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WO2024238130A2 - Systèmes et procédés d'analyse de morphologie cellulaire - Google Patents

Systèmes et procédés d'analyse de morphologie cellulaire Download PDF

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Publication number
WO2024238130A2
WO2024238130A2 PCT/US2024/026761 US2024026761W WO2024238130A2 WO 2024238130 A2 WO2024238130 A2 WO 2024238130A2 US 2024026761 W US2024026761 W US 2024026761W WO 2024238130 A2 WO2024238130 A2 WO 2024238130A2
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cell
cells
features
images
examples
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WO2024238130A3 (fr
Inventor
Stéphane C. BOUTET
Andreja Jovic
Cristian L. LUENGO HENDRIKS
Anastasia Mavropoulos
Senzeyu ZHANG
Ryan C. CARELLI
Kevin B. JACOBS
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Deepcell Inc
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Deepcell Inc
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/77—Processing image or video features in feature spaces; using data integration or data reduction, e.g. principal component analysis [PCA] or independent component analysis [ICA] or self-organising maps [SOM]; Blind source separation
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/60—Type of objects
    • G06V20/69—Microscopic objects, e.g. biological cells or cellular parts
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10056—Microscopic image
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20084—Artificial neural networks [ANN]
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30024—Cell structures in vitro; Tissue sections in vitro

Definitions

  • Analysis of a cell may be accomplished by examining, for example, one or more fluorescent images of the cell or sequencing data of the cell (e.g., gene fragment analysis, whole-genome sequencing, whole-exome sequencing, RNA-seq, etc.). Such methods may be used to identify cell type (e.g., stem cell or differentiated cell) or cell state (e.g., healthy or disease state). In some examples, cells may be directed through a flow channel for at least a portion of the analysis.
  • Examples herein provide systems and methods for cell morphology analysis. Provided below are several examples that may be employed in any combination to achieve the benefits as described herein.
  • Example 1 A method of processing images, comprising: providing one or more cell images to a plurality of encoders comprising a machine learning encoder and a computer vision encoder; extracting a set of machine learning (ML)-based features using the machine learning encoder, and extracting a set of cell morphometric features using the computer vision encoder; and using the machine learning encoder and the computer vision encoder to respectively encode the set of ML-based features and the set of cell morphometric features into a plurality of multi-dimensional vectors that represent morphology of at least one cell in the one or more cell images.
  • Example 2. The method of example 1, wherein the machine learning encoder comprises a deep learning encoder.
  • Example 3 Example 3.
  • Example 1 The method of example 1, wherein the plurality of multi-dimensional vectors comprises different numbers of the ML-based features and the cell morphometric features.
  • Example 4. The method of example 1, wherein the plurality of multi-dimensional vectors comprises a same number each of the ML-based features and the cell morphometric features.
  • Example 5. The method of any one of examples 1 to 5, wherein the ML-based features are not human-interpretable.
  • Example 6. The method of any one of examples 1 to 6, wherein the cell morphometric features are human-interpretable.
  • Example 7 The method of example 6, wherein the cell morphometric features comprise cell position, cell shape, pixel intensity, texture, focus, or any combination thereof.
  • Example 10 The method of any one of examples 1 to 7, wherein cells in the one or more cell images are unstained.
  • Example 9 The method of any one of examples 1 to 8, wherein the one or more cell images are brightfield cell images.
  • Example 10 The method of any one of examples 1 to 9, wherein the machine learning encoder uses a convolutional neural network or a vision transformer.
  • Example 11 The method of any one of examples 1 to 10, wherein the computer-vision encoder uses a human-constructed algorithm.
  • Example 13 The method of any one of examples 1 to 11, wherein the machine learning encoder extracts n ML-based features, the computer-vision encoder extracts m cell morphometric features, wherein the multi-dimensional vectors have n+m dimensions, and wherein n and m are positive integers.
  • Example 13 The method of example 12, wherein within each of the multi-dimensional vectors, each dimension of the n+m dimensions is an element of that multi-dimensional vector.
  • Example 14 The method of example 13, wherein the element is a numeric value.
  • Example 15 The method of any one of examples 1 to 14, wherein the ML-based features are orthogonal to one another. [0019] Example 16.
  • Example 17 The method of example 17 or example 18, wherein the cell shape features are selected from the group consisting of: area, perimeter, maximum caliper distance, minimum caliper distance, maximum radius, minimum radius, long ellipse axis, short ellipse axis, ellipse elongation, ellipse similarity, roundness, circle similarity, and convex shape.
  • Example 20 The method of any one of examples 17 to 19, wherein the pixel intensity features are selected from the group consisting of: mean pixel intensity, standard deviation of pixel intensity, pixel intensity 25th percentile, pixel intensity 75th percentile, positive fraction, and negative fraction.
  • Example 21 Example 21.
  • Example 22 The method of any one of examples 17 to 20, wherein the texture features are selected from the group consisting of: small set of connected bright pixels, integral; small set of connected dark pixels, integral; large set of connected bright pixels, integral; large set of connected dark pixels, integral; image moments; local binary patterns – center; local binary patterns – periphery; image sharpness; image focus; ring width; and ring intensity.
  • the texture features are selected from the group consisting of: small set of connected bright pixels, integral; small set of connected dark pixels, integral; large set of connected bright pixels, integral; large set of connected dark pixels, integral; image moments; local binary patterns – center; local binary patterns – periphery; image sharpness; image focus; ring width; and ring intensity.
  • a method of processing comprising: using a machine learning encoder to extract a set of machine learning (ML)-based features from a cell image; using a computer vision encoder to extract a set of cell morphometric features from the cell image; and using the set of ML-based features and the set of cell morphometric features to generate a feature vector that represents morphology of the cell.
  • ML machine learning
  • Example 26 The method of example 25, wherein the dimension reduction technique is selected from the group consisting of missing value ratio, low variance filter, high correlation filter, random forest, backward feature elimination, forward feature selection, factor analysis, principal component analysis (PCA), independent component analysis, methods based on projections, t-distributed stochastic neighbor embedding (t-SNE), and uniform manifold approximation and projection (UMAP).
  • PCA principal component analysis
  • UMAP uniform manifold approximation and projection
  • Example 27 The method of example 26, wherein the dimension reduction technique is PCA.
  • Example 28 The method of any one of examples 23 to 27, wherein the lower-dimensional vector comprises three or more dimensions.
  • Example 29 The method of any one of examples 23 to 28, further comprising generating, using at least in part on the assessing, a cell cluster map comprising a plurality of shapes representing the plurality of cells, wherein the plurality of shapes is arranged in a plurality of clusters using at least the similarity.
  • Example 30 The method of example 29, wherein the lower-dimensional vector and the cell cluster map are generated by using different dimension reduction techniques.
  • Example 31 The method of example 29 or example 30, wherein the cell cluster map is generated by using UMAP.
  • Example 32 The method of any one of examples 29 to 31, further comprising standardizing the multi-dimensional vector via a feature scaling technique.
  • Example 33 Example 33.
  • Example 34 The method of any one of examples 23 to 33, wherein the image data comprises a label free image of each of the plurality of cells. [0038] Example 35.
  • a method of classifying comprising: extracting, from image data of a plurality of cells, a multi-dimensional vector for each cell of the plurality of cells, wherein the multi-dimensional vector comprises (i) a set of machine-learning (ML)-based features extracted via a machine learning encoder and (ii) a set of cell morphometric features extracted via a computer vision encoder; and using at least in part the one or more ML-based features and the morphometric features to classify the plurality of cells into a plurality of subsets of melanoma cells comprising a first subset with a first level of cell pigmentation and a second subset with a second level of cell pigmentation, wherein the first level and the second level are different.
  • ML machine-learning
  • Example 36 The method of example 35, further comprising automatically generating a cell cluster map comprising a plurality of shapes representing the plurality of cells, wherein the plurality of shapes is arranged in a plurality of clusters that corresponds to the plurality of subsets of melanoma cells.
  • Example 37 The method of example 35 or example 36, further comprising sorting the first subset and the second subset into different sub-channels of a flow channel in fluid communication with the plurality of cells.
  • Example 38 The method of any one of examples 35 to 37, wherein the image data comprises a label-free image of each of the plurality of cells.
  • Example 39 Example 39.
  • a method of processing one or more cell images comprising: providing the one or more cell images to a plurality of encoders comprising a machine learning encoder and a computer vision encoder; extracting a set of machine learning (ML)-based features via the machine learning encoder, and extracting a set of cell morphological features via the computer vision encoder; and using the machine learning encoder and the computer vision encoder to respectively encode the set of ML-based features and the set of cell morphological features into a plurality of multi-dimensional vectors that represent morphology of at least one cell in the one or more cell images.
  • ML machine learning
  • Example 41 Example 41.
  • Example 42 The method of example 39, wherein the plurality of multi-dimensional vectors comprise a same number each of the ML-based features and the cell morphological features.
  • Example 43 Example 43.
  • a method for assessing similarity between cells comprising: (a) obtaining image data of a plurality of cells; (b) extracting, from the image data, a multi-dimensional vector for a cell of the plurality of cells, wherein the multi-dimensional vector comprises a plurality of vectors, and each vector of the multi- dimensional vector represents a unique morphological information of the cell; (c) reducing a dimensionality of the multi-dimensional vector to generate a lower-dimensional vector; and (d) using at least in part the lower-dimensional vector to assess a similarity between the cell and an additional cell of the plurality of cells.
  • Example 44 The method of example 43, wherein the similarity is assessed in a feature space corresponding to the lower-dimensional vector.
  • Example 45 The method of any one of examples 43 to 44, wherein, in (c), the dimensionality of the multi-dimensional vector is reduced using a dimension reduction technique.
  • Example 46 The method of any one of examples 43 to 45, wherein the dimension reduction technique is selected from the group consisting of missing value ratio, low variance filter, high correlation filter, random forest, backward feature elimination, forward feature selection, factor analysis, principal component analysis (PCA), independent component analysis, methods based on projections, t-distributed stochastic neighbor embedding (t-SNE), and uniform manifold approximation and projection (UMAP).
  • PCA principal component analysis
  • UMAP uniform manifold approximation and projection
  • Example 53 The method of any one of examples 43 to 51, wherein the multi-dimensional vector comprises (i) a set of machine-learning (ML)-based features extracted via a machine learning encoder and/or (ii) a set of cell morphological features extracted via a computer vision encoder.
  • Example 53 The method of any one of examples 43 to 52, further comprising, prior to (c), standardizing the multi-dimensional vector via a feature scaling technique.
  • Example 54 The method of example 53, wherein the feature scaling technique is selected from the group consisting of min-max normalization, mean normalization, and z-score normalization.
  • Example 55 Example 55.
  • Example 56 A method classifying a plurality of cells, the method comprising: (a) obtaining image data of the plurality of cells; (b) processing the image data to extract one or more machine learning (ML)-based features associated with the plurality of cells; and (c) using at least in part the one or more ML-based features to classify the plurality of cells into a plurality of subsets of melanoma cells comprising a first subset with a first level of cell pigmentation and a second subset with a second level of cell pigmentation, wherein the first level and the second level are different.
  • Example 57 A method classifying a plurality of cells, the method comprising: (a) obtaining image data of the plurality of cells; (b) processing the image data to extract one or more machine learning (ML)-based features associated with the plurality of cells; and (c) using at least in part the one or more ML-based features to classify the plurality of cells into a plurality of subsets of melanoma cells comprising
  • Example 56 further comprising automatically generating a cell cluster map comprising a plurality of shapes representing the plurality of cells, wherein the plurality of shapes is arranged in a plurality of clusters that corresponds to the plurality of subsets of melanoma cells.
  • Example 58 The method of examples 56 to 57, further comprising sorting the first subset and the second subset into different sub-channels of a flow channel in fluid communication with the plurality of cells.
  • Example 59 The method of any one of examples 56 to 58, wherein the ML-based feature comprises a plurality of different ML-based features.
  • Example 60 Example 60.
  • Example 61 The method of any one of examples 56 to 60, further comprising: - in (b), extracting a morphological feature via a computer vision encoder; and - in (c), using the ML-based features and the morphological feature to classify the plurality of cells into the plurality of subsets of melanoma cells.
  • Example 62 The method of example 61, wherein the morphological features comprise a plurality of different morphological features.
  • Example 63 The method of any one of examples 56 to 62, wherein the image data comprises a label-free image of each of the plurality of cells.
  • Example 64 A method of processing, the method comprising: using a machine learning encoder to extract respective sets of machine learning (ML)-based features from respective images of cells having Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) perturbations; using a computer vision encoder to extract respective sets of cell morphometric features from the respective images; using the respective sets of ML-based features and the respective sets of cell morphometric features to generate respective multi-dimensional feature vectors that represent morphologies of the cells; and using the respective feature vectors to screen the CRISPR perturbations.
  • Example 65 The method of example 64, wherein the images are of viable cells.
  • Example 66 Example 66.
  • Example 64 The method of example 64 or example 65, wherein the images are of unstained cells.
  • Example 67 The method of any one of examples 64 to 66, wherein cells of a first subset of the cells have a first CRISPR perturbation, and wherein cells of a second subset of the cells lack the first CRISPR perturbation.
  • Example 68 The method of example 67, wherein the cells of the second subset of the cells have a second CRISPR perturbation that is different from the first CRISPR perturbation.
  • Example 69 Example 69.
  • Example 70 The method of any one of examples 64 to 68, further comprising: reducing dimensionalities of the multi-dimensional feature vectors to generate lower-dimensional vectors; and using the lower-dimensional vectors to screen the CRISPR perturbations.
  • Example 70 The method of any one of examples 64 to 69, further comprising: flowing the cells through a microfluidic platform; and generating the respective images of the cells within the microfluidic platform.
  • Example 71 The method of example 70, further comprising collecting the cells after generating the respective images of the cells.
  • Example 72 The method of example 70 or example 71, further comprising sorting the cells into a plurality of collection wells using morphologies of the cells.
  • Example 73 Example 73.
  • Example 72 further comprising performing cell-level phenotyping on the sorted cells.
  • Example 74 The method of example 73, wherein the cell-level phenotyping comprises a multi- omic assay.
  • Example 75 The method of any one of examples 72 to 74, further comprising performing single- cell RNA sequencing on the sorted cells.
  • Example 76 The method of any one of examples 64 to 75, further comprising functionally screening the cells.
  • Example 77 The method of any one of examples 64 to 76, further comprising identifying genes or proteins that are common in a selected population of the cells and not in other cells.
  • Example 78 The method of example 78.
  • Example 77 wherein identifying the genes or proteins comprises using genomics data, transcriptomics data, proteomics data, or metabolomics data.
  • Example 79 The method of any one of examples 64 to 78, wherein the respective images are brightfield cell images.
  • Example 80 The method of any one of examples 64 to 79, wherein the machine learning encoder uses a convolutional neural network.
  • Example 81 The method of any one of examples 64 to 80, wherein the computer-vision encoder uses a human-constructed algorithm.
  • Example 82 The method of any one of examples 64 to 80, wherein the computer-vision encoder uses a human-constructed algorithm.
  • Example 83 The method of example 82, wherein within each of the multi-dimensional vectors, each dimension of the n+m dimensions is an element of that multi-dimensional vector.
  • Example 84 The method of example 83, wherein the element is a numeric value.
  • Example 85 The method of any one of examples 64 to 84, wherein the ML-based features are orthogonal to one another. [0089] Example 86.
  • a method of processing comprising: using a machine learning encoder to extract respective sets of machine learning (ML)-based features from respective images of non-viable cells in different states than one another; using a computer vision encoder to extract respective sets of cell morphometric features from the respective images; using the respective sets of ML-based features and the respective sets of cell morphometric features to generate respective multi-dimensional feature vectors that represent morphologies of the cells; and using the respective feature vectors to characterize the states of the non-viable cells.
  • ML machine learning
  • Example 89 The method of example 88, wherein the non-viable cells are undergoing necrosis, are in an early apoptotic state, or are in a late apoptotic state.
  • Example 91 The method of any one of examples 88 to 90, further comprising: reducing dimensionalities of the multi-dimensional feature vectors to generate lower-dimensional vectors; and using the lower-dimensional vectors to characterize the states of the non-viable cells.
  • Example 92 The method of any one of examples 88 to 91, further comprising: flowing the cells through a microfluidic platform; and generating the respective images of the cells within the microfluidic platform.
  • Example 93 The method of example 92, further comprising collecting the cells after generating the respective images of the cells.
  • Example 94 Example 94.
  • Example 95 The method of example 92 or example 93, further comprising sorting the cells into a plurality of collection wells using morphologies of the cells.
  • Example 95 The method of example 94, further comprising performing cell-level phenotyping on the sorted cells.
  • Example 96 The method of example 95, wherein the cell-level phenotyping comprises a multi- omic assay.
  • Example 97 The method of any one of examples 94 to 96, further comprising performing single- cell RNA sequencing on the sorted cells.
  • Example 98 The method of any one of examples 88 to 97, further comprising functionally screening the cells.
  • Example 99 Example 99.
  • Example 100 The method of any one of examples 88 to 98, further comprising identifying genes or proteins that are common in a selected population of the cells and not in other cells.
  • Example 100 The method of example 99, wherein identifying the genes or proteins comprises using genomics data, transcriptomics data, proteomics data, or metabolomics data.
  • Example 101 The method of any one of examples 88 to 100, wherein the respective images are brightfield cell images.
  • Example 102 The method of any one of examples 88 to 101, wherein the machine learning encoder uses a convolutional neural network.
  • Example 103 The method of any one of examples 88 to 102, wherein the computer- vision encoder uses a human-constructed algorithm.
  • Example 104 The method of any one of examples 88 to 98, further comprising identifying genes or proteins that are common in a selected population of the cells and not in other cells.
  • Example 105 The method of example 104, wherein within each of the multi-dimensional vectors, each dimension of the n+m dimensions is an element of that multi-dimensional vector.
  • Example 106 The method of example 105, wherein the element is a numeric value.
  • Example 107 The method of any one of examples 88 to 106, wherein the ML-based features are orthogonal to one another. [0111] Example 108.
  • Example 109 The method of any one of examples 88 to 107, wherein the ML-based features are orthogonal to the cell morphological features.
  • Example 109 The method of any one of examples 88 to 108, wherein the cell morphometric features are selected from the group consisting of position features, cell shape features, pixel intensity features, texture features, and focus features.
  • FIG.1 illustrates an example workflow of extracting features associated with cell morphology from cell images using the human foundation model, in accordance with some examples of the present disclosure.
  • FIG.2A illustrates cell classes, numbers of images used as training dataset to train a classifier using features extracted using the human foundation model, numbers of images processed by the human foundation model as test dataset, and corresponding representative cell images, in accordance with some examples of the present disclosure.
  • FIG. 1 illustrates an example workflow of extracting features associated with cell morphology from cell images using the human foundation model, in accordance with some examples of the present disclosure.
  • FIG.2A illustrates cell classes, numbers of images used as training dataset to train a classifier using features extracted using the human foundation model, numbers of images processed by the human foundation model as test dataset, and corresponding representative cell images, in accordance with some examples of the present disclosure.
  • FIG. 2B illustrates an example morphology Uniform Manifold Approximation and Projections (UMAP) of different cell lines and polystyrene beads as control, in accordance with some examples of the present disclosure.
  • FIG. 2C illustrates an example confusion matrix between predicted cell classes classified using features extracted using the human foundation model and actual cell classes, in accordance with some examples of the present disclosure.
  • FIG.2D illustrates example density plots of four differential features generated by the human foundation model for different cell lines and polystyrene beads as control, in accordance with some examples of the present disclosure.
  • FIG.3 illustrates an example system for cell morphology analysis, in accordance with some examples of the present disclosure.
  • FIG.4 illustrates an example interaction between a microfluidic platform, the human foundation model, and the data suite, in accordance with some examples of the present disclosure.
  • FIG.5 illustrates an example system for cell morphology analysis, in accordance with some examples of the present disclosure.
  • FIG.6A illustrates an example workflow from high-throughput imaging to cell characterization, classification and sorting based on cell morphology analysis, in accordance with some examples of the present disclosure.
  • FIG.6B illustrates another example workflow from sample preparation to cell characterization and sorting based on cell morphology profiling, in accordance with some examples of the present disclosure.
  • FIG.7 illustrates example characterization of cancer cells based on cell morphology profiling, in accordance with some examples of the present disclosure.
  • FIG.8A illustrates an example morphology UMAP from cell image feature embeddings colored by cell types, in accordance with some examples of the present disclosure.
  • FIG.8B illustrates an example morphology UMAP colored by cluster imputed using Leiden algorithm, with randomly selected representative images from each cluster shown, in accordance with some examples of the present disclosure.
  • FIG.9A illustrates an example morphology UMAP of a heterogeneous collection of melanoma cell lines and immune/stromal cells derived from patient biopsies in consistency with FIG.8A, in accordance with some examples of the present disclosure.
  • FIG.9B illustrates an example re-projected morphology UMAP using filtered data from FIG.9A showing only melanoma cells colored by cell lines, in accordance with some examples of the present disclosure.
  • FIG.9C illustrates an example morphology UMAP colored by cluster imputed using Leiden algorithm, with randomly selected representative images from each cluster shown and pixel density plot of three selected clusters, in accordance with some examples of the present disclosure.
  • FIG.10A illustrates an example morphology UMAP of cells based on multi-dimensional embeddings where the cells resided in a NSCLC DTC biopsy sample, in accordance with some examples of the present disclosure.
  • FIG.10B illustrates six example morphologically distinct clusters isolated by the platform as described herein via user-defined sorting and processed for copy number variation (CNV) profiling, in accordance with some examples of the present disclosure.
  • FIG.11A illustrates example bulk RNA-sequence analysis of cells resided in a NSCLC DTC biopsy sample, in accordance with some examples of the present disclosure.
  • FIG.11B illustrates example principal component analysis (PCA) performed on each morphology cluster, in accordance with some examples of the present disclosure.
  • FIG.12 illustrates an example flow of operations in a method of processing images.
  • FIG.13 illustrates an example flow of operations in a method for assessing.
  • FIG.14 illustrates an example flow of operations in a method for classifying.
  • FIG.15 schematically illustrates an example method for classifying a cell.
  • FIG.16 schematically illustrates, in one example, different ways of representing analysis data of image data of cells.
  • FIG.17 schematically illustrates, in one example, different representations of analysis of image data of a population of cells.
  • FIG.18 schematically illustrates, in one example, a method for a user to interact with a method for analyzing image data of cells.
  • FIG.19 schematically illustrates, in one example, a cell analysis platform for analyzing image data of one or more cells.
  • FIGS.20A-20B schematically illustrate, in one example, an example microfluidic system for sorting one or more cells.
  • FIGS.21A-21F schematically illustrate, in one example, an example system for classifying and sorting one or more cells.
  • FIGS.22A-22E schematically illustrate operations that may be performed in an example method.
  • FIG.23 shows a computer system, in one example, that is programmed or otherwise configured to implement methods provided herein.
  • Non-limiting examples of a shape of a cell may include, but are not limited to, circular, elliptic, shmoo-like, dumbbell, star-like, flat, scale-like, columnar, invaginated, having one or more concavely formed walls, having one or more convexly formed walls, prolongated, having appendices, having cilia, having angle(s), having corner(s), etc.
  • a morphological feature of a cell may be visible with treatment of a cell (e.g., small molecule or antibody staining). In another example, the morphological feature of the cell need not require any treatment to be visualized in an image or video.
  • the terms “unstructured” or “unsorted,” as used interchangeably herein, generally refers to a mixture of cells (e.g., an initial mixture of cells) that is not substantially sorted (or rearranged) into separate partitions.
  • An unstructured population of cells may comprise at least two types of cells that may be distinguished by exhibiting different properties (e.g., one or more physical properties, such as one or more different morphological characteristics as disclosed herein).
  • the unstructured population of cells may be a random (or randomized) mixture of the at least two types of cells.
  • the cells as disclosed herein may be viable cells.
  • a viable cell, as disclosed herein may be a cell that is not undergoing necrosis or a cell that is not in an early or late apoptotic state.
  • Assays for determining cell viability may include, e.g., as using propidium iodide (PI) staining which may be detected by flow cytometry.
  • the cells need not be viable (e.g., fixed cells).
  • a “viable cell” as disclosed herein may be characterized by exhibiting one or more characteristics (e.g., morphology, one or more gene expression profiles, etc.) that is substantially unaltered (or that is not substantially impacted by) by any operation or process of the methods disclosed herein (e.g., partitioning).
  • a characteristic of a viable cell may be a gene transcript accumulation rate, which may be characterized by a change in transcript levels of a same gene (e.g., a same endogenous gene) between mother and daughter cells over the time between cell divisions, as ascertained by single cell sequencing, polymerase chain reaction (PCR), etc.
  • high throughput when referring to a platform, system, model, and the like, means that such a platform, system, model, etc., is capable of generating an embedding for at least one image within a desired time, such as but not limited to approximately 5 ms to 30 ms.
  • a high- throughput setting may also include components to process approximately 10,000 frames/sec and/or approximately 1000 images/sec while being configured to correct per-pixel variation in background offset, camera gain, and/or illumination for the processed frames.
  • “high-throughput” systems may require relatively low-latency.
  • Relative terms, such as “about,” “substantially,” or “approximately” are used to include small variations with specific numerical values (e.g., +/- x%,), as well as including the situation of no variation (+/-0%).
  • the numerical value x is less than or equal to 10 – e.g., less than or equal to 5, to 2, to 1, or smaller, including 0.
  • a real time event may be performed almost immediately or within a short enough time span, such as within at least about 0.0001 ms, for example at least about 0.0005 ms, at least about 0.001 ms, at least about 0.005 ms, at least about 0.01 ms, at least about 0.05 ms, at least about 0.1 ms, at least about 0.5 ms, at least about 1 ms, at least about 5 ms, at least about 0.01 seconds, at least about 0.05 seconds, at least about 0.1 seconds, at least about 0.5 seconds, at least about 1 second, or more.
  • a real time event may be performed almost immediately or within a short enough time span, such as within at most about 1 second, for example at most about 0.5 seconds, at most about 0.1 seconds, at most about 0.05 seconds, at most about 0.01 seconds, at most about 5 ms, at most about 1 ms, at most about 0.5 ms, at most about 0.1 ms, at most about 0.05 ms, at most about 0.01 ms, at most about 0.005 ms, at most about 0.001 ms, at most about 0.0005 ms, at most about 0.0001 ms, or less.
  • any of the operations of a computer processor as provided herein may be performed (e.g., automatically performed) in real-time.
  • an “encoder” refers to a type of model that transforms or “encodes” an image into a vector.
  • Nonlimiting examples of encoders include machine learning-based models, and computer vision models.
  • Overview [0159] Morphology is an important cell property associated with identity, state, and function, but in some instances it is characterized crudely in a few standard dimensions such as diameter, perimeter, or area, or with subjective qualitative descriptions.
  • the present disclosure provides a method of processing that includes using a machine learning encoder to extract a set of ML-based features from a cell images, using a computer vision encoder to extract a set of cell morphometric features from the cell image, and using the set of ML-based features and the set of cell morphometric features to generate a feature vector that represents morphology of the cell.
  • the feature vector may be used in a variety of practical applications, e.g., in a manner such as described in the nonlimiting examples provided herein.
  • tumors are composed of heterogeneous assortments of cells with distinct genetic and phenotypic characteristics that may drive therapeutic resistance, immune evasion, and disease progression.
  • Cell morphology information has historically been used for cell and disease characterization but has been difficult to objectively and reproducibly quantify. Cell morphology in many instances is studied qualitatively through microscopes, which may be inherently slow, difficult to scale, and relies on human interpretation.
  • the present disclosure provides multi-dimensional morphology analysis (e.g., profiling) enabled by machine learning and computer vision morphometrics. The present disclosure has the benefit of enabling higher resolution and biological insight while reducing labor-intensive cell processing manipulations.
  • the multi-dimensional morphology profiling and sorting of unlabeled single cells using machine learning, advanced imaging, and microfluidics may be used to assess population heterogeneity beyond biomarkers.
  • the present disclosure provides a method for cell morphology analysis.
  • the method may combine deep learning and computer vision methods to extract features from cell images.
  • a deep learning model used in the method may provide quantitative descriptions of cell features using one or more neural network.
  • a computer vision model used in the method may provide a quantitative assessment of cell and biological features using discrete image analysis algorithms.
  • the method as described herein may allow for extracting and interpreting cell morphology features with a multidimensional, unbounded, and quantitative assessment.
  • the present disclosure provides a system for cell morphology analysis.
  • the system may comprise a benchtop single-cell imaging and sorting system for high-dimensional morphology analysis.
  • the system may combine label-free imaging, deep learning, computer vision morphometrics, and gentle cell sorting to leverage multidimensional single cell morphology as a quantitative readout.
  • the system may capture high-resolution brightfield cell images, from which features (e.g., dimensional embedding vectors) may be extracted representing the morphology of the cells.
  • features e.g., dimensional embedding vectors
  • the system and method may combine label-free imaging, deep learning, computer vision morphometrics, and gentle cell sorting to harness multi-dimensional single cell morphology as a quantitative biological readout.
  • the systems and methods disclosed herein have a variety of potential uses.
  • the systems and methods may be used in a variety of applications including but not limited to cancer research, developmental biology, cell and gene therapies, and drug and functional screening.
  • the systems and methods as described herein may be used in drug and Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) perturbation screening, using cell morphology as a novel biomarker for the screening.
  • CRISPR Clustered Regularly Interspaced Short Palindromic Repeats
  • the systems and methods as described herein may be used in sample-level profiling, including but not limited to heterogeneous sample evaluation and characterization, disease detection and enrichment, and sample clean-up.
  • the systems and methods as described herein may be used in cell-level phenotyping, including cell health status, cell state characterization, and multi-omic integration.
  • Human Foundation Model HMM
  • the present disclosure may provide a human foundation model (“HFM”) for cell morphology analysis (e.g., profiling).
  • the human foundation model may combine a deep learning model and a computer vision model and extract cell features from cell images.
  • the deep learning model may process cell images as input and provide quantitative descriptions of cell features.
  • the deep learning model may extract deep learning features that are information-rich metrics of cell morphology with powerful discriminative capabilities.
  • the deep learning features may not be human-interpretable.
  • the computer vision model may process cell images as input and provide morphometric features that are human- interpretable, quantitative metrics of cell morphology including cell size, shape, texture, and intensity.
  • the morphometrics may be computationally generated using discrete computer vision algorithms.
  • the deep learning model may overcome the limitation of the computer vision model by imputing the most computationally intensive morphometrics into the human foundation model.
  • the human foundation model as described herein may provide both accuracy and interpretability in real- time feature extraction, cell classification and sorting.
  • the human foundation model may also have strong generalization capabilities that enable hypothesis-free sample exploration and efficient generation of application-specific models.
  • FIG.1 illustrates an example workflow of extracting features associated with cell morphology from cell images using the human foundation model, in accordance with some examples of the present disclosure.
  • the human foundation model may process cell images 110 and generate features therefrom. In some examples, cells that are under analysis may be unstained, and the cell images 110 may be brightfield cell images.
  • the human foundation model may comprise a deep learning model 120 and a computer vision model 130.
  • the deep learning model 120 may comprise a deep learning encoder, for example, a convolutional neural network.
  • the deep learning model 120 may process cell images 110 as input and extract artificial intelligence (AI) features 140 therefrom.
  • AI artificial intelligence
  • the AI features 140 may comprise deep learning features 160, e.g., features that are extracted using a deep learning algorithm, such as a convolutional neural network or vision transformer, with other nonlimiting examples being provided elsewhere herein.
  • the dimensions of the deep learning features may be in a range of between about 1 and about 10, between about 1 and about 20, between about 1 and about 50, between about 1 and about 80, between about 1 and about 100, between about 1 and about 200, between about 1 and about 500, between about 1 and about 800, between about 1 and about 1,000, between about 1 and about 2,000, between about 1 and about 5,000, between about 1 and about 8,000, between about 1 and about 10,000, between about 1 and about 20,000, between about 1 and about 50,000, between about 1 and about 80,000, or between about 1 and about 100,000, or any value between any of the aforementioned numbers.
  • each feature in a data set comprising a plurality of deep learning features of the cell(s), each feature may be referred to as a dimension (e.g., a deep learning dimension). Any range of dimensions of the deep learning features may be contemplated, for example from 1 through any number greater than about 100,000. As illustrated in FIG.1, as one nonlimiting example, the deep learning model 120 generates about 64-dimensional deep learning features 160.
  • the computer vision model 130 may comprise a computer vision encoder including human-constructed algorithm(s), which in some cases may be referred to as “rule-based morphometrics.” The computer vision model 130 may process cell images 110 as input and extract cell features 150 therefrom.
  • the cell features 150 may comprise one or more of cell position, cell shape, pixel intensity, texture, or focus, or combinations thereof.
  • the cell features 150 may comprise morphometric features 170. Nonlimiting examples of morphometric features 170 are provided below in Table 2.
  • the cell features 150 may include any suitable number of morphometric features 170, for example, at least about 1 feature, for example, at least about 5 features, or at least about 10 features, or at least about 50 features, or at least about 100 features, or at least about 500 features, or at least about 1,000 features, or at least about 5,000 features, or at least about 10,000 features, or at least about 50,000 features, or at least about 100,000 features, or more.
  • each feature in a data set comprising a plurality of computer vision features of the cell(s), each feature may be referred to as a dimension (e.g., computer vision-based dimension). Any range of dimensions of the morphometric features may be contemplated, for example from 1 through any number greater than 100,000.
  • the computer vision model 130 may extract between about 5 and about 1000 morphometric features, e.g., between about 10 and about 500 morphometric features, e.g., between about 50 and about 100 features, and any values in between any of the aforementioned ranges, from each cell image. As illustrated in FIG.1, in one nonlimiting example, the computer vision model generates about 51- dimensional morphometric features 170. [0171] In some examples, the human foundation model may encode the deep learning features 160 and morphometric features 170 into multidimensional numerical vectors representing the cell morphology.
  • the human foundation model may generate one or more morphology maps based on one or more of deep learning features 160 and morphometric features 170, and in some examples based on a plurality of deep learning features and a plurality of morphometric features (e.g., based on a multi- dimensional vector that represents morphology of a cell).
  • the generation of a morphology map may be referred to as reducing dimensionalities of multi-dimensional feature vectors to generate lower-dimensional vectors.
  • a cell morphology map may be a visual (e.g., graphical) representation of one or more clusters of datapoints.
  • the cell morphology map may be a 1-dimensional (1D) representation (e.g., based on one morphological property as one parameter or dimension) or a multi-dimensional representation, such as a 2- dimensional (2D) representation (e.g., based on two morphological properties as two parameters or dimensions), a 3-dimensional (3D) representation (e.g., based on three morphological properties as three parameters or dimensions), a 4-dimensional (4D) representation, etc.
  • one morphological property of a plurality of morphological properties used for blotting the cell morphology map may be represented as a non-axial parameter (e.g., non-x, y, or z axis), such as, distinguishable colors (e.g., heatmap), numbers, letters (e.g., texts of one or more languages), and/or symbols (e.g., a square, oval, triangle, square, etc.).
  • a heatmap may be used as colorimetric scale to represent the classifier prediction percentages for each cell against a cell class, cell type, or cell state.
  • the cell morphology map may be generated based on one or more morphological features (e.g., characteristics, profiles, fingerprints, etc.) from the processed image data.
  • morphological features e.g., characteristics, profiles, fingerprints, etc.
  • Non-limiting examples of one or more morphological properties of a cell, as disclosed herein, that may be extracted from one or more images of the cell may include, but are not limited to (i) shape, curvature, size (e.g., diameter, length, width, circumference), area, volume, texture, thickness, roundness, etc.
  • One or more dimensions of the cell morphology map may be represented by various approaches (e.g., dimensionality reduction approaches), such as, for example, principal component analysis (PCA), multidimensional scaling (MDS), t-distributed stochastic neighbor embedding (t-SNE), and uniform manifold approximation and projection (UMAP).
  • PCA principal component analysis
  • MDS multidimensional scaling
  • t-SNE t-distributed stochastic neighbor embedding
  • UMAP uniform manifold approximation and projection
  • UMAP may be a machine learning technique for dimension reduction.
  • UMAP may be constructed from a theoretical framework based in Riemannian geometry and algebraic topology.
  • UMAP may be utilized for a practical scalable algorithm that applies to real world data, such as morphological properties of one or more cells.
  • the deep learning model of the human foundation model may be trained using a plurality of cell images from different types of biological samples and thus, be able to detect differences in cell morphology without labeled training data.
  • the deep learning model 120 of the human foundation model 160 may be trained using any suitable number of images of cells, for example between about 1 and about 200, about 1 to about 500, between about 1 and about 800, between about 1 and about 1,000, between about 1 and about 2,000, between about 1 and about 5,000, between about 1 and about 8,000, between about 1 and about 10,000, between about 1 and about 20,000, between about 1 and about 50,000, between about 1 and about 80,000, between about 1 and about 100,000, between about 1 and about 200,000, between about 1 and about 500,000, between about 1 and about 800,000, between about 1 and about 1,000,000, between about 1 and about 2,000,000, between about 1 and about 5,000,000, between about 1 and about 8,000,000, or between about 1 and about 10,000,000 images of cells.
  • the deep learning model 120 of the human foundation model is trained using a training dataset that includes at least about 10,000 images of cells – e.g., at least about 100,000 images of cells, at least about 1,000,000 images of cells, at least about 5,000,000 images of cells, at least about 10,000,000 images of cells, at least about 100,000,00 images of cells, at least about 1 billion, or more, images of cells.
  • the deep learning model 120 may be trained using between about 5,000,000 and about 1 billion images of cells.
  • the training set may include, or may consist essentially of (and in some examples may consist of), images of cells that are not physically stained and that are not computationally labeled in any manner.
  • the deep learning model 120 learns to recognize features from the cell images in a self-supervised manner.
  • the human foundation model may comprise parameters in a range of between about 1 and about 1,000, between about 1 and about 2,000, between about 1 and about 5,000, between about 1 and about 8,000, between about 1 and about 10,000, between about 1 and about 20,000, between about 1 and about 50,000, between about 1 and about 80,000, between about 1 and about 100,000, between about 1 and about 200,000, between about 1 and about 500,000, between about 1 and about 800,000, between about 1 and about 1,000,000, between about 1 and about 2,000,000, between about 1 and about 5,000,000, between about 1 and about 8,000,000, between about 1 and about 10,000,000, between about 1 and about 20,000,000, between about 1 and about 50,000,000, between about 1 and about 80,000,000, between about 1 and about 100,000,000, or between about 1 and about 500,000,000.
  • the deep learning model (e.g., backbone model) of the human foundation model, which extracts image features, may be based on a convolutional neural network architecture, a vision transformer architecture, or both.
  • the training process may apply a self-supervised learning approach that learns image features without labels and generate deep learning embeddings (vectors) that are orthogonal to each other and orthogonal to morphometric features.
  • embeddings that are “orthogonal” may be perpendicular to another embedding vector or set of embedding vectors. For example, vectors are considered to be orthogonal to each other if they are at right angles in n ⁇ dimensional space, where n is the size or number of elements in each vector.
  • “orthogonal” embeddings may have a covariance of about 0 and may be perfectly or completely orthogonal (e.g., have exactly a covariance of 0) or may be substantially orthogonal with a covariance that is greater than but close to 0.
  • “orthogonal” embeddings include features that are “independent” of one another, meaning, that the presence or absence of one feature does not affect the presence or absence of any of the other feature. For example, a vector is orthogonal if the dot product with another vector is zero.
  • a training algorithm may build a machine learning model capable of assigning features within images of cells into one category or the other, e.g., to make the model a non-probabilistic machine learning model.
  • the machine learning model may be used to create a new category to assign new examples/cases into the new category.
  • a machine learning model may be the actual trained model that is generated based on the training model.
  • the machine learning algorithm as disclosed herein may be configured to extract one or more morphological features of a cell from the image data of the cell.
  • the machine learning algorithm may form a new data set based on the extracted morphological features, and the new data set need not contain the original image data of the cell.
  • replicas of the original images in the image data may be stored in a database disclosed herein, e.g., prior to using any of the new images for training, e.g., to keep the integrity of the images of the image data.
  • processed images of the original images in the image data may be stored in a database disclosed herein during or subsequent to the classifier training.
  • any of the newly extracted morphological features as disclosed herein may be utilized as new molecular markers for a cell or population of cells of interest to the user.
  • a cell analysis platform as disclosed herein may be operatively coupled to one or more databases comprising non-morphological data of cells processed (e.g., genomics data, transcriptomics data, proteomics data, metabolomics data), a selected population of cells exhibiting the newly extracted morphological feature(s) may be further analyzed by their non-morphological properties to identify proteins or genes of interest that are common in the selected population of cells but not in other cells, thereby determining such proteins or genes of interest to be new molecular markers that may be used to identify such selected population of cells.
  • non-morphological data of cells processed e.g., genomics data, transcriptomics data, proteomics data, metabolomics data
  • a selected population of cells exhibiting the newly extracted morphological feature(s) may be further analyzed by their non-morphological properties to identify proteins or genes of interest that are common in the selected population of cells but not in other cells, thereby determining such proteins or genes of interest to be new molecular markers that may be used to identify such selected population of cells.
  • Non-limiting examples of machine learning algorithms for training a machine learning model may include supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, self- learning (also referred to as self-supervised learning), feature learning, anomaly detection, association rules, etc.
  • a machine learning model may be trained by using one or more learning models on such training dataset.
  • learning models may include artificial neural networks (e.g., convolutional neural networks, U-net architecture neural network, etc.), backpropagation, boosting, decision trees, support vector machines, regression analysis, Bayesian networks, genetic algorithms, kernel estimators, conditional random field, random forest, ensembles of machine learning models, minimum complexity machines (MCM), probably approximately correct learning (PACT), etc.
  • the neural networks are designed by the modification of neural networks such as AlexNet, VGGNet, GoogLeNet, ResNet (residual networks), DenseNet, and Inception networks.
  • the enhanced neural networks are designed by modification of ResNet (e.g. ResNet 18, ResNet 34, ResNet 50, ResNet 101, and ResNet 152) or inception networks.
  • the modification may include a series of network surgery operations that are mainly carried out to improve including inference time and/or inference accuracy.
  • a Vision Transformer may be used in addition to, or as an alternative neural network architecture to, a CNN.
  • the present machine learning machine learning model may include a hybrid architecture that incorporates that incorporates aspects of both a CNN and a Vision Transformer.
  • the present machine learning machine learning model may include a Vision Transformer which is used as a drop-in replacement for a CNN and is trained using the same procedure as would be used to train a CNN.
  • the Vision Transformer may function as an encoder (and in all respects may have the same function as the model as a feature extractor).
  • the Vision Transformer is configured to process tiles of images sequentially using interleaved self-attention operations with multilayer perceptrons (MLP).
  • MLP multilayer perceptrons
  • Nonlimiting examples of Vision Transformers and their use in encoding images are described in Dosovitskiy et al., “An image is worth 16x16 words: Transformers for image recognition at scale,” International Conference on Learning Representations (ICLR) (2021) (21 pages available at arxiv.org/abs/2010.11929), the entire contents of which are incorporated by reference herein.
  • the machine learning algorithm as disclosed herein may utilize one or more clustering algorithms to determine that objects (e.g., features) in the same cluster may be more similar (in one or more morphological features) to each other than those in other clusters.
  • Non-limiting examples of the clustering algorithms may include, but are not limited to, connectivity models (e.g., hierarchical clustering), centroid models (e.g. K-means algorithm), distribution models (e.g., expectation-maximization algorithm), density models (e.g., density-based spatial clustering of applications with noise (DBSCAN), ordering points to identify the clustering structure (OPTICS)), subspace models (e.g., biclustering), group models, graph- based models (e.g., highly connected subgraphs (HCS) clustering algorithms), single graph models, and neural models (e.g., using unsupervised neural network).
  • the machine learning algorithm may utilize a plurality of models, e.g., in equal weights or in different weights.
  • the graph-based models may include graph-based clustering algorithms that use modularity, e.g., such as described in the following references, the entire contents of each of which are incorporated by reference herein: Blondel et al., “Fast unfolding of communities in large networks,” Journal of Statistical Mechanics: Theory and Experiment 2008: P10008 (2008); and Traag et al., “From Louvain to Leiden: guaranteeing well-connected communities,” Scientific Reports 9: 5233, 12 pages, (2019).
  • unsupervised and self-supervised approaches may be used to expedite labeling of image data of cells (extract features from cells). For the example of unsupervised, an embedding for a cell image may be generated.
  • the embedding may be a representation of the image in a space with reduced dimensions than the original image data.
  • Such embeddings may be used to cluster images that are similar to one another.
  • the labeler may be configured to batch-label the cells and increase the throughput as compared to manually labeling one or more cells.
  • additional meta information e.g., additional non-morphological information
  • additional non-morphological information about the sample (e.g., what disease is known or associated with the patient who provided the sample) may be used for labeling of image data of cells.
  • embedding generation may use a neural net trained on predefined cell types.
  • an intermediate layer of the neural net that is trained on predetermined image data may be used. By providing enough diversity in image data/sample data to the trained model, this method may provide an accurate way to cluster future cells.
  • embedding generation may use neural nets trained for different tasks.
  • an intermediate layer of the neural net that is trained for a different task e.g., a neural net that is trained on a canonical dataset such as ImageNet).
  • autoencoders may be used for embedding generation.
  • autoencoders may be used, in which the input and the output may be substantially the same image and the squeeze layer may be used to extract the embeddings.
  • the squeeze layer may force the model to learn a smaller representation of the image, which smaller representation may have sufficient information to recreate the image (e.g., as the output).
  • an expanding training data set may be used for clustering-based labeling of image data or cells.
  • one or more revisions of labeling e.g., manual relabeling
  • Such manual relabeling may be intractable on a large scale and ineffective when done on a random subset of the data.
  • similar embedding-based clustering may be used to identify labeled images that may cluster with members of other classes.
  • adaptive image augmentation may be used.
  • one or more images with artifacts may be identified, and (2) such images identified with artifacts may be added to training pipeline (e.g., for training the model).
  • Identifying the image(s) with artifacts may comprise: (1a) while imaging cells, one or more additional sections of the image frame may be cropped, which frame(s) being expected to contain just the background without any cell; (2a) the background image may be checked for any change in one or more characteristics (e.g., optical characteristics, such as brightness); and (3a) flagging/labeling one or more images that have such change in the characteristic(s).
  • Adding the identified images to training pipeline may comprise: (2a) adding the one or more images that have been flagged/labeled as augmentation by first calculating an average feature of the changed characteristic(s) (e.g., the background median color); (2b) creating a delta image by subtracting the average feature from the image data (e.g., subtracting the median for each pixel of the image); and (3c) adding the delta image to the training pipeline.
  • the model(s) may be validated (e.g., for the ability to demonstrate accurate cell classification performance).
  • validation metrics may include, but are not limited to, threshold metrics (e.g., accuracy, F-measure, Kappa, Macro-Average Accuracy, Mean-Class-Weighted Accuracy, Optimized Precision, Adjusted Geometric Mean, Balanced Accuracy, etc.), the ranking methods and metrics (e.g., receiver operating characteristics (ROC) analysis or “ROC area under the curve (ROC AUC)”), and the probabilistic metrics (e.g., root-mean-squared error).
  • threshold metrics e.g., accuracy, F-measure, Kappa, Macro-Average Accuracy, Mean-Class-Weighted Accuracy, Optimized Precision, Adjusted Geometric Mean, Balanced Accuracy, etc.
  • the ranking methods and metrics e.g., receiver operating characteristics (ROC) analysis or “ROC area under the curve (ROC AUC)”
  • the probabilistic metrics e.g., root-mean-squared error
  • the model(s) may be determined to be balanced or accurate when the ROC AUC is greater than about 0.5, greater than about 0.55, greater than about 0.6, greater than about 0.65, greater than about 0.7, greater than about 0.75, greater than about 0.8, greater than about 0.85, greater than about 0.9, greater than about 0.91, greater than about 0.92, greater than about 0.93, greater than about 0.94, greater than about 0.95, greater than about 0.96, greater than about 0.97, greater than about 0.98, greater than about 0.99, or more.
  • the output of the machine learning encoder may include, or may consist essentially of, or may consist of, at least one multidimensional vector (which may also be referred to herein as an embedding).
  • Elements of the vector(s) for a given image may correspond to the values of respective features that the machine learning encoder extracted from that image.
  • Table 1 below describes example machine learning dimensions (In one example, deep learning dimensions), which correspond to different features that the machine learning encoder extracts from images.
  • the machine learning encoder extracts n ML-based features from each image (where n is a positive integer), and outputs an array of length n, which array may be considered to be an n-dimensional vector.
  • the output of the deep learning encoder may have the format: V 2 V 3 ....
  • n may be in any suitable range, e.g., may be between about 5 and about 1000, e.g., between about 10 and about 500, e.g., between about 50 and about 100, or range in between any of the aforementioned values. In the nonlimiting example shown in Table 1, n is equal to 64. Table 1.
  • one or more machine learning models may be used to automatically sort or categorize particles (e.g., cells) in the imaging data into one or more classes (e.g., one or more physical characteristics or morphological features, as used interchangeably herein).
  • cell imaging data may be analyzed using the machine learning algorithm(s) to classify (e.g., sort) a cell (e.g., a single cell) in a cell image or video.
  • cell imaging data may be analyzed using the machine learning algorithm(s) to determine a focus score of a cell (e.g., a single cell) in a cell image or video.
  • cell imaging data may be analyzed using the machine learning algorithm(s) to determine a relative distance between (i) a first plane of cells exhibiting first similar physical characteristic(s) and (ii) a second plane of cells exhibiting second similar physical characteristic(s), which first and second planes denote fluid streams flowing substantially parallel to each other in a channel.
  • one or more cell morphology maps as disclosed herein may be used to train one or more machine learning models (e.g., at least about 1, for example at least about 2, at least about 3, at least about 4, at least about 5, at least about 6, at least about 7, at least about 8, at least about 9, at least about 10, or more machine learning models) as disclosed herein.
  • Each machine learning model may be trained to analyze one or more images of a cell (e.g., to extract one or more morphological features of the cell) and categorize (or classify) the cell into one or more determined class or categories of a cell (e.g., based on a type of state of the cell).
  • the machine learning model may be trained to create a new category to categorize (or classify) the cell into the new category, e.g., when determining that the cell is morphologically distinct than any pre-existing categories of other cells.
  • the entire process of cell focusing as disclosed herein may be accomplished based on de novo AI- mediated analysis of each cell (e.g., using analysis of one or more images of each cell using machine learning algorithm). This may be a complete AI or a full AI approach for cell sorting and analysis.
  • a hybrid approach may be utilized, wherein AI-mediated analysis may analyze cells and one or more heterologous markers that are co-partitioned with the cells (e.g., into the same planar current flowing through the channel), confirm or determine the co-partitioning, after which a more conventional approach (e.g., imaging to detect presence of the heterologous markers, such as fluorescent imaging) may be utilized to sort a subsequent population of cells and the heterologous markers that are co-partitioned into the same planar current.
  • the machine learning model e.g., a metamodel
  • learning algorithms e.g., machine learning algorithms
  • a training algorithm may build a machine learning model capable of assigning new examples/cases (e.g., new datapoints of a cell or a group of cells) into one category or the other, e.g., to make the model a non-probabilistic machine learning model.
  • the machine learning model may be used to create a new category to assign new examples/cases into the new category.
  • a machine learning model may be the actual trained model that is generated based on the training model.
  • the machine learning algorithm as disclosed herein may be configured to extract one or more morphological features of a cell from the image data of the cell.
  • the machine learning algorithm may form a new data set based on the extracted morphological features, and the new data set need not contain the original image data of the cell.
  • replicas of the original images in the image data may be stored in a database disclosed herein, e.g., prior to using any of the new images for training, e.g., to keep the integrity of the images of the image data.
  • processed images of the original images in the image data may be stored in a database disclosed herein during or subsequent to the classifier training.
  • any of the newly extracted morphological features as disclosed herein may be utilized as new molecular markers for a cell or population of cells of interest to the user.
  • cell analysis platform as disclosed herein may be operatively coupled to one or more databases comprising non-morphological data of cells processed (e.g., genomics data, transcriptomics data, proteomics data, metabolomics data), a selected population of cells exhibiting the newly extracted morphological feature(s) may be further analyzed by their non-morphological properties to identify proteins or genes of interest that are common in the selected population of cells but not in other cells, thereby determining such proteins or genes of interest to be new molecular markers that may be used to identify such selected population of cells.
  • non-morphological data of cells processed e.g., genomics data, transcriptomics data, proteomics data, metabolomics data
  • a selected population of cells exhibiting the newly extracted morphological feature(s) may be further analyzed by their non-morphological properties to identify proteins or genes of interest that are common in the selected population of cells but not in other
  • a machine learning model may be trained by applying machine learning algorithms on at least a portion of one or more cell morphology maps as disclosed herein as a training dataset.
  • machine learning algorithms for training a machine learning model may include supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, self- learning, feature learning, anomaly detection, association rules, etc.
  • a machine learning model may be trained by using one or more learning models on such training dataset.
  • Non-limiting examples of learning models may include artificial neural networks (e.g., convolutional neural networks, U-net architecture neural network, etc.), backpropagation, boosting, decision trees, support vector machines, regression analysis, Bayesian networks, genetic algorithms, kernel estimators, conditional random field, random forest, ensembles of machine learning models, minimum complexity machines (MCM), probably approximately correct learning (PACT), etc.
  • the neural networks are designed by the modification of neural networks such as AlexNet, VGGNet, GoogLeNet, ResNet (residual networks), DenseNet, and Inception networks.
  • the enhanced neural networks are designed by modification of ResNet (e.g.
  • the modification comprises a series of network surgery operations that are mainly carried out to improve including inference time and/or inference accuracy.
  • a Vision Transformer may be used in addition to, or as an alternative neural network architecture to, a CNN.
  • the present machine learning machine learning model may include a hybrid architecture that incorporates that incorporates aspects of both a CNN and a Vision Transformer.
  • the present machine learning machine learning model may include a Vision Transformer which is used as a drop-in replacement for a CNN and is trained using the same procedure as would be used to train a CNN.
  • the Vision Transformer may function as an encoder (and in all respects may have the same function as the model as a feature extractor).
  • the Vision Transformer is configured to process tiles of images sequentially using interleaved self-attention operations with multilayer perceptrons (MLP).
  • MLP multilayer perceptrons
  • Nonlimiting examples of Vision Transformers and their use in encoding images are described in Dosovitskiy et al., “An image is worth 16x16 words: Transformers for image recognition at scale,” International Conference on Learning Representations (ICLR) (2021) (21 pages available at arxiv.org/abs/2010.11929), the entire contents of which are incorporated by reference herein.
  • the machine learning algorithm as disclosed herein may utilize one or more clustering algorithms to determine that objects (e.g., cells) in the same cluster may be more similar (in one or more morphological features) to each other than those in other clusters.
  • clustering algorithms may include, but are not limited to, connectivity models (e.g., hierarchical clustering), centroid models (e.g.
  • K- means algorithm
  • distribution models e.g., expectation-maximization algorithm
  • density models e.g., density-based spatial clustering of applications with noise (DBSCAN), ordering points to identify the clustering structure (OPTICS)
  • subspace models e.g., biclustering
  • group models e.g., graph-based models (e.g., highly connected subgraphs (HCS) clustering algorithms), single graph models, and neural models (e.g., using unsupervised neural network).
  • the machine learning algorithm may utilize a plurality of models, e.g., in equal weights or in different weights.
  • the graph-based models may include graph- based clustering algorithms that use modularity, e.g., such as described in the following references, the entire contents of each of which are incorporated by reference herein: Blondel et al., “Fast unfolding of communities in large networks,” Journal of Statistical Mechanics: Theory and Experiment 2008: P10008 (2008); and Traag et al., “From Louvain to Leiden: guaranteeing well-connected communities,” Scientific Reports 9: 5233, 12 pages, (2019).
  • unsupervised and self-supervised approaches may be used to expedite labeling of image data of cells. For the example of unsupervised, an embedding for a cell image may be generated.
  • the embedding may be a representation of the image in a space with reduced dimensions than the original image data.
  • Such embeddings may be used to cluster images that are similar to one another.
  • the labeler may be configured to batch-label the cells and increase the throughput as compared to manually labeling one or more cells.
  • additional meta information e.g., additional non-morphological information
  • additional non-morphological information about the sample (e.g., what disease is known or associated with the patient who provided the sample) may be used for labeling of image data of cells.
  • embedding generation may use a neural net trained on predefined cell types.
  • an intermediate layer of the neural net that is trained on predetermined image data may be used.
  • image data e.g., image data of known cell types and/or states
  • this method may have a benefit of providing an accurate way to cluster future cells.
  • embedding generation may use neural nets trained for different tasks.
  • an intermediate layer of the neural net that is trained for a different task e.g., a neural net that is trained on a canonical dataset such as ImageNet).
  • autoencoders may be used for embedding generation.
  • autoencoders may be used, in which the input and the output may be substantially the same image and the squeeze layer may be used to extract the embeddings.
  • the squeeze layer may force the model to learn a smaller representation of the image, which smaller representation may have sufficient information to recreate the image (e.g., as the output).
  • an expanding training data set may be used for clustering-based labeling of image data or cells.
  • one or more revisions of labeling e.g., manual relabeling
  • Such manual relabeling may be intractable on a large scale and ineffective when done on a random subset of the data.
  • similar embedding-based clustering may be used to identify labeled images that may cluster with members of other classes.
  • adaptive image augmentation may be used.
  • one or more images with artifacts may be identified, and (2) such images identified with artifacts may be added to training pipeline (e.g., for training the model).
  • Identifying the image(s) with artifacts may comprise: (1a) while imaging cells, one or more additional sections of the image frame may be cropped, which frame(s) being expected to contain just the background without any cell; (2a) the background image may be checked for any change in one or more characteristics (e.g., optical characteristics, such as brightness); and (3a) flagging/labeling one or more images that have such change in the characteristic(s).
  • Adding the identified images to training pipeline may comprise: (2a) adding the one or more images that have been flagged/labeled as augmentation by first calculating an average feature of the changed characteristic(s) (e.g., the background median color); (2b) creating a delta image by subtracting the average feature from the image data (e.g., subtracting the median for each pixel of the image); and (3c) adding the delta image to the training pipeline.
  • the model(s) may be validated (e.g., for the ability to demonstrate accurate cell classification performance).
  • validation metrics may include, but are not limited to, threshold metrics (e.g., accuracy, F-measure, Kappa, Macro-Average Accuracy, Mean-Class-Weighted Accuracy, Optimized Precision, Adjusted Geometric Mean, Balanced Accuracy, etc.), the ranking methods and metrics (e.g., receiver operating characteristics (ROC) analysis or “ROC area under the curve (ROC AUC)”), and the probabilistic metrics (e.g., root-mean-squared error).
  • threshold metrics e.g., accuracy, F-measure, Kappa, Macro-Average Accuracy, Mean-Class-Weighted Accuracy, Optimized Precision, Adjusted Geometric Mean, Balanced Accuracy, etc.
  • the ranking methods and metrics e.g., receiver operating characteristics (ROC) analysis or “ROC area under the curve (ROC AUC)”
  • the probabilistic metrics e.g., root-mean-squared error
  • the model(s) may be determined to be balanced or accurate when the ROC AUC is greater than about 0.5 - e.g., greater than about 0.55, greater than about 0.6, greater than about 0.65, greater than about 0.7, greater than about 0.75, greater than about 0.8, greater than about 0.85, greater than about 0.9, greater than about 0.91, greater than about 0.92, greater than about 0.93, greater than about 0.94, greater than about 0.95, greater than about 0.96, greater than about 0.97, greater than about 0.98, greater than about 0.99, or higher.
  • Computer Vision model of HFM [0211]
  • the computer vision model of the human foundation model may include a set of rules to identify cell morphometric features within an image, and to encode those features into a multidimensional vector.
  • the rules may be human defined, and may correspond to features that may be understood by a human.
  • the output of the computer vision encoder may include, or may consist essentially of, or may consist of, at least one multidimensional vector (which may also be referred to herein as an embedding). Elements of the vector(s) for a given image may correspond to the values of respective features that the computer vision encoder extracted from that image. Because the features are human defined, the features may be human-interpretable. Table 2 below describes example computer vision dimensions (morphometric features), which correspond to different features that the computer vision encoder may extract from images. Table 2. Example morphometric features generated using the human foundation model.
  • morphometric features may be categorized into different groups. For example, cell morphometric features may be selected from the group consisting of position features, cell shape features, pixel intensity features, texture features, and focus features. In some examples, position features may be selected from the group consisting of: centroid X axis and centroid Y axis, where Table 2 provides respective example descriptions for such features.
  • cell shape features may be selected from the group consisting of: area, perimeter, maximum caliper distance, minimum caliper distance, maximum radius, minimum radius, long ellipse axis, short ellipse axis, ellipse elongation, ellipse similarity, roundness, circle similarity, and convex shape, where Table 2 provides respective example descriptions for such features.
  • pixel intensity features are selected from the group consisting of: mean pixel intensity, standard deviation of pixel intensity, pixel intensity 25th percentile, pixel intensity 75th percentile, positive fraction, and negative fraction, where Table 2 provides respective descriptions for such features.
  • texture features may be selected from the group consisting of: small set of connected bright pixels, integral; small set of connected dark pixels, integral; large set of connected bright pixels, integral; large set of connected dark pixels, integral; image moments; local binary patterns – center; local binary patterns – periphery; image sharpness; image focus; ring width; and ring intensity.
  • the computer vision encoder extracts m morphometric features from each image (where m is a positive integer), and outputs an array of length m, which array may be considered to be an m-dimensional vector. For the illustrative computer vision dimensions listed in Table 2, the output of the computer vision encoder may have the format: [W1 W2 W3 ....
  • m may be in any suitable range, e.g., may be between about 5 and about 1000, e.g., between about 10 and about 500, e.g., between about 50 and about 100. In the nonlimiting example shown in Table 2, m is equal to 51. [0215] Because the morphometric features represent features that are visible by both human and computer vision, the features may be human-interpretable.
  • the computer vision encoder may be implemented using any suitable combination of hardware and software.
  • the system component which is implementing the HFM may include a processor and a computer-readable medium (such as a non-volatile computer-readable medium) that includes instructions for causing the processor to respectively process cell images using a computer vision encoder.
  • the computer vision encoder may be configured to quantify the characteristics (e.g., to measure dimensions or intensities) of different features within respective cell images, and to output a vector the dimensions (elements) of which correspond to the measured values of those respective characteristics.
  • the set of ML-based features extracted using the machine learning encoder and the set of cell morphometric features extracted using the computer vision encoder may be used to respectively encode the set of ML-based features and the set of cell morphometric features into a plurality of multi-dimensional vectors that represent morphology of a cell in a cell image.
  • the multi-dimensional vectors may have n+m dimensions, where n and m are positive integers.
  • each dimension of the n+m dimensions may be an element of that multi-dimensional vector, e.g., a numeric value.
  • the ML-based features and the cell morphometric features may be concatenated to generate a multi-dimensional vector having the format: where, similarly as above, the subscripts 1...n correspond to the respective deep learning dimension numbers, the letter V represents the value of the feature in that image that the deep learning encoder calculated, the subscripts 1...m correspond to the respective computer vision dimension numbers, and the letter W represents the value of the feature in that image that the computer vision encoder calculated.
  • the ML-based features may be orthogonal to one another.
  • the ML-based features may all be different than one another, and may all be uncorrelated to one another.
  • ML-based feature V 1 may be different than, and uncorrelated to, each of ML-based features V2...Vn.
  • the ML-based features may be orthogonal to the cell morphological features.
  • System for Cell Morphology Analysis [0219] Cell morphology may be highly indicative of a cell’s phenotype and function, but it is also highly dynamic and complex. Traditional analysis of cell morphology by human eyes has significant limitations.
  • Some examples of the present disclosure provide a quantitative, high-dimensional, unbiased platform to assess cell morphology and magnify insights into a cell’s phenotype and functions.
  • the system as described herein may provide imaging of single cells and label-free sorting in one platform. For example, the system may directly capture high-resolution brightfield images of cells in real time. The system may also enable cell sorting based on their morphology without involving any cell labels. The cells may remain viable and minimally perturbed after the sorting process. In addition, the system may allow collection of sorted cells for downstream analysis, for example, single-cell RNA sequencing.
  • the system may comprise, or be compatible with, the human foundation model for high- dimensional morphological feature analysis.
  • the system may comprise or be compatible with a data suite that may allow users to store, visualize, and analyze images and high-dimensional data.
  • the system may enable the end-to-end process including cell imaging, morphology analysis, sorting, and classification.
  • the system may comprise a microfluidic platform. When cells flow through the microfluidic platform, the system may capture high-resolution brightfield images of each individual cell. The images may be processed by the human foundation model for extracting high- dimensional features corresponding to the cells. The system may sort the cells in different categories, based on the distinct morphological features.
  • FIG. 3 illustrates an example system for cell morphology analysis, in accordance with some examples of the present disclosure.
  • the system 300 may comprise a benchtop microfluidic platform 310 that captures high-resolution brightfield images of single cells and sort cells in a label-free manner.
  • System 300 also may include data suite 330, which may be implemented using (and integrated with) microfluidic platform 310, or may be implemented using a separate device. Tables 3 and 4, provided further below, list example parameters, specifications, and components of system 300.
  • FIG.4 illustrates system 400 which includes, and illustrates the interaction between, a microfluidic platform 410 (e.g., corresponding to microfluidic platform 310 illustrated in FIG.3), the human foundation model 410, and a data suite 430, in accordance with some examples of the present disclosure.
  • the system 400 for cell morphology analysis may comprise a microfluidic platform 410, which may comprise or be compatible with the human foundation model 420 and the data suite 430.
  • Example interactions between the microfluidic platform 410, the human foundation model 420, and the data suite 430 will be described in further detail elsewhere herein, including below in accordance with FIGS.6A-6B.
  • FIG.4 illustrates system 400 which includes, and illustrates the interaction between, a microfluidic platform 410 (e.g., corresponding to microfluidic platform 310 illustrated in FIG.3), the human foundation model 410, and a data suite 430, in accordance with some examples of the present disclosure.
  • the system 500 may include a benchtop microfluidic platform that uses a set of reagents and chips for imaging and sorting, and may correspond to microfluidic platform 310 illustrated in FIG.3. Reagents may be placed in the reagent drawer 510 and chips are loaded onto the stage.
  • the system 500 may be controlled by a controller software.
  • the on-instrument display 520 may show real- time run status of cell imaging, characterization, sorting and classification.
  • the system 500 may use a microfluidic chip that allows for the input and flow of cells in suspension.
  • the high-speed system 500 may include an imager (e.g., including a light source, one or more objective lenses, and an image sensor) to collect brightfield images of cells as they flow through a microfluidic chip. High-resolution images may capture subcellular and subnuclear features of the cells in high contrast.
  • the system 500 may allow high- speed image capture in a range of about 1 to about 10, about 1 to about 50, about 1 to about 100, about 1 to about 200, about 1 to about 500, about 1 to about 800, about 1 to about 1,000, about 1 to about 2000, about 1 to about 5,000, or about 1 to about 8,000 image capturing events per second. Any range of image capturing events per second may be contemplated, for example from 1 through any number greater than about 8,000.
  • system 500 may include data suite 330 or 410 to perform further processing using the output of the human foundation model.
  • system 500 may include a computer processor and at least one computer-readable medium (e.g., non- volatile computer-readable medium) that stores instructions for causing the computer processor to collect images using the image sensor, and instructions to transmit the images to another, external system that uses the human foundation model to process the images and in one example further may perform further processing using the output of the human foundation model.
  • the human foundation model, and in one example also the data suite may process cell images for morphology analysis, allowing the cells to be sorted in a plurality of collection wells.
  • system 500 further may include a cell sorter, and the instructions may be for causing the computer to sort cells into one or more collection wells using the output of the human foundation model.
  • the processor may determine, using the human foundation model, the values of each of a plurality of machine learning features using a machine learning encoder and the values of each of a plurality of morphometric features using a computer vision encoder.
  • the data suite may be used determine that certain cells have one or more machine learning features and/or one or more morphometric features in common, and may sort those cells into the same collection well as one another.
  • the system may comprise at least about two, e.g., at least about three, at least about four, at least about five, at least about six, at least about seven, at least about eight, at least about nine, at least about ten, at least about fifteen, or at least about twenty collection wells, or more, such that cells may be classified and sorted based on their distinct morphological features.
  • the system 500 may further comprise a laser-based system that tracks cells in real-time to assist with imaging and sorting, and to report on the purity and yield of the run. Further details regarding options for systems 300, 400, 500, including options for analyzing, classifying, and/or sorting cells, are provided below with reference to FIGS. 15-23. [0227] FIG.
  • the workflow 600 may be streamlined, starting from preparing and loading cells onto the microfluidic platform (operation 610).
  • the preparation of samples may comprise dissociation of cells into a single-cell suspension and loading the suspension onto the microfluidic platform.
  • the system may capture images of the cells, and the human foundation model may characterize the cells in real time as they flow through the microfluidic chip (operation 620).
  • the human foundation model may process the images of the cells and generate high-dimensional features reflecting the cell morphology.
  • the images and extracted features may be stored in the data suite (operation 630).
  • the data suite may also provide in-depth data analysis, including selecting cell populations of interest to sort on the microfluidic platform.
  • the system may recover sorted cells in a plurality of collection wells (operation 640), which may be used for downstream analyses.
  • FIG.6B illustrates another example workflow from sample preparation to cell characterization and sorting based on cell morphology profiling, in accordance with some examples of the present disclosure.
  • the system for cell morphology profiling as described herein may comprise a benchtop microfluidic platform that captures high-resolution brightfield images of single cells and sort cells in a label-free manner.
  • the microfluidic platform may comprise or be compatible with the human foundation model and a data suite that may allow users to store, visualize, and analyze images and high-dimensional data.
  • FIG. 7 illustrates example characterization of tumor cells based on cell morphology profiling, in accordance with some examples of the present disclosure.
  • the tumor microenvironment (TME) is a highly complex ecosystem. Tumor cells may co-exist with immune cells (e.g., macrophages, polymorphonuclear cells, mast cells, natural killer cells, dendritic cells (DCs), T lymphocytes, and B lymphocytes) and non- immune cells.
  • immune cells e.g., macrophages, polymorphonuclear cells, mast cells, natural killer cells, dendritic cells (DCs), T lymphocytes, and B lymphocytes
  • the immune cell components of a tumor may determine the metastatic ability of the tumor.
  • some tumor cells e.g., melanoma cells
  • some tumor cells may have genomic instability.
  • the morphology of tumor cells may represent different status and characterization of tumor cells. For example, different types of tumor cells and non-tumor cells may have distinct morphological features within the same microenvironment.
  • the system and method as described herein may characterize and sort cells by analyzing the images of the cells, generate multi- dimensional morphometric features and based on which, differentiate cells by types and functions. [0230] It will be appreciated that the features and operations described herein may be used in any suitable combination with one another.
  • FIG.12 illustrates an example flow of operations in a method of processing images.
  • Method 1200 illustrated in FIG.12 includes providing the one or more cell images to a plurality of encoders including a machine learning encoder and a computer vision encoder (operation 1210).
  • any suitable component(s) of system 300, system 400, or system 500 may include a processor and a non-computer readable medium storing the machine learning encoder and the computer vision encoder, and instructions for causing the processor to use the machine learning encoder and the computer vision encoder to process the cell image(s).
  • the processor may be included within microfluidic platform 310 described with reference to FIG.
  • Method 1200 illustrated in FIG.12 also may include extracting a set of ML-based features via the machine learning encoder, and extracting a set of cell morphometric features via the computer vision encoder (operation 1220).
  • Nonlimiting examples of ML-based features that may be extracted using a machine learning encoder, and nonlimiting examples of cell morphometric features that may be extracted using a computer vision encoder, are provided elsewhere herein.
  • the machine learning encoder uses a convolutional neural network.
  • the convolutional neural network may have been trained using self-supervised learning, e.g., may have been trained to find features on its own using unlabeled images of unstained cells of a plurality of different types.
  • the computer- vision encoder uses a human-constructed algorithm. In a manner such as described above, the human- constructed algorithm may measure and output different selected characteristics of a cell that describe its morphology using user-defined rules, some nonlimiting examples of which are provided in Table 2.
  • Method 1200 illustrated in FIG. 12 also may include using the machine learning encoder and the computer vision encoder to respectively encode the set of ML-based features and the set of cell morphometric features into a plurality of multi-dimensional vectors that represent morphology of at least one cell in the one or more cell images.
  • the machine learning encoder may output vector(s) of the extracted ML-based features
  • the computer-vision encoder may vector(s) of the extracted cell morphometric features
  • the vectors of the extracted ML-based features and the extracted cell morphometric features may be combined (e.g., concatenated) to form a single data structure that includes a plurality of multi-dimensional vectors.
  • the plurality of multi-dimensional vectors are combined into a single column vector or into a single row vector.
  • the vector may have length (n+m), where n is the number of ML-based features in the set of ML-based features (and is a positive integer), and m is the number of cell morphometric features in the set of cell morphometric features (and is a positive integer).
  • each dimension of the n+m dimensions may be an element of that multi-dimensional vector.
  • the element may be a numeric value.
  • the multi-dimensional vectors may be expressed using the vector: [V 1 V 2 V 3 .... V n W 1 W 2 W 3 ....
  • the plurality of multi-dimensional vectors may include different numbers of the ML- based features and the cell morphological features; in these examples, m is not equal to n. In other examples, the plurality of multi-dimensional vectors may include a same number each of the ML-based features and the cell morphological features; in these examples m is equal to n.
  • the ML-based features may be orthogonal to one another.
  • Method 1300 illustrated in FIG.13 may include extracting, from image data of a plurality of cells, a multi-dimensional vector for a cell of the plurality of cells, wherein the multi-dimensional vector comprises (i) a set of machine-learning (ML)-based features extracted using a machine learning encoder and (ii) a set of cell morphometric features extracted using a computer vision encoder (operation 1310).
  • ML machine-learning
  • Nonlimiting examples of formats for the multi- dimensional vector, ML-based features, machine learning encoders, cell morphometric features, and computer vision encoders are provided elsewhere herein.
  • the image of the plurality of cells may be label-free.
  • the display of system 300, 400, or 500 may include a user interface via which a user may select from among the different ML-based features and/or cell morphometric features in the multi-dimensional vector for display (or other suitable output).
  • the user interface may, for example, prompt the user to select two of the ML-based features and/or cell morphometric features, or to select three of the ML-based features and/or cell morphometric features, or to select four of the ML-based features and/or cell morphometric features, or to select five or more of the ML-based features and/or cell morphometric features.
  • System 300, 400, or 500 then may generate a rendering of the selected features (e.g., a morphology map such as described with reference to FIG. 1).
  • Such rendering may, for example, include a multi-dimensional plot of which the axes correspond to the selected features.
  • the plot may be two-dimensional, where three features are selected, the plot may be three-dimensional, and so on.
  • the values of those features for each cell of the group of cells may be plotted along their respective axes in this rendering. [0237] In one example, say that features V 1 and W 3 are selected.
  • the lower-dimensional vector may be expressed, for each cell of the group of cells, as [V 1 W 3 ] where V 1 is the value of that ML-based feature extracted from the image of a respective cell, and W 3 is the value of that cell morphometric feature extracted from the image of a respective cell.
  • This reduced dimensionality vector may be rendered as a two- dimensional plot in which V 1 is one axis (e.g., the X-axis) and W 3 is another axis (e.g., the Y-axis). For each cell, a point may be rendered in this plot in which the coordinates of that point along the V 1 axis are that cell’s value of V1, and the coordinates of that point along the W3 axis are that cell’s extracted value of W 3 .
  • the dimensionality reduction may be performed using the data suite or other suitable component, e.g., of system 300, 400, or 500.
  • the dimension reduction technique is UMAP.
  • UMAPs are illustrated in, and described with reference to, FIGS.8A-8B, 9A-9C, and 10A.
  • the dimensionality of the multi-dimensional vector may be reduced using an algorithm that takes as input the full HFM feature vector and outputs a 2-dimensional or 3-dimensional vector that "compresses" the full HFM feature vector according to some criteria.
  • An example is UMAP where the full feature vector (e.g., 115 dimensional feature vector) is reduced to a 2-dimensional vector such that neighboring cells (cells that are closest together in distance) are the same in both 115 dimensions and 2 dimensions ("a nearest-neighbor preserving projection to 2D").
  • the UMAP coordinates correspond to the values of the 2-dimensional vector.
  • the color of each coordinate may be (1) categorical, such as a label derived from the sample (patient identifier), or the cluster assigned to the cell by a clustering algorithm, or any other categorical label, and/or may be (2) continuous: e.g., the value of a HFM feature.
  • Method 1300 illustrated in FIG. 13 also may include using at least in part the lower-dimensional vector to assess a similarity between the cell and an additional cell of the plurality of cells. The similarity is assessed in a feature space corresponding to the lower-dimensional vector.
  • Some examples further include, based at least in part on the assessing, generating a cell cluster map that includes a plurality of shapes representing the plurality of cells, wherein the plurality of shapes is arranged in a plurality of clusters using at least the similarity.
  • the lower-dimensional vector and the cell cluster map are generated by using different dimension reduction techniques.
  • the cell cluster map may be generated by using UMAP.
  • the different cells may be expected to differ in their extracted values along given dimensions more than would cells that are similar to one another.
  • the plot may render such similarities, or differences, in the extracted ML-based features and/or cell morphometric features using the locations of points along the respective axes of the plot.
  • a first subset of cells that are similar to one another may have points that form a first cluster in the plot, because their values of V 1 and W 3 are similar to one another, while a second subset of cells that are similar to one another and different than the first subset of cells may form a second cluster in the plot which is spaced apart from the first cluster along at least one of the axes, because their values of V 1 and W 3 are similar to one another and are different than those of the first subset of cells.
  • the reduced dimensionality plot provides a readily understandable, visual rendering of the manner in which different features of different subsets of cells are similar to, or different than, one another.
  • the lower dimensional vector in the simplified example described here has two dimensions, in some examples, the lower-dimensional vector may include three or more dimensions.
  • the user interface allows a user to select a point in the plot, and upon such selection provides further information about the cell to which that point corresponds. For example, upon such selection the user interface may display an image of the corresponding cell. The user interface also may display the HFM feature values for each cell and/or catel labels assigned to the sample that the cell came from (such as known cell type, patient number, and the like).
  • the multi-dimensional vector may be standardized via a feature scaling technique.
  • the feature scaling technique is selected from the group consisting of min- max normalization, mean normalization, and z-score normalization. For example, certain algorithms benefit from features being on consistent scales (e.g., PCA, clustering).
  • FIG. 14 illustrates an example flow of operations in a method for classifying.
  • Method 1400 may include extracting, from image data of a plurality of cells, a multi-dimensional vector for each cell of the plurality of cells, wherein the multi-dimensional vector comprises (i) a set of ML-based features extracted via a machine learning encoder and (ii) a set of cell morphometric features extracted via a computer vision encoder (operation 1410).
  • operation 1410 may be performed in a manner similar to that of operation 1310 described with reference to FIG.13.
  • the image data comprises a label- free image of each of the plurality of cells.
  • Method 1400 also may include using at least in part the one or more ML-based features and the morphometric features to classify the plurality of cells into a plurality of subsets of melanoma cells comprising a first subset with a first level of cell pigmentation and a second subset with a second level of cell pigmentation, wherein the first level and the second level are different (operation 1420).
  • the level of cell pigmentation may be characterized using at least one ML-based feature, at least one cell morphometric feature, or at least one ML-based feature and at least one cell morphometric feature.
  • the dimensionality of the multi-dimensional vectors of respective melanoma cells being classified may be reduced in a manner such as described with reference to operation 1320 of FIG.13.
  • a lower dimensional vector may be generated that includes one or more ML-based feature(s) and/or one or more cell morphometric features that characterize the level of cell pigmentation in those cells.
  • the lower dimensional vector may be used at least in part to assess similarity between melanoma cells in a manner such as described with reference to operation 1330 of FIG.13.
  • operation 1420 may include automatically generating a cell cluster map including a plurality of shapes representing the plurality of cells, wherein the plurality of shapes is arranged in a plurality of clusters that corresponds to the plurality of subsets of melanoma cells.
  • Method 1400 further may include sorting the first subset and the second subset into different sub-channels of a flow channel in fluid communication with the plurality of cells.
  • live cells may be captured with brightfield high resolution imaging and processed in real-time by self-supervised deep learning models to generate quantitative AI embeddings representative of cell morphology.
  • this platform uses an AI model to extract features from cell images of diverse human cells without prior knowledge of cell types, cell preparation, or other application-specific knowledge for an exploratory approach.
  • the Human Foundation Model includes a hybrid architecture that combines self-supervised learning (SSL) and morphometrics (computer vision) to extract 115 dimensional embeddings representing cell morphology from high-resolution cell images.
  • SSL produces a foundation model with high generalization capacity that enables hypothesis-free sample exploration and efficient generation of application-specific models.
  • ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇
  • the hybrid self-supervised Deep Learning model together with the Computer Vision model, produces 115 embeddings which are reproducible, quantitative, and high-dimensional descriptions that distinguish individual cells from one another.
  • deep learning embeddings may be analyzed through interactive UMAPs, population analytics, and image visualizations. Using these features, cell populations of interest may be defined, and select features may be reproducibly used for subsequent experiments or sort populations into six wells for further multi-omic and functional analysis.
  • the platform may be used to image patient-derived PBMC samples and purified immune cell subsets including CD4+ T cells (total, activated, and naive), CD8+ T cells (total, activated, and naive), CD14+ monocytes, CD19+ B cells, CD38+ plasma cells, CD56+ NK cells, and macrophages.
  • High-dimensional morphology analysis shows profiled immune cell subsets in distinct locations on the associated UMAP, indicating cell types are separable by morphological features extracted by the HFM.
  • Leiden clustering further distinguished cell groups by morphological traits, with several cell subsets overlapping with individual Leiden clusters.
  • An example training process for the HFM self-supervised backbone model, the discriminatory power added by supervised tasks, and validation of the reproducibility and generalization capabilities of the resulting model are described herein.
  • the Axon data suite a user-friendly tool designed for researchers to analyze data and create custom reusable workflows, also is described. This includes the ability to store and manage data, visualize high dimensional data as low-dimensional projections, and train classifiers to identify and sort cell populations on the REM-I instrument.
  • the tool provides data export options for images, plots, and embeddings.
  • the approach allows users of all computational skill levels to access and interpret AI- enabled morphological profiling.
  • Potential applications of the REM-I platform may include heterogeneous sample evaluation, tumor cell enrichment, cell state characterization, and multi-omic integration.
  • FIG. 15 schematically illustrates an example method for classifying a cell.
  • the method may comprise processing image data 1510 comprising tag-free images/videos of single cells (e.g., image data 1510 including, consisting essentially of, or consisting of tag-free images/videos of single cells).
  • image data 1510 including, consisting essentially of, or consisting of tag-free images/videos of single cells.
  • Various clustering analysis models 1520 as disclosed herein may be used to process the image data 1510 to extract one or more morphological properties of the cells from the image data 1510, and generate a cell morphology map 1530A based on the extracted one or more morphological properties.
  • the cell morphology map 1530A may be generated based on two morphological properties as dimension 1 and dimension 2.
  • the cell morphology map 1530A may comprise one or more clusters (e.g., clusters A, B, and C) of datapoints, each datapoint representing an individual cell from the image data 1510.
  • the cell morphology map 1530A and the clusters A-C therein may be used to train classifier(s) 1550.
  • a new image 1540 of a new cell may be obtained and processed by the trained classifier(s) 1550 to automatically extract and analyze one or more morphological features from the cellular image 1540 and plot it as a datapoint on the cell morphology map 1530A.
  • the classifier(s) 1550 may automatically classify the new cell.
  • the classifier(s) 1550 may determine a probability that the cell in the new image data 1540 belongs to cluster C (e.g., the likelihood for the cell in the new image data 1540 to share one or more commonalities and/or characteristics with cluster C more than with other clusters A/B).
  • the classifier(s) 1550 may determine and report that the cell in the new image data 1540 has a 95% probability of belonging to cluster C, 1% probability of belonging to cluster B, and 4% probability of belong to cluster A, solely based on analysis of the tag-free image 1540 and one or more morphological features of the cell extracted therefrom.
  • An image and/or video (e.g., a plurality of images and/or videos) of one or more cells as disclosed herein may be captured while the cell(s) is suspended in a fluid (e.g., an aqueous liquid, such as a buffer) and/or while the cell(s) is moving (e.g., transported across a microfluidic channel).
  • a fluid e.g., an aqueous liquid, such as a buffer
  • the cell(s) is moving (e.g., transported across a microfluidic channel).
  • the cell need not be suspended is a gel-like or solid-like medium.
  • the fluid may comprise a liquid that is heterologous to the cell(s)’s natural environment.
  • cells from a subject’s blood may be suspended in a fluid that comprises (i) at least a portion of the blood and (ii) a buffer that is heterologous to the blood.
  • the cell(s) may not be immobilized (e.g., embedded in a solid tissue or affixed to a microscope slide, such as a glass slide, for histology) or adhered to a substrate.
  • the cell(s) may be isolated from the natural environment or niche (e.g., a part of the tissue from which the cell(s) are retrieved from a subject) when the image and/or video of the cell(s) is captured.
  • the image and/or video need not be from a histological imaging.
  • each cell image may be annotated with the extracted one or more morphological features and/or with information that the cell image belongs to a particular cluster (e.g., a probability).
  • the cell morphology map may be a visual (e.g., graphical) representation of one or more clusters of datapoints.
  • the cell morphology map may be a 1-dimensional (1D) representation (e.g., based on one morphological property as one parameter or dimension) or a multi-dimensional representation, such as a 2- dimensional (2D) representation (e.g., based on two morphological properties as two parameters or dimensions), a 3-dimensional (3D) representation (e.g., based on three morphological properties as three parameters or dimensions), a 4-dimensional (4D) representation, etc.
  • 1D 1-dimensional
  • 2D 2- dimensional
  • 3D 3-dimensional
  • 4D 4-dimensional
  • one morphological property of a plurality of morphological properties used for blotting the cell morphology map may be represented as a non-axial parameter (e.g., non-x, y, or z axis), such as, distinguishable colors (e.g., heatmap), numbers, letters (e.g., texts of one or more languages), and/or symbols (e.g., a square, oval, triangle, square, etc.).
  • a heatmap may be used as colorimetric scale to represent the classifier prediction percentages for each cell against a cell class, cell type, or cell state.
  • the cell morphology map may be generated based on one or more morphological features (e.g., characteristics, profiles, fingerprints, etc.) from the processed image data.
  • morphological features e.g., characteristics, profiles, fingerprints, etc.
  • Non-limiting examples of one or more morphological properties of a cell, as disclosed herein, that may be extracted from one or more images of the cell may include, but are not limited to (i) shape, curvature, size (e.g., diameter, length, width, circumference), area, volume, texture, thickness, roundness, etc.
  • the cell or one or more components of the cell e.g., cell membrane, nucleus, mitochondria, etc.
  • number or positioning of one or more contents e.g., nucleus, mitochondria, etc.
  • optical characteristics of a region of the image(s) e.g., unique groups of pixels within the image(s) that correspond to the cell or a portion thereof (e.g., light emission, transmission, reflectance, absorbance, fluorescence, luminescence, etc.).
  • Non-limiting examples of clustering as disclosed herein may be hard clustering (e.g., determining whether a cell belongs to a cluster or not), soft clustering (e.g., determining a likelihood that a cell belongs to each cluster to a certain degree), strict partitioning clustering (e.g., determining whether each cell belongs to exactly one cluster), strict partitioning clustering with outliers (e.g., determining whether a cell may also belong to no cluster), overlapping clustering (e.g., determining whether a cell may belong to more than one cluster), hierarchical clustering (e.g., determining whether cells that belong to a child cluster may also belong to a parent cluster), and subspace clustering (e.g., determining whether clusters are not expected to overlap).
  • hard clustering e.g., determining whether a cell belongs to a cluster or not
  • soft clustering e.g., determining a likelihood that a cell belongs to each cluster to a certain degree
  • strict partitioning clustering e.
  • Cell clustering and/or generation of the cell morphology map may be based on a single morphological property of the cells.
  • cell clustering and/or generation the cell morphology map may be based on a plurality of different morphological properties of the cells.
  • the plurality of different morphological properties of the cells may have the same weight or different weights.
  • a weight may be a value indicative of the importance or influence of each morphological property relative to one another in training the classifier or using the classifier to (i) generate one or more cell clusters, (ii) generate the cell morphology map, or (iii) analyze a new cellular image to classify the cellular image as disclosed herein.
  • cell clustering may be performed by having 50% weight on cell shape, 40% weight on cell area, and 10% weight on texture (e.g., roughness) of the cell membrane.
  • the classifier as disclosed herein may be configured to adjust the weights of the plurality of different morphological properties of the cells during analysis of new cellular image data, thereby to yield a most optimal cell clustering and cell morphology map.
  • the plurality of different morphological properties with different weights may be utilized during the same analysis operation for cell clustering and/or generation of the cell morphology map.
  • the plurality of different morphological properties may be analyzed hierarchically.
  • a first morphological property may be used as a parameter to analyze image data of a plurality of cells to generate an initial set of clusters.
  • a second and different morphological property may be used as a second parameter to (i) modify the initial set of clusters (e.g., optimize arrangement among the initial set of clusters, re-group some clusters of the initial set of clusters, etc.) and/or (ii) generate a plurality of sub-clusters within a cluster of the initial set of clusters.
  • a first morphological property may be used as a parameter to analyze image data of a plurality of cells to generate an initial set of clusters, to generate a 1D cell morphology map.
  • a second morphological property may be used as a parameter to further analyze the clusters of the 1D cell morphology map, to modify the clusters and generate a 2D cell morphology map (e.g., a first axis parameter based on the first morphological property and a second axis parameter based on the second morphological property).
  • a 2D cell morphology map e.g., a first axis parameter based on the first morphological property and a second axis parameter based on the second morphological property.
  • the initial morphological feature may be cell type, such as stem cells (or not), and the sub-features may be different types of stem cells (e.g., embryonic stem cells, induced pluripotent stem cells, mesenchymal stem cells, muscle stem cells, etc.).
  • the initial may be cancer cells (or not), and the sub-feature may be different types of cancer cells (e.g., sarcoma cells, sarcoma cells, leukemia cells, lymphoma cells, multiple myeloma cells, melanoma cells, etc.).
  • the initial may be cancer cells (or not), and the sub-feature may be different stages of the cancer cell (e.g., quiescent, proliferative, apoptotic, etc.).
  • Each datapoint may represent an individual cell or a collection of a plurality of cells (e.g., at least about 2 - e.g., at least about 3, at least about 4, at least about 5, at least about 6, at least about 7, at least about 8, at least about 9, or at least about 10 cells or more).
  • Each datapoint may represent an individual image (e.g., of a single cell or a plurality of cells) or a collection of a plurality of images (e.g., at least about 2, - e.g., at least about 3, at least about 4, at least about 5, at least about 6, at least about 7, at least about 8, at least about 9, or at least about 10 images of the same single cell or different cells or more ).
  • the cell morphology map may comprise at least about 1, for example at least about 2, or at least about 3, or at least about 4, or at least about 5, or at least about 6, or at least about 7, or at least about 8, or at least about 9, or at least about 10, or at least about 15, or at least about 20, or at least about 30, or at least about 40, or at least about 50, or at least about 60, or at least about 70, or at least about 80, or at least about 90, or at least about 100, or at least about 150, or at least about 200, or at least about 300, or at least about 400, or at least about 500 clusters, or more.
  • Each cluster as disclosed herein may comprise a plurality of sub-clusters, e.g., at least about 2, or at least about 3, or at least about 4, or at least about 5, or at least about 6, or at least about 7, or at least about 8, or at least about 9, or at least about 10, or at least about 15, or at least about 20, or at least about 30, or at least about 40, or at least about 50, or at least about 60, or at least about 70, or at least about 80, or at least about 90, or at least about 100, or at least about 150, or at least about 200, or at least about 300, or at least about 400, or at least about 500 sub-clusters, or more.
  • sub-clusters e.g., at least about 2, or at least about 3, or at least about 4, or at least about 5, or at least about 6, or at least about 7, or at least about 8, or at least about 9, or at least about 10, or at least about 15, or at least about 20, or at least about 30, or at least about 40, or at least about 50, or at least about 60,
  • a cluster (or sub-cluster) may comprise datapoints representing cells of the same type/state. In another example,In another example, a cluster (or sub-cluster) may comprise datapoints representing cells of different types/states.
  • a cluster (or sub-cluster) may comprise at least about 1, for example at least about 2, or at least about 3, or at least about 4, or at least about 5, or at least about 6, or at least about 7, or at least about 8, or at least about 9, or at least about 10, or at least about 15, or at least about 20, or at least about 30, or at least about 40, or at least about 50, or at least about 60, or at least about 70, or at least about 80, or at least about 90, or at least about 100, or at least about 150, or at least about 200, or at least about 300, or at least about 400, or at least about 500, or at least about 1,000, or at least about 2,000, or at least about 3,000, or at least about 4,000, or at least about 5,000, or at least about 10000, or at least
  • Two or more clusters may overlap in a cell morphology map. In another example, no clusters may not overlap in a cell morphology map. In some examples, an allowable degree of overlapping between two or more clusters may be adjustable (e.g., manually or automatically by a machine learning algorithm) depending on the quality, condition, or size of data in the image data being processed.
  • a cluster (or sub-cluster) as disclosed herein may be represented with a boundary (e.g., a solid line or a dashed line). In another example, a cluster or sub-cluster need not be represented with a boundary, and may be distinguishable from other cluster(s) sub-cluster(s) based on their proximity to one another.
  • a cluster (or sub-cluster) or a data comprising information about the cluster may be annotated based on one or more annotation schema (e.g., predefined annotation schema).
  • annotation schema e.g., predefined annotation schema
  • Such annotation may be manual (e.g., by a user of the method or system disclosed herein) or automatically (e.g., by any of the machine learning algorithms disclosed herein).
  • the annotation of the clustering may be related the one or more morphological properties of the cells that have been analyzed (e.g., cell shape, cell area, optical characteristic(s), etc.) to generate the cluster or assign one or more datapoints to the cluster.
  • the annotation of the clustering may be related to information that has not been used or analyzed to generate the cluster or assign one or more datapoints to the cluster (e.g., genomics, transcriptomics, or proteomics, etc.). In such example, the annotation may be utilized to add additional “layers” of information to each cluster.
  • an interactive annotation tool may be provided that permits one or more users to modify any process of the method described herein. For example, the interactive annotation tool may allow a user to curate, verify, edit, and/or annotate the morphologically-distinct clusters.
  • the interactive annotation tool may process the image data, extract one or more morphological features from the image data, and allow the user to select one or more of the extracted morphological features to be used as a basis to generate the clusters and/or the cell morphology map.
  • the interactive annotation tool may allow the user to annotate each cluster and/or the cell morphology map using (i) a predefined annotation schema or (ii) a new, user- defined annotation schema.
  • the interactive annotation tool may allow user to assign different weights to different morphological features for the clustering and/or map plotting.
  • One or more cell morphology maps as disclosed herein may be used to train one or more classifiers (e.g., at least about 1, for example, at least about 2, at least about 3, at least about 4, at least about 5, at least about 6, at least about 7, at least about 8, at least about 9, at least about 10, or more classifiers) as disclosed herein.
  • Each classifier may be trained to analyze one or more images of a cell (e.g., to extract one or more morphological features of the cell) and categorize (or classify) the cell into one or more determined class or categories of a cell (e.g., based on a type of state of the cell).
  • the classifier may be trained to create a new category to categorize (or classify) the cell into the new category, e.g., when determining that the cell is morphologically distinct than any pre-existing categories of other cells.
  • the human foundation model e.g., machine learning algorithm and computer vision model
  • the human foundation model may be configured to extract one or more morphological feature of a cell from the image data of the cell.
  • the human foundation model may form a new data set based on the extracted morphological features, and the new data set need not contain the original image data of the cell.
  • replicas of the original images in the image data may be stored in a database disclosed herein, e.g., prior to using any of the new images for training, e.g., to keep the integrity of the images of the image data.
  • processed images of the original images in the image data may be stored in a database disclosed herein during or subsequent to the classifier training.
  • any of the newly extracted morphological features as disclosed herein may be utilized as new molecular markers for a cell or population of cells of interest to the user.
  • a selected population of cells exhibiting the newly extracted morphological feature(s) may be further analyzed by their non-morphological properties to identify proteins or genes of interest that are common in the selected population of cells but not in other cells, thereby determining such proteins or genes of interest to be new molecular markers that may be used to identify such selected population of cells.
  • a classifier may be trained by applying machine learning algorithm(s) on at least a portion of one or more cell morphology maps as disclosed herein, which maps may be used as a training dataset.
  • Non-limiting examples of machine learning algorithms for training a classifier may include supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, self-learning, feature learning, anomaly detection, association rules, etc.
  • a classifier may be trained by using one or more learning models on such training dataset.
  • learning models may include artificial neural networks (e.g., convolutional neural networks, U-net architecture neural network, etc.), backpropagation, boosting, decision trees, support vector machines, regression analysis, Bayesian networks, genetic algorithms, kernel estimators, conditional random field, random forest, ensembles of classifiers, minimum complexity machines (MCM), probably approximately correct learning (PACT), etc.
  • the neural networks are designed by the modification of neural networks such as AlexNet, VGGNet, GoogLeNet, ResNet (residual networks), DenseNet, and Inception networks.
  • the enhanced neural networks are designed by modification of ResNet (e.g. ResNet 18, ResNet 34, ResNet 50, ResNet 101, and ResNet 152) or inception networks.
  • the modification comprises a series of network surgery operations that are mainly carried out to improve including inference time and/or inference accuracy.
  • the machine learning algorithm as disclosed herein may utilize one or more clustering algorithms to determine that objects in the same cluster may be more similar (in one or more morphological features) to each other than those in other clusters.
  • Non-limiting examples of the clustering algorithms may include, but are not limited to, connectivity models (e.g., hierarchical clustering), centroid models (e.g. K-means algorithm), distribution models (e.g., expectation-maximization algorithm), density models (e.g., density- based spatial clustering of applications with noise (DBSCAN), ordering points to identify the clustering structure (OPTICS)), subspace models (e.g., biclustering), group models, graph-based models (e.g., highly connected subgraphs (HCS) clustering algorithms), single graph models, and neural models (e.g., using unsupervised neural network).
  • the machine learning algorithm may utilize a plurality of models, e.g., in equal weights or in different weights.
  • unsupervised and self-supervised approaches may be used to expedite labeling of image data of cells.
  • an embedding for a cell image may be generated.
  • the embedding may be a representation of the image in a space with reduced dimensions than the original image data.
  • Such embeddings may be used to cluster images that are similar to one another.
  • the labeler may be configured to batch-label the cells and increase the throughput as compared to manually labeling one or more cells.
  • additional meta information e.g., additional non-morphological information
  • additional meta information e.g., additional non-morphological information
  • embedding generation may use a neural net trained on predefined cell types.
  • an intermediate layer of the neural net that is trained on predetermined image data (e.g., image data of known cell types and/or states) may be used.
  • embedding generation may use neural nets trained for different tasks.
  • an intermediate layer of the neural net that is trained for a different task e.g., a neural net that is trained on a canonical dataset such as ImageNet.
  • this may allow to focus on features that matter for image classification (e.g., edges and curves) while removing a bias that may otherwise be introduced in labeling the image data.
  • autoencoders may be used for embedding generation.
  • autoencoders may be used, in which the input and the output may be substantially the same image and the squeeze layer may be used to extract the embeddings.
  • the squeeze layer may force the model to learn a smaller representation of the image, which smaller representation may have sufficient information to recreate the image (e.g., as the output).
  • an expanding training data set may be used for clustering-based labeling of image data or cells. With the expanding training data set, one or more revisions of labeling (e.g., manual relabeling) may be needed to, e.g., avoid the degradation of model performance due to the accumulated effect of mislabeled images.
  • Such manual relabeling may be intractable on a large scale and ineffective when done on a random subset of the data.
  • similar embedding-based clustering may be used to identify labeled images that may cluster with members of other classes. Such examples are likely to be enriched for incorrect or ambiguous labels, which may be removed (e.g., automatically or manually).
  • adaptive image augmentation may be used. In order to make the models and classifiers disclosed herein more robust to artifacts in the image data, (1) one or more images with artifacts may be identified, and (2) such images identified with artifacts may be added to training pipeline (e.g., for training the model/classifier).
  • Identifying the image(s) with artifacts may comprise: (1a) while imaging cells, one or more additional sections of the image frame may be cropped, which frame(s) being expected to contain just the background without any cell; (2a) the background image may be checked for any change in one or more characteristics (e.g., optical characteristics, such as brightness); and (3a) flagging/labeling one or more images that have such change in the characteristic(s).
  • Adding the identified images to training pipeline may comprise: (2a) adding the one or more images that have been flagged/labeled as augmentation by first calculating an average feature of the changed characteristic(s) (e.g., the background median color); (2b) creating a delta image by subtracting the average feature from the image data (e.g., subtracting the median for each pixel of the image); and (3c) adding the delta image to the training pipeline.
  • an average feature of the changed characteristic(s) e.g., the background median color
  • 2b) creating a delta image by subtracting the average feature from the image data e.g., subtracting the median for each pixel of the image
  • adding the delta image to the training pipeline may comprise: (2a) adding the one or more images that have been flagged/labeled as augmentation by first calculating an average feature of the changed characteristic(s) (e.g., the background median color); (2b) creating a delta image by subtracting the average feature from the image data (e.g., subtracting the median for each
  • One or more dimension of the cell morphology map may be represented by various approaches (e.g., dimensionality reduction approaches), such as, for example, principal component analysis (PCA), multidimensional scaling (MDS), t-distributed stochastic neighbor embedding (t-SNE), and uniform manifold approximation and projection (UMAP).
  • PCA principal component analysis
  • MDS multidimensional scaling
  • t-SNE t-distributed stochastic neighbor embedding
  • UMAP uniform manifold approximation and projection
  • UMAP may be a machine learning technique for dimension reduction.
  • UMAP may be constructed from a theoretical framework based in Riemannian geometry and algebraic topology.
  • UMAP may be utilized for a practical scalable algorithm that applies to real world data, such as morphological properties of one or more cells.
  • the cell morphology map as disclosed herein may comprise an ontology of the one or more morphological features.
  • the ontology may be an alternative medium to represent a relationship among various datapoints (e.g., each representing a cell) analyzed from an image data.
  • an ontology may be a data structure of information, in which nodes may be linked by edges. An edge may be used to define a relationship between two nodes.
  • a cell morphology map may comprise a cluster comprising sub-clusters, and the relationship between the cluster and the sub-clusters may be represented in an nodes/edges ontology (e.g., an edge may be used to describe the relationship as a subclass of, genus of, part of, stem cell of, differentiated from, progeny of, diseased state of, targets, recruits, interacts with, same tissue, different tissue, etc.).
  • one-to-one morphology to genomics mapping may be utilized. An image of a single cell or images of multiple “similar looking” cells may be mapped to its/their molecular profile(s) (e.g., genomics, proteomics, transcriptomics, etc.).
  • classifier-based barcoding may be performed.
  • Each sorting event e.g., positive classifier
  • a unique barcode e.g., nucleic acid or small molecule barcode.
  • the exact barcode(s) used for that individual classifier positive event may be recorded and tracked.
  • the cells may be lysed and molecularly analyzed together with the barcode(s).
  • the result of the molecular analysis may then be mapped (e.g., one-to-one) to the image(s) of the individual (or ensemble of) sorted cell(s) captured while the cell(s) are flowing in the flow channel.
  • class-based sorting may be utilized.
  • FIG.16 schematically illustrates different ways of representing analysis data of image data of cells.
  • Tag-free image data 1610 of cells e.g., circular cells and square cells
  • nuclei e.g., small nucleus and large nucleus
  • any of the classifier(s) disclosed herein may be used to analyze and plot the image data 1610 into a cell morphology map 1620, comprising four distinguishable clusters: cluster A (circular cell, small nucleus), cluster B (circular cell, large nucleus), cluster C (square cell, small nucleus), and cluster D (square cell, large nucleus).
  • the classifier(s) may also represent the analysis in a cell morphological ontology 1630, in which a top node (“cell shape”) may be connected to two sub-nodes (“circular cell” and “rectangular cell”) via an edge (“is a subclass of”) to define the relationship between the nodes.
  • Each sub-node may also be connected to its own sub-nodes (“small nucleus” and “large nucleus”) via an edge (“is a part of”) to define their relationships.
  • the sub-nodes e.g., “small nucleus” and “large nucleus”
  • the cell morphology map or cell morphological ontology as disclosed herein may be further annotated with one or more non-morphological data of each cell. As shown in FIG.17, the ontology 1630 from FIG.
  • Non-limiting examples of such non-morphological data may be from additional treatment and/or analysis, including, but not limited to, cell culture (e.g., proliferation, differentiation, etc.), cell permeabilization and fixation, cell staining by a probe, mass cytometry, multiplexed ion beam imaging (MIBI), confocal imaging, nucleic acid (e.g., DNA, RNA) or protein extraction, polymerase chain reaction (PCR), target nucleic acid enrichment, sequencing, sequence mapping, etc.
  • cell culture e.g., proliferation, differentiation, etc.
  • cell permeabilization and fixation cell staining by a probe
  • mass cytometry e.g., mass cytometry
  • MIBI multiplexed ion beam imaging
  • PCR polymerase chain reaction
  • target nucleic acid enrichment e.g., sequencing, sequence mapping, etc.
  • Examples of the probe used for cell staining may include, but are not limited to, a fluorescent probe (e.g., for staining chromosomes such as X, Y, 13, 18 and 21 in fetal cells), a chromogenic probe, a direct immunoagent (e.g.
  • an indirect immunoagent e.g., unlabeled primary antibody coupled to a secondary enzyme
  • a quantum dot e.g., a fluorescent nucleic acid stain (such as DAPI, Ethidium bromide, Sybr green, Sybr gold, Sybr blue, Ribogreen, Picogreen, YoPro-1, YoPro-2 YoPro-3, YOYo, Oligreen acridine orange, thiazole orange, propidium iodine, or Hoeste), another probe that emits a photon, or a radioactive probe.
  • the instrument(s) for the additional analysis may comprise a computer executable logic that performs karyotyping, in situ hybridization (ISH) (e.g., fluorescence in situ hybridization (FISH), chromogenic in situ hybridization (CISH), nanogold in situ hybridization (NISH)), restriction fragment length polymorphism (RFLP) analysis, polymerase chain reaction (PCR) techniques, flow cytometry, electron microscopy, quantum dot analysis, or detects single nucleotide polymorphisms (SNPs) or levels of RNA.
  • ISH in situ hybridization
  • FISH fluorescence in situ hybridization
  • CISH chromogenic in situ hybridization
  • NISH nanogold in situ hybridization
  • RFLP restriction fragment length polymorphism
  • PCR polymerase chain reaction
  • Analysis of the image data may be performed (e.g., automatically) within less than about 1 hour - e.g., less than about 50 minutes, or less than about 40 minutes, or less than about 30 minutes, or less than about 25 minutes, or less than about 20 minutes, or less than about 15 minutes, or less than about 10 minutes, or less than about 9 minutes, or less than about 8 minutes, or less than about 7 minutes, or less than about 6 minutes, or less than about 5 minutes, or less than about 4 minutes, or less than about 3 minutes, or less than about 2 minutes, or less than about 1 minute, or less than about 50 seconds, or less than about 40 seconds, or less than about 30 seconds, or less than about 20 seconds, or less than about 10 seconds, or less than about 5 seconds, about 1 second, or less.
  • One or more morphological features utilized for generating the clusters or the cell morphology map, as disclosed herein, may be selected automatically (e.g., by one or more machine learning algorithms) or, in another example, selected manually by a user via a user interface (e.g., graphical user interface (GUI)).
  • GUI graphical user interface
  • the GUI may show visualization of, for example, (i) the one or more morphological parameters extracted from the image data (e.g., represented as images, words, symbols, predefined codes, etc.), (ii) the cell morphology map comprising one or more clusters, or (iii) the cell morphological ontology.
  • the user may select, via the GUI, which morphological parameter(s) to be used to generate the clusters and the cell morphological map prior to actual generation of the clusters and the cell morphological map.
  • the user may, upon seeing or receiving a report about the generated clusters and the cell morphological map, retroactively modify the types of morphological parameter(s) to use, thereby to (i) modify the clustering or the cell morphological mapping and/or (ii) create new cluster(s) or new cell morphological map(s).
  • the user may select one or more regions to be excluded or included for further analysis or further processing of the cells (e.g., sorting in the future or in real-time).
  • a microfluidic system as disclosed herein may be utilized to capture image(s) of each cell from a population of cells, and any of the methods disclosed herein may be utilized to analyze such image data to generate a cell morphology map comprising clusters representing the population of cells.
  • the user may select one or more clusters or sub- clusters to be sorted, and the input may be provided to the microfluidic system to sort at least a portion of the cells into one or more sub-channels of the microfluidic system (e.g., in real-time) accordingly.
  • the user may select one or more clusters or sub-clusters to be excluded during sorting (e.g., to get rid of artifacts, debris, or dead cells), and the input may be provided to the microfluidic system to sort at least a portion of the cells into one or more sub-channels of the microfluidic system (e.g., in real-time) accordingly without such artifacts, debris, or dead cells.
  • the cell morphology map or cell morphological ontology as disclosed herein may be further annotated with one or more non -morphological data of each cell. As shown in FIG.17, the ontology 1630 from FIG.
  • Non-limiting examples of such non-morphological data may be from additional treatment and/or analysis, including, but not limited to, cell culture (e.g., proliferation, differentiation, etc.), cell permeabilization and fixation, cell staining by a probe, mass cytometry, multiplexed ion beam imaging (MIBI), confocal imaging, nucleic acid (e.g., DNA, RNA) or protein extraction, polymerase chain reaction (PCR), target nucleic acid enrichment, sequencing, sequence mapping, etc.
  • cell culture e.g., proliferation, differentiation, etc.
  • cell permeabilization and fixation cell staining by a probe
  • mass cytometry e.g., mass cytometry
  • MIBI multiplexed ion beam imaging
  • PCR polymerase chain reaction
  • target nucleic acid enrichment e.g., sequencing, sequence mapping, etc.
  • Examples of the probe used for cell staining may include, but are not limited to, a fluorescent probe (e.g., for staining chromosomes such as X, Y, 13, 18 and 21 in fetal cells), a chromogenic probe, a direct immunoagent (e.g.
  • an indirect immunoagent e.g., unlabeled primary antibody coupled to a secondary enzyme
  • a quantum dot e.g., a fluorescent nucleic acid stain (such as DAPI, Ethidium bromide, Sybr green, Sybr gold, Sybr blue, Ribogreen, Picogreen, YoPro-1, YoPro-2 YoPro-3, YOYo, Oligreen acridine orange, thiazole orange, propidium iodine, or Hoeste), another probe that emits a photon, or a radioactive probe.
  • the instrument(s) for the additional analysis may comprise a computer executable logic that performs karyotyping, in situ hybridization (ISH) (e.g., florescence in situ hybridization (FISH), chromogenic in situ hybridization (CISH), nanogold in situ hybridization (NISH)), restriction fragment length polymorphism (RFLP) analysis, polymerase chain reaction (PCR) techniques, flow cytometry, electron microscopy, quantum dot analysis, or detects single nucleotide polymorphisms (SNPs) or levels of RNA.
  • ISH in situ hybridization
  • FISH florescence in situ hybridization
  • CISH chromogenic in situ hybridization
  • NISH nanogold in situ hybridization
  • RFLP restriction fragment length polymorphism
  • PCR polymerase chain reaction
  • Analysis of the image data may be performed (e.g., automatically) within less than about 1 hour, 50 minutes, 40 minutes, 30 minutes, 25 minutes, 20 minutes, 15 minutes, 10 minutes, 9 minutes, 8 minutes, 7 minutes, 6 minutes, 5 minutes, 4 minutes, 3 minutes, 2 minutes, 1 minute, 50 seconds, 40 seconds, 30 seconds, 20 seconds, 10 seconds, 5 seconds, 1 second, or less. In some examples, such analysis may be performed in real-time.
  • One or more morphological features utilized for generating the clusters or the cell morphology map, as disclosed herein, may be selected automatically (e.g., by one or more machine learning algorithms) or, in another example, selected manually by a user via a user interface (e.g., graphical user interface (GUI)).
  • GUI graphical user interface
  • the GUI may show visualization of, for example, (i) the one or more morphological parameters extracted from the image data (e.g., represented as images, words, symbols, predefined codes, etc.), (ii) the cell morphology map comprising one or more clusters, or (iii) the cell morphological ontology.
  • the user may select, via the GUI, which morphological parameter(s) to be used to generate the clusters and the cell morphological map prior to actual generation of the clusters and the cell morphological map.
  • the user may, upon seeing or receiving a report about the generated clusters and the cell morphological map, retroactively modify the types of morphological parameter(s) to use, thereby to (i) modify the clustering or the cell morphological mapping and/or (ii) create new cluster(s) or new cell morphological map(s).
  • the user may select one or more regions to be excluded or included for further analysis or further processing of the cells (e.g., sorting in the future or in real-time).
  • a microfluidic system as disclosed herein may be utilized to capture image(s) of each cell from a population of cells, and any of the methods disclosed herein may be utilized to analyze such image data to generate a cell morphology map comprising clusters representing the population of cells.
  • the user may select one or more clusters or sub- clusters to be sorted, and the input may be provided to the microfluidic system to sort at least a portion of the cells into one or more sub-channels of the microfluidic system (e.g., in real-time) accordingly.
  • the user may select one or more clusters or sub-clusters to be excluded during sorting (e.g., to get rid of artifacts, debris, or dead cells), and the input may be provided to the microfluidic system to sort at least a portion of the cells into one or more sub channels of the microfluidic system (e.g., in real-time) accordingly without such artifacts, debris, or dead cells.
  • FIG.18 schematically illustrates a method for a user to interact (e.g., via GUI) with any one of the methods disclosed herein.
  • Image data 1810 of a plurality of cells may be processed, via any one of the methods disclosed herein, to generate a cell morphology map 1820A that represents the plurality of cells as datapoints in different clusters A, B, C, and D.
  • the cell morphology map 1820A may be displayed to the user via the GUI 1830.
  • the user may select each cluster or a datapoint within each cluster to visualize one or more images 1850a, b, c, or d of the cells classified into the cluster.
  • the user may draw a box 1840 (e.g., via any user-defined shape and/or size) around one or more datapoints or around a cluster.
  • the user may draw a box 1840 around a cluster of “debris” datapoints, to, e.g., remove the selected cluster and generate a new cell morphology map 1820B.
  • the user input may be used to update cell classifying algorithms, mapping algorithms, cell flowing mechanism (e.g., velocity of cells, positioning of the cells within a flow channel, adjusting imaging focal length/plane of one or more sensors/cameras of an imaging module (also referred to as an imaging device herein) that captures one or more images/videos of cells flowing through the cartridge, etc.), cell sorting mechanisms in the flow channel, cell sorting instructions in the flow channel, etc.
  • cell flowing mechanism e.g., velocity of cells, positioning of the cells within a flow channel, adjusting imaging focal length/plane of one or more sensors/cameras of an imaging module (also referred to as an imaging device herein) that captures one or more images/videos of cells flowing through the cartridge, etc.
  • the classifier may be trained to identify one or more common morphological features within the selected datapoints (e.g., features that distinguish the selected datapoints from the unselected data).
  • Features of the selected group may be used to further identify other cells from other samples having similar feature(s) for further analysis or discard cells having similar feature(s), e.g., for cell sorting.
  • the present disclosure also describes a cell analysis platform, e.g., for analyzing or classifying a cell.
  • the cell analysis platform may be a product of any one of the methods disclosed herein.
  • the cell analysis platform may be used as a basis to execute any one of the methods disclosed herein.
  • the cell analysis platform may be used to process image data comprising tag-free images of single cells to generate a new cell morphology map of various cell clusters.
  • the cell analysis platform may be used to process image data comprising tag-free images of single cells to compare the cell to pre-determined (e.g., pre-analyzed) images of known cells or cell morphology map(s), such that the single cells from the image data may be classified, e.g., for cell sorting.
  • FIG. 19 illustrates an example cell analysis platform (e.g., machine learning/artificial intelligence platform) for analyzing image data of one or more cells.
  • the cell analysis platform 1900 may comprise a cell morphology atlas (CMA) 1905.
  • CMA cell morphology atlas
  • the CMA 1905 may comprise a database 1910 having a plurality of annotated single cell images that are grouped into morphologically-distinct clusters (e.g., represented a texts, as cell morphology map(s), or cell morphological ontology(ies)) corresponding to a plurality of classifications (e.g., predefined cell classes).
  • the CMA 1905 may comprise a modeling unit comprising one or more models (e.g., modeling library 1920 comprising, such as, one or more machine learning algorithms disclosed herein) that are trained and validated using datasets from the CMA 1905, to process image data comprising images/videos of one or more cells to identify different cell types and/or states based at least on morphological features.
  • the CMA 1905 may comprise an analysis module 1930 comprising one or more classifiers as disclosed herein.
  • the classifier(s) may use one or more of the models from the modeling library 1920 to, e.g., (1) classify one or more images taken from a sample, (2) assess a quality or state of the sample based on the one or more images, (3) map one or more datapoints representing such one or more images onto a cell morphology map (or cell morphological ontology) via using a mapping module 1940.
  • the CMA 1905 may be operatively coupled to one or more additional database 1970 to receive the image data comprising the images/videos of one or more cells.
  • the image data from the database 1970 may be obtained from an imaging module 1992 of a cartridge 1990, which may also be operatively coupled to the CMA 1905.
  • the cartridge may direct flow of a sample comprising or suspected of comprising a target cell, and capture one or more images of contents (e.g., cells) within the sample by the imaging module 1992.
  • Any image data obtained by the imaging module 1992 may be transmitted directly to the CMA 1905 and/or to the new image database 1970.
  • the CMA 1905 may be operatively coupled to one or more additional databases 1980 comprising non-morphological data of any of the cells (e.g., genomics, transcriptomics, or proteomics, etc.), e.g., to further annotate any of the datapoint, cluster, map, ontology, images, as disclosed herein.
  • the CMA 1905 may be operatively coupled to a user device 1950 (e.g., a computer or a mobile device comprising a display) comprising a GUI 1960 for the user to receive information from and/or to provide input (e.g., instructions to modify or assist any portion of the method disclosed herein). Any classification made by the CMA and/or the user may be provided as an input to the sorting module 1994 of the cartridge 1990.
  • the sorting module may determine, for example, (i) when to activate one or more sorting mechanisms at the sorting junction of the cartridge 1990 to sort one or more cells of interest, (ii) which sub-channel of a plurality of sub channels to direct each single cell for sorting.
  • the sorted cells may be collected for further analysis, e.g., downstream molecular assessment and/or profiling, such as genomics, transcriptomics, proteomics, metabolomics, etc.
  • Any of the methods or platforms disclosed herein may be used as a tool that permits a user to train one or more models (e.g., from the modeling library) for cell clustering and/or cell classification.
  • a user may provide initial image dataset of a sample to the platform, and the platform may process the initial set of image data. Based on the processing, the platform may determine a number of labels and/or an amount of data that the user needs to train the one or more models, based on the initial image dataset of the sample. In some examples, the platform may determine that the initial set of image data may be insufficient to provide an accurate cell classification or cell morphology map.
  • the platform may plot an initial cell morphology map and recommend to the user the number of labels and/or the amount of data needed to for enhanced processing, classification, and/or sorting, based on proximity (or separability), correlation, or commonality of the datapoints in the map (e.g., whether there is no distinguishable clusters within the map, whether the clusters within the map are too close to each other, etc.).
  • the platform may allow the user to select different model (e.g., clustering model) or classifier, different combinations of models or classifiers, to re-analyze the initial set of image data.
  • any of the methods or platforms disclosed herein may be used to determine quality or state of the image(s) of the cell, that of the cell, or that of a sample comprising the cell.
  • the quality or state of the cell may be determined at a single cell level.
  • the quality or state of the cell may be determined at an aggregate level (e.g., as a whole sample, or as a portion of the sample).
  • the quality or state may be determined and reported based on, e.g., a number system (e.g., a number scale from about 1 to about 10, a percentage scale from about 1% to about 100%), a symbolic system, or a color system.
  • the quality or state may be indicative of a preparation or priming condition of the sample (e.g., whether the sample has a sufficient number of cells, whether the sample has too many artifacts, debris, etc.) or indicative of a viability of the sample (e.g., whether the sample has an amount of “dead” cells above a predetermined threshold).
  • a preparation or priming condition of the sample e.g., whether the sample has a sufficient number of cells, whether the sample has too many artifacts, debris, etc.
  • a viability of the sample e.g., whether the sample has an amount of “dead” cells above a predetermined threshold.
  • Any of the methods or platforms disclosed herein may be used to sort cells in silico (e.g., prior to actual sorting of the cells using a microfluidic channel).
  • the in silico sorting may be, e.g., to discriminate among and/or between, e.g., multiple different cell types (e.g., different types of cancer cells, different types of immune cells, etc.), cell states, cell qualities.
  • the methods and platforms disclosed herein may utilize pre-determined morphological properties (e.g., provided in the platform) for the discrimination.
  • newly abstracted morphological properties may be abstracted (e.g., generated) based on the input data for the discrimination.
  • new model(s) and/or classifier(s) may be trained or generated to process the image data.
  • the newly abstracted morphological properties may be used to discriminate among and/or between, e.g., multiple different cell types, cell states, cell qualities that are known.
  • the newly abstracted morphological properties may be used to create new class (or classifications) to sort the cells (e.g., in silico or via the microfluidic system).
  • the newly abstracted morphological properties as disclosed herein may enhance accuracy or sensitivity of cell sorting (e.g., in silico or via the microfluidic system).
  • the actual cell sorting of the cells (e.g., via the microfluidic system or cartridge) based on the in silico sorting may be performed within less than about 1 hours, for example less than about 50 minutes, or less than about 40 minutes, or less than about 30 minutes, or less than about 25 minutes, or less than about 20 minutes, or less than about 15 minutes, or less than about 10 minutes, or less than about 9 minutes, or less than about 8 minutes, or less than about 7 minutes, or less than about 6 minutes, or less than about 5 minutes, or less than about 4 minutes, or less than about 3 minutes, or less than about 2 minutes, or less than about 1 minute, or less than about 50 seconds, or less than about 40 seconds, or less than about 30 seconds, or less than about 20 seconds, or less than about 10 seconds, or less than about 5 seconds, or less than about 1 second, or less.
  • the in silico sorting and the actual sorting may occur in real-time.
  • the model(s) and/or classifier(s) may be validated (e.g., for the ability to demonstrate accurate cell classification performance).
  • validation metrics may include, but are not limited to, threshold metrics (e.g., accuracy, F-measure, Kappa, Macro-Average Accuracy, Mean-Class-Weighted Accuracy, Optimized Precision, Adjusted Geometric Mean, Balanced Accuracy, etc.), the ranking methods and metrics (e.g., receiver operating characteristics (ROC) analysis or “ROC area under the curve (ROC AUC)”), and the probabilistic metrics (e.g., root-mean-squared error).
  • threshold metrics e.g., accuracy, F-measure, Kappa, Macro-Average Accuracy, Mean-Class-Weighted Accuracy, Optimized Precision, Adjusted Geometric Mean, Balanced Accuracy, etc.
  • the ranking methods and metrics e.g., receiver operating characteristics (ROC) analysis or “ROC area under the curve (ROC AUC)”
  • the probabilistic metrics e.g., root-mean-squared error
  • the model(s) or classifier(s) may be determined to be balanced or accurate when the ROC AUC is greater than about 0.5, greater than about 0.55, greater than about 0.6, greater than about 0.65, greater than about 0.7, greater than about 0.75, greater than about 0.8, greater than about 0.85, greater than about 0.9, greater than about 0.91, greater than about 0.92, greater than about 0.93, greater than about 0.94, greater than about 0.95, greater than about 0.96, greater than about 0.97, greater than about 0.98, greater than about 0.99, or more.
  • the image(s) of the cell(s) may be obtained when the cell(s) are prepared and diluted in a sample (e.g., a buffer sample).
  • the cell(s) may be diluted, e.g., in comparison to real-life concentrations of the cell in the tissue (e.g., solid tissue, blood, serum, spinal fluid, urine, etc.) to a dilution concentration.
  • the methods or platforms disclosed herein may be compatible with a sample (e.g., a biological sample or derivative thereof) that is diluted by a factor of about 500 to about 1,000,000.
  • the methods or platforms disclosed herein may be compatible with a sample that is diluted by a factor of at least about 500.
  • the methods or platforms disclosed herein may be compatible with a sample that is diluted by a factor of at most about 1,000,000.
  • the methods or platforms disclosed herein may be compatible with a sample that is diluted by a factor of about 500 to about 1,000, about 500 to about 2,000, about 500 to about 5,000, about 500 to about 10,000, about 500 to about 20,000, about 500 to about 50,000, about 500 to about 100,000, about 500 to about 200,000, about 500 to about 500,000, about 500 to about 1,000,000, about 1,000 to about 2,000, about 1,000 to about 5,000, about 1,000 to about 10,000, about 1,000 to about 20,000, about 1,000 to about 50,000, about 1,000 to about 100,000, about 1,000 to about 200,000, about 1,000 to about 500,000, about 1,000 to about 1,000,000, about 2,000 to about 5,000, about 2,000 to about 10,000, about 2,000 to about 20,000, about 2,000 to about 50,000, about 2,000 to about 100,000, about 2,000 to about 200,000, about 2,000 to about 500,000, about 2,000 to about 1,000,000, about 5,000 to about 10,000, about 2,000
  • the methods or platforms disclosed herein may be compatible with a sample that is diluted by a factor of at least about 500, for example, at least about 1,000, at least about 2,000, at least about 5,000, at least about 10,000, at least about 20,000, at least about 50,000, at least about 100,000, at least about 200,000, at least about 500,000, or at least about 1,000,000, or more.
  • the classifier may generate a prediction probability (e.g., based on the morphological clustering and analysis) that an individual cell or a cluster of cells belongs to a cell class (e.g., within a predetermined cell class provided in the CMA as disclosed herein), e.g., via a reporting module.
  • the reporting module may communicate with the user via a GUI as disclosed herein.
  • the classifier may generate a prediction vector that an individual cell or a cluster of cells belongs to a plurality of cell classes (e.g., a plurality of all of predetermined cell classes from the CMA as disclosed herein).
  • the vector may be ID (e.g., a single row of different cell classes), 2D (e.g., two dimensions, such as tissue origin vs. cell type), 3D, etc.
  • the classifier may generate a report showing a composition of the sample, e.g., a distribution of one or more cell types, each cell type indicated with a relative proportion within the sample.
  • Each cell of the sample may also be annotated with a most probable cell type and one or more less probably cell types.
  • Any one of the methods and platforms disclosed herein may be capable of processing image data of one or more cells to generate one or more morphometric maps of the one or more cells.
  • Non-limiting examples of morphometric models may be utilized to analyze one or more images of single cells (or cell clusters) may include, e.g., simple morphometries (e.g., based on lengths, widths, masses, angles, ratios, areas, etc.), landmark-based geometric morphometries (e.g., spatial information, intersections, etc.
  • the morphometric map(s) may be multi- dimensional (e.g., 2D, 3D, etc.). The morphometric map(s) may be reported to the user via the GUI.
  • any of the methods or platforms disclosed herein may be used to process, analyze, classify, and/or compare two or more samples (e.g., at least about 2, for example at least about 3, or at least about 4, or at least about 5, or at least about 6, or at least about 7, or at least about 8, or at least about 9, or at least about 10, or more test samples).
  • the two or more samples may each be analyzed to determine a morphological profile (e.g., a cell morphology map) of each sample.
  • the morphological profiles of the two or more samples may be compared for identifying a disease state of a patient’s sample in comparison to a health cohort’s sample or a sample of image data representative of a disease of interest.
  • the morphological profiles of the two or more samples may be compared to monitor a progress of a condition of a subject, e.g., comparing first image data of a first set of cells from a subject before a treatment (e.g., a test drug maydidate, chemotherapy, surgical resection of solid tumors, etc.) and second image data of a second set of cells from the subject after the treatment.
  • the second set of cells may be obtained from the subject at least about 1 week, for example at least about 2 weeks, at least about 3 weeks, at least about 4 weeks, at least about 2 months, or at least about 3 months, or more, subsequent to obtaining the first set of cells from the subject.
  • the morphological profiles of the two or more samples may be compared to monitor effects of two or more different treatment options (e.g., different test drugs) in two or more different cohorts (e.g., human subjects, animal subjects, or cells being tested in vitro/ex vivo).
  • the systems and methods disclosed herein may be utilized (e.g., using sorting or enrichment of a cell type of interest or a cell exhibiting a characteristic of interest) to select a drug and/or a therapy that yields a desired effect (e.g., a therapeutic effect greater than equal to a threshold value).
  • Any of the platforms disclosed herein may provide an inline end-to- end pipeline solution for continuous labeling and/or sorting of multiple different cell types and/or states based at least in part on (e.g., based solely on) morphological analysis of imaging data provided.
  • a modeling library used by the platform may be scalable for large amount of data, extensible (e.g., one or more models or classifiers modified), and/or generalizable (e.g., more resistant to data perturbations - such as artifacts, debris, random objects in the background, image/video distortions - between samples. Any of the modeling library may be removed or updated with new model automatically by the machine learning algorithms or artificial intelligence, or by the user.
  • any of the methods and platforms disclosed herein may adjust one or more parameters of the microfluidic system as disclosed herein.
  • an imaging module e.g., sensors, cameras
  • the image data may be processed and analyzed (e.g., in real-time) by the methods and platforms of the present disclosure to train a model (e.g., machine learning model) to determine whether or not to adjust one or more parameters of the microfluidic system.
  • a model e.g., machine learning model
  • the model(s) may determine that the cells are flowing too fast or too slow, and send an instruction to the microfluidic system to adjust (i) the velocity of the cells (e.g., via adjusting velocity of the fluid medium carrying the cells) and/or (ii) image recording rate of a camera that is capturing images/videos of cells flowing through the flow channel.
  • the model(s) may determine that the cells are in-focus or out-of-focus in the images/videos, and send an instruction to the microfluidic system to (i) adjust a positioning of the cells within the cartridge (e.g., move the cell towards or away from the center of the flow channel via, for example, hydrodynamic focusing and/or inertial focusing) and/or (ii) adjust a focal length/plane of the camera that is capturing images/videos of cells flowing through the flow channel. Adjusting the focal length/plane may be performed for the same cell that has been analyzed (e.g., adjusting focal length/plane of a camera that is downstream) or a subsequent cell.
  • Adjusting the focal length/plane may enhance clarity or reduce blurriness in the images.
  • the focal length/plane may be adjusted based on a classified type or state of the cell. In some examples, adjusting the focal length/plane may allow enhanced focusing/clarity on all parts of the cell. In some examples, adjusting the focal length/plane may allow enhanced focusing/clarity on different portions (but not all parts) of the cell.
  • out-of-focus images may be usable for any of the methods disclosed herein to extract morphological feature(s) of the cell that otherwise may not be abstracted from in-focus images, or vice versa.
  • instructing the imaging module to capture both in-focus and out-of-focus images of the cells may enhance accuracy of any of the analysis of cells disclosed herein.
  • the model(s) may send an instruction to the microfluidic system to modify the flow and adjust an angle of the cell relative to the camera, to adjust focus on different portions of the cell or a subsequent cell.
  • Different portions as disclosed herein may comprise an upper portion, a mid portion, a lower portion, membrane, nucleus, mitochondria, etc. of the cell.
  • theoretic methods like Fourier Transform or Laplace transform.
  • bi-directional out-of-focus (OOF) images cells e.g., one or more first images that are OOF in a first direction, and one or more second images that are OOF in as second direction that is different — such as opposite — from the first direction.
  • images that are OOF in two opposite directions may be called “bright OOF” image(s) and “dark OOF” image(s), which may be obtained by changing the z-focus bi-directionally.
  • a classifier as disclosed herein may be trained with a image data comprising both bright OOF image(s) and dark OOF image(s).
  • the trained classifiers may be used to run inferences (e.g., in real-time) on new image data of cells to classify each image as bright OOF image, dark OOF image, and in one example image that is not OOF (e.g., not OOF relative to the bright/dark OOF images).
  • the classifier may also measure a percentage of bright OOF image, a percentage of dark OOF image, or a percentage of both bright and dark OOF images within the image data.
  • the classifier may determine that the imaging device (e.g., by the microfluidic system as disclosed herein) may not be imaging cells at the right focal length/plane.
  • the classifier may instruct the user, via GUI of a user device, to adjust the imaging device’s focal length/plane.
  • the classifier may determine, based on analysis of the image data comprising OOF images, direction and degree of adjustment of focal length/plane that may be required to adjust the imaging device, to yield a reduced amount of OOF imaging.
  • a threshold e.g., a predetermined threshold
  • a percentage of OOF images e.g., bright OOF, dark OOF, or both
  • a threshold e.g., a predetermined threshold
  • a percentage of OOF images e.g., bright OOF, dark OOF, or both
  • a threshold e.g., a predetermined threshold
  • a percentage of OOF images e.g., bright OOF, dark OOF, or both
  • a threshold (e.g., a predetermined threshold) of a percentage of OOF images may be at most about 20 %.
  • a threshold (e.g., a predetermined threshold) of a percentage of OOF images may be about 0.1 % to about 0.5 %, about 0.1 % to about 1 %, about 0.1 % to about 2 %, about 0.1 % to about 4 %, about 0.1 % to about 6 %, about 0.1 % to about 8 %, about 0.1 % to about 10 %, about 0.1 % to about 15 %, about 0.1 % to about 20 %, about 0.5 % to about 1 %, about 0.5 % to about 2 %, about 0.5 % to about 4 %, about 0.5 % to about 6 %, about 0.5 % to about 8 %, about 0.5 % %, about 0.5 % to about 10 %, about 0.5 % to about 15 %, about 0.1 % to about 20 %
  • a threshold (e.g., a predetermined threshold) of a percentage of OOF images may be at least about 0.1 %, for example at least about 0.5 %, or at least about 1 %, or at least about 2 %, or at least about 4 %, or at least about 6 %, or at least about 8 %, or at least about 10 %, or at least about 15 %, or at least or about 20 %, or more.
  • the model(s) may determine that images of different modalities are needed for any of the analysis disclosed herein.
  • Images of varying modalities may comprise a bright field image, a dark field image, a fluorescent image (e.g., of cells stained with a dye), an in-focus image, an out-of-focus image, a greyscale image, a monochrome image, a multi-chrome image, etc.
  • Any of the models or classifiers disclosed herein may be trained on a set of image data that is annotated with one imaging modality.
  • the models/classifiers may be trained on set of image data that is annotated with a plurality of different imaging modalities (e.g., about 2, about 3, about 4, about 5, or more different imaging modalities).
  • Any of the models/classifiers disclosed herein may be trained on a set of image data that is annotated with a spatial coordinate indicative of a position or location within the flow channel. Any of the models/classifiers disclosed herein may be trained on a set of image data that is annotated with a timestamp, such that a set of images may be processed based on the time they are taken.
  • An image of the image data may be processed in various image processing methods, such as horizontal or vertical image flips, orthogonal rotation, gaussian noise, contrast variation, or noise introduction to mimic microscopic particles or pixel-level aberrations. One or more of the processing methods may be used to generate replicas of the image or analyze the image.
  • the artifact(s) may be removed in silico by any of the models/classifiers disclosed herein, and any new replica or modified variant of the image/video excluding the artifact(s) may be stored in a database as disclosed herein.
  • the artifact(s) may be, for example, from debris (e.g., dead cells, dust, etc.), optical conditions during capturing the image/video of the cells (e.g., lighting variability, over- saturation, under-exposure, degradation of the light source, etc.), external factors (e.g., vibrations, misalignment of the microfluidic chip relative to the lighting or optical sensor/camera, power surges/fluctuations, etc.), and changes to the microfluidic system (e.g., deformation/shrinkage/expansion of the microfluidic channel or the microfluidic chip as a whole).
  • debris e.g., dead cells, dust, etc.
  • optical conditions during capturing the image/video of the cells e.
  • Weight assignments to the plurality of artifacts may be instructed manually by the user or determined automatically by the models/classifiers disclosed herein.
  • one or more reference images or videos of the flow channel may be stored in a database and used as a frame of reference to help identify, account for, and/or exclude any artifact.
  • the reference image(s)/video(s) may be obtained before use of the microfluidic system.
  • the reference image(s)/video(s) may be obtained during the use of the microfluidic system.
  • the reference image(s)/video(s) may be obtained periodically during the use of the microfluidic system, such as, each time the microfluidic system passes at least about 5, for example at least about 10, at least about 20, at least about 50, at least about 100, at least about 200, at least about 500, at least about 1,000, at least about 2,000, at least about 5,000, at least about 10,000, at least about 20,000, at least about 50,000, at least about 100,000 cells, or more.
  • the reference image(s)/video(s) may be obtained at landmark periods during the use of the system, such as, when the optical sensor/camera captures at least about 5, for example at least about 10, at least about 20, at least about 50, at least about 100, at least about 200, at least about 500, at least about 1,000, at least 2,000, at least about 5,000, at least about 10,000, at least about 20,000, at least about 50,000, at least about 100,000 images, or more.
  • the reference image(s)/video(s) may be obtained at landmark periods during the use of the microfluidic system, such as, when the microfluidic system passes at least about 5, for example at least about 10, at least about 20, at least about 50, at least about 100, at least about 200, at least about 500, at least about 1,000, at least about 2,000, at least about 5,000, at least about 10,000, at least about 20,000, at least about 50,000, at least about 100,000, or more, images.
  • the method and the platform as disclosed herein may be utilized to process (e.g., modify, analyze, classify) the image data at a rate of about 1,000 images/second to about 100,000,000 images/second.
  • the rate of image data processing may be at least about 1,000 images/second.
  • the rate of image data processing may be at most about 100,000,000 images/second.
  • the rate of image data processing may be about 1,000 images/second to about 5,000 images/second, about 1,000 images/second to about 10,000 images/second, about 1,000 images/second to about 50,000 images/second, about 1,000 images/second to about 100,000 images/second, about 1,000 images/second to about 500,000 images/second, about 1,000 images/second to about 1,000,000 images/second, about 1,000 images/second to about 5,000,000 images/second, about 1,000 images/second to about 10,000,000 images/second, about 1,000 images/second to about 50,000,000 images/second, about 1,000 images/second to about 100,000,000 images/second, about 5,000 images/second to about 10,000 images/second, about 5,000 images/second to about 50,000 images/second, about 5,000 images/second to about 100,000 images/second, about 5,000 images/second to about 500,000 images/second, about 5,000 images/second to about 1,000,000 images/second, about 5,000 images/second to about 5,000,000 images/second, about 5,000 images/second to about 10,000,000 images/second, about 5,000
  • the rate of image data processing may be at least about 1,000 images/second, for example at least about 5,000 images/second, at least about 10,000 images/second, at least about 50,000 images/second, at least about 100,000 images/second, at least about 500,000 images/second, at least about 1,000,000 images/second, at least about 5,000,000 images/second, at least about 10,000,000 images/second, at least about 50,000,000 images/second, or at least about 100,000,000 images/second, or more.
  • the method and the platform as disclosed herein may be utilized to process (e.g., modify, analyze, classify) the image data at a rate of about 1,000 cells/second to about 100,000,000 cells/second.
  • the rate of image data processing may be at least about 1,000 cells/second.
  • the rate of image data processing may be at most about 100,000,000 cells/second.
  • the rate of image data processing may be about 1,000 cells/second to about 5,000 cells/second, about 1,000 cells/second to about 10,000 cells/second, about 1,000 cells/second to about 50,000 cells/second, about 1,000 cells/second to about 100,000 cells/second, about 1,000 cells/second to about 500,000 cells/second, about 1,000 cells/second to about 1,000,000 cells/second, about 1,000 cells/second to about 5,000,000 cells/second, about 1,000 cells/second to about 10,000,000 cells/second, about 1,000 cells/second to about 50,000,000 cells/second, about 1,000 cells/second to about 100,000,000 cells/second, about 5,000 cells/second to about 10,000 cells/second, about 5,000 cells/second to about 50,000 cells/second, about 5,000 cells/second to about 100,000 cells/second, about 5,000 cells/second to about 500,000 cells/second, about 5,000 cells/second to about 1,000,000 cells/second, about 5,000 cells/second to about 5,000,000 cells/second, about 5,000 cells/second to about 10,000,000 cells/second, about 5,000
  • the rate of image data processing may be at least about 1,000 cells/second, for example at least about 5,000 cells/second, at least about 10,000 cells/second, at least about 50,000 cells/second, at least about 100,000 cells/second, at least about 500,000 cells/second, at least about 1,000,000 cells/second, at least about 5,000,000 cells/second, at least about 10,000,000 cells/second, at least about 50,000,000 cells/second, or at least about 100,000,000 cells/second, or more.
  • the method and the platform as disclosed herein may be utilized to process (e.g., modify, analyze, classify) the image data at a rate of about 1,000 datapoints/second to about 100,000,000 datapoints/second.
  • the rate of image data processing may be at least about 1,000 datapoints/second.
  • the rate of image data processing may be at most about 100,000,000 datapoints/second.
  • the rate of image data processing may be about 1,000 datapoints/second to about 5,000 datapoints/second, about 1,000 datapoints/second to about 10,000 datapoints/second, about 1,000 datapoints/second to about 50,000 datapoints/second, about 1,000 datapoints/second to about 100,000 datapoints/second, about 1,000 datapoints/second to about 500,000 datapoints/second, about 1,000 datapoints/second to about 1,000,000 datapoints/second, about 1,000 datapoints/second to about 5,000,000 datapoints/second, about 1,000 datapoints/second to about 10,000,000 datapoints/second, about 1,000 datapoints/second to about 50,000,000 datapoints/second, about 1,000 datapoints/second to about 100,000,000 datapoints/second, about 5,000 datapoints/second to about 10,000 datapoints/second, about 5,000 datapoints/second to about 50,000 datapoints/second, about 5,000 datapoints/second to about 100,000 datapoints/second, about 5,000 datapoint
  • the rate of image data processing may be at least about 1,000 datapoints/second, for example at least about 5,000 datapoints/second, at least about 10,000 datapoints/second, at least about 50,000 datapoints/second, at least about 100,000 datapoints/second, at least about 500,000 datapoints/second, at least about 1,000,000 datapoints/second, at least about 5,000,000 datapoints/second, at least about 10,000,000 datapoints/second, at least about 50,000,000 datapoints/second, or at least about 100,000,000 datapoints/second, or more.
  • Any of the methods or platforms disclosed herein may be operatively coupled to an online crowdsourcing platform.
  • the online crowdsourcing platform may comprise any of the database disclosed herein.
  • the database may store a plurality of single cell images that are grouped into morphologically-distinct clusters corresponding to a plurality of cell classes (e.g., predetermined cell types or states).
  • the online crowdsourcing platform may comprise one or more models or classifiers as disclosed herein (e.g., a modeling library comprising one or more machine learning models/classifiers as disclosed herein).
  • the online crowdsourcing platform may comprise a web portal for a community of users to share contents, e.g., (1) upload, download, search, curate, annotate, or edit one or more existing images or new images into the database, (2) train or validate the one or more model(s)/classifier(s) using datasets from the database, and/or (3) upload new models into the modeling library.
  • the online crowdsourcing platform may allow users to buy, sell, share, or exchange the model(s)/classifier(s) with one another.
  • the web portal may be configured to generate incentives for the users to update the database with new annotated cell images, model(s), and/or classifier(s). Incentives may be monetary. Incentives may be additional access to the global CMA, model(s), and/or classified s).
  • the web portal may be configured to generate incentives for the users to download, use, and review (e.g., rate or leave comments) any of the annotated cell images, model(s), and/or classifier(s) from, e.g., other users.
  • a global cell morphology atlas may be generated by collecting (i) annotated cell images, (ii) cell morphology maps or ontologies, (iii), and/or (iv) classifiers from the users via the web portal.
  • the global CMA may then be shared with the users via the web portal. All users may have access to the global CMA.
  • specifically defined users may have access to specifically defined portions of the global CMA.
  • cancer centers may have access to “cancer cells” portion of the global CMA, e.g., via a subscription based service.
  • a sample 2002 is prepared and injected by a pump 2004 (e.g., a syringe pump) into a cartridge 2005, or flow-through device.
  • the cartridge 2005 is a microfluidic device.
  • FIG. 20A illustrates a classification and/or sorting system utilizing a syringe pump, any of a number of perfusion systems may be used such as (but not limited to) gravity feeds, peristalsis, or any of a number of pressure systems.
  • the sample is prepared by fixation and staining.
  • the sample comprises live cells.
  • the specific manner in which the sample is prepared is largely dependent upon the requirements of a specific application.
  • Examples of the pump 2004, or other suitable flow unit for flowing a sample through cartridge 2005 may be, but are not limited to, a syringe pump, a vacuum pump, an actuator (e.g., linear, pneumatic, hydraulic, etc.), a compressor, or any other suitable device to exert pressure (positive, negative, alternating thereof, etc.) to a fluid that may or may not comprise one or more particles (e.g., one or more cells to be classified, sorted, and/or analyzed).
  • the pump or other suitable flow unit may be configured to raise, compress, move, and/or transfer fluid into or away from the microfluidic channel.
  • the pump or other suitable flow unit may be configured to deliver positive pressure, alternating positive pressure and vacuum pressure, negative pressure, alternating negative pressure and vacuum pressure, and/or only vacuum pressure.
  • the cartridge of the present disclosure may comprise (or otherwise be in operable communication with) at least about 1 - e.g., at least about 2, at least about 3, at least about 4, at least about 5, at least about 6, at least about 7, at least about 8, at least about 9, at least about 10, or more, pumps or other flow units.
  • the cartridge may comprise at most about 10, for example at most about 9, at most about 8, at most about 7, at most about 6, at most about 5, at most about 4, at most about 3, at most about 2, or at most about 1 pumps or other suitable flow units.
  • Each pump or other suitable flow unit may be in fluid communication with at least about 1, e.g., at least about 2, at least about 3, at least about 4, at least about 5, at least about 6, at least about 7, at least about 8, at least about 9, at least about 10, or more sources of fluid.
  • Each flow unit may be in fluid communication with at most about 10, for example at most about 9, at most about 8, at most about 7, at most about 6, at most about 5, at most about 4, at most about 3, at most about 2, or at most about 1 fluid.
  • the fluid may contain the particles (e.g., cells). In another example, the fluid may be particle-free.
  • the pump or other suitable flow unit may be configured to maintain, increase, and/or decrease a flow velocity of the fluid within the microfluidic channel of the flow unit.
  • the pump or other suitable flow unit may be configured to maintain, increase, and/or decrease a flow velocity (e.g., downstream of the microfluidic channel) of the particles.
  • the pump or other suitable flow unit may be configured to accelerate or decelerate a flow velocity of the fluid within the microfluidic channel of the flow unit, thereby accelerating or decelerating a flow velocity of the particles.
  • the fluid may be liquid or gas (e.g., air, argon, nitrogen, etc.).
  • the liquid may be an aqueous solution (e.g., water, buffer, saline, etc.). In another example, the liquid may be oil. In some examples, only one or more aqueous solutions may be directed through the microfluidic channels.
  • aqueous solution(s) and oil(s) may be directed through the microfluidic channels.
  • the aqueous solution may form droplets (e.g., emulsions containing the particles) that are suspended in the oil, or
  • the oil may form droplets (e.g., emulsions containing the particles) that are suspended in the aqueous solution.
  • any perfusion system including but not limited to peristalsis systems and gravity feeds, appropriate to a given classification and/or sorting system may be utilized.
  • the cartridge 2005 may be implemented as a fluidic device that focuses cells from the sample into a single streamline that is imaged continuously.
  • the cell line is illuminated by a light source 2006 (e.g., a lamp, such as an arc lamp) and an optical system 2010 that directs light onto an imaging region 2038 of the cartridge 2005.
  • An objective lens system 2012 magnifies the cells by directing light toward the sensor of a high-speed camera system 2014.
  • a 10x, 20x, 40x, 60x, 80x, 100x, or 200x objective is used to magnify the cells.
  • a 10x, objective is used to magnify the cells.
  • a 20x objective is used to magnify the cells.
  • a 40x objective is used to magnify the cells.
  • a 60x objective is used to magnify the cells.
  • a 80x objective is used to magnify the cells.
  • a 100x objective is used tog magnify the cells.
  • a 200x objective is used to magnify the cells.
  • a 10x to a 200x objective is used to magnify the cells, for example a 10x-20x, a 10x-40x, a 10x-60x, a 10x-80x, or 10x-100x objective is used to magnify the cells.
  • the exposure time is between about 0.75 ms and about 0.50 ms. In some instances, the exposure time is between about 0.75 ms and about 0.25 ms. In some instances, the exposure time is between about 0.50 ms and about 0.25 ms. In some instances, the exposure time is between about 0.25 ms and about 0.1 ms. In some instances, the exposure time is between about 0.1 ms and about 0.01 ms. In some instances, the exposure time is between about 0.1 ms and about 0.001 ms. In some instances, the exposure time is between about 0.1 ms and about 1 microsecond (ps). In some examples, the exposure time is between about 1 ps and about 0.1 ps.
  • the exposure time is between about 1 ps and about 0.01 ps. In some examples, the exposure time is between about 0.1 ps and about 0.01 ps. In some examples, the exposure time is between about 1 ps and about 0.001 ps. In some examples, the exposure time is between about 0.1 ps and about 0.001 ps. In some examples, the exposure time is between about 0.01 ps and about 0.001 ps.
  • Each of the plurality of imaging devices may use light from a same light source. In another example, each of the plurality of imaging devices may use light from different light sources.
  • the plurality of imaging devices may be configured in parallel and/or in series with respect to one another.
  • the plurality of imaging devices may be configured on one or more sides (e.g., two adjacent sides or two opposite sides) of the cartridge 2005.
  • the plurality of imaging devices may be configured to view the imaging region 2038 along a same axis or different axes with respect to (i) a length of the cartridge 2005 (e.g., a length of a straight channel of the cartridge 2005) or (ii) a direction of migration of one or more particles (e.g., one or more cells) in the cartridge 2005.
  • One or more imaging devices of the present disclosure may be stationary while imaging one or more cells, e.g., at the imaging region 2038.
  • one or more imaging devices may move with respect to the flow channel (e.g., along the length of the flow channel, towards and/or away from the flow channel, tangentially about the circumference of the flow channel, etc.) while imaging the one or more cells.
  • the one or more imaging devices may be operatively coupled to one or more actuators, such as, for example, a stepper actuator, linear actuator, hydraulic actuator, pneumatic actuator, electric actuator, magnetic actuator, and mechanical actuator (e.g., rack and pinion, chains, etc.).
  • the cartridge 2005 may comprise at least about 1 – e.g., at least about 2, about 3, about 4, about 5, about 6, about 7, about 8, about 9, about 10, or more, imaging regions (e.g., the imaging region 2038). In some examples, the cartridge 2005 may comprise at most about 10 – e.g., at most about 9, about 8, about 7, about 6, about 5, about 4, about 3, about 2, or about 1 imaging region. In some examples, the cartridge 2015 may comprise a plurality of imaging regions, and the plurality of imaging regions may be configured in parallel and/or in series with respect to each another. The plurality of imaging regions may or may not be in fluid communication with each other.
  • a first imaging region and a second imaging region may be configured in parallel, such that a first fluid that passes through the first imaging region does not pass through a second imaging region.
  • a first imaging region and a second imaging region may be configured in series, such that a first fluid that passes through the first imaging region also passes through the second imaging region.
  • the imaging device(s) e.g., the high-speed camera
  • the imaging system may comprise an electromagnetic radiation sensor (e.g., IR sensor, color sensor, etc.) that detects at least a portion of the electromagnetic radiation that is reflected by and/or transmitted from the cartridge or any content (e.g., the cell) in the cartridge.
  • the imaging device may be in operative communication with one or more sources (e.g., at least about 1 - e.g., at least about 2, at least about 3, at least about 4, at least about 5, or more) of the electromagnetic radiation.
  • the electromagnetic radiation may comprise one or more wavelengths from the electromagnetic spectrum including, but not limited to x-rays (about 0.1 nanometers (nm) to about 10.0 nm; or about 10 18 Hertz (Hz) to about 10 16 Hz), ultraviolet (UV) rays (about 10.0 nm to about 380 nm; or about 8x 10 16 Hz to about 10 15 Hz), visible light (about 380 nm to about 750 nm; or about 8x 10 14 Hz to about 4x 10 14 Hz), infrared (IR) light (about 750 nm to about 0.1 centimeters (cm); or about 4x 10 14 Hz to about 5x 10 11 Hz), and microwaves (about 0.1 cm to about 100 cm; or about 10 8 Hz to about
  • the source(s) of the electromagnetic radiation may be ambient light, and thus the cell sorting system may not have an additional source of the electromagnetic radiation.
  • the imaging device(s) may be configured to take a two-dimensional image (e.g., one or more pixels) of the cell and/or a three-dimensional image (e.g., one or more voxels) of the cell.
  • the exposure times may differ across different systems and may largely be dependent upon the requirements of a given application or the limitations of a given system such as but not limited to flow rates. Images are acquired and may be analyzed using an image analysis algorithm. [0339] In some examples, the images are acquired and analyzed post-capture.
  • the images are acquired and analyzed in real-time continuously. Using object tracking software, single cells may be detected and tracked while in the field of view of the camera. [0340] Background subtraction may then be performed.
  • the cartridge 2006 causes the cells to rotate as they are imaged, and multiple images of each cell are provided to a computing system 2016 for analysis.
  • the multiple images comprise images from a plurality of cell angles.
  • the flow rate and channel dimensions may be determined to obtain multiple images of the same cell from a plurality of different angles (i.e., a plurality of cell angles). A degree of rotation between an angle to the next angle may be uniform or non-uniform. In some examples, a full 360° view of the cell is captured.
  • 4 images are provided in which the cell rotates 90° between successive frames.
  • 8 images are provided in which the cell rotates 45° between successive frames.
  • 24 images are provided in which the cell rotates 15° between successive frames.
  • at least three or more images are provided in which the cell rotates at a first angle between a first frame and a second frame, and the cell rotates at a second angle between the second frame and a third frame, wherein the first and second angles are different.
  • less than the full 360° view of the cell may be captured, and a resulting plurality of images of the same cell may be sufficient to classify the cell (e.g., determine a specific type of the cell).
  • the cell may have a plurality of sides.
  • the plurality of sides of the cell may be defined with respect to a direction of the transport (flow) of the cell through the channel.
  • the cell may comprise a stop side, a bottom side that is opposite the top side, a front side (e.g., the side towards the direction of the flow of the cell), a rear side opposite the front side, a left side, and/or a right side opposite the left side.
  • the image of the cell may comprise a plurality of images captured from the plurality of angles, wherein the plurality of images comprise: (1) an image captured from the top side of the cell, (2) an image captured from the bottom side of the cell, (3) an image captured from the front side of the cell, (4) an image captured from the rear side of the cell, (5) an image captured from the left side of the cell, and/or (6) an image captured from the right side of the cell.
  • a two-dimensional “hologram” of a cell may be generated by superimposing the multiple images of the individual cell. The “hologram” may be analyzed to automatically classify characteristics of the cell based upon features including but not limited to the morphological features of the cell.
  • At least about 1, for example at least about 2, at least about 3, at least about 4, at least about 5, at least about 6, at least about 7, at least about 8, at least about 9, or at least about 10 images, or more, are captured for each cell.
  • at least about 5 or more images are captured for each cell.
  • from about 5 to about 10 images are captured for each cell.
  • at least about 10 or more images are captured for each cell.
  • from about 10 to about 20 images are captured for each cell.
  • at least about 20 or more images are captured for each cell.
  • from about 20 to about 50 images are captured for each cell.
  • at least about 50 or more images are captured for each cell.
  • from about 50 to about 100 images are captured for each cell.
  • 100 or more images are captured for each cell.
  • at least about 1, for example at least about 2, at least about 3, at least about 4, at least about 5, at least about 6, at least about 7, at least about 8, at least about 9, at least about 10, at least about 15, at least about 20, at least about 30, at least about 40, at least about 50, or more images may be captured for each cell at a plurality of different angles.
  • At most 50 for example at most about 40, at most about 30, at most about 20, at most about 15, at most about 10, at most about 9, at most about 8, at most about 7, at most about 6, at most about 5, at most about 4, at most about 3, or at most about 2 images may be captured for each cell at a plurality of different angles.
  • the imaging device is moved so as to capture multiple images of the cell from a plurality of angles.
  • the images are captured at an angle between 0 and 90 degrees to the horizontal axis.
  • the images are captured at an angle between 90 and 180 degrees to the horizontal axis.
  • the images are captured at an angle between 180 and 270 degrees to the horizontal axis.
  • the images are captured at an angle between 270 and 360 degrees to the horizontal axis.
  • multiple imaging devices for e.g. multiple cameras
  • each device captures an image of the cell from a specific cell angle.
  • at least about 2, for example at least about 3, at least about 4, at least about 5, at least about 6, at least about 7, at least about 8, at least about 9, or at least about 10 cameras, or more, are used.
  • more than about 10 cameras are used, wherein each camera images the cell from a specific cell angle.
  • the cartridge has different regions to focus, order, and/or rotate cells. Although the focusing regions, ordering regions, and cell rotating regions are discussed as affecting the sample in a specific sequence, a person having ordinary skill in the art would appreciate that the various regions may be arranged differently, where the focusing, ordering, and/or rotating of the cells in the sample may be performed in any order. Regions within a microfluidic device implemented in accordance with an example of the disclosure are illustrated in FIG. 20B. Cartridge 2005 may include a filtration region 2030 to prevent channel clogging by aggregates/debris or dust particles. Cells pass through a focusing region 2032 that focuses the cells into a single streamline of cells that are then spaced by an ordering region 2034.
  • the focusing region utilizes “inertial focusing” to form the single streamline of cells. In some examples, the focusing region utilizes “hydrodynamic focusing” to focus the cells into the single streamline of cells.
  • rotation may be imparted upon the cells by a rotation region 2036. In one example, the spinning cells may then pass through an imaging region 2038 in which the cells are illuminated for imaging prior to exiting the cartridge. These various regions are described and discussed in further detail below. In some examples, the rotation region 2036 may precede the imaging region 2038.
  • the rotation region 2036 may be a part (e.g., a beginning portion, a middle portion, and/or an end portion with respect to a migration of a cell within the cartridge) of the imaging region 2038.
  • the imaging region 2038 may be a part of the rotation region 2036.
  • a single cell is imaged in a field of view of the imaging device, e.g. camera.
  • multiple cells are imaged in the same field of view of the imaging device.
  • At least about 1, for example at least about 2, at least about 3, at least about 4, at least about 5, at least about 6, at least about 7, at least about 8, at least about 9, or at least about 10 cells, or more, are imaged in the same field of view of the imaging device. In some examples, up to about 100 cells are imaged in the same field of view of the imaging device.
  • about 10 to about 100 cells are imaged in the field of view, for example, about 10 to 20 cells, about 10 to about 30 cells, about 10 to about 40 cells, about 10 to about 50 cells, about 10 to about 60 cells, about 10 to about 80 cells, about 10 to about 90 cells, about 20 to about 30 cells, about 20 to about 40 cells, about 20 to about 50 cells, about 20 to about 60 cells, about 20 to about 70 cells, about 20 to about 80 cells, about 20 to about 90 cells, about 30 to about 40 cells, about 40 to about 50 cells, about 40 to about 60 cells, about 40 to about 70 cells, about 40 to about 80 cells, about 40 to about 90 cells, about 50 to about 60 cells, about 50 to about 70 cells, about 50 to about 80 cells, about 50 to about 90 cells, about 60 to about 70 cells, about 60 to about 80 cells, about 60 to about 90 cells, about 70 to about 80 cells, about 70 to about 90 cells, or about 90 to about 100 cells are imaged in the same field of view of the imaging device.
  • only a single cell may be allowed to be transported across a cross-section of the flow channel perpendicular to the axis of the flow channel.
  • a plurality of cells e.g., at least about 2, for example at least about 3, at least about 4, at least about 5, or more cells; or at most about 5, for example at most about 4, at most about 3, at most about 2, or at most about 1 cell
  • the imaging device or the processor operatively linked to the imaging device
  • the imaging system may include, among other things, a camera, an objective lens system and a light source.
  • cartridges similar to those described above may be fabricated using standard 2D microfluidic fabrication techniques, requiring minimal fabrication time and cost.
  • classification and/or sorting systems may be implemented in any of a variety of ways appropriate to the requirements of specific applications in accordance with various examples of the disclosure. Specific elements of microfluidic devices that may be utilized in classification and/or sorting systems in accordance with some examples of the disclosure are discussed further below.
  • the microfluidic system may comprise a microfluidic chip (e.g., comprising one or more microfluidic channels for flowing cells) operatively coupled to an imaging device (e.g., one or more cameras).
  • a microfluidic device may comprise the imaging device, and the chip may be inserted into the device, to align the imaging device to an imaging region of a channel of the chip.
  • the chip may comprise one or more positioning identifiers (e.g., pattern(s), such as numbers, letters, symbols, or other drawings) that may be imaged to determine the positioning of the chip (and thus the imaging region of the channel of the chip) relative to the device as a whole or relative to the imaging device.
  • one or more images of the chip may be capture upon its coupling to the device, and the image(s) may be analyzed by any of the methods disclosed herein (e.g., using any model or classifier disclosed herein) to determine a degree or score of chip alignment.
  • the positioning identifier(s) may be a “guide” to navigate the stage holding the chip within the device to move within the device towards a correct position relative to the imaging unit.
  • rule-based image processing may be used to navigate the stage to a precise range of location or a precise location relative to the image unit.
  • machine learning/artificial intelligence methods as disclosed herein may be modified or trained to identify the pattern on the chip and navigate the stage to the precise imaging location for the image unit, to increase resilience.
  • machine learning/artificial intelligence methods as disclosed herein may be modified or trained to implement reinforcement learning based alignment and focusing.
  • the alignment process for the chip to the instrument or the image unit may involve moving the stage holding the chip in, e.g., either X or Y axis and/or moving the imaging plane on the Z axis.
  • the chip may start at a X, Y, and Z position (e.g., randomly selected), (ii) based on one or more image(s) of the chip and/or the stage holding the chip, a model may determine a movement vector for the stage and a movement for the imaging plane, (iii) depending on whether such movement vector may take the chip closer to the optimum X, Y, and Z position relative to the image unit, an error term may be determined as a loss for the model, and (iv) the magnitude of the error may be either constant or be proportional to how far the current X, Y, and Z position is from an optimal X, Y, and Z position (e.g., may be predetermined).
  • a model may determine a movement vector for the stage and a movement for the imaging plane, (iii) depending on whether such movement vector may take the chip closer to the optimum X, Y, and Z position relative to the image unit, an error term may be determined as a loss for the model, and (iv)
  • Such trained model may be used to determine, for example, the movement vector and/or movement of the movement for the imaging plane, to enhance relative alignment between the chip and the image unit (e.g., one or more sensors).
  • the alignment may occur subsequent to capturing of the image(s). In another example, the alignment may occur real-time while capturing images/videos of the positioning identifier(s) of the chip.
  • One or more flow channels of the cartridge of the present disclosure may have various shapes and sizes. For example, referring to FIGS.
  • the system of the present disclosure comprises straight channels with rectangular or square cross-sections.
  • the system of the present disclosure comprises straight channels with round cross-sections.
  • the system comprises straight channels with half-ellipsoid cross-sections.
  • the system comprises spiral channels.
  • the system comprises round channels with rectangular cross-sections.
  • the system comprises round channels with rectangular channels with round cross-sections. In some examples, the system comprises round channels with half-ellipsoid cross-sections. In some examples, the system comprises channels that are expanding and contracting in width with rectangular cross-sections. In some examples, the system comprises channels that are expanding and contracting in width with round cross-sections. In some examples, the system comprises channels that are expanding and contracting in width with half-ellipsoid cross-sections. [0356]
  • the flow channel may comprise one or more walls that are formed to focus one or more cells into a streamline.
  • the flow channel may comprise a focusing region comprising the wall(s) to focus the cell(s) into the streamline.
  • Focusing regions on a microfluidic device may take a disorderly stream of cells and utilize a variety of forces (for e.g. inertial lift forces (wall effect and shear gradient forces) or hydrodynamic forces) to focus the cells within the flow into a streamline of cells.
  • the cells are focused in a single streamline.
  • the cells are focused in multiple streamlines, for example at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, or at least 10 streamlines.
  • the focusing region receives a flow of randomly arranged cells via an upstream section. The cells flow into a region of contracted and expanded sections in which the randomly arranged cells are focused into a single streamline of cells.
  • the focusing may be driven by the action of inertial lift forces (wall effect and shear gradient forces) acting on cells.
  • the focusing region is formed with curvilinear walls that form periodic patterns.
  • the patterns form a series of square expansions and contractions.
  • the patterns are sinusoidal.
  • the sinusoidal patterns are skewed to form an asymmetric pattern.
  • the focusing region may be effective in focusing cells over a wide range of flow rates. In the illustrated example, an asymmetrical sinusoidal-like structure is used as opposed to square expansions and contractions. This helps prevent the formation of secondary vortices and secondary flows behind the particle flow stream.
  • the illustrated structure allows for faster and more accurate focusing of cells to a single lateral equilibrium position.
  • Spiral and curved channels may also be used in an inertia regime; however, these may complicate the integration with other modules.
  • straight channels where channel width is greater than channel height may also be used for focusing cells onto single lateral position.
  • imaging since there will be more than one equilibrium position in the z-plane, imaging may become problematic, as the imaging focal plane is preferably fixed.
  • any of a variety of structures that provide a cross section that expands and contracts along the length of the microfluidic channel or are capable of focusing the cells may be utilized as appropriate to the requirements of specific applications.
  • the cell sorting system may be configured to focus the cell at a width and/or a height within the flow channel along an axis of the flow channel.
  • the cell may be focused to a center or off the center of the cross-section of the flow channel.
  • the cell may be focused to a side (e.g., a wall) of the cross-section of the flow channel.
  • a focused position of the cell within the cross-section of the channel may be uniform or non- uniform as the cell is transported through the channel.
  • Microfluidic channels may be designed to impose ordering upon a single streamline of cells formed by a focusing region in accordance with several examples of the disclosure.
  • Microfluidic channels in accordance with some examples of the disclosure include an ordering region having pinching regions and curved channels.
  • the ordering region orders the cells and distances single cells from each other to facilitate imaging. In some examples, ordering is achieved by forming the microfluidic channel to apply inertial lift forces and Dean drag forces on the cells.
  • Different geometries, orders, and/or combinations may be used.
  • pinching regions may be placed downstream from the focusing channels without the use of curved channels.
  • Architecture of the microfluidic channels of the cartridge of the present disclosure may be controlled (e.g., modified, optimized, etc.) to modulate cell flow along the microfluidic channels.
  • Examples of the cell flow may include (i) cell focusing (e.g., into a single streamline) and (ii) rotation of the one or more cells as the cell(s) are migrating (e.g., within the single streamline) down the length of the microfluidic channels.
  • microfluidic channels may be configured to impart rotation on ordered cells in accordance with a number of examples of the disclosure.
  • One or more cell rotation regions (e.g., the cell rotation region 2036) of microfluidic channels in accordance with some examples of the disclosure use co- flow of a particle-free buffer to induce cell rotation by using the co-flow to apply differential velocity gradients across the cells.
  • a cell rotation region may introduce co-flow of at least about 1, for example at least about 2, at least about 3, at least about 4, at least about 5, or more buffers (e.g., particle-free, or containing one or more particles, such as polymeric or magnetic particles) to impart rotation on one or more cells within the channel.
  • a cell rotation region may introduce co-flow of at most about 5, for example at most about 4, at most about 3, at most about 2, or at most about 1 buffer to impart the rotation of one or more cells within the channel.
  • the plurality of buffers may be co-flowed at a same position along the length of the cell rotation region, or sequentially at different positions along the length of the cell rotation region. In some examples, the plurality of buffers may be the same or different.
  • the cell rotation region of the microfluidic channel is fabricated using a two-layer fabrication process so that the axis of rotation is perpendicular to the axis of cell downstream migration and parallel to cell lateral migration.
  • Cells may be imaged in at least a portion of the cell rotating region, while the cells are tumbling and/or rotating as they migrate downstream.
  • the cells may be imaged in an imaging region that is adjacent to or downstream of the cell rotating region.
  • the cells may be flowing in a single streamline within a flow channel, and the cells may be imaged as the cells are rotating within the single streamline.
  • a rotational speed of the cells may be constant or varied along the length of the imaging region.
  • This may allow for the imaging of a cell at different angles (e.g., from a plurality of images of the cell taken from a plurality of angles due to rotation of the cell), which may provide more accurate information concerning cellular features than may be captured in a single image or a sequence of images of a cell that is not rotating to any significant extent.
  • This also allow a 3D reconstruction of the cell using available software since the angles of rotation across the images are known.
  • every single image of the sequence of image many be analyzed individually to analyze (e.g., classify) the cell from each image.
  • results of the individual analysis of the sequence of images may be aggregated to determine a final decision (e.g., classification of the cell).
  • a cell rotation region of a microfluidic channel incorporates an injected co-flow prior to an imaging region in accordance with an example of the disclosure.
  • Co-flow may be introduced in the z plane (perpendicular to the imaging plane) to spin the cells. Since the imaging is done in the x-y plane, rotation of cells around an axis parallel to the y-axis provides additional information by rotating portions of the cell that may have been occluded in previous images into view in each subsequent image. Due to a change in channel dimensions, at point xo, a velocity gradient is applied across the cells, which may cause the cells to spin.
  • a cell rotation region incorporates an increase in one dimension of the microfluidic channel to initiate a change in the velocity gradient across a cell to impart rotation onto the cell.
  • a cell rotation region of a microfluidic channel incorporates an increase in the z-axis dimension of the cross section of the microfluidic channel prior to an imaging region in accordance with an example of the disclosure.
  • the change in channel height may initiate a change in velocity gradient across the cell in the z axis of the microfluidic channel, which may cause the cells to rotate as with using co flow.
  • the system and methods of the present disclosure focuses the cells in microfluidic channels.
  • the term focusing as used herein broadly means controlling the trajectory of cell/cells movement and comprises controlling the position and/or speed at which the cells travel within the microfluidic channels. In some examples controlling the lateral position and/or the speed at which the particles travel inside the microfluidic channels, allows to accurately predict the time of arrival of the cell at a bifurcation. The cells may then be accurately sorted.
  • the parameters critical to the focusing of cells within the microfluidic channels include, but are not limited to channel geometry, particle size, overall system throughput, sample concentration, imaging throughput, size of field of view, and method of sorting.
  • the focusing is achieved using inertial forces.
  • the system and methods of the present disclosure focus cells to a certain height from the bottom of the channel using inertial focusing.
  • the distance of the cells from the objective is equal and images of all the cells will be clear.
  • cellular details, such as nuclear shape, structure, and size appear clearly in the outputted images with minimal blur.
  • the system disclosed herein has an imaging focusing plane that is adjustable. In some examples, the focusing plane is adjusted by moving the objective or the stage.
  • the best focusing plane is found by recording videos at different planes and the plane wherein the imaged cells have the highest Fourier magnitude, thus, the highest level of detail and highest resolution, is the best plane.
  • the system and methods of the present disclosure utilize a hydrodynamic-based z focusing system to obtain a consistent z height for the cells of interests that are to be imaged.
  • the design comprises hydrodynamic focusing using multiple inlets for main flow and side flow.
  • the hydrodynamic-based z focusing system is a triple-punch design.
  • the design comprises hydrodynamic focusing with three inlets, wherein the two side flows pinch cells at the center.
  • the design comprises hydrodynamic focusing with 2 inlets, wherein only one side flow channel is used and cells are focused near channel wall.
  • the hydrodynamic focusing comprises side flows that do not contain any cells and a middle inlet that contains cells. The ratio of the flow rate on the side channel to the flow rate on the main channel determines the width of cell focusing region.
  • the design is a combination of the above. In all examples, the design is integrable with the bifurcation and sorting mechanisms disclosed herein.
  • the hydrodynamic-based z focusing system is used in conjunction with inertia-based z focusing.
  • the cell is a live cell.
  • the cell is a fixed cell (e.g., in methanol or paraformaldehyde).
  • one or more cells may be coupled (e.g., attached covalently or non-covalently) to a substrate (e.g., a polymeric bead or a magnetic bead) while flowing through the cartridge.
  • the cell(s) may not be coupled to any substrate while flowing through the cartridge.
  • a variety of techniques may be utilized to classify images of cells captured by classification and/or sorting systems in accordance with various examples of the disclosure.
  • the image captures are saved for future analysis/classification either manually or by image analysis software. Any suitable image analysis software may be used for image analysis.
  • image analysis is performed using OpenCV.
  • analysis and classification is performed in real time.
  • the system and methods of the present disclosure comprise collecting a plurality of images of objects in the flow.
  • the plurality of images comprises at least 20 images of cells.
  • the plurality of images comprises at least about 19, at least about 18, at least about 17, at least about 16, at least about 15, at least about 14, at least about 13, at least about 12, at least about 11, at least about 10, at least about 9, at least about 8, at least about 7, at least about 6, at least about 5, at least about 4, at least about 3, or at least about 2 images of cells.
  • the plurality of images comprises images from multiple cell angles.
  • the plurality of images, comprising images from multiple cell angles help derive extra features from the particle which would be hidden if the particle is imaged from a single point-of-view.
  • the plurality of images comprising images from multiple cell angles, help derive extra features from the particle which would be hidden if a plurality of images are combined into a multi-dimensional reconstruction (e.g., a two-dimensional hologram or a three-dimensional reconstruction).
  • a multi-dimensional reconstruction e.g., a two-dimensional hologram or a three-dimensional reconstruction.
  • the systems and methods of present disclosure allow for a tracking ability, wherein the system and methods track a particle (e.g., cell) under the camera and maintain the knowledge of which frames belong to the same particle. In some examples, the particle is tracked until it has been classified and/or sorted.
  • the particle may be tracked by one or more morphological (e.g., shape, size, area, volume, texture, thickness, roundness, etc.) and/or optical (e.g., light emission, transmission, reflectance, absorbance, fluorescence, luminescence, etc.) characteristics of the particle.
  • each particle may be assigned a score (e.g., a characteristic score) based on the one or more morphological and/or optical characteristics, thereby to track and confirm the particle as the particle travels through the microfluidic channel.
  • the systems and methods of the disclosure comprise imaging a single particle in a particular field of view of the camera.
  • the same instrument that performs imaging operations may also perform sorting operations.
  • the system and methods of the present disclosure image multiple particles in the same field of view of camera. Imaging multiple particles in the same field of view of the camera may provide additional advantages, for example it will increase the throughput of the system by batching the data collection and transmission of multiple particles.
  • at least about 2, for example at least about 3, at least about 4, at least about 5, at least about 6, at least about 7, at least about 8, at least about 9, at least about 10, at least about 20, at least about 30, at least about 40, at least about 50, at least about 60, at least about 70, at least about 80, at least about 90, at least about 100, or more particles are imaged in the same field of view of the camera. In some instances, about 100 to about 200 particles are imaged in the same field of view of the camera.
  • At most about 100 for example at most about 90, at most about 80, at most about 70, at most about 60, at most about 50, at most about 40, at most about 30, at most about 20, at most about 10, at most about 9, at most about 8, at most about 7, at most about 6, at most about 5, at most about 4, at most about 3, or at most about 2 particles are imaged in the same field of view of the camera.
  • the number of the particles (e.g., cells) that are imaged in the same field of view may not be changed throughout the operation of the cartridge.
  • the number of the particles (e.g., cells) that are imaged in the same field of view may be changed in real-time throughout the operation of the cartridge, e.g., to increase speed of the classification and/or sorting process without negatively affecting quality or accuracy of the classification and/or soring process.
  • the imaging region maybe downstream of the focusing region and the ordering region. Thus, the imaging region may not be part of the focusing region and the ordering region. In an example, the focusing region may not comprise or be operatively coupled to any imaging device that is configured to capture one or more images to be used for particle analysis (e.g., cell classification).
  • the systems and the methods of the present disclosure actively sorts a stream of particles.
  • sort or sorting refers to physically separating particles, for e.g. cells, with one or more desired characteristics.
  • the desired characteristic(s) may comprise a morphometric feature of the cell(s) analyzed and/or obtained from the image(s) of the cell, or a combination of such morphometric features.
  • Examples of the morphometric feature of the cell(s) may comprise a size, shape, volume, electromagnetic radiation absorbance and/or transmittance (e.g., fluorescence intensity, luminescence intensity, etc.), or viability (e.g., when live cells are used), or a feature selected from Table 1 or from Table 2.
  • the flow channel may branch into a plurality of channels, and the cell sorting system may be configured to sort the cell by directing the cell to a selected channel of the plurality of channels based on the analyzed image of the cell.
  • the analyzed image may be indicative of one or more features of the cell, wherein the feature(s) are used as parameters of cell sorting.
  • one or more channels of the plurality of channels may have a plurality of sub channels, and the plurality of sub-channels may be used to further sort the cells that have been sorted once.
  • Cell sorting may comprise isolating one or more target cells from a population of cells.
  • the target cell(s) may be isolated into a separate reservoir that keeps the target cell(s) separate from the other cells of the population.
  • Cell sorting accuracy may be defined as a proportion (e.g., a percentage) of the target cells in the population of cells that have been identified and sorted into the separate reservoir.
  • the cell sorting accuracy of the cartridge provided herein may be at least about 80 %, for example at least about 81 %, at least about 82 %, at least about 83 %, at least about 84 %, at least about 85 %, at least about 86 %, at least about 87 %, at least about 88 %, at least about 89 %, at least about 90 %, at least about 91 %, at least about 92 %, at least about 93 %, at least about 94 %, at least about 95 %, at least about 96 %, a at least bout 97 %, at least about 98 %, at least about 99 %, or more (e.g., about 99.9% or about 100%).
  • the cell sorting accuracy of the cartridge provided herein may be at most about 100 %, for example at most about 99 %, at most about 98 %, at most about 97 %, at most about 96 %, at most about 95 %, at most about 94 %, at most about 93 %, at most about 92 %, at most about 91 %, at most about 90 %, at most about 89 %, at most about 88 %, at most about 87 %, at most about 86 %, at most about 85 %, at most about 84 %, at most about 83 %, at most about 82 %, at most about 81 %, or at most about 80 %, or less.
  • cell sorting may be performed at a rate of at least about 1 cell/second, for example at least about 5 cells/second, at least about 10 cells/second, at least about 50 cells/second, at least about 100 cells/second, at least about 500 cells/second, at least about 1,000 cells/second, at least about 5,000 cells/second, at least about 10,000 cells/second, at least about 50,000 cells/second, or more.
  • cell sorting may be performed at a rate of at most about 50,000 cells/second, for example at most about 10,000 cells/second, at most about 5,000 cells/second, at most about 1,000 cells/second, at most about 500 cells/second, at most about 100 cells/second, at most about 50 cells/second, at most about 10 cells/second, at most about 5 cells/second, or at most about 1 cell/second, or less.
  • the systems and methods disclosed herein use an active sorting mechanism.
  • the active sorting is independent from analysis and decision making platforms and methods.
  • the sorting is performed by a sorter, which receives a signal from the decision making unit (e.g.
  • bifurcation refers to the termination of the flow channel into two or more channels, such that cells with the one or more desired characteristics are sorted or directed towards one of the two or more channels and cell without the one or more desired characteristics are directed towards the remaining channels.
  • the flow channel terminates into at least about 2, for example at least about 3, at least about 4, at least about 5, at least about 6, at least about 7, at least about 8, at least about 9, at least about 10, or more channels.
  • the flow channel terminates into at most about 10, for example at most about 9, at most about 8, at most about 7, at most about 6, at most about 5, at most about 4, at most about 3, or at most about 2 channels.
  • the flow channel terminates in two channels and cells with one or more desired characteristics are directed towards one of the two channels (the positive channel), while cells without the one or more desired characteristics are directed towards the other channel (the negative channel).
  • the flow channel terminates in three channels and cells with a first desired characteristic are directed to one of the three channels, cells with a second desired characteristic are directed to another of the three channels, and cells without the first desired characteristic and the second desired characteristic are directed to the remaining of the three channels.
  • the sorting is performed by a sorter.
  • the sorter may function by predicting the exact time at which the particle will arrive at the bifurcation. To predict the time of particle arrival, the sorter may use any applicable method.
  • the sorter predicts the time of arrival of the particle by using (i) velocity of particles (e.g., downstream velocity of a particle along the length of the microfluidic channel) that are upstream of the bifurcation and (ii) the distance between velocity measurement/calculation location and the bifurcation. In some examples, the sorter predicts the time of arrival of the particles by using a constant delay time as an input. [0382] In some examples, prior to the cell’s arrival at the bifurcation, the sorter may measure the velocity of a particle (e.g., a cell) at least about 1, for example at least about 2, at least about 3, at least about 4, or at least about 5, or more times.
  • a particle e.g., a cell
  • the sorter may measure the velocity of the particle at most about 5, for example at most about 4, at most about 3, at most about 2, or at most about 1 time. In some examples, the sorter may use at least about 1, for example at least about 2, at least about 3, at least about 4, or at least about 5, or more sensors. In some examples, the sorter may use at most about 5, for example at most about 4, at most about 3, at most about 2, or at most about 1 sensor.
  • Example of the sensor(s) may be an imaging device (e.g., a camera such as a high-speed camera), one- or multi-point light (e.g., laser) detector, etc.
  • the sorter may use any one of the imaging devices (e.g., the high-speed camera system 2014) disposed at or adjacent to the imaging region 2038.
  • the same imaging device(s) may be used to capture one or more images of a cell as the cell is rotating and migrating within the channel, and the one or more images may be analyzed to (i) classify the cell and (ii) measure a rotational and/or lateral velocity of the cell within the channel and predict the cell’s arrival time at the bifurcation.
  • the sorter may use one or more sensors that are different than the imaging devices of the imaging region 2038.
  • the sorter may measure the velocity of the particle (i) upstream of the imaging region 2038, (ii) at the imaging region 2038, and/or (iii) downstream of the imaging region 2038.
  • the sorter may comprise or be operatively coupled to a processor, such as a computer processor.
  • a processor such as a computer processor.
  • Such processor may be the processor 2016 that is operatively coupled to the imaging device 2014 or a different processor.
  • the processor may be configured to calculate the velocity of a particle (rotational and/or downstream velocity of the particle) an predict the time of arrival of the particle at the bifurcation.
  • the processor may be operatively coupled to one or more valves of the bifurcation.
  • the processor may be configured to direct the valve(s) to open and close any channel in fluid communication with the bifurcation.
  • the processor may be configured to predict and measure when operation of the valve(s) (e.g., opening or closing) is completed.
  • the sorter may comprise a self-included unit (e.g., comprising the sensors, such as the imaging device(s)) which is capable of (i) predicting the time of arrival of the articles and/or (ii) detecting the particle as it arrives at the bifurcation.
  • the order at which the particles arrive at the bifurcation, as detected by the self-included unit may be matched to the order of the received signal from the decision making unit (e.g. a classifier).
  • controlled particles are used to align and update the order as necessary.
  • the calibration beads used are polystyrene beads with size ranging between about 1 mM to about 50 mM. In some examples the calibration beads used are polystyrene beads with size of least about 1 pM. In some examples the calibration beads used are polystyrene beads with size of at most about 50 pM.
  • the calibration beads used are polystyrene beads with size ranging between about 1 pM to about 3 pM, about 1 pM to about 5 pM, about 1 pM to about 6 pM, about 1 pM to about 10 pM, about 1 pM to about 15 pM, about 1 pM to about 20 pM, about 1 pM to about 25 pM, about 1 pM to about 30 pM, about 1 pM to about 35 pM, about 1 pM to about 40 pM, about 1 pM to about 50 pM, about 3 pM to about 5 pM, about 3 pM to about 6 pM, about 3 pM to about 10 pM, about 3 pM to about 15 pM, about 3 pM to about 20 pM, about 3 pM to about 25 pM, about 3 pM to about 30 pM, about 3 pM to about 35 pM, about 3 pM to about 35
  • the systems, methods, and platforms disclosed herein may dynamically adjust a delay time (e.g., a constant delay time) based on imaging of the cell(s) or based on tracking of the cell(s) with light (e.g., laser).
  • a delay time e.g., a constant delay time
  • the delay time e.g., time at which the cells arrive at the bifurcation
  • a feedback loop may be designed that may constantly read such changes and adjust the delay time accordingly.
  • the delay time may be adjusted for each cell/particle.
  • the delay time may be calculated separately for each individual cell, based on, e.g., its velocity, lateral position in the channel, and/or time of arrival at specific locations along the channel (e.g., using tracking based on lasers or other methods).
  • the calculated delay time may then be applied to the individual cell/particle (e.g., if the cell is a positive cell or a target cell, the sorting may be performed according to its specific delay time or a predetermined delay time).
  • the sorters used in the systems and methods disclosed herein are self-learning cell sorting systems or intelligent cell sorting systems, as disclosed herein. [0388] These sorting systems may continuously learn based on the outcome of sorting.
  • a sample of cells is sorted, the sorted cells are analyzed, and the results of this analysis are fed back to the classifier.
  • the cells that are sorted as “positive” i.e., target cells or cells of interest
  • the cells that are sorted as “negative” i.e., non-target cells or cells not of interest
  • both positive and negative cells may be validated.
  • Such validation of sorted cells e.g., based on secondary imaging and classification
  • a flush mechanism may be used during sorting.
  • the flush mechanism may ensure that the cell which has been determined to be sorted to a specific bucket or well will end up there (e.g., not be stuck in various parts of the channel or outlet).
  • the flush mechanism may ensure that the channel and outlets stay clean and debris-free for maximum durability.
  • the flush mechanism may inject additional solutions/reagents (e.g., cell lysis buffers, barcoded reagents, etc.) to the well or droplet that the cell is being sorted into.
  • the flush mechanism may be supplied by a separate set of channels and/or valves which are responsible to flow a fluid at a predefined cadence in the direction of sorting. [0390]
  • the methods and systems disclosed herein may use any sorting technique to sort particles.
  • the sorting technique comprises closing a channel on one side of the bifurcation to collect the desired cell on the other side.
  • the closing of the channels may be carried out by employing any known technique.
  • the closing is carried out by application of a pressure.
  • the pressure is pneumatic actuation.
  • the pressure may be positive pressure or negative pressure.
  • positive pressure is used.
  • one side of the bifurcation is closed by applying pressure and deflecting the soft membrane between top and bottom layers.
  • the systems and methods of the present disclosure comprise one or more reservoirs designed to collect the particles after the particles have been sorted.
  • the number of cells to be sorted is about 1 cell to about 1,000,000 cells. In some examples, the number of cells to be sorted is at least about 1 cell. In some examples, the number of cells to be sorted is at most about 1,000,000 cells.
  • the number of cells to be sorted is about 1 cell to about 100 cells, about 1 cell to about 500 cells, about 1 cell to about 1,000 cells, about 1 cell to about 5,000 cells, about 1 cell to about 10,000 cells, about 1 cell to about 50,000 cells, about 1 cell to about 100,000 cells, about 1 cell to about 500,000 cells, about 1 cell to about 1,000,000 cells, about 100 cells to about 500 cells, about 100 cells to about 1,000 cells, about 100 cells to about 5,000 cells, about 100 cells to about 10,000 cells, about 100 cells to about 50,000 cells, about 100 cells to about 100,000 cells, about 100 cells to about 500,000 cells, about 100 cells to about 1,000,000 cells, about 500 cells to about 1,000 cells, about 500 cells to about 5,000 cells, about 500 cells to about 10,000 cells, about 500 cells to about 50,000 cells, about 500 cells to about 100,000 cells, about 500 cells to about 100,000 cells, about 500 cells to about 100,000 cells, about 500 cells to about 100,000 cells, about 500 cells to about 100,000 cells, about 500 cells to about 100,000 cells, about 500 cells to about 100,000 cells, about 500 cells to about 100,000 cells, about 500 cells
  • the number of cells to be sorted is more than about 1 cell, more than about 100 cells, more than about 500 cells, more than about 1,000 cells, more than about 5,000 cells, more than about 10,000 cells, more than about 50,000 cells, more than about 100,000 cells, more than about 500,000 cells, or more than about 1,000,000 cells. [0392] In some examples, the number of cells to be sorted is about 100 to about 500 cells, about 200 to about 500 cells, about 300 to about 500 cells, about 350 to about 500 cells, about 400 to about 500 cells, or about 450 to about 500 cells. In some examples, the reservoirs may be milliliter scale reservoirs. In some examples, the one or more reservoirs are pre-filled with a buffer and the sorted cells are stored in the buffer.
  • the buffer is a phosphate buffer, for example phosphate-buffered saline (PBS).
  • PBS phosphate-buffered saline
  • the system and methods of the present disclosure comprise a cell sorting technique wherein pockets of buffer solution containing no negative objects are sent to the positive output channel in order to push rare objects out of the collection reservoir.
  • additional buffer solution is sent to the positive output channel to flush out all positive objects at the end of a run, once the channel is flushed clean (e.g., using the flush mechanism as disclosed herein).
  • the system and methods of the present disclosure comprise a cell retrieving technique, wherein sorted cells may be retrieved for downstream analysis (e.g., molecular analysis).
  • Non- limiting examples of the cell retrieving technique may include: retrieval by centrifugation; direct retrieval by pipetting; direct lysis of cells in well; sorting in a detachable tube; feeding into a single cell dispenser to be deposited into 96 or 384 well plates; etc.
  • the system and methods of the present disclosure comprise a combination of techniques, wherein a graphics processing unit (GPU) and a digital signal processor (DSP) are used to run artificial intelligence (AI) algorithms and apply classification results in real-time to the system.
  • GPU graphics processing unit
  • DSP digital signal processor
  • the feedback loop may be designed to monitor and/or handle degenerate scenarios, in which the microfluidic system is not responsive or malfunctioning (e.g., outputting a value read that is out of range of acceptable reads).
  • the system and methods of the present disclosure may adjust a cell classification threshold based on expected true positive rate for a sample type.
  • the expected true positive rate may come from statistics gathered in one or more previous runs from the same or other patients with similar conditions. Such approach may help neutralize run-to-run variations (e.g., illumination, chip fabrication variation, etc.) that would impact imaging and hence any inference therefrom.
  • the systems disclosed herein further comprise a validation unit that detects the presence of a particle without getting detailed information, such as imaging.
  • the validation unit may be used for one or more purposes.
  • the validation unit detects a particle approaching the bifurcation and enables precise sorting.
  • the validation unit detects a particle after the particle has been sorted to one of subchannels in fluid communication with the bifurcation.
  • the validation unit provides timing information with a plurality of laser spots, e.g., two laser spots.
  • the validation unit provides timing information by referencing the imaging time.
  • the validation unit provides precise time delay information and/or flow speed of particles.
  • the biological sample comprises, or is derived from, a biopsy sample from a subject.
  • the biological sample comprises a tissue sample from a subject.
  • the biological sample comprises liquid biopsy from a subject.
  • the biological sample may be a solid biological sample, e.g., a tumor sample.
  • the liquid biological sample may be a blood sample (e.g., whole blood, plasma, or serum). A whole blood sample may be subjected to separation of cellular components (e.g., plasma, serum) and cellular components by use of a Ficoll reagent.
  • the liquid biological sample may be a urine sample.
  • the liquid biological sample may be a perilymph sample.
  • the liquid biological sample may be a fecal sample.
  • the liquid biological sample may be saliva.
  • the liquid biological sample may be semen.
  • the liquid biological sample may be amniotic fluid.
  • the liquid biological sample may be cerebrospinal fluid.
  • the liquid biological sample may be bile.
  • the liquid biological sample may be sweat. In some examples, the liquid biological sample may be tears. In some examples, the liquid biological sample may be sputum. In some examples, the liquid biological sample may be synovial fluid. In some examples, the liquid biological sample may be vomit. [0401] In some examples, samples may be collected over a period of time and the samples may be compared to each other or with a standard sample using the systems and methods disclosed herein. In some examples the standard sample is a comparable sample obtained from a different subject, for example a different subject that is known to be healthy or a different subject that is known to be unhealthy. Samples may be collected over regular time intervals, or may be collected intermittently over irregular time intervals.
  • FIGS.21A-21F schematically illustrate an example system for classifying and sorting one or more cells.
  • the platform as disclosed herein may allow for the input and flow of cells in suspension with confinement along a single lateral trajectory to obtain a narrow band of focus across the z-axis (FIGS.21A- 21E).
  • FIG.21A shows the microfluidic chip and the inputs and output of the sorter platform according to one example of the present disclosure.
  • Cells in suspension and sheath fluid are inputted, along with run parameters entered by the user: target cell type(s) and a cap on the number of cells to sort, if sorting is of interest.
  • the system Upon run completion, the system generates reports of the sample composition (number and types of all of the processed cells) and the parameters of the run, including: length of run, number of analyzed cells, quality of imaging, quality of the sample. If sorting option is selected, it outputs isolated cells in a reservoir on the chip as well as a report of the number of sorted cells, purity of the collected cells and yield of the sort.
  • FIG.21B a combination of hydrodynamic focusing and inertial focusing is used to focus the cells on a single z plane and a single lateral trajectory.
  • FIGS. 21C and 21D the diagram shows the interplay between different components of the software (FIG.21C) and hardware pieces (FIG.21D).
  • the classifier is blown up in FIG.21E, depicting the process of image collection, and automated real-time assessment of single cells in flow.
  • individual cell images are cropped using an automated object detection module, the cropped images are then run through a deep neural networks model trained on the relevant cells.
  • the model For each image, the model generates a prediction vector over the available cell classes and an inference will be made according to a selection rule (e.g., argmax).
  • the model may also infer the z focusing plane of the image.
  • the percentage of debris and cell clumps may also be predicted by the neural network model as a proxy for “sample quality”.
  • FIG. 21F shows an example performance of sorting.
  • the platform may collect ultra high- speed bright-field images of cells as they pass through the imaging zone of the microfluidic chip (FIGS. 21A and 21B).
  • an automated object detection module may be incorporated to crop each image centered around the cell, before feeding the cropped images into a deep convolutional neural network (CNN) based on Inception architecture, which is trained on images of relevant cell types.
  • CNN deep convolutional neural network
  • the CNN may be trained to assess the focus of each image (in Z plane) and identify debris and cell clusters, thus providing information to assess sample quality (FIG.21E).
  • a feedback loop may be engineered so that the CNN inferred cell type is used in real time to regulate pneumatic valves for sorting a cell into either the positive reservoir (cell collection reservoir) for a targeted category of interest or a waste outlet (FIG. 21A). Sorted cells in the reservoir may then be retrieved for downstream processing and molecular analysis.
  • FIGS.22A-22E schematically illustrate operations that may be performed in an example method.
  • FIG. 22A shows high resolution images of single cells in flow are stored. Referring to FIG.
  • AIAIA AI Assisted Image Annotation
  • a user uses the labeling tool to adjust and batch-label the cell clusters.
  • one AML cell is mis-clustered into a group of WBC cells and an image showing a cell clump (debris) is mis-clustered in a NSCLC cell group. These errors are corrected by an “Expert clean-up” step.
  • the annotated cells are then integrated into a Cell Morphology Atlas (CMA).
  • CMA Cell Morphology Atlas
  • the pre-trained model shown in FIG.22D is used to infer the cell type (class) in real-time.
  • the enriched cells are retrieved from the device.
  • the retrieved cells are further processed for molecular profiling.
  • the platform may be run in multiple different modes.
  • the collected images of a sample may be fed to the AI-Assisted Image Annotation (AIAIA), which may be configured to use unsupervised learning to group cells into morphologically distinct sub-clusters.
  • AIAIA AI-Assisted Image Annotation
  • a user may clean up the sub-clusters by removing cells that are incorrectly clustered and annotates each cluster based on a predefined annotation schema.
  • the annotated cell images are then integrated into the Cell Morphology Atlas (CMA), a growing database of expert-annotated images of single cells.
  • CMA Cell Morphology Atlas
  • the CMA is broken down into training and validation sets and is used to train and evaluate CNN models aimed at identifying certain cell types and/or states.
  • FOG. 22D Under the analysis mode (FIG. 22D), the collected images are fed into models that had been previously trained using the CMA, and a report is generated demonstrating the composition of the sample of interest.
  • a UMAP visualization is used to depict the morphometric map of all the single cells within the sample.
  • a set of prediction probabilities is also generated showing the classifier prediction of each individual cell within the sample belonging to every predefined cell class within the CMA.
  • FIG. 23 shows a computer system that is programmed or otherwise configured to implement methods provided herein.
  • the present disclosure provides computer systems that are programmed to implement methods of the disclosure.
  • FIG. 23 shows a computer system 2301 that is programmed or otherwise configured to capture and/or analyze one or more images of the cell.
  • the computer system 2301 may regulate various aspects of components of the cell sorting system of the present disclosure, such as, for example, the pump, the valve, and the imaging device.
  • the computer system 2301 may be an electronic device of a user or a computer system that is remotely located with respect to the electronic device.
  • the electronic device may be a mobile electronic device.
  • the computer system 2301 includes a central processing unit (CPU, also “processor” and “computer processor” herein) 2305, which may be a single core or multi core processor, or a plurality of processors for parallel processing.
  • the computer system 2301 also includes memory or memory location 2310 (e.g., random-access memory, read-only memory, flash memory), electronic storage unit 2315 (e.g., hard disk), communication interface 2320 (e.g., network adapter) for communicating with one or more other systems, and peripheral devices 2325, such as cache, other memory, data storage and/or electronic display adapters.
  • the memory 2310, storage unit 2315, interface 2320 and peripheral devices 2325 are in communication with the CPU 2305 through a communication bus (solid lines), such as a motherboard.
  • the storage unit 2315 may be a data storage unit (or data repository) for storing data.
  • the computer system 2301 may be operatively coupled to a computer network (“network”) 2330 with the aid of the communication interface 2320.
  • the network 2330 may be the Internet, an internet and/or extranet, or an intranet and/or extranet that is in communication with the Internet.
  • the network 2330 in some cases is a telecommunication and/or data network.
  • the network 2330 may include one or more computer servers, which may enable distributed computing, such as cloud computing.
  • the network 2330 in some cases with the aid of the computer system 2301, may implement a peer-to-peer network, which may enable devices coupled to the computer system 2301 to behave as a client or a server.
  • the CPU 2305 may execute a sequence of machine-readable instructions, which may be embodied in a program or software. The instructions may be stored in a memory location, such as the memory 2310.
  • the instructions may be directed to the CPU 2305, which may subsequently program or otherwise configure the CPU 2305 to implement methods of the present disclosure. Examples of operations performed by the CPU 2305 may include fetch, decode, execute, and writeback.
  • the CPU 2305 may be part of a circuit, such as an integrated circuit. One or more other components of the system 2301 may be included in the circuit. In some cases, the circuit is an application specific integrated circuit (ASIC).
  • ASIC application specific integrated circuit
  • the storage unit 2315 may store files, such as drivers, libraries and saved programs.
  • the storage unit 2315 may store user data, e.g., user preferences and user programs.
  • the computer system 2301 in some cases may include one or more additional data storage units that are external to the computer system 2301, such as located on a remote server that is in communication with the computer system 2301 through an intranet or the Internet.
  • the computer system 2301 may communicate with one or more remote computer systems through the network 2330.
  • the computer system 2301 may communicate with a remote computer system of a user.
  • Examples of remote computer systems include personal computers (e.g., portable PC), slates, or tablets (e.g., Apple® iPad, Samsung® Galaxy Tab), telephones, Smart phones (e.g., Apple® iPhone, Android-enabled device, Blackberry®), or personal digital assistants.
  • the user may access the computer system 2301 via the network 2330.
  • Methods as described herein may be implemented by way of machine (e.g., computer processor) executable code stored on an electronic storage location of the computer system 2301, such as, for example, on the memory 2310 or electronic storage unit 2315.
  • the machine executable or machine readable code may be provided in the form of software.
  • the code may be executed by the processor 2305.
  • the code may be retrieved from the storage unit 2315 and stored on the memory 2310 for ready access by the processor 2305.
  • the electronic storage unit 2315 may be precluded, and machine-executable instructions are stored on memory 2310.
  • the code may be pre-compiled and configured for use with a machine having a processor adapted to execute the code, or may be compiled during runtime.
  • the code may be supplied in a programming language that may be selected to enable the code to execute in a pre compiled or as-compiled fashion.
  • Examples of the systems and methods provided herein, such as the computer system 2301, may be embodied in programming.
  • Various aspects of the technology may be thought of as “products” or “articles of manufacture” typically in the form of machine (or processor) executable code and/or associated data that is carried on or embodied in a type of machine readable medium.
  • Machine-executable code may be stored on an electronic storage unit, such as memory (e.g., read-only memory, random-access memory, flash memory) or a hard disk.
  • “Storage” type media may include any or all of the tangible memory of the computers, processors or the like, or associated modules thereof, such as various semiconductor memories, tape drives, disk drives and the like, which may provide non-transitory storage at any time for the software programming. All or portions of the software may at times be communicated through the Internet or various other telecommunication networks. Such communications, for example, may enable loading of the software from one computer or processor into another, for example, from a management server or host computer into the computer platform of an application server.
  • another type of media that may bear the software elements includes optical, electrical and electromagnetic waves, such as used across physical interfaces between local devices, through wired and optical landline networks and over various air-links.
  • Non-volatile storage media include, for example, optical or magnetic disks, such as any of the storage devices in any computer(s) or the like, such as may be used to implement the databases, etc.
  • Volatile storage media include dynamic memory, such as main memory of such a computer platform.
  • Tangible transmission media include coaxial cables; copper wire and fiber optics, including the wires that comprise a bus within a computer system.
  • Carrier-wave transmission media may take the form of electric or electromagnetic signals, or acoustic or light waves such as those generated during radio frequency (RF) and infrared (IR) data communications.
  • Computer-readable media therefore include for example: a floppy disk, a flexible disk, hard disk, magnetic tape, any other magnetic medium, a CD-ROM, DVD or DVD-ROM, any other optical medium, punch cards paper tape, any other physical storage medium with patterns of holes, a RAM, a ROM, a PROM and EPROM, a FLASH-EPROM, any other memory chip or cartridge, a carrier wave transporting data or instructions, cables or links transporting such a carrier wave, or any other medium from which a computer may read programming code and/or data. Many of these forms of computer readable media may be involved in carrying one or more sequences of one or more instructions to a processor for execution.
  • the computer system 2301 may include or be in communication with an electronic display 2335 that comprises a user interface (UI) 2340 for providing, for example, the one or more images of the cell that is transported through the channel of the cell sorting system.
  • UI user interface
  • the computer system 2301 may be configured to provide a live feedback of the images.
  • UIs include, without limitation, a graphical user interface (GUI) and web-based user interface.
  • GUI graphical user interface
  • Methods and systems of the present disclosure may be implemented by way of one or more algorithms. An algorithm may be implemented by way of software upon execution by the central processing unit 2305. The algorithm may include, for example, the human foundation model.
  • Example 1 Evaluation of the human foundation model using different cell lines.
  • Cancer cells lines A375 and Caov-3, and immune cell line Jurkat were used to evaluate the classification performance of the human foundation model.
  • Polystyrene beads with a size of 6 micrometers ( ⁇ m) were used as control.
  • the cell lines and polystyrene beads were imaged using the microfluidic platform (e.g., REM-I platform) as described herein and combined in silico to evaluate the performance of the human foundation model.
  • the microfluidic platform e.g., REM-I platform
  • the human foundation model processed the images of the cell lines and polystyrene beads, extracted deep learning and morphometric features. These features were standardized and projected into a lower dimensional principal components analysis (PCA) basis. Nearest neighbors were computed in the PCA space, then used to compute 2D Uniform Manifold Approximation and Projections (UMAP).
  • PCA principal components analysis
  • UMAP 2D Uniform Manifold Approximation and Projections
  • Table 1 (above) is a panel of deep learning derived features generated using the DL model of the human foundation model and used in the present examples.
  • Table 2 (above) is a panel of morphometric features generated using the computer vision model of the human foundation model and used in the present examples. It should be noted that the number and types of features listed in Tables 1 and 2 are provided only as examples without limiting the scope of the present disclosure.
  • FIG.2A illustrates cell classes, numbers of images used as training dataset to train a classifier using features extracted using the human foundation model, numbers of images processed by the human foundation model as test dataset, and corresponding representative cell images, in accordance with some examples of the present disclosure.
  • a scale bar of 10 ⁇ m is shown on the representative cell images.
  • FIG.2B illustrates a morphology UMAP of different cell lines and polystyrene beads as control, in accordance with some examples of the present disclosure.
  • FIG. 2C illustrates a confusion matrix between predicted cell classes classified using features extracted using the human foundation model and actual cell classes, in accordance with some examples of the present disclosure.
  • the human foundation model predicts cell classes with a high accuracy. The accuracy for predicting the Jurkat cell line, A375 cell line, and Caov-3 cell line is 90.5%, 89%, and 95.8%, respectively.
  • FIG. 2D illustrates density plots of four differential features generated by the human foundation model for different cell lines and polystyrene beads as control.
  • the differential features comprise computer vision derived morphometric features in the top row of FIG. 2D, including maximum radius (“HFMv1:MD007” on the top left panel), and small set of connected bright pixels (“HFMv1:MD022” on the top right panel).
  • the differential features also comprise deep learning derived features in the bottom row of FIG.2D (“HFMv1:DL005” and “HFMv1:DL011”).
  • the differential features of A375 and Caov-3 cancer cell lines are nearly identical.
  • the size by maximum radius (“HFMv1:MD007” on the top left panel), small set of connected bright pixels (“HFMv1:MD022” on the top right panel), and deep learning feature 5 (“HFMv1:DL005” on the bottom left panel) for the two cancer cell lines are substantially similar.
  • the similarity indicates that cell features other than size may contribute to differentiating the A375 and Caov-3 cancer cell lines, for example, deep learning feature (“HFMv1:DL011” on the bottom right panel).
  • Table 4 lists the example components of one example system. From this example, it may be understood that the present system may be constructed using commercially available components. Table 3. Parameters and specifications of REM-I system for cell morphology analysis. “*” in Table 3 denotes the specification is dependent on sample characteristics and/or sorting configurations. Table 4. Components of the REM-I system. Example 3 – Characterization of tumor cell population composition by morphology. [0426] This example evaluated the performance of the cell morphology profiling platform as described herein in differentiating different types of cells.
  • Melanoma lesions are generally composed of primary tumor cells as well as a diverse set of immune cells in various activation states. To simulate these tumors, an in- silico mixture of cell types was generated.
  • the mixture of cell types typically represented in solid tumors, comprised human melanoma cell lines (e.g., SK-MEL-1, SK-MEL-3, MNT-1), in vitro activated T cells from peripheral blood mononuclear cells (PBMCs), plasma cells from purified bone marrow, stromal cells from patient lymph nodes, and CD45+ immune cells (e.g., activated T cells, T cells, B cells, and NK cells), and macrophages isolated from metastatic melanoma biopsies.
  • PBMCs peripheral blood mononuclear cells
  • CD45+ immune cells e.g., activated T cells, T cells, B cells, and NK cells
  • macrophages isolated from metastatic melanoma biopsies e.g., activated T
  • FIG.8A illustrates an example morphology UMAP from cell image feature embeddings colored by cell types, in accordance with some implementations of the present disclosure.
  • FIG. 8B illustrates an example morphology UMAP colored by clusters imputed using Leiden algorithm, with randomly selected representative images from each cluster shown, in accordance with some implementations of the present disclosure.
  • tumor cell lines SK-MEL-1 and SK-MEL-3 are distinct from MNT-1 cells (cluster 3).
  • tumor cell lines SK-MEL-1 and SK- MEL-3 exhibit a smoother appearance, whereas MNT-1 cells (cluster 3) show higher granularity.
  • distinct immune cell types are variably located in the morphology UMAP, indicating cells have subtle but separable morphologies. For example, larger immune cells with more granular features (e.g., macrophages) are clustered toward the top of the morphology UMAP, while smaller cells with no granularity (e.g., activated T cells and plasma cells) are located in the lower clusters of the UMAP.
  • FIGS.8A and 8B suggest the platform for cell morphology profiling may differentiate cell types represented in melanoma tumors based solely on multi-dimensional morphological profiles.
  • the cell type differentiation includes differentiating tumor versus non-tumor cells, activated versus quiescent lymphocytes, and different immune cells (e.g., plasma cells, macrophages, and lymphocytes) with varying granularity features. From this example, it may be understood that the present platform may be used to differentiate different cell types from one another.
  • Example 4 – Computer vision morphometrics reveals differential cell features in melanoma cell lines.
  • Skin and hair pigmentation is the result of melanosome melanin biosynthesis in epidermal melanocytes.
  • Example 4 evaluated the performance of the platform as described herein in differentiating melanoma cell features.
  • FIG.9A illustrates an example morphology UMAP of a heterogeneous collection of melanoma cell lines and immune/stromal cells derived from patient biopsies, in consistency with FIG.8A, in accordance with some implementations of the present disclosure.
  • FIG.4B illustrates a re-projected morphology UMAP using filtered data from FIG. 9A showing only melanoma cells colored by cell lines, in accordance with some implementations of the present disclosure.
  • FIGS.9A and 9B pigmented melanoma cells (e.g., MNT-1) are morphologically distinct and clustered separately from other melanoma cells (e.g., SK- MEL-1 and SK-MEL-3) with low levels of melanin.
  • FIG. 9C illustrates an example morphology UMAP colored by clusters imputed using Leiden algorithm, with randomly selected representative images from each cluster shown and pixel density plot of three selected clusters, in accordance with some implementations of the present disclosure.
  • the left panel of FIG. 9C shows representative images of pigmented cells in clusters 6 and 9, respectively, as well as representative images of non-pigmented cells in cluster 2.
  • Lung cancer is highly heterogeneous, and generally divided into two groups: small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC).
  • SCLC small cell lung cancer
  • NSCLC non-small cell lung cancer
  • FIGS. 10A and 10B show example morphologically distinct cell clusters from human NSCLC tissue.
  • FIG. 10A illustrates a morphology UMAP of cells based on multi-dimensional embeddings where the cells resided in a NSCLC DTC biopsy sample, in accordance with some implementations of the present disclosure.
  • FIG.10B illustrates six morphologically distinct clusters isolated by the platform as described herein via user-defined sorting and processed for CNV profiling, in accordance with some implementations of the present disclosure. As illustrated, sorted morphology clusters exhibited distinct CNV patterns, consistent with cancer cell profiles in clusters 2, 5, and 6.
  • FIGS.11A and 11B demonstrate example transcriptomic characterization of morphology clusters.
  • FIG. 11A illustrates bulk RNA-sequence analysis of cells resided in a NSCLC DTC biopsy sample, in accordance with some implementations of the present disclosure.
  • FIG.11B illustrates principal component analysis (PCA) performed on each morphology cluster, in accordance with some implementations of the present disclosure.
  • PCA principal component analysis
  • isolated morphology clusters are viable and show high concordance with CNV and bulk RNA-Seq expression profiles.
  • sorted morphology clusters comprise increased populations of respective cell classes.
  • clusters 2, 5, and 6 show enrichment of tumor cells while clusters 1, 3, and 4 show enrichment of immune cells, indicating morphology may be reflective of cell identity and function.
  • the detection of multiple cancer cell clusters based on multi-dimensional morphology may be reflective of the heterogeneous nature of the tumor sample, which may not have been revealed by a constrained panel of biomarkers.

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Abstract

Un procédé de traitement d'images consiste à fournir une ou plusieurs images de cellule à une pluralité de codeurs comprenant un codeur à apprentissage automatique et un codeur de vision par ordinateur ; à extraire un ensemble de fonctionnalités utilisant l'apprentissage automatique (ML) à l'aide du codeur à apprentissage automatique, et à extraire un ensemble de caractéristiques morphométriques de cellule à l'aide du codeur de vision par ordinateur ; et à utiliser le codeur à apprentissage automatique et le codeur de vision par ordinateur pour coder respectivement l'ensemble de fonctionnalités utilisant le ML et l'ensemble de caractéristiques morphométriques de cellule en une pluralité de vecteurs multidimensionnels qui représentent la morphologie d'au moins une cellule dans la ou les images de cellule.
PCT/US2024/026761 2023-05-12 2024-04-29 Systèmes et procédés d'analyse de morphologie cellulaire Pending WO2024238130A2 (fr)

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