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WO2024201451A1 - Système et procédé de différenciation et de séparation de cellules spermatiques - Google Patents

Système et procédé de différenciation et de séparation de cellules spermatiques Download PDF

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Publication number
WO2024201451A1
WO2024201451A1 PCT/IL2024/050291 IL2024050291W WO2024201451A1 WO 2024201451 A1 WO2024201451 A1 WO 2024201451A1 IL 2024050291 W IL2024050291 W IL 2024050291W WO 2024201451 A1 WO2024201451 A1 WO 2024201451A1
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Prior art keywords
sperm cells
sperm
cell
cells
trained
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English (en)
Inventor
Lior OFER
Roy Porat
Nadav SEGAL
Achia KRONMAN
Dafna KETTER
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Foreseed Ltd
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Foreseed Ltd
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    • 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
    • G06V20/698—Matching; Classification
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
    • G01N15/10—Investigating individual particles
    • G01N15/14—Optical investigation techniques, e.g. flow cytometry
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B03—SEPARATION OF SOLID MATERIALS USING LIQUIDS OR USING PNEUMATIC TABLES OR JIGS; MAGNETIC OR ELECTROSTATIC SEPARATION OF SOLID MATERIALS FROM SOLID MATERIALS OR FLUIDS; SEPARATION BY HIGH-VOLTAGE ELECTRIC FIELDS
    • B03B—SEPARATING SOLID MATERIALS USING LIQUIDS OR USING PNEUMATIC TABLES OR JIGS
    • B03B9/00—General arrangement of separating plant, e.g. flow sheets
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12N—MICROORGANISMS OR ENZYMES; COMPOSITIONS THEREOF; PROPAGATING, PRESERVING, OR MAINTAINING MICROORGANISMS; MUTATION OR GENETIC ENGINEERING; CULTURE MEDIA
    • C12N5/00—Undifferentiated human, animal or plant cells, e.g. cell lines; Tissues; Cultivation or maintenance thereof; Culture media therefor
    • C12N5/06—Animal cells or tissues; Human cells or tissues
    • C12N5/0602—Vertebrate cells
    • C12N5/0608—Germ cells
    • C12N5/061—Sperm cells, spermatogonia
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
    • G01N15/10—Investigating individual particles
    • G01N15/14—Optical investigation techniques, e.g. flow cytometry
    • G01N15/1468—Optical investigation techniques, e.g. flow cytometry with spatial resolution of the texture or inner structure of the particle
    • G01N15/147—Optical investigation techniques, e.g. flow cytometry with spatial resolution of the texture or inner structure of the particle the analysis being performed on a sample stream
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
    • 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
    • G06V10/82—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks

Definitions

  • the present invention is in the field of sperm cell analysis and separation. Particularly, for obtaining enriched populations of sperm cells with desired characteristics.
  • sperm cells in a semen sample Differentiating between types of sperm cells in a semen sample according to desired characteristics is needed in various practices such as livestock insemination, in vitro fertilization, genetic consultation, and the like.
  • the desired characteristics include for example, fertilization potential, motility, morphology, and genetic content of the sperm cells.
  • One of the sperm cells’ traits that is of major importance is the sex chromosome content of the sperm cells, namely, the determination whether a sperm cell carries an X chromosome, or a Y chromosome, or any aberration thereof.
  • Determination of the sex chromosome content of sperm cells is used for selecting the sex of the offspring, for example in the livestock industry, to maximize husbandry efficiency. Sex selection in livestock animals is mainly performed prior to fertilization, by increasing the content of X chromosome-bearing or Y chromosome-bearing sperm cells in semen samples intended for artificial insemination. The process of differentiating sperm cells according to their sex chromosome content is known as semen sexing.
  • the current disclosure provides a method and system that make use of sperm visualization and a pretrained Al-based model for the purpose of detecting individual sperm cells and one or more sperm cell features of the individual sperm cells. Based on these features sperm cells may be classified, for example, among others, into such that contain an X or a Y chromosome, into living and dead cells, or into cells with high and low fertility potential.
  • the disclosure comprises classifying genetic or biological traits that may be unknown or may not be detectable by standard visual inspection techniques.
  • pre-trained as used in relation to the Al -based model or algorithm is intended to denote that the algorithm has been subjected to some pre -training.
  • an algorithm while pre-trained, may also undergo continued training when used in practice; for example, with a training set specific for a specific ejaculate -providing mail mammal (e.g. a bull), a set specific for a defined herd, etc.
  • the algorithm may undergo continued refinement when in use.
  • the refinement may be by external data.
  • sperm cells suspension may be used herein to denote a medium that comprises moving sperm cells suspended therein (namely sperm cells that are not immobilized and freely swim in the medium) that were not exposed to or pre-stained with dyes.
  • the sperm cells suspension is typically a semen sample diluted in an appropriate medium.
  • the sperm cells suspension may also be referred to in short as “suspension ".
  • One of the purposes of such classification is to separate sperm cells that meet certain defined criteria from other cells, particularly for the purpose of semen sexing.
  • the classification is performed without (i) immobilizing the cells, (ii) use of laser beams to illuminate the sperm cells, or without (iii) exposing the cells to a stain or dye that stains cells or identifies certain biomarkers, prior or during the performance of the classification.
  • the classification is performed without any alteration to the sperm cells natural properties or behavior.
  • This disclosure concerns two independent aspects: a method for such classification, and a system for such classification. These aspects may be referred to herein as the “method aspect” and the “system aspect”, respectively.
  • a method for classification of mammalian sperm cells that comprises passing the sperm cells suspension in front of a video capturing utility, capturing one or more video frames of individual sperm cells in the suspension, and feeding the captured video frames into a computing utility for analysis and classification. Said passing is carried out under conditions that permit the video capturing utility to capture images of single sperm cells.
  • the sperm cells suspension is devoid of any cell-staining or biomarker-identifying dyes, the sperm cells or at least a portion thereof are motile, and furthermore, the cells in the suspension were not exposed to or pre-stained with dyes.
  • the computing utility is configured for analyzing the fed video frames using one or more pre-trained Al models to detect individual sperm cells and one or more sperm cell features of the individual sperm cells. Based thereon the detected sperm cells can be classified as belonging to a defined category based on the sperm cell features identified in said analyzing.
  • video stream may be used herein to denote a feed of data representative of one or more video frames that are fed into the computing utility.
  • a system for the classification of mammalian sperm cells that comprises (1) a flow channel configured for transferring the sperm cells suspension therethrough; (2) a video capturing utility configured for capturing images of single sperm cells flowing through the flow channel and generating a video stream; and (3) a computing utility running one or more pre-trained Al models and configured for receiving and analyzing the video stream to (i) detect individual sperm cells and one or more sperm cells features of the individual sperm cells and (ii) classify the detected sperm cells as belonging to a defined category based on said features.
  • the features that are being detected and analyzed as well as the classification may, by some embodiments, comprise one or more of the following:
  • Embodiments (c) - (g) may, for example, be carried out using the detection and analysis of embodiments (a) - (b).
  • the computing utility is configured to analyze the data using one or more pre-trained Al models.
  • the pre-trained Al models may, by some embodiments, be an ensemble of two or more pre-trained Al models.
  • the one or more pre-trained Al models comprise a first pre-trained Al model and a second pre-trained Al model, the first pre-trained Al model being configured for analyzing the video stream and providing an input to a second pre-trained Al model.
  • at least one of the Al models is based on one or more of the following: computer vision-based Al, machine learning (e.g. deep learning), and computer vision-based Al combined with machine learning.
  • the classification of the sperm cells serves the purpose of physically separating sperm cells with defined characteristics from other cells.
  • the separation may comprise one or more of the following:
  • the system may comprise a cell separating utility controllable by the computing utility and configured for separating sperm cells with defined characteristics from other cells.
  • the cell separating utility may, for example (and without limitation) be a cell sorter or a microfluidic unit. The separation may be carried out at different temperatures, for example between about 4°C to about 39°C.
  • a specific, but non-limiting embodiment of the separation is for obtaining a sperm cell-containing suspension with a sperm cell population enriched with X chromosome-containing or Y chromosome-containing sperm cells.
  • the system may comprise at least one light source that is configured for illuminating the sperm cells suspension passing in the flow channel.
  • the at least one light source may be selected from a group consisting of an artificial visible light source, a natural visible light source, an ultraviolet (UV) light source, a far infra-red (IR) light source, a near IR light source; it may be polarized or non-polarized light; it may be reflected light, transmitted light, a combination of reflected light and transmitted light; it may be bright field illumination, dark field illumination, phase contrast illumination; etc.
  • UV ultraviolet
  • IR far infra-red
  • a near IR light source it may be polarized or non-polarized light
  • it may be reflected light, transmitted light, a combination of reflected light and transmitted light
  • it may be bright field illumination, dark field illumination, phase contrast illumination; etc.
  • this disclosure is not limited by the nature of light that is used to visualize the sperm cells (and other matter) in the sperm cells suspension.
  • the at least one light source may also be configured to illuminate the sperm cells suspension passing in the flow channel by a combination of lights having different characteristics: combination of different discrete wavelengths, combination of different illumination angles, combination of different light focusing techniques, etc.
  • Combination of light of different characteristics may be by illuminating light from different sources at the same time or alternating between lights of different characteristics, for example, a pulse of light of one characteristic, then a pulse of another, and so forth.
  • each captured cell may be captured by light of different characteristics, which may aid in the analysis that is carried out in accordance with this disclosure.
  • Fig. 1 is a schematic illustration of a system for differentiating between various types of sperm cells in a sperm cells suspension.
  • Fig. 2 is a schematic illustration of a system similar to that of Fig. 1, coupled to a cell sorter.
  • Fig. 3 is a schematic illustration of an artificial neural network for determining a type of individual sperm cells according to visual signals.
  • the present disclosure provides a technology for classifying and differentiating between various types of sperm cells in a sperm cells suspension, typically in real-time while the sperm cells are imaged as they pass through a flow channel in front of a video images capturing device. Individual sperm cell images are then analyzed using a pretrained artificial intelligence (Al) model.
  • Al artificial intelligence
  • one aspect concerns a method for classifying mammalian sperm cell; the other aspect concerns a system for such classification.
  • the method may, typically, comprise a step for separating the sperm cells into two or more distinct populations of cells, each population enriched with cells of specific trait, e.g. population of cells enriched with X or Y chromosome-containing cells within the framework of sperm sexing.
  • the system may, comparably, include a cell separation utility.
  • the sperm cells are not manipulated (other than being suspended and diluted in a medium), namely they are not exposed to any dyes prior to or during the classification procedure.
  • the sperm cells suspension is, typically, a diluted semen sample.
  • the semen sample is, typically, diluted with a medium to yield a sperm cells suspension such that when passing it in front of the video capturing utility, most of the cells will not overlap and be spaced apart; for example, will pass in front of the video capturing utility one after the other.
  • dilution in some embodiments may be in the range of 1:5, 1: 10, 1:20, 1:50, 1: 100, 1:200, 1:500, 1: 1,000, 1:2,000, 1:5,000, 1: 10,000 or in some embodiment even a higher dilution.
  • the dilution may depend on the sperm cells concentration in the original ejaculate, the system features, such as the diameter of the flow channel, and other considerations.
  • the diluting medium may be any medium suitable for that purpose, including such known or used in the art. Specific, and non-limiting, examples are common semen extenders, custom-made mediums, saline, PBS, or other buffers.
  • the sperm cells suspension comprises mobile sperm cells that were not exposed to a dye that stains such cells and does not contain such a dye.
  • the present disclosure encompasses the analysis of any type of sperm cells suspension, from any organism and in any condition. It may, for example be a fresh sperm cells suspension, namely one that is analyzed immediately after collection, a frozen sperm cells suspension that was thawed, and the like.
  • the sperm cells suspension is passed through a flow channel under conditions that permit the video capturing utility to capture images of single sperm cells. Typically, although not exclusively, this includes passing the flowing sperm cells suspension through a narrow flow channel under conditions such that the cells would pass in front of the video-capturing utility spaced apart to permit it to capture images of one cell at a time. Such conditions may also include certain concentrations of the sperm cells or, conversely, the extent of dilution of the original semen sample.
  • the video capturing utility captures one or more video frames of each of the individual sperm cells in the medium.
  • the video frames may be successive video frames or selected, non-successive video frames.
  • the video stream is analyzed to detect individual sperm cells and sperm cell visual features.
  • These features may comprise one or more visual motility features and/or one or more visual structure-related features e.g. features that are identifiable in a single image.
  • the visual motility features that may be taken into consideration by the Al algorithm may include, but are not limited to, speed of movement, lateral head displacement, linearity of motion path, straight-line velocity, curvilinear velocity, average path velocity, amplitude of lateral head displacement, beat frequency, spinning rate, motion dynamics, dynamic positioning, direction of movement, bending dynamics, swimming dynamics, response to flow of medium in which the sperm cell swims, or any combination thereof.
  • the visual structure-related features that may be taken into consideration by the Al algorithm include, but are not limited to, color, transparency, phase contrast, size, shape, positioning, position relative to other objects in the at least one image, orientation, bending, circularity, eccentricity, solidity, convexity, curvature, skeleton, center of mass, center of gravity, fill factor, Feret diameter, compactness, inertia tensor, fractal dimensions, density, symmetry, texture, organelle key points, length of tail, bending of tail, relative depth of the sperm cell or of parts of the sperm cell, Z position, spatial relationship between the sperm cell and other objects, spatial relationship between different parts of the sperm cell, aspect ratio of different parts of the sperm cell, or any combination thereof.
  • CV computer vision
  • Al artificial intelligence
  • ML machine learning
  • DL deep learning
  • the captured video frames are fed into a computing utility (e.g., a processor) configured for analyzing the video stream using one or more pre-trained Al models.
  • a computing utility e.g., a processor
  • the pre-trained Al models are an ensemble of two or more pre-trained Al models.
  • the one or more pre-trained Al models comprise a first pretrained Al model and a second pre-trained Al model
  • the first pre-trained Al model is configured for analyzing the video stream that comprises one or more imaged frames, and providing an input to a second pre-trained Al model that is configured to classify the at least one sperm cell according to the input provided by the first pre-trained Al model.
  • any of the pre-trained Al models may be a supervised Al model or an unsupervised Al model.
  • One embodiment of the present disclosure is a method and system for semen sexing, namely classifying the sperm cells as X chromosome-containing or Y chromosome-containing sperm cells and separating them into two populations, as aforesaid. It should be noted that such classification and separation is not absolute, but rather, it is a determination that the individual sperm cell includes the specific trait, in this case the type of sex chromosome, at a high probability. Thus, a sperm cell population enriched with X chromosome-containing sperm cells, may contain a smaller proportion of Y chromosome-containing sperm cells.
  • the method and system of the present disclosure can be used in various practices such as livestock insemination, in vitro fertilization, genetic consultation, and the like.
  • sperm cells refers to a sperm cell which presents visual features outside the normal spectrum, i.e., which shows a discrepancy e.g., in a chromosomal content of a sperm cell, for example, but not limited to: aneuploidy, polyploidy, DNA fragmentation and the like; in the structure of the sperm cell, for example, but not limited to: macrocephaly, microcephaly, tapered head, amorphous head, pinhead sperm, thickened neck, thin neck, bent tail, short tail, multiple tails, coiled tail, absent tail (acephaly), long tail (flagellated head), agglutination and the like; or in the motility of a sperm cell for
  • the method and system by some embodiments of the present disclosure enables a determination of the fertility potential based on the sperm cells’ visual features.
  • the method and system of embodiments of the present disclosure can be used in offspring sex selection in assisted reproduction procedures, like artificial insemination, in vitro fertilization (IVF) and the like.
  • Such organisms include mammals, such as, but not limited to, cows, buffalo, pigs, sheep, horses, and humans.
  • the semen sexing technology of the present subject matter can be used in the livestock industry, for example for artificial insemination of farm animals, and it can also be used for sexing of pets or wild animals.
  • this technology can also be used in sexing of human semen samples, for example during assisted reproduction treatments, selection of the sex of the offspring for example in cases when one of the parents, or both, is a carrier of an X-linked disorder, and the like.
  • a unique characteristic of the method and system of the present disclosure is that the DNA of the sperm cells is not labeled or stained prior to or during the classification process. Moreover, the sperm cells are not manipulated by any other means, in contrast with currently existing technologies, thereby reducing potential damage to the sperm cells.
  • Fig. 1 schematically illustrating an exemplary embodiment of a system 1, intended for the analysis of sperm cells, for example whether they are viable, and whether they contain an X or Y chromosome.
  • System 1 comprises an analysis sub-system 2 comprising at least one light source 12 configured to emit light and illuminate a sperm cells suspension 500 (represented by the sperm cell cartoon) passing through a flow channel 502.
  • a video capturing utility 13 that comprises at least one camera 14 with an associated optical assembly 15, is configured to acquire images of the illuminated sperm cells suspension.
  • a computing utility 100 operates an algorithm that executes at least one CV Al, is in data communication with video capturing utility 13 via a data link represented by arrow 16 and is configured to receive data feed representative of the captured video images and analyze the data using the CV Al algorithms, and generates an output 17 of classification of an individual sperm cell in the sperm cells suspension 500 that can be relayed to an output device 18 that may, for example, be a computer display.
  • the at least one light source 12 may be any suitable light that can illuminate in a manner permitting the visualization of an image captured by the video capturing utility 13.
  • the at least one light source is natural light, for example sunlight.
  • the light source is an artificial light in the visible, ultraviolet (UV) or infrared (IR) including (near IR light or a far IR light) range.
  • the light emitted by light source 12 may be of a broadband illumination.
  • the illumination may be a narrow bandwidth illumination of a distinct wavelength, a combination of two or more lights of a distinct wavelength, etc.
  • the video-capturing utility 13 should be configured for capturing lights that match the nature of the light emitted by the light source 12.
  • the at least one light source 12 is schematically illustrated as a light source that is positioned opposite the video capturing utility 13, whereby the sperm cells suspension 500 is illuminated by transmitted light (namely light passing through the sperm cells suspension coming from a light source that is substantially opposite the videocapturing utility).
  • the light source may be reflected light, a combination of reflected and transmitted light, bright field illumination or dark field illumination, the light may be polarized, the illumination may be a phase contrast illumination or any combination of the aforesaid.
  • the illumination by some embodiments may be continuous or may be pulsed. It may also be configured by yet other embodiments for a combination of pulsed and continuous illumination; for example, continuous at one wavelength and pulsed at another.
  • the captured video images captured by the video capturing utility 13 may be successive video frames or selected, non-successive video frames.
  • Video capturing utility 13 may be an ensemble of two or more cameras, for example each recording images at a different wavelength, at a different focal point within channel 502, at a different magnification, etc.
  • the optical assembly 15 may be geared and optimized to the nature of the light illumination and may also be configured to provide a certain image magnification or focus, as the case may be.
  • the at least one camera 14 may comprise a charged-coupled device (CCD) camera, e.g., a 3 CCD camera whose imaging system uses three separate CCDs, a complementary metal oxide semiconductor (CMOS) camera, a combination of a CCD camera and a CMOS.
  • CCD charged-coupled device
  • CMOS complementary metal oxide semiconductor
  • the at least one camera 14, by some embodiments, may comprise a line scan camera, an area scan camera, a three- dimensional (3D) scan camera, or any combination of such cameras.
  • the optical assembly 15 may comprise a set of filters, for example interchangeable filters. By some embodiments the filters may be digital filters.
  • the computer utility 100 is configured to use either all, or part of, the visual features of at least one of the images captured by the at least one camera 14, for the purpose of classifying a sperm cell in suspension 500.
  • the analysis may comprise determining an X/Y chromosome content of individual sperm cells.
  • the visual features of the sperm cells in suspension 500 that are analyzed may comprise (without limitation) one or more of the following: color, transparency, phase contrast, size, shape, positioning, position relative to other objects in the at least one image, orientation, bending, circularity, eccentricity, solidity, convexity, curvature, skeleton, center of mass, center of gravity, fill factor, Feret diameter, compactness, inertia tensor, fractal dimensions, density, symmetry, texture, organelle key points, length of tail, bending of tail, relative depth of the sperm cell or of parts of the sperm cell, Z position, spatial relationship between the sperm cell 500 and other objects, spatial relationship between different parts of the sperm cell 500, aspect ratio of different parts of the sperm cell 500, or any combination thereof.
  • Computing utility 100 may also be configured to use visual motion (also referred to herein as “motility”) features, that may be determined by the analysis of successive frames, to classify the sperm cells.
  • the motility features include (without limitation): overall motion pattern, motion patterns of one part of the versus others, or a combination thereof.
  • the visual motion feature may include, but not limited to, speed of movement, motion dynamics, dynamic positioning, direction of movement, bending dynamics, swimming dynamics, response to flow of medium in which the sperm cell swims, or any combination thereof.
  • the data received in computing utility 100 from the at least one camera 14 may be configured to preprocess the data that may comprise, but not limited to, at least one of the following: image filtering, image enhancement, image transformation, image segmentation, image registration, image restoration, image compression, image fusion and the like.
  • Computing utility 100 executes the sperm cell classification algorithms that comprise pre-trained Al algorithms.
  • the algorithms may be based on classical computer vision, machine learning and deep learning principles and are pre-trained with large data sets of sperm cells. Training the algorithm to differentiate between sperm cells may involve exposing the algorithm to images of defined cells or cell populations, optionally under the same conditions in which the classification of the sperm cells is intended to be performed.
  • a ground truth approach cells may be separated after being imaged by the image capturing utility and each cell is then examined for one or more specific biological properties or traits.
  • each sperm cell may be tested, by histological/molecular/immunological/labeling methods and this information can then be fed to the algorithm, as part of its training set. This may, for example, involve obtaining a ground truth on the sex chromosome content. Also, such ground truth can also be the sex of the embryo that is formed when using the specific cell for IVF. In the case of fertility potential, the ground truth can be the fertilization outcome of the specific sperm cell as determined, for example, by a variety of techniques known per se.
  • the algorithm can be trained by imaging a population of sperm cells separated by other techniques and know to contain a high proportion (e.g. above 80% or above 90%) of sperm cells that meet a defined criterion, e.g. population of sperm cells containing a majority of cells carrying a defined sex chromosome or sperm cells having a high-fertility potential or both.
  • a defined criterion e.g. population of sperm cells containing a majority of cells carrying a defined sex chromosome or sperm cells having a high-fertility potential or both.
  • the algorithm may be continuously improved or fine-tuned, for example, by continuously testing a sample of sperm cells classified as being all of a defined class and obtaining ground truth data and feeding it back to the algorithm.
  • the algorithm may be fine-tuned for a specific herd or individual males in a herd that exhibit properties that may be different to an extent from the average population on which the Al model of the algorithm was based and this approach may permit such fine-tuning.
  • the Al model at the heart of the classifying algorithm may also be continuously fine-tuned from data feed from extraneous sources that include data collected and finetuned in other Al-based sperm cells classification systems concomitantly operating in the same or remote facilities.
  • the computing utility 100 is configured to perform any type of segmentation method and provide any type of segmentation output accordingly, for example, but not limited to: pixel-wise segmentation method and output, super pixel segmentation method and output, regions of X/Y chromosomes segmentation method and output, background segmentation method and output, and any other segmentation method and output.
  • the algorithms of the computing utility 100 are configured to determine whether an individual sperm cell in suspension 500 contains an X-chromosome, or a Y-chromosome. According to yet a further exemplary embodiment, the algorithms of the analyzer 100 are configured to identify chromosomal abnormalities of individual sperm cells 500.
  • the analysis by computing utility 100 is based on two separate CV Al models.
  • a first of these two may comprise a first CV Al model configured to analyze at least one image and provide an input to the second CV Al model that is configured to classify the at least one sperm cell according to the input provided by the first computer-vision Al model.
  • the computing utility may, by some embodiments, be configured to determine any type of irregularity in a chromosomal content of a sperm cell, for example, but not limited to aneuploidy, polyploidy, DNA fragmentation and the like. It may also, by other embodiments, be configured to determine any type of structural irregularity of a sperm cell, for example, but not limited to: macrocephaly, microcephaly, tapered head, amorphous head, pinhead sperm, thickened neck, thin neck, bent tail, short tail, multiple tails, coiled tail, absent tail (acephaly), long tail (flagellated head), agglutination and the like.
  • it may be configured to determine any type of motility irregularity of a sperm cell, for example, but not limited to: asthenozoospermia, hyperactivated motility, hypo activated motility, immotile sperm cell, circular motility, non-progressive motility, and the like.
  • Output device 18 may be a display configured to display results of the analysis of a single sperm cell or of a population of cells to a user, the display having one of a myriad of possible display configurations.
  • the output device 18 may also be a cell sorting utility for sorting cells into separate populations that meet defined criteria, such as for the purpose of semen sexing, as described in reference to Figs. 2 and 3.
  • separating sperm cells from non-sperm cells i. separating X chromosome-containing from Y chromosome-containing sperm cells; iii. separating dead cells from living cells; iv. separating abnormal cells from normal cells; and separating sperm cells with a fertility potential above a defined threshold from other sperm cells.
  • the separation can be performed at a temperature range of between about 4°C to about 39°C.
  • the separation can be performed in any dedicated cell separation device, for example in a cell sorter or in a microfluidic unit.
  • FIG. 2 schematically illustrating an exemplary embodiment of a system 1A, for separating between sperm cell populations with different characteristics, for example separating X-containing from Y-containing sperm cells within the framework of semen sexing.
  • a system 1A output device 18 of Fig, 1 is constituted by a cell sorting utility 182.
  • Cell sorting utility 182 may operate by one of a variety of cell sorting techniques, for example such techniques known per se.
  • the exemplary cell sorting utility 182 shown in Fig. 2 is a flow cytometry cell sorter configured to sort the sperm cells into a plurality of groups according to the classification received from the analyzer 100 through output link 17, for example, according to their sex chromosome content.
  • the analyzed sperm cells suspension 500A passing through flow channel 502 A may be analyzed, for example as being either X chromosome - containing sperm cells or Y chromosome-containing sperm cells and then sorted, as represent by arrows 1823 and 1825 into two (or at times more) respective collecting vessels, such as vessels 1827 and 1829.
  • the cell sorter 182 is a cytometry cell sorter configured to electrically charge cells, or droplets of liquid containing at least one cell, according to their classification, and deflect the cells, or the droplets of liquid containing at least one cell, to a pre-determined collection vessel by using positively and negatively charged deflection plates 1826 and 1828, respectively.
  • cell sorter 182 is of a kind that deflects the sperm cells or droplets comprising sperm cells into specific vessels by the use of a laser beam; or of a kind that deflects droplets that contain sperm cells by the application of a controlled flow of air or a fluid; or of a kind that uses a controlled valving arrangement; or of a kind that deflects droplets that contain sperm cells by the use of a magnetic field; etc. It should be noted that this disclosure is not limited by the kind of cell sorting that is carried out.
  • An exemplary application of system 1A is preparation of semen samples for artificial insemination.
  • separation of the sperm cells into suspension enriched with X chromosome-containing or Y chromosome-containing sperm cells increases the likelihood that the artificial insemination will give rise to a high proportion of the desired livestock gender.
  • exemplary applications include (i) separating sperm cells from non-sperm cells, (ii) separating living sperm cells from non-living sperm cells, (iii) separating aberrant sperm cells from normal sperm cells, and (iv) separating sperm cells with a fertility potential above a defined threshold from other sperm cells, all intended to increase the potency of the sperm cells suspension and the likelihood that the artificial insemination will be successful and give rise to offsprings.
  • the (i) through (iv) exemplary applications may be combined with the semen sexing application discussed above to obtain potent sperm cells suspension enriched with cells of the desired sex chromosome.
  • system 1A Another example of an application of system 1A is the elimination of sperm cells that contain chromosome abnormalities, namely autosome abnormalities, or sex chromosome abnormalities, or both autosome and sex chromosome abnormalities.
  • the system 1 can identify sperm cells in the sperm cells suspension that contain an abnormal number of autosomal chromosomes, an abnormal number of sex chromosomes, namely aneuploid sperm cells 500, and the like.
  • Computing utility 100 is configured to execute one or more pre-trained CV Al algorithms.
  • the computing utility 100 is linked to a cloud computing server executing one or more such pre-trained CV Al algorithms which may (i) function to validate the analysis carried by computing utility 100, (ii) periodically update the one or more CV Al algorithms being executed on computing utility 100 with newer versions of such algorithms, (iii) receive input from the computing utility 100 to improve the algorithms on the serve, and (iv) any combination of (i) to (iii).
  • Fig. 3 schematically illustrating an exemplary embodiment, in the form of a diagram presentation of an artificial neural network for determining a type of individual sperm cell.
  • the computer utility 100 of the embodiments illustrated in Figs. 1 and 2 is configured to analyze acquired images of individual sperm cells in a sperm cells suspension 500 and 500A, respectively, and determine a type of each individual sperm cell.
  • the uniqueness of the present disclosure is the use of an Al-based computational algorithm without the use of any dyes or markers that stain the sperm cells or bind to specific elements in sperm cells, whether during analysis or prior thereto.
  • Many machine learning techniques may be employed to train the algorithm to identify sperm cells of interest, for example to identify X chromosomecontaining or Y chromosome-containing sperm cells.
  • the exemplary artificial neural network comprises a plurality of nodes organized in layers. These comprise an input layer 42, with a plurality of input nodes 422, one or more hidden layers 44, two - 442 and 444 in this specific example, each with a plurality of respective hidden nodes 4422 and 4442, and an output layer 46 with a number of output nodes 462.
  • the nodes are interconnected with weighted connections 48, and calculations are performed between each node and the nodes in the next layer.
  • the flow of calculation is from the input layer 42, through the hidden layers 44 toward the output layer 46.
  • the flow of calculation can also be in the opposite direction.
  • the input nodes 422 receive input data, for example, different tagged images of individual sperm cells. For example, one input node 422 receiving data relating to the size of a sperm cell, another receiving data on velocity of swimming of the cell, one receiving data relating to size of the head of the sperm cell, one receiving data on the tail length of the sperm cell. Calculations on these data are performed in the nodes of hidden layer 44, and results of the calculations are forwarded to the nodes of the output layer 46. For example, one of output nodes 462 receiving an indication when the sperm cell contains an X-chromosome, and another output node 462 receiving an indication when the sperm cell contains a Y-chromosome.

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Abstract

L'invention concerne un procédé et un système de classification de spermatozoïdes de mammifères. La classification peut, par exemple, se trouver dans le cadre du sexage de sperme. Une suspension avec des spermatozoïdes motiles en suspension dans un milieu est passée devant un outil de capture vidéo dans des conditions qui permettent audit outil de capturer des images de spermatozoïdes uniques. Le milieu est dépourvu de tout colorant d'identification de cellules ou d'identification de biomarqueurs et les cellules dans le milieu n'ont pas été pré-colorées avec de tels colorants. Une ou plusieurs images vidéo de spermatozoïdes individuels dans le support sont capturées et introduites dans un outil informatique qui est configuré pour analyser le flux vidéo à l'aide d'un ou de plusieurs modèles d'Al pré-entraînés pour détecter des spermatozoïdes individuels et une ou plusieurs caractéristiques de spermatozoïdes des spermatozoïdes individuels et pour classifier les spermatozoïdes détectés comme appartenant à une catégorie définie sur la base des caractéristiques de spermatozoïdes identifiées dans ladite analyse.
PCT/IL2024/050291 2023-03-27 2024-03-21 Système et procédé de différenciation et de séparation de cellules spermatiques Pending WO2024201451A1 (fr)

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IL301727A IL301727A (en) 2023-03-27 2023-03-27 System and method for differentiating types of sperm cells according to optically observed characteristics of the sperm cells

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Citations (15)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US3687806A (en) 1969-11-04 1972-08-29 Bio Controls Inc Method for controlling sex of mammalian offspring
US4083957A (en) 1974-07-26 1978-04-11 Lang John L Process for the alteration of the sex-ratio of mammals
US4191749A (en) 1977-10-11 1980-03-04 Bryant Bernard J Method and material for increasing the percentage of mammalian offspring of either sex
US5021244A (en) 1988-12-06 1991-06-04 Cytogam, Inc. Sex-associated membrane antibodies and their use for increasing the probability that offspring will be of a desired sex
US5514537A (en) 1994-11-28 1996-05-07 Board Of Supervisors Of Louisiana State University And Agricultural And Mechanical College Process and apparatus for sorting spermatozoa
US6153373A (en) 1997-07-01 2000-11-28 Vicam, L.P. Method for sex determination of mammalian offspring
US20030087860A1 (en) 1999-11-04 2003-05-08 Alan Mileham Methods for sexing non-human mammals
US20030162238A1 (en) 1999-12-07 2003-08-28 University Of Guelph Sex-chromosome-specific proteins, species specific and sperm specific proteins and methods for their identification and isolation
EP2308418A2 (fr) * 2003-03-28 2011-04-13 Inguran, LLC Appareil et procédés pour fournir du sperme d'animaux triés selon le sexe
US20140182005A1 (en) 2009-04-01 2014-06-26 Biocern, Inc. Sperm cell separation methods and compositions containing sperm cell targeting ligands for use therein
EP2761275A1 (fr) 2011-09-30 2014-08-06 Inguran, LLC Méthode de coloration et de tri de spermatozoïdes
US20170205390A1 (en) * 2015-05-07 2017-07-20 Technology Innovation Momentum Fund (Israel) Limited Partnership Interferometric system and method for use with biological cells and organisms including sperm
KR101916959B1 (ko) 2015-07-13 2018-11-08 김동구 정자의 성 감별용 항체 및 이의 용도
EP3865849A1 (fr) * 2020-02-14 2021-08-18 Smart-Pick GmbH Système de collecte de sperme
WO2022178095A1 (fr) * 2021-02-19 2022-08-25 Deepcell, Inc. Systèmes et procédés d'analyse cellulaire

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6149867A (en) * 1997-12-31 2000-11-21 Xy, Inc. Sheath fluids and collection systems for sex-specific cytometer sorting of sperm
CA2408939C (fr) * 2000-05-09 2011-11-08 Xy, Inc. Populations de spermatozoides tres purs, porteurs de chromosomes x et y
US11584662B2 (en) * 2020-03-16 2023-02-21 Inguran, Llc Systems and method for correction of positionally dependent electromagnetic radiation detected from objects within a fluid column
FR3126043A1 (fr) * 2021-08-04 2023-02-10 Univers 2020 Installation de cytométrie en flux comportant un système de détection optique, notamment pour le traitement de cellules du type spermatozoïde

Patent Citations (17)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US3687806A (en) 1969-11-04 1972-08-29 Bio Controls Inc Method for controlling sex of mammalian offspring
US4083957A (en) 1974-07-26 1978-04-11 Lang John L Process for the alteration of the sex-ratio of mammals
US4191749A (en) 1977-10-11 1980-03-04 Bryant Bernard J Method and material for increasing the percentage of mammalian offspring of either sex
US5021244A (en) 1988-12-06 1991-06-04 Cytogam, Inc. Sex-associated membrane antibodies and their use for increasing the probability that offspring will be of a desired sex
US5514537A (en) 1994-11-28 1996-05-07 Board Of Supervisors Of Louisiana State University And Agricultural And Mechanical College Process and apparatus for sorting spermatozoa
US6153373A (en) 1997-07-01 2000-11-28 Vicam, L.P. Method for sex determination of mammalian offspring
US6489092B1 (en) 1997-07-01 2002-12-03 Vicam, L.P. Method for sex determination of mammalian offspring
US20030087860A1 (en) 1999-11-04 2003-05-08 Alan Mileham Methods for sexing non-human mammals
US20030162238A1 (en) 1999-12-07 2003-08-28 University Of Guelph Sex-chromosome-specific proteins, species specific and sperm specific proteins and methods for their identification and isolation
EP2308418A2 (fr) * 2003-03-28 2011-04-13 Inguran, LLC Appareil et procédés pour fournir du sperme d'animaux triés selon le sexe
US20140182005A1 (en) 2009-04-01 2014-06-26 Biocern, Inc. Sperm cell separation methods and compositions containing sperm cell targeting ligands for use therein
EP2761275A1 (fr) 2011-09-30 2014-08-06 Inguran, LLC Méthode de coloration et de tri de spermatozoïdes
EP2761275B1 (fr) 2011-09-30 2017-06-21 Inguran, LLC Méthode de coloration et de tri de spermatozoïdes
US20170205390A1 (en) * 2015-05-07 2017-07-20 Technology Innovation Momentum Fund (Israel) Limited Partnership Interferometric system and method for use with biological cells and organisms including sperm
KR101916959B1 (ko) 2015-07-13 2018-11-08 김동구 정자의 성 감별용 항체 및 이의 용도
EP3865849A1 (fr) * 2020-02-14 2021-08-18 Smart-Pick GmbH Système de collecte de sperme
WO2022178095A1 (fr) * 2021-02-19 2022-08-25 Deepcell, Inc. Systèmes et procédés d'analyse cellulaire

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
DARYL PREECE: "Red light improves spermatozoa motility and does not induce oxidative DNA damage", SCIENTIFIC REPORTS, vol. 7, no. 1, 20 April 2017 (2017-04-20), US, XP093166470, ISSN: 2045-2322, Retrieved from the Internet <URL:https://www.nature.com/articles/srep46480> DOI: 10.1038/srep46480 *

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