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WO2025101870A1 - Systèmes et procédés de formation d'image optique biologique avec des particules artificielles en tant que références - Google Patents

Systèmes et procédés de formation d'image optique biologique avec des particules artificielles en tant que références Download PDF

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Publication number
WO2025101870A1
WO2025101870A1 PCT/US2024/055081 US2024055081W WO2025101870A1 WO 2025101870 A1 WO2025101870 A1 WO 2025101870A1 US 2024055081 W US2024055081 W US 2024055081W WO 2025101870 A1 WO2025101870 A1 WO 2025101870A1
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WO
WIPO (PCT)
Prior art keywords
biological sample
artificial particles
solution
beads
particles
Prior art date
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Pending
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PCT/US2024/055081
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English (en)
Inventor
Jeremy Hammond
Jason Aguiar
Lucy Ericson
Timothy BUTCHER
Jui-Ming Lin
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Idexx Laboratories Inc
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Idexx Laboratories Inc
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Publication date
Priority claimed from US18/940,011 external-priority patent/US20250147031A1/en
Application filed by Idexx Laboratories Inc filed Critical Idexx Laboratories Inc
Publication of WO2025101870A1 publication Critical patent/WO2025101870A1/fr
Pending legal-status Critical Current
Anticipated expiration legal-status Critical

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Classifications

    • 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
    • G01N15/04—Investigating sedimentation of particle suspensions
    • 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
    • G01N15/01—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials specially adapted for biological cells, e.g. blood cells
    • 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
    • G01N15/1429—Signal processing
    • G01N15/1433—Signal processing using image recognition
    • 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
    • G01N15/1434—Optical arrangements
    • 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
    • G01N15/02—Investigating particle size or size distribution
    • G01N15/0205—Investigating particle size or size distribution by optical means
    • G01N15/0227—Investigating particle size or size distribution by optical means using imaging; using holography
    • 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
    • G01N15/01—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials specially adapted for biological cells, e.g. blood cells
    • G01N2015/012—Red blood cells
    • 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
    • G01N2015/1006—Investigating individual particles for cytology
    • 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
    • G01N15/1012—Calibrating particle analysers; References therefor
    • G01N2015/1014—Constitution of reference particles
    • 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
    • G01N15/1434—Optical arrangements
    • G01N2015/1447—Spatial selection
    • 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
    • G01N15/1434—Optical arrangements
    • G01N2015/1452—Adjustment of focus; Alignment

Definitions

  • the present specification relates to optical microscopy imaging, and more particularly, to optical microscopy imaging using reference particles.
  • Dynamic imaging of biological samples using optical microscopy devices involves biological sample preparation and real-time tracking of the biological samples, such as cells or tissues.
  • the viability of the biological samples and post-treatment on the biological samples demands a fast and accurate imaging process and technology. Accordingly, there is a desire to use artificial particles as a reference to enhance the dynamic imaging capacity of the optical microscopy device.
  • a method for use with a biological sample includes mixing artificial particles and the biological sample with a diluent to form a solution, conducting focus sequences with the artificial particles as reference markers in the solution using a microscopy device, and imaging the biological sample in the solution using the microscopy device.
  • the artificial particles are suspended in the solution independent from the biological sample.
  • a settling time of artificial particles is shorter than or equal to a settling time of the biological sample.
  • a system for use with a biological sample includes a microscopy device, a diluent, a sample holder, and one or more of lyophilized cakes comprising artificial particles.
  • FIG. 4 schematically depicts example imaging modes using the microscopy device, according to one or more embodiments shown and described herein
  • FIG. 5 schematically depicts example artificial particles suspended in the solution, according to one or more embodiments shown and described herein;
  • FIG. 6 visually depicts example distributions of artificial particles suspended in the solution, with and without any lyophilization treatments, according to one or more embodiments shown and described herein;
  • FIG. 9 depicts a flowchart of an example method of confirmation of reagent using the biological optical imaging system, according to one or more embodiments shown and described herein;
  • the solution 311 may include a diluent 312, artificial particles 313, biological samples 315, uninterested particles 317, and dye 319 (e.g., as illustrated in FIG. 3).
  • the solution 311 may further include cakes that are lyophilized and contain the artificial particles 313 such that the artificial particles do not aggregate in the solution 311.
  • the connections may include a combination of conductive traces, conductive wires, connectors, and buses that cooperate to permit the transmission of electrical data signals to components such as processors, memories, sensors, input devices, output devices, and communication devices.
  • signal means a waveform (e.g., electrical, optical, magnetic, mechanical or electromagnetic), such as DC, AC, sinusoidal-wave, triangular-wave, square-wave, vibration, and the like, capable of traveling through a medium.
  • the controller 201 may receive inputs from the components and provide outputs to the components of the microscopy device 101.
  • the biological imaging system 100 depicted in FIG. 1 includes the microscopy device 101.
  • the microscopy device 101 is, without limitations, an optical microscopy device, a light microscopy device, an electron microscopy device, a confocal microscopy device, a multiphoton microscopy device, or a fluorescence microscopy device.
  • the microscopy device 101 is a digital microscope.
  • the microscopy device 101 may include, without limitations, one or more light sources, one or more lenses (such as, without limitations, a condenser lens and an objective lens), one or more mirrors and fdters, one or more specimen stages, such as a well or a chamber, and any components and parts suitable for the operation of the microscopy device 101.
  • the microscopy device 101 may switch between, without limitations, a bright field mode, a dark field mode, and a fluorescent mode.
  • the condenser lens (not shown in the figure) may focus and direct the light onto the solution 311 including one or more biological samples 315 or artificial particles 313 (FIG. 3), creating a bright and uniform background.
  • the objective lens 618 may collect the transmitted light from the sample and form an image on the eyepiece or camera.
  • one or more excitation filters of different specific wavelengths such as the blue excitation filter 608 and the ultraviolet excitation filter 610, may be used to select the excitation light that matches the absorption spectrum of the fluorophores in the solution 311.
  • the imaging dichroic mirror 616 as a beamsplitter may reflect the excitation light towards the sample holder 301 while allowing emitted fluorescence to pass through and directed to the camera 624.
  • the objective lens 618 may collect the emitted fluorescence from the biological samples 315 or the artificial particles 313 that are dyed with fluorescent stain or dye 319 (FIG. 3).
  • the microscopy device 101 may utilize microscopy techniques to determine attributes associated with sample preparation, reagent preparation, and biological analysis process.
  • the attributes may be used to determine, without limitations, the settlement of the biological samples 315, a confirmation of the reagent process workflow, a confirmation of acellular sample in the solution, a confirmation of magnification of the microscopy device 101, and estimation of incubation time.
  • the microscopy device 101 may identify other attributes.
  • the biological imaging system 100 may include the controller 201.
  • the controller 201 may include various components, such as a memory component 202, a processor 204, an input/output interface 205, a network interface hardware 206, a data storage component 207, a display 208 including a user interface 218, and a local interface 203.
  • the controller 201 may include one or more displays 208 with one or more user interfaces 218.
  • the controller 201 may include one or more modules, such as an automatic focusing module 222 to conduct realtime sequence focusing at various depths of the solution 311 (FIGS. 1, 3, and 4), an artificial particle module 232 to identify artificial particles 313 (FIG. 3), and an biological sample module 242 to identify biological samples (FIG. 3).
  • the controller 201 may be any device or combination of components comprising the processor 204 and the memory component 202, such as a non-transitory computer readable memory.
  • the processor 204 may be any device capable of executing the machine-readable instruction set stored in the non-transitory computer readable memory. Accordingly, the processor 204 may be an electric controller, an integrated circuit, a microchip, a computer, or any other computing device.
  • the processor 204 may include any processing component(s) configured to receive and execute programming instructions (such as from the data storage component 207 and/or the memory component 202). The instructions may be in the form of a machine-readable instruction set stored in the data storage component 207 and/or the memory component 202.
  • the processor 204 is communicatively coupled to the other components of the controller 201 by the local interface 203. Accordingly, the local interface 203 may communicatively couple any number of processors 204 with one another, and allow the components coupled to the local interface 203 to operate in a distributed computing environment.
  • the local interface 203 may be implemented as a bus or other interface to facilitate communication among the components of the controller 201. While the embodiment depicted in FIG. 2 includes a single processor, other embodiments may include more than one processor.
  • the memory component 202 may include RAM, ROM, flash memories, hard drives, or any non-transitory memory device capable of storing machine -readable instructions such that the machine-readable instructions can be accessed and executed by the processor 204.
  • the machine-readable instruction set may include logic or algorithm(s) written in any programming language of any generation (e.g., 1GL, 2GL, 3GL, 4GL, or 5GL) such as, for example, machine language that may be directly executed by the processor 204, or assembly language, object-oriented programming (OOP), scripting languages, microcode, etc., that may be compiled or assembled into machine readable instructions and stored in the memory component 202.
  • any programming language of any generation e.g., 1GL, 2GL, 3GL, 4GL, or 5GL
  • OOP object-oriented programming
  • the machine-readable instruction set may be written in a hardware description language (HDL), such as logic implemented via either a field-programmable gate array (FPGA) configuration or an applicationspecific integrated circuit (ASIC), or their equivalents.
  • HDL hardware description language
  • FPGA field-programmable gate array
  • ASIC applicationspecific integrated circuit
  • the functionality described herein may be implemented in any conventional computer programming language, as preprogrammed hardware elements, or as a combination of hardware and software components.
  • the memory component 202 may be a machine-readable memory (which may also be referred to as a non-transitory processor-readable memory or medium) that stores instructions that, when executed by the processor 204, causes the processor 204 to perform a method or control scheme as described herein. While the embodiment depicted in FIG.
  • the memory component 202 may be used to store the one or more modules.
  • the one or more modules during operating may be in the form of operating systems, application program modules, and other program modules.
  • Such program modules may include, but are not limited to, routines, subroutines, programs, objects, components, and data structures for performing specific tasks or executing specific abstract data types according to the present disclosure as will be described below.
  • the program module may include the automatic focusing module 222 to conduct real-time sequence focusing at various depths of the solution 311 (FIGS. 1, 3, and 4), the artificial particle module 232 to identify artificial particles 313 (FIG. 3), the biological sample module 242 to identify biological samples (FIG. 3).
  • the input/output interface 205 may include a monitor, keyboard, mouse, printer, camera, microphone, speaker, and/or other device for receiving, sending, and/or presenting data.
  • the network interface hardware 206 may include any wired or wireless networking hardware, such as a modem, FAN port, Wi-Fi card, WiMax card, mobile communications hardware, and/or other hardware for communicating with other networks and/or devices.
  • the data storage component 207 may store the one or more modules.
  • the input/output interface 205 and the network interface hardware 206 allow a user to send input to the controller 201 of the biological imaging system 100 to control and manipulate the components of the biological imaging system 100, such as the fluorescent blue light source 600, the fluorescent ultraviolet light source 602, the collector lenses 604 and 606, the blue excitation fdter 608, the ultraviolet excitation fdter 610, the excitation dichroic 612, the field lens 614, the imaging dichroic mirror 616, the objective lens 618, the triband filter 620, the tube lens 622, and the camera 624, and receive output from the controller 201.
  • the components of the biological imaging system 100 such as the fluorescent blue light source 600, the fluorescent ultraviolet light source 602, the collector lenses 604 and 606, the blue excitation fdter 608, the ultraviolet excitation fdter 610, the excitation dichroic 612, the field lens 614, the imaging dichroic mirror 616, the objective lens 618, the triband filter 620, the tube lens 622, and the camera 624, and
  • the camera 624 may capture image data and communicates the image data to the processor 204.
  • the image data may be received by the processor 204, which may process the image data using one or more image processing algorithms.
  • Any known or yet-to-be developed video and image processing algorithms may be applied to the image data in order to identify an item or situation.
  • Example video and image processing algorithms include, but are not limited to, kernel-based tracking (such as, for example, mean-shift tracking) and contour processing algorithms.
  • video and image processing algorithms may detect objects and movement from sequential or individual frames of image data.
  • One or more object recognition algorithms may be applied to the image data to extract objects and determine their relative locations to each other.
  • Example object recognition algorithms include, but are not limited to, scale-invariant feature transform (“SIFT”), speeded up robust features (“SURF”), and edge-detection algorithms.
  • SIFT scale-invariant feature transform
  • SURF speeded up robust features
  • edge-detection algorithms edge-detection algorithms
  • the data storage component 207 stores collected imaging data and data of operating various components of the microscopy device 101.
  • the various modules may also be stored in the data storage component 207 during operating or after operation.
  • the display 208 include a user interface 218, a screen, one or more devices in communication with the one or more processors 204 (such as smartphones, tables, and the like), and/or any other device or interface suitable for displaying data.
  • the display 208 is a touchscreen and may be configured as an input device to receive user input.
  • Each of the various modules may include one or more machine learning algorithms or neural networks. Each module may be trained and provided machine learning capabilities via a neural network as described herein.
  • the neural network may utilize one or more artificial neural networks (ANNs).
  • ANNs artificial neural networks
  • connections between nodes may form a directed acyclic graph (DAG).
  • ANNs may include node inputs, one or more hidden activation layers, and node outputs, and may be utilized with activation functions in the one or more hidden activation layers such as a linear function, a step function, logistic (sigmoid) function, a tanh function, a rectified linear unit (ReLu) function, or combinations thereof.
  • ANNs are trained by applying such activation functions to training data sets to determine an optimized solution from adjustable weights and biases applied to nodes within the hidden activation layers to generate one or more outputs as the optimized solution with a minimized error.
  • new inputs may be provided (such as the generated one or more outputs) to the ANN model as training data to continue to improve accuracy and minimize error of the ANN model.
  • the one or more ANN models may utilize one to one, one to many, many to one, and/or many to many (e.g., sequence to sequence) sequence modeling.
  • the one or more ANN models may employ a combination of artificial intelligence techniques, such as, but not limited to, Deep Learning, Random Forest Classifiers, Feature extraction from audio, images, clustering algorithms, or combinations thereof.
  • a convolutional neural network may be utilized.
  • a CNN may be used as an ANN that, in a field of machine learning, for example, is a class of deep, feed-forward ANNs applied for audio analysis of the recordings.
  • CNNs may be shift or space invariant and utilize shared-weight architecture and translation.
  • each of the various modules may include generative artificial intelligence algorithms.
  • the generative artificial intelligence algorithm may include a general adversarial network (GAN) that has two networks, a generator model and a discriminator model.
  • GAN general adversarial network
  • the generative artificial intelligence algorithm may also be based on variation autoencoder (VAE) or transformer-based models.
  • VAE variation autoencoder
  • the artificial particles 313 may have uniform sizes, with a deviation distribution below a threshold deviation value.
  • the artificial particles 313 may have a uniform size or two uniform sizes.
  • the diameters of the artificial particles 313 may have a nominal size of 5 microns with a size range of between 1 micron and 10 microns and a standard deviation of less than 1.5 microns.
  • the artificial particles 313 may have a size less than the biological sample 315.
  • the artificial particles 313 may have a density greater than the diluent 312 and the reagent.
  • metal or magnetic elements may be added to the artificial particles 313 to increase the density of the artificial particles 313 or otherwise provide an alternate means to accelerate the settling of the artificial particles 313.
  • the artificial particles 313 can be used for magnification reference.
  • the microscopy device 101 may include a magnification based on the parameters of the components of the microscopy device 101. After detecting the artificial particles 313 in the solution, which include known particle sizes with a controlled size variation, the system 100 may use the size of the artificial particle 313 as a reference to verify the system-based magnification. The magnification verification may be conducted when optical artifacts, such as, without limitations, halos around cells, are present to use the artificial particles 313 around the biological samples 315 for each run to confirm the size calculations of the biological samples 315 are correct.
  • the artificial particles 313 can be used for fluorescence signal reference. Fluorescence signals may change due to several factors within the optics and within the reagents.
  • the artificial particles 313 may be fluorescent and may be used to determine the dynamics of the fluorescence signals in the reagents. For example, the artificial particles 313 may be chemically altered to respond to the fluorescent stains and fluoresce in a controlled manner.
  • the controller chemical changes of the artificial particles may be used by one or more algorithms of the system 100 to interpret the kinetics of fluorescent signals of the biological samples 315. This method can also provide information about the efficacy of the stain. The stain may degrade over time or/and due to thermal or light exposure).
  • the artificial particles 313 can be used for confirmation of the biological samples 315 that include acellular samples. For example, sample collection for certain cytology may result in no cells being aspirated into needles used to collect the biological sample 315.
  • the system 100 may select one or more areas of interest 503 and conduct sequence focus using the automatic focusing module to determine whether acellular samples are present in the solution 311.
  • the system 100 may focus on an area of interest 503 in the solution 311 to search for the expected presence of biological samples 315 and cells during a pre-scan in the sample holder 301 placed in the well or the chamber of the microscopy device 101.
  • the pre-scan step may be conducted during a biological sample or cells distribution step before the biological samples 315 are settled in the solution 311.
  • the system 100 may include a predetermined concentration of the artificial particles 313 for a confirmation that more than a threshold amount of artificial particles 313 are present in the solution but less than a threshold amount or concentration of biological samples 315 are found in the solution 311.
  • the threshold concentration of biological sample 315 is a minimum value to secure the presence of biological sample 315.
  • the threshold value of the biological sample 315 may be predetermined based on the type and size of the biological sample 315 and may evolve as more data are collected during the usage of the system 100 using a machine learning algorithm associated with the biological sample module.
  • the artificial particles 313 can be used for confirmation of reagent steps.
  • various reagent steps are performed before the solution 311 is formed.
  • the user may dispense the biological sample 315 into the diluent 312. This diluent 312 may temporarily preserve the cells in the biological sample 315.
  • the user may further add the lyophilized reagents, which includes fluorescent dyes 319 and the artificial particles 313, to the dilute 312.
  • the lyophilized reagents may dissolve in the diluent 312 within a few seconds and be agitated to form a uniformly distributed solution 311.
  • the mixed solution 311 may then be transmitted into the sample holder 301 for further analysis.
  • the system 100 may conduct a pre-scan step using the automatic focusing module 222 and the artificial particle module 232 to determine whether the reagent steps are conducted properly and whether the solution 311 is satisfied for further characterization.
  • the prescanning under the fluorescence mode may detect a lack of presence of the fluorescent dyes suggesting that a lyophilization step is missed, or an absence of the artificial particles 313 indicating that the sample steps may not have been followed correctly.
  • the system 100 displays a corresponding error message at the user interface 218 and may further provide reasons of the error(s) and instruct further steps to correct the errors.
  • the artificial particles 313 may be polymer beads.
  • the artificial particles 313 may have refractive index values similar to blood cells.
  • the artificial particles 313 may have similar sizes (e.g. 2-20 microns or 4-6 microns to represent red blood cells).
  • the artificial particles 313 may be in a spherical shape 531 and stable at room temperature for a long period, such as 1-10 years.
  • the artificial particles 313 are further cross-polymerized via cross-linking which changes the spherical nature of the artificial particles 313 to a golfball shape or popcorn shape 511.
  • the impact of the cross-linking is that the spherical artificial particles 531 form a series of larger artificial particles of the popcorn shape particles 511 that are made up of smaller spheres 513, as shown in image 505 in FIG. 5.
  • the morphology of the artificial particles 313, such as the spherical shape, or the golf ball shape or popped corn shape, may be used by the modules, such as the artificial particle module 232 and the biological sample module 242 to identify the artificial particles 313 from the biological sample 315, such as natural cells.
  • both the artificial particles 313 having the spherical shape 531 and the popcorn shape 511 can illuminate with specific brightfield wavelengths that allow the system 100 to differentiate the artificial particles 313 from the biological sample 315 and the uninterested particles 317.
  • the system 100 may be capable of distinguishing between the biological sample 315, which may contain RNA and/or DNA and is stained with either brightfield or fluorescent stains, and the artificial particles 313 that remain unaffected by these stains.
  • the disclosed method can be used to detect Red Blood Cells (RBC) such that an image captured using brightfield microscopy using violet light, green light, or other lights can find artificial particles 313 in bright color and RBC in dark color.
  • RBC Red Blood Cells
  • the artificial particle module 232 and the biological sample module 242 may include an algorithm to identify and differentiate the artificial particles 313 and the biological sample 315.
  • the algorithm may adopt one or more offsets in differentiating the artificial particles 313 and the biological sample 315 based on the size, the fluorescent wavelength, or the surface morphology between the artificial particles 313 and the biological sample 315.
  • the size of the artificial particles 313 may be more than 10 microns smaller than the biological sample, include no fluorescent activated light, and have a popcorn-shape morphology
  • the biological sample 315 may be at least 10 microns larger in diameter, be dyed to emit fluorescent activated light, and have a typical cell shape, such as spherical, oval, elliptical, spindle shape, polygonal, isodiametric, or flat plate-like.
  • FIG. 6 distributions of artificial particles and other particles in the liquid suspension with and without lyophilization treatment in the solution are depicted.
  • the spherical particles 711 which include the artificial particles 313, are separate after the lyophilized cake dissolved in the diluent 312.
  • the artificial particles 313 are not aggregated as a cluster 733 as shown in image 713.
  • the spherical particles 731 which include the artificial particles 313, may partially aggregate to form a cluster 733 after being resuspended in the diluent 312.
  • the nonaggregate particle 313 in the cakes are lyophilized under one or more freezing cycles and one or more drying cycles, wherein temperatures of the freezing cycles are greater than or equal to -50 °C and less than or equal to 5 °C, and temperatures of the drying cycles are greater than or equal to -50 °C and less than or equal to 25 °C.
  • the components contained in the cakes are prepared in the form of bulk reagent and are pretreated before lyophilization.
  • the bulk reagent in a tube may be inverted several times to keep the particles well-suspended.
  • the resuspended bulk reagent may be dispensed into a glass vial or plastic tube. All the glass vials may be placed in a metal tray and the tray may be placed in a lyophilizer.
  • Sample lyophilization cycles are provided below. In the sample lyophilization cycles, there are four freezing cycles and nine drying cycles. The freezing cycles are conducted under temperatures between -50 °C to 5 °C, for 0 min to 200 min. The drying cycles are conducted under temperatures between -50 °C to 25 °C, at pressure of 40 mTorr, for 0 to 1300 min.
  • the method for use with a biological sample 315 includes mixing artificial particles 313 and the biological sample 315 with a diluent 312 to form a solution 311.
  • the artificial particles 313 are suspended in the solution 311 independent from the biological sample 315.
  • a settling time of artificial particles 313 is shorter than or equal to a settling time of the biological sample 315.
  • the method for use with a biological sample 315 includes, conducting focus sequences with the artificial particles 313 as reference markers in the solution 311 using a microscopy device.
  • the method for use with a biological sample includes imaging the biological sample in the solution using the microscopy device.
  • the method 700 may include, after mixing to form the solution, dispensing the solution into the sample holder 301, such as a cartridge used by the microscopy unit.
  • the method 700 may include an imaging algorithm that adopts one or more offsets, based on a difference between the artificial particle 313 and the biological sample 315 in size, fluorescent wavelength, or surface morphology, to identify the artificial particles and the biological sample.
  • the size of the artificial particles 313 may be, without limitations, greater than or equal to 1 micron and less than or equal to 20 microns.
  • the artificial particles 313 may have the surface morphology of, without limitations, a spherical shape, a golfball shape, or a popped-corn shape.
  • the biological sample 315 may include a fluorescent stain.
  • the mixing may further include a reagent process.
  • the reagent process may include dispensing the biological sample 315 into a sample holder 301 containing the diluent 312 including the fluorescent stain 319, adding lyophilized cakes into the sample holder 301, and agitating the sample holder 301 to mix the biological sample 315, the lyophilized cakes that may include the artificial particles 313 and the dye 319, and the diluent 312.
  • the biological sample 315 may be dyed with the dye 319 that may be fluorescent stain during the reagent process.
  • the method 700 may include arranging a focal point 413 of the microscopy device 101 in one or more depths of interest, such as near the surface area 405 of the solution 311, within a region 403 expected to contain desired particles, or within the lowermost region 401 of the solution 311 .
  • the method 700 may further include capturing an in- focus image of the artificial particles 313 at the one or more depths of interest.
  • the method may include in response to capturing the in-focus image of the artificial particles 313, determining whether the biological sample 315 is present at the one or more depths of interest.
  • the method 700 may include, in response to the determination of the biological sample 315 at the one or more depths of interest, capturing an in-focus image of the biological sample 315 to determine one or more imaging events, the imaging events may be, without limitation, a settlement of the biological sample, a confirmation of reagent, a confirmation of acellular sample, a confirmation of magnification, or an estimation of incubation time.
  • the focus sequences of the method of biological sample imaging may include determining whether the artificial particles 313 have settled in the solution 311.
  • the focus sequences may include determining whether the biological sample 315 has settled.
  • the focus sequences may include imaging the biological sample 315 in the solution.
  • the focus sequences may include providing an unsettledness of the biological sample on a user interface.
  • the focus sequences may further include providing a reason for the unsettledness of the biological sample on the user interface, wherein the reason is associated with an error message of a pre-analytical step.
  • the artificial particles 313 and the biological samples 315 may have a density lower than or greater than the solution 311 or the diluent 312. In some embodiments, when the density of the artificial particles 313 and the biological samples 315 are greater than the density of the solution 311 or the diluent 312, the artificial particles 313 and the biological samples 315 may settle to the bottom of the solution 311.
  • the settlement of the artificial particles 313 or the biological sample 315 may be determined by arranging a focal point 413 of the microscopy device 101 in a depth around the bottom 401 of the solution 311 and capturing an in- focus image of the artificial particles 313 or the biological sample 315.
  • the density of the artificial particles 313 and the biological samples 315 are less than the density of the solution 311 or the diluent 312, the artificial particles 313 and the biological samples 315 may settle to the top of the solution 311.
  • the settlement of the artificial particles 313 or the biological sample 315 may be determined by arranging a focal point 413 of the microscopy device 101 in a depth around top 413 of the solution 311, and capturing an in- focus image of the artificial particles or the biological sample around the top 413 of the solution 311.
  • the method 900 of confirmation of reagents may include vertically changing a focal point 413 at one or more depths of the solution 311 to determine presence of the artificial particles 313.
  • the depths of the solution to focus may be determined based on the settling speed and the settling time of the artificial particles 313.
  • the method 900 of confirmation of reagents using the biological optical imaging system 100 may further include providing an error message regarding reagent process workflow at a user interface 218.
  • the method 1000 of confirmation of acellular using the biological optical imaging system 100 may include vertically changing a focal point 413 at a plurality of depths of the solution 311 to determine whether the artificial particles 313 are present at the depths of the solution 311, wherein the depths are selected based on a settling speed and the settling time of the artificial particles 311.
  • the method 1000 of confirmation of acellular using the biological optical imaging system 100 may include, in response to the determination of the presence of the artificial particles 313 at one or more presence depths, determine a cellular sample distribution at the one or more presence depths.
  • the method 1000 of confirmation of acellular using the biological optical imaging system 100 may include determining whether the cellular sample distribution is below a threshold density.
  • the method 1000 of confirmation of acellular using the biological optical imaging system 100 may include, in response to the determination that the cellular sample distribution is below the threshold density, providing an error message regarding the cellular sample distribution at a user interface.

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  • Chemical & Material Sciences (AREA)
  • Dispersion Chemistry (AREA)
  • Physics & Mathematics (AREA)
  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Analytical Chemistry (AREA)
  • Biochemistry (AREA)
  • General Health & Medical Sciences (AREA)
  • General Physics & Mathematics (AREA)
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Abstract

La présente invention porte sur des systèmes et des procédés destinés à être utilisés avec un échantillon biologique qui comprennent un dispositif de microscopie, un diluant, un porte-échantillon et un ou plusieurs gâteaux lyophilisés comprenant des particules artificielles. Les gâteaux lyophilisés et l'échantillon biologique sont mélangés avec le diluant dans le porte-échantillon pour former une solution. L'image de l'échantillon biologique est formée avec les particules artificielles en tant que marqueurs de référence à l'aide du dispositif de microscopie. Un temps de stabilisation des particules artificielles est inférieur ou égal à un temps de stabilisation de l'échantillon biologique.
PCT/US2024/055081 2023-11-08 2024-11-08 Systèmes et procédés de formation d'image optique biologique avec des particules artificielles en tant que références Pending WO2025101870A1 (fr)

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US202363597095P 2023-11-08 2023-11-08
US63/597,095 2023-11-08
US18/940,011 US20250147031A1 (en) 2023-11-08 2024-11-07 Systems and methods for biological optical imaging with artificial particles references
US18/940,011 2024-11-07

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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20140270459A1 (en) * 2012-10-29 2014-09-18 Mbio Diagnostics, Inc. Particle Identification System, Cartridge And Associated Methods
US20230003622A1 (en) * 2019-12-12 2023-01-05 S.D. Sight Diagnostics Ltd Detecting platelets in a blood sample
WO2023139741A1 (fr) * 2022-01-21 2023-07-27 株式会社日立ハイテク Appareil de mesure de particule

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20140270459A1 (en) * 2012-10-29 2014-09-18 Mbio Diagnostics, Inc. Particle Identification System, Cartridge And Associated Methods
US20230003622A1 (en) * 2019-12-12 2023-01-05 S.D. Sight Diagnostics Ltd Detecting platelets in a blood sample
WO2023139741A1 (fr) * 2022-01-21 2023-07-27 株式会社日立ハイテク Appareil de mesure de particule

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