Automated configuration of flow cytometry machines

Computer based methods, systems, and computer readable media are provided for intelligently sorting cells using machine learning. A biological cell analysis sorting machine, wherein the biological cell analysis sorting machine comprises a flow cytometry system and a cell analytics sorting system, may be configured to detect configuration issues by analyzing results of a sorting experiment performed by the biological cell analysis sorting machine. An analysis of a history of prior sorting experiments and associated configuration settings may be performed and a corpus of documents pertaining to the sorting experiment based on the detected configuration issues may be analyzed. Updated configuration settings for the biological cell analysis sorting machine based on the performed analysis may be determined, and the biological cell analysis sorting machine may be configured with the updated configuration settings to conduct a desired sorting experiment.

1. TECHNICAL FIELD

Present invention embodiments relate to automation of flow cytometry machines, and more specifically, to automating configuration of flow cytometry machines to improve cell throughput and cell classification.

2. DISCUSSION OF THE RELATED ART

Immunophenotyping is a complex laboratory technique in which cells are imaged and studied based on size, complexity, and often, specific biomarkers. This technique is broadly used in research and clinical settings of biomedical fields. In the clinic, immunophenotyping is fundamental for the accurate diagnosis of patients with blood cancers or other diseases. In the research laboratory, immunophenotyping helps differentiate between different types of cells, and may be useful for generating research models for investigating the effects of different treatments as well as designing novel immunotherapies.

Immunophenotyping may also be used in flow cytometry to identify and sort different types of cells. For example, cells may be labeled using a fluorescently labeled antibody, and subjected to cell sorting by placing the cells in a fluid stream in which the cells move past a set of detectors. In this approach, the principal investigator manually sets up the sorting experiment, configuring voltages, flow rates, and gates to collect cells based on identification of cell populations of interest, without receiving corrections or feedback from the flow cytometry device as the sorting experiment progresses.

SUMMARY

According to embodiments of the present invention, methods, systems and computer readable media are provided for intelligently sorting cells using a biological cell analysis sorting machine, wherein the biological cell analysis sorting machine comprises a flow cytometry system and a cell analytics sorting system that sorts cells. The cell analysis sorting machine may be configured to detect configuration issues by analyzing results of a sorting experiment performed by the biological cell analysis sorting machine. An analysis of a history of prior sorting experiments and associated configuration settings may be performed and a corpus of documents pertaining to the sorting experiment based on the detected configuration issues may be analyzed. Updated configuration settings for the biological cell analysis sorting machine based on the performed analysis may be determined, and the biological cell analysis sorting machine may be configured with the updated configuration settings to conduct a desired sorting experiment. Accordingly, the present techniques may be used to automate aspects of flow cytometry, namely selection of flow cytometry configuration settings. Flow cytometry configuration settings have traditionally been selected by a user in a heuristic manner. Accordingly, reproducibility of cell sorting is often challenging. Present techniques, by automating selection of configuration settings, may help standardize and optimize configuration settings for a flow cytometry system. Additionally, existing flow cytometry systems do not learn from historical sorting experiments to provide feedback in order to improve future sorting experiments.

Typically, configuration settings include one or more of a flow rate, various voltages, and gates. In some cases, configuration settings may also include a type of flow cytometry device or machine. Accordingly, automated selection of specific configuration settings allows cell sorting experiments to become more standardized and reproducible.

In one aspect, updated configuration settings may be determined using a machine learning system, wherein the machine learning system is trained using configuration settings and optionally other information from prior sorting experiments. Thus, machine learning techniques may be used to select configuration settings to automatically configure a flow cytometer.

In another aspect, cell sorting results are evaluated based upon a determined experimental error. The experimental error is provided as feedback to the biological cell analysis sorting machine, and the configuration settings are modified automatically to reduce the experimental error. In this case, real-time or near real-time evaluation of experimental error may be performed to provide feedback to the biological cell analysis sorting machine to modify configuration settings to improve cell sorting results. Present techniques may evaluate cell sorting results of the biological cell analysis sorting machine in order to optimize configuration settings.

Optionally, the configuration settings are selected based upon a cell type. This allows specific configuration settings to be associated with specific cell types to optimize cell sorting in a cell-specific manner.

Typically, a ranked list of updated configuration settings is provided by the biological cell analysis sorting machine. An updated configuration setting from the ranked list may be implemented by the flow cytometry system. When a desired cell sorting efficiency is not achieved, an alternative updated configuration setting of the ranked list may be implemented until the desired cell sorting efficiency is achieved. The system, by generating the ranked list, may predict optimal or near optimal configuration settings for cell sorting.

Optionally, the biological cell analysis sorting machine is configured to sort cells based on the presence of two to six biomarkers. Traditionally, separation of cells with high complexity is challenging and difficult to replicate. Present techniques help to ensure reproducibility and may help optimize configuration settings for complex sorting experiments.

Historical data may comprise flow cytometry sorting experiments and associated configuration settings from one or more resources including but not limited to scientific literature, lab protocols, academic institutions, research institutions, and previously conducted sorting experiments by the flow cytometry system. The system may consider configuration settings from a variety of resources in order to predict optimal configuration settings (or at least to improve sorting results) for a given flow cytometry system.

DETAILED DESCRIPTION

Methods, systems, and computer readable media are provided to automate and improve cell sorting using a biological cell sorting analysis machine. The machine may also utilize machine learning to select and adjust configuration settings prior to and during cell sorting to improve results.

Currently, flow cytometry devices, including fluorescence-activated cell sorting (FACS) devices, are pre-configured for each sorting experiment. Typically, such configuration is performed manually by personnel having training and expertise in flow cytometry. Determining the configuration settings is a time consuming and complex process, which may vary from one flow cytometry machine to another making reproducibility between machines difficult.

For given cell types and/or types of cell sorting experiments, present techniques may employ machine learning to automatically select flow cytometry (including FACS) configuration settings, configure the flow cytometry system, and monitor and update flow cytometry configuration settings during the process of cell sorting, without human intervention, to improve or optimize experimental results. Thus, these techniques may be applied to automate and adapt the configuration of biological cell analysis sorting machines at setup and during cell sorting. Historical data from previous experiments may be analyzed and used to automatically configure the biological cell analysis sorting machine for a desired cell sorting experiment.

An example environment100for use with present invention embodiments is illustrated inFIG.1. Specifically, the environment includes one or more server systems10, and one or more client or end-user systems20. Server systems10and client systems20may be remote from each other and communicate over a network35. Server systems10may be connected to the flow cytometry system50through network35, or alternatively, server systems10may be integrated with flow cytometry system50. The network may be implemented by any number of any suitable communications media (e.g., wide area network (WAN), local area network (LAN), Internet, Intranet, etc.). Alternatively, server systems10and client systems20may be local to each other, and communicate via any appropriate local communication medium (e.g., local area network (LAN), hardwire, wireless link, Intranet, etc.).

Client systems20enable users to submit inputs to a cell analytics sorting system15(e.g., types of cells, type of sorting experiment, fluorescent labels, etc.) of server systems10to control the flow cytometry system50. The client systems may present a graphical user (e.g., GUI, etc.) or other interface (e.g., command line prompts, menu screens, etc.) to solicit information from users pertaining to the desired analysis, and may provide reports including analysis results (e.g., suggested flow cytometry/FACS configuration settings, results of cell sorting, error, ranked configuration settings, etc.).

A database system40may store various information for the analysis (e.g., machine learning training data42, scientific clinical literature46, configuration settings48, etc.). The database system40may be implemented by any conventional or other database or storage unit, may be local to or remote from server systems10and client systems20, and may communicate via any appropriate communication medium (e.g., local area network (LAN), wide area network (WAN), Internet, hardwire, wireless link, Intranet, etc.).

Server systems10and client systems20may be implemented by any conventional or other computer systems preferably equipped with a display or monitor, a base (e.g., including at least one processor16,22, one or more memories17,23and/or internal or external network interfaces or communications devices18,24(e.g., modem, network cards, etc.)), optional input devices (e.g., a keyboard, mouse or other input device) as part of a user interface19,25and a display26, and any commercially available and custom software (e.g., server/communications software, cell analytics sorting system15, browser/interface software, etc.).

Alternatively, one or more client systems20may automate operation of the flow cytometry system50, when operating as a stand-alone unit. In a stand-alone mode of operation, the client system stores or has access to the data (e.g., machine learning training data42, scientific/clinical literature46, configuration settings48, etc.), and includes a cell analytics sorting system15to control the flow cytometry system50. The graphical user (e.g., GUI, etc.) or other interface (e.g., command line prompts, menu screens, etc.) solicits information from a corresponding user pertaining to the desired analysis, and may provide reports including analysis results (e.g., suggested flow cytometry/FACS configuration settings, results of cell sorting, error, ranked configuration settings, etc.).

Machine learning training data42may include configuration settings from previous flow cytometry experiments, adjusted and compensated for different types of cell separation experiments and different cell types, as well as configuration settings extracted from published scientific/clinical literature46(e.g., scientific publications, clinical documents, laboratory protocols, and documents from other FACS facilities (e.g., clinical facilities and hospitals, companies, research institutions and academic centers using flow cytometry, etc.)). Cell samples from commercially available sources, academic research institutions, clinical facility sources, etc. may be sorted using the techniques provided herein. Additionally, sorting experiments performed by the flow cytometry system50may generate additional machine learning training data sets, which may be stored in machine learning training data42, and used to update the trained machine learning sorting module72, both prior to sorting operations and during sorting operations, to further improve results of sorting experiments. Machine learning training data may include feature sets extracted from scientific/clinical literature46and configuration settings48(e.g., configuration settings determined from sorting experiments performed by flow cytometry system50) for training the machine learning sorting module72. In some cases, training data may include data from sorting experiments performed on mammalian cells (e.g., mouse, human, etc.) or other types of cells.

The cell analytics sorting system, once trained, may provide feedback on experimental configuration settings to detect potential issues when analyzing a given flow cytometry setup. For example, the present system may analyze results of sorting experiments (e.g., from scientific/clinical literature46and other information including configuration settings48), based on presence and frequency of different cell populations to indicate configuration issues, and may provide alternative configurations for improving sorting of the different cell populations. In some aspects, the system is trained to automatically configure and set up photomultiplier tubes (PMT) voltages, electrical charge rings, gates for cell sorting experiments, voltages applied to plates, and flow rates. In some aspects, gating may involve selecting regions (e.g., defining a polygon region of a 2D plot, such as a scatter or intensity plot), wherein each region corresponds to a particular cell type. These regions may help establish configuration settings with which to collect each of these respective cell populations. The system may make adjustments to the configuration settings as the sorting experiment proceeds.

Scientific/clinical literature46may include information from the literature, lab protocols, FACS facilities, or databases that corresponds to flow cytometry configuration settings for various types of cells and/or types of flow cytometry experiments. For example, configuration settings may include voltage configuration settings for photomultiplier tubes (PMTs), plate voltages, electrical charge rings, cell types, gating configuration settings, flow rate configuration settings, fluorescent labels, etc. for a particular flow cytometry experiment involving a particular type of cell. Configuration settings48may include experimentally determined configuration settings for sorting cells by flow cytometry system50.

Cell analytics sorting system15may include one or more modules or units to perform the various functions of present invention embodiments described herein. The various modules (e.g., NLP module70, machine learning sorting module72, cell sorting analytics module74, ranked configuration settings module76, cell sorting error module77, etc.) may be implemented by any combination of any quantity of software and/or hardware modules or units, and may reside within memory17,23of the server and/or client systems for execution by processor16,22. In some cases, the cell analytics sorting system15may reside within the flow cytometry system50or may be on a separate computing device (e.g., server systems10) in communication with the flow cytometry system50, to automatically configure the flow cytometry system. The cell analytics sorting system15may automatically determine configuration settings prior to a sorting experiment and may automatically adjust the configuration settings of the flow cytometry system as the sorting experiment progresses.

Once the sorting experiment is underway, the flow cytometry system50may provide feedback to the cell analytics sorting system15to improve sorting. For example, if cells are not sufficiently separated into a single stream, the cell analytics sorting system15may adjust the configuration settings (e.g., flow rate) and provide the updated configuration settings to the flow cytometry system50. As another example, if cells are not sorted properly, gating may be adjusted. In some aspects, these techniques may be integrated with current flow cytometry systems, and machine learning may be applied to adjust the flow rate, PMT voltages, detector voltages, plate voltages, electrical charging ring voltages, and/or gating to adapt and optimize flow cytometry configuration settings to specific cell populations in real or quasi-real time.

Natural language processing (NLP) module70extracts information from scientific/clinical literature46, which may include but is not limited to research publications, clinical trial information, laboratory procedures and protocols, review articles, pathology guides, scientific literature, information from research institutions/clinical facilities, or any other scientific source of information that may be analyzed by NLP module70. In general, the articles will be machine readable. Types of sorting experiments, expected sorting results, and configuration settings available in cytometry protocols and in the scientific literature may be mined using NLP and other similar approaches.

NLP module70may also extract other features from scientific/clinical literature46, including but not limited to morphological features of respective types of cells (e.g., cell size (e.g., from 0.5 μM to 100 μm), cell shape, cell radius, cell appearance, cell diameter, presence and intensity of biomarker(s), etc.) from flow cytometry experiments. NLP module70may be used to extract information on biomarkers used to label different cell types.

From this extracted information, machine learning training data42may be generated to train the machine learning sorting module72to select configuration settings before (e.g., during set up) and during sorting experiments. In some aspects, the data for training the machine learning module72is obtained from the above referenced sources and may be validated by subject matter experts.

Machine learning sorting module72, once trained, may be used to automatically select and adjust configuration settings associated with flow cytometry system50, including PMT voltages, the type of sorting experiment, flow rates, detector voltages, gating, biomarkers, and other cell characteristics. In some aspects, the machine learning system may consider the specific type of flow cytometry system, given that different flow cytometry systems may comprise different components with different characteristics, and therefore, configurations settings may be optimized on a system-by-system basis.

Machine learning sorting module72may use any suitable machine learning technique, including but not limited to statistical classification, supervised learning, unsupervised learning, artificial neural networks, deep learning neural networks, cluster analysis, random forest, dimensionality reduction, binary classification, decision tree, etc. to select configuration settings and to adjust configuration settings during cell sorting.

Machine learning sorting module72may also analyze historical information of cell sorting to determine configuration settings from various data sources (e.g., unpublished experiments, publications, lab protocols, academic and clinical facilities, previous sorting experiments using flow cytometry system50, etc.), and may automatically provide configuration settings to configure a cytometry device (e.g., a FACS machine) for a desired sorting experiment. In other aspects, machine learning sorting module72may identify potential configuration errors, and may suggest modifications to a predetermined configuration or may provide a new configuration to the flow cytometry system50. Reconfiguration may be performed at the start of a sorting experiment or during a sorting experiment to correct the configuration error. In some cases, the system may identify damaged cells or debris, and may exclude these components from cytometry analysis, with such components directed to a fraction that is discarded during cell collection.

Cell sorting analytics module74may classify the cells into respective categories based upon forward scatter and side scatter and/or fluorescence of a labeled cell. Gating may be determined based upon the classification, in some cases, by the cell sorting analytics module74. Once gating has been performed, characteristics may be defined with which to collect cells. For example, the flow cytometry system may charge a droplet containing a cell, wherein the cell has a side scatter and forward scatter or other characteristic such as fluorescent intensity falling within a gated area, and may apply a voltage to plates to deflect the charged cell into a corresponding receiving container for cell collection. Sorting of cells may be based on various properties, including but not limited to cell shape, cell size, intensity of fluorescent label, density of the sample, etc. as determined by forward and side scatter techniques, as well as the intensity of one or more fluorescent tags.

Ranked configuration settings module76may return a list of sets of configuration settings for a given cell type and/or type of experiment. The system may rank the configuration settings according to optimal sorting results (e.g., based on recovery, purity, etc. with regard to cell sorting). In some cases, the flow cytometry system may start the cell sorting experiment with the top ranked set of configuration settings, and may alter the configuration settings (e.g., to select the second ranked set, or the third ranked set, etc.) by selecting a different set in the ranked list to optimize cell sorting.

Cell sorting error module77evaluates cell sorting results to determine experimental error. Techniques for determining error in flow cytometry/FCAS are known in the art. The experimental error may be provided as feedback to the biological cell analysis sorting machine, and the configuration settings may be modified automatically to reduce experimental error. In this case, real-time or near real-time evaluation of experimental error may be provided as feedback to the biological cell analysis sorting machine, which modifies the configuration settings until cell sorting results are improved.

Flow cytometry system50comprises a cell suspension51, an ultrasonic nozzle52for creating droplets (each droplet preferably containing a single cell), a laser54, a detector55, an analyzer56, voltage plates57, containers58for cells, electrical charging ring59, and corresponding tubing (not shown). A suspension of cells may be placed in a reservoir. A stream of fluid from the reservoir may join a sheath flow (a saline-based fluid without cells) in order to form a stream comprising single cells. The stream of cells may be contacted with a laser, with light scatter or fluorescence intensity detected by the detectors (PMTs) and provided to the analyzer. The detectors detect light scatter or fluorescence intensity from the laser interactions with the cells, and this information may be used to generate scatter plots (e.g., forward scatter and side scatter plots), which may be used for gating cells.

The analyzer may analyze forward and side light scatter or intensity of fluorescent cells and this information may be provided to cell sorting analytics module74for gating.

Forward scatter is proportional to the size of the cell (e.g., with smaller cells having smaller forward scatter and larger cells having larger forward scatter), and may be converted into a voltage signal proportional to the amount of forward scattered light. Side scatter is proportional to the shape and internal granularity and complexity of the cell, and may be converted into a voltage signal proportional to the amount of side scattered light. The detector for forward scatter may be in-line with the laser, while the detector for side scatter is perpendicular to the laser. In some cases, the forward scatter and side scatter voltages for each cell may be plotted respectively on an x and y axis to generate a scatter plot. Typically, groups of cells with different characteristics will appear as clusters on the scatter plot. Polygons may be drawn around these clusters, in a process known as gating to select features (e.g., ranges of light or intensity scatter) with which to collect cells.

Fluorescence-activated cell sorting (FACS) is a subcategory of flow cytometry. Fluorescent molecules (e.g., fluorescent labeled antibodies) may be used to tag cells. When contacted with a laser, the fluorophore may be excited to a higher energy level. The fluorophore returns to the ground energy state and emits light corresponding to a specific wavelength. The emitted light follows the same path as side scattered light, traveling through filters and mirrors, to direct wavelength ranges of light to detectors (e.g., PMTs). The fluorescence can be converted into a corresponding voltage, wherein the magnitude of the voltage corresponds to the intensity of the fluorescence.

For cell collection, once gating has been performed, the stream may pass through the ultrasonic nozzle to form droplets of single cells. The droplets may pass through an electrical charging ring in order to be charged, positively or negatively. Cells that are to be collected fall within a gating region and are charged, while cells outside of the gating regions are not charged and flow to the waste contained. In FACS, cells may be charged in proportion to their fluorescent intensity. The charged droplets pass through plates to which a voltage is applied, e.g., two plates, one having a negative charge and the other having a positive charge. The positively charged cells are directed to the negatively charged plate and the negatively charged cells are directed to the positively charged plate. Accordingly, the trajectory of the cell can be altered, such that the cell is directed to a specific collection tube, based upon the magnitude of the deflection.

FIG.2is a flow diagram showing determination of and adaptive application of flow cytometry configuration settings. At operation210, a biological sample is obtained. At operation220, the biological sample is processed for flow cytometry. Processing may include staining/dyes, labeling with a fluorophore conjugated antibody, purification, etc. At operation230, the cell sorting device is configured automatically, with predetermined configuration settings for a given cell type and type of sorting experiment. At operation240, the cell sorting experiment is performed. At operation250, the output of the cell analytics sorting system is analyzed to determine purity of cell sorting. If the results do not meet a specified threshold (e.g., achieving a suitable level of purity), the machine learning system may suggest updated configuration settings to improve sorting, at operation255. If the configuration settings are updated, due to the output of the cell analytics sorting system being below an acceptable threshold (e.g., for single cells in a droplet, for achieving a suitable level of cell sorting with regard to contamination, etc.), the flow cytometry system will implement the updated configuration settings, and the sorting experiment will proceed with the updated configuration settings at operation230. If the sorting experiment is proceeding according to desired results, the configuration settings will not be updated, and the sorting experiment may continue at operation240while undergoing further monitoring and analysis at operation250.

FIG.3shows a flow chart for generation of training data for the machine learning sorting module72. At operation310, literature and other information46are pre-processed to be machine readable (e.g., by optical character recognition, etc.). The literature may include but is not limited to scientific publications, clinical reports, internal company information, experimental protocols, databases, abstracts, conference proceedings, etc. At operation320, NLP module72may be used to extract information pertaining to configuration settings for flow cytometry for a given cell type suitable for generating a training data set. These configuration settings may include flow rates, voltages of the PMTs, plate voltages, number of collection tubes, cell types, cell labeling, type of sorting experiment, etc. Any suitable cell type may be included, including blood cells and other cells present in the circulating blood (e.g., white blood cells such as monocytes, lymphocytes, basophils, eosinophils, granulocytes, natural killer cells, T cells, red blood cells, cancer cells, epithelial cells, or other cells present in a blood sample at a low concentration (cancer cells), etc.). At operation330, the extracted information may be curated and/or annotated prior to providing to the machine learning sorting module. For example, the extracted data may be curated by a subject matter expert, such as a flow cytometry expert or scientist with expertise in configuring FACS machines for cell sorting. The curated and/or annotated data may be provided to the machine learning sorting module72as training data42, and the machine learning sorting module may be trained at operation340. In some aspects, the training data may include a cell type (e.g., including cell size, cell shape, other cell properties, etc.) along with cytometry configuration settings including the type of stain or label used to visualize the cell along with flow rates, voltages of the PMTs, gates, plate voltages, electrical charging ring voltages, type of sorting experiment, etc. The training data is provided to the cell analytics sorting system15to train the machine learning sorting module72to sort a cell sample into respective components.

Once trained, machine learning sorting module72can automatically select conditions for a cell sorting experiment and may use this information to configure flow cytometry system50. By applying machine learning to the flow cytometry system, conditions may be adapted to specific cell populations in real time or in quasi-real time. The present techniques may also provide feedback from flow cytometry experiments to cell analytics sorting system15to detect and flag potential issues from analyzing and sorting a given sample containing a mixture of cell populations.

The present flow cytometry system is not limited to sorting and analyzing a mixture of positive and negatively charged cells for one biomarker. The system may be trained to simultaneously sort cells based on multiple biomarkers depending upon the experiment, for instance, two to six biomarkers for cell proliferation, two to six biomarkers for immunophenotyping, or more (e.g., two to eighteen different fluorescent markers), etc. Thus, the present techniques may sort cells based on any suitable feature, including one or more biomarkers.

The cell analytics sorting system may analyze sorting experiments and provide recommendations and analysis pertaining to the sorting experiment. For example, the cell analytics sorting system may identify potentially failed experiments (e.g., by identifying user configurations that are substantially different from machine learning generated configuration settings, etc.). In addition, the system may provide experimental conclusions based on the presence and frequency of the different cell populations (e.g., by analyzing light or fluorescence intensity scatter plots to determine whether a sufficient level of separation for a given cell type is reached). Additionally, the cell analytics sorting system may analyze different types of sorting experiments to determine results including but not limited to proliferation studies, viability studies, or immunophenotyping studies, e.g., from blood samples. For example, for a proliferation sorting experiment, a population of cells exposed to conditions to promote cell proliferation may be evaluated as compared to a control population. For an inhibition experiment, a population of cells exposed to an inhibitor may be evaluated as compared to a control population. For a viability experiment, a population of cells exposed to conditions to promote viability may be evaluated as compared to a control population.

FIG.4shows a flowchart of inputs and outputs to machine learning sorting module72. A machine learning process may be used to automatically and dynamically adjust FACS configuration settings, e.g., such as gating, flow rates, various voltages, etc. as well as other alignment and calibration configuration settings, during sorting in real time or quasi-real time to reach optimal or improved conditions. Machine learning sorting module72may comprise a plurality of sorting models, with a particular machine learning algorithm, specific to particular cell types, particular biological assays, and one or more biomarkers. As previously discussed, training data42may be provided to the machine learning module in order to train the respective machine learning model (e.g., for particular cell type(s)) to identify and sort cells from a biological sample, such as a blood sample. Output490of the machine learning sorting module may include various flow cytometry configuration settings450.

Once the machine learning sorting module is trained, it may provide or analyze configuration settings for similar sorting assays. For current cell sorting experiments420, the machine learning sorting module may provide updated configuration settings based on the type of cell, cell size, biolabel(s), and type of sorting experiment, PMT voltages, plate voltages, flow rates, gating, electrical charge ring voltages, cell characteristics, etc. In particular, the machine learning system may provide parameters for a sorting experiment, including PMT voltages, electrical charge ring voltages, flow rates, and plate voltages. Additionally, the machine learning system may determine gating, identifying areas around particular populations of cells from 2D scatter or intensity plots, which may be used to collect the cells. If the updated settings are different from the current configuration settings, the cell analytics sorting system may notify the user of a possible configuration error, and the user may have the option of adjusting the configuration settings. Alternatively, the system may automatically select an updated set of configuration settings and may provide the configuration settings to the flow cytometry system.

A blood sample may comprise a variety of cell types, including but not limited to normal red blood cells, abnormal red blood cells, white blood cells (including granulocytes/polymorphonuclear leukocytes (e.g., neutrophils, eosinophils, basophils, etc.), mononuclear leukocytes (lymphocytes, monocytes, etc.)), and other cells (e.g., circulating cancer/tumor cells, epithelial cells, etc.). The system may be used to sort these cell types as well as cultured cells (e.g., cells frown in a laboratory for research purposes, mammalian cells, cells from tissue samples, etc.). Alternatively, the system may be used to sort cells from tissue samples that have been cultured or subjected to resuspension.

FIG.5shows a flow chart of example operations for using machine learning to sort cells. At operation610, results of a sorting experiment performed by the biological cell analysis sorting machine are analyzed to detect configuration issues. At operation620, a history of prior sorting experiments and associated configuration settings and a corpus of documents pertaining to the sorting experiment are analyzed based on the detected configuration issues. At operation630, updated configuration settings are determined for the biological cell analysis sorting machine based on the performed analysis. At operation640, the biological cell sorting machine is configured with the updated configuration settings and a desired experiment is conducted.

Present techniques provide a variety of advantages over existing approaches. These approaches speed up, simplify, and improve robustness of flow cytometry techniques for immunophenotyping and other types of experiments.

Advantages include using a machine learning process to automatically configure FACS configuration settings, such as gates, flow rates, various voltages, etc. as well as other alignment and calibration configuration settings. The machine learning process may be used to automatically and dynamically adjust FACS configuration settings during sorting in real time or quasi-real time to reach optimal or improved conditions. Particular FACS machine learning models may be generated and used to sort particular cell types for particular biological assays, and the FACS machine learning models may be trained on multiple biomarkers. Present techniques improve reproducibility of flow cytometry experiments, and automate aspects of this process.

It will be appreciated that the embodiments described above and illustrated in the drawings represent only a few of the many ways of implementing embodiments for automating flow cytometry/FACS techniques.

It is to be understood that the software (e.g., cell analytics sorting system15, including NLP module70, machine learning sorting module72, cell sorting analytics module74, ranked configuration settings module76, cell sorting error module77, etc.) of the present invention embodiments may be implemented in any desired computer language and could be developed by one of ordinary skill in the computer arts based on the functional descriptions contained in the specification and flow charts illustrated in the drawings. Further, any references herein of software performing various functions generally refer to computer systems or processors performing those functions under software control. The computer systems of the present invention embodiments may alternatively be implemented by any type of hardware and/or other processing circuitry.

The software of the present invention embodiments (e.g., cell analytics sorting system15, including NLP module70, machine learning sorting module72, cell sorting analytics module74, ranked configuration settings module76, cell sorting error module77, etc.) may be available on a non-transitory computer useable medium (e.g., magnetic or optical mediums, magneto-optic mediums, floppy diskettes, CD-ROM, DVD, memory devices, etc.) of a stationary or portable program product apparatus or device for use with stand-alone systems or systems connected by a network or other communications medium.

The system may employ any number of any conventional or other databases, data stores or storage structures (e.g., files, databases, data structures, data or other repositories, etc.) to store information (e.g., machine learning training data42, scientific clinical literature46, configuration settings48, etc.). The database system may be implemented by any number of any conventional or other databases, data stores or storage structures (e.g., files, databases, data structures, data or other repositories, etc.) to store information (e.g., machine learning training data42, scientific clinical literature46, configuration settings48, etc.). The database system may be included within or coupled to the server and/or client systems. The database systems and/or storage structures may be remote from or local to the computer or other processing systems, and may store any desired data (e.g., machine learning training data42, scientific clinical literature46, configuration settings48, etc.).

The report may include a listing of prioritized configuration settings along with any other information arranged in any fashion, and may be configurable based on rules or other criteria to provide desired information to a user (e.g., flow cytometry analytics, ranked configuration settings, error analysis, etc.).

The present invention embodiments are not limited to the specific tasks or algorithms described above, but may be utilized for any application in which sorting cells using flow cytometry/FACS is being performed.