Optimized sorting gate

Abstract

Included herein are systems, devices, and methods for determining a gating strategy. An acquisition system can cause a particle analyzer to collect parameter measurements for a portion of particles in a sample. An analysis system can receive the parameter measurements and determine a gating strategy from the parameter measurements. The analysis system can receive the parameter measurements and determine a gating strategy from the parameter measurements, and the acquisition system can collect measurements for some or all of the remaining particles in the sample using the gating strategy determined by the analysis system.

Description

Technical Field

[0001] (Cross - Reference to Related Applications) This application claims priority to the filing dates of U.S. Provisional Patent Application No. 62 / 791,316, filed on January 11, 2019, and U.S. Provisional Patent Application No. 62 / 817,555, filed on March 12, 2019, under 35 U.S.C. § 119(e), and the disclosures of these applications are incorporated herein by reference.

[0002] (Technical Field) The present disclosure generally relates to automated particle evaluation, and more specifically, to the fields of sample analysis methods and particle characterization methods.

Background Art

[0003] Particle analyzers such as flow cytometers can enable the characterization of particles based on electro - optical measurements such as light scattering and fluorescence. In a flow cytometer, for example, particles such as molecules, analyte - conjugated beads, or individual cells in a fluid suspension method pass through a detection region where the particles are typically exposed to excitation light from one or more lasers, and the light scattering characteristics and fluorescence characteristics of the particles are measured. The particles or components of the particles are typically labeled with a fluorescent dye to facilitate detection. A number of different particles or components can be detected simultaneously by labeling different particles or components with spectrally different fluorescent dyes. Different cell types can be identified by their light scattering characteristics and fluorescence emissions that result from labeling different cellular proteins or other components with antibodies or other fluorescent probes labeled with fluorescent dyes. The data obtained from the analysis of cells (or other particles) by multi - color cytometry is multi - dimensional, where each cell corresponds to a point in a multi - dimensional space defined by the measured parameters. A population of cells or particles can be identified as a population of points in the data space.

Summary of the Invention

[0004] Disclosed herein are embodiments of a collection system for classifying a plurality of particles of a sample. In some embodiments, the collection system includes a non-transitory storage device configured to store executable instructions, and a processor (e.g., a hardware processor and a processor of a virtual machine) that exchanges information with the non-transitory storage device, and the processor, by the executable instructions, (1) measures parameters of a first plurality of particles of a sample related to an experiment collected by a particle analyzer, and (2) is programmed to send the first related experiment information of the experiment in a first format to an analysis system (e.g., a SaaS (software as a service) system). The processor can be programmed by the executable instructions to receive, from the analysis system, the second related experiment information of the experiment in the first format. The analysis system is programmed to receive reference sorting criteria for a first plurality of particles of a sample related to an experiment, (1) based on the measured values of the parameters of the first plurality of particles of a sample related to the experiment, and (2) based on the reference sorting criteria for the first plurality of particles of a sample related to the experiment, to generate a gating strategy, and to generate second related experiment information of the experiment including the first related experiment information of the experiment and the gating strategy (or an explanation of the gating strategy) in the first format. Alternatively or additionally, the received second related experiment information is generated by the analysis system by receiving reference sorting criteria for a first plurality of particles of a sample related to the experiment, (1) based on the measured values of the parameters of the first plurality of particles of a sample related to the experiment, and (2) based on the reference sorting criteria for the first plurality of particles of a sample related to the experiment, by generating a gating strategy (or an explanation of the gating strategy), and by generating second related experiment information of the experiment including the first related experiment information of the experiment and the gating strategy (or an explanation of the gating strategy) in the first format.Alternatively or additionally, the received second relevant experimental information indicates that the analysis system has received reference classification criteria for a first plurality of particles of a sample related to the experiment, (1) generated a gating strategy (or an explanation of the gating strategy) based on measured values of parameters of the first plurality of particles of the sample related to the experiment and (2) based on the reference classification criteria for the first plurality of particles of the sample related to the experiment, and has generated first relevant experimental information of the experiment and second relevant experimental information of the experiment including the gating strategy in the first format (or an explanation of the gating strategy). The processor can be programmed by executable instructions to represent the gating strategy (e.g., the gating strategy or an explanation of the gating strategy) and transmit the particle analyzer configuration generated from the second relevant experimental information of the experiment to the particle analyzer.

[0005] Disclosed herein are embodiments of a collection system for classifying a plurality of particles of a sample. In some embodiments, the collection system comprises a non-transitory storage device configured to store executable instructions, and a processor (e.g., a hardware processor of a virtual processor of a virtual machine) that exchanges information with the non-transitory storage device, and the processor, by the executable instructions, (1) measures parameters of a first plurality of particles of a sample related to an experiment collected by a particle analyzer, and (2) is programmed to transmit to an analysis system (e.g., a SaaS system) first related experiment information of the experiment in a first format, and the analysis system is programmed to receive a reference classification criterion for a first plurality of particles of a sample related to the experiment, and to generate a gating strategy (or an explanation of the gating strategy) based on (1) the measured values of the parameters of the first plurality of particles of the sample related to the experiment, and (2) the reference classification criterion for the first plurality of particles of the sample related to the experiment. In some embodiments, the analysis system is programmed to generate in a first format second related experiment information of the experiment including the first related experiment information of the experiment and the gating strategy (or an explanation of the gating strategy), and the processor, by the executable instructions, is programmed to receive in a first format the second related experiment information of the experiment from the analysis system, and to represent the gating strategy (e.g., the gating strategy or an explanation of the gating strategy), and to transmit to the particle analyzer a particle analyzer configuration generated from the second related experiment information of the experiment. In some embodiments, the analysis system is programmed to generate a particle analyzer configuration including the first related experiment information and representing in a first format (e.g., readable by the collection system) the gating strategy (e.g., the gating strategy or an explanation of the gating strategy), and to transmit the particle analyzer configuration to the collection system, where the hardware processor is programmed by the executable instructions to receive the particle analyzer configuration.

[0006] Disclosed in this specification are embodiments of a collection system for classifying a plurality of particles of a sample. In some embodiments, the collection system includes a non-transitory storage device configured to store executable instructions, and a processor (e.g., a hardware processor of a virtual processor of a virtual machine) that exchanges information with the non-transitory storage device. The processor is programmed by the executable instructions to: (1) transmit to an analysis system (e.g., a SaaS system) measurement values of parameters of a first plurality of particles of a sample related to an experiment collected by a particle analyzer, and (2) first relevant experimental information of the experiment in a first format. The analysis system is programmed to receive a reference classification criterion for a first plurality of particles of a sample related to the experiment, and to generate a gating strategy (or an explanation of the gating strategy) based on: (1) the measurement values of the parameters of the first plurality of particles of the sample related to the experiment, and (2) the reference classification criterion for the first plurality of particles of the sample related to the experiment, and to receive an indicator indicating that the gating strategy has been generated. In some embodiments, the analysis system is programmed to generate, in the first format, second relevant experimental information of the experiment including the first relevant experimental information of the experiment and the gating strategy (or an explanation of the gating strategy). The indicator regarding the generation of the gating strategy includes the second relevant experimental information of the experiment. The processor is programmed by the executable instructions to represent the gating strategy (e.g., the gating strategy or an explanation of the gating strategy), and to transmit to the particle analyzer a particle analyzer configuration generated from the second relevant experimental information of the experiment. In some embodiments, the analysis system is programmed to generate a particle analyzer configuration including the first relevant experimental information and representing the gating strategy (e.g., the gating strategy or an explanation of the gating strategy). The indicator regarding the generation of the gating strategy includes the particle analyzer configuration. The processor is programmed by the executable instructions to transmit the particle analyzer configuration to the particle analyzer.

[0007] In some embodiments, the analysis system is programmed to generate a gating strategy using a gating method (e.g., a gating method implemented by an analysis tool) based on (1) measurements of parameters of a first plurality of particles of a sample related to an experiment and (2) a reference classification criterion for the first plurality of particles of the sample related to the experiment. The second related experimental information can include a gating method, one or more input parameters related to the gating method, one or more output parameters related to the gating method, and / or an analysis tool. The particle analyzer configuration can include a gating method, one or more input parameters related to the gating method, one or more output parameters related to the gating method, and / or an analysis tool. The analysis system can be programmed to generate a file including the first related experimental information, the gating strategy, the second related experimental information, the gating method for generating the gating strategy, one or more input parameters related to the gating method, one or more output parameters related to the gating method, and / or the analysis tool for implementing the gating method to generate the second related experimental information. The analysis system can be programmed to generate an explanation of the gating strategy from the gating strategy (e.g., in a first format readable by a collection system).

[0008] In some embodiments, a gating strategy (or description of the gating strategy) can comprise a plurality of gates corresponding to a set of parameters. A reference classification criterion can include a first plurality of particles of a target selected from a first plurality of particles. An analysis system is programmed to determine a number of the plurality of gates based on an evaluation criterion that differentiates a first plurality of particles of a target from remaining particles of the first plurality of particles using the gating strategy (or description of the gating strategy), where the plurality of gates corresponds to some or all of the parameters, and is programmed to generate a plurality of polygons based on measured values of parameters of the first plurality of particles of a sample, the classification criterion, and the plurality of gates, where at least two of the plurality of polygons consist of polygons of different shapes, and the number of the plurality of gates is the same as the number of the plurality of polygons. The evaluation criterion can include a purity measurement and a yield measurement that differentiates a first plurality of particles of a target from remaining particles of the first plurality of particles using the gating strategy (or description of the gating strategy). The analysis system is programmed to generate a plurality of polygons that enclose some or all of the plurality of particles of a target to generate the plurality of polygons.

[0009] In some embodiments, a processor is programmed by executable instructions to receive measured values of parameters of a first plurality of particles of a sample related to an experiment from a particle analyzer.

[0010] In some embodiments, the processor can be programmed by executable instructions to receive first relevant experimental information in a second format from a particle analyzer, generate the first relevant experimental information in a first format from the first relevant experimental information in the second format, and generate second relevant experimental information in the second format from the second relevant experimental information in the first format. The processor is programmed by executable instructions to represent a gating strategy (e.g., a gating strategy or an explanation of the gating strategy) and transmit to the particle analyzer a particle analyzer configuration generated from the second relevant experimental information in the second format in order to transmit a particle analyzer configuration representing the gating strategy (e.g., the gating strategy or an explanation of the gating strategy) to the particle analyzer. The first relevant experimental information in the second format can include an identifier of the particle analyzer, in which case the first relevant experimental information in the first format includes an identifier of another particle analyzer. The second relevant experimental information in the second format can include an identifier of the particle analyzer, in which case the second relevant experimental information in the first format includes an identifier of another particle analyzer. The analysis system can be programmed to determine that the first relevant experimental information is in the first format before generating a gating strategy (or an explanation of the gating strategy).

[0011] In some embodiments, the first relevant experimental information in the first format includes an identifier of the particle analyzer. The first relevant experimental information in the first format can include an identifier of the collection system. The processor can be programmed by executable instructions to generate a particle analyzer configuration for a particle analyzer representing a gating strategy (e.g., a gating strategy or an explanation of the gating strategy), and the particle analyzer configuration can cause the particle analyzer to collect measurements of parameters of a second plurality of particles of a sample related to the experiment, at least in part based on the particle analyzer configuration. The reference classification criteria can include gating information that identifies a plurality of values of parameters of the first plurality of particles of the sample for distinguishing the first plurality of particles of interest from the remaining particles of the first plurality of particles.

[0012] In some embodiments, to receive a reference classification criterion for a first plurality of particles of a sample, the analysis system is programmed to receive a selection of the first plurality of particles from a first plurality of particles of interest. To receive a selection of the first plurality of particles of interest, the analysis system can be programmed to display a plurality of images corresponding to measurements of parameters of the first plurality of particles of the sample relevant to the experiment.

[0013] In some embodiments, the measurements of the parameters of the first plurality of particles of the sample include measurements of light fluorescence-emitted by the first plurality of particles. The light fluorescence-emitted by the first plurality of particles can include light fluorescence-emitted by a cell component binder bound to the first plurality of particles.

[0014] Disclosed herein are embodiments of an analysis system (e.g., a SaaS system) that generates a gating strategy (or an explanation of the gating strategy). The analysis system can include a non-transitory storage device configured to store executable instructions, and a processor (e.g., a hardware processor and a processor of a virtual machine) that exchanges information with the non-transitory storage device. The processor is programmed by the executable instructions to receive from a collection system that exchanges information with a particle analyzer: (1) measurement values of parameters of a first plurality of particles of a sample related to an experiment, and (2) first related experiment information of an experiment in a first format. The processor can be programmed by the executable instructions to receive a reference classification criterion for the case of the first plurality of particles of the sample related to the experiment. The processor is programmed by the executable instructions to generate a gating strategy (or an explanation of the gating strategy) based on: (1) the measurement values of the parameters of the first plurality of particles of the sample related to the experiment, and (2) the reference classification criterion for the case of the first plurality of particles of the sample related to the experiment. The processor is programmed by the executable instructions to generate second related experiment information of the experiment including the first related experiment information of the experiment and the gating strategy (or an explanation of the gating strategy) in the first format. The processor is programmed by the executable instructions to transmit the second related experiment information of the experiment to the collection system.

[0015] Disclosed herein are embodiments of an analysis system (e.g., a SaaS system) that generates a gating strategy (or an explanation of the gating strategy). The analysis system can include a non-transitory storage device configured to store executable instructions and a processor (e.g., a processor and a processor of a virtual machine) that exchanges information with the non-transitory storage device. The processor is programmed by the executable instructions to receive from a collection system that exchanges information with a particle analyzer: (1) measurements of parameters of a first plurality of particles of a sample related to an experiment, (2) first related experimental information of an experiment in a first format, to receive a reference classification criterion for a case of the first plurality of particles of a sample related to an experiment, and to generate a gating strategy based on (1) the measurements of parameters of the first plurality of particles of a sample related to an experiment and (2) the reference classification criterion for a case of the first plurality of particles of a sample related to an experiment. In some embodiments, the processor is programmed by the executable instructions to generate second related experimental information of an experiment that includes the first related experimental information of the experiment and a gating strategy in a first format (or an explanation of the gating strategy), and to transmit the second related experimental information of the experiment to the collection system. In some embodiments, the processor is programmed by the executable instructions to generate a particle analyzer configuration that includes the first related experimental information and represents a gating strategy (e.g., a gating strategy or an explanation of the gating strategy), and to transmit the particle analyzer configuration to the collection system, where in this case the collection system is programmed to receive the particle analyzer configuration.

[0016] Disclosed herein are embodiments of an analysis system (e.g., a SaaS system) that generates a gating strategy (or an explanation of the gating strategy). The analysis system can include a non-transitory storage device configured to store executable instructions, and a processor (e.g., a hardware processor and a processor of a virtual machine) that exchanges information with the non-transitory storage device. The processor, by the executable instructions, receives from a collection system that exchanges information with a particle analyzer: (1) measurement values of parameters of a first plurality of particles of a sample related to an experiment, (2) first related experimental information of an experiment in a first format, receives a reference classification criterion for a case of the first plurality of particles of a sample related to the experiment, and generates a gating strategy (or an explanation of the gating strategy) based on (1) the measurement values of the parameters of the first plurality of particles of a sample related to the experiment and (2) the reference classification criterion for the case of the first plurality of particles of a sample related to the experiment, and is programmed to transmit an indicator that the gating strategy has been generated for the collection system. In some embodiments, the processor, by the executable instructions, generates second related experimental information of an experiment that includes the first related experimental information of the experiment and a gating strategy (or an explanation of the gating strategy) in the first format, and is programmed to transmit the second related experimental information of the experiment to the collection system, and the indicator that the gating strategy has been generated includes the second related experimental information. In some embodiments, the processor is programmed by the processor to generate a particle analyzer configuration that includes the first related experimental information and represents a gating strategy (e.g., a gating strategy or an explanation of the gating strategy), and to transmit the particle analyzer configuration to the collection system, and in this case, the indicator that the gating strategy has been generated includes the particle analyzer configuration.

[0017] In some embodiments, to generate a gating strategy (or an explanation of the gating strategy), the processor is programmed by executable instructions to use a gating method of an analysis tool to generate a gating strategy (or an explanation of the gating strategy) based on (1) measured values of parameters of a first plurality of particles of a sample related to an experiment and (2) a reference classification criterion for the first plurality of particles of the sample related to the experiment. The second related experimental information can include a gating method, one or more input parameters related to the gating method, one or more output parameters related to the gating method, and / or an analysis tool. The particle analyzer configuration can include a gating method, one or more input parameters related to the gating method, one or more output parameters related to the gating method, and / or an analysis tool. To generate the second related experimental information, the processor can be programmed by executable instructions to generate a file including the first related experimental information, the gating strategy, the second related experimental information, the gating method for generating the gating strategy, one or more input parameters related to the gating method, one or more output parameters related to the gating method, and / or the analysis tool for implementing the gating method. The processor can be programmed by executable instructions to generate an explanation of the gating strategy (e.g., in a format readable by a collection system) from the gating strategy.

[0018] In some embodiments, the gating strategy (or the description of the gating strategy) comprises a plurality of gates corresponding to a set of parameters. The reference classification criteria can include a first plurality of particles of a target selected from the first plurality of particles. To generate the gating strategy (or the description of the gating strategy), the processor can be programmed by executable instructions to determine the number of the plurality of gates based on an evaluation criterion that differentiates the first plurality of particles of the target from the remaining particles of the first plurality of particles using the gating strategy (or the description of the gating strategy), where the plurality of gates corresponds to some or all of the parameters, and can be programmed to generate a plurality of polygons based on the measured values of the parameters of the first plurality of particles of the sample, the classification criteria, and the plurality of gates, where at least two of the plurality of polygons are composed of polygons of different shapes, and the number of the plurality of gates is the same as the number of the plurality of polygons. The evaluation criterion can include a purity measurement and a yield measurement that differentiates the first plurality of particles of the target from the remaining particles of the first plurality of particles using the gating strategy (or the description of the gating strategy). To generate the plurality of polygons, the processor can be programmed by executable instructions to generate a plurality of polygons surrounding some or all of the plurality of particles of the target.

[0019] In some embodiments, the collection system is programmed to generate first relevant experimental information from a second format to a first format and to generate second relevant experimental information from the first format to the second format. The first relevant experimental information in the second format can include an identifier of a particle analyzer, and in this case, the first relevant experimental information in the first format includes an identifier of another particle analyzer. The second relevant experimental information in the second format can include an identifier of a particle analyzer, and in this case, the second relevant experimental information in the first format includes an identifier of another particle analyzer. The processor can be programmed by executable instructions to determine that the first relevant experimental information is in the first format before generating a gating strategy (or an explanation of the gating strategy).

[0020] In some embodiments, the first relevant experimental information in the first format includes an identifier of a particle analyzer. The first relevant experimental information in the first format can include an identifier of the collection system. The collection system can be programmed to generate a particle analyzer configuration for a particle analyzer representing a gating strategy (e.g., a gating strategy or an explanation of the gating strategy) and to transmit the particle analyzer configuration to the particle analyzer. The particle analyzer can be programmed to collect measurements of parameters of a second plurality of particles of a sample relevant to the experiment, at least in part based on the particle analyzer configuration. The reference classification criteria can include gating information that identifies a plurality of values of parameters of the first plurality of particles of the sample for distinguishing the first plurality of particles of interest from the remaining particles of the first plurality of particles.

[0021] In some embodiments, to receive a reference classification criterion for a first plurality of particles of a sample, the processor is programmed by executable instructions to receive a selection of the first plurality of particles from the first plurality of particles of interest. To receive a selection of the first plurality of particles of interest, the processor can be programmed by executable instructions to display a plurality of images corresponding to measurements of parameters of the first plurality of particles of a sample related to an experiment.

[0022] In some embodiments, the measurements of parameters of the first plurality of particles of the sample include measurements of light fluorescence emitted by the first plurality of particles. The light fluorescence emitted by the first plurality of particles can include light fluorescence emitted by a cell component binder bound to the first plurality of particles.

[0023] Disclosed herein are embodiments of a method for classifying a plurality of particles of a sample. In some embodiments, the method, under the control of a processor (e.g., a hardware processor and a processor of a virtual machine), includes receiving (1) measurements of parameters of a first plurality of particles of a sample related to an experiment collected by a particle analyzer and (2) first relevant experimental information of an experiment in a first format. The method can include receiving a reference classification criterion for the case of the first plurality of particles of the sample related to the experiment. The method can include generating a gating strategy (or an explanation of the gating strategy) (1) based on the measurements of the parameters of the first plurality of particles of the sample related to the experiment and (2) based on the reference classification criterion for the case of the first plurality of particles of the sample related to the experiment. The method can include generating second relevant experimental information of the experiment including the first relevant experimental information of the experiment and the gating strategy (or an explanation of the gating strategy) in the first format. The method can include causing the particle analyzer to represent a gating strategy (e.g., the gating strategy or an explanation of the gating strategy) and collect measurements of parameters of a second plurality of particles of the sample related to the experiment, at least partially based on a particle analyzer configuration generated from the second relevant experimental information of the experiment.

[0024] Disclosed in this specification are embodiments of a method for classifying a plurality of particles of a sample. In some embodiments, the method, under the control of a processor (e.g., a hardware processor and a processor of a virtual machine), (1) receives measurements of parameters of a first plurality of particles of a sample related to an experiment collected by a particle analyzer, (2) receives first relevant experimental information of an experiment in a first format, and receives a reference classification criterion for the case of the first plurality of particles of the sample related to the experiment, and includes generating a gating strategy based on (1) the measurements of parameters of the first plurality of particles of the sample related to the experiment and (2) the reference classification criterion for the case of the first plurality of particles of the sample related to the experiment. In some embodiments, the method includes generating second relevant experimental information of the experiment including the first relevant experimental information of the experiment and a gating strategy in a first format (or an explanation of the gating strategy), and causing the particle analyzer to collect measurements of parameters of a second plurality of particles of the sample related to the experiment, at least partially based on a particle analyzer configuration representing the gating strategy (e.g., the gating strategy or an explanation of the gating strategy) and generated from the second relevant experimental information of the experiment. The method can include generating a particle analyzer configuration representing the gating strategy (e.g., the gating strategy or an explanation of the gating strategy) from the second relevant experimental information of the experiment and transmitting the particle analyzer configuration to the particle analyzer. In some embodiments, the method can include generating a particle analyzer configuration including the first relevant experimental information and representing a gating strategy in a first format (e.g., the gating strategy or an explanation of the gating strategy), and causing the particle analyzer to collect measurements of parameters of a second plurality of particles of the sample related to the experiment, at least partially based on the particle analyzer configuration. The method can include transmitting the particle analyzer configuration to the particle analyzer.

[0025] In some embodiments, generating a gating strategy (or an explanation of the gating strategy) includes using a gating method and / or an analysis tool implementing the gating method to generate (1) based on measurements of parameters of a first plurality of particles of a sample related to an experiment, and (2) based on a reference classification criterion for the first plurality of particles of the sample related to the experiment, a gating strategy (or an explanation of the gating strategy). Second related experimental information can include a gating method, one or more input parameters related to the gating method, one or more output parameters related to the gating method, and / or an analysis tool. A particle analyzer configuration can include a gating method, one or more input parameters related to the gating method, one or more output parameters related to the gating method, and / or an analysis tool. Generating second related experimental information can include generating a file including the first related experimental information, the gating strategy, the second related experimental information, the gating method for generating the gating strategy, one or more input parameters related to the gating method, one or more output parameters related to the gating method, and / or the analysis tool implementing the gating method. The method can include generating an explanation of the gating strategy from the gating strategy.

[0026] In some embodiments, the gating strategy (or the description of the gating strategy) comprises a plurality of gates corresponding to a set of parameters. The reference classification criteria can include a first plurality of particles of a target selected from the first plurality of particles. Generating the gating strategy (or the description of the gating strategy) can include determining the number of the plurality of gates based on an evaluation criterion that uses the gating strategy (or the description of the gating strategy) to distinguish a first plurality of particles of a target from the remaining particles of the first plurality of particles, where the plurality of gates corresponds to some or all of the parameters, and generating a plurality of polygons based on the measured values of the parameters of the first plurality of particles of the sample, the classification criteria, and the plurality of gates, where at least two of the plurality of polygons are composed of polygons of different shapes, and the number of the plurality of gates is the same as the number of the plurality of polygons. The evaluation criterion can include a purity measurement and a yield measurement that use the gating strategy (or the description of the gating strategy) to distinguish a first plurality of particles of a target from the remaining particles of the first plurality of particles. Generating a plurality of polygons can include generating a plurality of polygons that enclose some or all of the plurality of particles of the target.

[0027] In some embodiments, the method can include transmitting to an analysis system (e.g., a SaaS system) (1) measured values of parameters of a first plurality of particles of a sample related to an experiment and (2) first related experimental information in a first format, where receiving a reference classification criterion includes receiving, by the analysis system, a reference classification criterion for a first plurality of particles of a sample related to an experiment, where generating a gating strategy (or an explanation of the gating strategy) includes generating, using the analysis system, a gating strategy (or an explanation of the gating strategy) based on (1) measured values of parameters of a first plurality of particles of a sample related to an experiment and (2) a reference classification criterion for a first plurality of particles of a sample related to an experiment, and where generating second related experimental information includes generating, using the analysis system, second related experimental information of the experiment in the first format that includes the first related experimental information of the experiment and the gating strategy (or an explanation of the gating strategy).

[0028] In some embodiments, the method includes receiving first related experimental information in a second format, generating first related experimental information in a first format from the first related experimental information in the second format, and generating second related experimental information in the second format from second related experimental information in the first format. The first related experimental information in the second format includes an identifier of a particle analyzer, and in this case, the first related experimental information in the first format includes an identifier of another particle analyzer. The second related experimental information in the second format can include an identifier of a particle analyzer, and in this case, the second related experimental information in the first format includes an identifier of another particle analyzer. The method can include determining that the first related experimental information is in the first format. Determining that the first related experimental information is in the first format can include determining, by the analysis system, that the first related experimental information is in the first format.

[0029] In some embodiments, the first relevant experimental information in the first format includes an identifier of a particle analyzer. The first relevant experimental information in the first format can include an identifier of a collection system. The reference classification criteria can include gating information that identifies a plurality of values of parameters of a first plurality of particles of a sample that distinguish the first plurality of particles of interest from the remaining particles of the first plurality of particles.

[0030] In some embodiments, receiving the reference classification criteria for the first plurality of particles of a sample includes receiving a selection of the first plurality of particles from the first plurality of particles of interest. Receiving a selection of the first plurality of particles of interest can include displaying a plurality of images corresponding to measured values of parameters of the first plurality of particles of a sample relevant to an experiment.

[0031] In some embodiments, the measured values of parameters of the first plurality of particles of a sample include measured values of light fluoresced by the first plurality of particles. The light fluoresced by the first plurality of particles can include light fluoresced by a cell component binder bound to the first plurality of particles.

Brief Description of the Drawings

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[0033] In the following detailed description, reference is made to the accompanying drawings, which form a part hereof. In the drawings, like reference numerals typically identify like components unless otherwise indicated. The exemplary embodiments described in the detailed description, drawings, and claims are not meant to be limiting. Other embodiments may be used and other changes may be made without departing from the spirit and scope of the content presented herein. The aspects of the present disclosure as described throughout this specification and shown in the figures can be configured, substituted, combined, separated, and designed in a variety of different configurations, all of which are clearly intended herein and form a part of the disclosure of this specification.

[0034] Particle analyzers such as flow cytometers and scanning cytometers are analytical tools that enable the characterization of particles based on electro-optical measurements such as light scattering and fluorescence. For example, in a flow cytometer, particles such as molecules, analyte-bound beads, or individual cells in a fluid suspension pass through a detection region where the particles are typically exposed to excitation light from one or more lasers, and the light scattering and fluorescence characteristics of the particles are measured. The particles or components of the particles are typically labeled with a fluorescent dye to facilitate detection. A number of different particles or components can be detected simultaneously using spectrally distinct fluorescent dyes to label the different particles or components. In some embodiments, the analyzer includes a number of photodetectors for each scattering parameter to be measured and one or more for each different dye to be measured. For example, some embodiments include a spectroscopic configuration in which one or more sensors or detectors are used for each dye. The data obtained includes signals measured for each of the light scattering detectors and fluorescence emissions.

[0035] The particle analyzer can further comprise means for recording the measured data and analyzing the data. For example, the storage and analysis of the data can be performed using a computer connected to the detection electronics. For example, the data can be stored in tabular form where each row corresponds to the data for one particle and the columns correspond to each of the measured morphological configurations. The use of a standard file format such as the "FCS (Flow Cytometry Standard)" file format for storing data from the particle analyzer facilitates the analysis of the data using independent programs and / or machines. Using current analytical methods, the data is typically displayed in a one-dimensional histogram or two-dimensional (2D) plot to facilitate visualization, although other methods can also be used to visualize multi-dimensional data.

[0036] For example, parameters measured using a flow cytometer typically include light scattered by particles mainly at a narrow angle along the forward direction (referred to as forward scatter (FSC)), light scattered by particles in a direction orthogonal to the excitation laser (referred to as side scatter (SSC)), light emitted from fluorescent molecules in one or more detectors that measure signals over a range of spectral wavelengths, or light emitted by fluorescent dyes that are mainly detected in a specific detector or an array of detectors. Different cell types can be discriminated by their light scattering characteristics and fluorescence emission, which are generated by labeling different cellular proteins or other components with antibodies or other fluorescent probes labeled with fluorescent dyes.

[0037] Both flow cytometers and scanning cytometers are commercially available, for example, from BD Biosciences (San Jose, Calif.). Flow cytometry is described, for example, in Landy et al. (eds.), Clinical Flow Cytometry, Annals of the New York Academy of Sciences Volume 677 (1993), Bauer et al. (eds.), Clinical Flow Cytometry: Principles and Applications, Williams & Wilkins (1993), Ormerod (ed.), Flow Cytometry: A Practical Approach, Oxford Univ. Press (1994), Jaroszeski et al. (eds.), Flow Cytometry Protocols, Methods in Molecular Biology No. 91, Humana Press (1997) and Practical Shapiro, Flow Cytometry, 4th ed., Wiley-Liss (2003), each of which is hereby incorporated by reference into the present specification. Fluorescence image microscopy is described, for example, in Pawley (ed.), Handbook of Biological Confocal Microscopy, 2nd Edition, Plenum Press (1989), which is hereby incorporated by reference into the present specification.

[0038] Data obtained from the analysis of cells (or other particles) by multi-color flow cytometry is multi-dimensional, where in this case each cell corresponds to a single point in a multi-dimensional space defined by the measured parameters. A population of cells or particles can be identified as a population of points in the data space. Identification of the population, and thereby the population can be performed by manually drawing a gate around the population displayed in one or more two-dimensional plots, also called a "scatter plot" or "dot plot" of the data. Alternatively, the population can be identified and the gate defining the limits of the population can be determined automatically. Examples of methods of automated gating are already described, for example, in U.S. Patent Nos. 4,845,653, 5,627,040, 5,739,000, 5,795,727, 5,962,238, 6,014,904, 6,944,338 and 8,990,047, each of which is hereby incorporated by reference into the present specification.

[0039] Flow cytometry is a useful method for the analysis and separation of biological particles such as cells and constituent molecules. Therefore, it has various diagnostic and therapeutic applications. The method uses a fluid flow to linearly separate the particles so that they can pass through a detection device in a single file. Individual cells can be identified according to their position in the fluid flow and the presence of detectable markers. Therefore, a flow cytometer can be used to generate a description of the diagnostic profile characteristics of a population of biological particles.

[0040] The separation of biological particles is achieved by adding sorting or collection capabilities to a flow cytometer. Particles in the separated stream that are detected as having one or more desired characteristics can be separated individually from the sample stream by mechanical or electrical separation. This method of flow sorting has already been used to distinguish different types of cells, to study sperm behavior for animal breeding, to distinguish X and Y chromosomes, to sort chromosomes for genetic analysis, and to separate specific organisms from complex biological populations.

[0041] The gating method can be used to assist in sorting and elucidating the large amount of data that can be generated from a sample. Given the large amount of data presented for a given sample, there is a need to efficiently control the graphic display of the data.

[0042] Fluorescence-activated particle sorting or cell sorting is a specialized type of flow cytometry. Fluorescence-activated particle sorting or cell sorting can perform a method of sorting a heterogeneous mixture of particles, one cell at a time, into one or more containers based on specific light scattering and the fluorescence characteristics of each cell. It records the fluorescence signal from individual cells and physically separates the cells of interest. The abbreviation FACS is trademarked and owned by Becton, Dickinson and Company (Franklin Lakes, NJ) and can be used to refer to an apparatus that performs fluorescence-activated particle sorting or cell sorting.

[0043] The particle suspension is placed near the center of a narrow and rapidly flowing liquid stream. The stream is generally configured such that there is a large spacing between the particles relative to their particle size when the particles probabilistically reach the detection region (e.g., a Poisson process). The vibration mechanism can stably separate the emerging fluid stream into individual droplets containing particles that have already been characterized in the detection region. The system can generally be adjusted such that it is unlikely that more than one particle is in a single droplet. When the particles are sorted to be collected, a charge can be applied to the flow cell and the emerging stream while one or more droplets are formed and then separated from the stream. These charged droplets then move through an electrostatic deflection system that directs the droplets to a target container based on the charge applied to the droplets.

[0044] The sample can contain thousands, if not millions, of cells. The cells can be sorted to purify the sample into the cells of interest. The sorting process can generally distinguish between three types of cells, namely, the cells of interest, the cells that are not of interest, and the cells that cannot be identified. To sort the cells to a high purity (e.g., a high concentration of the cells of interest), the droplet generation cell sorter can electronically abort the sorting if the desired cells are too close to another unwanted cell, thereby reducing the contamination of the sorted population due to inadvertent inclusion of unwanted particles within the droplets containing the cells of interest.

[0045] Disclosed herein are embodiments of a collection system for classifying a plurality of particles of a sample. In some embodiments, the collection system includes a non-transitory storage device configured to store executable instructions and a hardware processor or a processor of a virtual machine that exchanges information with the non-transitory storage device, and the hardware processor is programmed by the executable instructions to: (1) transmit measurement values of parameters of a first plurality of particles of a sample related to an experiment collected by a particle analyzer and (2) first related experiment information of the experiment in a first format to an analysis system (e.g., a SaaS system). The hardware processor can be programmed by the executable instructions to receive, from the analysis system, second related experiment information of the experiment in the first format. The analysis system is programmed to receive a reference classification criterion for a first plurality of particles of a sample related to the experiment and to generate a gating strategy based on: (1) the measurement values of the parameters of the first plurality of particles of the sample related to the experiment and (2) the reference classification criterion for the first plurality of particles of the sample related to the experiment, and to generate second related experiment information of the experiment including the first related experiment information of the experiment and the gating strategy in the first format (or an explanation of the gating strategy). Alternatively or additionally, the received second related experiment information is generated by the analysis system by receiving a reference classification criterion for a first plurality of particles of a sample related to the experiment and generating a gating strategy based on: (1) the measurement values of the parameters of the first plurality of particles of the sample related to the experiment and (2) the reference classification criterion for the first plurality of particles of the sample related to the experiment, and generating second related experiment information of the experiment including the first related experiment information of the experiment and the gating strategy in the first format.Alternatively or additionally, the received second relevant experimental information indicates that the analysis system received a reference classification criterion for a first plurality of particles of a sample related to the experiment, (1) based on the measured values of the parameters of the first plurality of particles of the sample related to the experiment, and (2) generated a gating strategy based on the reference classification criterion for the first plurality of particles of the sample related to the experiment, and generated second relevant experimental information of the experiment including the first relevant experimental information of the experiment and the gating strategy in the first format (or an explanation of the gating strategy). The hardware processor can be programmed by executable instructions to represent the gating strategy and transmit the particle analyzer configuration generated from the second relevant experimental information of the experiment to the particle analyzer.

[0046] Disclosed herein are embodiments of an analysis system (e.g., a SaaS system) that generates a gating strategy. The analysis system can include a non-transitory storage device configured to store executable instructions, and a hardware processor or a processor of a virtual machine that exchanges information with the non-transitory storage device. The hardware processor is programmed by the executable instructions to receive (1) measurement values of parameters of a first plurality of particles of a sample related to an experiment, and (2) first related experiment information of the experiment in a first format, from a collection system that exchanges information with a particle analyzer. The hardware processor can be programmed by the executable instructions to receive a reference classification criterion for the case of a first plurality of particles of a sample related to the experiment. The hardware processor is programmed by the executable instructions to generate a gating strategy (1) based on measurement values of parameters of a first plurality of particles of a sample related to the experiment, and (2) based on the reference classification criterion for the case of a first plurality of particles of a sample related to the experiment. The hardware processor is programmed by the executable instructions to generate second related experiment information of the experiment, including the first related experiment information of the experiment and the gating strategy in the first format (or an explanation of the gating strategy). The hardware processor is programmed by the executable instructions to transmit the second related experiment information of the experiment to the collection system.

[0047] Disclosed herein are embodiments of a method for classifying a plurality of particles of a sample. In some embodiments, the method includes receiving, under the control of a hardware processor or a processor of a virtual machine, (1) measurements of parameters of a first plurality of particles of a sample relevant to an experiment collected by a particle analyzer, and (2) first relevant experimental information of an experiment in a first format. The method can include receiving a reference classification criterion for the case of a first plurality of particles of a sample relevant to the experiment. The method can include generating a gating strategy (1) based on measurements of parameters of a first plurality of particles of a sample relevant to the experiment and (2) based on a reference classification criterion for the case of a first plurality of particles of a sample relevant to the experiment. The method can include generating second relevant experimental information of the experiment including the first relevant experimental information of the experiment and a gating strategy in the first format (or an explanation of the gating strategy). The method can include causing the particle analyzer to collect measurements of parameters of a second plurality of particles of a sample relevant to the experiment, representing the gating strategy, and at least partially based on a particle analyzer configuration generated from the second relevant experimental information of the experiment. Definitions

[0048] The terms described in detail below, when used in this specification, have the following definitions. Unless otherwise defined in this chapter, all terms used in this specification generally have the meanings understood by those skilled in the art related to this invention.

[0049] As used in this specification, "system", "instrument", "apparatus", "device" generally include both (e.g., mechanical and electronic) hardware components and, in some embodiments, related software (e.g., a dedicated computer program for graphics control) components.

[0050] As used herein, "event" or "event data" generally refers to data (e.g., packets of assembled data) measured from a single particle such as a cell or synthetic cell. Typically, data measured from a single particle includes a number of parameters or features including one or more light scattering parameters or features and at least one other parameter or feature derived from fluorescence detected from the particle, such as fluorescence intensity. Thus, each event can be represented as a vector consisting of parameter and feature extraction, where each measured parameter or feature corresponds to a one-dimensionality of the data space. In some embodiments, data measured from a single particle includes image, electrical, time, or acoustic data. An event can be associated with a sample source that can be identified in relation to an experiment, test, or measurement data.

[0051] As used herein, "population" or "subpopulation" of particles such as cells or other particles generally refers to a group of particles having characteristics (e.g., optical characteristics, impedance characteristics, or temporal characteristics) regarding one or more measured parameters such that the measured parameter data forms a cluster within the data space. Thus, a population can be recognized as a cluster within the data. Conversely, each data population is generally interpreted as corresponding to a population consisting of a particular type of cell or particle, although populations corresponding to noise or background are also typically observed. A population can be defined in a subset of dimensions, e.g., with respect to a subset of measured parameters, which corresponds to populations that differ only in a subset of the measured parameters or features extracted from the measured values of the cells or particles.

[0052] As used in this specification, "gate" generally refers to a classifier boundary that identifies a subset of the data of interest. In cytometry, a gate can delimit a group of specific target events. As used in this specification, "gating" generally refers to the process of classifying data using a predetermined gate for a given data set, where the gate can be one or more regions of interest combined with Boolean logic.

[0053] Specific examples of various embodiments and systems that are implemented will be further described below. Sorting control system

[0054] FIG. 1 shows a functional block diagram of an example of a sorting control system, such as an analysis control device 100 that analyzes and displays biological events. The analysis control device 100 can be configured to perform various processes for controlling the graphic display of biological events.

[0055] The particle analyzer or sorting system 102 can be configured to acquire biological event data. For example, a flow cytometer can generate flow cytometry event data. The particle analyzer 102 can be configured to provide biological event data to the analysis control device 100. A data communication channel can be included between the particle analyzer 102 and the analysis control device 100. The biological event data can be supplied to the analysis control device 100 via the data communication channel.

[0056] The analysis control device 100 can be configured to receive biological event data from the particle analyzer 102. The biological event data received from the particle analyzer 102 can include flow cytometry event data. The analysis control device 100 can be configured to supply a graphic display including a first plot of the biological event data to the display device 106. The analysis control device 100 can further be configured to set a target region, for example, as a gate around a population of biological event data indicated by the display device 106 and superimposed on the first plot. In some embodiments, the gate can be a logical combination of one or more graphic regions of interest drawn on a single-parameter histogram or a two-variable plot.

[0057] The analysis control device 100 can further be configured to display the biological event data on the display device 106 within a gate that is different from other events in the biological event data outside the gate. For example, the analysis control device 100 can be configured to make the color of the biological event data contained within the gate different from the color of the biological event data outside the gate. The display device 106 can be implemented as a monitor, a tablet computer, a smartphone, or other electronic device configured to present a graphic interface.

[0058] The analysis control device 100 can be configured to receive a gate selection signal for identifying a gate from a first input device. For example, the first input device can be implemented as a mouse 110. The mouse 110 can activate a gate selection signal for the analysis control device 100 to identify the gate so as to be displayed on the display device 106 (e.g., by activating or clicking the desired gate when the cursor is located thereon) or to be operated via the display device. In some embodiments, the first device can be implemented as a keyboard 108 or as other means for providing an input signal to the analysis control device 100, such as a touch screen, a touch pen, a light detector, or a voice recognition system. Some input devices can include multiple input functions. In such embodiments, each input function can be regarded as an input device. For example, as shown in FIG. 1, the mouse 110 can include a right mouse button and a left mouse button, each of which can generate a trigger event.

[0059] The trigger event can cause the analysis control device 100 to change the way data is displayed, with portions of the data being actually displayed on the display device 106 and / or to provide an input for further processing such as the selection of a population of objects for particle classification.

[0060] In some embodiments, the analysis control device 100 can be configured to detect that the gate selection has been activated by the mouse 110. The analysis control device 100 can further be configured to automatically modify the visualization of the plot to facilitate the gating process. The modification can be based on the clear distribution of the biological event data received by the analysis control device 100.

[0061] The analysis control device 100 can be connected to the storage device 104. The storage device 104 can be configured to receive and store biological event data from the analysis control device 100. Further, the storage device 104 can also be configured to receive and store flow cytometry event data from the analysis control device 100. The storage device 104 can further be configured to enable the analysis control device 100 to search for biological event data such as flow cytometry event data.

[0062] The display device 106 can be configured to receive display data from the analysis control device 100. The display data can include a plot of biological event data and a gate that depicts the contour of the area of the plot. The display device 106 can further be configured to change the information presented according to the input received from the analysis control device 100, together with the inputs from the particle analyzer 102, the storage device 104, the keyboard 108, and / or the mouse 110.

[0063] In some embodiments, the analysis control device 100 can generate a user interface for receiving events of examples to be classified. For example, the user interface can include a control unit for receiving an event of an example or an image of an example. The event of an example or an image or a gate of an example can be generated before the collection of event data for the sample, or based on an initial set of events in the case of a part of the sample. Particle sorter system

[0064] A conventional flow sorting method, which can be called "electrostatic cell sorting", uses droplet sorting in which a stream or flowing fluid column containing linearly separated particles becomes droplet-like, and droplets containing the target particles are charged and deflected into a collection tube by a passage through an electric field. The droplet sorting system is capable of forming droplets at a rate of 100,000 droplets per second in a fluid flow passing through a nozzle having a diameter of less than 100 micrometers. Droplet sorting typically requires that the droplets separate from the flow at a certain distance from the tip of the nozzle. The distance is usually on the order of a few millimeters from the tip of the nozzle, and the tip of the nozzle can be vibrated at a predetermined frequency having an amplitude for keeping the separation constant, so as to be stabilized and maintained for a non-perturbed fluid flow. For example, in some embodiments, adjusting the amplitude of a sinusoidal voltage pulse at a predetermined frequency keeps the separation stable and constant.

[0065] Typically, the particles entrained linearly in the stream are characterized as they pass through a flow cell or cuvette, or an observation point located directly below the tip of the nozzle. Once a particle is identified as meeting one or more desired criteria, it is possible to predict the time at which it will separate from the stream into droplets in response to the droplet separation point. Ideally, the droplet containing the selected particle separates from the stream, and then a short-term charge is applied to the fluid flow immediately before the droplet falls immediately after separation. The droplets to be sorted maintain their charge as they separate from the fluid flow, and all other droplets remain uncharged. The charged droplets are deflected laterally from the downward trajectory of the other droplets by the electric field and collected into the sample tube. The uncharged droplets fall directly into the drain.

[0066] FIG. 2A is a schematic diagram of a particle sorter system 200 (e.g., particle analyzer 102) according to one embodiment presented in the present specification. In some embodiments, the particle sorter system 200 is a cell sorter system. As shown in FIG. 2A, a droplet generation transducer 202 (e.g., piezoelectric oscillator) is coupled to a fluid conduit 201 such as a nozzle. Within the fluid conduit 201, a sheath fluid 204 hydrodynamically focuses a sample fluid 206 into a moving fluid column 208 (e.g., a stream). Within the moving fluid column 208, particles (e.g., cells) are secured in a single file to cross a monitor region 210 that is irradiated by an irradiation source 212 (e.g., a laser) (e.g., where a laser stream intersects). The vibration of the droplet generation transducer 202 separates the moving fluid column 208 into a plurality of droplets 209.

[0067] During operation, a detection station 214 (e.g., an event detector) identifies that a target particle (or a target cell) has crossed the monitor region 210. The detection station 214 is coupled to a timing circuit 228, which then is coupled to a flash charge circuit 230. At a droplet break-off point, known by a timed drop delay (Δt), a flash charge can be applied to the moving fluid column 208 such that a target droplet carries a charge. The target droplet can contain one or more particles or cells to be sorted. Thereafter, the charged droplet can be sorted by activating a deflection plate (not shown) to deflect the droplet into a container such as a collection tube or a multi-well or microwell sample plate, where the well or microwell can be associated with a particular target droplet. As shown in FIG. 2A, the droplets can be collected in a drain container 238.

[0068] The detection system 216 (e.g., a droplet interface detector) operates to automatically determine the phase of the droplet drive signal when a target particle passes through the monitor region 210. An exemplary droplet interface detector is described in U.S. Patent No. 7,679,039, which is hereby incorporated by reference in its entirety. The detection system 216 enables the instrument to accurately calculate the location of each detected particle within the droplet. The detection system 216 can be coupled to the amplitude signal 220 and / or the phase signal 218, which can then enter (via amplifier 222) the amplitude control circuit 226 and / or the frequency control circuit 224. The amplitude control circuit 226 and / or the frequency control circuit 224 then controls the droplet generation transducer 202. The amplitude control circuit 226 and / or the frequency control circuit 224 can be included in one control system.

[0069] In some embodiments, the sorting electronics (e.g., detection system 216, detection station 214, and processor 240) can be coupled to a storage device configured to store detected events and sorting decisions based thereon. The sorting decisions can be included in the event data for the particles. In some embodiments, the detection system 216 and the detection station 214 can be implemented as a single detection unit such that event measurements can be collected by one of the detection system 216 or the detection station 214 and supplied to non-collecting elements, and can be coupled to exchange information.

[0070] Figure 2B is a schematic diagram of a particle sorter system according to one embodiment presented in the specification of the present application. The particle sorter system 200 shown in Figure 2B includes deflection plates 252 and 254. Charge can be applied through a stream charging wire within barb 256. This generates a stream 260 of particles for analysis. The particles can be illuminated with one or more light sources (e.g., a laser) to generate light scattering and fluorescence information. The particle information is analyzed, for example, by a sorting electronic device or other detection system (not shown in Figure 2B). The deflection plates 252 and 254 can be independently controlled to attract or not attract charged droplets and guide the droplets towards a desired collection container (e.g., one of reference numerals 272, 274, 276, or 278). As shown in Figure 2B, the deflection plates 252 and 254 can be controlled to direct the particles along a first path 262 towards container 274 or along a second path 268 towards container 278. If the particles are not of interest (e.g., do not exhibit scattering or illumination information within a particular sort range), the deflection plates can be made to allow the particles to continue along flow path 264. Such uncharged droplets can flow into a waste container, for example, via aspirator 270.

[0071] The sorting electronic device can be included to start collecting measurements, receive the fluorescence signal of the particles, and determine how to adjust the deflection plates to effect sorting of the particles. An example implementation of the embodiment shown in Figure 2B includes the BD FACSAria (trademark) line of flow cytometers commercially available from Becton, Dickinson and Company (Franklin Lakes, NJ).

[0072] In some embodiments, one or more of the components described in the context of the particle sorter system 200 can be used to analyze and characterize particles, whether or not they physically sort the particles into a collection container. Similarly, one or more of the components described below in the context of the particle analysis system 300 (FIG. 3) can be used to analyze and characterize particles, whether or not they physically sort the particles into a collection container. For example, particles can be sorted or displayed in a tree-like manner that includes at least three groups as described herein using one or more components of the particle sorter system 200 or the particle analysis system 300.

[0073] FIG. 3 shows a functional block diagram of a particle analysis system for computer-based sample analysis and particle characterization. In some embodiments, the particle analysis system 300 is a flow system. The particle analysis system 300 shown in FIG. 3 can be configured to perform all or part of the methods described herein, for example. The particle analysis system 300 includes a fluidics system 302. The fluidics system 302 can include or be coupled to a sample tube 310 and a dynamic fluid column within the sample tube through which sample particles 330 (e.g., cells) move along a common sample path 320.

[0074] The particle analysis system 300 includes a detection system 304 configured to collect signals from each particle as the particle passes through one or more detection stations along the common sample path. The detection station 308 generally refers to a monitoring region 340 of the common sample path. In some embodiments, detection includes detecting the light or one or more other properties of the particle 330 as the particle passes through the monitoring region 340. FIG. 3 shows one detection station 308 having one monitoring region 340. Some embodiments of the particle analysis system 300 can include multiple detection stations. Further, some detection stations can monitor one or more regions.

[0075] Each signal is assigned a signal value for constructing the data points of each particle. As described above, this data can be called event data. The data points can be multi-dimensional data points including the values of the respective characteristics measured for one particle. The detection system 304 is configured to collect a series of such data points in a first time interval.

[0076] The particle analysis system 300 can also include a control system 306. The control system 306 can include one or more processors and an amplitude control circuit 226 and / or a frequency control circuit 224 as shown in FIG. 2B. The illustrated control system 206 can be operatively coupled to the fluidics system 302. The control system 206 can be configured to generate a signal frequency calculated during at least a part of the first interval based on the Poisson distribution and the number of data points collected by the detection system 304 during the first time interval. The control system 306 can further be configured to generate a test signal frequency based on the number of data points in a portion of the first time interval. The control system 306 can additionally compare the test signal frequency with the calculated signal frequency or a predetermined signal frequency. Sorting Configuration Generation System

[0077] FIG. 4 is a schematic diagram showing an event selection system according to an embodiment for dynamically identifying event data to generate a sorting configuration, for example. The event selection system 400 includes a selection device 420. The selection device 420 includes an event data receiver 422. The event data receiver 422 can receive event data 402 from a particle analyzer such as the particle analyzer 102 shown in FIG. 1. In some embodiments, the event data 402 can be generated by a particle analyzer, for example, by receiving it from an analysis workstation. For example, a user can supply the event data 402 obtained from the particle analyzer to the event data receiver 422. The event data receiver 422 can include a transceiver for wireless communication or a port for connecting to a wired network such as an Ethernet local area network or a device via, for example, a Universal Serial Bus or a THUNDERBOLT (registered trademark) connection.

[0078] The event data receiver 422 can supply at least a part of the event data 402 to an event data processor 424 included in the selection device 420. The event data processor 424 can identify a conversion to apply to the event data. The identification can include detecting a value in the event data such as an identifier for an assay or experiment. The available conversions can be stored in a data store 440 accessible to the event data processor 424. The conversion of the event data executed by the event data processor 424 can be a parametric or non-parametric conversion. In some embodiments, the conversion can be specified by the device that supplies the event data 402. For example, an analysis workstation can submit a message requesting processing of the event data 402. The message can include a desired conversion (e.g., tSNE). In some embodiments, event data processing can be guided based on user input. For example, a user can identify a conversion to apply to the received event data.

[0079] The selection device 420 can include a gate selection unit 426. The gate selection unit 426 can receive a selection of a target event from an input device. The selection can be referred to as a gate. The selection can define one or more ranges of parameter values of the target event. The one or more ranges can be used by the gate selection unit 426 to generate a classifier or other sorting configuration 490. The sorting configuration 490 can be represented as a truth table or decision tree for identifying those events associated with the gate. As described herein, manual acquisition of a gate can be error-prone and, in some cases, can miss important parameters or potential hardware bottlenecks or efficiencies. Thus, the initial selection can be used as an example of a desired sort that can be adapted through further processing without manual intervention to optimize the search method.

[0080] FIG. 5 is a diagram showing an example of a system for adaptively generating a sorting configuration. The adaptive sorting device 520 in FIG. 5 includes a shaping configuration for receiving a selection from a selection device such as the selection device 420 and then generating an optimized search method for the events to be evaluated.

[0081] The system 500 includes an adaptive sorting device 520. The adaptive sorting device 520 includes an event data receiver 522. The event data receiver 522 can receive event data 502 from a particle analyzer such as the particle analyzer 102 shown in FIG. 1. In some embodiments, the event data 502 can be generated by a particle analyzer, for example, by receiving it from an analysis workstation. For example, a user can supply the event data 502 obtained from the particle analyzer to the event data receiver 522. The event data receiver 522 can include a wireless communication transceiver or a network port connected to a wired network such as an Ethernet local area network.

[0082] The event data receiver 522 can supply at least a part of the event data 502 to the selection device 420. The selection device 420 can obtain the sorting configuration of the embodiment from the user. The embodiment can be supplied to the sorting strategy optimizer 524 together with the received event data 502. The sorting strategy optimizer 524 can iteratively generate a sorting strategy that approximates the sorting configuration of the embodiment. The sorting strategy can include a dynamic pipeline of event data transformation or selection. The available transformation or sorting steps can be stored in the data store 514 accessible to the sorting strategy optimizer 524. The candidate strategy includes automatically detecting a population and scoring each event against related target and off-target populations (computer sorting). The strategy can include an approximation of a (non-parametric or parametric) transformation using a neural network or other machine learning method. The strategy can include projecting a target population from a non-sortable parameter space to a sortable space. As part of projecting the target population, the system can identify computable parameters that approximate a combination of non-easily computable parameters. The strategy can include, for example, automatically extracting features from an image or high-dimensional measurements including time-series waveform data. One example of automatic feature extraction is an autoencoder neural network for learning relevant image features used in sorting decisions.

[0083] In some embodiments, the neural network can be used for both feature generation and gating strategy generation. For feature generation, a neural network that receives raw image data as the input and output measure of what the image "looks like". Another neural network, such as a neural network that receives the calculated parameters (e.g., the area or height of a pulse) as new input and output parameters. These new parameters can perform a projection from the original parameter space to a new parameter space. For gating strategy generation, it can be used in sorting decisions, can be trained to generate sorting decisions, or a neural network that receives various parameters as a single input and output value for use. Sorting Configuration Generation Method

[0084] FIG. 6 is a process flow diagram showing an example of a method for adaptively generating an optimized sorting strategy. Method 600 can be implemented in whole or in part by an adaptive sorting device, such as the adaptive sorting device 520 shown in FIG. 5.

[0085] Method 600 begins at block 602. At block 610, initial event data for some cases of the sample can be received. The event data can be collected after operating a particle analyzer to process the portion of the sample. Processing the sample can include measuring the characteristics of the particles, such as graphic characteristics, electrical characteristics, time characteristics, or acoustic characteristics. In some embodiments, the collection of the initial event data can be omitted, and method 600 can proceed from block 602 to block 620. This may be desirable in cases where the sample size is small. In such cases, the sorting configuration can evaluate the sample without consuming any of the sample in order to maintain the amount of sample available for classification.

[0086] In block 620, a sort selection of the sample is received from the communication device. The sort selection can represent an example of a population of events to be classified. The sort selection can include a gate, an image of an example, or an event of an example. The sort selection can be identified for the event data of the first portion of the sample received in block 610. For example, a researcher can draw a polygon on a graph of event data measurements to define a range of data values to be classified. The polygon can be used as a sorting criterion or can define a gate that can be associated with the sorting criterion.

[0087] The sort selection can act as a criterion for generating an optimized sort configuration. In some embodiments, the sort selection can include information identifying the sorting device or sorting electronics of interest. As discussed, different hardware can have different capabilities for implementing a sort configuration. The identity of the device can be considered so that the sort configuration is reliably adapted to the hardware of interest.

[0088] In block 630, candidate event classifiers for the sample can be identified. The identification can include selecting an event classifier from a data store. The event classifier can include a neural network model, an event data transformation, an autoencoder, or other machine-implemented elements for evaluating event data. The selection can be based in part on the type of sample (e.g., blood, urine, tissue, etc.). The selection can be based in part on the particle analyzer that will be used to process the sample. The selection can be based in part on the sort selection received from the communication device. For example, if the sort selection includes an image, a graphics-based classifier can be selected as a candidate event classifier. As another example, if the distribution of events in the sort selection features is statistically a normal distribution, it may be desirable to use a Mahalanobis-based classifier.

[0089] In some embodiments, the user interface can be provided to gather information specifying which classifiers to include in the candidate event classifiers. In some embodiments, the system can consider a set of parameters and classifiers defined in a data store. The classifiers can be filtered, for example, based on how the events of the sample were identified by the user. When a classifier is identified, the history identification information can be used to identify a common workflow that bundles together the classifier and parameters that are jointly selected for an experiment, the target particle, the particle analyzer that generates the data, or other detectable characteristics of the experiment or the data generated during the experiment.

[0090] In block 640, a sorting strategy can be generated using one or more candidate event classifiers. Generating a sorting strategy includes prioritizing one or more candidate event classifiers to form a pipeline for processing event data. Generating a sorting strategy can include informing how different classifiers / parameters should be combined using the user's example gating strategy. For example, if the user reached the population of their examples using hierarchical gates, we can use a similar hierarchical structure. Another example is when the user uses a transformed space at any point in judging the data of their examples, and the system can detect the transformation and generate an approximation of the transformation for the sorting strategy. Yet another example is to attempt to detect multiple hierarchical gates, draw a manifold in a high-dimensional space, and employ tools such as relationships that maintain a statistical model such as a transformation or Mahalanobis distance. When a sorting strategy is identified, the history strategy information can be used to identify a common workflow that prioritizes specific identifiers for an experiment, the target particle, the particle analyzer that generates the data, or other detectable characteristics of the experiment or the data generated during the experiment.

[0091] The sorting strategy generated by block 640 can be evaluated using a metric. In block 650, a metric indicating the accuracy of the sorting strategy is generated. The metric can represent the accuracy (e.g., purity) of the events classified from the samples. The metric can be generated based on the reliability of the classifiers included in the sorting strategy. In some embodiments, the metric can be generated based on a comparison of the sorting selection with the sorting configuration generated using the sorting strategy. One way to generate a metric is to use the F-measure for the sorting strategy, where precision indicates the level of sorting purity and recall indicates the amount or yield of the classified samples. For example, the events of the examples provided by the user can be divided into a "training" subset and a "validation" subset. The division can be based on a pseudo-random selection of a portion of the events. The training subset can be used to train a number of candidate gating strategies. In that case, those gating strategies can be used to evaluate the validation subset. The results of this validation will be used to generate the F-measure.

[0092] In block 660, a determination is made as to whether the metric of the sorting strategy corresponds to a threshold. The threshold can be a predetermined configuration value indicating the minimum purity or yield of the sorting strategy. If the determination in block 660 is affirmative, the generated sorting strategy can be considered appropriate for the sample. In such a case, method 600 proceeds to block 670.

[0093] In block 670, a sorting electronic device (e.g., a sorting circuit) can be configured using the sorting strategy generated in block 640. Configuring the sorting electronic device can include storing a model or transformation included in the strategy in a storage location accessible to the sorting electronic device. In that case, the sorting strategy can be used to process event data for evaluation against the sorting criteria included in the sorting strategy.

[0094] In block 680, the analyzer can evaluate and classify the remaining portion of the sample using the configured sorting electronics. When new event measurements are collected, the measurements can be processed in real time using the configured sorting electronics and classified into the designated containers according to the sorting configuration. For example, the deflection plates of the particle analyzer can be actuated to direct the target particles into the designated collection tubes.

[0095] Method 600 can end at block 690. However, it will be correctly recognized that method 600 can be repeated for additional events, samples, or experiments. In some embodiments, it may be desirable to generate a new sorting strategy to adapt to some change in the sample or to reveal changes within the same source. For example, in a therapeutic setting, a biological sample can be collected during the administration of a drug or other compound. The sorting strategy may need to be adjusted to reveal the presence of the drug or compound once administered or if there are unexpected variations from the selection of the first instance used to guide the generation of the adaptation strategy. In such cases, since the original search method was trained, the sorting strategy can be regenerated based in part on the data collected. For example, the first strategy may be able to identify a normal distribution of events, but the actual event data collected for the sample may indicate events with a non-normal distribution. Determination of Sorting Gates

[0096] Disclosed herein are software tools that enable a user to collect data from a sample to be classified and analyze the data within the software, or workflows that use an analysis tool to analyze flow cytometry data, such as FlowJo® (Ashford, OR), to determine a gating strategy and / or to collect data from a sample. For example, the method can include collecting data (e.g., parameter measurements of a portion of the sample) using a flow cytometer (e.g., within an experimental file), performing an analysis on the data, and converting the results of the analysis into a package (e.g., another experimental file) that can be understood by the flow cytometer so that the flow cytometer can collect additional data (e.g., parameter measurements of some or all of the remaining sample).

[0097] FIG. 7A shows a non-limiting, exemplary workflow for determining and using an improved (e.g., optimized) sorting gate. In some embodiments, the workflow can include identifying one or more populations to be classified (e.g., receiving user input regarding one or more populations to be classified), generating a gating strategy (e.g., an optimal gating strategy), and importing the gating strategy into acquisition software (e.g., FACSDiva™ (Becton, Dickinson and Company (Franklin Lakes, N.J.))) for sorting. In some embodiments, initial data for an experiment (e.g., data for a very small portion of the sample) can be collected using cytometer system acquisition software. The initial data can be analyzed within flow cytometry data analysis software or tools (e.g., FlowJo®) by appropriate means such as manual gating, cluster analysis, or other computational techniques. The workflow or a portion thereof can be implemented as a software component of software for analyzing flow cytometry data, such as FlowJo®.

[0098] In some embodiments, the identified population (e.g., a population of target cells) can be used within the workspace of flow cytometry data analysis software (e.g., FlowJo®) as a training set to computationally create a set of gates (e.g., an optimal set) for defining each desired population. The gates can be refined using an F - measure (e.g., the harmonic mean of purity and yield) that allows for optimal purity and yield. To find a good (e.g., the best) set of parameters for creating the gates with respect thereto, combinations of two parameters can be analyzed at a time. The boundaries of the gates can be refined using one or more techniques, such as bi - stable k - means clustering and Voronoi tessellation. The resulting gates can be loaded into the flow cytometry data analysis software.

[0099] In some embodiments, a copy of the original flow cytometry experimental data (e.g., from a BD FACSDiva™ experiment) using computationally derived gates from flow cytometry data analysis software (e.g., FlowJo®) added as a new worksheet to the original flow cytometry experimental data. The new worksheet can include plots that display a new gating strategy as well as a new gating hierarchy from the flow cytometry data analysis software. The new gating hierarchy becomes available when the user selects which populations to classify.

[0100] In some embodiments, the workflow can include performing computationally complex analysis of a cell population and classifying cells using the results from the analysis. The workflow or a portion thereof can be implemented by flow cytometry analysis software. Gates used to classify cells can be defined manually by a user drawing graphic regions on a data plot. Using computational techniques, the number of gates required to define a population can be reduced by determining and using a smaller set of parameters. By using a smaller set of parameters and gates, the number of comparisons that need to be performed on a sorter when determining cell classifications during sorting can be reduced. In some embodiments, gate boundaries that more accurately fit the shape of a population can be drawn computationally.

[0101] In some embodiments, the workflow can include determining (e.g., optimizing) the number of gates (e.g., the required number of gates) to define a population. For example, the F-measure resulting from gates drawn for different sets of parameters can be used to determine the number of gates. In some embodiments, the workflow can include generating an improved gate shape (e.g., determining an optimal polygon gate shape) for each bivariate gate. For example, generating an improved gate shape can include over-clustering the data using the bistable k-means method, partitioning the data space according to the data density using Voronoi tessellation, and constructing the gate boundaries using the boundaries of the resulting partitions. In some embodiments, the workflow can include importing the number of gates for defining a population and the improved gate shape into flow cytometry data analysis software. In some embodiments, a software plugin can determine the number of gates for defining a population and the gate shape. For example, a FlowJo® plugin can perform gate optimization from one or more populations identified by a user, and the resulting gates can be exported, for example, using another FlowJo® plugin, to a Diva experiment that can be used by a cell sorter.

[0102] In some embodiments, flow cytometry data (e.g., FACSDiva™ data) can be imported into R (or another programming or scripting language), gate optimization can be performed within R, the resulting gates can be viewed by plotting, and those gates can be created within cell sorter software (e.g., FACSDiva™ software) for use in sorting.

[0103] In some embodiments, the workflow uses the F-scale, clustering method, FACSDiva™ software experiment memory format, data space partitioning, FlowJo® code structure and plugin API and / or the data scaling function in FACSDiva™ and FlowJo®. Sorting gate determination system

[0104] FIG. 8 is an interaction diagram showing a non-limiting exemplary method of classifying particles. A collection system 804 (e.g., a particle analyzer system, a particle analyzer control system, the sorting control system described in connection with FIG. 1, the control system 306 described in connection with FIG. 3, and a computing system 1100) can be operably coupled to a particle analyzer 808 (e.g., the particle analyzer 102 described in connection with FIG. 1, and the particle sorter system 200 described in connection with FIGS. 2A and 2B, etc.). In some embodiments, the collection system 804 can comprise the particle analyzer 808. The collection system 804 can instruct or cause the particle analyzer 808 to receive flow cytometry event data, and to receive flow cytometry event data collected from the particle analyzer 808. For example, a collection program (e.g., flow cytometry data collection software or program such as FACSDiva (trademark) (Becton, Dickinson and Company (Franklin Lakes, NJ))) stored as executable instructions in a non-transitory storage device of the collection system 804 can instruct the particle analyzer 808 to collect flow cytometry event data. The collection system 804 can communicate information with an analysis system 812 (e.g., the analysis control unit 100 described in connection with FIG. 3, and the computing system 1100). In some embodiments, the collection system 804 can be a SaaS system or can implement a SaaS system. The collection system 804 can transmit the received flow cytometry event data to the analysis system 812 for display and analysis. For example, analysis software (e.g., flow cytometry data analysis software or program (e.g., FlowJo (registered trademark))) stored as executable instructions in a non-transitory storage device of the analysis system 812 can display the flow cytometry event data to a user.

[0105] In interaction 816, the collection system 804 can send a training particle analyzer configuration to the particle analyzer 816 instructing it to collect measurements of parameters of a first plurality of particles of the sample. For example, the collection system 804 can send a training particle analyzer configuration to the particle analyzer 816 instructing it to collect measurements of parameters of a first plurality of particles of the sample via a collection and control software package. Thereafter, the analysis system 812 can use the measurements of the parameters collected by the particle analyzer 808 as a training set for determining an improved (e.g., optimized) gating strategy.

[0106] In interaction 820, the particle analyzer 816 can collect measurements of parameters of a first plurality of particles of the sample. The measurements of the parameters of the first plurality of particles of the sample include measurements of light fluorescently emitted by the first plurality of particles. The light fluorescently emitted by the first plurality of particles can include light fluorescently emitted by a cell component binder bound to the first plurality of particles.

[0107] In interaction 824, the collection system 804 can receive measurements of parameters of a first plurality of particles of the sample related to the experiment from the particle analyzer 808. In some embodiments, the collection system 804 can receive first relevant experimental information of the experiment in a first format from the particle analyzer 808. The first relevant experimental information in the first format can include an identifier of the particle analyzer.

[0108] In interaction 828, the collection system 804 can send to the analysis system 812 (1) measurements of parameters of a first plurality of particles of a sample related to an experiment collected by a particle analyzer, and (2) first relevant experiment information of an experiment in a first format. The first relevant experiment information in the first format can include an identifier of the collection system. For example, the collection system 804 can send the experimental file of the collection and a control software package (e.g., a FACSDiva (trademark) experimental file). The experimental file can include (1) measurements of parameters of a first plurality of particles of a sample related to an experiment collected by a particle analyzer, and (2) first relevant experiment information of the experiment in the file format of the experiment (e.g., the file format of FACSDiva (trademark)).

[0109] In interaction 832, the analysis system 812 can receive a reference classification criterion for a first plurality of particles of a sample related to an experiment. The reference classification criterion can include a first plurality of target particles selected from the first plurality of particles. To receive the reference classification criterion for the first plurality of particles of the sample, the analysis system 812 can receive a selection of the first plurality of target particles from the first plurality of target particles. To receive the selection of the first plurality of target particles, the analysis system 812 can display a plurality of images corresponding to measurements of parameters of the first plurality of particles of a sample related to the experiment. In some embodiments, the reference classification criterion can include gating information that identifies a plurality of values of parameters of the first plurality of particles of the sample to distinguish the first plurality of target particles from the remaining particles of the first plurality of particles.

[0110] In interaction 836, the analysis system 812 can generate a gating strategy. For example, the analysis system 812 can generate a gating strategy based on (1) the measured values of the parameters of the first plurality of particles of the sample related to the experiment and (2) the reference classification criteria for the first plurality of particles of the sample related to the experiment. For example, the analysis system 812 can generate a gating strategy using the method 600 described in relation to FIG. 6. In some embodiments, to generate a gating strategy, the analysis system 812 uses a gating method (e.g., a gating method implemented by an analysis tool such as a gate discovery plugin implemented by FlowJo®) based on (1) the measured values of the parameters of the first plurality of particles of the sample related to the experiment and (2) the reference classification criteria for the first plurality of particles of the sample related to the experiment. The gating strategy can include a plurality of gates corresponding to a set of parameters.

[0111] To generate a gating strategy, the analysis system 812 can determine the number of the plurality of gates based on a strategy for distinguishing the first plurality of particles of the object from the remaining particles of the first plurality of particles using the gating strategy. The plurality of gates can correspond to some or all of the parameters. The analysis system 812 can generate a plurality of polygons based on the measured values of the parameters of the first plurality of particles of the sample, the classification criteria, and the plurality of gates. At least two of the plurality of polygons can include polygons of different shapes. The number of the plurality of gates and the number of the plurality of polygons can be the same. The strategy can include a purity measurement and a yield measurement for distinguishing the first plurality of particles of the object from the remaining particles of the first plurality of particles using the gating strategy. To generate a plurality of polygons, the analysis system 812 can generate a plurality of polygons surrounding some or all of the plurality of particles of the object.

[0112] In interaction 840, the analysis system 812 can generate second relevant experimental information for the experiment, including the first relevant experimental information of the experiment and a gating strategy (or an explanation of the gating strategy) in a first format. The file can include the second relevant experimental information. The second relevant experimental information can be in a format readable by the collection system 804 (e.g., the first format). The second relevant experimental information can include a gating method used to determine the gating strategy, one or more input parameters of the gating method, one or more output parameters of the gating method, and / or an analysis tool used to determine the gating strategy. The collection system 804 can, for example, after generating a particle analyzer configuration as described below, use the second relevant experimental information to collect measurement values of some or all of the remaining parameters of the sample particles. The explanation of the gating strategy in the second relevant experimental information can be regarded as a conversion of the gating strategy in a format (e.g., the first format) usable by the collection system 804 for collecting measurement values of some or all of the remaining parameters of the sample particles.

[0113] Alternatively, or additionally, the analysis system 812 can generate a particle analyzer configuration that includes the first relevant experimental information and represents a gating strategy (e.g., a gating strategy or an explanation of the gating strategy). The particle analyzer configuration can include a gating method used to determine the gating strategy, one or more input parameters of the gating method, one or more output parameters of the gating method, and / or an analysis tool used to determine the gating method.

[0114] To generate second related experimental information, the analysis system 812 can generate a file consisting of the first related experimental information, a gating strategy, the second related experimental information, a gating method for generating the gating strategy, one or more input parameters of the gating method, one or more output parameters of the gating method, and / or an analysis tool for implementing the gating method. For example, the analysis system 812 can generate a file or document readable by a collection and control software package (e.g., the collection and control software package of the analysis system 812). The file can include information related to the determined gating strategy and / or measured parameter values of a first plurality of particles of an experiment in a machine-readable format. The information related to the gating strategy can include the gating strategy and information related to the gating method used to determine the gating strategy. The information related to the gating method can include information about the analysis tool used to generate the gating strategy, such as the gating method used (e.g., tSNE) and parameters of the gating method used (e.g., the number of clusters). The file can be an experimental file of the collection and control software package (e.g., a FACSDiva (trademark) experimental file). The file is readable by the collection and control software. The file, (1) the measured values of the parameters of the first plurality of particles of the sample related to the experiment collected by the particle analyzer, and (2) the first related experimental information of the experiment received by the analysis system 812 in the interaction 828 can be in the same format (e.g., the first format) or different formats.

[0115] The acquisition and control software can gate some or all of the remaining particles of the sample classified by the particle analyzer 808 using the experimental file. For example, the acquisition and control software of the acquisition system 804 can cause the particle analyzer 808 to collect measurements of some or all of the remaining particles of the sample in interaction 856 in interaction 852. In some embodiments, the acquisition and control software can cause the particle analyzer 808 to directly collect measurements of some or all of the remaining particles of the sample. Alternatively, or additionally, the acquisition and control software can generate a configuration file for the particle analyzer 808 and use the configuration file to cause the particle analyzer 808 to collect measurements of some or all of the remaining particles of the sample.

[0116] To generate second relevant experimental information, the analysis system is programmed to use an exporter plugin to generate second relevant experimental information of the experiment, including first relevant experimental information of the experiment and a gating strategy in a first format.

[0117] In interaction 844, the acquisition system 804 can receive an indication that a gating strategy has already been generated. For example, the acquisition system 804 can receive second relevant experimental information of the experiment in a first format from the analysis system 812. The second relevant experimental information received by the acquisition system 804 indicates that the analysis system 812 received a reference classification criterion for a first plurality of particles of the sample related to the experiment, and based on (1) measurements of parameters of the first plurality of particles of the sample related to the experiment and (2) the reference classification criterion for the first plurality of particles of the sample related to the experiment, generated a gating strategy, and generated second relevant experimental information of the experiment including first relevant experimental information of the experiment and a gating strategy in a first format.

[0118] In interaction 848, the collection system 804 can generate a particle analyzer configuration for the particle analyzer that represents the gating strategy determined by the analysis system 812. In interaction 852, the collection system 804 can send a particle analyzer configuration that represents the gating strategy and is generated from the second relevant experimental information of the experiment to the particle analyzer 808. In interaction 856, the particle analyzer 808 can collect measurements of parameters of a second plurality of particles of a sample related to the experiment (e.g., some or all of the remaining particles of the sample after the first plurality of particles were used to collect measurements of the parameters used as a training set) at least partially based on the particle analyzer configuration. The measurements of the parameters of the second plurality of particles of the sample include measurements of the light fluoresced by the second plurality of particles. The light fluoresced by the second plurality of particles can include the light fluoresced by a cell component binder bound to the second plurality of particles. The parameters of the first plurality of particles and the parameters of the second plurality of particles can be made the same or different.

[0119] In interaction 860, the particle analyzer 808 can send the measurements of the parameters of the second plurality of particles of the collected sample to the collection system 804. In some embodiments, the measurements of the parameters of the second plurality of particles of the collected sample can be analyzed, for example, by the analysis system 812.

[0120] FIG. 9 is an interaction diagram showing another non-limiting exemplary embodiment of classifying particles. The collection system 804 can be operably coupled to or can comprise the particle analyzer 808. In interaction 816, the collection system 804 can send a particle analyzer configuration that instructs the particle analyzer 816 to collect measurements of parameters of a first plurality of particles of a sample. In interaction 820, the particle analyzer 816 can collect measurements of the parameters of the first plurality of particles of the sample.

[0121] In interaction 824, the collection system 804 can receive from the particle analyzer 808 measurements of parameters of a first plurality of particles of a sample related to an experiment. For example, the collection system 804 can receive from the particle analyzer first relevant experimental information in a second format. In interaction 926, the collection system 804 can generate from the first relevant experimental information in the second format (e.g., using a plug-in of a collection program or an independent conversion program) first relevant experimental information in a first format. The first relevant experimental information in the second format can include an identifier of the particle analyzer, in which case the first relevant experimental information in the first format can include an identifier of a different particle analyzer.

[0122] In interaction 828, the collection system 804 can send to the analysis system 812 (1) measurements of parameters of a first plurality of particles of a sample related to an experiment collected by the particle analyzer, and (2) first relevant experimental information in a first format. In some embodiments, the collection system 804 can be or can implement a SaaS system. In interaction 832, the analysis system 812 can receive a reference classification criterion for a first plurality of particles of a sample related to an experiment. In interaction 836, the analysis system 812 can generate a gating strategy based on (1) measurements of parameters of a first plurality of particles of a sample related to an experiment, and (2) the reference classification criterion for the first plurality of particles of a sample related to the experiment.

[0123] The analysis system 812 can determine in interaction 930 that the first relevant experimental information is in the first format before receiving the reference classification criterion in interaction 832 and / or before generating the gating strategy in interaction 836. For example, the analysis system 812 can generate a gating strategy when the measurements of the parameters are collected by a specific particle analyzer, a specific type of particle analyzer, or a specific brand of particle analyzer.

[0124] In interaction 840, the analysis system 812 can generate second relevant experimental information for the experiment, including the first relevant experimental information of the experiment and a gating strategy in a first format. In interaction 844, the collection system 804 can receive an indication that the gating strategy has already been generated. For example, the collection system 804 can receive the second relevant experimental information of the experiment from the analysis system 812 in the first format.

[0125] In interaction 946, the collection system 804 can generate second relevant experimental information in a second format from the second relevant experimental information in the first format. The second relevant experimental information in the second format can include an identifier of a particle analyzer, in which case the second relevant experimental information in the first format includes an identifier of another particle analyzer.

[0126] In interaction 848, the collection system 804 can generate a particle analyzer configuration for the particle analyzer, representing the gating strategy determined by the analysis system 812. In interaction 852, the collection system 804 can transmit the particle analyzer configuration, which represents the gating strategy and is generated from the second relevant experimental information in the second format, to the particle analyzer 808. In interaction 856, the particle analyzer 808 can collect measurement values of parameters of a second plurality of particles of a sample related to the experiment, at least partially based on the particle analyzer configuration. In interaction 860, the particle analyzer 808 can transmit the measurement values of the parameters of the second plurality of particles of the collected sample to the collection system 804. In some embodiments, the measurement values of the parameters of the second plurality of particles of the collected sample can be analyzed, for example, by the analysis system 812.

[0127] Figures 8 and 9 show embodiments of the collection system 804 and the analysis system 812 as two different systems, but these embodiments are merely illustrative and not intended to be limiting. In some embodiments, a single system (e.g., system 1100) can perform the collection functions of the collection system 804 and the analysis functions of the analysis system 812. For example, a single system can instruct a particle analyzer 808 (e.g., via a collection program) to collect flow cytometry event data, and can determine a gating strategy based on the collected flow cytometry event data (e.g., via a plug-in of an analysis program). Sorting gate determination method

[0128] Figure 10 is a flowchart showing an exemplary method 1000 for classifying a plurality of particles of a sample. The method 1000 can be embodied as a set of executable program instructions stored on a computer-readable medium such as one or more disk drives of a computing system. For example, the computing system 1100 shown in Figure 11 and described in detail below can execute a set of executable program instructions for implementing the method 1000. When the method 1000 is started, the executable program instructions can be loaded into a storage device such as RAM and executed by one or more processors of the computing system 1100. The method 1000 is described in relation to the computing system 1100 shown in Figure 11, but this description is merely illustrative and not intended to be limiting. In some embodiments, the method 1000 or a portion thereof can be executed by a number of computing systems sequentially or in parallel.

[0129] After method 1000 starts at block 1004, method 1000 proceeds to block 1008, where the computing system (e.g., the collection system) receives (1) measurements of parameters of a first plurality of particles of a sample related to the experiment collected by a particle analyzer and (2) first relevant experimental information of an experiment in a first format. In some embodiments, the measurements of parameters of the first plurality of particles of the sample include measurements of light fluoresced by the first plurality of particles. The light fluoresced by the first plurality of particles can include light fluoresced by a cell component binder, such as an antibody, bound to the first plurality of particles.

[0130] In some embodiments, the method can include transmitting (1) measurements of parameters of a first plurality of particles of a sample related to the experiment and (2) first relevant experimental information of an experiment in a first format from (e.g., from a collection system) to an analysis system.

[0131] Method 1000 proceeds from block 1008 to block 1012, where the computing system (e.g., the analysis system) receives a reference classification criterion for the case of a first plurality of particles of a sample related to the experiment. In some embodiments, the reference classification criterion includes a first plurality of particles of a target selected from the first plurality of particles. In some embodiments, the reference classification criterion can include gating information that identifies a plurality of values of parameters of the first plurality of particles of the sample for distinguishing the first plurality of particles of the target from the remaining particles of the first plurality of particles.

[0132] In some embodiments, receiving a reference classification criterion for the case of a first plurality of particles of the sample includes receiving a selection of the first plurality of particles of the target from the first plurality of particles of the target. Receiving a selection of the first plurality of particles of the target can include (e.g., by an analysis system) displaying a plurality of images corresponding to measurements of parameters of the first plurality of particles of a sample related to the experiment.

[0133] In some embodiments, receiving a reference classification criterion includes receiving, by an analysis system, a reference classification criterion for a first plurality of particles of a sample related to an experiment.

[0134] In block 1012, after receiving the reference classification criterion, method 1000 proceeds to block 1016 where a computing system (e.g., an analysis system) generates a gating strategy based on (1) measured values of parameters of a first plurality of particles of a sample related to an experiment and (2) the reference classification criterion for the first plurality of particles of the sample related to the experiment. In some embodiments, generating a gating strategy includes using a gate discovery plugin to generate a gating strategy based on (1) measured values of parameters of a first plurality of particles of a sample related to an experiment and (2) the reference classification criterion for the first plurality of particles of the sample related to the experiment.

[0135] In some embodiments, the gating strategy includes a plurality of gates corresponding to sets of parameters. Generating the gating strategy can include using the gating strategy to determine the number of the plurality of gates based on a strategy for distinguishing a first plurality of particles of an object from the remaining particles of the first plurality of particles. The plurality of gates can correspond to some or all of the parameters. The strategy can include a purity measurement and a yield measurement for distinguishing a first plurality of particles of an object from the remaining particles of the first plurality of particles using the gating strategy. Generating the gating strategy can include generating a plurality of polygons based on measured values of parameters of a first plurality of particles of a sample, a classification criterion, and the plurality of gates. At least two of the plurality of polygons can include polygons of different shapes. The number of the plurality of gates and the number of the plurality of polygons can be the same. Generating the plurality of polygons can include generating a plurality of polygons enclosing some or all of the plurality of particles of the object.

[0136] In some embodiments, generating a gating strategy includes using an analysis system to generate a gating strategy based on (1) measured values of parameters of a first plurality of particles of a sample related to an experiment and (2) a reference classification criterion for the case of the first plurality of particles of the sample related to the experiment.

[0137] Method 1000 proceeds from block 1016 to block 1020, where a computing system (e.g., a collection system) generates second relevant experimental information for the experiment, including the first relevant experimental information for the experiment and a gating strategy in a first format. In some embodiments, generating the second relevant experimental information can include using an exporter plugin to generate the second relevant experimental information for the experiment, including the first relevant experimental information for the experiment and the gating strategy in the first format.

[0138] In some embodiments, generating the second relevant experimental information can include using an analysis system to generate the second relevant experimental information for the experiment, including the first relevant experimental information for the experiment and the gating strategy in the first format.

[0139] At block 1020, after generating the second relevant experimental information, method 1000 proceeds to block 1024, where the computing system (e.g., the collection system) causes a particle analyzer to collect measured values of parameters of a second plurality of particles of a sample related to the experiment, representing the gating strategy and at least partially based on a particle analyzer configuration generated from the second relevant experimental information for the experiment.

[0140] In some embodiments, the measured values of parameters of the second plurality of particles of the sample include measured values of light fluoresced by the second plurality of particles. The light fluoresced by the second plurality of particles can include light fluoresced by a cell component binder, e.g., an antibody, bound to the second plurality of particles.

[0141] The method can include generating a particle analyzer configuration representing a gating strategy from second relevant experimental information of the experiment (e.g., by a collection system). In some embodiments, the method can include transmitting the particle analyzer configuration to a particle analyzer (e.g., from a collection system).

[0142] (Format) In some embodiments, the method includes receiving first relevant experimental information in a second format (e.g., a file format generated by an analysis system for a particle analyzer). The method can include generating first relevant experimental information in a first format (e.g., a file format generated by an analysis system for another particle analyzer) from first relevant experimental information in the second format (e.g., using a software program, tool, or plugin). The method can include generating second relevant experimental information in the second format from second relevant experimental information in the first format (e.g., using a software program, tool, or plugin). The first relevant experimental information in the second format can include an identifier of the particle analyzer and / or identification information of the collection system of the particle analyzer, and the first relevant experimental information in the first format can include an identifier of another particle analyzer and / or identification information of the collection of the other particle analyzer. The second relevant experimental information in the second format can include an identifier of the particle analyzer and / or identification information of the collection system of the particle analyzer, and the second relevant experimental information in the first format can include an identifier of another particle analyzer and / or identification information of the collection of the other particle analyzer. The method can include determining that the first relevant experimental information is in the first format. Determining that the first relevant experimental information is in the first format can include determining by the analysis system that the first relevant experimental information is in the first format.

[0143] In some embodiments, the first relevant experimental information in the first format includes an identifier of a particle analyzer. The first relevant experimental information in the first format can include an identifier of a collection system. Method 1000 ends at block 1028. Execution environment

[0144] FIG. 11 shows the overall structure of a computing device 1100 of an example configured to implement the metabolite annotation and gene insertion systems disclosed herein. The overall structure of the computing device 1100 shown in FIG. 11 includes a configuration consisting of computer hardware components and software components. The computing device 1100 may include more (or fewer) elements than those shown in FIG. 11. However, in order to provide a disclosure of an implementable degree, it is not necessarily the case that all of these generally conventional elements are illustrated. As shown, the computing device 1100 includes a processing unit 1110, a network interface 1120, a computer-readable media drive 1130, an input / output device interface 1140, a display 1150, and an input device 1160, all of which can exchange information with each other via a communication bus. The network interface 1120 can provide connectivity to one or more networks or computing systems. Thus, the processing unit 1110 can receive information and instructions via the network from other computing systems or services. The processing unit 1110 can also communicate with, or from, a storage device 1170, and can further supply output information for any display 1150 via the input / output device interface 1140. The input / output device interface 1140 can also receive inputs from any input device 1160 such as a keyboard, mouse, digital pen, microphone, touch screen, gesture recognition system, voice recognition system, game pad, accelerometer, gyroscope, or other input device.

[0145] The memory device 1170 can include computer program instructions (grouped as modules or components in some embodiments) that are executed by the processing unit 1110 to implement one or more embodiments. The memory device 1170 generally includes RAM, ROM, and / or other persistent, auxiliary, or non-transitory computer-readable media. The memory device 1170 can store an operating system 1172 that supplies computer program instructions for use by the processing unit 1110 in the overall management and operation of the computing device 1100. The memory device 1170 can further include computer program instructions for implementing aspects of the present disclosure and other information.

[0146] For example, in one embodiment, the memory device 1170 includes a collection module 1174 for receiving flow cytometry event data such as measurements of sample particle parameters (e.g., for collection by a particle analyzer). The memory device 1170 can additionally or alternatively include an analysis module 1176 for determining a gating strategy from the flow cytometry event data. Further, the memory device 1170 can include a data store 1190 and / or one or more other data stores for storing the collected flow cytometry event data and / or the determined gating strategy, or can communicate such data store information. The term

[0147] The term "determine" or "determining" as used in this specification encompasses various operations. For example, "determining" may include calculating, computing, processing, deriving, investigating, searching (e.g., searching a table, database, or other data structure), verifying, etc. Also, "determine" may include receiving (e.g., receiving information), accessing (e.g., accessing data in a storage device), etc. Further, "determine" may include deciding, selecting, choosing, ascertaining, etc.

[0148] The term "provide" or "providing" as used in this specification encompasses various operations. For example, "provide" may include storing a value in a storage location of a storage device for later retrieval, directly transmitting a value to a recipient via at least one wired or wireless communication medium, transmitting or storing a criterion for a value, etc. Also, "provide" may include encoding, decoding, encrypting, decrypting, authenticating, verifying, etc. via a hardware element.

[0149] The term "selectively" or "selective" as used in this specification encompasses various operations. For example, a "selective" process may include determining one option from a plurality of options. A "selective" process may include one or more of dynamically determined inputs, preconfigured inputs, or user-initiated inputs to perform a determination. In some implementations, an n-input switch can be made to provide a selective function, where n is the number of inputs used to perform the selection.

[0150] As used in this specification, the term "message" encompasses various formats for communicating information (e.g., transmitting or receiving). A message may include a machine-readable collection of information such as an XML document, a fixed-field message, a comma-separated message, etc. In some implementations, a message includes a signal used to transmit one or more descriptions of information. Although described in the singular, it will be understood that a message may be composed, transmitted, stored, received, etc. in multiple parts.

[0151] As used in this specification, a "user interface" (also referred to as an interactive user interface, a graphical user interface, or a UI) can refer to a network-based interface that includes data fields, buttons, or other interactive controls for receiving an input signal, generating electronic information, or providing information to a user in response to any received input signal. The UI can be implemented in whole or in part using techniques such as HTML (hyper-text mark-up language), JAVASCRIPT (registered trademark), FLASH (registered trademark), JAVA (registered trademark),.NET (trademark), WINDOWS OS (registered trademark), macOS (trademark), web services, or RSS (rich site summary). In some embodiments, the UI can be included in a stand-alone client (e.g., a thin client, a fat client) configured to communicate information (e.g., transmit or receive data) according to one or more of the described embodiments.

[0152] "Data store", as used in this specification, can be embodied in a hard disk drive, a solid state storage device, and / or any other type of non-transitory computer-readable storage medium that is accessible to or accessible by an access device, a server, or other computing device such as those described. Further or alternatively, the data store may be distributed or partitioned across many local and / or remote storage devices known in the art without departing from the scope of the present disclosure. In yet other embodiments, the data store may be included in or embodied as a data storage web service.

[0153] One of ordinary skill in the art will appreciate that information, messages, and signals can be represented using any of a variety of different techniques and technologies. For example, data, instructions, commands, information, signals, bits, symbols, and chips that can be referenced throughout the above description can be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof.

[0154] One of ordinary skill in the art will further correctly recognize that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments disclosed in this specification can be implemented as electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been generally described above in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. One of ordinary skill in the art may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be construed as causing a departure from the scope of the present invention.

[0155] The technology described in this specification can be implemented in hardware, software, firmware, or any combination thereof. Such technology can be implemented in various devices such as a specially programmed event processing computer, a wireless communication device, or an integrated circuit device. Any form configuration described as a module or component can be implemented together within an integrated logic device or independently as discrete but mutually usable logic devices. When implemented in software, the technology can be at least partially realized by a computer-readable storage medium comprising program code that executes one or more of the methods described above during execution. The computer-readable storage medium can form part of a computer program product that may include a packaging material. The computer-readable medium may consist of a storage device or data storage medium such as a random access memory (RAM) like synchronous dynamic random access memory (synchronous DRAM), read-only memory (ROM), non-volatile random access memory (NVRAM), electrically erasable programmable read-only memory (EEPROM), FLASH memory, magnetic or optical data storage media, etc. The computer-readable medium may be a non-transitory storage medium. The technology can further or alternatively be at least partially realized by a computer-readable communication medium that carries the program code in the form of instructions or data structures, or transmits the program code, and that is accessible, readable, and / or executable by a computing device such as a propagated signal or a propagated wave.

[0156] The program code can be executed by a specially programmed sorting strategy processor, which may include one or more processors such as one or more digital signal processors (DSPs), configurable microprocessors, application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), or other equivalent integrated or discrete logic circuits. Such a graphics processor can be specially configured to execute any of the techniques described in this disclosure. Combinations of computing devices, such as combinations of DSPs and microprocessors, multiple microprocessors, one or more microprocessors coupled to a DSP, or any other such configuration in at least partial data connectivity, can implement one or more of the described configurations. In some aspects, the functions described herein can be executed within dedicated software modules or hardware modules configured for encoding and decoding, or can be incorporated into a dedicated sorting control card.

[0157] Notwithstanding the appended claims, the present disclosure is also defined by the following items.

[0158] 1. A collection system for classifying a plurality of particles of a sample, a non-transitory memory configured to store executable instructions, a hardware processor in communication with the non-transitory memory and comprising, the hardware processor being programmed by the executable instructions to (1) transmit to an analysis system measurements of parameters of a first plurality of particles of a sample related to an experiment collected by a particle analyzer and (2) first associated experiment information of the experiment in a first format, the analysis system being Receive a reference classification criterion for the first plurality of particles of the sample related to the experiment, and be programmed to generate a gating strategy based on (1) the measured values of the parameters of the first plurality of particles of the sample related to the experiment and (2) the reference classification criterion for the first plurality of particles of the sample related to the experiment.

[0159] 2. The analysis system is programmed to generate second related experiment information of the experiment consisting of the first related experiment information of the experiment and the gating strategy in the first format, and the hardware processor, by the executable instructions, receives the second related experiment information of the experiment in the first format from the analysis system, and is programmed to transmit to the particle analyzer a particle analyzer configuration representing the gating strategy and generated from the second related experiment information of the experiment. The collection system according to item 1.

[0160] 3. The analysis system generates a particle analyzer configuration including the first related experiment information and representing the gating strategy in the first format, and is programmed to transmit the particle analyzer configuration to the collection system, and the hardware processor, by the executable instructions, is programmed to receive the particle analyzer configuration. The collection system according to item 1.

[0161] 4. A collection system for classifying a plurality of particles of a sample, a non - transient memory configured to store executable instructions and a hardware processor communicating with the non - transient memory, comprising and the hardware processor, by the executable instructions, (1) Measurement values of parameters of a first plurality of particles of a sample related to an experiment collected by a particle analyzer, and (2) first associated experiment information of the experiment in a first format are transmitted to an analysis system, which is programmed to: The analysis system: Receives a reference classification criterion for a first plurality of particles of a sample related to the experiment, and generates a gating strategy based on (1) measurement values of parameters of the first plurality of particles of the sample related to the experiment and (2) the reference classification criterion for the first plurality of particles of the sample related to the experiment, and is programmed to receive an indicator indicating that the gating strategy has been generated.

[0162] 5. The analysis system: Is programmed to generate second associated experiment information of the experiment, including the first associated experiment information of the experiment and the gating strategy in the first format, The indicator indicating that the gating strategy has been generated includes the second associated experiment information of the experiment, The hardware processor, by the executable instructions, Represents the gating strategy and is programmed to transmit a particle analyzer configuration generated from the second associated experiment information of the experiment to the particle analyzer, the collection system according to item 4.

[0163] 6. The analysis system: Is programmed to generate a particle analyzer configuration representing the gating strategy and constituting the first associated experiment information, The indicator indicating that the gating strategy has been generated includes the particle analyzer configuration, The hardware processor, by the executable instructions, Is programmed to transmit the particle analyzer configuration to the particle analyzer, the collection system according to item 4.

[0164] 7. A collection system for classifying a plurality of particles of a sample, comprising: A non-transitory memory configured to store executable instructions, A hardware processor that communicates with the non-transitory memory, and comprises the hardware processor is programmed by the executable instructions to (1) send to an analysis system measurements of parameters of a first plurality of particles of a sample related to an experiment collected by a particle analyzer, and (2) first experiment information related to the experiment in a first format, be programmed to receive, from the analysis system, second experiment information related to the experiment in the first format, The analysis system receives a reference classification criterion for a first plurality of particles of a sample related to the experiment, generates a gating strategy based on (1) measurements of parameters of a first plurality of particles of a sample related to the experiment and (2) the reference classification criterion for the first plurality of particles of the sample related to the experiment, generates second experiment information of the experiment, comprising the first related experiment information of the experiment and the gating strategy in the first format, is programmed to represent the gating strategy and transmit a particle analyzer configuration generated from the second experiment information of the experiment to the particle analyzer.

[0165] 8. The collection system according to any one of items 1 - 7, wherein, in order to generate a gating strategy, the analysis system is programmed to use a gating method implemented by an analysis tool to generate a gating strategy based on (1) measurements of parameters of a first plurality of particles of a sample related to an experiment and (2) the reference classification criterion for the first plurality of particles of a sample related to the experiment.

[0166] 9. The collection system according to item 8, wherein the second related experiment information includes a gating method, one or more input parameters of the gating method, one or more output parameters of the gating method, and / or an analysis tool.

[0167] 10. The particle analyzer configuration includes a gating method, one or more input parameters of the gating method, one or more output parameters of the gating method, and / or an analysis tool, for the collection system according to item 8 or item 9.

[0168] 11. To generate second related experimental information, the analysis system is programmed to generate a file including first related experimental information, a gating strategy, second related experimental information, a gating method for generating the gating strategy, one or more input parameters of the gating method, one or more output parameters of the gating method, and / or an analysis tool for implementing the gating method, for the collection system according to any one of items 1 to 10.

[0169] 12. The gating strategy includes a plurality of gates corresponding to the pairs of the parameters, for the collection system according to any one of items 1 to 11.

[0170] 13. The reference classification criteria includes a first plurality of particles of interest selected from a first plurality of particles, for the collection system according to item 12.

[0171] 14. To generate a gating strategy, the analysis system uses a gating strategy to determine the number of a plurality of gates corresponding to some or all of the parameters based on a scale for distinguishing a first plurality of particles of interest from the remaining particles of the first plurality of particles, and is programmed to generate a plurality of polygons based on the measured values of the parameters of the first plurality of particles of the sample, the classification criteria, and the plurality of gates. At least two of the plurality of polygons are composed of polygons of different shapes, and the number of the plurality of gates is the same as the number of the plurality of polygons, for the collection system according to item 13.

[0172] 15. The collection system according to item 14, wherein the measured values include a purity measurement value and a yield measurement value for distinguishing the first plurality of particles of interest from the remaining particles of the first plurality of particles using the gating strategy.

[0173] 16. The collection system according to item 14 or item 15, wherein the analysis system is programmed to generate a plurality of polygons that enclose some or all of the plurality of particles of interest.

[0174] 17. The collection system according to any one of items 1 to 16, wherein the hardware processor is programmed by executable instructions to receive measurement values of parameters of a first plurality of particles of a sample related to the experiment from a particle analyzer.

[0175] 18. The hardware processor, by the executable instructions, receives first related experiment information in a second format from the particle analyzer, generates first related experiment information in the first format from the first related experiment information in the second format, is programmed to generate second related experiment information in the second format from second related experiment information in the first format, and to transmit a particle analyzer configuration representing a gating strategy to the particle analyzer, the hardware processor, by the executable instructions, transmits to the particle analyzer a particle analyzer configuration representing the gating strategy and generated from the second related experiment information in the second format. The collection system according to any one of items 1 to 17.

[0176] 19. The collection system according to item 18, wherein the first related experiment information in the second format includes an identifier of the particle analyzer, and the first related experiment information in the first format includes an identifier of another particle analyzer.

[0177] 20. The second related experimental information of the second format includes the identifier of the particle analyzer, and the second related experimental information of the first format includes the identifier of another particle analyzer. The collection system according to item 18.

[0178] 21. The collection system according to any one of items 18 to 20, wherein the analysis system is programmed to determine that the first related experimental information is in the first format before generating the gating strategy.

[0179] 22. The collection system according to any one of items 1 to 21, wherein the first related experimental information of the first format includes the identifier of the particle analyzer.

[0180] 23. The collection system according to any one of items 1 to 22, wherein the first related experimental information of the first format includes the identifier of the collection system.

[0181] 24. The hardware processor, by the executable instructions, represents the gating strategy, generates a particle analyzer configuration for the particle analyzer, and is programmed to cause the particle analyzer to collect measurement values of parameters of a second plurality of particles of a sample related to the experiment, at least partially based on the particle analyzer configuration. The collection system according to any one of items 1 to 23.

[0182] 25. The reference classification criteria includes gating information that identifies a plurality of values of parameters of a first plurality of particles of a sample for distinguishing a first plurality of particles of interest from the remaining particles of the first plurality of particles. The collection system according to any one of items 1 to 24.

[0183] 26. The collection system according to any one of items 1 to 25, wherein the analysis system is programmed as follows to receive the reference classification criteria of the first plurality of particles of the sample: receive a selection of a first plurality of particles of interest from the first plurality of particles.

[0184] 27. To receive the selection of the first plurality of particles of interest, the analysis system is programmed to display a plurality of images corresponding to measured values of parameters of the first plurality of particles of a sample related to the experiment, the collection system according to item 26.

[0185] 28. The measured values of the parameters of the first plurality of particles of the sample include measured values of light fluoresced by the first plurality of particles, the collection system according to any one of items 1 to 27.

[0186] 29. The light fluoresced by the first plurality of particles includes light fluoresced by a cell component binding reagent bound to the first plurality of particles, the collection system according to item 28.

[0187] 30. An analysis system for generating a gating strategy, A non-transitory memory configured to store executable instructions, A hardware processor communicating with the non-transitory memory Comprising, The hardware processor, by the executable instructions, Receives measured values of parameters of a first plurality of particles of a sample related to an experiment and first related experiment information of the experiment in a first format from a collection system communicating with a particle analyzer, Receives a reference classification criterion for a first plurality of particles of a sample related to the experiment, Is programmed to generate a gating strategy based on (1) measured values of parameters of a first plurality of particles of a sample associated with the experiment and (2) a reference classification criterion for the first plurality of particles of the sample associated with the experiment.

[0188] 31. The hardware processor, by the executable instructions, Generate second related experimental information of the experiment, including the first related experimental information of the experiment and the gating strategy in the first format. The analysis system according to item 30, which is programmed to transmit the second related experimental information of the experiment to the collection system.

[0189] 32. The hardware processor, by the executable instructions, Generate a particle analyzer configuration including the first related experimental information and representing the gating strategy, and is programmed to transmit the particle analyzer configuration to the collection system. The collection system is programmed to receive the configuration of the particle analyzer, and the analysis system according to item 30.

[0190] 33. An analysis system for generating a gating strategy, A non - temporary memory configured to store executable instructions, A hardware processor communicating with the non - temporary memory And comprising, The hardware processor, by the executable instructions, Receive measurement values of parameters of a first plurality of particles of a sample related to an experiment and first related experimental information of the experiment in the first format from a collection system communicating with a particle analyzer, Receive a reference classification criterion for a first plurality of particles of a sample related to the experiment, Generate a gating strategy based on (1) measurement values of parameters of a first plurality of particles of a sample associated with the experiment and (2) a reference classification criterion for the first plurality of particles of the sample associated with the experiment, And is programmed to transmit an indicator indicating that the gating strategy has been generated to the collection system.

[0191] 34. The hardware processor, by the executable instructions, Generate second related experimental information of the experiment, including the first related experimental information of the experiment and the gating strategy in the first format. Programmed to send the second related experimental information of the experiment to a collection system, An analysis system according to item 33, including the second related experimental information, wherein an indicator indicating that a gating strategy has been generated is provided.

[0192] 35. The hardware processor, by the executable instructions, Generates a particle analyzer configuration representing the gating strategy from the first related experimental information, Sends the particle analyzer configuration to the collection system, An analysis system according to item 33, including the particle analyzer configuration, wherein an indicator indicating that a gating strategy has been generated is provided.

[0193] 36. An analysis system for generating a gating strategy, comprising: A non-transitory memory configured to store executable instructions; A hardware processor communicating with the non-transitory memory, And, The hardware processor, by the executable instructions, Receives measurement values of parameters of a first plurality of particles of a sample related to an experiment and first related experimental information of the experiment in a first format from a collection system communicating with a particle analyzer, Receives a reference classification criterion for a first plurality of particles of a sample related to the experiment, Generates a gating strategy based on (1) measurement values of parameters of a first plurality of particles of a sample associated with the experiment and (2) a reference classification criterion for a first plurality of particles of a sample associated with the experiment, Generates second related experimental information of the experiment, including the first related experimental information of the experiment and the gating strategy in the first format, And is programmed to send the second related experimental information of the experiment to a collection system.

[0194] 37. To generate the gating strategy, the hardware processor is programmed by the executable instructions to generate a gating strategy based on (1) measured values of parameters of a first plurality of particles of a sample associated with an experiment and (2) a reference classification criterion of the first plurality of particles of the sample associated with the experiment, using a gating method of an analysis tool. The analysis system according to any one of items 30 to 36.

[0195] 38. The second related experiment information includes a gating method, one or more input parameters of the gating method, one or more output parameters of the gating method, and / or an analysis tool. The analysis system according to item 37.

[0196] 39. The particle analyzer configuration includes a gating method, one or more input parameters of the gating method, one or more output parameters of the gating method, and / or an analysis tool. The analysis system according to item 37 or item 38.

[0197] 40. To generate the second related experiment information, the hardware processor is programmed by the executable instructions to generate a file including first related experiment information, a gating strategy, second related experiment information, a gating method for generating the gating strategy, one or more input parameters of the gating method, one or more output parameters of the gating method, and / or an analysis tool implementing the gating method. The analysis system according to any one of items 30 to 39.

[0198] 41. The gating strategy includes a plurality of gates corresponding to the pairs of parameters. The analysis system according to any one of items 30 to 40.

[0199] 42. The reference classification criterion includes a first plurality of particles of interest selected from the first plurality of particles. The analysis system according to item 38.

[0200] 43. To generate the gating strategy, the hardware processor, by the executable instructions, using the gating strategy, determines the number of a plurality of gates corresponding to some or all of the parameters based on a scale that distinguishes a first plurality of particles of interest from the remaining particles of the first plurality of particles, is programmed to generate a plurality of polygons based on the measured values of the parameters of the first plurality of particles of the sample, the classification criteria, and the plurality of gates, The analysis system according to item 42, wherein at least two of the plurality of polygons are composed of polygons of different shapes, and the number of the plurality of gates is the same as the number of the plurality of polygons.

[0201] 44. The analysis system according to item 43, wherein the measured values include a purity measurement value and a yield measurement value that distinguish the first plurality of particles of interest from the remaining particles of the first plurality of particles using the gating strategy.

[0202] 45. The analysis system according to any one of items 43 to 44, wherein to generate a plurality of polygons, the hardware processor is programmed by the executable instructions to generate a plurality of polygons surrounding some or all of the plurality of particles of interest.

[0203] 46. The collection system is programmed to generate first relevant experimental information in a first format from a second format and second relevant experimental information in the second format from the first format, the analysis system according to any one of items 30 to 45.

[0204] 47. The analysis system according to item 46, wherein the first relevant experimental information in the second format includes an identifier of a particle analyzer, and the first relevant experimental information in the first format includes an identifier of another particle analyzer.

[0205] 48. The second related experimental information of the second format includes the identifier of the particle analyzer, and the second related experimental information of the first format includes the identifier of another particle analyzer. The analysis system according to item 46.

[0206] 49. The hardware processor is programmed by the executable instructions to determine that the first related experimental information is in the first format before generating the gating strategy. The analysis system according to any one of items 46 to 48.

[0207] 50. The first related experimental information of the first format includes the identifier of the particle analyzer. The analysis system according to any one of items 30 to 49.

[0208] 51. The first related experimental information of the first format includes the identifier of the collection system. The analysis system according to any one of items 30 to 50.

[0209] 52. The collection system is programmed to generate a particle analyzer configuration for the particle analyzer representing the gating strategy and transmit the particle analyzer configuration to the particle analyzer. The particle analyzer is programmed to collect measurement values of parameters of a second plurality of particles of the sample related to the experiment based at least in part on the particle analyzer configuration. The analysis system according to any one of items 30 to 51.

[0210] 53. The reference classification criteria includes gating information that identifies a plurality of values of parameters of a first plurality of particles of the sample for distinguishing a first plurality of particles of interest from the remaining particles of the first plurality of particles. The analysis system according to any one of items 30 to 52.

[0211] 54. To receive the reference classification criteria for the first plurality of particles of the sample, the hardware processor, by the executable instructions, The analysis system according to any one of items 30 to 53, which receives the selection of the first plurality of particles of interest from among the first plurality of particles of interest.

[0212] 55. To receive the selection of the first plurality of particles of interest, the hardware processor, by the executable instructions, The analysis system according to item 54, which displays a plurality of images corresponding to measurement values of parameters of the first plurality of particles of a sample related to the experiment.

[0213] 56. The analysis system according to any one of items 30 to 55, wherein the measurement values of the parameters of the first plurality of particles of the sample include the measurement values of the light fluoresced by the first plurality of particles.

[0214] 57. The analysis system according to item 56, wherein the light fluoresced by the first plurality of particles includes the light fluoresced by a cell component binding reagent bound to the first plurality of particles.

[0215] 58. A method for classifying a plurality of particles of a sample, comprising: Under the control of a hardware processor, (1) receiving measurement values of parameters of the first plurality of particles of a sample related to an experiment collected by a particle analyzer, and (2) first related experiment information of the first experiment in a first format; Receiving a reference classification criterion for the first plurality of particles of the sample related to the experiment; Generating a gating strategy based on (1) the measurement values of the parameters of the first plurality of particles of the sample related to the experiment and (2) the reference classification criterion for the first plurality of particles of the sample related to the experiment.

[0216] 59. Generating second related experiment information of the experiment, including the first related experiment information of the experiment and the gating strategy in the first format; The method according to item 58, wherein the particle analyzer is caused to collect measurement values of parameters of a second plurality of particles of a sample related to the experiment, based at least in part on a particle analyzer configuration representing a gating strategy and generated from second related experiment information of the experiment.

[0217] 60. Generating a particle analyzer configuration representing a gating strategy from second related experiment information of the experiment, The method according to item 59, wherein the particle analyzer configuration is transmitted to the particle analyzer.

[0218] 61. Generating a particle analyzer configuration that includes first related experiment information and represents a gating strategy in a first format, The method according to item 58, wherein the particle analyzer is caused to collect measurement values of parameters of a second plurality of particles of a sample related to the experiment, based at least in part on the particle analyzer configuration.

[0219] 62. The method according to item 59, wherein the particle analyzer configuration is transmitted to the particle analyzer.

[0220] 63. A method for classifying a plurality of particles of a sample, comprising: under the control of a hardware processor, (1) receiving measurement values of parameters of a first plurality of particles of a sample related to the experiment collected by a particle analyzer and (2) first related experiment information of the experiment in a first format, receiving a reference classification criterion for the first plurality of particles of the sample related to the experiment, generating a gating strategy based on (1) the measurement values of the parameters of the first plurality of particles of the sample related to the experiment and (2) the reference classification criterion for the first plurality of particles of the sample related to the experiment, generating second related experiment information of the experiment, including the first related experiment information of the experiment and the gating strategy in the first format, causing the particle analyzer to collect measurement values of parameters of a second plurality of particles of the sample related to the experiment, based at least in part on a particle analyzer configuration representing the gating strategy, and generating from the second related experiment information of the experiment.

[0221] 64. Using a gating method and / or an analysis tool implementing the gating method, based on (1) measurement values of parameters of a first plurality of particles of a sample related to the experiment and (2) a first plurality of particle reference classification criteria of the sample related to the experiment, to generate a gating strategy, the method according to any one of items 58 to 63.

[0222] 65. The second related experiment information includes a gating method, one or more input parameters of the gating method, one or more output parameters of the gating method, and / or an analysis tool, the method according to item 64.

[0223] 66. The particle analyzer configuration consists of a gating method, one or more input parameters of the gating method, one or more output parameters of the gating method, and / or an analysis tool, the method according to any one of items 64 to 65.

[0224] 67. Generating the second related experiment information includes generating a file including first related experiment information, a gating strategy, second related experiment information, a gating method for generating the gating strategy, one or more input parameters of the gating method, one or more output parameters of the gating method, and / or an analysis tool for implementing the gating method, the method according to any one of items 58 to 66.

[0225] 68. The gating strategy includes a plurality of gates corresponding to parameter pairs, the method according to any one of items 58 to 67.

[0226] 69. The reference classification criteria include a first plurality of particles of interest selected from the first plurality of particles, the method according to item 68.

[0227] 70. Generating the gating strategy is Determine the number of a plurality of gates corresponding to some or all of the parameters based on a scale that distinguishes a first plurality of particles from the remaining particles of the first plurality of particles using the gating strategy, including generating a plurality of polygons based on the measured values of the parameters of the first plurality of particles of the sample, the classification criteria, and the plurality of gates, s The method according to item 69, wherein at least two of the plurality of polygons include polygons of different shapes, and the number of the plurality of gates is the same as the number of the plurality of polygons.

[0228] 71. The method according to item 70, wherein the measured values include a purity measurement value and a yield measurement value that distinguish a first plurality of particles of interest from the remaining particles of the first plurality of particles using a gating strategy.

[0229] 72. The method according to any one of items 70 to 71, wherein generating the plurality of polygons includes generating a plurality of polygons surrounding some or all of the plurality of particles of interest.

[0230] 73. (1) Transmit the measured values of the parameters of the first plurality of particles of the sample associated with the experiment and (2) the first associated experimental information of the experiment in the first format to an analysis system, Receive, by the analysis system, a reference classification criterion for the first plurality of particles of the sample related to the experiment, Using the analysis system, generate a gating strategy based on (1) the measured values of the parameters of the first plurality of particles of the sample related to the experiment and (2) the reference classification criterion for the first plurality of particles of the sample related to the experiment, The method according to any one of items 58 to 72, wherein the analysis system is used to generate second associated experimental information of the experiment including the first associated experimental information of the experiment and the gating strategy in the first format.

[0231] 74. Receive the first associated experimental information in a second format, Generate the first relevant experimental information of the first format from the first relevant experimental information of the second format. The method according to any one of items 58 to 73, generating the second relevant experimental information of the second format from the second relevant experimental information of the first format.

[0232] 75. The method according to item 74, wherein the first relevant experimental information of the second format includes an identifier of a particle analyzer, and the first relevant experimental information of the first format includes an identifier of another particle analyzer.

[0233] 76. The method according to item 74, wherein the second relevant experimental information of the second format includes an identifier of a particle analyzer, and the second relevant experimental information of the first format includes an identifier of another particle analyzer.

[0234] 77. The method according to any one of items 74 to 76, wherein the first relevant experimental information is determined to be in the first format.

[0235] 78. The method according to item 77, wherein the analysis system determines that the first relevant experimental information is in the first format.

[0236] 79. The method according to any one of items 58 to 78, wherein the first relevant experimental information of the first format includes an identifier of a particle analyzer.

[0237] 80. The method according to any one of items 58 to 79, wherein the first relevant experimental information of the first format includes an identifier of a collection system.

[0238] 81. The method according to any one of items 58 to 80, wherein the reference classification criterion includes gating information for identifying a plurality of values of parameters of the first plurality of particles of the sample to distinguish the first plurality of particles of interest from the remaining particles of the first plurality of particles.

[0239] 82. Receiving a reference classification criterion for the first plurality of particles of the sample includes receiving a selection of the first plurality of particles of interest from among the first plurality of particles, according to any one of items 58 to 81.

[0240] 83. Receiving a selection of the first plurality of particles of interest includes displaying a plurality of images corresponding to measured values of parameters of the first plurality of particles of the sample related to the experiment, according to the method of item 82.

[0241] 84. The measured values of the parameters of the first plurality of particles of the sample include measured values of light fluoresced by the first plurality of particles, according to any one of items 58 to 83.

[0242] 85. The light fluoresced by the first plurality of particles includes the light fluoresced by a cell component binding reagent bound to the first plurality of particles, according to the method of item 84.

[0243] Generally, it will be understood by those skilled in the art that the terms used herein, particularly in the appended claims (e.g., the body of the appended claims), are generally intended to be open terms (e.g., the term "including" should be construed as "including but not limited to", the term "having" should be construed as "having at least", the term "include" should be construed as "including but not limited to", etc.). It will further be understood by those skilled in the art that if a specific number of introductions of claim descriptions is intended, such intent is explicitly stated in the claim, and if there is no such description, there is no such intent. For example, for the purpose of illustration, the following appended claims include the use of introductory phrases such as "at least one" and "one or more" to introduce claim descriptions. However, the use of such phrases should not be construed as meaning that the introduction of a claim description by the indefinite article "a" or "an" limits a particular claim including such introduced claim to an embodiment containing only one such description. The same applies to the use of definite articles used in the introduction of claim descriptions even if the same claim includes introductory phrases such as "one or more" or "at least one" and indefinite articles such as "a" or "an" (where "a" or "an" should be construed as meaning "at least one" or "one or more"). Further, even if a specific number of introductions of claim descriptions is explicitly stated, those skilled in the art will recognize that such description should be construed as meaning at least the stated number (e.g., the description "two reproductions" without other modifiers means at least two reproductions, or two or more reproductions). Further, when a convention analogous to "at least one of A, B, C, etc." is used, generally such a construction is intended in the sense that those skilled in the art understand the convention (e.g., "a system having at least one of A, B, C" includes, but is not limited to, a system having A alone, B alone, C alone, a combination of A and B, a combination of A and C, a combination of B and C, and / or a combination of A, B, and C, etc.).When a convention similar to “at least one of A, B, or C, etc.” is used, generally, such a construction is intended in the sense that a person skilled in the art understands the convention (for example, “a system having at least one of A, B, or C” includes, but is not limited to, a system having A alone, B alone, C alone, a combination of A and B, a combination of A and C, a combination of B and C, and / or a combination of A, B, and C, etc.). As will be further understood by a person skilled in the art, in substantially all separate words and / or phrases presenting two or more alternative terms in any of the specification, claims, and drawings, it should be understood that they are intended to cover the possibility of including any one of the terms, any combination of the terms, or both terms. For example, the phrase “A or B” is understood to include the possibilities of “A” or “B” or “A and B”.

[0244] Furthermore, when a feature or aspect of the present disclosure is described from the perspective of a Markush group, a person skilled in the art will recognize that the present disclosure is also described from the perspective of any individual member or subgroup of the members of the Markush group thereby.

[0245] As will be understood by those skilled in the art, for all purposes, such as providing a written description, all ranges disclosed herein include any and all possible sub-ranges and combinations thereof. Any range recited can be readily recognized as also fully disclosing and enabling the same range to be broken down into at least equal halves, thirds, quarters, fifths, tenths, etc. As a non-limiting example, each range disclosed herein can be readily broken down into lower thirds, middle thirds, upper thirds, etc. Also, as will be understood by those skilled in the art, words such as "up to", "at least", "greater than", "less than", etc. include the recited numbers and then refer to ranges that can be broken down into sub-ranges as described above. Finally, as will be understood by those skilled in the art, ranges include individual members. Thus, for example, a group having 1 to 3 items refers to a group having 1, 2, or 3 items. Similarly, a group having 1 to 5 items means a group having 1, 2, 3, 4, or 5 items, and so on.

[0246] The methods disclosed herein include one or more steps or actions for achieving the recited methods. The steps and / or actions of the methods can be interchanged with one another without departing from the scope of the claims. In other words, the order and / or use of particular steps and / or actions can be changed without departing from the scope of the claims, unless a particular order of steps or actions is specified.

[0247] Although various aspects and embodiments have been disclosed herein, other aspects and embodiments will be apparent to those skilled in the art. The various aspects and embodiments disclosed herein are for illustrative purposes and not intended to be limiting, and the true scope and spirit are set forth by the following claims.

Claims

1. A collection system for classifying a plurality of particles of a sample, comprising: a non-transitory memory configured to store executable instructions; a hardware processor that exchanges information with the non-transitory memory; and wherein the hardware processor is programmed by the executable instructions to (1) transmit to an analysis system a measurement value of a parameter of a first plurality of particles of a sample related to an experiment collected by a particle analyzer, and (2) first relevant experiment information of the experiment including an identifier of the particle analyzer in a first format that is a file format readable by the hardware processor; wherein the analysis system receives a reference classification criterion for the first plurality of particles of the sample related to the experiment, and generates a gating strategy based on (1) the measurement value of the parameter of the first plurality of particles of the sample related to the experiment, and (2) the reference classification criterion for the first plurality of particles of the sample related to the experiment; generates second relevant experiment information of the experiment including the first relevant experiment information of the experiment and the gating strategy in the first format, and is programmed to transmit the second relevant experiment information of the experiment to the collection system; wherein the hardware processor is programmed by the executable instructions to receive, from the analysis system, the second relevant experiment information of the experiment in the first format; represent the gating strategy, and transmit to the particle analyzer a particle analyzer configuration generated from the second relevant experiment information of the experiment; a collection system.

2. A collection system for classifying a plurality of particles of a sample, comprising: a non-transitory memory configured to store executable instructions; a hardware processor that exchanges information with the non-transitory memory; and wherein the hardware processor is programmed by the executable instructions to (1) transmit to an analysis system a measurement value of a parameter of a first plurality of particles of a sample related to an experiment collected by a particle analyzer, and (2) first relevant experiment information of the experiment including an identifier of the particle analyzer in a first format that is a file format readable by the hardware processor; wherein the analysis system Receive a reference classification criterion for the first plurality of particles of the sample related to the experiment, and generate a gating strategy based on (1) the measured values of the parameters of the first plurality of particles of the sample related to the experiment and (2) the reference classification criterion for the first plurality of particles of the sample related to the experiment. Generate a particle analyzer configuration that includes the first related experimental information and represents the gating strategy in the first format. Programmed to send the particle analyzer configuration to the collection system. The hardware processor, By the executable instructions, is programmed to receive the particle analyzer configuration and send the received particle analyzer configuration to the particle analyzer. Collection system. **Claim 3** A collection system for classifying a plurality of particles of a sample, A non-transitory memory configured to store executable instructions, A hardware processor that exchanges information with the non-transitory memory, Comprising, The hardware processor, by the executable instructions, (1) Transmit to the analysis system the measured values of the parameters of the first plurality of particles of the sample related to the experiment collected by the particle analyzer and (2) the first related experimental information of the experiment including the identifier of the particle analyzer in a first format that is a file format readable by the hardware processor. The analysis system, Receive a reference classification criterion for the first plurality of particles of the sample related to the experiment, and generate a gating strategy based on (1) the measured values of the parameters of the first plurality of particles of the sample related to the experiment and (2) the reference classification criterion in the case of the first plurality of particles of the sample related to the experiment. Generate second related experimental information of the experiment, including the first related experimental information of the experiment and the gating strategy in the first format. Programmed to send to the collection system an indicator regarding the generation of the gating strategy, including the second related experimental information of the experiment. The hardware processor, by the executable instructions, Is programmed to send to the particle analyzer a particle analyzer configuration that represents the gating strategy and is generated from the second related experimental information of the experiment. Collection system. Claim 4. A collection system for classifying a plurality of particles of a sample, comprising: A non-transitory memory configured to store executable instructions; and A hardware processor for communicating with the non-transitory memory; The hardware processor is programmed by the executable instructions to: Transmit to an analysis system (1) measured values of parameters of a first plurality of particles of a sample related to an experiment collected by a particle analyzer, and (2) first relevant experimental information of the experiment including an identifier of the particle analyzer in a first format that is a file format readable by the hardware processor; The analysis system is configured to: Receive a reference classification criterion for the first plurality of particles of the sample related to the experiment, and generate a gating strategy based on (1) the measured values of the parameters of the first plurality of particles of the sample related to the experiment, and (2) the reference classification criterion for the first plurality of particles of the sample related to the experiment; Generate a particle analyzer configuration including the first relevant experimental information and representing the gating strategy; Transmit to the collection system an indicator that the gating strategy has been generated, the indicator including the particle analyzer configuration; The hardware processor is programmed by the executable instructions to: Receive the indicator and transmit the particle analyzer configuration to the particle analyzer; The collection system according to claim 4. Claim 5. The collection system according to claim 1 or claim 3, wherein, in order to generate the gating strategy, the analysis system is programmed to generate the gating strategy based on (1) the measured values of the parameters of the first plurality of particles of the sample related to the experiment, and (2) the reference classification criterion for the first plurality of particles of the sample related to the experiment, using a gating strategy generation method implemented by an analysis tool. Claim 6. The collection system according to claim 5, wherein the second relevant experimental information includes the gating strategy generation method, one or more input parameters of the gating strategy generation method, one or more output parameters of the gating strategy generation method, and / or the analysis tool. Claim 7. The collection system according to claim 6. Claim 8. The collection system according to claim 7, further comprising a display configured to display at least one of the first relevant experimental information, the second relevant experimental information, the gating strategy, and the particle analyzer configuration. Claim 9. The collection system according to claim 8, wherein the display is further configured to display a graphical user interface for receiving user input for adjusting the gating strategy. The particle analyzer configuration includes the gating strategy generation method, one or more input parameters of the gating strategy generation method, one or more output parameters of the gating strategy generation method, and / or the analysis tool The collection system according to claim 5 or claim 6

8. To generate the second related experimental information, the analysis system is programmed to generate a file including the first related experimental information, the gating strategy, the gating strategy generation method for generating the gating strategy, one or more input parameters of the gating strategy generation method, one or more output parameters of the gating strategy generation method, and / or the analysis tool for implementing the gating strategy generation method The collection system according to claim 5

9. The gating strategy includes a plurality of gates corresponding to the set of parameters The collection system according to any one of claims 1 to 8

10. Receiving, from the collection system according to any one of claims 1 to 9, (1) measurement values of parameters of a first plurality of particles of a sample related to an experiment collected by a particle analyzer, and (2) first related experimental information of the experiment including an identifier of the particle analyzer in a first format which is a file format readable by the hardware processor Receiving a reference classification criterion for the first plurality of particles of the sample related to the experiment Programmed to generate a gating strategy based on (1) the measurement values of the parameters of the first plurality of particles of the sample related to the experiment and (2) the reference classification criterion for the first plurality of particles of the sample related to the experiment, and transmit it to the collection system Analysis system

11. A method for classifying a plurality of particles of a sample, comprising Under the control of a hardware processor An analysis system receives from a collection system (1) measurement values of parameters of a first plurality of particles of a sample related to an experiment collected by a particle analyzer, and (2) first related experimental information of the experiment including an identifier of the particle analyzer in a first format which is a file format readable by the hardware processor Receiving a reference classification criterion for the first plurality of particles of the sample related to the experiment A method comprising generating a gating strategy based on (1) the measured values of the parameters of the first plurality of particles of the sample related to the experiment and (2) the reference classification criteria in the case of the first plurality of particles of the sample related to the experiment, and transmitting the gating strategy to the collection system.

12. The collection system is the collection system according to any one of Claims 1 to 9 The method according to Claim 11.

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