Method and system for group-wise cytometric data analysis

Abstract

Aspects of the present disclosure include methods for processing cytometer data, such as group-wise analysis of cytometer data (e.g., flow cytometry data in FCS format, mass cytometry data, genomic cytometry data). Methods according to certain embodiments include generating a composite population of events including data accessors from the cytometry data, for example, the composite population of cytometer data is from two or more different samples that are kept as separate raw data files (e.g., not concatenated to form a single combined data file). Systems are also described that have an input module for receiving cytometer data and a processor with a memory having instructions for performing the subject methods. Non-transitory computer readable storage media are also provided.

Description

[Technical field]

[0001] Light detection is often used to characterize components of a sample, such as when the sample (e.g., a biological sample) is used to diagnose a disease or medical condition. When a sample is illuminated, light can be scattered by the sample, transmitted through the sample, and emitted by the sample (e.g., by fluorescence). Variations in sample components, such as morphology, absorbance, and the presence of fluorescent labels, can cause variations in the light scattered, transmitted, or emitted by the sample. These variations can be used to characterize and identify the presence of components in the sample. To quantify these variations, the light is collected and directed to a detector surface. [Background technology]

[0002] One technique that utilizes optical detection to characterize components in a sample is flow cytometry. A flow cytometer includes an optical detection system made up of optics, photodetectors, and electronics that allow for efficient detection of an optical signal and its conversion into a corresponding electrical signal. The electronic signal is processed to obtain parameters that a user can utilize to perform a desired analysis. The cytometer further includes a means for recording and analyzing the measurement data. For example, data storage and analysis can be performed using a computer connected to the detection electronics. The data can be stored in a table format, with each row corresponding to data for one particle and columns corresponding to each of the measured parameters. The use of a standard file format, such as the "FCS" file format, for storing data from a particle analyzer makes it easy to analyze the data using separate programs and / or machines. Using current analysis methods, the data is typically displayed in one-dimensional histograms or two-dimensional (2D) plots for ease of visualization.

[0003] Data obtained from the analysis of particles (e.g., cells) by flow cytometry is often multidimensional, with each particle corresponding to a point in a multidimensional space defined by the measured parameters. Populations of particles or cells can be identified as clusters of points in the data space. For example, identification of a population of interest can be performed by drawing a gate around the displayed population in one or more two-dimensional plots, referred to as "scatter plots" or "dot plots" of the data. Summary of the Invention [Means for solving the problem]

[0004] Aspects of the present disclosure include methods for processing cytometer data, such as group-wise analysis of cytometer data (e.g., flow cytometry data in FCS format, mass cytometry data, genomic cytometry data). The method of some embodiments includes generating a composite population of events including data accessors from the cytometry data, for example, the composite population of cytometer data is from two or more different samples that are kept as separate raw data files (e.g., not concatenated to form a single combined data file). A system is also described having an input module for receiving cytometer data and a processor with a memory having instructions for performing the subject methods. A non-transitory computer readable storage medium is also provided.

[0005] In carrying out the subject method, a composite population of events including a data accessor is generated from cytometry data collected from one or more samples having particles, e.g., particles are illuminated by a light source in a flow stream. In some embodiments, the cytometry data is generated based on detection of one or more of light absorption, light scattering, and light emission (e.g., fluorescence) from the samples. In some examples, the composite population is generated from cytometry data from two or more different samples, e.g., three or more different samples, e.g., four or more different samples, e.g., five or more different samples, including generating a composite population from cytometry data collected from ten or more different samples. In certain embodiments, the composite population is generated from cytometry data from a single sample. In some embodiments, the composite population includes a data accessor for each event of cytometry data. In some embodiments, the data accessor is configured to access metadata for each event of cytometry data, such as accessing metadata associated with a raw data file collected for each sample. In some embodiments, the data accessor includes a source identity for each event of the sample. In some examples, a composite population is generated from flow cytometry data from two or more different samples, and the raw data from each sample (i.e., data acquired from the optical detection system without any type of post-acquisition processing) is kept as a separate data file. For example, a composite population is generated from flow cytometry data from two or more different samples, and the raw data files from each sample are not concatenated to form a single combined data file.

[0006] In some embodiments, the method includes applying a data gate to the composite population to generate a gated composite population. In some embodiments, the method includes applying a hierarchy of data gates to the composite population. In some examples, applying a hierarchy of data gates to the composite population is sufficient to generate a hierarchy of gated composite populations. In some examples, applying a data gate to the composite population is sufficient to apply the data gate to a plurality of events in the composite population. In certain examples, applying a data gate to the composite population provides applying the data gate to every event in the composite population. In some embodiments, the method includes defining one or more subpopulations of events of the composite population, and applying the data gate is sufficient to apply the data gate to all of the events of the subpopulations. In some embodiments, an analytical algorithm is applied to the gated composite population, such as applying a clustering algorithm or a compensation matrix to the gated composite population.

[0007] In certain embodiments, the data gate is desynchronized for one or more samples of the gated composite population. In some examples, desynchronizing the data gate includes changing the geometry of a data gate applied to one or more samples of the gated composite population. In certain examples, the method includes desynchronizing (e.g., changing the gate geometry) the data gate for multiple samples, e.g., the data gate is desynchronized sequentially (i.e., one at a time) for each sample of the composite population.

[0008] In some embodiments, the composite population of events is displayed on a graphical user interface. In some examples, the graphical user interface includes a first pane configured to display one or more ungated composite populations including cytometry data of one or more groups of samples, a second pane configured to display one or more gated composite populations, and a third pane configured to display data files for each of the samples used to generate the composite population. In some examples, the gated composite populations in the second pane are displayed as a hierarchy. In some examples, the second pane is configured to display an analytical algorithm applied to the events of the composite population selected in the first pane, such as a clustering algorithm or compensation matrix applied to the composite population displayed in the first pane. In some embodiments, the hierarchy of the gated composite population is color coded in the second pane. In one example, each of the inherited data gates is labeled with the same color. In some examples, the desynchronized data gates are visualized in the second pane. In some examples, each desynchronized data gate is visualized in the second pane by a different text font. In some embodiments, a third pane of the graphical user interface is configured to display a data file for each sample having an event in the gated composite population selected in the second pane.

[0009] In some embodiments, the method includes applying the analytical algorithm displayed in the first pane to one or more of the gated composite populations displayed in the second pane. In certain examples, applying the analytical algorithm to the one or more gated composite populations includes dragging the analytical algorithm displayed in the first pane to the gated composite population displayed in the second pane. In other examples, applying the analytical algorithm to the one or more gated composite populations includes selecting an analytical algorithm from a menu of analytical algorithms and applying the selected algorithm to the gated composite population displayed in the second pane. In certain examples, an icon is displayed in the second pane on the gated composite population in response to applying the analytical algorithm from the first pane. In some embodiments, applying the analytical algorithm to the gated composite population in the second pane is sufficient to apply the analytical algorithm to one or more gated composite populations in a hierarchy of gated composite populations. In some examples, applying the analytical algorithm to the gated composite population is sufficient to apply the analytical algorithm to all of the gated composite populations in a hierarchy of gated composite populations.

[0010] In some embodiments, the method includes applying an analytical algorithm displayed in the first pane to one or more of the data files for the sample displayed in the third pane. In a particular example, applying the analytical algorithm includes dragging the analytical algorithm displayed in the first pane to the data files for the sample displayed in the third pane. In another example, applying the analytical algorithm to one or more of the data files for the sample displayed in the third pane includes selecting an analytical algorithm from a menu of analytical algorithms and applying the selected algorithm to one or more of the data files for the sample displayed in the third pane. In some embodiments, the method includes applying an analytical algorithm displayed in the second pane (e.g., tSNE, x-shift algorithm) to one or more of the data files for the sample displayed in the third pane. In a particular example, applying the analytical algorithm includes dragging the analytical algorithm displayed in the second pane to the data files for the sample displayed in the third pane.

[0011] Aspects of the present disclosure also include a system for processing flow cytometer data. The system according to certain embodiments includes an input module configured to receive flow cytometer data from one or more samples having particles illuminated by a light source in a flow stream, and a processor having a memory operably coupled to the processor, the memory including instructions stored in the memory that, when executed by the processor, cause the processor to generate a composite population of events having a data accessor from the flow cytometry data. In some examples, the system includes a light detection system configured to detect light from particles of the samples in the flow stream illuminated by the light source (e.g., a laser). In some embodiments, the light detection system may include a light scatter light detector, a fluorescence light detector, and a light loss light detector. In some examples, the flow cytometer data is generated based on data signals from a scatter light detector channel (e.g., forward scatter image data, side scatter image data). In other examples, the flow cytometer data is generated based on data signals from one or more fluorescence detector channels. In other examples, the flow cytometer data is generated based on data signals from one or more light loss detector channels. In yet other examples, the flow cytometer data is generated based on data signals from a combination of data signals from two or more of a light scatter detector channel, a fluorescence detector channel, and a light loss detector channel. In certain embodiments, the subject system is a flow cytometer configured to visualize and sort one or more particles in a flow stream.

[0012] In some examples, the memory includes instructions stored thereon for generating a composite population from flow cytometry data from two or more different samples, e.g., three or more different samples, e.g., four or more different samples, e.g., five or more different samples, and also includes instructions for generating a composite population from flow cytometry data collected from ten or more different samples. In some embodiments, the memory includes instructions for generating a composite population including a data accessor for each event of the flow cytometry data. In some embodiments, the memory includes instructions for accessing metadata for each event of the flow cytometry data using the data accessor, such as accessing metadata associated with raw data files collected for each sample. In some embodiments, the data accessor includes a source identity for each event of the sample. In some examples, the memory includes instructions for generating a composite population from flow cytometry data from two or more different samples, where the raw data from each sample is held as a separate data file. In certain examples, the memory includes instructions for generating a composite population from flow cytometry data from two or more different samples, where the raw data files from each sample are not concatenated to form a single combined data file.

[0013] In some embodiments, the memory includes instructions stored in the memory for applying a data gate to the composite population to generate a gated composite population. In some examples, the memory includes instructions for applying a hierarchy of data gates to the composite population. In some examples, the memory includes instructions for applying a hierarchy of data gates to the composite population to generate a hierarchy of gated composite populations. In some examples, the memory includes instructions for applying a data gate to one event of the composite population, and in certain examples, applying the data gate to multiple events in the composite population. In certain examples, applying a data gate to a single event of the composite population provides for applying the data gate to every event in the composite population. In some embodiments, the memory includes instructions for defining one or more subpopulations of events of the composite population, and application of the data gate is sufficient to apply the data gate to all of the events of the subpopulation. In some embodiments, the memory includes instructions for applying an analytical algorithm to the gated composite population, such as instructions for applying a clustering algorithm or a compensation matrix to the gated composite population.

[0014] In certain embodiments, the memory includes instructions for desynchronizing a data gate for one or more samples of the gated composite population. In some examples, the memory includes instructions for desynchronizing a data gate by changing a geometry of a data gate applied to one or more samples of the gated composite population. In certain examples, the memory includes instructions for desynchronizing (e.g., changing a gate geometry) a data gate for multiple samples, e.g., a data gate is desynchronized sequentially (i.e., one at a time) for each sample of the composite population.

[0015] In some embodiments, the system includes a display configured to display a composite population of events on a graphical user interface. In some examples, the system includes a memory having instructions stored therein, which when executed by the processor, cause the processor to generate a graphical user interface including a first pane configured to display one or more ungated composite populations including cytometry data for one or more groups of samples, a second pane displaying one or more gated composite populations, and a third pane displaying data files for each of the samples used to generate the composite population. In some examples, the memory includes instructions for generating a graphical user interface in which the second pane displays a hierarchy of gated composite populations. In some examples, the second pane displays an analytical algorithm applied to the events of the composite population selected in the first pane, such as a clustering algorithm or compensation matrix applied to the composite population displayed in the first pane. In some embodiments, the hierarchy of gated composite populations is color coded in the second pane. In some examples, each of the inherited data gates is labeled with the same color. In some examples, the memory includes instructions for generating a graphical user interface visualizing desynchronized data gates in the second pane. In some examples, each desynchronized data gate is visualized in the second pane with a different text font. In some embodiments, the memory includes instructions for displaying in a third pane of the graphical user interface a data file for each sample having an event in the gated composite population selected in the second pane.

[0016] In some embodiments, the memory includes instructions for applying an analytical algorithm displayed in the first pane to one or more of the gated composite populations displayed in the second pane. In certain examples, the memory includes instructions for applying an analytical algorithm to one or more of the gated composite populations by dragging an analytical algorithm displayed in the first pane to the gated composite population displayed in the second pane. In other examples, the memory includes instructions for applying an analytical algorithm to one or more of the gated composite populations by selecting an analytical algorithm from a menu of analytical algorithms and applying the selected algorithm to the gated composite population displayed in the second pane. In certain examples, the memory includes instructions for displaying an icon in the second pane of the graphical user interface on the gated composite population in response to applying the analytical algorithm from the first pane. In some embodiments, the memory includes instructions for applying an analytical algorithm to the gated composite population in the second pane such that the analytical algorithm is applied to one or more subgroups in the hierarchy of applied data gates. In some examples, applying an analytical algorithm to the gated composite population is sufficient to apply the analytical algorithm to all of the gated composite populations in the hierarchy of applied data gates.

[0017] In some embodiments, the memory includes instructions for applying an analytical algorithm displayed in the first pane to one or more of the data files for the sample displayed in the third pane. In certain examples, the memory includes instructions for applying an analytical algorithm by dragging an analytical algorithm displayed in the first pane to the data files for the sample displayed in the third pane. In other examples, the memory includes instructions for applying an analytical algorithm to one or more of the data files for the sample displayed in the third pane by selecting an analytical algorithm from a menu of analytical algorithms and applying the selected algorithm to one or more of the data files for the sample displayed in the third pane. In some embodiments, the memory includes instructions for applying an analytical algorithm displayed in the second pane (e.g., tSNE, x-shift algorithm) to one or more of the data files for the sample displayed in the third pane. In certain examples, the memory includes instructions for applying an analytical algorithm by dragging an analytical algorithm displayed in the second pane to the data files for the sample displayed in the third pane.

[0018] Aspects of the present disclosure also include a non-transitory computer readable storage medium for processing cytometer data (e.g., flow cytometry data in FCS format, mass cytometry data, genomic cytometry data). A non-transitory computer readable storage medium according to certain embodiments includes instructions having an algorithm for generating a composite population of events including a data accessor from the cytometry data. In some examples, the non-transitory computer readable storage medium includes an algorithm for processing cytometer data generated based on data signals from a scattered light detector channel (e.g., forward scatter image data, side scatter image data). In other examples, the non-transitory computer readable storage medium includes an algorithm for processing cytometer data generated based on data signals from one or more fluorescence detector channels. In other examples, the non-transitory computer readable storage medium includes an algorithm for processing cytometer data generated based on data signals from one or more light loss detector channels. In yet other examples, the non-transitory computer readable storage medium includes an algorithm for processing cytometer data generated based on data signals from a combination of data signals from two or more of a light scatter detector channel, a fluorescence detector channel, and a light loss detector channel.

[0019] In some examples, the non-transitory computer readable storage medium includes an algorithm for generating a composite population from cytometry data from two or more different samples, e.g., three or more different samples, e.g., four or more different samples, e.g., five or more different samples, and also includes instructions for generating a composite population from cytometry data collected from ten or more different samples. In some embodiments, the non-transitory computer readable storage medium includes an algorithm for generating a composite population, including a data accessor for each event of the cytometry data. In some embodiments, the non-transitory computer readable storage medium includes an algorithm for accessing metadata for each event of the cytometry data using the data accessor, such as accessing metadata associated with a raw data file collected for each sample. In some embodiments, the data accessor includes a source identity for each event of the sample. In some examples, the non-transitory computer readable storage medium includes an algorithm for generating a composite population from cytometry data from two or more different samples, and the raw data from each sample is maintained as a separate data file. In certain examples, the non-transitory computer readable storage medium includes an algorithm for generating a composite population from cytometry data from two or more different samples, where the raw data files from each sample are not concatenated to form a single combined data file.

[0020] In some embodiments, the non-transitory computer readable storage medium includes an algorithm for applying a data gate to a composite population to generate a gated composite population. In some examples, the non-transitory computer readable storage medium includes an algorithm for applying a hierarchy of data gates to the composite population. In some examples, the non-transitory computer readable storage medium includes an algorithm for applying a hierarchy of data gates to the composite population to generate a hierarchy of gated composite populations. In some examples, the non-transitory computer readable storage medium includes an algorithm for applying a data gate to one event of the composite population, and in certain examples, applying the data gate to multiple events in the composite population. In certain examples, applying a data gate to a single event of the composite population provides for applying the data gate to every event in the composite population. In some embodiments, the non-transitory computer readable storage medium includes an algorithm for defining one or more subpopulations of events of the composite population, and applying the data gate is sufficient to apply the data gate to all events of the subpopulation. In some embodiments, the non-transitory computer readable storage medium comprises an algorithm for applying an analytical algorithm to the gated composite population, such as instructions for applying a clustering algorithm or a compensation matrix to the gated composite population.

[0021] In certain embodiments, the non-transitory computer-readable storage medium includes an algorithm for desynchronizing data gates for one or more samples of the gated composite population. In some examples, the non-transitory computer-readable storage medium includes an algorithm for desynchronizing data gates by changing the geometry of the data gates applied to one or more samples of the gated composite population. In certain examples, the non-transitory computer-readable storage medium includes an algorithm for desynchronizing data gates (e.g., changing the gate geometry) for multiple samples, e.g., the data gates are desynchronized sequentially (i.e., one at a time) for each sample of the composite population.

[0022] In some embodiments, the non-transitory computer-readable storage medium includes an algorithm for generating a graphical user interface including a first pane configured to display one or more ungated composite populations including cytometry data of one or more groups of samples, a second pane displaying one or more gated composite populations, and a third pane displaying data files for each of the samples used to generate the composite population. In some examples, the non-transitory computer-readable storage medium includes an algorithm for generating a graphical user interface, the second pane displaying a hierarchy of gated composite populations. In some examples, the second pane displays an analytical algorithm applied to events of the composite population selected in the first pane, such as a clustering algorithm or compensation matrix applied to the composite population displayed in the first pane. In some embodiments, the hierarchy of gated composite populations is color coded in the second pane. In some examples, each of the inherited data gates is labeled with the same color. In some examples, the non-transitory computer-readable storage medium includes an algorithm for generating a graphical user interface visualizing desynchronized data gates in the second pane. In some examples, each desynchronized data gate is visualized in a different text font in the second pane. In some embodiments, the non-transitory computer-readable storage medium comprises an algorithm for displaying in a third pane of the graphical user interface a data file for each sample having an event in the gated composite population selected in the second pane.

[0023] In some embodiments, the non-transitory computer readable storage medium includes an algorithm for applying an analytical algorithm displayed in a first pane to one or more of the gated composite populations displayed in a second pane. In certain examples, the non-transitory computer readable storage medium includes an algorithm for applying an analytical algorithm to one or more of the gated composite populations by dragging an analytical algorithm displayed in a first pane to the gated composite population displayed in the second pane. In other examples, the non-transitory computer readable storage medium includes an algorithm for applying an analytical algorithm to one or more of the gated composite populations by selecting an analytical algorithm from a menu of analytical algorithms and applying the selected algorithm to the gated composite population displayed in the second pane. In certain examples, the non-transitory computer readable storage medium includes an algorithm for displaying an icon in the second pane of the graphical user interface on the gated composite population in response to applying the analytical algorithm from the first pane. In some embodiments, the non-transitory computer readable storage medium includes an algorithm for applying an analytical algorithm to the gated composite population in the second pane such that the analytical algorithm is applied to one or more subgroups in a hierarchy of applied data gates. In some instances, applying an analytical algorithm to a gated composite population is sufficient to apply the analytical algorithm to all of the gated composite populations within the hierarchy of the applied data gate.

[0024] In some embodiments, the non-transitory computer readable storage medium includes an algorithm for applying the analytical algorithm displayed in the first pane to one or more of the data files for the sample displayed in the third pane. In certain examples, the non-transitory computer readable storage medium includes an algorithm for applying the analytical algorithm by dragging the analytical algorithm displayed in the first pane to the data files for the sample displayed in the third pane. In other examples, the non-transitory computer readable storage medium includes an algorithm for applying the analytical algorithm to one or more of the data files for the sample displayed in the third pane by selecting an analytical algorithm from a menu of analytical algorithms and applying the selected algorithm to one or more of the data files for the sample displayed in the third pane. In some embodiments, the non-transitory computer readable storage medium includes an algorithm for applying the analytical algorithm displayed in the second pane (e.g., tSNE, x-shift algorithm) to one or more of the data files for the sample displayed in the third pane. In certain examples, the non-transitory computer readable storage medium includes an algorithm for applying the analytical algorithm displayed in the second pane to the data files for the sample displayed in the third pane by dragging the analytical algorithm displayed in the second pane to the data files for the sample displayed in the third pane. [Brief description of the drawings]

[0025] The invention may be best understood from the following detailed description when read in conjunction with the accompanying drawing figures, in which:

[0026] [Figure 1] 1 shows a flow chart for group-wise analysis of flow cytometry data from one or more samples according to certain embodiments. [Diagram 2] 1 illustrates a graphical user interface for group-wise analysis of flow cytometry data according to certain embodiments. [Diagram 3]1 illustrates the use of a graphical user interface for group-wise analysis of flow cytometry data according to certain embodiments. [Figure 4A] FIG. 1 illustrates a functional block diagram of a particle analysis system according to certain embodiments. [Figure 4B] 1 illustrates a flow cytometer according to certain embodiments. [Diagram 5] FIG. 2 illustrates a functional block diagram of an example of a particle analyzer control system in accordance with certain embodiments. [Figure 6A] FIG. 1 shows a schematic diagram of a particle sorter system according to certain embodiments. [Figure 6B] FIG. 1 shows a schematic diagram of a particle sorter system according to certain embodiments. [Figure 7] FIG. 1 illustrates a block diagram of a computing system according to certain embodiments. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0027] Aspects of the present disclosure include methods for processing cytometer data, such as group-wise analysis of cytometer data (e.g., flow cytometry data in FCS format, mass cytometry data, genomic cytometry data). Methods according to certain embodiments include generating a composite population of events including data accessors from the flow cytometry data, for example, the composite population of flow cytometer data is from two or more different samples that are kept as separate raw data files (e.g., not concatenated to form a single combined data file). Systems are also described that include an input module for receiving cytometer data and a processor with a memory having instructions for performing the subject methods. Non-transitory computer readable storage media are also provided.

[0028] Before the present invention is described in more detail, it is to be understood that this invention is not limited to particular embodiments described, as such may, of course, vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting, since the scope of the present invention will be limited only by the appended claims.

[0029] Where a range of values ​​is provided, unless the context clearly dictates otherwise, it is understood that each intervening value between the upper and lower limits of that range, to the tenth of the unit of the lower limit, and any otherwise stated or intervening value in that stated range, is included in the invention. The upper and lower limits of these smaller ranges may independently be included in the smaller ranges, and are also included in the invention, subject to any specifically excluded limit in the stated range. Where the stated range includes one or both limits, ranges excluding either or both of those included limits are also included in the invention.

[0030] A range is presented herein, and numerical values ​​are preceded by the term "about". The term "about" is used herein to provide literal support for the exact number it precedes, as well as a number that is close to or approximately the number it precedes. When determining whether a number is close to or approximately equal to a specifically stated number, the unstated number that is close or approximate may be a number that, in the context in which it is presented, results in a substantial equivalence to the specifically stated number.

[0031] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Although any methods and materials similar or equivalent to those described herein can also be used in the practice or testing of the present invention, representative exemplary methods and materials are described herein.

[0032] All publications and patents cited in this specification are incorporated herein by reference as if each individual publication or patent was specifically and individually indicated to be incorporated by reference, and are incorporated herein by reference to describe and explain the methods and / or materials in connection with which the publications are cited. The citation of any publication is for its disclosure prior to the filing date and should not be construed as an admission that the present invention is not entitled to antedate such publication by virtue of prior invention. Further, the publication dates provided may be different from the actual publication dates which may need to be independently confirmed.

[0033] It should be noted that, as used in this specification and the appended claims, the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise. It should be further noted that the claims may be drafted to exclude any optional element. As such, this statement is intended to serve as a prior basis for using exclusive terminology such as "solely," "only," or "negative" limitations in connection with the recitation of claim elements.

[0034] As will be apparent to those skilled in the art upon reading this disclosure, each of the individual embodiments described and illustrated herein has distinct components and features which may be readily separated from or combined with the features of any of the other several embodiments without departing from the scope or spirit of the invention. Any recited method can be carried out in the order of events recited or in any other order which is logically possible.

[0035] Although the apparatus and methods have been described or will be described for grammatical fluidity with functional descriptions, unless expressly formulated under 35 U.S.C. § 112, the claims should not be construed as necessarily limited in any way by means- or step-limitation constructions, but should be given the full scope of meaning and equivalents of the definitions provided by the claims under the doctrine of equivalents, and where a claim is expressly formulated under 35 U.S.C. § 112, it should be expressly understood that the full statutory equivalents under 35 U.S.C. § 112 are accorded.

[0036] As summarized above, the present disclosure provides a method for processing flow cytometer data, such as for group-wise analysis of flow cytometer data. In further describing embodiments of the present disclosure, a method for generating a composite population of events including data accessors from flow cytometry data and applying one or more data gates or analysis algorithms to the composite population is first described in more detail. Then, a system is provided that includes an input module for receiving flow cytometer data and a processor with a memory having instructions for performing the subject method. A graphical user interface and a non-transitory computer-readable storage medium are further described.

[0037] Methods for group-wise analysis of flow cytometer data

[0038] Aspects of the present disclosure include methods for processing cytometry data. In some embodiments, the cytometry data includes data provided or displayed in a flow cytometry standard format (FCS format). In certain embodiments, the cytometry data is selected from one or more of flow cytometry data, mass cytometry data, or genomic cytometry (e.g., RNA sequence data). In certain examples, the cytometry data is flow cytometry data. As described in more detail below, in some examples, flow cytometry data for performing the subject methods is generated by detecting light from a sample having particles in a flow stream illuminated with a light source. In some examples, the method provides for group-wise analysis of cytometer data, e.g., samples may be arranged in a hierarchy of groups, and data analysis (e.g., applying data gates or analysis algorithms) may be performed on events within multiple different samples without generating a cytometry data file that combines all of the raw data from multiple different samples. As described in more detail below, in certain examples, data gates or analysis algorithms may be applied to events from two or more different samples without concatenating the raw cytometry data files for each sample. In some embodiments, the subject methods provide comparative analysis of collections of samples based on controlled characteristics while preserving source identity without encoding sample groups together (e.g., by filename, folder structure, or staining panel). In some embodiments, group-wise analysis of cytometry data according to the subject methods eliminates the need to apply data gates to events from each individual sample data set. In embodiments, group-wise analysis of cytometry data as described herein provides improved management and navigation of sample cytometry data (including metadata associated with the cytometry data). Additionally, the methods described herein provide improved efficiency in creating complex data analyses and calculating results from the data analyses.

[0039] In carrying out the subject method, a composite population of events comprising data accessors is generated from cytometry data collected from one or more samples having particles illuminated by a light source in a flow stream. "Composite population" refers to a set of events grouped together from cytometry data collected from one or more samples. In some embodiments, the composite population can be cytometry data collected for 2 or more, such as 3 or more, such as 5 or more, such as 10 or more, such as 25 or more, such as 50 or more, such as 100 or more, such as 250 or more, such as 500 or more, such as 1000 or more, such as 2500 or more, such as 5000 or more, including when the composite population comprises cytometry data collected for 10000 or more events. A composite population may include cytometry data of 1% or more, such as 2% or more, such as 3% or more, such as 4% or more, such as 5% or more, such as 10% or more, such as 15% or more, such as 25% or more, such as 50% or more, such as 75% or more, such as 90% or more of the events collected for each sample, including cytometry data of 99% or more of the events collected for two or more samples.

[0040] As described in more detail below, a composite population may include events from one or more, such as two or more, such as three or more, such as four or more, such as five or more, such as six or more, such as seven or more, such as eight or more, such as nine or more, such as ten or more, such as fifteen or more, such as twenty-five or more different samples, including cytometry data collected from fifty or more different samples. In some examples, a composite population is a gated composite population generated by applying a data gate (e.g., a gate for lymphocytes or a gate for one or more fluorescent markers) to events from one or more different samples.

[0041] The term "data accessor" is used herein in its conventional sense to refer to a data access object that provides an interface to the raw data of a cytometry data file collected for one or more samples. In some embodiments, the data accessor is an accessor algorithm having programming for retrieving one or more components of the raw data from a cytometry data file. For example, the data accessor in some examples includes programming for retrieving the photodetector data signals collected from the side scatter light detector, the forward scatter light detector, the fluorescence light detector, and the light loss light detector for each event in the sample. In some examples, the source identity of the data collected for each event is maintained with the raw data file, and the data accessor includes programming for retrieving the photodetector data signals using the source identity. In certain examples, metadata for each event is maintained with the raw data file, and the data accessor includes programming for retrieving the metadata for each event from the raw data file.

[0042] "Cytometer data," as used herein, refers to information about parameters of events (e.g., cells, particles) collected by any number of optical detectors (described in more detail below) in a particle analyzer. In some embodiments, the flow cytometer data is received from a forward scatter detector. For example, the forward scatter detector may, in some instances, provide information about the overall size of the particle. In some embodiments, the cytometer data is received from a side scatter detector. The side scatter detector may, in some instances, be configured to detect refracted and reflected light from the surface and internal structure of the particle, which tends to increase with increasing particle complexity of structure. In some embodiments, the cytometer data is received from a fluorescence detector. The fluorescence detector may, in some instances, be configured to detect fluorescent emissions from fluorescent molecules, such as labeled specific binding members (such as labeled antibodies that specifically bind to markers of interest) associated with the particles in the flow cell. In certain embodiments, the method comprises detecting fluorescence from the sample by one or more, such as two or more, for example three or more, such as four or more, for example five or more, six or more, such as seven or more, for example eight or more, such as nine or more, for example ten or more, such as fifteen or more fluorescence detectors, and twenty-five or more fluorescence detectors.

[0043] In embodiments, the cytometry data of the composite population is kept as separate raw data files collected for each of the samples. In some examples, the raw data files are not concatenated to form a single combined data file. The term "concatenated" is used herein in its conventional sense to refer to flow cytometry data being processed to generate a combined data file that includes raw data files collected for two or more different samples. In some examples, the concatenated data includes cytometry data in which all or part of the cytometry data collected for two or more samples are combined into a single data file. For example, 1% or more, such as 2% or more, such as 3% or more, such as 4% or more, such as 5% or more, such as 10% or more, such as 15% or more, such as 25% or more, such as 50% or more, such as 75% or more, such as 90% or more of the flow cytometry data collected for each sample may be combined together to form a single data file, including cases where concatenating the data includes combining 99% or more of the cytometry data collected for two or more samples into a single data file. In embodiments, the data of the composite population is not concatenated.

[0044] In some embodiments, the method includes applying a data gate to the composite population to generate a gated composite population. The term "gate" is used herein in its conventional sense to refer to a classifier boundary that identifies a subset of data of interest. In some examples, a gate may bound a group of events of particular interest. Additionally, "gating" may refer to the process of classifying data using a defined gate for a given set of data, where a gate may be one or more regions of interest combined with Boolean logic. In some embodiments, a gate defines a boundary for classifying a population of flow cytometer data from one or more samples. In some embodiments, a gate identifies cytometer data exhibiting the same parameter. Examples of methods for gating are 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, the disclosures of which are incorporated herein by reference. In some embodiments, the gate bounds a population of cytometer data from one or more different samples that has been previously determined (e.g., by a user) to correspond to a property of interest. Data obtained from the analysis of particles (e.g., cells) by cytometry can be multidimensional, with each particle (e.g., cell) corresponding to a point in a multidimensional space defined by measurement parameters. A population of cells or particles can be identified as a cluster of points in the data space. In some embodiments, the method includes generating one or more population clusters from the composite population based on the determined parameters of the analytes (e.g., cells, particles) in the sample. As used herein, a "population" or "subpopulation" of analytes, such as cells or other particles, refers to a group of analytes that have properties related to one or more measured parameters (e.g., optical, impedance, or temporal properties), such that the measured parameter data form a cluster in data space.In embodiments, the data includes signals from multiple, for example, 2 or more, 3 or more, 4 or more, 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, 10 or more, and 20 or more different parameters. Thus, populations are recognized as clusters in the data. Conversely, each data cluster can be interpreted as corresponding to a complex population of a particular type of cell or analyte, although clusters corresponding to noise or background are typically observed. Clusters can be defined in a subset of dimensions, for example, with respect to a subset of measurement parameters, which corresponds to a complex population that differs only in a subset of measurement parameters or features extracted from cell or particle measurements.

[0045] In some embodiments, the method includes receiving cytometer data, calculating parameters for each analyte, and clustering the analytes together based on the calculated parameters. For example, an experiment may include particles labeled with several fluorophores or fluorescently labeled antibodies, and groups of particles may be defined by populations corresponding to one or more fluorescence measurements. In an example, a first group may be defined by a particular range of light scattering for a first fluorophore, and a second group may be defined by a particular range of light scattering for a second fluorophore. If the first and second fluorophores are represented on the x and y axes, respectively, when the information is to be displayed graphically, two different color-coded populations may appear to define each group of particles. Any number of analytes may be assigned to a cluster, including 5 or more analytes, e.g., 10 or more analytes, e.g., 50 or more analytes, e.g., 100 or more analytes, e.g., 500 analytes, and 1000 analytes. In certain embodiments, the method groups rare events detected in the sample (e.g., rare cells in a sample, such as cancer cells) together in a cluster. In these embodiments, the generated analyte clusters may include 10 or fewer assigned analytes, such as 9 or fewer assigned analytes, and even 5 or fewer assigned analytes.

[0046] In some embodiments, applying a data gate to a single event of the composite population is sufficient to apply the data gate to multiple events of the composite population. For example, a data gate applied to an event of the composite population may be applied to 1% or more, such as 2% or more, such as 3% or more, such as 4% or more, such as 5% or more, such as 10% or more, such as 25% or more, such as 50% or more, such as 75% or more, such as 90% or more, such as 95% or more, such as 97% or more of the remaining events of the composite population, and to 99% or more of the events of the composite population. In certain examples, applying a data gate to a single event of the composite population is sufficient to apply the data gate to all events (i.e., 100%) of the composite population.

[0047] In some embodiments, a hierarchy of data gates is applied to the composite population. In some examples, applying a hierarchy of data gates to the composite population is sufficient to generate a hierarchy of gated composite populations. In some examples, the hierarchy of data gates generates at least one parent gating composite population and at least one child gating composite population. In certain examples, two or more tiers of data gates are applied to the composite population generating two or more, such as three or more, such as four or more, such as five or more, and even ten or more different child gating composite populations.

[0048] In one example, a hierarchy of data gates may be applied to generate composite populations from cytometry data collected from a biological sample. In a particular example, a first gated composite population corresponds to events of diseased sample cells and a second gated composite population corresponds to events of normal sample cells. The first gated composite population (composed of event data from diseased sample cells) may include a composite population corresponding to lymphocytes. The lymphocyte composite population includes single cells. The single cells include a composite population corresponding to B cells and T cells. In this example, the first hierarchy of data gates applied to the composite populations generates a gated composite population of diseased cells, as well as gated composite populations corresponding to lymphocytes, single cells, B cells, and T cells.

[0049] In some embodiments, applying a data gate to a gated composite population is sufficient to apply the data gate to one or more of the other gated composite populations in the hierarchy (i.e., the data gate is inherited). In certain embodiments, a data gate applied to a composite population is a group-owned data gate. "Group-owned" means that a data gate applied to a group of events is attributed to the group, not the sample. In some examples, to maintain group-by-group analysis, a data gate or analysis algorithm applied to even a single event of a sample is attributed to (and executed on) the entire group. In certain examples, a data gate or analysis algorithm is applied individually to each sample of the composite population and is attributed to the gated composite population.

[0050] In some examples, an analytical algorithm is applied to the composite population. In one example, the first composite population may include events to which a spectral compensation algorithm has been applied, and the second composite population may include events to which a spectral compensation algorithm has not been applied. In another example, the first composite population may include events to which a clustering algorithm has been applied, and the second composite population may include events to which a clustering algorithm has not been applied. In certain examples, the analytical algorithm is applied to one or more gated composite populations. In some examples, applying an analytical algorithm to a gated composite population is sufficient to apply the analytical algorithm to one or more other gated composite populations in a hierarchy of the gated composite populations. For example, applying an analytical algorithm to a gated composite population is sufficient to apply the analytical algorithm to a child gated composite population in the hierarchy. Any convenient analytical algorithm can be applied to the events of the composite population, such as, for example, a spectral compensation algorithm, a t-distributed stochastic neighborhood embedding (tSNE) algorithm, an x-shift algorithm, or a clustering algorithm. In a particular example, the analysis algorithm is a spectral unmixing algorithm such as those described in U.S. Patent No. 11,009,400, filed August 19, 2021, and International Patent Application No. PCT / US2021 / 46741, the disclosures of which are incorporated herein by reference.

[0051] In certain embodiments, the data gate is desynchronized for one or more samples of the gated composite population. In some examples, desynchronizing the data gate is sufficient to exclude one or more events from the gated composite population. For example, desynchronizing the data gate for one or more samples of the gated composite population is sufficient to exclude 2 or more, such as 5 or more, such as 10 or more, such as 25 or more, such as 50 or more, such as 100 or more, and 250 or more events from the gated composite population. In some embodiments, desynchronizing the data gate includes changing the geometry of the data gate applied to one or more samples of the gated composite population. In certain examples, the method includes desynchronizing (e.g., changing the gate geometry) the data gate for multiple samples, for example, the data gate is desynchronized for each sample of the composite population sequentially (i.e., one at a time).

[0052] In certain embodiments, the method includes implementing a dynamic algorithm, such as a machine learning algorithm, to generate or desynchronize one or more data gates. In some examples, the geometry of the data gate for one or more samples of the composite population may be determined by a machine learning algorithm. In certain examples, the change in the geometry of the data gate is determined by a machine learning algorithm. For example, in some embodiments, the change in the geometry of the data gate may be sufficient to increase the number of events that enter the gate. In other embodiments, the change in the geometry of the data gate is sufficient to decrease the number of events that enter the gate. The term "machine learning" is used herein in its conventional sense to refer to adjustments to a data gate (e.g., the geometry of the data gate) by computational methods that ascertain and implement information directly from the data, without relying on a predetermined formula as a model. In certain embodiments, machine learning includes a learning algorithm that finds patterns in a data signal (e.g., from multiple particles in a sample). In these embodiments, the learning algorithm is configured to generate better and more accurate decisions and predictions as a function of the number of data signals (i.e., the learning algorithm becomes more robust as the number of characterizing particles from the sample increases). Machine learning protocols of interest may include, but are not limited to, artificial neural networks, decision tree learning, decision tree predictive modeling, support vector machines, Bayesian networks, dynamic Bayesian networks, genetic algorithms, and other machine learning protocols.

[0053] FIG. 1 shows a flow chart for group-wise analysis of flow cytometry data from one or more samples, according to certain embodiments. In step 101, particles in a flow stream are illuminated by a light source, and in step 102, light from the particles is detected. In step 103, flow cytometry data is generated from the light detector signals. In step 104, flow cytometry data from one or more illuminated samples is received (e.g., by a processor or data server). A composite population is generated from the flow cytometry data (step 105), with the events of the composite population having data accessors associated with the raw data of the flow cytometry data received in step 104. As explained above, the composite population is a virtually linked cluster of events obtained from the flow cytometry data, and a single distinct data file (i.e., concatenated data) is not generated that combines data from different sample populations. Additionally, the composite population retains source identity and access to metadata from the raw data signals of the flow cytometry data, as opposed to concatenated data that is combined into a newly generated flow cytometer data file. One or more data gates (e.g., a hierarchy of data gates with group-wise inheritance of gating) may be applied to the composite population as shown in step 106a1, or may be applied to the composite population applying an analytical algorithm in step 106a2. An analytical algorithm, such as a compensation matrix or clustering algorithm, may also be applied to the composite population in step 106b1, or may be applied to one or more of the subgroups generated by the applied data gates in step 106b2.

[0054] In some embodiments, the composite population is displayed on a graphical user interface. In some examples, the graphical user interface is a three-pane graphical user interface, e.g., the user interface is optimized for visualizing and applying data gates to a composite population generated from raw data files of two or more different samples. In some examples, the graphical user interface includes a first pane configured to display one or more ungated composite populations including cytometry data of one or more groups of samples, a second pane configured to display one or more gated composite populations (composite populations with applied data gates or analytical algorithms), and a third pane configured to display data files for each of the samples used to generate the composite population. In some examples, the second pane is configured to display a hierarchy of the gated composite population selected in the first pane of the graphical user interface. In some examples, the second pane is configured to display an analytical algorithm applied to the events of the composite population selected in the first pane, such as a clustering algorithm or compensation matrix applied to the composite population displayed in the first pane. In some embodiments, the hierarchy of the gated composite population is color coded in the second pane. In one example, each of the inherited data gates is labeled with the same color. In some examples, the desynchronized data gates are visualized in the second pane. In some examples, each desynchronized data gate is visualized in the second pane with a different text font. In some embodiments, a third pane of the graphical user interface is configured to display data files for each sample having an event in the gated composite population selected in the second pane.

[0055] FIG. 2 illustrates a graphical user interface for group-wise analysis of flow cytometry data according to certain embodiments. The graphical user interface 200 includes a first pane 201 showing a composite population with a hierarchy of distinct sample groups. The first pane 201 includes a composite population 201A ("All Samples") that includes a hierarchy of different sample groups. The composite population 201A includes subgroups corresponding to events from healthy donors (population 201A1) and events from patient samples (population 201A2). As shown in FIG. 2, the population 201A2 ("Patient") subgroup further includes a composite population of events from samples collected from patients (population 201A2a) in a hospital ward ("Ward" subgroup) and from samples collected from patients (population 201A2b) in a hospital intensive care unit ("ICU" subgroup). The population 201A2a ("Ward") and population 201A2b ("ICU") subgroups each include a further subgroup including "recovered" patients. The number of events in each of the subgroups is also shown in column 201D of the first pane 201. The first pane 201 of the graphical user interface 200 also includes an icon 201B for adding a new composite population, as well as an icon 201C for searching for different composite populations shown in the first pane 201.

[0056] The graphical user interface 200 includes a second pane 202 configured to display a hierarchy of gated composite populations that can be selected from the group of samples displayed in the first pane. As shown in FIG. 2, population 201A2b (events from samples of patients in a hospital intensive care unit "ICU") is selected in the first pane 201 and a hierarchy of gated populations 201A2b is displayed in the second pane 202. Gated composite population 201A2b has a group-owning hierarchy of applied data gates that produces a gated composite population 202A for lymphocytes that further includes a gated composite population 202A1 for T cells. The gated composite population 202A1 further includes gated population 202A1a (naive T cells), gated population 202A1b (memory T cells), gated population 202A1c (activated T cells), gated population 202A1d (cytokine A) and gated population 202A1e (cytokine B). As explained in detail above, in some embodiments, the applied data gates remain group-owned (i.e., remain with the generated composite population) and are indicated by being color-coded in the second pane. As shown in FIG. 2, the hierarchy of data gates held by the composite population 201A2b are all shown in the same color indicating that these gates are inherited across the groups of samples of each composite population. Here, the gates inherited by the "ICU" group 201A2b are from the "All Samples" group 201A. The second pane 202 includes an icon 202B indicating the composite population selected in the second pane.

[0057] The graphical user interface 200 includes a third pane 203 configured to display sample and flow cytometry data accessed (through a data accessor) by the composite populations listed in the first pane 201 and the data gates shown in the second pane 202. The third pane 203 includes an icon 203A indicating that an analysis algorithm (spectral compensation matrix) has been applied to the sample data, and an icon 203B indicating that a quality control algorithm has been applied to the sample data.

[0058] In some embodiments, the method includes applying an analytical algorithm displayed in the first pane to one or more of the gated composite populations displayed in the second pane. In certain examples, applying the analytical algorithm to the one or more gated composite populations includes dragging the analytical algorithm displayed in the first pane to the gated composite population displayed in the second pane. In other examples, applying the analytical algorithm to the one or more gated composite populations includes selecting an analytical algorithm from a menu of analytical algorithms and applying the selected algorithm to the gated composite population displayed in the second pane. In certain examples, an icon is displayed in the second pane on the gated composite population in response to applying the analytical algorithm from the first pane. In some embodiments, applying the analytical algorithm to the gated composite population in the second pane is sufficient to apply the analytical algorithm to one or more subgroups in a hierarchy of applied data gates. In some examples, applying the analytical algorithm to the gated composite population is sufficient to apply the analytical algorithm to all subgroups in a hierarchy of applied data gates. In some embodiments, the method includes applying an analytic algorithm (e.g., tSNE, x-shift algorithm) displayed in the second pane to one or more of the data files for the samples displayed in the third pane. In a particular example, applying the analytic algorithm includes dragging the analytic algorithm displayed in the second pane to the data files for the samples displayed in the third pane.

[0059] FIG. 3 illustrates the use of a graphical user interface for group-wise analysis of flow cytometry data according to certain embodiments. The graphical user interface 300 includes a first pane 301 showing a composite population including cytometry data of one or more groups of samples as described above in FIG. 2. An analytical algorithm (e.g., compensation matrix 301M or 310N) can be applied to one or more of the composite populations in the first pane 301 by dragging the analytical algorithm to the composite population of interest. This is illustrated in FIG. 3 by the arrow from compensation matrix 301N to population 301A1 ("healthy donors"). In some embodiments, dragging compensation matrix 301M to population 301A1 is sufficient to apply the compensation matrix to all of the subgroups of composite population 301A1. In some embodiments, the analytical algorithm can be applied to the entire sample, where compensation matrix 301M is dragged to the sample in the third pane 303 as shown. Applying the analytical algorithm from the first pane 301 in certain instances is sufficient to apply the analytical algorithm to all of the composite populations including events from the sample. Samples from the third pane 303 can be added to different composite populations in the first pane 301. To add flow cytometry data from the samples to the composite population (e.g., to generate a composite population having events with data accessors to the raw data in the selected samples), one or more of the samples shown in the third pane 303 can be dragged to the composite population shown in the first pane 301. As shown in Figure 3, sample 303A from the third pane 303 is dragged to composite population 301A2a (the hospital "ward" subgroup).

[0060] In some embodiments, the cytometry data includes data provided or displayed in a flow cytometry standard format. In certain embodiments, the cytometry data is selected from one or more of flow cytometry data, mass cytometry data, or genomic cytometry data (e.g., RNA sequence data). In certain examples, the cytometry data is flow cytometry data. Flow cytometry data for implementing the subject methods is, in some examples, generated by detecting light from a sample having particles in a flow stream illuminated with a light source. In some embodiments the method comprises illuminating the sample propagating through the flow stream over an interrogation area of ​​the flow stream of 5 μm or more, such as 10 μm or more, for example 15 μm or more, such as 20 μm or more, for example 25 μm or more, such as 50 μm or more, for example 75 μm or more, such as 100 μm or more, for example 250 μm or more, such as 500 μm or more, for example 750 μm or more, such as 1 mm or more, 2 mm or more, for example 3 mm or more, such as 4 mm or more, for example 5 mm or more, such as 6 mm or more, for example 7 mm or more, such as 8 mm or more, for example 9 mm or more, and 10 mm or more.

[0061] In some embodiments, the method includes illuminating the sample in the flow stream with a continuous wave light source, e.g., the light source provides an uninterrupted light flux, maintaining illumination of the particles in the flow stream with little or no undesired changes in light intensity. In some embodiments, the continuous light source emits non-pulsed or non-stroboscopic illumination. In certain embodiments, the continuous light source provides a substantially constant luminescence intensity. For example, the method may include illuminating the sample in the flow stream with a continuous light source that provides a luminescence intensity during an illumination time interval that varies by 10% or less, such as 9% or less, such as 8% or less, such as 7% or less, such as 6% or less, such as 5% or less, such as 4% or less, such as 3% or less, such as 2% or less, such as 1% or less, such as 0.5% or less, such as 0.1% or less, such as 0.01% or less, such as 0.001% or less, such as 0.0001% or less, such as 0.00001% or less, including cases where the luminescence intensity during an illumination time interval varies by 0.000001% or less. The intensity of the light output can be measured with any convenient protocol, including, but not limited to, a scanning slit profiler, a charge coupled device (CCD such as an intensified charge coupled device ICCD), a positioning sensor, a power sensor (e.g., a thermopile power sensor), an optical power sensor, an energy meter, a digital laser photometer, a laser diode detector, and other types of optical detectors.

[0062] In other embodiments, the method includes illuminating a sample propagating through the flow stream with a pulsed light source, e.g., light is emitted at predetermined time intervals, each time interval having a predetermined illumination time (i.e., pulse width). In certain embodiments, the method includes illuminating particles with a pulsed light source at each interrogation region of the flow stream with a periodic flash of light. For example, the frequency of each light pulse can be 0.0001 kHz or more, e.g., 0.0005 kHz or more, e.g., 0.001 kHz or more, e.g., 0.005 kHz or more, e.g., 0.01 kHz or more, e.g., 0.05 kHz or more, e.g., 0.1 kHz or more, e.g., 0.5 kHz or more, e.g., 1 kHz or more, e.g., 2.5 kHz or more, e.g., 5 kHz or more, e.g., 10 kHz or more, e.g., 25 kHz or more, e.g., 50 kHz or more, and 100 kHz or more. In certain examples, the frequency of the pulsed irradiation by the light source is in the range of 0.00001 kHz to 1000 kHz, such as 0.00005 kHz to 900 kHz, for example, 0.0001 kHz to 800 kHz, for example, 0.0005 kHz to 700 kHz, for example, 0.001 kHz to 600 kHz, for example, 0.005 kHz to 500 kHz, for example, 0.01 kHz to 400 kHz, for example, 0.05 kHz to 300 kHz, for example, 0.1 kHz to 200 kHz, and 1 kHz to 100 kHz. The duration of light illumination for each light pulse (i.e., pulse width) may vary and may be 0.000001 ms or more, such as 0.000005 ms or more, such as 0.00001 ms or more, such as 0.00005 ms or more, such as 0.0001 ms or more, such as 0.0005 ms or more, such as 0.001 ms or more, such as 0.005 ms or more, such as 0.01 ms or more, such as 0.05 ms or more, such as 0.1 ms or more, such as 0.5 ms or more, such as 1 ms or more, such as 2 ms or more, such as 3 ms or more, such as 4 ms or more, such as 5 ms or more, such as 10 ms or more, 25 ms or more, such as 50 ms or more, such as 100 ms or more, and 500 ms or more.For example, the duration of light irradiation may be in the range of 0.000001 ms to 1000 ms, such as 0.000005 ms to 950 ms, for example 0.00001 ms to 900 ms, for example 0.00005 ms to 850 ms, for example 0.0001 ms to 800 ms, for example 0.0005 ms to 750 ms, for example 0.001 ms to 700 ms, for example 0.005 ms to 650 ms, for example 0.01 ms to 600 ms, for example 0.05 ms to 550 ms, for example 0.1 ms to 500 ms, for example 0.5 ms to 450 ms, for example 1 ms to 400 ms, for example 5 ms to 350 ms, and 10 ms to 300 ms.

[0063] The flow stream may be illuminated with any convenient light source, including laser and non-laser light sources (e.g., light emitting diodes). In certain embodiments, the method includes illuminating the sample with a laser, such as a pulsed laser or a continuous wave laser. For example, the laser may be a diode laser, such as an ultraviolet diode laser, a visible diode laser, and a near infrared diode laser. In other embodiments, the laser may be a helium neon (HeNe) laser. In some examples, the laser may be a helium neon laser, an argon laser, a krypton laser, a xenon laser, a nitrogen laser, a CO laser, or a fluorine laser. 2 The laser is a gas laser, such as a CO laser, an argon-fluorine (ArF) excimer laser, a krypton-fluorine (KrF) excimer laser, a xenon-chlorine (XeCl) excimer laser, or a xenon-fluorine (XeF) excimer laser, or a combination thereof. In other examples, the subject systems include dye lasers, such as stilbene, coumarin, or rhodamine lasers. In still other examples, the lasers of interest include metal vapor lasers, such as helium cadmium (HeCd) lasers, helium mercury (HeHg) lasers, helium selenium (HeSe) lasers, helium silver (HeAg) lasers, strontium lasers, neon copper (NeCu) lasers, copper lasers, or gold lasers, and combinations thereof. In still other examples, the subject systems include ruby ​​lasers, Nd:YAG lasers, NdCrYAG lasers, Er:YAG lasers, Nd:YLF lasers, Nd:YVO 4 Laser, Nd:YCa4 O(BO 3 ) 3 Laser, Nd:YCOB laser, titanium sapphire laser, thulium YAG laser, ytterbium YAG laser, ytterbium 2 O 3 Examples of lasers that may be used include solid state lasers such as lasers, fluorine-doped lasers, or cerium-doped lasers, and combinations thereof.

[0064] In some embodiments, the light source outputs a particular wavelength, such as 200 nm to 1500 nm, for example 250 nm to 1250 nm, for example 300 nm to 1000 nm, for example 350 nm to 900 nm, and 400 nm to 800 nm. In certain embodiments, the continuous wave light source emits light having a wavelength of 365 nm, 385 nm, 405 nm, 460 nm, 490 nm, 525 nm, 550 nm, 580 nm, 635 nm, 660 nm, 740 nm, 770 nm, or 850 nm.

[0065] The flow stream may be illuminated by the light source from any suitable distance, such as, for example, from 0.001 mm or more, such as 0.005 mm or more, such as 0.01 mm or more, such as 0.05 mm or more, such as 0.1 mm or more, such as 0.5 mm or more, such as 1 mm or more, such as 5 mm or more, such as 10 mm or more, such as 25 mm or more, including distances of 100 mm or more. Furthermore, illumination of the flow stream may be at any suitable angle, such as, for example, from 10° to 90°, such as from 15° to 85°, such as from 20° to 80°, such as from 25° to 75°, and from 30° to 60°, such as an angle of 90°.

[0066] In some embodiments, the method includes further conditioning the light from the sample before detecting the light. For example, the light from the sample source may pass through one or more lenses, mirrors, pinholes, slits, gratings, optical refractors, and any combination thereof. In some examples, the collected light passes through one or more focusing lenses, such as to reduce the light profile. In other examples, the emitted light from the sample passes through one or more collimators to reduce the light beam divergence.

[0067] In certain embodiments, the method includes illuminating the sample with two or more beams of frequency shifted light. As described above, a light beam generator component having a laser and an acousto-optical device for frequency shifting the laser light may be employed. In these embodiments, the method includes illuminating the acousto-optical device with a laser. Depending on the desired wavelength of light generated in the output laser beam (e.g., for use in illuminating the sample in the flow stream), the laser may have a variety of specific wavelengths ranging from 200 nm to 1500 nm, e.g., 250 nm to 1250 nm, e.g., 300 nm to 1000 nm, e.g., 350 nm to 900 nm, and 400 nm to 800 nm. The acousto-optical device may be illuminated with one or more lasers, e.g., two or more lasers, e.g., three or more lasers, e.g., four or more lasers, e.g., five or more lasers, and ten or more lasers. The lasers may include any combination of types of lasers. For example, in some embodiments, the method includes illuminating the acousto-optical device with an array of lasers, such as an array having one or more gas lasers, one or more dye lasers, and one or more solid state lasers.

[0068] When more than one laser is employed, the acousto-optical device may be irradiated with the lasers simultaneously or sequentially, or a combination thereof. For example, the acousto-optical device may be irradiated with each of the lasers simultaneously. In other embodiments, the acousto-optical device is irradiated with each of the lasers sequentially. When more than one laser is employed to irradiate the acousto-optical device sequentially, the time that each laser irradiates the acousto-optical device may independently be 0.001 microseconds or more, such as 0.01 microseconds or more, such as 0.1 microseconds or more, such as 1 microsecond or more, such as 5 microseconds or more, such as 10 microseconds or more, such as 30 microseconds or more, and 60 microseconds or more. For example, the method may include irradiating the acousto-optical device with the laser for a duration in the range of 0.001 microseconds to 100 microseconds, such as 0.01 microseconds to 75 microseconds, such as 0.1 microseconds to 50 microseconds, such as 1 microsecond to 25 microseconds, and 5 microseconds to 10 microseconds. In embodiments in which the acousto-optical device is illuminated sequentially with two or more lasers, the duration for which the acousto-optical device is illuminated by each laser may be the same or different.

[0069] In an embodiment, the method includes applying high frequency drive signals to an acousto-optical device to generate an angularly deflected laser beam. Two or more high frequency drive signals, such as three or more high frequency drive signals, for example four or more high frequency drive signals, for example five or more high frequency drive signals, for example six or more high frequency drive signals, for example seven or more high frequency drive signals, for example eight or more high frequency drive signals, for example nine or more high frequency drive signals, for example ten or more high frequency drive signals, for example fifteen or more high frequency drive signals, for example twenty-five or more high frequency drive signals, for example fifty or more high frequency drive signals, and one hundred or more high frequency drive signals may be applied to the acousto-optical device to generate an output laser beam having a desired number of angularly deflected laser beams.

[0070] Each of the angularly deflected laser beams generated by the high frequency drive signals has an intensity based on the amplitude of the applied high frequency drive signal. In some embodiments, the method includes applying a high frequency drive signal having an amplitude sufficient to generate an angularly deflected laser beam having a desired intensity. In some examples, each applied high frequency drive signal independently has an amplitude of about 0.001V to about 500V, e.g., about 0.005V to about 400V, e.g., about 0.01V to about 300V, e.g., about 0.05V to about 200V, e.g., about 0.1V to about 100V, e.g., about 0.5V to about 75V, e.g., about 1V to about 50V, e.g., about 2V to about 40V, e.g., 3V to about 30V, and about 5V to about 25V. Each applied high frequency drive signal, in some embodiments, has a frequency of about 0.001 MHz to about 500 MHz, such as about 0.005 MHz to about 400 MHz, for example, about 0.01 MHz to about 300 MHz, for example, about 0.05 MHz to about 200 MHz, for example, about 0.1 MHz to about 100 MHz, for example, about 0.5 MHz to about 90 MHz, for example, about 1 MHz to about 75 MHz, for example, about 2 MHz to about 70 MHz, for example, about 3 MHz to about 65 MHz, for example, about 4 MHz to about 60 MHz, and about 5 MHz to about 50 MHz.

[0071] In these embodiments, the angularly deflected laser beams in the output laser beam are spatially separated. Depending on the applied high frequency drive signal and the desired illumination profile of the output laser beam, the angularly deflected laser beams can be separated by 0.001 μm or more, such as 0.005 μm or more, such as 0.01 μm or more, such as 0.05 μm or more, such as 0.1 μm or more, such as 0.5 μm or more, such as 1 μm or more, such as 5 μm or more, such as 10 μm or more, such as 100 μm or more, such as 500 μm or more, such as 1000 μm or more, and 5000 μm or more. In some embodiments, the angularly deflected laser beams overlap with adjacent angularly deflected laser beams along the horizontal axis of the output laser beam. The overlap between adjacent angularly deflected laser beams (such as overlap of beam spots) can be 0.001 μm or more overlap, such as 0.005 μm or more overlap, such as 0.01 μm or more overlap, such as 0.05 μm or more overlap, such as 0.1 μm or more overlap, such as 0.5 μm or more overlap, such as 1 μm or more overlap, such as 5 μm or more overlap, such as 10 μm or more overlap, and 100 μm or more overlap.

[0072] In certain examples, the flow stream is illuminated with multiple beams of frequency-shifted light to generate a frequency-encoded image, see Diebold et al., Nature Photonics Vol. 7(10); 806-810 (2013) and U.S. Pat. Nos. 9,423,353; 9,784,661; 9,983,132; 10,006,852; 10,078,045; 10,036,699; 10,222,316; 10,288,546; 10,324,019; 10,408,758; 10,451,538; 10,62 Cells within the flow stream are imaged by fluorescent imaging using radio frequency tagged emission (FIRE), such as those described in U.S. Patent Publication Nos. 2017 / 0133857; 2017 / 0328826; 2017 / 0350803; 2018 / 0275042; 2019 / 0376895 and 2019 / 0376894.

[0073] In certain embodiments, light from an illuminated sample in the flow stream is detected. In embodiments, the method may include detecting light at 10 or more locations (e.g., a segment of a predetermined length) across the flow stream, such as 25 or more locations, such as 50 or more locations, such as 75 or more locations, such as 100 or more locations, such as 150 or more locations, such as 200 or more locations, such as 250 or more locations, and 500 or more locations in the flow stream. In some embodiments, the light from the flow stream is detected by a photodetector. The photodetector may be any convenient light detection protocol, including, but not limited to, an active pixel sensor (APS), an avalanche photodiode (APD), a quadrant photodiode, an image sensor, a charge-coupled device (CCD), an intensified charge-coupled device (ICCD), a light-emitting diode, a photon counter, a bolometer, a pyroelectric detector, a photoresistor, a photocell, a photodiode, a photomultiplier tube, a phototransistor, a quantum dot photoreceptor or photodiode, and combinations thereof, other photodetectors, and the like. In certain embodiments, the photodetector has a resolution of 0.01 cm. 2 ~10cm 2 , e.g. 0.05 cm 2 ~9cm 2 , e.g. 0.1 cm 2 ~8cm 2 , e.g. 0.5 cm 2 ~7cm 2 , and 1 cm 2 ~5cm 2 and a photomultiplier tube such as a photomultiplier tube having an active detection surface area in each region in the range of 100 nm to 150 nm.

[0074] The light may be measured by the photodetector at one or more wavelengths, e.g., two or more wavelengths, e.g., five or more different wavelengths, e.g., ten or more different wavelengths, e.g., twenty-five or more different wavelengths, e.g., fifty or more different wavelengths, e.g., one hundred or more different wavelengths, e.g., two or more different wavelengths, e.g., twenty-five or more different wavelengths, e.g., fifty or more different wavelengths, e.g., one hundred or more different wavelengths, e.g., three or more different wavelengths, including measuring light from particles in the flow stream at four hundred or more different wavelengths. The light may be measured continuously or at discrete intervals. In some examples, the detector of interest is configured to make measurements of the light continuously. In other examples, the detector of interest is configured to make measurements at discrete intervals, such as measuring light every 0.001 milliseconds, every 0.01 milliseconds, every 0.1 milliseconds, every 1 milliseconds, every 10 milliseconds, every 100 milliseconds, and every 1000 milliseconds, or some other interval. Measurements of light from the entire flow stream may be made one or more times, e.g., two or more times, e.g., three or more times, e.g., five or more times, and ten or more times during each discrete time interval. In certain embodiments, light from the flow stream is measured by a photodetector two or more times, and in certain instances the data is averaged.

[0075] System for group-wise analysis of cytometer data.

[0076] Aspects of the present disclosure also include systems for processing cytometer data. The system according to certain embodiments includes an input module configured to receive cytometer data from one or more samples having particles, and a processor having a memory operably coupled to the processor, the memory including instructions stored in the memory that, when executed by the processor, cause the processor to generate a composite population of events having data accessors from the cytometry data. As described above, the subject system provides for group-wise analysis of cytometer data, e.g., samples may be arranged in a hierarchy of groups, and data analysis (e.g., applying data gates or analysis algorithms) may be performed on events within multiple different samples without generating a cytometry data file that combines all of the raw data. In certain examples, the system includes a memory having instructions for applying data gates or analysis algorithms to events from two or more different samples without concatenating the raw cytometry data files for each sample. In some embodiments, the memory includes instructions for comparatively analyzing a collection of samples based on a controlled characteristic while preserving source identity without encoding the sample groups together (e.g., by file name, folder structure, or stain panel).

[0077] In an embodiment, the system includes a processor having a memory operably coupled to the processor, the memory including instructions stored thereon, which when executed by the processor, causes the processor to generate a composite population of events including a data accessor from cytometry data collected from one or more samples having particles. In some embodiments, the composite population includes 2 or more, such as 3 or more, such as 5 or more, such as 10 or more, such as 25 or more, such as 50 or more, such as 100 or more, such as 250 or more, such as 500 or more, such as 1000 or more, such as 2500 or more, such as 5000 or more events, including cases where the composite population includes cytometry data collected for 10000 or more events. A composite population may include cytometry data of 1% or more, such as 2% or more, such as 3% or more, such as 4% or more, such as 5% or more, such as 10% or more, such as 15% or more, such as 25% or more, such as 50% or more, such as 75% or more, such as 90% or more of the events collected for each of the samples, including cytometry data of 99% or more of the events collected for two or more samples.

[0078] In some embodiments, the memory includes instructions for generating a composite population including events from one or more, such as two or more, such as three or more, such as four or more, such as five or more, such as six or more, such as seven or more, such as eight or more, such as nine or more, such as ten or more, such as fifteen or more, such as twenty-five or more different samples, including cytometry data collected from fifty or more different samples. In some examples, the memory includes instructions for generating a gated composite population by applying a data gate (e.g., a gate for lymphocytes or a gate for one or more fluorescent markers) to events from one or more different samples.

[0079] In some embodiments, the memory includes instructions for generating a composite population from cytometer data generated from data signals collected from one or more of the side scatter light detector, the forward scatter light detector, the fluorescence light detector, and the light loss light detector for each event in the sample. In some embodiments, the memory includes instructions for retaining the cytometry data of the composite population as separate raw data files collected for each of the samples. In some examples, the memory includes instructions for not concatenating the raw data files to form a single combined data file.

[0080] In some embodiments, the memory includes instructions for applying a data gate to the composite population to generate a gated composite population. In some examples, the memory includes instructions for applying a data gate to a plurality of events of the composite population by applying the data gate to a single event of the composite population. For example, the memory includes instructions for applying a data gate to events of the composite population such that the data gate may be applied to 1% or more of the remaining events of the composite population, such as 2% or more, such as 3% or more, such as 4% or more, such as 5% or more, such as 10% or more, such as 25% or more, such as 50% or more, such as 75% or more, such as 90% or more, such as 95% or more, such as 97% or more, and 99% or more of the events of the composite population. In a particular example, the memory includes instructions for applying a data gate to all events (i.e., 100%) of the composite population by applying the data gate to a single event of the composite population.

[0081] In some embodiments, the memory includes instructions for applying a hierarchy of data gates to the composite population. In some examples, applying the hierarchy of data gates to the composite population is sufficient to generate a hierarchy of child gating composite populations. In some examples, the hierarchy of data gates generates at least one child gating composite population. In particular examples, the memory includes instructions for applying two or more hierarchies of data gates to the composite population to generate two or more, e.g., three or more, e.g., four or more, e.g., five or more, and ten or more different child gating composite populations.

[0082] In one example, the hierarchy of applied data gates may include data gates that gate composite populations generated from cytometry data collected from a biological sample. In a particular example, a first gated composite population corresponds to events of diseased sample cells, and a second gated composite population corresponds to events of normal sample cells. The first gated composite population (composed of event data from diseased sample cells) may include a gated composite population corresponding to lymphocytes. The lymphocyte composite population includes single cells. The single cell population includes a composite population corresponding to B cells and T cells. In this example, the first hierarchy of data gates applied to the composite populations generates a gated composite population of diseased cells, as well as gated composite populations corresponding to lymphocytes, single cells, B cells, and T cells.

[0083] In some examples, the memory includes instructions for applying an analytical algorithm to the composite population. Any convenient analytical algorithm can be applied to the events of the composite population, such as, for example, a compensation algorithm or a clustering algorithm. In a particular example, the memory includes instructions for applying a spectral unmixing algorithm, such as those described in U.S. Pat. No. 11,009,400 and International Patent Application No. PCT / US2021 / 46741, filed Aug. 19, 2021, the disclosures of which are incorporated herein by reference.

[0084] In certain embodiments, the memory includes instructions for desynchronizing the data gates for one or more samples of the gated composite population. In some examples, desynchronizing the data gates is sufficient to exclude one or more events from the gated composite population. For example, desynchronizing the data gates for one or more samples of the gated composite population is sufficient to exclude 2 or more, such as 5 or more, such as 10 or more, such as 25 or more, such as 50 or more, such as 100 or more events from the gated composite population, including excluding 250 or more. In some embodiments, the memory includes instructions for desynchronizing one or more events from the composite population by changing the geometry of the data gates applied to one or more samples of the gated composite population. In some embodiments, the memory includes instructions for desynchronizing the data gates based on some parameter of interest, such as particle size, particle center of mass, particle eccentricity, or optical, impedance, or temporal properties. In some embodiments, the memory includes instructions for desynchronizing the data gates sequentially (i.e., one at a time for each sample of the composite population) for multiple samples (e.g., changing the gate geometry).

[0085] In some examples, the system includes a display having a graphical user interface for use in the group-wise analysis of cytometry data according to the methods described herein. In some examples, the graphical user interface is a three-pane graphical user interface, e.g., the user interface is optimized for visualizing and applying data gates to a composite population generated from raw data files of two or more different samples. In some examples, the graphical user interface includes a first pane configured to display one or more ungated composite populations including cytometry data of one or more groups of samples, a second pane to display one or more gated composite populations, and a third pane configured to display data files for each of the samples used to generate the composite population. In some examples, the second pane is configured to display a hierarchy of the gated composite population selected in the first pane of the graphical user interface. In some examples, the second pane is configured to display an analytical algorithm applied to the events of the composite population selected in the first pane, such as a clustering algorithm or compensation matrix applied to the composite population displayed in the first pane. In some embodiments, the hierarchy of the gated composite population is color coded in the second pane. In one example, each of the inherited data gates is labeled with the same color. In some examples, the desynchronized data gates are visualized in the second pane. In some examples, each desynchronized data gate is visualized in the second pane with a different text font. In some embodiments, a third pane of the graphical user interface is configured to display data files for each sample having an event in the gated composite population selected in the second pane.

[0086] In some embodiments, the memory includes instructions for applying an analytical algorithm displayed in the first pane to one or more of the gated composite populations displayed in the second pane. In certain examples, the memory includes instructions for applying an analytical algorithm to one or more of the gated composite populations by dragging an analytical algorithm displayed in the first pane to the gated composite population displayed in the second pane. In other examples, the memory includes instructions for applying an analytical algorithm to one or more of the gated composite populations by selecting an analytical algorithm from a menu of analytical algorithms and applying the selected algorithm to the gated composite population displayed in the second pane. In certain examples, the memory includes instructions for displaying an icon in the second pane of the graphical user interface on the gated composite population in response to applying the analytical algorithm from the first pane. In some embodiments, the memory includes instructions for applying an analytical algorithm to the gated composite population in the second pane such that the analytical algorithm is applied to one or more subgroups in the hierarchy of applied data gates. In some examples, applying the analytical algorithm to the gated composite population is sufficient to apply the analytical algorithm to all of the subgroups in the hierarchy of applied data gates. In some embodiments, the memory includes instructions for applying the analytic algorithm displayed in the second pane (e.g., tSNE, x-shift algorithm) to one or more of the data files for the samples displayed in the third pane. In a particular example, applying the analytic algorithm includes dragging the analytic algorithm displayed in the second pane to the data files for the samples displayed in the third pane.

[0087] In some embodiments, the memory includes instructions for applying the analytical algorithm displayed in the first pane to one or more of the data files for the sample displayed in the third pane. In certain examples, the memory includes instructions for applying the analytical algorithm by dragging the analytical algorithm displayed in the first pane to the data files for the sample displayed in the third pane. In other examples, the memory includes instructions for applying the analytical algorithm to one or more of the data files for the sample displayed in the third pane by selecting an analytical algorithm from a menu of analytical algorithms and applying the selected algorithm to one or more of the data files for the sample displayed in the third pane.

[0088] In some embodiments, the system is part of or operably coupled to a particle analyzer system (e.g., a flow cytometer) for generating flow cytometer data as described herein. In some examples, the system includes a light source for illuminating a sample having particles in a flow stream. The system of interest includes a light source configured to illuminate a sample in a flow stream. In embodiments, the light source can be any suitable broadband or narrowband light source. Depending on the components in the sample (e.g., cells, beads, non-cellular particles, etc.), the light source can be configured to emit various wavelengths of light ranging from 200 nm to 1500 nm, e.g., 250 nm to 1250 nm, e.g., 300 nm to 1000 nm, e.g., 350 nm to 900 nm, and 400 nm to 800 nm. For example, the light source can include a broadband light source that emits light having a wavelength of 200 nm to 900 nm. In other examples, the light source includes a narrowband light source that emits wavelengths in the range of 200 nm to 900 nm. For example, the light source can be a narrowband LED (1 nm to 25 nm) that emits light having a wavelength in the range of 200 nm to 900 nm.

[0089] In some embodiments, the light source is a laser. Lasers of interest may include pulsed or continuous wave lasers. For example, lasers may include helium neon lasers, argon lasers, krypton lasers, xenon lasers, nitrogen lasers, CO 2 gas lasers, such as CO lasers, argon-fluorine (ArF) excimer lasers, krypton-fluorine (KrF) excimer lasers, xenon-chlorine (XeCl) excimer lasers or xenon-fluorine (XeF) excimer lasers or combinations thereof; dye lasers, such as stilbene, coumarin or rhodamine lasers; metal vapor lasers, such as helium cadmium (HeCd) lasers, helium mercury (HeHg) lasers, helium selenium (HeSe) lasers, helium silver (HeAg) lasers, strontium lasers, neon copper (NeCu) lasers, copper lasers or gold lasers, and combinations thereof; ruby ​​lasers, Nd:YAG lasers, NdCrYAG lasers, Er:YAG lasers, Nd:YLF lasers, Nd:YVO 4 Laser, Nd:YCa 4 O(BO 3 ) 3 Laser, Nd:YCOB laser, titanium sapphire laser, thulium YAG laser, ytterbium YAG laser, ytterbium 2 O 3 The laser may be a solid-state laser, such as a 1000 MHz frequency-doubled laser or a cerium-doped laser, and combinations thereof; a semiconductor diode laser, an optically pumped semiconductor laser (OPSL), or a frequency-doubled or frequency-tripled implementation of any of the lasers mentioned above.

[0090] In other embodiments, the light source is a non-laser light source, such as a lamp, including but not limited to a halogen lamp, a deuterium arc lamp, a xenon arc lamp, a light emitting diode, such as a broadband LED with a continuous spectrum, a superluminescent light emitting diode, a semiconductor light emitting diode, a wide spectrum LED white light source, a multi-LED integrated, etc. In some examples, the non-laser light source is a stabilized fiber coupled broadband light source, a white light source, or any combination thereof, among other light sources.

[0091] In certain embodiments, the light source is an optical beam generator configured to generate two or more beams of frequency-shifted light. In some examples, the optical beam generator includes a laser and a radio frequency generator configured to apply a radio frequency drive signal to an acousto-optical device to generate two or more angularly deflected laser beams. In these embodiments, the laser may be a pulsed laser or a continuous wave laser. The acousto-optical device may be any convenient acousto-optical protocol configured to frequency shift the laser light using an applied acoustic wave. In certain embodiments, the acousto-optical device is an acousto-optical deflector. The acousto-optical device in the subject system is configured to generate the angularly deflected laser beam from the light from the laser and the applied radio frequency drive signal. The radio frequency drive signal may be applied to the acousto-optical device by any suitable radio frequency drive signal source, such as a direct digital synthesizer (DDS), an arbitrary waveform generator (AWG), or an electrical pulse generator.

[0092] In an embodiment, the controller is configured to apply high frequency drive signals to the acousto-optical device to generate a desired number of angularly deflected laser beams in the output laser beam, such as configured to apply three or more high frequency drive signals, for example four or more high frequency drive signals, for example five or more high frequency drive signals, for example six or more high frequency drive signals, for example seven or more high frequency drive signals, for example eight or more high frequency drive signals, for example nine or more high frequency drive signals, for example ten or more high frequency drive signals, for example fifteen or more high frequency drive signals, for example twenty-five or more high frequency drive signals, for example fifty or more high frequency drive signals, and configured to apply one hundred or more high frequency drive signals.

[0093] In some examples, to generate an intensity profile of the angularly deflected laser beam within the output laser beam, the controller is configured to apply high frequency drive signals having various amplitudes, such as from about 0.001 V to about 500 V, for example, from about 0.005 V to about 400 V, for example, from about 0.01 V to about 300 V, for example, from about 0.05 V to about 200 V, for example, from about 0.1 V to about 100 V, for example, from about 0.5 V to about 75 V, for example, from about 1 V to about 50 V, for example, from about 2 V to about 40 V, for example, from 3 V to about 30 V, and from about 5 V to about 25 V. Each applied high frequency drive signal, in some embodiments, has a frequency of about 0.001 MHz to about 500 MHz, such as about 0.005 MHz to about 400 MHz, for example, about 0.01 MHz to about 300 MHz, for example, about 0.05 MHz to about 200 MHz, for example, about 0.1 MHz to about 100 MHz, for example, about 0.5 MHz to about 90 MHz, for example, about 1 MHz to about 75 MHz, for example, about 2 MHz to about 70 MHz, for example, about 3 MHz to about 65 MHz, for example, about 4 MHz to about 60 MHz, and about 5 MHz to about 50 MHz.

[0094] In certain embodiments, the controller includes a processor having a memory operably coupled to the processor, whereby the memory includes instructions stored in the memory that, when executed by the processor, cause the processor to generate an output laser beam having an angularly deflected laser beam with a desired intensity profile. For example, the memory may include instructions for generating two or more, e.g., three or more, e.g., four or more, e.g., five or more, e.g., ten or more, e.g., twenty-five or more, e.g., fifty or more angularly deflected laser beams having the same intensity, including where the memory may include instructions for generating one hundred or more angularly deflected laser beams having the same intensity. In other embodiments, the memory may include instructions for generating two or more, e.g., three or more, e.g., four or more, e.g., five or more, e.g., ten or more, e.g., twenty-five or more, e.g., fifty or more angularly deflected laser beams having different intensities, including where the memory may include instructions for generating one hundred or more angularly deflected laser beams having different intensities.

[0095] In particular examples, a light beam generator configured to generate two or more beams of frequency-shifted light includes a laser excitation module as described in U.S. Pat. Nos. 9,423,353, 9,784,661, and 10,006,852 and U.S. Patent Publication Nos. 2017 / 0133857 and 2017 / 0350803, the disclosures of which are incorporated herein by reference.

[0096] In an embodiment, the system includes a light detection system having one or more light detectors for detecting and measuring light from the sample. Light detectors of interest may be configured to measure light absorption (e.g., for bright field light data), light scattering (e.g., forward or side scatter light data), light emission (e.g., fluorescent light data) from the sample, or a combination thereof. Light detectors of interest may include optical sensors such as, but not limited to, active pixel sensors (APS), avalanche photodiodes (APDs), image sensors, charge-coupled devices (CCDs), intensified charge-coupled devices (ICCDs), light-emitting diodes, photon counters, bolometers, pyroelectric detectors, photoresistors, photocells, photodiodes, photomultiplier tubes, phototransistors, quantum dot photoconductors or photodiodes, and combinations thereof, other light detectors. In certain embodiments, the light from the sample is measured by a charge-coupled device (CCD), a semiconductor charge-coupled device (CCD), an active pixel sensor (APS), a complementary metal-oxide semiconductor (CMOS) image sensor, or an N-type metal-oxide semiconductor (NMOS) image sensor.

[0097] In some embodiments, the light detection system of interest includes a plurality of light detectors. In some examples, the light detection system includes a plurality of solid-state detectors, such as photodiodes. In certain examples, the light detection system includes a light detector array, such as an array of photodiodes. In these embodiments, the light detector array may include four or more light detectors, such as ten or more light detectors, such as twenty-five or more light detectors, such as fifty or more light detectors, such as one hundred or more light detectors, such as two hundred and fifty or more light detectors, such as one hundred and more light detectors, such as two hundred and fifty or more light detectors, such as one hundred and more light detectors. For example, the detector may be a photodiode array having four or more photodiodes, such as ten or more photodiodes, such as two hundred and more photodiodes, such as two hundred and more photodiodes, such as one hundred and more photodiodes, such as two hundred and more photodiodes, such as one hundred and more photodiodes.

[0098] The photodetectors may be arranged in any geometric configuration as desired, with configurations of interest including, but not limited to, square, rectangular, trapezoidal, triangular, hexagonal, heptagonal, octagonal, nonagonal, decagonal, dodecagonal, circular, elliptical, and irregularly patterned configurations. The photodetectors in a photodetector array may be oriented relative to one another at angles ranging from 10° to 180°, such as 15° to 170°, such as 20° to 160°, such as 25° to 150°, such as 30° to 120°, and 45° to 90° (as referenced in the XZ plane). The photodetector array may be of any suitable shape, including rectilinear shapes, such as square, rectangular, trapezoidal, triangular, hexagonal, etc., curvilinear shapes, such as circular, elliptical, as well as irregular shapes, such as a parabolic base coupled to a planar top. In certain embodiments, the photodetector array has a rectangular active surface.

[0099] Each photodetector (e.g. photodiode) in the array may have an active surface with a width in the range of 5 μm to 250 μm, such as 10 μm to 225 μm, for example 15 μm to 200 μm, such as 20 μm to 175 μm, for example 25 μm to 150 μm, for example 30 μm to 125 μm, and 50 μm to 100 μm, and a length in the range of 5 μm to 250 μm, for example 10 μm to 225 μm, for example 15 μm to 200 μm, such as 20 μm to 175 μm, for example 25 μm to 150 μm, for example 30 μm to 125 μm, and 50 μm to 100 μm, and the surface area of ​​each photodetector (e.g. photodiode) in the array may be less than 25 μm. 2 ~10000μm 2 , e.g. 50 μm 2 ~9000μm 2 , e.g. 75 μm 2 ~8000μm 2 , e.g. 100 μm 2 ~7000μm 2 , e.g. 150 μm 2 ~6000μm 2 , and 200 μm 2 ~5000μm 2 The range is.

[0100] The size of the photodetector array may vary depending on the amount and intensity of light, the number of photodetectors, and the desired sensitivity, and may have a length in the range of 0.01 mm to 100 mm, e.g., 0.05 mm to 90 mm, e.g., 0.1 mm to 80 mm, e.g., 0.5 mm to 70 mm, e.g., 1 mm to 60 mm, e.g., 2 mm to 50 mm, e.g., 3 mm to 40 mm, e.g., 4 mm to 30 mm, and 5 mm to 25 mm. The width of the photodetector array may also vary in the range of 0.01 mm to 100 mm, e.g., 0.05 mm to 90 mm, e.g., 0.1 mm to 80 mm, e.g., 0.5 mm to 70 mm, e.g., 1 mm to 60 mm, e.g., 2 mm to 50 mm, e.g., 3 mm to 40 mm, e.g., 4 mm to 30 mm, and 5 mm to 25 mm. Thus, the active surface of the photodetector array may be as small as 0.1 mm. 2 ~10000mm 2 , e.g. 0.5mm 2 ~5000mm 2 , e.g. 1mm 2~1000mm 2 , e.g. 5mm 2 ~500mm 2 , and 10mm 2 ~100mm 2 The range may be:

[0101] Optical detectors of interest are configured to measure light collected at one or more wavelengths, such as two or more wavelengths, for example five or more different wavelengths, such as ten or more different wavelengths, for example twenty-five or more different wavelengths, such as fifty or more different wavelengths, for example one hundred or more different wavelengths, such as two hundred or more different wavelengths, for example three hundred or more different wavelengths, including measuring light emitted by a sample in the flow stream at four hundred or more different wavelengths.

[0102] In some embodiments, the photodetector is configured to measure collected light over a range of wavelengths (e.g., 200 nm to 1000 nm). In certain embodiments, the photodetector of interest is configured to collect a spectrum of light over a range of wavelengths. For example, the system may include one or more detectors configured to collect a spectrum of light over one or more of the wavelength ranges of 200 nm to 1000 nm. In still other embodiments, the detector of interest is configured to measure light from a sample in the flow stream at one or more specific wavelengths. For example, the system may include one or more detectors configured to measure light at one or more of 450 nm, 518 nm, 519 nm, 561 nm, 578 nm, 605 nm, 607 nm, 625 nm, 650 nm, 660 nm, 667 nm, 670 nm, 668 nm, 695 nm, 710 nm, 723 nm, 780 nm, 785 nm, 647 nm, 617 nm, and any combination thereof.

[0103] The light detection system is configured to measure light continuously or at discrete intervals. In some examples, the light detector of interest is configured to make measurements of the collected light continuously. In other examples, the light detection system is configured to make measurements at discrete intervals, such as measuring light every 0.001 milliseconds, every 0.01 milliseconds, every 0.1 milliseconds, every 1 millisecond, every 10 milliseconds, every 100 milliseconds, every 1000 milliseconds, or some other interval.

[0104] In some embodiments, the light detection system is configured to detect light from a plurality of different locations of the flow stream. In some embodiments, the light detection system is configured to detect light from 10 or more locations (e.g., a segment of a predetermined length) of the flow stream, such as 25 or more locations, such as 50 or more locations, such as 75 or more locations, such as 100 or more locations, such as 150 or more locations, such as 200 or more locations, such as 250 or more locations of the flow stream, and from 500 or more locations of the flow stream. In some embodiments, the light detection system is configured to detect light from each location of the flow stream simultaneously. In some embodiments, the light detection system includes an imaging light detector that detects light simultaneously across the flow stream at a plurality of pixel locations. For example, the imaging light detector may be configured to detect light from the flow stream at 10 or more pixel locations across the flow stream, such as 25 or more pixel locations, such as 50 or more pixel locations, such as 75 or more pixel locations, such as 100 or more pixel locations, such as 150 or more pixel locations, such as 200 or more pixel locations, such as 250 or more pixel locations, including 500 or more pixel locations across the horizontal axis of the flow stream. In some examples, each pixel location corresponds to a different point in the flow stream.

[0105] In certain embodiments, the system further comprises a flow cell configured to propagate the sample in the flow stream. Any convenient flow cell that propagates the fluid sample to the sample interrogation region may be employed, and in some embodiments, the flow cell comprises a proximal cylindrical portion defining a longitudinal axis and a distal frustoconical portion terminating in a flat surface having an orifice transverse to the longitudinal axis. The length of the proximal cylindrical portion (measured along the longitudinal axis) may vary from 1 mm to 15 mm, e.g., 1.5 mm to 12.5 mm, e.g., 2 mm to 10 mm, e.g., 3 mm to 9 mm, and 4 mm to 8 mm. The length of the distal frustoconical portion (measured along the longitudinal axis) may also vary from 1 mm to 10 mm, e.g., 2 mm to 9 mm, e.g., 3 mm to 8 mm, and 4 mm to 7 mm. The diameter of the flow cell nozzle chamber may vary in some embodiments from 1 mm to 10 mm, such as from 2 mm to 9 mm, such as from 3 mm to 8 mm, and from 4 mm to 7 mm.

[0106] In certain examples, the flow cell does not include a cylindrical portion, and the entire flow cell inner chamber is frustoconical in shape. In these embodiments, the length of the frustoconical inner chamber (measured along a longitudinal axis transverse to the nozzle orifice) may vary from 1 mm to 15 mm, such as 1.5 mm to 12.5 mm, such as 2 mm to 10 mm, such as 3 mm to 9 mm, and 4 mm to 8 mm. The diameter of the proximal portion of the frustoconical inner chamber may range from 1 mm to 10 mm, such as 2 mm to 9 mm, such as 3 mm to 8 mm, and 4 mm to 7 mm.

[0107] In some embodiments, the sample flow stream is emitted from an orifice at the distal end of the flow cell. Depending on the desired characteristics of the flow stream, the flow cell orifice can be of any suitable shape, with cross-sectional shapes of interest including, but not limited to, rectilinear cross-sectional shapes, e.g., square, rectangular, trapezoidal, triangular, hexagonal, etc., curved cross-sectional shapes, e.g., circular, elliptical, as well as irregular shapes, e.g., a parabolic bottom joined to a planar top. In certain embodiments, the flow cell of interest has a circular orifice. The size of the nozzle orifice may vary in some embodiments from 1 μm to 20000 μm, such as from 2 μm to 17500 μm, such as from 5 μm to 15000 μm, such as from 10 μm to 12500 μm, such as from 15 μm to 10000 μm, such as from 25 μm to 7500 μm, such as from 50 μm to 5000 μm, such as from 75 μm to 1000 μm, such as from 100 μm to 750 μm, and from 150 μm to 500 μm. In a particular embodiment, the nozzle orifice is 100 μm.

[0108] In some embodiments, the flow cell includes a sample injection port configured to provide a sample to the flow cell. In an embodiment, the sample injection system is configured to provide a suitable sample flow to the flow cell inner chamber. Depending on the desired characteristics of the flow stream, the speed of the sample delivered by the sample injection port to the flow cell chamber can be 1 μL / min or more, such as 2 μL / min or more, such as 3 μL / min or more, such as 5 μL / min or more, such as 10 μL / min or more, such as 15 μL / min or more, such as 25 μL / min or more, such as 50 μL / min or more, and 100 μL / min or more, and in some examples, the speed of the sample delivered by the sample injection port to the flow cell chamber is 1 μL / sec or more, such as 2 μL / sec or more, such as 3 μL / sec or more, such as 5 μL / sec or more, such as 10 μL / sec or more, such as 15 μL / sec or more, such as 25 μL / sec or more, such as 50 μL / sec or more, and 100 μL / sec or more.

[0109] The sample injection port may be an orifice disposed in the wall of the inner chamber, or may be a conduit disposed at the proximal end of the inner chamber. When the sample injection port is an orifice disposed in the wall of the inner chamber, the sample injection port orifice may be of any suitable shape, with cross-sectional shapes of interest including, but not limited to, rectilinear cross-sectional shapes, such as square, rectangular, trapezoidal, triangular, hexagonal, etc., curved cross-sectional shapes, such as circular, elliptical, etc., as well as irregular shapes, such as a parabolic bottom joined to a planar top. In certain embodiments, the sample injection port has a circular orifice. The size of the sample injection port orifice may vary depending on the shape, with certain examples having an opening in the range of 0.1 mm to 5.0 mm, such as 0.2 mm to 3.0 mm, such as 0.5 mm to 2.5 mm, such as 0.75 mm to 2.25 mm, such as 1 mm to 2 mm, and 1.25 mm to 1.75 mm, such as 1.5 mm.

[0110] In certain examples, the sample injection port is a conduit disposed at the proximal end of the flow cell internal chamber. For example, the sample injection port can be a conduit disposed to have the orifice of the sample injection port in line with the flow cell orifice. When the sample injection port is a conduit disposed in line with the flow cell orifice, the cross-sectional shape of the sample injection tube can be any suitable shape, and cross-sectional shapes of interest include, but are not limited to, rectilinear cross-sectional shapes, such as square, rectangular, trapezoidal, triangular, hexagonal, etc., curved cross-sectional shapes, such as circular, elliptical, and irregular shapes, such as a parabolic bottom joined to a flat top. The orifice of the conduit can vary depending on the shape, and in certain examples has an opening in the range of 0.1 mm to 5.0 mm, such as 0.2 to 3.0 mm, such as 0.5 mm to 2.5 mm, such as 0.75 mm to 2.25 mm, such as 1 mm to 2 mm, and 1.25 mm to 1.75 mm, such as 1.5 mm. The shape of the tip of the sample injection port may be the same as or different from the cross-sectional shape of the sample injection tube. For example, the orifice of the sample injection port may include a bevel tip having a bevel angle in the range of 1° to 10°, such as 2° to 9°, such as 3° to 8°, such as 4° to 7°, and a bevel angle of 5°.

[0111] In some embodiments, the flow cell also includes a sheath fluid injection port configured to provide sheath fluid to the flow cell. In embodiments, the sheath fluid injection system is configured to provide a flow of sheath fluid to the flow cell interior chamber, for example, in conjunction with the sample to generate a laminar flow stream of sheath fluid surrounding the sample flow stream. Depending on the desired characteristics of the flow stream, the velocity of the sheath fluid delivered to the flow cell chamber can be 25 μL / sec or more, such as 50 μL / sec or more, for example 75 μL / sec or more, for example 100 μL / sec or more, for example 250 μL / sec or more, for example 500 μL / sec or more, for example 750 μL / sec or more, for example 1000 μL / sec or more, and 2500 μL / sec or more.

[0112] In some embodiments, the sheath fluid injection port is an orifice disposed in the wall of the inner chamber. The sheath fluid injection port orifice may be of any suitable shape, and cross-sectional shapes of interest include, but are not limited to, rectilinear cross-sectional shapes, such as square, rectangular, trapezoidal, triangular, hexagonal, etc., curved cross-sectional shapes, such as circular, elliptical, and irregular shapes, such as a parabolic bottom coupled to a flat top. The size of the sample injection port orifice may vary depending on the shape, and in certain examples has an opening in the range of 0.1 mm to 5.0 mm, such as 0.2 to 3.0 mm, such as 0.5 mm to 2.5 mm, such as 0.75 mm to 2.25 mm, such as 1 mm to 2 mm, and 1.25 mm to 1.75 mm, such as 1.5 mm.

[0113] In some embodiments, the system further includes a pump in fluid communication with the flow cell for propagating the flow stream through the flow cell. Any convenient fluid pumping protocol may be employed to control the flow of the flow stream through the flow cell. In certain examples, the system includes a peristaltic pump, such as a peristaltic pump with a pulse damper. The pump in the subject system is configured to convey fluid through the flow cell at a rate suitable for detecting light from the sample in the flow stream. In some examples, the rate of sample flow in the flow cell is 1 μL / min (microliters per minute) or more, such as 2 μL / min or more, such as 3 μL / min or more, such as 5 μL / min or more, such as 10 μL / min or more, such as 25 μL / min or more, such as 50 μL / min or more, such as 75 μL / min or more, such as 100 μL / min or more, such as 250 μL / min or more, such as 500 μL / min or more, such as 750 μL / min or more, and 1000 μL / min or more. For example, the system may include a pump configured to flow the sample through the flow cell at a rate in the range of 1 μL / min to 500 μL / min, such as 1 μL / min to 250 μL / min, such as 1 μL / min to 100 μL / min, such as 2 μL / min to 90 μL / min, such as 3 μL / min to 80 μL / min, such as 4 μL / min to 70 μL / min, such as 5 μL / min to 60 μL / min, and 10 μL / min to 50 μL / min. In certain embodiments, the flow rate of the flow stream is between 5 μL / min to 6 μL / min.

[0114] In certain embodiments, a light detection system having multiple light detectors as described above is part of or located within a particle analyzer, such as a particle sorter, hi certain embodiments, the subject system is a flow cytometry system that includes a photodiode and amplifier components as part of the light detection system for detecting light emitted by a sample in a flow stream. Suitable flow cytometry systems include, but are not limited to, those described in Ormerod (ed.), Flow Cytometry: A Practical Approach, Oxford Univ. Press (1997); Jaroszeski et al. (eds.), Flow Cytometry Protocols, Methods in Molecular Biology No. 91, Humana Press (1997); Practical Flow Cytometry, 3rd ed., Wiley-Liss (1995); Virgo et al. (2012) Ann Clin Biochem. Jan; 49(pt1):17-28; Linden et al., Semin Thromb Hemost. Oct. 2004; 30(5):502-11; Alison et al. J Pathol, Dec. 2010; 222(4):335-344; and Herbig et al. (2007) Crit Rev Ther Drug Carrier Syst. 24(3):203-255, the disclosures of which are incorporated herein by reference.In particular examples, flow cytometry systems of interest include BD Biosciences FACSCanto™ flow cytometers, BD Biosciences FACSCanto™ II flow cytometers, BD Accuri™ flow cytometers, BD Accuri™ C6 Plus flow cytometers, BD Biosciences FACSCelesta™ flow cytometers, BD Biosciences FACSLyric™ flow cytometers, BD Biosciences FACSVerse™ flow cytometers, BD Biosciences FACSymphony™ flow cytometers, BD Biosciences LSRFortessa™ flow cytometers, BD Biosciences LSRFortessa™ X-20 flow cytometers, BD Biosciences FACSPresto™ flow cytometers, BD Biosciences FACSVia™ flow cytometers, and BD Biosciences FACSCalibur™ cell sorters, BD Biosciences FACSCount™ cell sorters, BD Biosciences FACSLyric™ cell sorters, BD These include BD Biosciences Via™ cell sorters, BD Biosciences Influx™ cell sorters, BD Biosciences Jazz™ cell sorters, BD Biosciences Aria™ cell sorters, BD Biosciences FACSAria™ II cell sorters, BD Biosciences FACSAria™ III cell sorters, BD Biosciences FACSAria™ Fusion cell sorters and BD Biosciences FACSMelody™ cell sorters, BD Biosciences FACSymphony™ S6 cell sorters, and others.

[0115] In some embodiments, the subject systems may be implemented using the same or similar technology as disclosed in U.S. Patent Nos. 10,663,476; 10,620,111; 10,613,017; 10,605,713; 10,585,031; 10,578,542; 10,578,469; 10,481,074; 10,302,5 No. 45; No. 10,145,793; No. 10,113,967; No. 10,006,852; No. 9,952,076; No. 9,933,341; No. 9 ,726,527;No.9,453,789;No.9,200,334;No.9,097,640;No.9,095,494;No.9,092,034 and flow cytometry systems such as those described in Nos. 8,975,595; 8,753,573; 8,233,146; 8,140,300; 7,544,326; 7,201,875; 7,129,505; 6,821,740; 6,813,017; 6,809,804; 6,372,506; 5,700,692; 5,643,796; 5,627,040; 5,620,842; 5,602,039; 4,987,086; and 4,498,766; the disclosures of which are incorporated by reference in their entireties herein.

[0116] In some embodiments, the subject system is a particle sorting system configured to sort particles with an encapsulated particle sorting module, such as that described in U.S. Patent Publication No. 2017 / 0299493, the disclosure of which is incorporated herein by reference. In certain embodiments, particles (e.g., cells) of a sample are sorted using a sort decision module having multiple sort decision units, such as that described in U.S. Patent Publication No. 2020 / 0256781, the disclosure of which is incorporated herein by reference. In some embodiments, the subject system includes a particle sorting module having a deflector plate, such as that described in U.S. Patent Publication No. 2017 / 0299493, filed March 28, 2017, the disclosure of which is incorporated herein by reference.

[0117] In certain examples, the flow cytometry system of the present invention may be implemented using the methods disclosed in Diebold et al., Nature Photonics Vol. 7(10); 806-810 (2013) and U.S. Pat. Nos. 9,423,353; 9,784,661; 9,983,132; 10,006,852; 10,078,045; 10,036,699; 10,222,316; 10,288,546; 10,324,019; 10,408,758; 10,451,538; 10,620,110; No. 2017 / 0328826; 2017 / 0350803; 2018 / 0275042; 2019 / 0376895; and 2019 / 0376894.

[0118] In some embodiments, the system is a particle analyzer, and a particle analysis system 401 (FIG. 4A) can be used to analyze and characterize particles with or without physically sorting the particles into a collection vessel. FIG. 4A 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 401 is a flow system. The particle analysis system 401 shown in FIG. 4A can be configured in whole or in part to perform methods, such as those described herein. The particle analysis system 401 includes a fluid system 402. The fluid system 402 can include or be coupled to a sample tube 405 and a moving fluid column within the sample tube, in which particles 403 (e.g., cells) of the sample move along a common sample path 409.

[0119] The particle analysis system 401 includes a detection system 404 configured to collect a signal from each particle as it passes through one or more detection stations along a common sample path. A detection station 408 generally refers to a monitoring region 407 of the common sample path. Detection, in some implementations, can include detection of light or one or more other properties of the particle 403 as it passes through the monitoring region 407. In FIG. 4A, one detection station 408 is shown with one monitoring region 407. Some implementations of the particle analysis system 401 can include multiple detection stations. Additionally, some detection stations can monitor more than one region.

[0120] Each signal is assigned a signal value to form a data point for each particle. As described above, this data may be referred to as event data. The data points may be multi-dimensional data points that include values ​​for each property measured for the particle. The detection system 404 is configured to collect a series of such data points over a first time interval.

[0121] The particle analysis system 401 may also include a control system 406. The control system 406 may include one or more processors, amplitude control circuitry, and / or frequency control circuitry. The illustrated control system may be operatively associated with the fluid system 402. The control system may be configured to generate a calculated signal frequency for at least a portion of the first time interval based on the Poisson distribution and the number of data points collected by the detection system 404 during the first time interval. The control system 406 may further be configured to generate an experimental signal frequency based on the number of data points in that portion of the first time interval. The control system 406 may further compare the experimental signal frequency to that of the calculated signal frequency or a predetermined signal frequency.

[0122] 4B illustrates a system 400 for flow cytometry according to an exemplary embodiment of the invention. System 400 includes a flow cytometer 410, a controller / processor 490, and a memory 495. Flow cytometer 410 includes one or more excitation lasers 415a-415c, a focusing lens 420, a flow chamber 425, a forward scatter detector 430, a side scatter detector 435, a fluorescence collecting lens 440, one or more beam splitters 445a-445g, one or more bandpass filters 450a-450e, one or more longpass ("LP") filters 455a-455b, and one or more fluorescence detectors 460a-460f.

[0123] The excitation lasers 115a-c emit light in the form of laser light. The wavelengths of the laser beams emitted from the excitation lasers 415a-415c are 488 nm, 633 nm, and 325 nm, respectively, in the exemplary system of FIG. 4B. The laser beams are first directed through one or more of the beam splitters 445a and 445b. The beam splitter 445a transmits the 488 nm light and reflects the 633 nm light. The beam splitter 445b transmits the UV light (light with wavelengths in the range of 10-400 nm) and reflects the 488 nm and 633 nm lights.

[0124] The laser beam is then directed to a focusing lens 420, which focuses the beam onto the portion of the fluid stream where the sample particles are located, in a flow chamber 425. The flow chamber is part of a fluidic system that directs particles, typically one at a time, in the stream towards the focused laser beam for interrogation. The flow chamber can comprise a flow cell in a benchtop cytometer, or a nozzle tip in a stream-in-air cytometer.

[0125] Light from the laser beam(s) interacts with particles in the sample by diffraction, refraction, reflection, scattering, and absorption with re-emission at a variety of different wavelengths depending on the particle's properties, such as the particle's size, internal structure, and the presence of one or more fluorescent molecules attached to or naturally occurring on or in the particle. The fluorescent emission and diffracted, refracted, reflected, and scattered light may be routed through one or more of beam splitters 445a-445g, bandpass filters 450a-450e, longpass filters 455a-455b, and fluorescence collection lens 440 to one or more of a forward scatter detector 430, a side scatter detector 435, and one or more fluorescence detectors 460a-460f.

[0126] Fluorescence collection lens 440 collects light emitted from particle-laser beam interactions and routes the light towards one or more beam splitters and filters. Bandpass filters, such as bandpass filters 450a-450e, allow a narrow range of wavelengths to pass through the filter. For example, bandpass filter 450a is a 510 / 20 filter. The first number represents the center of the spectral band. The second number provides the range of the spectral band. Thus, a 510 / 20 filter extends 10 nm on each side of the center of the spectral band, i.e., from 500 nm to 520 nm. Shortpass filters transmit wavelengths of light at or below a specified wavelength. Longpass filters, such as longpass filters 455a-455b, transmit wavelengths of light at or above a specified wavelength. For example, longpass filter 455a, a 670 nm longpass filter, transmits light at or above 670 nm. Filters are often selected to optimize the specificity of the detector for a particular fluorochrome. The filters can be configured so that the spectral band of light transmitted to the detector is close to the emission peak of the fluorescent dye.

[0127] Beam splitters direct different wavelengths of light in different directions. Beam splitters can be characterized by filter properties such as short pass and long pass. For example, beam splitter 445g is a 620SP beam splitter, meaning that beam splitter 445g transmits wavelengths of light equal to or less than 620 nm and reflects wavelengths of light longer than 620 nm in different directions. In one embodiment, beam splitters 445a-445g can comprise optical mirrors, such as dichroic mirrors.

[0128] The forward scatter detector 430 is positioned slightly off-axis from the direct beam through the flow cell and configured to detect diffracted light, while the excitation light travels through or around the particle in a generally forward direction. The intensity of light detected by the forward scatter detector depends on the overall size of the particle. The forward scatter detector can include a photodiode. The side scatter detector 435 is configured to detect refracted and reflected light from the surface and internal structure of the particle, which tends to increase with increasing particle complexity of structure. Fluorescence emission from fluorescent molecules associated with the particle can be detected by one or more fluorescence detectors 460a-460f. The side scatter detector 435 and the fluorescence detector can include photomultiplier tubes. The signals detected by the forward scatter detector 430, the side scatter detector 435 and the fluorescence detector can be converted to electronic signals (voltages) by the detectors. This data can provide information about the sample.

[0129] Those skilled in the art will recognize that flow cytometers according to embodiments of the present invention are not limited to the flow cytometer shown in Figure 4B, but may include any flow cytometer known in the art. For example, a flow cytometer may have any number of lasers, beam splitters, filters, and detectors at various wavelengths and in a variety of different configurations.

[0130] During operation, cytometer operation is controlled by controller / processor 490, and measurement data from the detectors can be stored in memory 495 and processed by controller / processor 490. Although not explicitly shown, controller / processor 190 can also be coupled to electrical and electromechanical components of flow cytometer 400 to couple to detectors to receive output signals therefrom, and to control lasers, fluid flow parameters, and the like. Input / output (I / O) functionality 497 can also be provided in the system. Memory 495, controller / processor 490, and I / O 497 can be provided entirely as an integral part of flow cytometer 410. In such an embodiment, a display can also form part of I / O functionality 497 for presenting experimental data to a user of cytometer 400. Alternatively, memory 495 and controller / processor 490 and some or all of the I / O functionality can be part of one or more external devices, such as a general purpose computer. In some embodiments, memory 495 and some or all of controller / processor 490 can be in wireless or wired communication with cytometer 410. The controller / processor 490, in conjunction with the memory 495 and the I / O 497, can be configured to perform a variety of functions associated with the preparation and analysis of a flow cytometer experiment.

[0131] The system shown in FIG. 4B includes six different detectors that detect fluorescence in six different wavelength bands (which may also be referred to herein as "filter windows" for a given detector), as defined by the configuration of filters and / or splitters in the beam path from the flow cell 425 to each detector. Different fluorescent molecules used in a flow cytometer experiment will emit light in their own characteristic wavelength bands. The particular fluorescent labels and their associated fluorescent emission bands used in the experiment may be selected to generally match the filter windows of the detectors. However, when more detectors are provided and more labels are utilized, perfect correspondence between filter windows and fluorescent emission spectra is not possible. While the peak of the emission spectrum of a particular fluorescent molecule may lie within the filter window of one particular detector, it is generally true that some of the emission spectrum of that label also overlaps the filter window of one or more other detectors. This may be referred to as spillover. The I / O 497 may be configured to receive data regarding a flow cytometer experiment having a panel of fluorescent labels and multiple cell populations having multiple markers, each cell population having a subset of the multiple markers. The I / O 497 can also be configured to receive biological data assigning one or more markers to one or more cell populations, marker density data, emission spectrum data, data assigning labels to one or more markers, and cytometer configuration data. Flow cytometer experimental data, such as label spectral characteristics and flow cytometer configuration data, can also be stored in memory 495. The controller / processor 490 can be configured to evaluate one or more assignments of labels to markers.

[0132] 5 shows a functional block diagram of one embodiment of a particle analyzer system for analyzing and displaying biological events, such as an analysis controller 500. The analysis controller 500 can be configured to implement various processes for controlling the graphical display of the biological events.

[0133] The particle analyzer or sorting system 502 can be configured to acquire biological event data. For example, a flow cytometer can generate flow cytometric event data. The particle analyzer 502 can be configured to provide the biological event data to the analysis controller 500. A data communication channel can be included between the particle analyzer or sorting system 502 and the analysis controller 500. The biological event data can be provided to the analysis controller 500 via the data communication channel.

[0134] The analysis controller 500 can be configured to receive biological event data from a particle analyzer or sorting system 502. The biological event data received from the particle analyzer or sorting system 502 can include flow cytometric event data. The analysis controller 500 can be configured to provide a graphical display on a display device 506 including a first plot of the biological event data. The analysis controller 500 can be further configured to render a region of interest as a gate around a population of the biological event data shown by the display device 506, for example overlaid on the first plot. In some embodiments, the gate can be a logical combination of one or more graphical regions of interest depicted on a single parameter histogram or bivariate plot. In some embodiments, the display can be used to display particle parameters or saturation detector data.

[0135] Analysis controller 500 can be further configured to display the biological event data within the gate on display device 506 in a manner different from other events within the biological event data outside the gate. For example, analysis controller 500 can be configured to render the colors of the biological event data contained within the gate distinct from the colors of the biological event data outside the gate. Display device 506 can be implemented as a monitor, tablet computer, smartphone, or other electronic device configured to present a graphical interface.

[0136] The analysis controller 500 can be configured to receive a gate selection signal identifying a gate from a first input device. For example, the first input device can be implemented as a mouse 510. The mouse 510 can initiate a gate selection signal to the analysis controller 500 to identify a gate to be displayed on or manipulated via the display device 506 (e.g., by clicking on or within the desired gate when a cursor is there). In some implementations, the first device can be implemented as a keyboard 508 or other means for providing an input signal to the analysis controller 500, such as a touch screen, a stylus, an optical detector, or a voice recognition system. Some input devices can include multiple input functions. In such implementations, each input function can be considered an input device. For example, as shown in FIG. 5, the mouse 510 can include a right mouse button and a left mouse button, each of which can generate a trigger event.

[0137] The trigger event can cause the analysis controller 500 to change the way the data is displayed, what portions of the data are actually displayed on the display device 506, and / or provide input to further processing, such as selection of a population of interest for particle sorting.

[0138] In some embodiments, the analysis controller 500 can be configured to detect when a gate selection is initiated by the mouse 510. The analysis controller 500 can be further configured to automatically modify the plot visualization to facilitate the gating process. The modification can be based on a particular distribution of the biological event data received by the analysis controller 500.

[0139] The analysis controller 500 can be connected to a memory device 504. The memory device 504 can be configured to receive and store biological event data from the analysis controller 500. The memory device 504 can also be configured to receive and store flow cytometric event data from the analysis controller 500. The memory device 504 can be further configured to enable acquisition of biological event data, such as flow cytometric event data, by the analysis controller 500.

[0140] The display device 506 can be configured to receive display data from the analysis controller 500. The display data can include plots of the biological event data and gates that delineate sections of the plots. The display device 506 can be further configured to modify the information presented according to input received from the analysis controller 500 in conjunction with input from the particle analyzer 502, the memory device 504, the keyboard 508, and / or the mouse 510.

[0141] In some implementations, the analysis controller 500 can generate a user interface for receiving the exemplary events for sorting. For example, the user interface can include a control for receiving the exemplary events or exemplary images. The exemplary events or images, or the exemplary gates, can be provided prior to collecting event data for the sample or based on an initial set of events for a portion of the sample.

[0142] FIG. 6A is a schematic diagram of a particle sorter system 600 (e.g., particle analyzer or sorting system 502) according to one embodiment presented herein. In some embodiments, the particle sorter system 600 is a cell sorter system. As shown in FIG. 6A, a droplet forming transducer 602 (e.g., a piezoelectric oscillator) is coupled to a fluid conduit 601, which can be coupled to, can include, or can be a nozzle 603. Within the fluid conduit 601, a sheath fluid 604 hydrodynamically focuses a sample fluid 606 with particles 609 into a moving fluid column 608 (e.g., a stream). Within the moving fluid column 608, the particles 609 (e.g., cells) are lined up in single file across a monitoring region 611 (e.g., where laser streams intersect) illuminated by an illumination source 612 (e.g., a laser). The vibration of the droplet forming transducer 602 causes the moving fluid column 608 to break up into a number of droplets 610 , some of which contain particles 609 .

[0143] In operation, the detection station 614 (e.g., an event detector) identifies when a particle of interest (or cell of interest) crosses the monitoring region 611. The detection station 614 feeds into a timing circuit 628, which in turn feeds into a flash charge circuit 630. At a droplet break-off point, signaled by a timed droplet delay (Δt), a flash charge is applied to the moving fluid column 608, causing the droplet of interest to carry a charge. The droplet of interest may contain one or more particles or cells to be sorted. The charged droplets may then be sorted by activating a deflection plate (not shown) to deflect the droplets into a container, such as a collection tube or a multi-well or microwell sample plate, where a well or microwell may be associated with a particular droplet of interest. The droplets may be collected in a drain receptacle 638, as shown in FIG. 6A.

[0144] The detection system 616 (e.g., a droplet boundary detector) functions to automatically determine the phase of the droplet drive signal as a particle of interest passes through the monitoring region 611. An exemplary droplet boundary detector is described in U.S. Pat. No. 7,679,039, which is incorporated by reference herein in its entirety. The detection system 616 allows the instrument to accurately calculate the position of each detected particle within the droplet. The detection system 616 can feed into an amplitude 620 and / or phase 618 signal, which are then fed (via amplifier 622) to an amplitude control circuit 626 and / or a frequency control circuit 624. The amplitude control circuit 626 and / or the frequency control circuit 624 then control the droplet forming transducer 602. The amplitude control circuit 626 and / or the frequency control circuit 624 can be included in the control system.

[0145] In some implementations, the sorting electronics (e.g., detection system 616, detection station 614, and processor 640) can be coupled with a memory configured to store the detected events and a sorting decision based thereon. The sorting decision can be included in the event data for the particles. In some implementations, the detection system 616 and detection station 614 can be implemented as a single detection unit or communicatively coupled such that event measurements are collected by one of the detection system 616 or detection station 614 and provided to a non-collection element.

[0146] FIG. 6B is a schematic diagram of a particle sorter system according to one embodiment presented herein. The particle sorter system 600 shown in FIG. 6B includes deflection plates 652 and 654. An electric charge can be applied via a stream of charging wires in the barbs. This creates a stream of droplets 610 containing the particles 610 for analysis. The particles can be illuminated with one or more light sources (e.g., lasers) to generate light scattering and fluorescence information. The particle information is analyzed, such as by sorting electronics or other detection systems (not shown in FIG. 6B). The deflection plates 652 and 654 can be independently controlled to attract or repel the charged droplets and guide them toward a desired collection receptacle (e.g., one of 672, 674, 676, or 678). 6B, deflection plates 652 and 654 can be controlled to direct particles along a first path 662 toward a receptacle 674, or along a second path 668 toward a receptacle 678. If a particle is not of interest (e.g., does not exhibit scattering or illumination information within a specified sorting range), the deflection plates may allow the particle to continue along flow path 664. Such uncharged droplets may be passed to a waste receptacle, such as via an aspirator 670.

[0147] Sorting electronics can be included to initiate collection of measurements, receive fluorescent signals for the particles, and determine how to adjust the deflection plates to cause sorting of the particles. An exemplary embodiment of the embodiment shown in Figure 6B includes the BD FACSAria™ line of flow cytometers commercially offered by Becton, Dickinson and Company (Franklin Lakes, NJ).

[0148] Computer Control System

[0149] Aspects of the present disclosure further include a computer control system, the system further comprising one or more computers for full or partial automation of the methods described herein. In some embodiments, the system includes a computer having a computer readable storage medium having a computer program stored thereon, the computer program including instructions for generating a composite population of events including a data accessor from the cytometry data when loaded into the computer. In some embodiments, the computer program includes instructions for generating a composite population of events including a data accessor from the cytometry data. In some examples, the computer program includes instructions for processing cytometer data generated based on data signals from a scattered light detector channel (e.g., forward scatter image data, side scatter image data). In other examples, the computer program includes instructions for processing cytometer data generated based on data signals from one or more fluorescence detector channels. In other examples, the computer program includes instructions for processing cytometer data generated based on data signals from one or more light loss detector channels. In still other examples, the computer program includes instructions for processing cytometer data generated based on data signals from a combination of two or more of a light scatter detector channel, a fluorescence detector channel, and a light loss detector channel.

[0150] In some examples, the computer program includes instructions for generating a composite population from cytometry data from two or more different samples, such as three or more different samples, such as four or more different samples, such as five or more different samples, and also includes instructions for generating a composite population from cytometry data collected from ten or more different samples. In some embodiments, the computer program includes instructions for generating a composite population including a data accessor for each event of cytometry data. In some embodiments, the computer program includes instructions for accessing metadata for each event of cytometry data using the data accessor, such as accessing metadata associated with the raw data files collected for each sample. In some embodiments, the data accessor includes a source identity for each event of the sample. In some examples, the computer program includes instructions for generating a composite population from cytometry data from two or more different samples, where the raw data from each sample is held as a separate data file. In certain examples, the computer program includes instructions for generating a composite population from cytometry data from two or more different samples, where the raw data files from each sample are not concatenated to form a single combined data file.

[0151] In some embodiments, the computer program includes instructions for applying a data gate to the composite population to generate a gated composite population. In some examples, the computer program includes instructions for applying a hierarchy of data gates to the composite population. In some examples, the computer program includes instructions for applying a hierarchy of data gates to the composite population to generate a hierarchy of gated composite populations. In some examples, the computer program includes instructions for applying a data gate to one event of the composite population, and in certain examples, applying the data gate to multiple events in the composite population. In certain examples, applying the data gate to a single event of the composite population provides for applying the data gate to every event in the composite population. In some embodiments, the computer program includes instructions for defining one or more subpopulations of events of the composite population, and application of the data gate is sufficient to apply the data gate to all of the events of the subpopulation. In some embodiments, the computer program includes instructions for applying an analytical algorithm to the gated composite population, such as instructions for applying a clustering algorithm or a compensation matrix to the gated composite population.

[0152] In certain embodiments, the computer program includes instructions for desynchronizing data gates for one or more samples of the gated composite population. In some examples, the computer program includes instructions for desynchronizing data gates by changing the geometry of the data gates applied to one or more samples of the gated composite population. In certain examples, the computer program includes instructions for desynchronizing data gates (e.g., changing the gate geometry) for multiple samples, e.g., data gates are desynchronized sequentially (i.e., one at a time) for each sample of the composite population.

[0153] In some embodiments, the computer program includes instructions for generating a graphical user interface including a first pane configured to display one or more ungated composite populations including cytometry data of one or more groups of samples, a second pane displaying one or more gated composite populations, and a third pane displaying data files for each of the samples used to generate the composite population. In some examples, the computer program includes instructions for generating a graphical user interface, the second pane displays a hierarchy of gated composite populations. In some examples, the second pane displays an analytical algorithm applied to the events of the composite population selected in the first pane, such as a clustering algorithm or compensation matrix applied to the composite population displayed in the first pane. In some embodiments, the hierarchy of gated composite populations is color coded in the second pane. In some examples, each of the inherited data gates is labeled with the same color. In some examples, the computer program includes instructions for generating a graphical user interface visualizing desynchronized data gates in the second pane. In some examples, each desynchronized data gate is visualized in the second pane by a different text font. In some embodiments, the computer program includes instructions for displaying in a third pane of the graphical user interface a data file for each sample having an event in the gated composite population selected in the second pane.

[0154] In some embodiments, the computer program includes instructions for applying the analytical algorithm displayed in the first pane to one or more of the gated composite populations displayed in the second pane. In certain examples, the computer program includes instructions for applying the analytical algorithm to one or more of the gated composite populations by dragging the analytical algorithm displayed in the first pane to the gated composite population displayed in the second pane. In other examples, the computer program includes instructions for applying the analytical algorithm to one or more of the gated composite populations by selecting an analytical algorithm from a menu of analytical algorithms and applying the selected algorithm to the gated composite population displayed in the second pane. In certain examples, the computer program includes instructions for displaying an icon in the second pane of the graphical user interface on the gated composite population in response to applying the analytical algorithm from the first pane. In some embodiments, the computer program includes instructions for applying the analytical algorithm to the gated composite population in the second pane such that the analytical algorithm is applied to one or more subgroups in the hierarchy of applied data gates. In some examples, applying the analytical algorithm to the gated composite population is sufficient to apply the analytical algorithm to all of the gated composite populations in the hierarchy of applied data gates.

[0155] In some embodiments, the computer program includes instructions for applying the analytical algorithm displayed in the first pane to one or more of the data files for the sample displayed in the third pane. In certain examples, the computer program includes instructions for applying the analytical algorithm by dragging the analytical algorithm displayed in the first pane to the data files for the sample displayed in the third pane. In other examples, the computer program includes instructions for applying the analytical algorithm to one or more of the data files for the sample displayed in the third pane by selecting an analytical algorithm from a menu of analytical algorithms and applying the selected algorithm to one or more of the data files for the sample displayed in the third pane. In some embodiments, the computer program includes instructions for applying the analytical algorithm displayed in the second pane (e.g., tSNE, x-shift algorithm) to one or more of the data files for the sample displayed in the third pane. In certain examples, the computer program includes instructions for applying the analytical algorithm by dragging the analytical algorithm displayed in the second pane to the data files for the sample displayed in the third pane.

[0156] In an embodiment, the system includes an input module, a processing module, and an output module. The subject systems may include both hardware and software components, and the hardware components may take the form of one or more platforms, e.g., servers, such that the functional elements, i.e., those elements of the system that perform specific tasks of the system (managing the input and output of information, processing information, etc.), may be performed by executing software applications on and across one or more computer platforms representing the system.

[0157] The system may include a display and an operator input device. The operator input device may be, for example, a keyboard, a mouse, and the like. The processing module includes a processor that can access a memory having instructions stored thereon to perform the steps of the subject method. The processing module may include an operating system, a graphical user interface (GUI) controller, a system memory, a memory storage device, and an input / output controller, a cache memory, a data backup unit, and many other devices. The processor may be a commercially available processor, or it may be one of other processors that are available or will become available. The processor executes an operating system, which interfaces with firmware and hardware in a well-known manner to facilitate the processor coordinating and executing the functions of various computer programs, which may be written in a variety of programming languages, such as Java, Perl, C++, other high-level or low-level languages ​​known in the art, and combinations thereof. The operating system typically works in conjunction with the processor to coordinate and execute the functions of the other components of the computer. The operating system also provides scheduling, input / output control, file and data management, memory management, and communication control and related services, all according to known techniques. The processor may be any suitable analog or digital system. In some embodiments, the processor includes analog electronics that allow a user to manually align the light source with the flow stream based on the first and second light signals, hi some embodiments, the processor includes analog electronics that provide feedback control, e.g., negative feedback control.

[0158] The system memory may be any of a variety of known or future memory storage devices. Examples include commonly available random access memory (RAM), magnetic media such as a resident hard disk or tape, optical media such as a read and write compact disk, flash memory devices, or other memory storage devices. The memory storage device may be any of a variety of known or future devices, including a compact disk drive, a tape drive, a removable hard disk drive, or a diskette drive. Such types of memory storage devices typically read from and / or write to a program storage medium (not shown), such as a compact disk, a magnetic tape, a removable hard disk, or a floppy diskette. Any of these program storage media, or others now in use or that may later be developed, may be considered a computer program product. As will be appreciated, these program storage media typically store computer software programs and / or data. Computer software programs, also referred to as computer control logic, are typically stored in the system memory and / or in program storage devices used in combination with the memory storage devices.

[0159] In some embodiments, a computer program product is described that comprises a computer usable medium having control logic (computer software program including program code) stored thereon. The control logic, when executed by a processor, a computer, causes the processor to perform the functions described herein. In other embodiments, some functions are implemented primarily in hardware using, for example, hardware state machines. The implementation of hardware state machines to perform the functions described herein will be apparent to one skilled in the relevant art.

[0160] The memory may be any suitable device from which the processor can store and retrieve data, such as magnetic, optical, or solid-state storage devices (including magnetic or optical disks or tapes or RAM, or any other suitable device, either fixed or portable). The processor may include a general-purpose digital microprocessor suitably programmed from a computer-readable medium carrying the necessary program code. The programming may be provided remotely to the processor over a communication channel, or may be pre-stored in a computer program product, such as a memory or some other portable or fixed computer-readable storage medium, using any of those devices in conjunction with the memory. For example, a magnetic or optical disk may carry the programming and may be read with a disk writer / reader. The system of the present invention also includes programming, for example in the form of a computer program product, algorithms for use in implementing the above-described methods. The programming according to the present invention may be recorded on a computer-readable medium, for example any medium that can be directly read and accessed by a computer. Such media include, but are not limited to: magnetic storage media such as floppy disks, hard disk storage media, and magnetic tape; optical storage media such as CD-ROMs; electrical storage media such as RAM and ROM; portable flash drives; and hybrids of these categories such as magnetic / optical storage media.

[0161] The processor may also have access to a communication channel to communicate with a user at a remote location, meaning that the user does not have direct contact with the system, but rather relays input information to the input manager from an external device, such as a computer connected to a wide area network ("WAN"), a telephone network, a satellite network, or any other suitable communication channel, including a mobile phone (i.e., a smartphone).

[0162] In some embodiments, a system according to the present disclosure may be configured to include a communication interface. In some embodiments, the communication interface includes a receiver and / or a transmitter for communicating with a network and / or another device. The communication interface may be configured for wired or wireless communication, including, but not limited to, radio frequency (RF) communication (e.g., radio frequency identification (RFID), Zigbee communication protocol, Wi-Fi, infrared, wireless universal serial bus (USB), ultra-wideband (UWB), Bluetooth® communication protocol, and cellular communication such as Code Division Multiple Access (CDMA) or Global System for Mobile Communications (GSM)).

[0163] In one embodiment, the communications interface is configured to include one or more communications ports, e.g., a physical port or interface such as a USB port, an RS-232 port, or any other suitable electrical connection port that enables data communications between the subject system and other external devices, such as a computer terminal (e.g., in a clinic or hospital environment) configured for similar complementary data communications.

[0164] In one embodiment, the communications interface is configured for infrared communications, Bluetooth® communications, or any other suitable wireless communications protocol to enable the subject system to communicate with other devices, such as computer terminals and / or networks, communications-enabled mobile phones, personal digital assistants, or any other communications devices that a user may use in combination.

[0165] In one embodiment, the communications interface is configured to provide connectivity for data transfer utilizing Internet Protocol (IP) over a cellular network, Short Message Service (SMS), a wireless connection to a personal computer (PC) on a local area network (LAN) connected to the Internet, or a Wi-Fi connection to the Internet at a Wi-Fi hotspot.

[0166] In one embodiment, the subject system is configured to wirelessly communicate with a server device over a communications interface using common standards such as, for example, 802.11 or Bluetooth RF protocols, or the IrDA infrared protocol. The server device may be another portable device, such as a smart phone, personal digital assistant (PDA), or notebook computer, or a larger device, such as a desktop computer, appliance, etc. In some embodiments, the server device has a display, such as a liquid crystal display (LCD), and input devices, such as buttons, a keyboard, a mouse, or a touch screen.

[0167] In some embodiments, the communications interface is configured to automatically or semi-automatically communicate the subject system, e.g., data stored in the optional data storage unit, with a network or server device using one or more of the communications protocols and / or mechanisms described above.

[0168] The output controller may include a controller for any of a variety of known display devices for presenting information to a user, whether local or remote, human or machine. Where one of the display devices provides visual information, this information may typically be logically and / or physically organized as an array of image elements. A Graphical User Interface (GUI) controller provides a graphical input / output interface between the system and the user and may include any of a variety of known or future software programs for processing user input. The functional elements of the computer may communicate with each other via a system bus. Some of these communications may be accomplished in alternative embodiments using a network or other type of remote communication. The output manager may also provide information generated by the processing modules to a user at a remote location, for example, via the Internet, telephone or satellite networks, in accordance with known techniques. The presentation of data by the output manager may be implemented in accordance with a variety of known techniques. As some examples, the data may include SQL, HTML or XML documents, email or other files, or other formats of data. The data may include Internet URL addresses so that the user may obtain additional SQL, HTML, XML, or other documents or data from remote sources. The one or more platforms present in the subject system may be any type of known or future developed computer platform, but they will typically be of the class of computers commonly referred to as servers. However, they may also be mainframe computers, workstations, or other types of computers. They may be connected via other communication systems, including cabled or wireless systems of known or future types, whether networked or not. They may be co-located, or they may be physically separated.A variety of operating systems may be employed on any computer platform, possibly depending on the type and / or make of the computer platform selected. Suitable operating systems include Windows, iOS, Oracle Solaris, Linux, IBMi, Unix, etc.

[0169] FIG. 7 illustrates the general architecture of an exemplary computing device 700 according to certain embodiments. The general architecture of the computing device 700 illustrated in FIG. 7 includes an arrangement of computer hardware and software components. The computing device 700 may include more (or less) elements than those illustrated in FIG. 7. However, not all of these generally conventional elements need to be illustrated to provide a valid disclosure. As illustrated, the computing device 700 includes a processing unit 710, a network interface 720, a computer-readable medium drive 730, an input / output device interface 740, a display 750, and an input device 760, all of which may communicate with each other via a communication bus. The network interface 720 may provide a connection to one or more networks or computing systems. The processing unit 710 may thus receive information and instructions from other computing systems or services over the network. The processing unit 710 may also communicate with a memory 770 and further provide output information for the optional display 750 via the input / output device interface 740. The input / output device interface 740 may also accept input from optional input device(s) 760, such as a keyboard, mouse, digital pen, microphone, touch screen, gesture recognition system, voice recognition system, game pad, accelerometer, gyroscope, or other input device.

[0170] Memory 770 may include computer program instructions (grouped in some embodiments as modules or components) that processing unit 710 executes to implement one or more embodiments. Memory 770 generally includes RAM, ROM, and / or other persistent, secondary, or non-transitory computer-readable media. Memory 770 may store an operating system 772 that provides computer program instructions used by processing unit 710 in the overall management and operation of computing device 700. Memory 770 may further include computer program instructions and other information for implementing aspects of the present disclosure.

[0171] Non-transitory computer-readable storage medium

[0172] Aspects of the present disclosure further include a non-transitory computer-readable storage medium having instructions for carrying out the subject methods. The computer-readable storage medium may be employed on one or more computers for full or partial automation of a system for carrying out the methods described herein. In certain embodiments, instructions for the methods described herein may be encoded on a computer-readable medium in the form of "programming," and the term "computer-readable medium" as used herein refers to any non-transitory storage medium involved in providing instructions and data to a computer for execution and processing. Examples of suitable non-transitory storage media include floppy disks, hard disks, optical disks, magneto-optical disks, CD-ROMs, CD-Rs, magnetic tapes, non-volatile memory cards, ROMs, DVD-ROMs, Blu-ray disks, solid-state disks, and network-attached storage (NAS), regardless of whether such devices are internal or external to the computer. A file containing information may be "stored" on a computer-readable medium, where "storing" means recording information so that it can be accessed and retrieved at a later date by a computer. The computer-implemented methods described herein may be executed using programming that may be written in one or more of any number of computer programming languages. Such languages ​​include, for example, Python, Java, Java Script, C, C#, C++, Go, R, Swift, PHP, as well as any others.

[0173] A non-transitory computer readable storage medium according to certain embodiments includes instructions having an algorithm for generating a composite population of events including a data accessor from cytometry data. In some examples, the non-transitory computer readable storage medium includes an algorithm for processing cytometer data generated based on data signals from a scattered light detector channel (e.g., forward scatter image data, side scatter image data). In other examples, the non-transitory computer readable storage medium includes an algorithm for processing cytometer data generated based on data signals from one or more fluorescence detector channels. In other examples, the non-transitory computer readable storage medium includes an algorithm for processing cytometer data generated based on data signals from one or more light loss detector channels. In yet other examples, the non-transitory computer readable storage medium includes an algorithm for processing cytometer data generated based on data signals from a combination of data signals from two or more of a light scatter detector channel, a fluorescence detector channel, and a light loss detector channel.

[0174] In some examples, the non-transitory computer readable storage medium includes an algorithm for generating a composite population from flow cytometry data from two or more different samples, e.g., three or more different samples, e.g., four or more different samples, e.g., five or more different samples, and also includes instructions for generating a composite population from cytometry data collected from ten or more different samples. In some embodiments, the non-transitory computer readable storage medium includes an algorithm for generating a composite population, including a data accessor for each event of the cytometry data. In some embodiments, the non-transitory computer readable storage medium includes an algorithm for accessing metadata for each event of the cytometry data using the data accessor, such as accessing metadata associated with a raw data file collected for each sample. In some embodiments, the data accessor includes a source identity for each event of the sample. In some examples, the non-transitory computer readable storage medium includes an algorithm for generating a composite population from cytometry data from two or more different samples, and the raw data from each sample is maintained as a separate data file. In certain examples, the non-transitory computer readable storage medium includes an algorithm for generating a composite population from cytometry data from two or more different samples, where the raw data files from each sample are not concatenated to form a single combined data file.

[0175] In some embodiments, the non-transitory computer readable storage medium includes an algorithm for applying a data gate to a composite population to generate a gated composite population. In some examples, the non-transitory computer readable storage medium includes an algorithm for applying a hierarchy of data gates to the composite population. In some examples, the non-transitory computer readable storage medium includes an algorithm for applying a hierarchy of data gates to the composite population to generate a hierarchy of gated composite populations. In some examples, the non-transitory computer readable storage medium includes an algorithm for applying a data gate to one event of the composite population, and in certain examples, applying the data gate to multiple events in the composite population. In certain examples, applying a data gate to a single event of the composite population provides for applying the data gate to every event in the composite population. In some embodiments, the non-transitory computer readable storage medium includes an algorithm for defining one or more subpopulations of events of the composite population, and applying the data gate is sufficient to apply the data gate to all events of the subpopulation. In some embodiments, the non-transitory computer readable storage medium comprises an algorithm for applying an analytical algorithm to the gated composite population, such as instructions for applying a clustering algorithm or a compensation matrix to the gated composite population.

[0176] In certain embodiments, the non-transitory computer-readable storage medium includes an algorithm for desynchronizing data gates for one or more samples of the gated composite population. In some examples, the non-transitory computer-readable storage medium includes an algorithm for desynchronizing data gates by changing the geometry of the data gates applied to one or more samples of the gated composite population. In certain examples, the non-transitory computer-readable storage medium includes an algorithm for desynchronizing data gates (e.g., changing the gate geometry) for multiple samples, e.g., the data gates are desynchronized sequentially (i.e., one at a time) for each sample of the composite population.

[0177] In some embodiments, the non-transitory computer-readable storage medium includes an algorithm for generating a graphical user interface including a first pane configured to display one or more ungated composite populations including cytometry data of one or more groups of samples, a second pane displaying one or more gated composite populations, and a third pane displaying data files for each of the samples used to generate the composite population. In some examples, the non-transitory computer-readable storage medium includes an algorithm for generating a graphical user interface, the second pane displaying a hierarchy of gated composite populations. In some examples, the second pane displays an analytical algorithm applied to the events of the composite population selected in the first pane, such as a clustering algorithm or compensation matrix applied to the composite population displayed in the first pane. In some embodiments, the hierarchy of gated composite populations is color coded in the second pane. In some examples, each of the inherited data gates is labeled with the same color. In some examples, the non-transitory computer-readable storage medium includes an algorithm for generating a graphical user interface visualizing desynchronized data gates in the second pane. In some examples, each desynchronized data gate is visualized in a different text font in the second pane. In some embodiments, the non-transitory computer-readable storage medium comprises an algorithm for displaying in a third pane of the graphical user interface a data file for each sample having an event in the gated composite population selected in the second pane.

[0178] In some embodiments, the non-transitory computer readable storage medium includes an algorithm for applying an analytical algorithm displayed in a first pane to one or more of the gated composite populations displayed in a second pane. In certain examples, the non-transitory computer readable storage medium includes an algorithm for applying an analytical algorithm to one or more of the gated composite populations by dragging an analytical algorithm displayed in a first pane to the gated composite population displayed in the second pane. In other examples, the non-transitory computer readable storage medium includes an algorithm for applying an analytical algorithm to one or more of the gated composite populations by selecting an analytical algorithm from a menu of analytical algorithms and applying the selected algorithm to the gated composite population displayed in the second pane. In certain examples, the non-transitory computer readable storage medium includes an algorithm for displaying an icon in the second pane of the graphical user interface on the gated composite population in response to applying the analytical algorithm from the first pane. In some embodiments, the non-transitory computer readable storage medium includes an algorithm for applying an analytical algorithm to the gated composite population in the second pane such that the analytical algorithm is applied to one or more subgroups in a hierarchy of applied data gates. In some instances, applying an analytical algorithm to a gated composite population is sufficient to apply the analytical algorithm to all of the gated composite populations within the hierarchy of the applied data gate.

[0179] In some embodiments, the non-transitory computer readable storage medium includes an algorithm for applying the analytical algorithm displayed in the first pane to one or more of the data files for the sample displayed in the third pane. In certain examples, the non-transitory computer readable storage medium includes an algorithm for applying the analytical algorithm by dragging the analytical algorithm displayed in the first pane to the data files for the sample displayed in the third pane. In other examples, the non-transitory computer readable storage medium includes an algorithm for applying the analytical algorithm to one or more of the data files for the sample displayed in the third pane by selecting an analytical algorithm from a menu of analytical algorithms and applying the selected algorithm to one or more of the data files for the sample displayed in the third pane. In some embodiments, the non-transitory computer readable storage medium includes an algorithm for applying the analytical algorithm displayed in the second pane (e.g., tSNE, x-shift algorithm) to one or more of the data files for the sample displayed in the third pane. In certain examples, the non-transitory computer readable storage medium includes an algorithm for applying the analytical algorithm displayed in the second pane to the data files for the sample displayed in the third pane by dragging the analytical algorithm displayed in the second pane to the data files for the sample displayed in the third pane.

[0180] The non-transitory computer-readable storage medium may be employed on one or more computer systems having a display and an operator input device. The operator input device may be, for example, a keyboard, a mouse, and the like. The processing module includes a processor having access to a memory in which instructions for performing the steps of the subject method are stored. The processing module may include an operating system, a graphical user interface (GUI) controller, a system memory, a memory storage device, an input / output controller, a cache memory, a data backup unit, and many other devices. The processor may be a commercially available processor, or it may be one of other processors that are available or will become available. The processor executes an operating system, which interfaces with firmware and hardware in a well-known manner to facilitate the processor coordinating and executing the functions of various computer programs, which may be written in a variety of programming languages, such as the programming languages ​​mentioned above, other high-level or low-level languages ​​known in the art, and combinations thereof. The operating system typically works in conjunction with the processor to coordinate and execute the functions of the other components of the computer. The operating system also provides scheduling, input / output control, file and data management, memory management, and communication control and related services, all according to known techniques.

[0181] kit

[0182] Aspects of the present disclosure further include kits, which include one or more of the components of the optical detection system described herein. In some embodiments, the kits include a plurality of optical detectors and programming for the subject system, such as in the form of a computer-readable medium (e.g., flash drive, USB storage, compact disc, DVD, Blu-ray disc, etc.), or instructions for downloading programming from an Internet web protocol or cloud server. The kits may also include optical conditioning components, such as lenses, mirrors, filters, optical fibers, wavelength separators, pinholes, slits, collimating protocols, and combinations thereof.

[0183] The kit may further include instructions for carrying out the subject method. These instructions may be present in the subject kit in a variety of forms, one or more of which may be present in the kit. One form in which these instructions may be present is as information printed on a suitable medium or substrate, such as a sheet or sheets of paper on which the information is printed, kit packaging, package inserts, etc. Yet another form in which these instructions may be present is a computer readable medium on which the information is recorded, such as a diskette, compact disc (CD), portable flash drive, etc. Yet another form in which these instructions may be present is a website address that may be used via the Internet to access information at the destination site.

[0184] Utilities

[0185] The subject methods, systems and computer systems find use in a variety of applications where it is desirable to optimize the analysis of flow cytometer data. The subject methods and systems also find use in particle analyzers having multiple optical detectors used to analyze and sort particle components in samples in fluid media, such as biological samples. The present disclosure finds use in flow cytometry, where it is desirable to provide a flow cytometer with improved cell sorting accuracy, enhanced particle collection, reduced energy consumption, particle loading efficiency, more accurate particle charging and enhanced particle deflection during cell sorting. In embodiments, the present disclosure reduces the need for user input or manual adjustments (e.g., data concatenation) of sample analysis of flow cytometer data.

[0186] Notwithstanding the claims appended hereto, the present disclosure is also defined by the following clauses. 1. A method for processing cytometry data, the method comprising generating a composite population of events comprising a data accessor from cytometry data from one or more samples comprising particles. 2. The method of claim 1, wherein the composite population is generated from cytometry data from two or more different samples. 3. The method of any one of clauses 1-2, wherein the data accessor is configured to access metadata for each event of the cytometry data from one or more samples. 4. The method of any one of clauses 1-3, wherein the data accessor includes a source identity for each event of the cytometry data from the one or more samples. 5. The method of any one of clauses 2 to 4, wherein the cytometry data of the composite populations from the two or more different samples are kept in separate raw data files. 6. The method of claim 5, wherein the raw data files containing the cytometry data are not concatenated to form a single combined data file. 7. The method of any one of clauses 1-6, wherein the method further comprises applying a data gate to the composite population to generate a gated composite population. 8. The method of clause 7, wherein applying a data gate to a single event in the composite population is sufficient for applying the data gate to multiple events in the composite population. 9. The method of claim 8, wherein applying a data gate to the plurality of events of the composite population is sufficient to apply the data gate to all of the events of the composite population. 10. The method of any one of clauses 7 to 9, wherein the method further comprises applying an analytical algorithm to the gated composite population. 11. The method of claim 10, wherein the method comprises applying a clustering algorithm to the gated composite population. 12. The method of claim 10, wherein the method includes applying a spectral compensation matrix to the gated composite population. 13. The method of any one of clauses 7 to 12, wherein the method comprises desynchronizing gates for one or more samples of the gated composite population. 14. The method of any one of clauses 7 to 13, wherein desynchronizing a gate for one or more samples of the gated composite population comprises changing a gate geometry of the gate. 15. A method according to any one of clauses 13 to 14, wherein the gate geometry is altered to increase or decrease the number of sample events that fall within the gate. 16. The method of any one of clauses 7 to 15, wherein the method includes applying a hierarchy of data gates to the composite population. 17. A method according to any one of clauses 1 to 16, wherein the composite population of events is displayed on a graphical user interface. 18. The graphical user interface comprises: a first pane configured to display one or more non-gated composite populations comprising cytometry data for one or more groups of samples; a second pane configured to display one or more gated composite populations; a third pane configured to display a data file for each of the samples used to generate the composite population; 18. The method according to claim 17, comprising: 19. The method of claim 18, wherein the gated composite population in the second pane is displayed as a hierarchy. 20. The method of claim 18, wherein the second pane is configured to display an analysis algorithm. 21. The method of any one of clauses 19 to 20, wherein the hierarchy of the gated composite population is color-coded in the second pane. 22. The method of any one of clauses 18 to 21, wherein the desynchronized data gate is visualized in the second pane. 23. The method of claim 22, wherein the desynchronized data gates are visualized in the second pane with a different text font. 24. The method of any one of clauses 18 to 23, wherein the third pane is configured to display a data file for each of the samples containing events within the gated composite population selected in the second pane. 25. The method of any one of clauses 18 to 24, wherein the method further comprises applying an analytical algorithm displayed in the first pane to one or more of the gated composite populations displayed in the second pane. 26. The method of claim 25, wherein applying the analysis algorithm comprises dragging the analysis algorithm displayed in the first pane to the gated composite population displayed in the second pane. 27. The method of any one of clauses 24 to 26, wherein the analysis algorithm is a spectral compensation matrix. 28. The method of any one of clauses 25 to 27, wherein an icon is displayed in the second pane on the gated composite population in response to applying the analysis algorithm from the first pane. 29. A method according to any one of clauses 26 to 28, wherein applying the analytical algorithm to the gated composite population in the second pane is sufficient to apply the analytical algorithm to each of the composite populations within the hierarchy of gated composite populations. 30. The method of any one of clauses 18 to 24, wherein the method further comprises applying an analysis algorithm displayed in the first pane to one or more of the data files for the sample displayed in the third pane. 31. The method of claim 30, wherein applying the analysis algorithm comprises dragging the analysis algorithm displayed in the first pane onto a data file for the sample displayed in the third pane. 32. The method of any one of clauses 1-31, wherein the cytometry data comprises flow cytometry data from particles illuminated by a light source in a flow stream. 33. The method of any one of clauses 1 to 32, wherein the cytometry data is represented in Flow Cytometry Standard (FCS) format. 34. A system for processing cytometric data, comprising: an input module configured to receive cytometry data from one or more samples comprising particles; a processor comprising a memory operatively coupled to the processor; Equipped with The memory includes instructions stored in the memory that, when executed by the processor, cause the processor to generate a composite population of events from the cytometry data with a data accessor. 35. The system of clause 34, wherein the input module is configured to receive cytometry data from two or more samples, and the memory includes instructions for generating a composite population from the cytometry data from two or more different samples. 36. The system of any one of claims 34-35, wherein the memory includes instructions for accessing metadata using the data accessor for each event in the cytometry data. 37. The system of any one of clauses 34-36, wherein the data accessor includes a source identity for each event of the cytometry data from the one or more samples. 38. The system of any one of clauses 35-37, wherein the memory includes instructions for retaining the cytometry data of the composite populations from the two or more different samples as separate raw data files. 39. The system of claim 38, wherein the raw data files containing the cytometry data are not concatenated to form a single combined data file. 40. The system of any one of clauses 34-39, wherein the memory includes instructions for applying a data gate to the composite population to generate a gated composite population. 41. The system of clause 40, wherein applying a data gate to a single event in the composite population is sufficient to apply the data gate to multiple events in the composite population. 42. The system of clause 41, wherein applying a data gate to the plurality of events of the composite population is sufficient to apply the data gate to all events of the composite population. 43. The system of any one of clauses 40-42, wherein the memory includes instructions for applying an analytical algorithm to the gated composite population. 44. The system of clause 43, wherein the memory includes instructions for applying a clustering algorithm to the gated composite population. 45. The system of clause 43, wherein the memory includes instructions for applying a spectral compensation matrix to the gated composite population. 46. ​​The system of any one of clauses 40-45, wherein the memory includes instructions for desynchronizing gates for one or more samples of the gated composite population. 47. The system of clause 46, wherein the memory includes instructions for desynchronizing a gate for one or more samples of the gated composite population by altering a gate geometry of the gate. 48. The system of any one of clauses 40-47, wherein the memory includes instructions for applying a hierarchy of data gates to the composite population. 49. A system according to any one of clauses 47-48, wherein the memory includes instructions for altering the gate geometry to increase or decrease the number of sample events that fall within the gate. 50. The system of any one of clauses 34 to 49, further comprising a display configured to display the composite population of events on a graphical user interface. 51. The graphical user interface: a first pane configured to display one or more non-gated composite populations comprising cytometry data for one or more groups of samples; a second pane configured to display one or more gated composite populations; a third pane configured to display a data file for each of the samples used to generate the composite population; 51. The system of claim 50, comprising: 52. The system of clause 51, wherein the gated composite population in the second pane is displayed as a hierarchy. 53. The system of clause 51, wherein the second pane is configured to display an analysis algorithm. 54. A system described in any one of clauses 52 to 53, wherein the hierarchy of the gated composite population is color-coded in the second pane. 55. The system of any one of clauses 51 to 54, wherein a desynchronized data gate is visualized in the second pane. 56. The system of clause 55, wherein the desynchronized data gates are visualized in the second pane with a different text font. 57. The system of any one of clauses 51 to 56, wherein the third pane is configured to display data files for each of the samples containing events within the gated composite population selected in the second pane. 58. The system of any one of clauses 51 to 57, wherein the memory includes instructions for applying an analytical algorithm displayed in the first pane to one or more of the gated composite populations displayed in the second pane. 59. The system of clause 58, wherein the memory includes instructions for applying an analytical algorithm by dragging the analytical algorithm displayed in the first pane to the gated composite population displayed in the second pane. 60. The system of any one of clauses 51-59, wherein the analysis algorithm is a spectral compensation matrix. 61. The system of any one of clauses 58-60, wherein the memory includes instructions for displaying an icon in the second pane on the gated composite population in response to applying the analysis algorithm from the first pane. 62. The system of any one of clauses 58-61, wherein the memory includes instructions for applying the analysis algorithm to each of the composite populations within the hierarchy of gated composite populations. 63. A system described in any one of clauses 51 to 62, wherein the memory includes instructions for applying an analysis algorithm displayed in the first pane to one or more of the data files for the sample displayed in the third pane. 64. The system of clause 63, wherein the memory includes instructions for applying the analysis algorithm displayed in the first pane by dragging the analysis algorithm to a data file for a sample displayed in the third pane. 65. The system of any one of claims 34-64, wherein the cytometry data includes flow cytometry data from particles illuminated by a light source in a flow stream. 66. A system described in any one of clauses 34 to 64, wherein the cytometry data is represented in Flow Cytometry Standard (FCS) format. 67. A first pane configured to display one or more non-gated composite populations of events including a data accessor including cytometry data for one or more groups of samples comprising particles; a second pane configured to display one or more gated composite populations; a third pane configured to display a data file for each of the samples used to generate the composite population; A graphical user interface, including 68. The graphical user interface of clause 67, wherein the gated composite population in the second pane is displayed as a hierarchy. 69. The graphical user interface of clause 67, wherein the second pane is configured to display an analysis algorithm. 70. A graphical user interface described in any one of clauses 68-69, wherein the hierarchy of the gated composite population is color coded in the second pane. 71. The graphical user interface of any one of clauses 67-70, wherein the second pane includes a visualization of one or more desynchronization gates. 72. The graphical user interface of clause 71, wherein desynchronization gates are visualized in the second pane with a different text font. 73. The graphical user interface of any one of clauses 67 to 72, wherein the third pane is configured to display data files for each of the samples containing events within the gated composite population selected in the second pane. 74. A graphical user interface described in any one of clauses 67 to 73, wherein the graphical user interface is configured to apply an analytical algorithm displayed in the first pane to one or more of the gated composite populations displayed in the second pane. 75. The graphical user interface of clause 74, wherein the graphical user interface is configured to apply an analytical algorithm by dragging the analytical algorithm displayed in the first pane to the gated composite population displayed in the second pane. 76. A graphical user interface according to any one of clauses 74 to 75, wherein the analysis algorithm is a spectral compensation matrix. 77. A graphical user interface as described in any one of clauses 74 to 76, wherein the graphical user interface is configured to display an icon in the second pane on the gated composite population in response to applying the analysis algorithm from the first pane. 78. A graphical user interface according to any one of clauses 74 to 77, wherein the graphical user interface is configured to apply the analytical algorithm to each of the composite populations within the hierarchy of gated composite populations. 79. A graphical user interface described in any one of clauses 67 to 78, wherein the graphical user interface is configured to apply an analysis algorithm displayed in the first pane to one or more of the data files for the sample displayed in the third pane. 80. The graphical user interface of clause 79, wherein the graphical user interface is configured to drag an analysis algorithm displayed in the first pane onto a data file for a sample displayed in the third pane. 81. A graphical user interface as described in any one of clauses 67 to 80, wherein the cytometry data includes flow cytometry data from particles illuminated by a light source in a flow stream. 82. A graphical user interface according to any one of clauses 67 to 80, wherein the cytometry data is represented in Flow Cytometry Standard (FCS) format. 83. A non-transitory computer readable storage medium having stored thereon instructions for processing cytometry data, the instructions including an algorithm for generating a composite population of events including a data accessor from cytometry data from one or more samples comprising particles. 84. The non-transitory computer-readable storage medium of clause 83, wherein the composite population is generated from cytometry data from two or more different samples. 85. A non-transitory computer-readable storage medium according to any one of clauses 83 to 84, wherein the non-transitory computer-readable storage medium includes an algorithm for accessing metadata using the data accessor for each event of the cytometry data. 86. The non-transitory computer-readable storage medium of any one of clauses 83 to 85, wherein the data accessor includes a source identity for each event of the cytometry data from the one or more samples. 87. The non-transitory computer readable storage medium of any one of clauses 84 to 86, wherein the non-transitory computer readable storage medium includes an algorithm for persisting the cytometry data of the composite populations from the two or more different samples as separate raw data files. 88. The non-transitory computer-readable storage medium of clause 87, wherein the raw data files containing the cytometry data are not concatenated to form a single combined data file. 89. The non-transitory computer readable storage medium of any one of clauses 83-88, wherein the non-transitory computer readable storage medium includes an algorithm for applying a data gate to the composite population to generate a gated composite population. 90. The non-transitory computer-readable storage medium of clause 89, wherein applying a data gate to a single event in the composite population is sufficient to apply the data gate to multiple events in the composite population. 91. The non-transitory computer-readable storage medium of clause 90, wherein applying a data gate to the plurality of events of the composite population is sufficient to apply the data gate to all of the events of the composite population. 92. The non-transitory computer readable storage medium of any one of clauses 89 to 91, wherein the non-transitory computer readable storage medium comprises an algorithm for applying an analytical algorithm to the gated composite population. 93. The non-transitory computer-readable storage medium of clause 92, wherein the non-transitory computer-readable storage medium comprises an algorithm for applying a clustering algorithm to the gated composite population. 94. The non-transitory computer-readable storage medium of clause 92, wherein the non-transitory computer-readable storage medium includes an algorithm for applying a spectral compensation matrix to the gated composite population. 95. The non-transitory computer readable storage medium of any one of clauses 89 to 94, wherein the non-transitory computer readable storage medium includes an algorithm for desynchronizing gates for one or more samples of the gated complex population. 96. The non-transitory computer readable storage medium of clause 95, wherein the non-transitory computer readable storage medium includes an algorithm for desynchronizing a gate for one or more samples of the gated complex population by altering a gate geometry of the gate. 97. The non-transitory computer readable storage medium of clause 95, wherein the non-transitory computer readable storage medium includes an algorithm for altering the gate geometry to increase or decrease the number of sample events that fall within the gate. 98. The non-transitory computer readable storage medium of any one of clauses 89-97, wherein the non-transitory computer readable storage medium includes an algorithm for applying a hierarchy of data gates to the composite population. 99. The non-transitory computer-readable storage medium of any one of clauses 82-98, wherein the non-transitory computer-readable storage medium includes an algorithm for displaying one or more of the composite populations on a graphical user interface. 100. The graphical user interface comprises: a first pane configured to display one or more non-gated composite populations comprising cytometry data for one or more groups of samples; a second pane configured to display one or more gated composite populations; a third pane configured to display a data file for each of the samples used to generate the composite population; 99. A non-transitory computer readable storage medium as described in claim 99, comprising: 101. The non-transitory computer-readable storage medium of claim 100, wherein the gated composite populations in the second pane are displayed in a hierarchy. 102. The non-transitory computer-readable storage medium of clause 100, wherein the second pane is configured to display an analysis algorithm. 103. The non-transitory computer-readable storage medium of any one of clauses 100 to 102, wherein the hierarchy of gated composite populations is color-coded in the second pane. 104. The non-transitory computer-readable storage medium of any one of clauses 100 to 103, wherein a desynchronized data gate is visualized in the second pane. 105. The non-transitory computer-readable storage medium of clause 104, wherein the desynchronized data gate is visualized in the second pane with a different text font. 106. The non-transitory computer-readable storage medium of any one of clauses 100 to 105, wherein the third pane is configured to display a data file for each of the samples containing events within the gated composite population selected in the second pane. 107. The non-transitory computer-readable storage medium of any one of clauses 100-106, wherein the memory includes instructions for applying an analytical algorithm displayed in the first pane to one or more of the gated composite populations displayed in the second pane. 108. The non-transitory computer-readable storage medium of clause 107, wherein the non-transitory computer-readable storage medium includes an algorithm for applying an analytic algorithm by dragging the analytic algorithm displayed in the first pane to the gated composite population displayed in the second pane. 109. The non-transitory computer-readable storage medium of any one of clauses 106 to 108, wherein the analysis algorithm is a spectral compensation matrix. 110. The non-transitory computer readable storage medium of any one of clauses 106-109, wherein the non-transitory computer readable storage medium includes an algorithm for displaying an icon in the second pane on the gated composite population in response to applying the analysis algorithm from the first pane. 111. The non-transitory computer-readable storage medium of any one of clauses 107 to 110, wherein the non-transitory computer-readable storage medium includes an algorithm for applying the analysis algorithm to each of the composite populations in the stratum of gated composite populations. 112. The non-transitory computer-readable storage medium of any one of clauses 100 to 111, wherein the non-transitory computer-readable storage medium includes an algorithm for applying an analysis algorithm displayed in the first pane to one or more data files for the sample displayed in the third pane. 113. The non-transitory computer-readable storage medium of clause 112, wherein the non-transitory computer-readable storage medium includes an algorithm for applying the analytical algorithm displayed in the first pane by dragging the analytical algorithm to a data file for a sample displayed in the third pane. 114. The non-transitory computer-readable storage medium of any one of clauses 83 to 113, wherein the cytometry data comprises flow cytometry data from particles illuminated by a light source in a flow stream. 115. The non-transitory computer-readable storage medium of any one of clauses 83 to 113, wherein the cytometry data is represented in Flow Cytometry Standard (FCS) format.

[0187] Although the foregoing invention has been described in some detail by way of illustration and example for clarity of understanding, it will be readily apparent to those skilled in the art in light of the teachings of this invention that certain changes and modifications may be made thereto without departing from the spirit or scope of the appended claims.

[0188] Thus, the above merely describes the principles of the invention. It will be appreciated that those skilled in the art can devise various configurations that embody the principles of the invention and fall within its spirit and scope, although not expressly described or shown herein. Furthermore, all examples and conditional language described herein are intended primarily to aid the reader in understanding the principles of the invention and the concepts the inventors contribute to furthering the art, and should not be construed as being limited to such specifically recited examples and conditions. Furthermore, all statements herein describing the principles, aspects, and embodiments of the invention, as well as specific examples thereof, are intended to encompass both structural and functional equivalents thereof. Moreover, such equivalents are intended to include both currently known equivalents and equivalents developed in the future, i.e., any developed elements that perform the same function, regardless of structure. Furthermore, nothing disclosed herein is intended to be dedicated to the public, regardless of whether such disclosure is expressly set forth in the claims.

[0189] Accordingly, the scope of the present invention is not intended to be limited to the exemplary embodiments shown and described herein. Rather, the scope and spirit of the present invention is embodied in the appended claims. In the claims, 35 U.S.C. §112(f) or 35 U.S.C. §112(6) is expressly defined to apply to a limitation of a claim only if the exact phrase "means for" or the exact phrase "step for" is prefaced in such limitation, and if such exact phrase is not used in a limitation in a claim, then 35 U.S.C. §112(f) or 35 U.S.C. §112(6) does not apply. CROSS-REFERENCE TO RELATED APPLICATIONS

[0190] Pursuant to 35 U.S.C. § 119(e), this application claims priority to the filing date of U.S. Provisional Patent Application No. 63 / 309,956, filed February 14, 2022, the disclosure of which is incorporated herein by reference in its entirety.

Claims

1. 1. A method for processing cytometry data, comprising: A method comprising generating a composite population of events comprising a data accessor from cytometry data from one or more samples comprising particles.

2. The method of claim 1 , wherein the composite population is generated from cytometry data from two or more different samples.

3. The method of any one of claims 1 to 2, wherein the data accessor is configured to access metadata for each event of the cytometry data from one or more samples.

4. The method of any one of claims 1 to 3, wherein the data accessor includes a source identity for each event of the cytometry data from the one or more samples.

5. The method of any one of claims 2 to 4, wherein the cytometry data of the complex populations from the two or more different samples are kept in separate raw data files.

6. The method of claim 5 , wherein the raw data files containing the cytometry data are not concatenated to form a single combined data file.

7. The method of any one of claims 1 to 6, further comprising applying a data gate to the composite population to generate a gated composite population.

8. The method of claim 7 , wherein applying a data gate to a single event of the composite population is sufficient for applying the data gate to multiple events of the composite population.

9. The method of claim 8 , wherein applying a data gate to the plurality of events of the composite population is sufficient to apply the data gate to all of the events of the composite population.

10. The method of any one of claims 7 to 9, wherein the method further comprises applying an analytical algorithm to the gated composite population.

11. 11. The method of claim 10, wherein the method comprises applying a clustering algorithm to the gated composite population.

12. The method of claim 10 , wherein the method comprises applying a spectral compensation matrix to the gated composite population.

13. 1. A system for processing cytometry data, comprising: an input module configured to receive cytometry data from one or more samples comprising particles; a processor comprising a memory operatively coupled to the processor; Equipped with The memory includes instructions stored therein that, when executed by the processor, cause the processor to generate a composite population of events from the cytometry data that includes a data accessor.

14. a first pane configured to display one or more ungated composite populations of events including a data accessor including cytometry data for one or more groups of samples comprising particles; a second pane configured to display one or more gated composite populations; a third pane configured to display a data file for each of the samples used to generate the composite population; and A graphical user interface, including

15. A non-transitory computer readable storage medium having stored thereon instructions for processing cytometry data, the non-transitory computer readable storage medium comprising: The non-transitory computer readable storage medium, the instructions including an algorithm for generating a composite population of events including a data accessor from cytometry data from one or more samples comprising particles.

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