Method and system for assessing the suitability of a fluorochrome panel for use in a flow cytometry protocol

Abstract

A method for assessing the suitability of a fluorochrome panel for use in a flow cytometry protocol for analyzing a biological sample is provided. The subject method includes receiving, using a processor, an initial fluorochrome panel, a plurality of population identifiers, each of which refers to a particle population, and an instrument identifier. The method further includes creating a set of population-marker pairs, generating a set of separability metrics, each of which predicts a measure of statistical distance between the particle populations in flow cytometer data space, aggregating the set of separability metrics into a panel score, and assessing the panel score. A system and a non-transitory computer-readable storage medium for assessing the suitability of a fluorochrome panel for use in a flow cytometry protocol for analyzing a biological sample are also provided.

Description

[Background technology]

[0001] Flow cytometry is a technique used to characterize and often sort biological materials, such as cells in a blood sample or particles of interest in another type of biological or chemical sample. A flow cytometer typically includes a sample reservoir for receiving a fluid sample, such as a blood sample, and a sheath reservoir containing a sheath fluid. The flow cytometer directs the sheath fluid toward a flow cell, transporting particles (including cells) in the fluid sample as a cell stream to the flow cell. To characterize the components of the flow stream, light is irradiated onto the flow stream. Variations in the material in the flow stream, such as morphology or the presence of fluorescent labels, can cause variations in the observed light, enabling characterization and separation. For example, particles, such as molecules in fluid suspension, analyte-bound beads, or individual cells, pass through a detection region where the particles are exposed to excitation light, typically from one or more lasers, and the particles' light scattering and fluorescence properties are measured. Particles or their components are typically labeled with fluorescent dyes to facilitate detection. By labeling different particles or components with spectrally distinct fluorescent dyes, multiple different particles or components can be detected simultaneously. In some implementations, the analyzer includes multiple detectors, one for each scattering parameter to be measured and one or more for each distinct dye to be detected. For example, some embodiments include spectral configurations in which two or more sensors or detectors are used per dye. The acquired data includes measured signals for each of the light scattering detectors and the fluorescence emission.

[0002] Parameters measured using particle analyzers typically include light at the excitation wavelength scattered by particles at a narrow angle along a roughly forward direction, called forward scatter (FSC), excitation light scattered by particles in a direction orthogonal to the excitation laser, called side scatter (SSC), and light emitted from fluorescent molecules or dyes. Different cell types can be distinguished by their light scattering characteristics and fluorescence emission resulting from labeling various cellular proteins or other components with fluorochrome-conjugated antibodies or other fluorescent probes. The forward scattered light, side scattered light, and fluorescence are detected by photodetectors located within the particle analyzer.

[0003] When a flow cytometry protocol involves the detection of fluorescence, experimental design typically involves the identification of a fluorochrome panel—i.e., a collection of fluorochromes to be used together in a given flow cytometry workflow. The process of fluorochrome panel design is necessary because biological resolution (i.e., the ability to distinguish different components of interest within or between particles of interest) is directly affected by both the measurement variance of the "raw" flow cytometry data and the mathematical process of spectral correction or decomposition. Both of these factors are highly dependent on the selection of fluorochromes within the panel. First, measurement variance (i.e., noise) in flow cytometry arises from a wide range of sources, including constant baseline measurement noise in the cytometer electronics, optical shot noise, which varies linearly with signal intensity, and multiplicative measurement noise resulting from random fluctuations in the cytometer's lasers and fluidics, which vary quadratically with signal intensity. The measurement noise itself depends on the selection of fluorochromes. For example, brighter fluorochromes induce more shot noise than dimmer fluorochromes, and dimmer fluorochromes have smaller signal magnitudes compared to the constant "noise floor" of the instrument's optics and electronics. Second, the raw measurement noise in "detector space" (with a dimensionality equal to the number of detectors in the instrument) is propagated through the mathematical process of fluorescence correction (in conventional cytometers) or spectral decomposition (in full-spectrum cytometers) to the final biological data in "corrected space" or "resolved space" (with a dimensionality equal to the number of fluorophores in the sample). The variance of the "resolved space" is important because it is the space in which the final biological analyses of interest (e.g., gating, clustering, sorting, marker quantification, etc.) are performed. This mathematical mapping of noise to biological space is highly dependent on the spectral signatures of the fluorophores themselves.

[0004] Traditional flow cytometry, in which separate photodetectors are dedicated to dye-specific fluorescence emission bands, imposes severe limitations on the number of fluorochromes that can be used simultaneously in a flow experiment; the number of fluorochromes may not exceed the number of fluorescence detection channels on the instrument. In contrast, full-spectrum flow cytometers, by definition, use more detectors than fluorochromes, and commercially available full-spectrum flow cytometers are available with over 180 fluorescence channels. Given current photonic technology (laser sources, optics, and detectors) and fluorochrome availability, flow cytometry typically operates in the ultraviolet (300 nm) to near-infrared (850 nm) range, representing an "area" of only approximately 550 nm. While the operating wavelength range can be stacked by using more lasers, it is often unavoidable to use fluorochromes with overlapping emission spectra, thus requiring correction or spectral decomposition to recover true median fluorochrome abundances. Spectral overlap adds additional noise to the detector, thus increasing the spread of the corrected / decomposed data and reducing the ability to distinguish populations. The phenomenon of increased spread due to spectral overlap is called spillover spread. Therefore, the process of panel design, i.e., strategically pairing biomarkers with appropriate fluorochromes, is crucial to the success of flow experiments and becomes increasingly important as flow cytometry moves towards higher parameter space where the potential for spillover diffusion is greater. Summary of the Invention

[0005] The present inventors have recognized that there is a practical limit to the number of fluorochromes and biomarkers that can be used simultaneously in a flow cytometry experiment. Despite the commercial availability of nearly 100 distinct fluorochromes for flow cytometry, panel size remains limited. This practical limit arises from the inevitable spectral overlap and similarity of the fluorochromes used. Therefore, methods and systems for evaluating and selecting suitable fluorochrome panels are desirable. Embodiments of the present invention fulfill this need.

[0006] Aspects of the present invention include methods for evaluating the suitability of a fluorochrome panel for use in a flow cytometry protocol for analyzing a biological sample. The subject method includes receiving, using a processor, an initial fluorochrome panel including a set of fluorochrome identifiers, each of which refers to a fluorochrome within the set of fluorochromes, and a set of biological marker identifiers, each associated with a fluorochrome identifier within the set of fluorochrome identifiers; a plurality of population identifiers, each of which refers to a particle population; and an instrument identifier. In some cases, the processor also receives a gating strategy, and the initial fluorochrome panel is determined based on the gating strategy. In some cases, the method includes receiving a randomly determined fluorochrome panel. The method also includes creating a set of population-marker pairs by associating each population identifier within the plurality of population identifiers with a biological marker identifier from the set of biological marker identifiers. A set of separability metrics is then generated, each of which predicts a measure of statistical distance between particle populations in flow cytometer data space. Each measure of statistical distance is related to the detected signal intensity resulting from each fluorochrome associated with each population-marker pair used in the flow cytometry protocol using the instrument associated with the instrument identifier. The separability metrics can then be aggregated into a panel score that can then be assessed to evaluate the suitability of the initial fluorochrome panel for use in a flow cytometry protocol.

[0007] In some cases, creating the set of population-marker pairs includes creating population-marker pairs for implicit particle populations present in the biological sample that are not referenced in the received plurality of population identifiers. In further cases, creating the set of population-marker pairs further includes defining one or more quantitative pairs of biological marker identifiers for assessing quantitative expression of the particle populations. Generating the set of separability metrics, in embodiments, includes predicting statistical moments of each biological marker identifier in the set of biological marker identifiers based on the detected signal intensities. In such embodiments, predicting statistical moments may include predicting a covariance matrix of the detected signal intensities, predicting a variance-covariance matrix of the detected signal intensities, or predicting a mean matrix of the detected signal intensities. In some cases, predicting statistical moments of each biological marker identifier in the set of biological marker identifiers includes incorporating the effects of a noise model (e.g., a Gaussian noise model, a Poisson noise model) on the detected signal intensities. Incorporating the effect of the noise model on the detected signal strengths, in some examples, includes performing a Monte Carlo simulation and / or obtaining an analytical expression relating the predicted statistical moments to the noise model (e.g., by incorporating the effect of the noise model on the detected signal strengths based on a spillover diffusion matrix). In some cases, generating the set of separability metrics includes stabilizing the variance of the detected signal strengths (e.g., by biexponential scaling or inverse hyperbolic scaling). In certain aspects, stabilizing the variance of the detected signal strengths includes solving an optimization problem having an objective function that is a measure of the similarity of the variances of different distributions of the detected signal strengths. In select cases, stabilizing the variance of the detected signal strengths includes determining an analytical relationship between the variance and mean of the detected signal strengths.

[0008] Aggregating the sets of separability metrics into a panel score may include, for example, negating the value of the lowest separability score. In certain embodiments, the method includes aggregating the sets of separability metrics for each population-marker pair and each quantitative pair. In some such embodiments, determining the panel score includes calculating a vector of aggregated sets of separability metrics for the population-marker pairs and aggregated sets of separability metrics for the quantitative pairs. Aggregating the sets of separability metrics may include, for example, comparing each separability metric to a threshold.

[0009] The subject methods may also include generating an optimized fluorochrome panel based on evaluating the suitability of the initial fluorochrome panel for use in the flow cytometry protocol, e.g., by determining a fluorochrome panel having an optimized panel number. Generating the optimized fluorochrome panel may include, e.g., adjusting (e.g., iteratively adjusting) fluorochromes in the initial fluorochrome panel and evaluating the suitability of the adjusted (e.g., iteratively adjusted) fluorochrome panel for use in the flow cytometry protocol.

[0010] Aspects of the invention further include systems, where the subject systems are configured to perform the subject methods (e.g., as briefly described above). The subject systems include a processor configured to receive an initial fluorochrome panel including a set of fluorochrome identifiers, each of which refers to a fluorochrome within the set of fluorochrome identifiers, and a set of biological marker identifiers, each associated with a fluorochrome identifier within the set of fluorochrome identifiers; a plurality of population identifiers, each of which refers to a particle population; and an instrument identifier. The processor is configured to create a set of population-marker pairs by associating each population identifier within the plurality of population identifiers with a biological marker identifier from the set of biological marker identifiers; generate a set of separability metrics, each predicting a measure of statistical distance between the particle populations in flow cytometer data space; and correlate each measure of statistical distance with a detected signal intensity resulting from each fluorochrome associated with each population-marker pair used in a flow cytometry protocol using an instrument associated with the instrument identifier. In embodiments, the processor is also configured to aggregate the set of separability metrics into a panel score and assess the panel score to evaluate the suitability of the initial fluorochrome panel for use in the flow cytometry protocol. In some cases, the system is or includes a flow cytometer. Systems of interest may further include a display configured to output an evaluation of the initial fluorochrome panel and / or an optimized fluorochrome panel. Aspects of the invention also include a non-transitory computer-readable storage medium having stored thereon instructions that, when executed by a processor, result in an evaluation of the suitability of the fluorochrome panel for use in a flow cytometry protocol for analyzing a biological sample. [Brief explanation of the drawings]

[0011] The invention can be best understood from the following detailed description when read in conjunction with the accompanying drawings, in which:

[0012] [Figure 1A]1 shows a flowchart for performing a method for assessing the suitability of a fluorochrome panel for use in a flow cytometry protocol for analyzing a biological sample, according to certain embodiments of the present invention. [Figure 1B] 1 shows a flowchart for performing a method for assessing the suitability of a fluorochrome panel for use in a flow cytometry protocol for analyzing a biological sample, according to certain embodiments of the present invention. [Figure 2] FIG. 1 shows a functional block diagram of a flow cytometry system, according to certain embodiments. [Figure 3] 1 illustrates a control system according to a particular embodiment. [Figure 4A] 1 shows a schematic diagram of a particle sorter system, in accordance with certain embodiments. [Figure 4B] 1 shows a schematic diagram of a particle sorter system, in accordance with certain embodiments. [Figure 5] 1 illustrates a block diagram of a computing system in accordance with certain embodiments. [Figure 6] 1 shows an exemplary gating strategy used in the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0013] A method for assessing the suitability of a fluorochrome panel for use in a flow cytometry protocol for analyzing a biological sample is provided. The subject method includes receiving, using a processor, an initial fluorochrome panel, a plurality of population identifiers, each of which refers to a particle population, and an instrument identifier. The method further includes creating a set of population-marker pairs, generating a set of separability metrics, each of which predicts a measure of statistical distance between the particle populations in flow cytometer data space, aggregating the set of separability metrics into a panel score, and assessing the panel score. A system and a non-transitory computer-readable storage medium for assessing the suitability of a fluorochrome panel for use in a flow cytometry protocol for analyzing a biological sample are also provided.

[0014] 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.

[0015] Where a range of values ​​is provided, unless the context clearly indicates otherwise, it is understood that each intervening value is included, to the tenth of the unit of the lower limit, between the upper and lower limit of that range and any other stated or intervening value in that stated range. The upper and lower limits of these smaller ranges may independently be included in the smaller ranges and are also encompassed within the invention, subject to any specific excluded limit in the stated range. Where the stated range includes one or both of the limits, ranges excluding either or both of those included limits are also included in the invention.

[0016] Certain ranges are presented herein with the term "about" preceding the numerical value. The term "about" is used herein to provide literal support for the exact number preceded by the term, as well as a number that is close to or approximately the number preceded by the term. In determining whether a number is close to or approximately a specifically recited number, the unrecited number that is close to or approximately the number may be a number that, in the context provided, provides substantial equivalence to the specifically recited number.

[0017] 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 now described.

[0018] All publications and patents cited herein are incorporated by reference to the same extent as if each individual publication or patent was specifically and individually indicated to be incorporated by reference, and are incorporated by reference herein to disclose and describe 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 dates of publication provided may be different from the actual publication dates, which may need to be independently confirmed.

[0019] 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. Accordingly, this statement is intended to serve as a precedent for the use of exclusive terminology such as "solely," "solely," and the like, or the use of "negative" limitations in connection with the recitation of claim elements.

[0020] As will be apparent to those skilled in the art upon reading this disclosure, each of the separate 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 various embodiments without departing from the scope or spirit of the invention. Any recited method may be carried out in the order of events recited or in any other order which is logically possible.

[0021] Although the systems and methods have been or will be described for grammatical fluidity with functional descriptions, it is expressly understood that the claims should not be construed as necessarily limited by "means" or "step" limitation constructions unless expressly formulated under 35 U.S.C. 112, but should be accorded the full scope of meaning and equivalents of the definitions provided by the claims under the statutory foundational doctrine of equivalents, and that if a claim is expressly formulated under 35 U.S.C. 112, it should be accorded the full legal equivalents under 35 U.S.C. 112.

[0022] Methods for evaluating fluorescent dye panels As described above, aspects of the present invention include evaluating the suitability of a fluorochrome panel for use in a flow cytometry protocol for analyzing biological samples. As described herein, a "fluorochrome panel" refers to a set of different fluorescent molecular substances (i.e., dyes) that can be used to identify particles or specific moieties or components associated therewith in a sample. As described herein, a "fluorochrome panel" may also refer to a set of identifiers (e.g., digital identifiers) that uniquely reference and are associated with specific fluorescent molecular substances. Such identifiers may be referred to herein as "fluorochrome identifiers." Different fluorochromes within a fluorochrome panel may differ with respect to properties such as absorption spectrum, extinction coefficient, emission spectrum, and quantum efficiency (i.e., the number of photons emitted for each photon absorbed), or a combination thereof. Thus, different or distinct fluorochromes may differ from one another with respect to chemical composition and / or with respect to one or more properties of the dyes. For example, a given pair of fluorochromes may be considered different if they differ from each other in terms of excitation and / or emission maxima, the magnitude of such difference in some cases being 5 nm or more, e.g., 10 nm or more (including 15 nm or more), and in some cases the magnitude of the difference being in the range of 5-400 nm, e.g., 10-200 nm (including 15-100 nm), e.g., 25-50 nm.

[0023] Evaluating the suitability of a fluorochrome panel for use in a flow cytometry protocol refers to predicting the quality of flow cytometer data that will be generated when the fluorochrome panel is used in a flow cytometry protocol. In other words, a fluorochrome panel can be described as "suitable for use" in a flow cytometry protocol when the fluorochrome panel generates understandable flow cytometer data that reliably provides insight into properties of interest in the sample under investigation. In some embodiments, a fluorochrome panel is suitable for use in a flow cytometry protocol if the panel provides increased biological resolution. "Biological resolution" refers to the ability to distinguish between different entities of interest (e.g., molecules, antigens, moieties, epitopes, etc.) in a biological specimen. In some cases, the fluorochrome panels identified herein produce maximum biological resolution, regardless of measurement variance and variance in the flow cytometer data space (e.g., flow cytometer data that has undergone fluorescence compensation or spectral decomposition). "Maximum" biological resolution, in certain embodiments, is assessed relative to the biological resolution achieved using one or more other sets of fluorescent dyes that differ from the optimal fluorescent dye panel described herein (i.e., contain one or more different fluorescent dyes relative to the optimal fluorescent dye panel).

[0024] The methods of the present invention include receiving an initial fluorochrome panel. The initial fluorochrome panel can be determined in any convenient manner. In some embodiments, the initial fluorochrome panel is determined randomly (e.g., by a processor). In certain cases, the method includes receiving a gating strategy and determining the initial fluorochrome panel based on the gating strategy. In other words, the processor can select the initial fluorochrome panel based on fluorochromes typically used with particles having a particular phenotypic characteristic of interest in the gating strategy. The fluorochromes available for use in a given fluorochrome panel can be varied as desired. In some embodiments, one skilled in the art practicing the method can limit the population of fluorochromes from which the initial fluorochrome panel is formed to, for example, those readily available to one skilled in the art.

[0025] A fluorochrome panel of the present invention may also include a set of biological marker identifiers, each associated with a fluorochrome identifier in the set of fluorochrome identifiers. As described herein, a "biological marker" may refer to any distinguishable feature of a biological sample (e.g., an organ, tissue, cell, macromolecule, etc.) that can be associated with a fluorochrome for analysis (e.g., as part of an antibody-dye conjugate). In some embodiments, a biological marker includes one or more cell surface proteins. In certain cases, a biological marker includes a cluster of differentiation (CD) molecule. A "biological marker identifier" refers to a set of identifiers (e.g., digital identifiers) associated with a particular biological marker that uniquely identifies that biological marker in data space. An exemplary fluorochrome panel with fluorochrome identifiers and associated biological marker identifiers is shown in Table 5, presented below in the Experimental Section. In certain cases, one skilled in the art practicing the method may limit the population of fluorochromes from which the initial fluorochrome panel is formed to, for example, a set of fluorochromes for which antibody conjugation is available for all markers of interest.

[0026] The methods of the present invention may further include receiving multiple population identifiers, each of which refers to a particle population. "Particle population" is referred to herein in its conventional sense, describing a grouping of particles having the same or sufficiently similar characteristics for purposes of a particular protocol. In some cases, particle populations may be described as positive or negative with respect to one or more biological markers, such as those described above. In other words, a "population" or "subpopulation" of analytes, such as cells or other particles, generally refers to a group of analytes having characteristics (e.g., optical, impedance, or temporal characteristics) related to one or more measured fluorescence parameters, such that the measured parameter data form clusters in data space. Thus, populations are recognized as clusters in the data. Conversely, each data cluster is generally interpreted as corresponding to a population of a particular type of cell or analyte, although clusters corresponding to noise or background are also typically observed. Clusters may be defined as dimensional subsets, e.g., with respect to a subset of measured fluorescence parameters (i.e., fluorescent dyes), corresponding to populations that differ only in a subset of measured parameters or features extracted from sample measurements.

[0027] In some embodiments, the method also includes receiving a gating strategy, where the gating strategy provides rules for how clusters (e.g., populations) of flow cytometer data having certain characteristics are distinguished from one another. In certain instances, the gating strategy includes a gating hierarchy. The gating hierarchy described herein defines criteria for grouping fluorescent flow cytometer data into specific populations. In some embodiments, the hierarchy establishes how data points that are positive or negative for the same parameter (e.g., biological marker) are grouped together. In some aspects, the gating hierarchy is a series of gating steps for identifying populations of interest in a series of one- or two-dimensional histogram plots. For example, a partial gating hierarchy for clustering T cells by determining whether the cells are positive or negative for the presence of CD4 and CD8 is shown below. CD4 + and CD8 - →CD4T cells CD4 - and CD8 + →CD8T cells CD4 + and CD8 + →Double positive T cells CD4 - and CD8 - →Double negative T cells

[0028] As noted above, cells that are positive for CD4 but negative for CD8 are "CD4 T cells," cells that are positive for both markers are "double positive T cells," and so on.

[0029] The method of the present invention also includes receiving an instrument identifier. "Instrument identifier" refers to information or data that identifies a particular instrument (e.g., particle analyzer, flow cytometer). In certain cases, the instrument identified via the instrument identifier is a flow cytometer. Any convenient flow cytometer configured to analyze fluorescent particle-modulated light can be used. In certain cases, flow cytometers of interest include those manufactured by BD Biosciences.Exemplary flow cytometers include the BD Biosciences FACSCanto™ flow cytometer, the BD Biosciences FACSCanto™ II flow cytometer, the BD Accuri™ flow cytometer, the BD Accuri™ C6 Plus flow cytometer, the BD Biosciences FACSCelesta™ flow cytometer, the BD Biosciences FACSLyric™ flow cytometer, the BD Biosciences FACSVerse™ flow cytometer, the BD Biosciences FACSymphony™ flow cytometer, the BD Biosciences LSRFortessa™ flow cytometer, the BD Biosciences LSRFortessa™ X-20 flow cytometer, the BD Biosciences FACSPresto™ flow cytometer, the BD Biosciences FACSVia™ flow cytometer, and the BD Biosciences FACSCalibur™ cell sorter, the BD Biosciences FACSCount™ cell sorter, the BD Biosciences FACSLyric™ cell sorter, and the BD Biosciences These include the Via™ cell sorter, BD Biosciences Influx™ cell sorter, BD Biosciences Jazz™ cell sorter, BD Biosciences Aria™ cell sorter, BD Biosciences FACSAria™ II cell sorter, BD Biosciences FACSAria™ III cell sorter, BD Biosciences FACSAria™ Fusion cell sorter, and BD Biosciences FACSMelody™ cell sorter, BD Biosciences FACSymphony™ S6 cell sorter, and BD FACSDiscover™ S8 Cell Sorter.

[0030] In some cases, receiving an instrument identifier enables instrument-specific evaluation of a fluorochrome panel for use in a flow cytometry protocol. For example, differences between distinct instruments in the number and arrangement of lasers and detection channels can result in different spectral signatures associated with each instrument. A "spectral signature" refers to the fluorescence spectral characteristics of an individual fluorochrome, expressed as one or more numerical values. Accordingly, embodiments of the subject methods perform analysis specific to the particular instrument (or class / type of instrument) on which the fluorochromes in the evaluated fluorochrome panel will hypothetically be used. Due to instrument-to-instrument differences, it is possible that the same fluorochrome panel may be associated with more or less variance in flow cytometer data if the fluorochromes are applied on two different types of machines (e.g., flow cytometers). The subject methods can enable fluorochrome panels to be evaluated in an instrument-specific context, thereby enabling those skilled in the art practicing the subject methods to more reliably gain insight into the quality of flow cytometer data when the fluorochromes in a given panel are used on a particular instrument.

[0031] The methods of the present invention further include creating a set of population-marker pairs by associating each population identifier in the plurality of population identifiers with a biological marker identifier from the set of biological marker identifiers. In other words, markers that can be used to distinguish one population from another are identified and associated with the respective populations. If the method includes receiving a gating strategy, creating the set of population-marker pairs can include dividing the gating strategy. For example, if the purpose of a flow experiment is to determine the phenotype of CD8+ and CD4+ T cell subsets and examine the quantitative expression of CD27 and CD28 for those subsets, the biological hypothesis can be described as a sample composed of CD8+ naive T cells (CD8N), CD8+ central memory T cells (CD8CM), CD8+ effector memory T cells (CD8EM), CD8+ effector cells (CD8Eff), counterparts for CD4+ T cell subsets (CD4N, CD4CM, CD4EM, CD4Eff), and regulatory T cells (Treg). A possible gating strategy is to first gate out the CD3+ population, based on which the CD8+ and CD4+ populations can be gated, respectively. Different subsets can be differentiated among CD8+ or CD4+ cells by examining their expression patterns for CCR7 and CD45RA. Furthermore, from CD4+ cells, Tregs can be found in the CD25+ and CD127- populations. Therefore, in this example, the relevant marker pairs used for gating are {CD3}, {CD8, CD4}, {CCR7, CD45RA}, and {CD25, CD127}.

[0032] In some embodiments, the method includes creating population-marker pairs for implicit particle populations that are not referenced in the received plurality of population identifiers but are present in the biological sample. In other words, populations that are not explicitly targeted in the experimental design but are still part of the biological sample are assigned to population marker pairs (e.g., in the manner described above). For example, some populations, such as CD3- cells (non-T) or CD8-CD4- cells (double-negative cells, T-dn), are not explicitly defined in a hypothesis (such as the one described above), but they may be present in the sample and affect how well explicitly defined cells can be gated. Thus, embodiments of the present invention include adding these implicit populations when formulating population-marker pairs.

[0033] Creating a set of population-marker pairs may, in some cases, further include defining one or more quantitative pairs of biological marker identifiers for assessing the quantitative expression of particle populations. In other words, the methods of the present invention may further include categorizing population-marker pairs into gating pairs and quantitative pairs according to their purpose in the gating strategy. Gating pairs refer to pairs used to gate out / classify populations, while quantitative pairs (e.g., {CD27, CD28}) are primarily used to examine the quantitative expression levels of gated populations. When it is desirable to measure the quantitative expression of these markers, it is advantageous to be able to separate populations in flow cytometer data that have different profiles of these quantitative markers. Therefore, aspects of the present invention include considering population-marker pairs constructed for quantitative markers in conjunction with the explicitly defined populations described above (i.e., populations that are part of the biological hypothesis and / or gating strategy). Similar to the implicit gating populations defined above, implicit quantitative populations can also be defined, along with the corresponding explicit populations, to constitute the complete set of four expression patterns of the quantitative pair, i.e., (+,+), (+,-), (-,+), and (-,-), where "+" and "-" are qualitative notations of abundance indicating positive and negative populations, respectively, and (+,-) means, for example, that the first marker is positive and the second marker is negative. Thus, the goal of having a narrow distribution for the quantitative markers can be translated into separating the four explicit and implicit quantitative populations from each other, thus matching the logic of how the gating pairs are scored.For example, the expression pattern of {CD27,CD28} for CD8N is (+,++), based on which three additional implicit populations are constructed, including CD8N\*CD27\*CD28\*q2, CD8N\*CD27\*CD28\*q3, and CD8N\*CD27\*CD28\*q4, where the notation is structured as {explicit cell population}\*{first marker}\*{second marker}\*{quadrant number}, where the quadrant number indicates the relative abundance of the two markers, specifically, q1 means (+,++), q2 means (-,++), q3 means (-,-), and q4 means (+,-). Thus, the expression patterns of these implicit populations are (-,++), (-,-), and (+,-). A good panel requires that these four populations are separated from each other to ensure reliable quantitative analysis of CD8N {CD27, CD28}.

[0034] Embodiments of the present invention also include generating a set of separability metrics, each of which predicts a measure of statistical distance between particle populations in flow cytometer data space. The "statistical distance measure" described herein relates to the detected signal intensity resulting from each fluorochrome associated with each population-marker pair used in a flow cytometry protocol using an instrument associated with the instrument identifier. The result is a table of separability scores for all pairs defined in the gating hierarchy. Any distance measure that quantifies the separability between populations may be used. For example, the distance between two univariate distributions may be defined as the difference in means normalized by the square root of the sum of their variances. In the case of two bivariate distributions, the method may include projecting the bivariate distributions in the direction in which the two distributions are most separated and then assessing the distance between the projected distributions by taking the difference in means normalized by the square root of the sum of their variances. "Predicting" a statistical distance measure means estimating what the statistical distance measure would be if the fluorochrome panel being evaluated were used in a flow cytometry protocol. Thus, in some cases, the signal intensities associated with the statistical measures are estimated values ​​(e.g., simulated values). In certain cases, generating a set of separability metrics that each predict a measure of statistical distance between particle populations in flow cytometer data space includes calculating the Earth Mover Distance (EMD) of the distributions.

[0035] In some embodiments, generating the set of separability metrics includes predicting a statistical moment of each biological marker identifier in the set of biological marker identifiers based on the detected signal strengths. "Statistical moment" is referred to herein in its conventional sense, referring to a quantitative measure related to the shape of a graph of a function. As understood in the art, the first moment is the mean, the second moment is the variance, the third moment is skewness, and the fourth moment is kurtosis. In some cases, predicting the statistical moment of each biological marker identifier in the set of biological marker identifiers based on the detected signal strengths includes calculating a matrix. In such cases, predicting the statistical moment includes predicting a covariance matrix of the detected signal strengths. In further cases, predicting the statistical moment includes predicting a variance-covariance matrix of the detected signal strengths. In still other cases, predicting the statistical moment includes predicting a mean matrix of the detected signal strengths.

[0036] Protocols for predicting statistical moments can vary. In some embodiments, predicting statistical moments involves performing a simulation. Any suitable simulation protocol can be used. In certain cases, the simulation is a Monte Carlo simulation. Monte Carlo simulation is described, for example, in Mooney, CZ (1997) Monte Carlo Simulation, the entire contents of which are incorporated herein by reference. As understood in the art, Monte Carlo simulation uses repeated random sampling to obtain numerical results. Such simulations can be used to generate simulated data sets of signal strengths that can be expected on a given device (i.e., associated with a device identifier). Statistical moments can then be calculated from the simulated data sets. In certain cases, performing a simulation involves parallel computing. In such cases, the method of the present invention can use multiple processors to perform the simulation. The number of processors in the multiple processors can vary. For example, the simulation can be performed by a processor including two or more processors, three or more processors, four or more processors, five or more processors, six or more processors, seven or more processors, eight or more processors, nine or more processors, and ten or more processors.

[0037] In certain aspects, the methods of the present invention involve directly calculating the propagation of relevant statistical moments in forward (i.e., from fluorophore to detector) mode and / or reverse (i.e., from detector to fluorophore) mode. Specifically, in forward mode, the distribution of fluorochrome abundances, instrument system noise, photon statistical noise, and all other relevant information are used in a noise model to predict the median and standard deviation of the signal at each detector. Meanwhile, in reverse mode, the noise model is evaluated in the reverse order, i.e., the median and spread of fluorochrome abundances are predicted using the signal distribution at each detector and all other relevant information. In some embodiments, the propagation of relevant statistical moments is calculated in forward mode. In other embodiments, the propagation of relevant statistical moments is calculated in reverse mode. In still other embodiments, the propagation of relevant statistical moments is calculated in forward mode and reverse mode.

[0038] In embodiments, predicting the statistical moments of each biological marker identifier in the set of biological marker identifiers includes incorporating the effect of a noise model on the (predicted) detected signal strength. For example, if predicting the statistical moments includes a simulation, the subject method includes incorporating an appropriate noise model into the simulation to capture dispersion introduced from different sources. In some cases, the noise model is a Gaussian noise model. As understood in the art, Gaussian noise is signal noise characterized by a probability density function equal to that of a Gaussian distribution. In embodiments, the Gaussian noise model may include a probability function p of a Gaussian random variable z, as follows:

[0039]

number

[0040] where z represents the gray level, u represents the mean gray value, and σ represents its standard deviation. In another embodiment, the noise model is a Poisson noise model. Poisson noise, also known as "shot noise," describes the fluctuations in the number of detected photons. Such photocurrent fluctuations rise and fall as the square root of the mean intensity, as follows:

[0041]

number

[0042] In some embodiments, the method includes incorporating one noise model (i.e., Gaussian noise or Poisson noise). In other embodiments, the method includes incorporating multiple noise models (e.g., Gaussian noise and Poisson noise).

[0043] In certain cases, incorporating the effect of a noise model on detected signal intensity involves obtaining an analytical expression relating moments of interest to parameters of the noise model. In other words, to more realistically model the effect of noise caused by multiple fluorochromes used at once in a single flow cytometry protocol, error associated with spillover can be incorporated. For example, after forward and backward propagation of statistical moments is calculated (e.g., as described above), an analytical model showing the relationship between noise and moments can be obtained. In certain cases, the method involves using a characteristic noise tensor (i.e., describing a multilinear relationship between a set of algebraic objects related to a vector space) to predict the diffusion of a particular marker. For example, in some cases, incorporating the effect of a noise model on detected signal intensity based on a spillover diffusion matrix (SSM) is performed. Spillover is the phenomenon in which particle-modulated light representing a particular fluorochrome is received by one or more detectors not configured to measure that parameter. Meanwhile, "spillover diffusion" refers to the error contributed by spillover to fluorescence flow cytometer data. In some cases, the spillover diffusion noise is constructive, resulting in higher signal intensities than normally observed, while in other cases the noise is destructive, resulting in lower intensities. In certain embodiments, the spillover diffusion matrix indicates how the detection of a particular fluorochrome by its corresponding detector is affected by spillover from other fluorochromes. Spillover diffusion matrices and methods for their calculation are described in U.S. Patent Application Publication Nos. 2021 / 0239592 and 2021 / 0349004, and Nguyen et al., Cytometry Part A, 83(3), 306-315, the disclosures of which are incorporated herein in their entireties.

[0044] In some embodiments, the spillover diffusion matrix is ​​calculated by the AutoSpread algorithm. The AutoSpread algorithm, created by Becton Dickinson and described in U.S. Patent Application Publication No. 2021 / 0349004, is configured to create a spillover diffusion matrix (e.g., as described above) without requiring a distinction between a population of flow cytometers that are positive and a population of flow cytometers that are negative for a given fluorochrome. AutoSpread characterizes the diffusion contributed to the detection signal of a first fluorochrome by including a second fluorochrome in the same flow cytometry panel. AutoSpread generates one coefficient for each interaction between the fluorescence detector and the fluorochrome and arranges the coefficients in a matrix similar to the spillover diffusion matrix described above. In embodiments, calculating the spillover diffusion coefficient includes assuming that the intensity of the fluorescence collected by the fluorescence detector for the negative population of the flow cytometer data is 0 and the corresponding standard deviation is an unknown quantity. In some embodiments, the spillover diffusion coefficient is calculated as follows:

[0045]

number

[0046] As shown in Equation 2, SS is the spillover diffusion coefficient and σ 2 is the standard deviation of the positive population of the fluorescence flow cytometer data,

[0047]

number

[0048] is an estimate of the standard deviation of the negative population of the fluorescent flow cytometer data, and d is the intensity of light collected by the fluorescent detector. In some embodiments, the estimate of the standard deviation of the negative population of the fluorescent flow cytometer data, assuming that the intensity of the fluorescence collected by the fluorescent detector for the negative population of the flow cytometer data is 0, is

[0049]

number

[0050] To obtain the spillover diffusion coefficient, the spillover diffusion coefficient is calculated according to a series of linear regressions. The fluorescence flow cytometer data is first sorted into quantiles according to the intensity values ​​detected by the fluorescence detector. The number of quantiles is 256 by default, but is adjusted downward to only 8 to ensure that each quantile has a sufficient number of data points to allow a reliable estimation of the standard deviation. Next, the robust standard deviation of the light emitted from the fluorochrome is regressed against the square root of the median light intensity detected for each quantile. The y-intercept of the ordinary least-squares fit is taken as an estimate of the standard deviation in the negative population of the flow cytometer data, assuming that the light intensity detected for the negative population is zero. This estimate of the standard deviation of the light emitted from the fluorochrome is used to obtain a new zero-adjusted standard deviation. The zero-adjusted standard deviation of the fluorochrome is regressed against the square root of the median fluorescence intensity for each quantile detected by the fluorescence detector. The slope of the ordinary least-squares fit is taken as the spillover diffusion coefficient.

[0051] In selected embodiments, generating a set of separability metrics involves stabilizing the variance of detected signal intensities. Corrected or decomposed flow data typically have a wide dynamic range, requiring appropriate scaling before calculating statistical distances. Examples include biexponential scaling or inverse hyperbolic (arcsinh) scaling. The parameters used in these scaling methods can be determined by automated processes that can be either data-driven or model-driven. Data-driven approaches essentially solve an optimization problem in which the objective function is the similarity of the variances of different distributions. Model-driven approaches avoid the need for simulated fluorescence datasets but instead seek to find an analytical relationship between the variance and mean of the corrected data, based on which a variance-stabilizing transformation can be determined.

[0052] After the set of separability metrics is generated, the result is a table of separability scores for all pairs defined in the gating hierarchy. An example of such a table is Table 9, shown below in the Experimental Section. The methods of the present invention further include aggregating the set of separability metrics into a panel score. As described herein, a "panel score" is a metric that provides a measure of the suitability of a given fluorochrome panel for use in a particular flow cytometry protocol performed using a particular instrument formulated using the separability metrics. To aggregate the table of separability scores into a panel score, an aggregation procedure is required. In some embodiments, the panel score is a scalar (e.g., in embodiments involving single-objective optimization). In other cases, the panel score is a vector (e.g., in embodiments involving multi-objective optimization). In certain cases, aggregating the set of separability metrics into a panel score includes negating the value of the lowest separability score. In some such cases, the negated value can be considered the panel score. In some instances, the method includes using the number of pairs whose scores are below a certain threshold as an objective function. In some cases, determining the panel score includes calculating a vector of a set of aggregated separability metrics for population-marker pairs and a set of aggregated separability metrics for quantitative pairs. In such cases, the method includes separating the quantitative and classification markers, using a different aggregation strategy for each, and using the vector of the two elements as the panel score.

[0053] Once the panel score is defined, it can be used as an objective function in combinatorial optimization routines. Thus, in embodiments, a method includes optimizing a fluorochrome panel based on an assessment of the suitability of the fluorochrome panel for use in generating flow cytometer data, i.e., such that the fluorochrome panel is suitable for use in a flow cytometry protocol. In some embodiments, optimizing the fluorochrome panel includes the use of a panel optimization algorithm. In some cases, the panel optimization algorithm is a constrained optimization algorithm. "Constrained optimization" is referred to herein in its conventional sense to describe the process of optimizing variables in the presence of constraints on those variables. Any suitable constrained optimization method may be used. Examples of constrained optimization techniques that may be used include, but are not limited to, local search, local repair, backtracking, and constraint propagation, random restart hill climbing, and tabu search. These may, in certain cases, be combined with minimization techniques such as simulated annealing and genetic (evolutionary) algorithms. In some cases, the fluorochrome panels described herein can be optimized in conjunction with the optimization protocol described in U.S. Provisional Patent Application No. 63 / 305,010, filed January 31, 2022 (Attorney Docket No. BECT-310PRV (P-26714)), the disclosure of which is incorporated herein by reference. In embodiments, the variable being optimized is the panel score. For problems with small search spaces, techniques for intelligently traversing the search space can be used. Examples include, but are not limited to, dynamic programming and depth-first or breath-first search. For problems where the search space is too large to traverse, heuristic search techniques, such as greedy algorithms, genetic algorithms, and the algorithms introduced in U.S. Provisional Patent Application No. 63 / 305,010, filed January 31, 2022 (Attorney Docket No. BECT-310PRV (P-26714)), can be used.

[0054] In certain cases, optimizing a fluorochrome panel includes adjusting fluorochromes in the fluorochrome panel and evaluating the suitability of the adjusted fluorochrome panel for use in generating flow cytometer data. "Adjusting" fluorochromes in a fluorochrome panel means switching fluorochromes, i.e., fluorochrome identifiers associated with fluorochromes, for different fluorochromes. One or more fluorochromes in a panel can be adjusted at any time. In some cases, the method includes switching a single fluorochrome in the panel at a given time. In certain cases, optimizing a fluorochrome panel includes maintaining a fluorochrome panel with a constant size. In other words, the number of fluorochromes in an acquired fluorochrome panel does not change when one or more fluorochromes are adjusted. For example, an evaluated fluorochrome panel with N fluorochromes continues to have N fluorochromes after adjustment. In certain cases, fluorochromes in the fluorochrome panel are not replaced with fluorochromes already in the fluorochrome panel. After the adjusted fluorochrome panel is generated, the subject method further includes evaluating the adjusted fluorochrome panel (e.g., as described above). The subject methods further involve comparing the evaluation of the first fluorochrome panel to the evaluation of the adjusted fluorochrome panel. For example, the method may include determining which of the first fluorochrome panel and the adjusted fluorochrome panel results in an optimized separability metric instead of a panel score.

[0055] In certain cases, the method includes iteratively adjusting a fluorochrome panel and evaluating the suitability of each iteratively adjusted fluorochrome panel. In embodiments, either the first fluorochrome panel evaluated to have a high panel score or the adjusted fluorochrome panel can serve as a seed for the next part of the iterative process. A "seed" refers to a fluorochrome panel determined in one iteration of the method to be associated with a high panel score compared to one or more slightly modified fluorochrome panels. In some embodiments, the iterative process repeats itself until a condition is met. Any suitable condition can be used to terminate the iterative process. In some cases, the iterative process terminates when a certain runtime has elapsed. In other cases, the iterative process terminates when the evaluations generated for each iteratively adjusted fluorochrome panel converge. Stated another way, the iterative process terminates when only minor panel score differences are observed between subsequent fluorochrome panels.

[0056] In some embodiments, the method also includes generating a visualization of the evaluated suitability of the fluorochrome panel for use in generating flow cytometer data. Any suitable visualization may be used. In some embodiments, the visualization includes a plot of simulated flow cytometer data based on the given fluorochrome panel. Stated another way, the visualization includes exemplary flow cytometer data that would be generated if the sample were run on a particular instrument with a particular fluorochrome panel.

[0057] FIG. 1A presents a flowchart illustrating one embodiment of the subject method. In step 101, a biological hypothesis, an antigen table (i.e., a list of biological markers), a gating strategy, an initial fluorochrome panel, and an instrument identifier (i.e., instrument configuration) are received. After the initial fluorochrome panel is received, the method includes evaluating the fluorochrome panel by defining marker pairs of interest in step 112 (step 110), assessing separability in step 113, and aggregating separability metrics into a panel score in step 114. In the embodiment of FIG. 1A, defining markers of interest (step 112) includes splitting the gating hierarchy in step 112a, adding implicit populations in step 112b, and defining quantitative pairs in step 112c. Assessing population separability in step 113 also includes estimating statistical moments in step 113a, stabilizing variance in step 113b, and calculating statistical distances 113c. After the panel scores are generated in step 114, an optimized fluorochrome panel can be obtained (step 120) by running an optimization routine (step 121) to create a panel with good separation performance, which is output in step 122.

[0058] FIG. 1B presents an alternative illustration of the subject method. The method begins by receiving an initial fluorochrome panel in step 101. Next, a panel score is generated (step 110, described above with respect to FIG. 1A). In a panel optimization routine 121, it is determined whether the panel score generated in step 110 is optimized in step 123. The determination of whether the panel is optimized may be based on criteria (e.g., thresholds) provided by the user. If optimized, this optimized panel score is output to the user. If not optimized, the fluorochrome panel is adjusted. The adjusted fluorochrome panel is then provided to a panel score generation process 110. This process may be repeated until an optimized fluorochrome is determined.

[0059] The fluorescent dye panels of the present invention can include any suitable set of fluorescent dyes. Fluorescent dyes of interest in certain embodiments have excitation maxima in the range of 100 nm to 800 nm, including, for example, 150 nm to 750 nm, such as, for example, 200 nm to 700 nm, such as, for example, 250 nm to 650 nm, including, for example, 300 nm to 600 nm, and 400 nm to 500 nm. Fluorescent dyes of interest in certain embodiments have emission maxima in the range of 400 nm to 1000 nm, including, for example, 450 nm to 950 nm, including, for example, 500 nm to 900 nm, including, for example, 550 nm to 850 nm, and 600 nm to 800 nm. In certain cases, the fluorescent dye is a luminescent dye, such as a fluorescent dye having a peak emission wavelength of 200 nm or more, for example, 250 nm or more, for example, 300 nm or more, for example, 350 nm or more, such as 400 nm or more, for example, 450 nm or more, for example, 500 nm or more, such as 550 nm or more, for example, 600 nm or more, for example, 650 nm or more, such as 700 nm or more, for example, 750 nm or more, for example, 800 nm or more, such as 850 nm or more, for example, 900 nm or more, for example, 950 nm or more, such as 1000 nm or more, and including 1050 nm or more. For example, the fluorescent dye may be a fluorescent dye having a peak emission wavelength in the range of, for example, 200 nm to 1200 nm, for example, 300 nm to 1100 nm, for example, 400 nm to 1000 nm, for example, 500 nm to 900 nm (including a fluorescent dye having a peak emission wavelength of 600 nm to 800 nm).

[0060] Fluorescent dyes of interest may include, but are not limited to, bodipy dyes, coumarin dyes, rhodamine dyes, acridine dyes, anthraquinone dyes, arylmethane dyes, diarylmethane dyes, chlorophyll-containing dyes, triarylmethane dyes, azo dyes, diazonium dyes, nitro dyes, nitroso dyes, phthalocyanine dyes, cyanine dyes, asymmetric cyanine dyes, quinoneimine dyes, azine dyes, eurodine dyes, safranine dyes, indamines, indophenol dyes, fluorine dyes, oxazine dyes, oxazone dyes, thiazine dyes, thiazole dyes, xanthene dyes, fluorene dyes, pyronine dyes, fluorine dyes, rhodamine dyes, phenanthridine dyes, squaraines, bodipy, squaraine-roxitanes, naphthalenes, coumarins, oxadiazoles, anthracenes, pyrenes, acridines, arylmethines, or tetrapyrroles, and combinations thereof. In certain embodiments, the conjugate may comprise two or more dyes, for example, two or more dyes selected from bodipy dyes, coumarin dyes, rhodamine dyes, acridine dyes, anthraquinone dyes, arylmethane dyes, diarylmethane dyes, chlorophyll-containing dyes, triarylmethane dyes, azo dyes, diazonium dyes, nitro dyes, nitroso dyes, phthalocyanine dyes, cyanine dyes, asymmetric cyanine dyes, quinoneimine dyes, azine dyes, eurodine dyes, safranine dyes, indamines, indophenol dyes, fluorine dyes, oxazine dyes, oxazone dyes, thiazine dyes, thiazole dyes, xanthene dyes, fluorene dyes, pyronine dyes, fluorine dyes, rhodamine dyes, phenanthridine dyes, squaraines, bodipy, squaraine-roxitanes, naphthalenes, coumarins, oxadiazoles, anthracenes, pyrenes, acridines, arylmethines, or tetrapyrroles, and combinations thereof.

[0061] In certain embodiments, fluorescent dyes of interest may include, but are not limited to, fluorescein isothiocyanate (FITC), phycoerythrin (PE) dyes, peridinin chlorophyll protein-cyanine dyes (e.g., PerCP-Cy5.5), phycoerythrin-cyanine (PE-Cy) dyes (PE-Cy7), allophycocyanin (APC) dyes (e.g., APC-R700), allophycocyanin-cyanine dyes (e.g., APC-Cy7), and coumarin dyes (e.g., V450 or V500). In certain cases, the fluorescent dye may include one or more of 1,4-bis-(o-methylstyryl)-benzene (bis-MSB 1,4-bis[2-(2-methylphenyl)ethenyl]-benzene), C510 dye, C6 dye, Nile Red dye, T614 dye (e.g., N-[7-(methanesulfonamido)-4-oxo-6-phenoxycyclomen-3-yl]formamide), LDS821 dye ((2-(6-(p-dimethylaminophenyl)-2,4-neopentylene-1,3,5-hexatrienyl)-3-ethylbenzothiazolium perchlorate), mFluor dye (e.g., mFluor Red dye such as mFluor 780NS).

[0062] The fluorescent dyes covered include fluorescein, hydroxycoumarin, aminocoumarin, methoxycoumarin, Cascade Blue, Pacific Blue, Pacific Orange, Lucifer yellow, NBD, R-phycoerythrin (PE), PE-Cy5 conjugate, PE-Cy7 conjugate, Red 613, PerCP, TruRed, FluorX, BODIPY-FL, TRITC, X-rhodamine, Lissamine rhodamine B, Texas Red, allophycocyanin (APC), APC-Cy7 conjugate, Cy2, Cy3, Cy3B, Cy3.5, Cy5, Cy5.5, Cy7, Hoechst 33342, DAPI, Hoechst 33258, SYTOX Blue, chromomycin A3, mithramycin, YOYO-1, ethidium bromide, acridine orange, and SYTOX. Green, TOTO-1, TO-PRO-1, thiazole orange, propidium iodide (PI), LDS751, 7-AAD, SYTOXOrange, TOTO-3, TO-PRO-3, DRAQ5, Indo-1, Fluo-3, DCFH, DHR, SNARF, Y66H, Y66F, EBFP, EBFP2, Azurite, GFPuv, T-Sapphire, TagBFP, Cerulean, mCFP, ECFP, CyPet, Y66W, dKeima-Red, mKeima-Red, TagCFP , AmCyan1, mTFP1(Teal), S65A, Midoriishi-Cyan, Wild type GFP, S65C, TurboGFP, TagGFP, TagGFP2, AcGFP1, S65L, Emerald, S65T, EGFP, Azami-Green, ZsGreen1, Dronpa-Green, TagYFP, EYFP, Topaz, Venu s, mCitrine, YPet, TurboYFP, PhiYFP, PhiYFP-m, ZsYellow1, mBanana, Kusabira-Orange, mOrange, mOrange2, mKO, TurboRFP, tdTomato, DsRed-Express2, TagRFP, DsRed monomer, DsRed2 ("RFP"), mStrawberry, TurboFP602, AsRed2, mRFP1, J-Red, mCherry, HcRed1, mKate2, Katushka (TurboFP635), mKate (TagFP635), TurboFP635, mPlum, mRaspberry, mNeptune, E2-Crimson, monochlorobimane, calcein, Alexa Alexa Fluor 350, Alexa Fluor 405, Alexa Fluor 430, Alexa Fluor 488, Alexa Fluor 500, Alexa Fluor 514, Alexa Fluor 532, Alexa Fluor 546, Alexa Fluor 555, Alexa Fluor 568, Alexa Fluor 594, Alexa Fluor 610, Alexa Fluor 633, Alexa Fluor 647, Alexa Fluor 660, Alexa Fluor 680, Alexa Fluor 700, Alexa Fluor 750, Alexa Fluor 790, and HyperFluor.

[0063] In some cases, the fluorescent dye panel includes one or more polymer dyes (e.g., fluorescent polymer dyes). The fluorescent polymer dyes that may find use in the subject methods and systems are diverse. In some cases of the present methods, the polymer dye includes a conjugated polymer. Conjugated polymers (CPs) are characterized by a delocalized electronic structure that includes a backbone of alternating unsaturated (e.g., double and / or triple) and saturated (e.g., single) bonds, where π electrons can move from one bond to another. In this manner, the conjugated backbone can restrict the bond angles between the repeat units of the polymer, imparting an elongated, linear structure to the polymer dye. For example, proteins and nucleic acids are also macromolecules, but in some cases, they do not form elongated rod structures but rather fold into highly ordered, three-dimensional shapes. In addition, CPs can form "rigid rod" polymer backbones, where the bending (e.g., twist) angles between the monomer repeat units along the polymer backbone chain are restricted. In some cases, the polymer dye includes a CP with a rigid rod structure. The structural features of the polymer dye can affect the fluorescent properties of the molecule.

[0064] Any convenient polymer dye can be utilized in the subject devices and methods. In some cases, the polymer dye is a multichromophore having a structure capable of harvesting light to amplify the fluorescent output of the fluorophore. In some cases, the polymer dye can harvest light and efficiently convert it to longer wavelength emission. In some cases, the polymer dye has a light-harvesting multichromophore system that can efficiently transfer energy to a nearby emissive species (e.g., a "signaling chromophore"). Mechanisms for energy transfer include, for example, resonance energy transfer (e.g., Förster (or fluorescence) resonance energy transfer, FRET), quantum charge exchange (Dexter energy transfer), etc. In some cases, these energy transfer mechanisms are relatively short-range, i.e., the proximity of the light-harvesting multichromophore system to the signaling chromophore provides efficient energy transfer. Under conditions for efficient energy transfer, amplification of emission from the signaling chromophore occurs when the number of individual chromophores in the light-harvesting multichromophore system is large. That is, the emission from the signaling chromophore is stronger when the incident light (the "excitation light") is at a wavelength that is absorbed by the light-harvesting multichromophore system than when the signaling chromophore is directly excited by the pump light.

[0065] The multichromophore can be a conjugated polymer. Conjugated polymers (CPs) feature a delocalized electronic structure and can be used as highly responsive optical reporters for chemical and biological targets. Because the effective conjugation length is significantly shorter than the length of the polymer chain, the backbone contains many closely spaced conjugated segments. Therefore, conjugated polymers are efficient at harvesting light, enabling light amplification via Forster energy transfer.

[0066] Polymeric dyes of interest are disclosed in U.S. Pat. Nos. 7,270,956, 7,629,448, 8,158,444, 8,227,187, 8,455,613, 8,575,303, 8,802,450, 8,969,509, 9,139,869, 9,371,559, 9,547,008, 10,094,838, 10,302,648, 10,458,989, 10,641,775, and 10,962,546, the disclosures of which are incorporated herein by reference in their entireties, and in U.S. Pat. Nos. 5,811,979, ... al., J. Am. Chem. Soc., 2001, 123(26), pp 6417-6418; Feng et al., Chem. Soc. Rev., 2010, 39, 2411-2419; and Traina et al., J. Am. Chem. Soc., 2011, 133(32), pp 12600-12607. Specific polymeric dyes that can be used include, but are not limited to, BD Horizon Brilliant™ dyes, such as BD Horizon Brilliant™ purple dyes (e.g., BV421, BV510, BV605, BV650, BV711, BV786), BD Horizon Brilliant™ ultraviolet dyes (e.g., BUV395, BUV496, BUV737, BUV805), and BD Horizon Brilliant™ blue dyes (e.g., BB515) (BD Biosciences, San Jose, Calif.). Any fluorescent dye known to those of skill in the art, including but not limited to those listed above, or yet to be discovered, can be used in the subject methods.

[0067] The subject fluorescent dye panels and / or fluorescent dyes referenced in the spectral matrix may or may not be conjugated to biomolecules, such as biopolymers. Biopolymers may be biopolymers. A "biopolymer" is a polymer of one or more types of repeating units. Biopolymers are typically found in biological systems and include, among others, polysaccharides (e.g., carbohydrates), peptides (a term used to include polypeptides and proteins, whether or not bound to polysaccharides), and polynucleotides, as well as their analogs (e.g., those compounds composed of or containing amino acid analogs or non-amino acid groups, or nucleotide analogs or non-nucleotide groups). This includes polynucleotides in which the conventional backbone is replaced with a non-naturally occurring or synthetic backbone, and nucleic acids (or synthetic or naturally occurring analogs) in which one or more of the conventional bases are replaced with groups (natural or synthetic) capable of participating in Watson-Crick hydrogen bonding interactions. Polynucleotides include single-stranded or multi-stranded arrangements, where one or more of the strands may or may not be perfectly aligned with another strand. Specifically, "biopolymer" includes DNA (including cDNA), RNA, and oligonucleotides, regardless of origin. Thus, biomolecules can include polysaccharides, nucleic acids, and polypeptides. For example, a nucleic acid can be an oligonucleotide, truncated or full-length DNA or RNA. In embodiments, oligonucleotides, truncated and full-length DNA or RNA are comprised of nucleotide monomers containing 10 or more, e.g., 15 or more, e.g., 25 or more, e.g., 50 or more, e.g., 100 or more, e.g., 250 or more, and 500 or more nucleotide monomers. For example, oligonucleotides, truncated and full-length DNA or RNA of interest can be comprised of 10 to 10 nucleotide monomers. 8 Nucleotides, e.g., 10 2 Nucleotides ~10 7 Nucleotide length range, and 10 3 Nucleotides ~10 6The term "full-length" refers to a nucleic acid polymer having 70% or more, such as 75% or more, such as 80% or more, such as 85% or more, such as 90% or more, such as 95% or more, such as 97% or more, such as 99% or more of the DNA or RNA's complete sequence (e.g., as found in nature), and may be a range including any nucleotide length. In embodiments, the biopolymer is not a single base or a short oligonucleotide (e.g., less than 10 bases). "Full-length" refers to a nucleic acid polymer having 70% or more, such as 75% or more, such as 80% or more, such as 85% or more, such as 90% or more, such as 95% or more, such as 97% or more, such as 99% or more of the DNA or RNA's complete sequence (e.g., as found in nature), and a nucleic acid polymer that contains 100% of the DNA or RNA's full-length sequence (e.g., as found in nature).

[0068] Polypeptides may, in certain cases, be truncated or full-length proteins, enzymes, or antibodies. In embodiments, polypeptides, truncated and full-length proteins, enzymes, or antibodies consist of 10 or more amino acid monomers, e.g., 15 or more, e.g., 25 or more, e.g., 50 or more, e.g., 100 or more, e.g., 250 or more, and even 500 or more amino acid monomers. For example, polypeptides, truncated and full-length proteins, enzymes, or antibodies of interest may consist of 10 to 10 amino acid monomers. 8 Amino acids, e.g., 10 2 Amino acids ~10 7 Amino acid length range, and 10 3 Amino acids ~10 6 The length of a biopolymer may be a range including any amino acid length. In embodiments, the biopolymer is not a single amino acid or a short polypeptide (e.g., less than 10 amino acids). By "full length" is meant a protein, enzyme, or antibody having 70% or more, such as 75% or more, for example 80% or more, such as 85% or more, for example 90% or more, such as 95% or more, for example 97% or more, for example 99% or more of its complete sequence (e.g., as found in nature), as well as a polypeptide polymer that includes 100% of the full-length sequence (e.g., as found in nature) of the protein, enzyme, or antibody.

[0069] In some cases, the fluorescent dye is conjugated to a specific binding member. The specific binding member and the fluorescent dye may be conjugated (e.g., covalently bonded) to one another at any convenient position on the two molecules via an optional linker. As used herein, the term "specific binding member" refers to a member of a pair of molecules that have binding specificity for one another. One member of the pair of molecules may have a surface region or depression that specifically binds to a surface region or depression of the other member of the pair of molecules. Thus, the members of the pair have the property of specifically binding to one another to form a binding complex. In some embodiments, the affinity between the specific binding members in the binding complex is greater than or equal to 10 -6 M or less, e.g., 10 -7 M or less (10 -8 M or less), e.g., 10 -9 M or less, 10 -10 M or less, 10 -11 M or less, 10 -12 M or less, 10 -13 M or less, 10 -14 M or less (10 -15 K (including M and below) d In some embodiments, a specific binding member specifically binds with high avidity. High avidity means that the binding member specifically binds with a dissociation constant greater than 10×10 -9 M or less, e.g., 1×10 -9 M or less, 3×10 -10 M or less, 1×10 -10 M or less, 3×10 -11 M or less, 1×10 -11 M or less, 3×10 -12 M or less, or 1 x 10 -12 Apparent K below M d This means that the antibody specifically binds with an apparent affinity characterized by:

[0070] A specific binding member can be proteinaceous. As used herein, the term "proteinaceous" refers to a moiety composed of amino acid residues. A proteinaceous moiety can be a polypeptide. In certain cases, a proteinaceous specific binding member is an antibody. In certain embodiments, a proteinaceous specific binding member is an antibody fragment, e.g., a binding fragment of an antibody that specifically binds to a polymeric dye. As used herein, the terms "antibody" and "antibody molecule" are used interchangeably and refer to a protein consisting of one or more polypeptides substantially encoded by all or part of recognized immunoglobulin genes. Recognized immunoglobulin genes include, for example, in humans, the kappa (k), lambda (l), and heavy chain loci (which together contain numerous variable region genes), as well as the constant region genes mu (u), delta (d), gamma (g), sigma (e), and alpha (a) (which encode the IgM, IgD, IgG, IgE, and IgA isotypes, respectively). An immunoglobulin light or heavy chain variable region consists of a framework region (FR) interrupted by three hypervariable regions, also called "complementarity-determining regions" or "CDRs." The extent of the framework region and CDRs has been precisely defined (see "Sequences of Proteins of Immunological Interest," E. Kabat et al., USDapartment of Health and Human Services, (1991)). The sequences of the framework regions of different light or heavy chains are relatively conserved within a species. The framework region of an antibody, the combined framework regions of the constituent light and heavy chains, serves to position and align the CDRs. The CDRs primarily contribute to binding to an epitope of an antigen. The term "antibody" is meant to include full-length antibodies and may refer to natural antibodies from any organism, engineered antibodies, or antibodies recombinantly produced for experimental, therapeutic, or other purposes, as further defined below.Antibody fragments of interest include, but are not limited to, Fab, Fab', F(ab')2, Fv, scFv, or other antigen-binding subsequences of antibodies, either produced by modification of whole antibodies or synthesized de novo using recombinant DNA technology. Antibodies may be monoclonal or polyclonal and may have other specific activities on cells (e.g., antagonist, agonist, neutralizing, inhibitory, or stimulatory antibodies). It is understood that antibodies may have additional conservative amino acid substitutions that do not substantially affect antigen binding or other antibody functions. In certain embodiments, the specific binding member is a Fab fragment, F(ab')2 fragment, scFv, diabody, or triabody. In certain embodiments, the specific binding member is an antibody. In some cases, the specific binding member is a murine antibody or binding fragment thereof. In certain instances, the specific binding member is a recombinant antibody or binding fragment thereof.

[0071] As explained above, biological markers of interest for the subject methods include cluster of differentiation (CD) molecules. Non-limiting examples of CD molecules that can be used include: CD1, CD1a, CD1b, CD1c, CD1d, CD1e, CD2, CD3, CD3d, CD3e, CD3g, CD4, CD5, CD6, CD7, CD8, CD8a, CD8b, CD9, CD10, CD11a, CD11b, CD11c, CD11d, CD13, CD14, CD15, CD16, CD16a, CD16b, CD17, CD18, CD19, CD20, CD21, CD22, CD23, CD24, CD25, CD26, CD27, CD28, CD29, CD30, CD31, CD32A, CD32B, CD33, CD34, CD35, CD36, CD37, CD38, CD39, CD40, CD41, CD42, CD42a, CD42b, CD42c, CD42d, CD43, CD44, CD45, CD4 6, CD47, CD48, CD49a, CD49b, CD49c, CD49d, CD49e, CD49f, CD50, CD51, CD52, CD53, CD54, CD55, CD56, CD57, CD58, CD59, CD60a, CD60b, CD60c, CD6 1, CD62E, CD62L, CD62P, CD63, CD64a, CD65, CD65s, CD66a, CD66b, CD66c, CD66d, CD66e, CD66f, CD68, CD69, CD70, CD71, CD72, CD73, CD74, CD75, C D75s, CD77, CD79A, CD79B, CD80, CD81, CD82, CD83, CD84, CD85A, CD85B, CD85C, CD85D, CD85F, CD85G, CD85H, CD85I, CD85J, CD85K, CD85M, CD86, C D87, CD88, CD89, CD90, CD91, CD92, CD93, CD94, CD95, CD96, CD97, CD98, CD99, CD100, CD101, CD102, CD103, CD104, CD105, CD106, CD107, CD107a, CD107b, CD108, CD109, CD110, CD111, CD112, CD113, CD114, CD115, CD116, CD117, CD118, CD119, CD120, CD120a, CD120b, CD121a, CD121b, CD122,CD123、CD124、CD125、CD126、CD127、CD129、CD130、CD131、CD132、CD133、CD134、CD135、CD136、CD137、CD138、CD139、CD140A、CD140B、CD141、CD142、CD143、CD144、CDw145、CD146、CD147、CD148、CD150、CD151、CD152、CD153、CD154、CD155、CD156、CD156a、CD156b、CD156c、CD157、CD158、CD158A、CD158B1、CD158B2、CD158C、CD158D、CD158E1、CD158E2、CD158F1、CD158F2、CD158G、CD158H、CD158I、CD158J、CD158K、CD159a、CD159c、CD160、CD161、CD162、CD163、CD164、CD165、CD166、CD167a、CD167b、CD168、CD169、CD170、CD171、CD172a、CD172b、CD172g、CD173、CD174、CD175、CD175s、CD176、CD177、CD178、CD179a、CD179b、CD180、CD181、CD182、CD183、CD184、CD185、CD186、CD187、CD188、CD189、CD190、CD191、CD192、CD193、CD194、CD195、CD196、CD197、CDw198、CDw199、CD200、CD201、CD202b、CD203a、CD203c、CD204、CD205、CD206、CD207、CD208、CD209、CD210、CDw210a、CDw210b、CD211、CD212、CD213a1、CD213a2、CD214、CD215、CD216、CD217、CD218a、CD218b、CD219、CD220、CD221、CD222、CD223、CD224、CD225、CD226、CD227、CD228、CD229、CD230、CD231、CD232、CD233、CD234、CD235a、CD235b、CD236、CD237、CD238、CD239、CD240CE、CD240D、CD241、CD242、CD243、CD244、CD245、CD246、CD247、CD248、CD249、CD250、CD251, CD252, CD253, CD254, CD255, CD256, CD257, CD258, CD259, CD260, CD261, CD262, CD263, CD264, CD265, CD266, CD267, CD268, CD269, CD270, CD271, CD272, CD273, CD274, CD275, CD276, CD277, CD278, CD279, CD280, CD281, CD282, CD283, CD284, CD285, CD286, CD287, CD288, CD289, CD290, CD291, CD292, CDw293, CD294, CD295, CD296, CD297, CD298, CD299, CD300A, CD300C, CD301, CD302, CD303, CD304, CD305, CD306, CD307, CD307a, CD307b, CD307c, CD307d, CD307e, CD308, CD309, CD310, CD311, CD312, CD313, CD314, CD315, CD316, CD317, CD318, CD319, CD320, CD321, CD322, CD323, CD324, CD325, CD326, CD327, CD328, CD329, CD330, CD331, CD332, CD333, CD334, CD335, CD336, CD337, CD338, CD339, CD340, CD344, CD349, CD351, CD352, CD353, CD354, CD355, CD357, CD358, CD360, CD361, CD362, CD363, CD364, CD365, CD366, CD367, CD368, CD369, CD370 and CD371.,

[0072] In embodiments, the subject fluorescent dye panels are used to analyze a sample. In some cases, the sample analyzed is a biological sample. The term "biological sample" is used in its conventional sense to refer to a whole organism, a plant, a fungus, or, in certain cases, a subset, cell, or component part of an animal tissue that may be found in blood, mucus, lymph, synovial fluid, cerebrospinal fluid, saliva, bronchoalveolar lavage, amniotic fluid, amniotic cord blood, urine, vaginal fluid, and semen. Thus, a "biological sample" refers to both a natural organism or a subset of its tissues, as well as homogenates, lysates, or extracts prepared from an organism or a subset of its tissues, including, but not limited to, plasma, serum, spinal fluid, lymph, skin sections, respiratory tract, gastrointestinal tract, cardiovascular, and urinary tract, tears, saliva, milk, blood cells, tumors, and organs. A biological sample can be any type of biological tissue, including both healthy and diseased tissue (e.g., cancerous, malignant, necrotic, etc.). In certain embodiments, the biological sample is a liquid sample such as blood or a derivative thereof, e.g., plasma, tears, urine, semen, etc., and in some cases the sample is a blood sample, including whole blood, such as blood obtained from a venipuncture or fingerstick (which may or may not be combined with any reagents, such as preservatives, anticoagulants, etc., prior to assay).

[0073] In certain embodiments, the sample source is a "mammal" or "mammalian animal," terms used broadly to describe organisms within the mammalian family, including carnivores (e.g., dogs and cats), rodents (e.g., mice, guinea pigs, and rats), and primates (e.g., humans, chimpanzees, and monkeys). In some cases, the subject is a human. The methods may be applied to samples obtained from human subjects of both genders and at any stage of development (i.e., newborn, infant, juvenile, adolescent, adult); in certain embodiments, the human subject is a juvenile, adolescent, or adult. It should be understood that while the present invention may be applied to samples from human subjects, it may also be practiced on samples from other animal subjects (i.e., "non-human subjects"), such as, but not limited to, birds, mice, rats, dogs, cats, livestock, and horses.

[0074] The fluorescent dyes in the fluorescent dye panel may be configured to target different types of cells (e.g., via antibodies targeting the cells, etc.). A variety of cells may be characterized using the subject methods. Target cells of interest include, but are not limited to, stem cells, T cells, dendritic cells, B cells, granulocytes, leukemia cells, lymphoma cells, viral cells (e.g., HIV cells), NK cells, macrophages, monocytes, fibroblasts, epithelial cells, endothelial cells, and erythroid cells. Target cells of interest include cells bearing a favorable cell surface marker or cell surface antigen that can be captured or labeled by a favorable affinity agent or conjugate thereof. For example, target cells may comprise cell surface antigens such as CD11b, CD123, CD14, CD15, CD16, CD19, CD193, CD2, CD25, CD27, CD3, CD335, CD36, CD4, CD43, CD45RO, CD56, CD61, CD7, CD8, CD34, CD1c, CD23, CD304, CD235a, T cell receptor alpha / beta, T cell receptor gamma / delta, CD253, CD95, CD20, CD105, CD117, CD120b, Notch4, Lgr5 (N-terminus), SSEA-3, TRA-1-60 antigen, disialoganglioside GD2, and CD71. In some embodiments, the target cells are selected from HIV-containing cells from whole blood, bone marrow or umbilical cord blood, Treg cells, antigen-specific T cell populations, tumor cells, or hematopoietic progenitor cells (CD34+).

[0075] In certain embodiments, the fluorochrome panel identified via the present methods can be used in a flow cytometry protocol (e.g., to analyze samples such as those described above). In performing such methods, a sample (e.g., in the flow stream of a flow cytometer) is illuminated with light from a light source. In some embodiments, the light source is a broadband light source that emits light having a broad range of wavelengths, e.g., spanning 50 nm or more, e.g., 100 nm or more, e.g., 150 nm or more, e.g., 200 nm or more, e.g., 250 nm or more, e.g., 300 nm or more, e.g., 350 nm or more, e.g., 400 nm or more, and 500 nm or more. For example, one suitable broadband light source emits light having a wavelength between 200 nm and 1500 nm. Another example of a suitable broadband light source includes a light source that emits light having a wavelength between 400 nm and 1000 nm. Where the method includes irradiating with a broadband light source, broadband light source protocols of interest may include, but are not limited to, halogen lamps, deuterium arc lamps, xenon arc lamps, stabilized fiber-coupled broadband light sources, broadband LEDs with continuous spectra, ultra-bright light emitting diodes, semiconductor light emitting diodes, broad spectrum LED white light sources, multi-LED integrated white light sources, or any combination thereof, among other broadband light sources.

[0076] In other embodiments, methods of the present invention include irradiating with a narrowband light source that emits a specific wavelength or a narrow range of wavelengths, such as a light source that emits light in a narrow range of wavelengths, such as a range of 50 nm or less, such as 40 nm or less, such as 30 nm or less, such as 25 nm or less, such as 20 nm or less, such as 15 nm or less, such as 10 nm or less, such as 5 nm or less, 2 nm or less, including light sources that emit light of a specific wavelength (i.e., monochromatic light). When the method includes irradiating with a narrowband light source, narrowband light source protocols of interest may include, but are not limited to, narrow wavelength LEDs, laser diodes, or broadband light sources coupled to one or more optical bandpass filters, diffraction gratings, monochromators, or any combination thereof.

[0077] In certain embodiments, the method includes irradiating the sample with one or more lasers. As explained above, the type and number of lasers can vary depending on the sample and the desired light to be collected, and can be gas lasers such as a helium-neon laser, an argon laser, a krypton laser, a xenon laser, a nitrogen laser, a CO laser, a CO laser, an argon-fluorine (ArF) excimer laser, a krypton-fluorine (KrF) excimer laser, a xenon-chlorine (XeCl) excimer laser, a xenon-fluorine (XeF) excimer laser, or a combination thereof. In other cases, the method includes irradiating the flowstream with a dye laser, such as a stilbene laser, a coumarin laser, or a rhodamine laser. In still other instances, the method includes irradiating the flowstream with a metal-vapor laser, such as a helium-cadmium (HeCd) laser, a helium-mercury (HeHg) laser, a helium-selenium (HeSe) laser, a helium-silver (HeAg) laser, a strontium laser, a neon-copper (NeCu) laser, a copper laser, or a gold laser, and combinations thereof. In still other instances, the method includes irradiating the flowstream with a solid-state laser, such as a ruby ​​laser, a Nd:YAG laser, a NdCrYAG laser, an Er:YAG laser, a Nd:YLF laser, a Nd:YVO4 laser, a Nd:YCa4O(BO3)3 laser, a Nd:YCOB laser, a titanium sapphire laser, a slim YAG laser, a ytterbium YAG laser, a Yb2O3 laser, or a cerium-doped laser, and combinations thereof.

[0078] The sample may be illuminated with one or more of the light sources mentioned above, such as two or more light sources, such as three or more light sources, such as four or more light sources, five or more light sources, etc., including ten or more light sources. The light sources may include any type of combination of light sources. For example, in some embodiments, the method includes illuminating the sample in the flowstream 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.

[0079] The sample may be illuminated with a wavelength in the range of 200 nm to 1500 nm, such as 250 nm to 1250 nm, such as 300 nm to 1000 nm, or 350 nm to 900 nm, including 400 nm to 800 nm. For example, if the light source is a broadband light source, the sample may be illuminated with a wavelength of 200 nm to 900 nm. In other cases, if the light source includes multiple narrowband light sources, the sample may be illuminated with a specific wavelength in the range of 200 nm to 900 nm. For example, the light source may be multiple narrowband LEDs (1 nm to 25 nm), each independently emitting light having a wavelength range of 200 nm to 900 nm. In other embodiments, the narrowband light source includes one or more lasers (e.g., a laser array), and the sample is illuminated with a specific wavelength in the range of 200 nm to 700 nm, such as a laser array having a gas laser, excimer laser, dye laser, metal vapor laser, and solid-state laser, as described above.

[0080] When two or more light sources are used, the sample can be illuminated with the light sources simultaneously or sequentially, or a combination thereof. For example, the sample can be illuminated with each of the light sources simultaneously. In other embodiments, the flow stream is illuminated sequentially with each of the light sources. When two or more light sources are used to sequentially illuminate the sample, the time for which each light source illuminates the sample can independently be 0.001 microseconds or more, such as 0.01 microseconds or more, such as 0.1 microseconds or more, such as 1 microseconds or more, such as 5 microseconds or more, such as 10 microseconds or more, such as 30 microseconds or more, including 60 microseconds or more. For example, the method can include illuminating the sample with a light source (e.g., a laser) for a duration ranging from 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, including 5 microseconds to 10 microseconds. In embodiments in which the sample is sequentially illuminated with two or more light sources, the duration for which the sample is illuminated by each light source may be the same or different.

[0081] The period between illumination by each light source can also vary as desired, independently separated by a delay of 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 15 microseconds or more, such as 30 microseconds or more, including 60 microseconds or more. For example, the period between illumination by each light source can range from 0.001 microseconds to 60 microseconds, such as 0.01 microseconds to 50 microseconds, such as 0.1 microseconds to 35 microseconds, such as 1 microsecond to 25 microseconds, including 5 microseconds to 10 microseconds. In certain embodiments, the period between illumination by each light source is 10 microseconds. In embodiments in which the sample is illuminated sequentially by more than two (i.e., three or more) light sources, the delay between illumination by each light source can be the same or different.

[0082] The sample can be illuminated continuously or at discrete intervals. In some cases, the method includes illuminating the sample in the sample with the light source continuously. In other cases, the sample is illuminated with the light source at discrete intervals, such as illuminating every 0.001 milliseconds, every 0.01 milliseconds, every 0.1 milliseconds, every 1 millisecond, every 10 milliseconds, every 100 milliseconds, or some other interval, including every 1000 milliseconds.

[0083] Depending on the light source, the sample may be illuminated from different distances, 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 2.5 mm or more, such as 5 mm or more, such as 10 mm or more, such as 15 mm or more, such as 25 mm or more, including 50 mm or more. The angle or illumination may also vary from 10° to 90°, such as 15° to 85°, such as 20° to 80°, such as 25° to 75°, including 30° to 60°, for example an angle of 90°.

[0084] In embodiments, light from the illuminated sample is transmitted to a light detection system and measured by one or more photodetectors. In practicing the subject method, light from the sample is transmitted to three or more wavelength separators, each configured to pass light having a predetermined spectral range. The spectral range of light from each of the wavelength separators is transmitted to one or more photodetection modules having optical components configured to transmit light having the predetermined subspectral range to the photodetector.

[0085] The light can be measured continuously or at discrete intervals with the light detection system. In some cases, the method includes measuring the light continuously. In other cases, the light is measured at discrete intervals, such as measuring the light every 0.001 milliseconds, every 0.01 milliseconds, every 0.1 milliseconds, every 1 millisecond, every 10 milliseconds, every 100 milliseconds, or some other interval, including every 1000 milliseconds.

[0086] Measurements of collected light may be made one or more times during the subject methods, such as two or more times, such as three or more times, five or more times, including ten or more times. In certain embodiments, light propagation is measured two or more times, and in certain cases, the data is averaged.

[0087] In some embodiments, the methods include conditioning the light before detecting it with a subject light detection system. For example, light from the sample source may pass through one or more lenses, mirrors, pinholes, slits, gratings, optical refractors, and any combination thereof. In some cases, the collected light passes through one or more focusing lenses to reduce the profile of the light directed to the light detection system or optical collection system, as described above. In other cases, light emitted from the sample passes through one or more collimators to reduce the divergence of the light beam being transmitted to the light detection system.

[0088] System for evaluating a panel of fluorescent dyes - Patent Application 20070122997 Aspects of the present invention additionally include systems configured to implement the above-described methods. The subject systems include a processor configured to evaluate the suitability of a fluorochrome panel for use in a flow cytometry protocol. In embodiments, the subject processor operates in conjunction with programmable logic, which may be implemented in hardware, software, firmware, or any combination thereof, to evaluate the fluorochrome panel. For example, when the programmable logic is implemented in software, the fluorochrome panel evaluation may be realized at least in part by a computer-readable data storage medium including program code with instructions configured to, when executed, receive an initial fluorochrome panel including a set of fluorochrome identifiers, each of which refers to a fluorochrome in the set of fluorochromes, and a set of biological marker identifiers, each associated with the fluorochrome identifier in the set of fluorochrome identifiers; a plurality of population identifiers, each of which refers to a particle population; and an instrument identifier. The processor is further configured to create a set of population-marker pairs by associating each population identifier in the plurality of population identifiers with a biological marker identifier from the set of biological marker identifiers, and generate a set of separability metrics, each of which predicts a measure of statistical distance between particle populations in flow cytometry data space. As described above, each measure of statistical distance is related to the detected signal intensity resulting from each fluorochrome associated with each population-marker pair employed in the flow cytometry protocol using the instrument associated with the instrument identifier. The processor described herein is also configured to aggregate the set of separability metrics into a panel score and assess the panel score to evaluate the suitability of the initial fluorochrome panel for use in the flow cytometry protocol.

[0089] The processor may additionally be configured to optimize the fluorochrome panel based on the evaluation of the suitability of the fluorochrome panel for use in generating flow cytometer data. As described above, panel optimization algorithms for use in optimizing the fluorochrome panel include, but are not limited to, constrained optimization methods. In some embodiments, the processor is configured to generate a visualization of the evaluated suitability of the fluorochrome panel for use in generating flow cytometer data. In some such embodiments, the system includes a display configured to depict the visualization. Any suitable display may be used. The subject device may include, but is not limited to, a monitor, a tablet computer, a smartphone, or other electronic device configured to present a graphical interface.

[0090] The subject programmable logic may be implemented in any of a variety of devices, such as a specially programmed event processing computer, a wireless communication device, an integrated circuit device, etc. In some embodiments, the programmable logic may be executed by a specially programmed processor, which may include one or more processors, such as one or more digital signal processors (DSPs), configurable microprocessors, application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), or other equivalent integrated circuits or discrete logic circuits. Combinations of computing devices, such as a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration in at least partial data connectivity, may implement one or more of the described features.

[0091] In certain cases, the system is or includes a particle analyzer. Particle analyzers of interest may include a flow cell for transporting particles in a flow stream, a light source for illuminating particles in the flow stream at an inspection point, and a particle-modulated light detector for detecting the particle-modulated light. In certain embodiments, the particle analyzer is a flow cytometer. In some cases where the particle analyzer is a flow cytometer, the flow cytometer is a full-spectrum flow cytometer.

[0092] As described herein, a "flow cell" is described in its conventional sense to refer to a component, such as a cuvette, that includes a flow channel with a liquid flow stream for transporting particles in a sheath fluid. A cuvette of interest includes a container having a passageway extending therethrough. The flow stream may include a liquid sample injected from a sample tube. A flow cell of interest includes an optically accessible flow channel. In some instances, the flow cell includes a transparent material (e.g., quartz) that allows the passage of light. In some embodiments, the flow cell is a stream-in-air flow cell in which optical interrogation of particles occurs outside the flow cell (i.e., in free space).

[0093] In some cases, the flow stream is configured to be illuminated with light from a light source at an inspection point. The flow stream comprising the flow channel may include a liquid sample injected from a sample tube. In certain embodiments, the flow stream may include a thin, rapidly flowing stream of liquid arranged such that linearly separated particles transported therein are separated from one another in a single-file manner. As used herein, a "test point" refers to a region within a flow cell where particles are illuminated by light from a light source, e.g., for analysis. The size of the test point may vary as desired. For example, if 0 μm represents the axis of light emitted by the light source, the test point may range from -100 μm to 100 μm, such as -50 μm to 50 μm, or -25 μm to 40 μm, including -15 μm to 30 μm.

[0094] After particles are illuminated in a flow cell, particle-modulated light can be observed. "Particle-modulated light" refers to light received from particles in a flow stream after illuminating the particles with light from a light source. In some cases, the particle-modulated light is side-scattered light. As described herein, side-scattered light refers to light that is diffracted and reflected from the surface and internal structure of a particle. In additional embodiments, the particle-modulated light includes forward-scattered light (i.e., light that travels mostly in a forward direction through or around the particle). In still other cases, the particle-modulated light includes fluorescence (i.e., light emitted from a fluorescent dye after being illuminated with excitation wavelength light).

[0095] As discussed above, aspects of the present invention also include a light source configured to illuminate particles passing through the flow cell at an inspection point. Any convenient light source can be used as the light source described herein. In some embodiments, the light source is a laser. In embodiments, the laser can be any convenient laser, such as a continuous wave laser. For example, the laser can 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 can be a helium-neon (HeNe) laser. In some cases, the laser is a gas laser, such as a helium-neon laser, an argon laser, a krypton laser, a xenon laser, a nitrogen laser, a CO laser, 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 cases, the subject flow cytometer includes a dye laser, such as a stilbene laser, a coumarin laser, or a rhodamine laser. In still other instances, the lasers of interest include metal vapor lasers such as helium-cadmium (HeCd), helium-mercury (HeHg), helium-selenium (HeSe), helium-silver (HeAg), strontium, neon-copper (NeCu), copper, or gold lasers, and combinations thereof. In still other instances, the flow cytometer includes solid-state lasers such as ruby, Nd:YAG, NdCrYAG, Er:YAG, Nd:YLF, Nd:YVO, Nd:YCaO(BO), Nd:YCOB, titanium sapphire, slim YAG, ytterbium YAG, YbO, or cerium-doped lasers, and combinations thereof.

[0096] A laser light source according to certain embodiments may also include one or more optical conditioning components. In certain embodiments, the optical conditioning components may include any device located between the light source and the flow cell that can change the spatial width of the illumination or some other characteristic of the illumination from the light source, such as the illumination direction, wavelength, beam width, beam intensity, and focus. The optical conditioning protocol may include any convenient device that adjusts one or more characteristics of the light source, including, but not limited to, lenses, mirrors, filters, optical fibers, wavelength separators, pinholes, slits, collimating protocols, and combinations thereof. In certain embodiments, a subject flow cytometer includes one or more focusing lenses. In one example, the focusing lens may be a non-magnifying lens. In yet other embodiments, a subject flow cytometer includes an optical fiber.

[0097] When the optical adjustment component is configured to move, it may be configured to move continuously or at discrete intervals, such as increments of 0.01 μm or more, including increments of 25 mm 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 10 μm or more, such as 100 μm or more, such as 500 μm or more, such as 1 mm or more, such as 5 mm or more, such as 10 mm or more.

[0098] Any displacement protocol can be used to move the optical adjustment component structure, such as coupled to a movable support stage or directly coupled to a geared translation device such as a motorized translation stage, a lead screw translation assembly, or one that uses a stepper motor, servo motor, brushless electric motor, brushed DC motor, microstep drive motor, or high resolution stepper motor, among other motor types.

[0099] The light source can be positioned any suitable distance from the flow cell, such as when the light source and flow cell are separated by 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 at a distance of 100 mm or more. Additionally, the light source can be positioned at any suitable angle relative to the flow cell, such as at an angle ranging from 10 degrees to 90 degrees, such as 15 degrees to 85 degrees, such as 20 degrees to 80 degrees, or 25 degrees to 75 degrees, including 30 degrees to 60 degrees, for example, at an angle of 90 degrees.

[0100] In some embodiments, the target light source includes multiple lasers configured to provide laser light for discrete illumination of the flowstream, such as two or more lasers, such as three or more lasers, such as four or more lasers, such as five or more lasers, such as ten or more lasers, including fifteen or more lasers configured to provide laser light for discrete illumination of the flowstream. Depending on the desired wavelength of light for illuminating the flowstream, each laser may have a specific wavelength ranging from 200 nm to 1500 nm, such as 250 nm to 1250 nm, such as 300 nm to 1000 nm, such as 350 nm to 900 nm, including 400 nm to 800 nm. In certain embodiments, the target lasers may include one or more of a 405 nm laser, a 488 nm laser, a 561 nm laser, and a 635 nm laser.

[0101] As discussed above, subject particle analyzers may further include one or more particle-modulated light detectors for detecting particle-modulated light intensity data. In some embodiments, the particle-modulated light detector includes one or more forward-scattered light detectors configured to detect forward-scattered light. For example, the subject particle analyzers may include one forward-scattered light detector or multiple (e.g., two or more, e.g., three or more, e.g., four or more, and including five or more) forward-scattered light detectors. In certain embodiments, the particle analyzer includes one forward-scattered light detector. In other embodiments, the particle analyzer includes two forward-scattered light detectors.

[0102] Any convenient detector for detecting collected light can be used in the forward scattered light detectors described herein. Detectors of interest can include, but are not limited to, optical sensors or detectors such as active pixel sensors (APS), avalanche photodiodes, image sensors, charge-coupled devices (CCDs), intensified charge-coupled devices (ICCDs), light-emitting diodes, photon counters, bolometers, pyroelectric detectors, photoresistors, solar cells, photodiodes, photomultiplier tubes (PMTs), phototransistors, quantum dot photoconductors, or photodiodes, as well as combinations thereof, among other detectors. In certain embodiments, the collected light is measured with 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. In certain embodiments, the detector is a 1 cm 2 ~5cm 2 Including 0.05cm 2 ~9cm 2 etc., 0.1cm 2 ~8cm 2 etc., 0.5cm 2 ~7cm 2 etc., 0.01cm 2 ~10cm 2 and a photomultiplier tube having an active detection surface area in each region in the range of 1000 nm to 1000 nm.

[0103] In embodiments, the forward scattered light detector is configured to measure light continuously or at discrete intervals. In some cases, the detector in question is configured to measure the collected light continuously. In other cases, the detector in question is configured to measure at discrete intervals, such as measuring light every 0.001 milliseconds, 0.01 milliseconds, 0.1 milliseconds, 1 millisecond, 10 milliseconds, 100 milliseconds, or some other interval, including every 1000 milliseconds.

[0104] In additional embodiments, the one or more particle modulation light detectors may include one or more side scattered light detectors for detecting side scattered wavelengths of light (i.e., light refracted and reflected from the surface and internal structure of the particle). In some embodiments, the particle analyzer includes a single side scattered light detector. In other embodiments, the particle analyzer includes multiple (e.g., two or more, including three or more, e.g., four or more, and five or more) side scattered light detectors.

[0105] Any convenient detector for detecting collected light can be used in the side scatter light detectors described herein. Detectors of interest can include, but are not limited to, optical sensors or detectors such as active pixel sensors (APS), avalanche photodiodes, image sensors, charge-coupled devices (CCDs), intensified charge-coupled devices (ICCDs), light-emitting diodes, photon counters, bolometers, pyroelectric detectors, photoresistors, solar cells, photodiodes, photomultiplier tubes (PMTs), phototransistors, quantum dot photoconductors, or photodiodes, as well as combinations thereof, among other detectors. In certain embodiments, the collected light is measured with 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. In certain embodiments, the detector is a 1 cm 2 ~5cm 2 Including 0.05cm 2 ~9cm 2 etc., 0.1cm 2 ~8cm 2 etc., 0.5cm 2 ~7cm 2 etc., 0.01cm 2 ~10cm 2 and a photomultiplier tube having an active detection surface area in each region in the range of 1000 nm to 1000 nm.

[0106] In embodiments, the subject particle analyzers also include a fluorescence detector configured to detect one or more fluorescent wavelengths of light, hi other embodiments, the particle analyzers include a plurality (e.g., including two or more, e.g., three or more, e.g., four or more, five or more, ten or more, fifteen or more, and twenty or more) fluorescence detectors.

[0107] Any convenient detector for detecting collected light can be used in the fluorescence detectors described herein. Detectors of interest can include, but are not limited to, optical sensors or detectors such as active pixel sensors (APS), avalanche photodiodes, image sensors, charge-coupled devices (CCDs), intensified charge-coupled devices (ICCDs), light-emitting diodes, photon counters, bolometers, pyroelectric detectors, photoresistors, solar cells, photodiodes, photomultiplier tubes (PMTs), phototransistors, quantum dot photoconductors, or photodiodes, as well as combinations thereof, among other detectors. In certain embodiments, the collected light is measured with 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. In certain embodiments, the detector is a 1 cm 2 ~5cm 2 Including 0.05cm 2 ~9cm 2 etc., from etc., 0.1cm 2 ~8cm 2 etc., 0.5cm 2 ~7cm 2 0.01cm etc. 2 ~10cm 2 and a photomultiplier tube such as a photomultiplier tube having an active detection surface area in each region in the range of 1000 nm to 1000 nm.

[0108] When a subject particle analyzer includes multiple fluorescence detectors, each fluorescence detector can be the same, or the collection of fluorescence detectors can be a combination of different types of detectors. For example, when a subject particle analyzer includes two fluorescence detectors, in some embodiments, the first fluorescence detector is a CCD-based device and the second fluorescence detector (or image sensor) is a CMOS-based device. In other embodiments, the first and second fluorescence detectors are both CCD-based devices. In still other embodiments, the first and second fluorescence detectors are both CMOS-based devices. In still other embodiments, the first fluorescence detector is a CCD-based device and the second fluorescence detector is a photomultiplier tube (PMT). In still other embodiments, the first fluorescence detector is a CMOS-based device and the second fluorescence detector is a photomultiplier tube. In still other embodiments, the first and second fluorescence detectors are both photomultiplier tubes.

[0109] In embodiments of the present disclosure, a subject fluorescence detector is configured to measure collected light of one or more wavelengths, such as two or more wavelengths, such as five or more different wavelengths, such as ten or more different wavelengths, such as twenty-five or more different wavelengths, such as fifty or more different wavelengths, such as one hundred or more different wavelengths, such as two or more different wavelengths, including measuring light emitted by particles in a flow stream of 400 or more different wavelengths. In some embodiments, two or more detectors in a particle analyzer described herein are configured to measure the same or overlapping wavelengths of collected light.

[0110] In some embodiments, the subject fluorescence detector is configured to measure light collected over a range of wavelengths (e.g., 200 nm to 1000 nm). In certain embodiments, the subject detector is configured to collect a spectrum of light over a range of wavelengths. For example, a particle analyzer may include one or more detectors configured to collect a spectrum of light over one or more wavelength ranges from 200 nm to 1000 nm. In still other embodiments, the subject detector is configured to measure light emitted by a sample in a flow stream at one or more specific wavelengths. For example, a particle analyzer may include one or more detectors configured to measure light at one or more of the following wavelengths: 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. In certain embodiments, one or more detectors may be configured to pair with a particular fluorophore, such as one used with a sample in a fluorescence assay.

[0111] In some embodiments, the particle analyzer includes one or more wavelength separators disposed between the flow cell and the particle-modulated light detector. The term "wavelength separator" is used herein in its conventional sense to refer to an optical component configured to separate light collected from a sample into predetermined spectral ranges. In some embodiments, the particle analyzer includes a single wavelength separator. In other embodiments, the particle analyzer includes a plurality of wavelength separators, including 100 or more wavelength separators, e.g., two or more wavelength separators, e.g., three or more, e.g., four or more, e.g., five or more, e.g., six or more, e.g., seven or more, e.g., eight or more, e.g., nine or more, e.g., ten or more, e.g., fifteen or more, e.g., twenty-five or more, e.g., fifty or more, e.g., seventy-five or more, e.g., seventy-five or more, e.g., seventy-five or more. In some embodiments, the wavelength separators are configured to separate light collected from a sample into predetermined spectral ranges by passing light having the predetermined spectral ranges and reflecting light in one or more remaining spectral ranges. In other embodiments, the wavelength separator is configured to separate the light collected from the sample into predetermined spectral ranges by passing light having the predetermined spectral ranges and absorbing light in one or more remaining spectral ranges. In still other embodiments, the wavelength separator is configured to spatially diffract the light collected from the sample into predetermined spectral ranges. Each wavelength separator can be any convenient light separation protocol, such as one or more dichroic mirrors, bandpass filters, diffraction gratings, beam splitters, or prisms. In some embodiments, the wavelength separator is a prism. In other embodiments, the wavelength separator is a diffraction grating. In certain embodiments, the wavelength separator in the subject optical detection system is a dichroic mirror.

[0112] 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(pt 1):17-28; Linden, et al., Semin Thromb Hemost. 2004 Oct; 30(5):502-11; Alison, et al. J Pathol, 2010 Dec; 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 certain instances, the flow cytometry systems of interest are the BD Biosciences FACSCanto™ flow cytometer, BD Biosciences FACSCanto™ II flow cytometer, BD Accuri™ flow cytometer, BD Accuri™ C6 Plus flow cytometer, BD Biosciences FACSCelesta™ flow cytometer, BD Biosciences FACSLyric™ flow cytometer, BD Biosciences FACSVerse™ flow cytometer, BD Biosciences FACSymphony™ flow cytometer, BD Biosciences LSRFortessa™ flow cytometer, BD Biosciences LSRFortessa™ X-20 flow cytometer, BD Biosciences FACSPresto™ flow cytometer, BD Biosciences FACSVia™ flow cytometer, and the BD Biosciences FACSCalibur™ cell sorter, BD Biosciences FACSCount™ cell sorter, BD Biosciences FACSLyric™ cell sorter, and BD Biosciences These include the Via™ cell sorter, BD Biosciences Influx™ cell sorter, BD Biosciences Jazz™ cell sorter, BD Biosciences Aria™ cell sorter, BD Biosciences FACSAria™ II cell sorter, BD Biosciences FACSAria™ III cell sorter, BD Biosciences FACSAria™ Fusion cell sorter, and BD Biosciences FACSMelody™ cell sorter, BD Biosciences FACSymphony™ S6BD, and FACSDiscover™ S8 cell sorters.

[0113] 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 herein by reference in their entireties.

[0114] In certain instances, the flow cytometry system of the present invention may be any of those described in Diebold, et al. Nature Photonics Vol. 7(10); 806-810 (2013), as well as U.S. Patent 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, and 2019 / 0376895, and 2019 / 0376894, the disclosures of which are incorporated herein by reference. In such cases, the flow cytometry data may include image data of particles, e.g., cells, present in the sample. See, for example, Schraivogel et al., Science Vol. 375(6578) 315-320(2022), the disclosures of which are incorporated herein in their entirety, and U.S. Provisional Patent Application No. 63 / 256,974, the disclosures of which are incorporated herein in their entirety. One example of such a system is the FACSDiscover™ S8 cell sorter.

[0115] 2 shows a system 200 for flow cytometry, according to an exemplary embodiment of the invention. System 200 includes a flow cytometer 210, a controller / processor 290, and a memory 295. Flow cytometer 210 includes one or more excitation lasers 215a-215c, a focusing lens 220, a flow chamber 225, a forward scatter detector 230, a side scatter detector 235, a fluorescence collection lens 240, one or more beam splitters 245a-245g, one or more bandpass filters 250a-250e, one or more longpass ("LP") filters 255a-255b, and one or more fluorescence detectors 260a-260f.

[0116] Pump lasers 215a-c emit light in the form of laser beams. In the exemplary system of FIG. 2, the wavelengths of the laser beams emitted from pump lasers 215a-c are 488 nm, 633 nm, and 325 nm, respectively. The laser beams are first directed through one or more of beam splitters 245a and 245b. Beam splitter 245a transmits 488 nm light and reflects 633 nm light. Beam splitter 245b transmits UV light (light having a wavelength in the range of 10-400 nm) and reflects 488 nm and 633 nm light.

[0117] The laser beam is then directed onto a focusing lens 220, which focuses the beam onto the portion of the fluid stream where the sample particles are located, within a flow chamber 225. The flow chamber is part of a fluidics system that directs particles in the stream, usually one at a time, into the focused laser beam for investigation. The flow chamber can comprise a flow cell in a benchtop flow cytometer or a nozzle tip in a stream-in air cytometer.

[0118] Light from the laser beam interacts with particles in the sample by diffraction, refraction, reflection, scattering, and absorption, with re-emission at a variety of different wavelengths depending on particle properties such as particle size, internal structure, and the presence of one or more fluorescent molecules attached to or naturally present on or in the particle. Fluorescence emission and diffracted, refracted, reflected, and scattered light can be routed through one or more of beam splitters 245c-245g, bandpass filters 250a-250e, longpass filters 255a-255b, and fluorescence collection lens 240 to one or more of forward scatter detector 230, side scatter detector 235, and one or more fluorescence detectors 260a-260f.

[0119] The fluorescence collection lens 240 collects light emitted from particle-laser beam interactions and routes it toward one or more beam splitters and filters. Bandpass filters, such as bandpass filters 250a-250e, allow a narrow wavelength range to pass through the filter. For example, bandpass filter 250a 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, or from 500 nm to 520 nm. Shortpass filters transmit wavelengths of light below a specified wavelength. Longpass filters, such as longpass filters 255a-255b, transmit wavelengths of light above a specified wavelength. For example, longpass filter 255b, a 670 nm longpass filter, transmits light above 670 nm. Filters are often selected to optimize the specificity of the detector for a particular fluorochrome. The filters may be configured so that the spectral band of light transmitted to the detector is close to the emission peak of the fluorescent dye.

[0120] The forward scatter detector 230 is positioned slightly off-axis from the direct beam through the flow cell and is configured to detect diffracted light, or excitation light traveling mostly forward through or around the particles. The intensity of light detected by the forward scatter detector depends on the overall particle size. The forward scatter detector may include a photodiode. The side scatter detector 235 is configured to detect refracted and reflected light from the particle's surface and internal structure, which tends to increase as particle structure becomes more complex. Fluorescence emission from fluorescent molecules associated with the particles may be detected by one or more fluorescence detectors 260a-260f. The side scatter detector 235 and the fluorescence detector may include photomultiplier tubes. The signals detected by the forward scatter detector 230, side scatter detector 235, and fluorescence detector may be converted to electronic signals (voltage) by the detectors. This data can provide information about the sample.

[0121] Those skilled in the art will recognize that a flow cytometer according to an embodiment of the present invention is not limited to the flow cytometer shown in Figure 2, but can include any flow cytometer known in the art. For example, a flow cytometer can have any number of lasers, beam splitters, filters, and detectors at various wavelengths and in a variety of different configurations.

[0122] During operation, the operation of the flow cytometer is controlled by controller / processor 290, and measurement data from the detectors may be stored in memory 295 and processed by controller / processor 290. Although not explicitly shown, controller / processor 290 may be coupled to the detectors to receive output signals therefrom and may also be coupled to electrical and electromechanical components of flow cytometer 210 to control lasers, fluid flow parameters, etc. Input / output (I / O) functionality 297 may also be provided within the system. Memory 295, controller / processor 290, and I / O 297 may be provided entirely as an integral part of flow cytometer 210. In such an embodiment, a display may also form part of I / O functionality 297 for presenting experimental data to a user of cytometer 210. Alternatively, memory 295 and controller / processor 290 and some or all of the I / O functionality may be part of one or more external devices, such as a general-purpose computer. In some embodiments, memory 295 and some or all of controller / processor 290 may be in wireless or wired communication with cytometer 210. In conjunction with memory 295 and I / O 297, controller / processor 290 may be configured to perform a variety of functions related to the preparation and analysis of flow cytometer experiments.

[0123] The system illustrated in FIG. 2 includes six different detectors that detect fluorescence within six different wavelength ranges (which may be referred to herein as the “filter windows” of a given detector) defined by the configuration of filters and / or splitters in the beam path from flow cell 225 to each detector. Different fluorescent molecules in a fluorochrome panel used in a flow cytometer experiment emit light in their own characteristic wavelength bands. The particular fluorescent labels used in the experiment and their associated fluorescence emission bands can be selected to generally match the filter windows of the detectors. I / O 297 can 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. I / O 297 can also be configured to receive biological data assigning one or more markers to one or more cell populations, marker concentration data, emission spectrum data, data assigning labels to one or more markers, and cytometer configuration data. Flow cytometer experiment data, such as label spectral characteristics and flow cytometer configuration data, can also be stored in memory 295. The controller / processor 290 may be configured to assess one or more assignments of labels to markers.

[0124] In some embodiments, the subject system is a particle sorting system configured to sort particles using an enclosed particle sorting module, 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. In certain embodiments, particles (e.g., cells) of a sample are sorted using a sort determination module having multiple sort determination units, such as that described in U.S. Patent Publication No. 2020 / 0256781, filed December 23, 2019, the disclosure of which is incorporated herein by reference. In some embodiments, a system for sorting components of a sample includes a particle sorting module with deflection plates, 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.

[0125] 3 shows a functional block diagram of an example of a control system for analyzing and displaying biological events, such as a processor 300. The processor 300 can be configured to implement various processes for controlling the graphical display of the biological events.

[0126] The flow cytometer or sorting system 302 can be configured to acquire bio-event data. For example, the flow cytometer can generate flow cytometry event data (e.g., particle-modulated optical data). The flow cytometer 302 can be configured to provide the bio-event data to the processor 300. A data communication channel can be included between the flow cytometer 302 and the processor 300. The bio-event data can be provided to the processor 300 via the data communication channel.

[0127] The processor 300 can be configured to receive bio-event data from the flow cytometer 302. The bio-event data received from the flow cytometer 302 can include flow cytometry event data. The processor 300 can be configured to provide a graphical display including a first plot of the bio-event data on the display device 306. The processor 300 can be further configured to render a region of interest as a gate (e.g., a first gate) around a population of the bio-event data shown by the display device 306, e.g., 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 saturated detector data.

[0128] The processor 300 may further be configured to display the bioevent data within the gate differently on the display device 306 than other events within the bioevent data outside the gate. For example, the processor 300 may be configured to render the color of the bioevent data contained within the gate differently from the color of the bioevent data outside the gate. The display device 306 may be implemented as a monitor, a tablet computer, a smartphone, or other electronic device configured to present a graphical interface.

[0129] The processor 300 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 310. The mouse 310 can initiate a gate selection signal to the processor 300 identifying a gate to be displayed or manipulated via the display device 306 (e.g., by clicking on or at the desired gate when a cursor is positioned thereon). In some implementations, the first device can be implemented as a keyboard 308 or other means of providing an input signal to the processor 300, such as a touchscreen, a stylus, a photodetector, 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. 3, the mouse 310 can include a right mouse button and a left mouse button, each of which can generate a trigger event.

[0130] The trigger event can cause the processor 300 to change the manner in which the data is displayed, what portions of the data are actually displayed on the display device 306, and / or provide input to further processing, such as selecting a population for particle sorting.

[0131] In some embodiments, the processor 300 can be configured to detect when gate selection is initiated by the mouse 310. The processor 300 can further be configured to automatically modify the visualization of the plot to facilitate the gating process. The modification can be based on a particular distribution of the biological event data received by the processor 300. In some embodiments, the processor 300 extends the first gate such that a second gate is generated (e.g., as described above).

[0132] The processor 300 may be connected to a storage device 304. The storage device 304 may be configured to receive and store biological event data from the processor 300. The storage device 304 may also be configured to receive and store flow cytometry event data from the processor 300. The storage device 304 may be further configured to enable retrieval of biological event data, such as flow cytometry event data, by the processor 300.

[0133] The display device 306 can be configured to receive display data from the processor 300. The display data can include a plot of the biological event data and a gate that delineates a section of the plot. The display device 306 can be further configured to modify the presented information according to input received from the processor 300 in conjunction with input from the flow cytometer 302, the storage device 304, the keyboard 308, and / or the mouse 310.

[0134] The processor 300 may be further configured to evaluate the fluorochrome panel. In such cases, the processor 300 is configured to receive an initial fluorochrome panel including a set of fluorochrome identifiers, each of which refers to a fluorochrome in the set of fluorochromes, a set of biological marker identifiers, each associated with a fluorochrome identifier in the set of fluorochrome identifiers, a plurality of population identifiers, each of which refers to a particle population, and an instrument identifier. The processor 300 may also be configured to create a set of population-marker pairs by associating each population identifier in the plurality of population identifiers with a biological marker identifier from the set of biological marker identifiers, generate a set of separability metrics, each of which predicts a measure of statistical distance between particle populations in flow cytometer data space, aggregate the set of separability metrics into a panel score, and assess the panel score to evaluate the suitability of the initial fluorochrome panel for use in a flow cytometry protocol. In certain cases, the processor 300 is also configured to generate a visualization based on the evaluation of the fluorochrome panel. This visualization may, in some cases, be displayed on the display device 306.

[0135] In some implementations, the processor 300 can generate a user interface for receiving example events for sorting. For example, the user interface can include a mechanism for receiving example events or example images. The example events or images, or example gates, can be provided prior to collection of event data for the sample or based on an initial set of events for a portion of the sample.

[0136] FIG. 4A is a schematic diagram of a particle sorter system 400 (e.g., flow cytometer 302) according to one embodiment presented herein. In some embodiments, particle sorter system 400 is a cell sorter system. As shown in FIG. 4A, a droplet-forming transducer 402 (e.g., a piezoelectric oscillator) is coupled to a fluid conduit 401, which can be coupled to, include, or be a nozzle 403. Within fluid conduit 401, a sheath fluid 404 hydrodynamically focuses a sample fluid 406 containing particles 409 into a moving fluid column 408 (e.g., a stream). Within moving fluid column 408, particles 409 (e.g., cells) are aligned single file across a monitored area 411 (e.g., where laser streams intersect) and are illuminated by an illumination source 412 (e.g., a laser). Vibration of droplet-forming transducer 402 causes moving fluid column 408 to break up into multiple droplets 410 , some of which contain particles 409 .

[0137] During operation, the detection station 414 (e.g., an event detector) identifies when a particle of interest (or cell of interest) crosses the monitored area 411. The detection station 414 feeds a timing circuit 428, which in turn feeds a flash charge circuit 430. At a droplet break-off point, signaled by a timed droplet delay (Δt), a flash charge can be applied to the moving fluid column 408 so that the droplets of interest carry a charge. The droplets of interest may contain one or more particles or cells to be sorted. The charged droplets can then be sorted by activating deflection plates (not shown) to deflect the droplets into a collection tube or a container such as a multi-well or microwell sample plate where wells or microwells can be associated with particular droplets of interest. As shown in FIG. 4A, the droplets can be collected in a drain container 438.

[0138] The detection system 416 (e.g., a droplet boundary detector) serves to automatically determine the phase of the droplet drive signal as a particle of interest passes through the monitored area 411. An exemplary droplet boundary detector is described in U.S. Pat. No. 7,679,039, which is incorporated herein by reference in its entirety. The detection system 416 allows the instrument to accurately calculate the position of each detected particle within the droplet. The detection system 416 can provide an amplitude signal 420 and / or a phase signal 418, which then provide (via amplifier 422) to an amplitude control circuit 426 and / or a frequency control circuit 424. The amplitude control circuit 426 and / or the frequency control circuit 424 then control the droplet forming transducer 402. The amplitude control circuit 426 and / or the frequency control circuit 424 can be included within a control system.

[0139] In some implementations, the sort electronics (e.g., detection system 416, detection station 414, and processor 440) can be coupled with a memory configured to store detected events and sort decisions based on the detected events. The sort decisions can be included in the particle event data. In some implementations, the detection system 416 and detection station 414 can be implemented as a single detection unit or communicatively coupled such that event measurements can be collected by either the detection system 416 or the detection station 414 and provided to a non-collection element.

[0140] FIG. 4B is a schematic diagram of a particle sorter system according to one embodiment presented herein. The particle sorter system 400 shown in FIG. 4B includes deflection plates 452 and 454. An electric charge can be applied via stream charging wires within the barbs, creating a stream of droplets 410 containing particles 409 for analysis. The particles can be illuminated with one or more light sources (e.g., lasers) to generate light scattering and fluorescence information. The information about the particles is analyzed, such as by sorting electronics or other detection systems (not shown in FIG. 4B). Deflection plates 452 and 454 can be independently controlled to attract or repel charged droplets, directing them toward a desired collection vessel (e.g., one of 472, 474, 476, or 478). As shown in FIG. 4B, deflection plates 452 and 454 can be controlled to direct particles along a first path 462 toward vessel 474 or along a second path 468 toward vessel 478. If the particle is not of interest (e.g., does not exhibit scattering or illumination information within a specified sort range), the deflector may allow the particle to continue along flow path 464. Such uncharged droplets may be diverted into a waste container, such as via aspirator 470.

[0141] Sorting electronics may be included to initiate the collection of measurements, receive fluorescent signals for the particles, and determine how to adjust the deflection plates to cause particle sorting. Exemplary implementations of the embodiment shown in Figure 4B include the BD FACSAria™ system of flow cytometers, commercially available from Becton, Dickinson and Company (Franklin Lakes, NJ).

[0142] Computer Control System Aspects of the present disclosure further include a computer control system, the system including one or more computers for full or partial automation. In some embodiments, the system includes a computer having a computer program stored thereon, the computer program including instructions, when loaded onto the computer, for evaluating the suitability of a fluorochrome panel for use in a flow cytometry protocol. In some cases, the instructions cause the computer to receive an initial fluorochrome panel including a set of fluorochrome identifiers, each of which refers to a fluorochrome within the set of fluorochrome identifiers, a set of biological marker identifiers, each associated with a fluorochrome identifier within the set of fluorochrome identifiers, a plurality of population identifiers, each of which refers to a particle population, and an instrument identifier. The instructions also cause the processor to create a set of population-marker pairs by associating each population identifier within the plurality of population identifiers with a biological marker identifier from the set of biological marker identifiers, and generate a set of separability metrics, each predicting a measure of statistical distance between the particle populations in flow cytometer data space, the measure of statistical distance being related to the detected signal intensity resulting from each fluorochrome associated with each population-marker pair used in the flow cytometry protocol using an instrument associated with the instrument identifier. In embodiments, the instructions also cause the processor to aggregate the set of separability metrics into a panel score and evaluate the panel score to assess the suitability of the initial fluorochrome panel for use in a flow cytometry protocol. In some cases, the system is or includes a flow cytometer.

[0143] The system may include a display and an operator input device. The operator input device may be, for example, a keyboard, a mouse, etc. The processing module includes a processor that accesses a memory having stored instructions 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 one of other processors that are or become available. The processor executes an operating system, which interfaces with firmware and hardware in a well-known manner and facilitates the processor's coordination and execution of functions of various computer programs, which may be written in a variety of programming languages, such as Java, Perl, C++, Python, other high-level or low-level languages, and combinations thereof, as known in the art. The operating system typically cooperates with the processor to coordinate and execute 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 in accordance with known techniques. In some embodiments, the processor includes analog electronics that provide feedback control, such as, for example, negative feedback control.

[0144] System memory can be any of a variety of known or future memory storage devices. Examples include any commonly available random access memory (RAM), magnetic media such as a resident hard disk or tape, optical media such as a read-write compact disk, flash memory devices, or other memory storage devices. The memory storage device can be any of a variety of known or future devices, including a compact disk drive, tape drive, or disk 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. 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 system memory and / or program storage devices used in conjunction with memory storage devices.

[0145] In some embodiments, a computer program product is described that includes a computer-usable medium having stored thereon control logic (a computer software program including program code). The control logic, when executed by a processor, causes the processor to perform the functions described herein. In other embodiments, some functions are implemented primarily in hardware, for example, using hardware state machines. Implementations of hardware state machines to perform the functions described herein will be apparent to those skilled in the relevant art.

[0146] The memory may be any suitable device from which a processor can store and retrieve data, such as a magnetic, optical, or solid-state storage device (including a magnetic or optical disk, or tape, 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 to the processor remotely via a communications 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 these devices together with the memory. For example, a magnetic or optical disk may carry the programming and be readable by a disk writer / reader. The inventive system also includes programming in the form of a computer program product, for example, algorithms for use in implementing the above-described methods. The programming according to the present invention may be recorded on a computer-readable medium, e.g., any medium that can be directly read and accessed by a computer. Such media include, but are not limited to, magnetic storage media, 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.

[0147] The processor may also have access to a communication channel for communicating 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).

[0148] In some embodiments, a system according to the present disclosure may be configured to include a communications interface. In some embodiments, the communications interface includes a receiver and / or a transmitter for communicating with a network and / or another device. The communications interface may be configured for wired or wireless communications, including, but not limited to, radio frequency (RF) communications (e.g., radio frequency identification (RFID), ZigBee communications protocol, Wi-Fi, infrared, wireless universal serial bus (USB), ultra-wideband (UWB), Bluetooth® communications protocol, and cellular communications such as code division multiple access (CDMA) or global system for mobile communications (GSM).

[0149] 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, a USB-C port, an RS-232 port, or any other suitable electrical connection port, to enable data communications between the subject system and other external devices, such as computer terminals (e.g., in a clinic or hospital environment), configured for similar complementary data communications.

[0150] In one embodiment, the communication interface is configured for infrared communication, Bluetooth® communication, or any other suitable wireless communication protocol, allowing the subject system to communicate with other devices, such as a computer terminal and / or network, a communication-enabled mobile phone, a personal digital assistant, or any other communication device that a user may use in conjunction with the device.

[0151] In one embodiment, the communication interface is configured to provide a connection for data transfer utilizing Internet Protocol (IP) via 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.

[0152] In one embodiment, the subject system is configured to communicate wirelessly with a server device via 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 smartphone, 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 touchscreen.

[0153] In some embodiments, the communications interface is configured to automatically or semi-automatically communicate data stored within the subject system, e.g., 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.

[0154] The output controller may include a controller for any of a variety of known display devices for presenting information to a user, whether human or machine, local or remote. When one of the display devices provides visual information, this information may typically be logically and / or physically organized as an array of pixels. The graphical user interface (GUI) controller may include any of a variety of known or future software programs for providing a graphical input and output interface between the system and the user and 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 achieved in alternative embodiments using a network or other type of remote communication. The output manager may also provide information generated by the processing module to a user at a remote location, for example, via the Internet, telephone, or satellite network, in accordance with known techniques. Presentation of data by the output manager may be accomplished in accordance with a variety of known techniques. As some examples, the data may include SQL, HTML, or XML documents, emails or other files, or other forms of data. The data may include Internet URL addresses so that the user can retrieve additional SQL, HTML, XML, or other documents or data from remote sources. The one or more platforms present in the subject system are typically of a class of computers commonly referred to as servers, but may be any type of known or future-developed computer platform. However, they may be mainframe computers, workstations, or other computer types. They may be connected via any known or future type of cabling or other communication systems, including wireless systems, either networked or not. They may be co-located or physically separated.In some cases, various operating systems may be used on any of the computer platforms, depending on the type and / or configuration of the computer platform selected. Suitable operating systems include Windows NT, Windows XP, Windows 7, Windows 8, Windows 10, iOS, macOS, Linux, Ubuntu, Fedora, OS / 400, i5 / OS, IBM i, Android, SGI IRIX, Oracle Solaris, and others.

[0155] FIG. 5 illustrates the general architecture of an exemplary computing device 500 according to a particular embodiment. The general architecture of computing device 500 illustrated in FIG. 5 includes an arrangement of computer hardware and software components. However, not all of these typically conventional elements need be shown to provide a useful disclosure. As illustrated, computing device 500 includes a processing unit 510, a network interface 520, a computer-readable medium drive 530, an input / output device interface 540, a display 550, and input devices 560, all of which may communicate with each other via a communications bus. Network interface 520 may provide connectivity to one or more networks or computing systems. Thus, processing unit 510 may receive information and instructions from other computing systems or services via a network. Processing unit 510 may also communicate with memory 570 and may further provide output information to optional display 550 via input / output device interface 540. For example, analysis software (e.g., data analysis software or program such as FlowJo®) stored as executable instructions in the analysis system's non-transitory memory can display flow cytometry event data to a user. The input / output device interface 540 may also receive input from optional input devices 560, such as a keyboard, mouse, digital pen, microphone, touch screen, gesture recognition system, voice recognition system, gamepad, accelerometer, gyroscope, or other input device.

[0156] Memory 570 may include computer program instructions (grouped, in some embodiments, as modules or components) that processing unit 510 executes to implement one or more embodiments. Memory 570 typically includes RAM, ROM, and / or other persistent, secondary, or non-transitory computer-readable media. Memory 570 may store an operating system 572 that provides computer program instructions for use by processing unit 510 in the general management and operation of computing device 500. Data may be stored in data storage device 590. Memory 570 may further include computer program instructions and other information for implementing aspects of the present disclosure.

[0157] computer-readable storage medium Aspects of the present disclosure further include non-transitory computer-readable storage media having instructions for implementing the subject methods. The computer-readable storage media may be used in conjunction with one or more computers to fully or partially automate a system for implementing the methods described herein. In certain embodiments, instructions according to the methods described herein may be encoded on a computer-readable medium in the form of "programming," in which case 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. In some cases, the instructions, when executed by a computer or processor, cause the computer or processor to receive an initial fluorochrome panel including a set of fluorochrome identifiers, each of which refers to a fluorochrome within the set of fluorochromes, and a set of biological marker identifiers each associated with a fluorochrome identifier within the set of fluorochrome identifiers, a plurality of population identifiers, each of which refers to a particle population, and an instrument identifier. The instructions also cause the processor to create a set of population-marker pairs by associating each population identifier in the plurality of population identifiers with a biological marker identifier from the set of biological marker identifiers, and generate a set of separability metrics, each predicting a measure of statistical distance between particle populations in flow cytometer data space, where each measure of statistical distance is related to a detected signal intensity resulting from each fluorochrome associated with each population-marker pair used in a flow cytometry protocol using an instrument associated with the instrument identifier. In embodiments, the instructions also cause the processor to aggregate the set of separability metrics into a panel score and evaluate the panel score to assess the suitability of the initial fluorochrome panel for use in the flow cytometry protocol. In some cases, the system is or includes a flow cytometer.

[0158] Examples of suitable non-transitory storage media include magnetic disks, hard disks, optical disks, magneto-optical disks, CD-ROMs, CD-Rs, magnetic tapes, non-volatile memory cards, ROMs, DVD-ROMs, Blue-ray discs, solid-state disks, flash drives, and network-attached storage devices (NAS), 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 the information so that it can be accessed and retrieved at a later date by a computer. The computer-implemented methods described herein may be implemented using programming, which may be written in one or more of any number of computer programming languages. Such languages ​​include, for example, Java, Python, Visual Basic, and C++, as well as many others.

[0159] usefulness The methods, systems, and computer-readable media of the present invention may find use where it is desirable to automatically determine a set of usable fluorochromes for particle analysis (e.g., flow cytometry). In certain instances, the present invention finds particular use in experimental design for full-spectrum (i.e., "spectral") flow cytometry panels. Stated differently, the present invention serves as the first step in spectral panel design by specifying whether a set of dyes can be used simultaneously. In some instances, the methods, systems, and computer-readable media described herein are useful for determining which set of fluorochromes is likely to provide the best quality data (e.g., greatest biological resolution). The present invention accomplishes this through an automated optimization algorithm, the use of the spectral signatures of the fluorochromes as readily available and easy-to-measure inputs to the algorithm, and the use of a spectral matrix as a computationally efficient heuristic for optimization.

[0160] Embodiments of the present invention find use in applications where cells prepared from a biological sample may be desirable for research, laboratory testing, or therapeutic use. In some embodiments, the subject methods and devices may facilitate obtaining individual cells prepared from a target fluid or tissue biological sample. For example, the subject methods and systems facilitate obtaining cells from fluid or tissue samples used as research or diagnostic specimens for diseases such as cancer. Similarly, the subject methods and systems may facilitate obtaining cells from fluid or tissue samples used in therapy. The disclosed methods and devices enable the separation and collection of cells from biological samples (e.g., organs, tissues, tissue fragments, bodily fluids) with improved efficiency and at lower cost compared to conventional flow cytometry systems.

[0161] kit Aspects of the present disclosure further include kits, which include instructions and / or programmable logic for performing the claimed methods. For example, the kits include programming configured to evaluate and optionally optimize a fluorescent panel (e.g., as described above in the Methods section), such as in the form of a computer-readable medium (e.g., a flash drive, USB storage, a compact disc, a DVD, a Blu-ray disc, etc.) or instructions for downloading the programming from an Internet web protocol or cloud server.

[0162] The kits may further include instructions for carrying out the subject methods. These instructions may be present in the subject kits 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 printed information on a suitable medium or substrate, such as on a piece of paper on which the information is printed, in the kit packaging, in a package insert, etc. Another form in which these instructions may be present is as a computer-readable medium having the information recorded thereon, such as a diskette, compact disc (CD), portable flash drive, etc. Another form in which these instructions may be present is as a website address that can be used via the Internet to access the information at a remote site.

[0163] The following examples are offered by way of illustration and not by way of limitation.

[0164] experiment Question Layout A nine-color panel was used to determine the phenotype of CD8+ and CD4+ T cell subsets, examine the quantitative expression levels of CD27 and CD28 for these subsets, and gate out the regulatory T cell population. The relevant markers were CD3, CD4, CD8, CCR7, CD45RA, CD27, CD28, CD25, and CD127. A BD FACSLyric™ was used as a demonstration. This instrument has three lasers and 12 detectors. The three lasers are violet, blue, and red.

[0165] A gating strategy was then identified. Singlet lymphocytes were gated out according to the appropriate scatter gate. This gating strategy is shown in Figure 6. The CD3+ population (shown in plot 601) was gated and then displayed in a CD4-CD8 bivariate scatter plot 602. For each of these populations, the expression levels of CD45RA and CCR7 were used to differentiate naive T cells, effector T cells, central memory T cells, and effector memory T cells (plot 604). Each of these subsets can then be examined for their expression levels of CD27 and CD28 (plot 605). For CD4+ T cells, a combination of CD25 and CD127 was used to gate out regulatory T cells (plot 603).

[0166] We identified a set of reagents from which a fluorochrome panel could be derived. While any reagent inventory could be used as the search space, we selected 22 fluorochromes, assuming their antibody conjugation was available for all markers of interest. Specifically, the fluorochromes used in the search space were BV421, V450, BV480, Pacific Blue, BV605, BV711, BV786, AF488, BB515, FITC, PE, BB700, PE-Cy5.5, PerCP-Cy5.5, BB790, PE-Cy7, APC, AF647, AF700, APC-R700, APC-Cy7, and APC-H7. The number of possible combinations for constructing a 9-color panel was approximately 5e5, which is not acceptable for brute-force screening.

[0167] Automated Panel Design Workflow Most gating strategies result in comparing the separability of two populations using one or two features. The gating strategy described above (i.e., with respect to FIG. 6) can then be divided into a list of cell and marker pairs whose degree of separability should be assessed. A partial view of such a list is provided in Table 1.

[0168] [Table 1]

[0169] Next, implicit populations were added. While this biological question concerns only T cells, there will naturally be non-T cells in the tube that will co-stain and need to be gated out. To assess the separability of T cells, these non-T cells must also be included. Essentially, we must also define populations that are not of biological interest in this study but could potentially appear in the gating hierarchy. Some examples of such implicit populations are shown in Table 2.

[0170] [Table 2]

[0171] Quantitative markers were then added. In the gating hierarchy, several markers, such as CD3, CD4, and CD8, are used to classify populations. However, some markers, such as CD27 and CD28, are used to examine their quantitative expression levels. It is useful to distinguish between these two types in the algorithm. To have a uniform approach to assess the ability to distinguish between different expression levels, pseudopopulations were defined by constructing four populations (i.e., (+,+), (+,-), (-,+), and (-,-)) for each pair of quantitative markers. The separability between these four pseudopopulations could then be assessed. In this example, q1 (quadrant 1) was used to represent (+,+), q2 (quadrant 2) was used to represent (-,+), q3 (quadrant 3) was used to represent (-,-), and q4 (quadrant 4) was used to represent (+,-). The pseudopopulations synthesized for the quantitative markers are shown in Table 3.

[0172] [Table 3]

[0173] By performing these three steps, we obtain a global list of cell / marker pairs (Table 4). This list includes the biological question and the corresponding gating hierarchy. A good panel will be one that can adequately separate these pairs.

[0174] [Table 4-1]

[0175] [Table 4-2]

[0176] [Table 4-3]

[0177] [Table 4-4]

[0178] [Table 4-5]

[0179] [Table 4-6]

[0180] The statistical moments of the different markers were then determined. Essentially, a flow cytometer can be viewed as performing a linear transformation that converts a vector of fluorophore abundances into a vector of detector signals. Based on this model, the mean and variance-covariance matrix of the detector signals can be predicted by incorporating different noise sources (Poisson noise, baseline noise, system noise, etc.) into this transformation. The distribution in detector space is then calculated. The variance-covariance matrix can be back-propagated to the variance-covariance matrix of fluorophore abundances through a process of decomposition or correction.

[0181] As an example, the model included Poisson noise, a baseline noise of 30 statistical photoelectrons (spe) per detector, and a system coefficient of variation (cv) of 0.03 per detector. Given the panels listed in Table 5, the mean and variance of the detector signal were predicted for each population, all in photons.

[0182] [Table 5]

[0183] The variance for each detector for the selected cell population was calculated and is listed in Table 6.

[0184] [Table 6]

[0185] The backpropagation dispersion of fluorophore abundance was calculated as shown in Table 7.

[0186] [Table 7]

[0187] From Table 7, it is clear that the variances of the same marker across different populations have large variations. To facilitate subsequent separability assessment, a variance stabilization procedure needs to be performed to stabilize these variances.

[0188] In this example, a transformation based on the one-parameter inverse hyperbolic sine function (arcsinh(x / c)) was used. An optimization routine was run to find the optimal value for parameter c to minimize the heteroscedasticity of the markers across different populations. The dispersion-stabilizing fluorophore dispersions are listed in Table 8.

[0189] [Table 8]

[0190] Given the processed statistical moments of different markers on different populations, the statistical distance between pairs defined in the gating hierarchy can be calculated. In this example, for one-dimensional histograms, such as the CD3 gating step, the Earth Mover distance (EMD) between two distributions was calculated. For two-dimensional scatter plots, such as the CD4-CD8 bivariate plot, the two distributions are first projected in the direction in which they are most separated, and then the EMD between the projected distributions is calculated. An exemplary list of separability scores is shown in Table 9.

[0191] [Table 9]

[0192] The separability score, defined in Table 9 above, is a dimensionless quantity that describes the degree of separation between two univariate or bivariate distributions. Therefore, an absolute threshold can be defined to give a binary classification as to whether a particular pair is separable or not. A value of 16 was found to be a good threshold number and was used in this example. Therefore, the panel score is used as the objective function to be minimized in the combinatorial optimization routine. The algorithm performed all of the above steps to identify panels with zero inseparable pairs, as listed in Table 5.

[0193] Regardless of the scope of the appended claims, the present disclosure is also defined by the following notes.

[0194] 1. A method for evaluating the suitability of a fluorochrome panel for use in a flow cytometry protocol for analyzing a biological sample, the method comprising: using a processor: an initial fluorochrome panel including a set of fluorochrome identifiers each referring to a fluorochrome in the set of fluorochromes and a set of biological marker identifiers each associated with a fluorochrome identifier in the set of fluorochrome identifiers; a plurality of population identifiers each referring to a particle population; and Device Identifier receiving the creating a set of population-marker pairs by associating each population identifier in the plurality of population identifiers with a biological marker identifier from the set of biological marker identifiers; generating a set of separability metrics each predicting a measure of statistical distance between particle populations in flow cytometer data space, each measure of statistical distance relating to detected signal intensities resulting from each fluorochrome associated with each population-marker pair used in a flow cytometry protocol using an instrument associated with the instrument identifier; aggregating the set of separability metrics into a panel score; A panel score is assessed to evaluate the suitability of the initial fluorochrome panel for use in a flow cytometry protocol. A method comprising: 2. The method of claim 1, wherein creating a set of population-marker pairs includes creating population-marker pairs for implicit particle populations that are not referenced in the received plurality of population identifiers but are present in the biological sample. 3. The method of claim 1 or 2, wherein creating a set of population-marker pairs further comprises defining one or more quantitative pairs of biological marker identifiers for assessing quantitative expression of particle populations. 4. A method according to any one of appendices 1 to 3, wherein generating a set of separability metrics includes predicting a statistical moment of each biological marker identifier in the set of biological marker identifiers based on detected signal strength. 5. The method of claim 4, wherein predicting the statistical moments includes predicting a covariance matrix of the detected signal intensities.

[0195] 6. The method of claim 4, wherein predicting the statistical moments includes predicting a variance-covariance matrix of the detected signal intensities. 7. The method of claim 4, wherein predicting statistical moments includes predicting a mean matrix of detected signal strengths. 8. The method of any one of claims 4 to 7, wherein estimating the statistical moments comprises performing a Monte Carlo simulation. 9. The method of any one of appendices 4 to 8, wherein predicting the statistical moment of each biological marker identifier in the set of biological marker identifiers includes incorporating the effect of a noise model on the detected signal strength. 10. The method of claim 9, wherein the noise model is a Gaussian noise model.

[0196] 11. The method of claim 9, wherein the noise model is a Poisson noise model. 12. The method of any one of appendices 9 to 11, wherein incorporating the effect of the noise model on the detected signal strength includes obtaining an analytical expression relating the predicted statistical moments to the noise model. 13. The method of claim 12, further comprising incorporating the effect of a noise model into the detected signal strength based on a spillover diffusion matrix (SSM). 14. The method of any one of claims 1 to 13, wherein generating the set of separability metrics includes stabilizing the variance of the detected signal strengths. 15. The method of claim 14, wherein stabilizing the variance of the detected signal strength includes biexponential scaling.

[0197] 16. The method of claim 14, wherein stabilizing the variance of the detected signal strength includes inverse hyperbolic function scaling. 17. The method of claim 14, wherein stabilizing the variance of the detected signal strengths includes solving an optimization problem having an objective function that is a measure of the similarity of the variances of different distributions of the detected signal strengths. 18. The method of claim 14, wherein stabilizing the variance of the detected signal strengths includes determining an analytical relationship between the variance and mean of the detected signal strengths. 19. The method of any one of Appendices 1 to 18, wherein aggregating the set of separability metrics into a panel score includes negating the value of the lowest separability score. 20. The method of claim 3, further comprising aggregating a set of separability metrics separately for each population-marker pair and quantitative pair.

[0198] 21. The method of claim 20, wherein determining the panel score includes calculating a vector of a set of aggregated separability metrics for population-marker pairs and a set of aggregated separability metrics for quantitative pairs. 22. The method of any one of Appendices 1 to 18, wherein aggregating the set of separability metrics includes comparing each separability metric to a threshold. 23. The method of any one of appendices 1 to 22, further comprising generating an optimized fluorochrome panel based on an evaluation of the suitability of the initial fluorochrome panel for use in a flow cytometry protocol. 24. The method of claim 23, wherein generating an optimized fluorochrome panel includes determining a fluorochrome panel having an optimized panel number. 25. The method of claim 23 or 24, wherein generating an optimized fluorochrome panel comprises adjusting the fluorochromes in the initial fluorochrome panel and evaluating the suitability of the adjusted fluorochrome panel for use in the flow cytometry protocol.

[0199] 26. The method of claim 25, wherein generating an optimized fluorochrome panel comprises iteratively adjusting an initial fluorochrome panel and evaluating the suitability of each iteratively adjusted fluorochrome panel for use in a flow cytometry protocol. 27. Receiving a gating strategy; and Determine the initial fluorochrome panel based on your gating strategy 27. The method of any one of claims 1 to 26, further comprising: 28. The method of any one of appendices 1 to 27, wherein the initial fluorochrome panel is randomly determined. 29. The method of any one of appendices 1 to 22, further comprising determining an initial fluorochrome panel using a processor.

[0200] 30. An initial fluorochrome panel including a set of fluorochrome identifiers each referring to a fluorochrome in the set of fluorochromes and a set of biological marker identifiers each associated with a fluorochrome identifier in the set of fluorochrome identifiers; a plurality of population identifiers each referring to a particle population; and Device Identifier Receive, creating a set of population-marker pairs by associating each population identifier in the plurality of population identifiers with a biological marker identifier from the set of biological marker identifiers; generating a set of separability metrics each predicting a measure of statistical distance between particle populations in flow cytometer data space, wherein each measure of statistical distance relates to a detected signal intensity resulting from each fluorochrome associated with each population-marker pair employed in a flow cytometry protocol using an instrument associated with an instrument identifier; Aggregating the set of separability metrics into a panel score, A panel score is assessed to evaluate the suitability of the initial fluorochrome panel for use in the flow cytometry protocol. A processor configured to Including, the system. 31. The system of claim 30, wherein creating the set of population-marker pairs includes creating population-marker pairs for implicit particle populations that are not referenced in the received plurality of population identifiers but are present in the biological sample. 32. The system of claim 30 or 31, wherein creating a set of population-marker pairs further includes defining one or more quantitative pairs of biological marker identifiers for assessing quantitative expression of particle populations. 33. The system of any one of appendices 30 to 32, wherein generating a set of separability metrics includes predicting a statistical moment of each biological marker identifier in the set of biological marker identifiers based on detected signal strength. 34. The system of claim 33, wherein predicting the statistical moments includes predicting a covariance matrix of the detected signal strengths.

[0201] 35. The system of claim 33, wherein predicting the statistical moments includes predicting a variance-covariance matrix of the detected signal strengths. 36. The system of claim 33, wherein predicting the statistical moments includes predicting a mean matrix of detected signal strengths. 37. The system of any one of notes 33 to 36, wherein predicting the statistical moments includes performing a Monte Carlo simulation. 38. A system described in any one of appendices 33 to 37, wherein predicting the statistical moment of each biological marker identifier in the set of biological marker identifiers includes incorporating the effect of a noise model on the detected signal strength. 39. The system of claim 38, wherein the noise model is a Gaussian noise model.

[0202] 40. The system of claim 38, wherein the noise model is a Poisson noise model. 41. The system of any one of appendixes 38 to 40, wherein incorporating the effect of the noise model on the detected signal strength includes obtaining an analytical expression relating the predicted statistical moments to the noise model. 42. The system of claim 41, wherein the processor is configured to incorporate the effect of a noise model into the detected signal strength based on a spillover diffusion matrix (SSM). 43. The system of any one of appendixes 30 to 42, wherein generating the set of separability metrics includes stabilizing the variance of detected signal strengths. 44. The system of claim 43, wherein stabilizing the variance of the detected signal strength includes biexponential scaling.

[0203] 45. The system of claim 43, wherein stabilizing the variance of the detected signal strength includes inverse hyperbolic function scaling. 46. ​​The system of claim 43, wherein stabilizing the variance of the detected signal strength includes solving an optimization problem having an objective function that is a similarity of the variances of different distributions of the detected signal strength. 47. The system of claim 43, wherein stabilizing the variance of the detected signal strength includes determining an analytical relationship between the variance and mean of the detected signal strength. 48. The system of any one of Appendices 30 to 47, wherein aggregating the set of separability metrics into a panel score includes negating the value of the lowest separability score. 49. The system of claim 32, wherein the processor is configured to aggregate the set of separability metrics separately for each population-marker pair and quantitative pair.

[0204] 50. The system of claim 49, wherein determining the panel score includes calculating a vector of a set of aggregated separability metrics for population-marker pairs and a set of aggregated separability metrics for quantitative pairs. 51. The system of any one of appendixes 30 to 47, wherein aggregating the set of separability metrics includes comparing each separability metric to a threshold. 52. The system of any one of appendices 30 to 51, wherein the processor is configured to generate an optimized fluorochrome panel based on an evaluation of the suitability of the initial fluorochrome panel for use in a flow cytometry protocol. 53. The system of claim 52, wherein generating an optimized fluorescent dye panel includes determining a fluorescent dye panel having an optimized panel number. 54. The system described in Appendix 52 or 53, wherein generating an optimized fluorochrome panel includes adjusting fluorochromes in the initial fluorochrome panel and evaluating the suitability of the adjusted fluorochrome panel for use in a flow cytometry protocol.

[0205] 55. The system described in Appendix 54, wherein generating an optimized fluorochrome panel includes iteratively adjusting an initial fluorochrome panel and evaluating the suitability of each iteratively adjusted fluorochrome panel for use in a flow cytometry protocol. 56. The system of any one of claims 30 to 55, wherein the system is a flow cytometer. 57. The system of any one of appendixes 30 to 56, further comprising a display configured to output an evaluation of the initial fluorochrome panel. 58. The processor shall: Receive the gating strategy, Determine the initial fluorochrome panel based on your gating strategy 58. The system of any one of clauses 30 to 57, configured to: 59. The system of any one of claims 30 to 57, wherein the processor is configured to randomly determine the initial fluorochrome panel.

[0206] 60. An initial fluorochrome panel including a set of fluorochrome identifiers each referring to a fluorochrome in the set of fluorochromes and a set of biological marker identifiers each associated with a fluorochrome identifier in the set of fluorochrome identifiers; a plurality of population identifiers each referring to a particle population; and Device Identifier receiving the creating a set of population-marker pairs by associating each population identifier in the plurality of population identifiers with a biological marker identifier from the set of biological marker identifiers; generating a set of separability metrics each predicting a measure of statistical distance between particle populations in flow cytometer data space, each measure of statistical distance relating to detected signal intensities resulting from each fluorochrome associated with each population-marker pair used in a flow cytometry protocol using an instrument associated with the instrument identifier; aggregating the set of separability metrics into a panel score; A panel score is assessed to evaluate the suitability of the initial fluorochrome panel for use in a flow cytometry protocol. A non-transitory computer-readable storage medium having stored thereon instructions for evaluating the suitability of a fluorochrome panel for use in a flow cytometry protocol for analyzing a biological sample by a method comprising: 61. The non-transitory computer-readable storage medium of claim 60, wherein creating a set of population-marker pairs includes creating population-marker pairs for implicit particle populations that are not referenced in the received plurality of population identifiers but are present in the biological sample. 62. A non-transitory computer-readable storage medium as described in Appendix 60 or 61, wherein creating a set of population-marker pairs further includes defining one or more quantitative pairs of biological marker identifiers for assessing quantitative expression of particle populations. 63. A non-transitory computer-readable storage medium according to any one of appendices 60 to 62, wherein generating a set of separability metrics includes predicting a statistical moment of each biological marker identifier in the set of biological marker identifiers based on detected signal strength. 64. The non-transitory computer-readable storage medium of claim 63, wherein predicting the statistical moments includes predicting a covariance matrix of the detected signal strengths.

[0207] 65. The non-transitory computer-readable storage medium of claim 63, wherein predicting the statistical moments includes predicting a variance-covariance matrix of the detected signal strengths. 66. The non-transitory computer-readable storage medium of claim 63, wherein predicting statistical moments includes predicting a mean matrix of detected signal strengths. 67. The non-transitory computer-readable storage medium of any one of Appendices 63 to 66, wherein predicting the statistical moments includes performing a Monte Carlo simulation. 68. A non-transitory computer-readable storage medium according to any one of appendices 63 to 67, wherein predicting the statistical moment of each biological marker identifier in the set of biological marker identifiers includes incorporating the effect of a noise model on the detected signal strength. 69. The non-transitory computer-readable storage medium of claim 68, wherein the noise model is a Gaussian noise model.

[0208] 70. The non-transitory computer-readable storage medium of claim 68, wherein the noise model is a Poisson noise model. 71. The non-transitory computer-readable storage medium of any one of Appendices 68 to 70, wherein incorporating the effect of the noise model on the detected signal strength includes obtaining an analytical expression relating the predicted statistical moments to the noise model. 72. The non-transitory computer-readable storage medium of claim 71, wherein the method further includes incorporating the effect of a noise model into the detected signal strength based on a spillover diffusion matrix (SSM). 73. The non-transitory computer-readable storage medium of any one of Appendices 60 to 72, wherein generating the set of separability metrics includes stabilizing a variance of the detected signal strengths. 74. The non-transitory computer-readable storage medium of claim 73, wherein stabilizing the variance of the detected signal strength includes bi-exponential scaling.

[0209] 75. The non-transitory computer-readable storage medium of claim 73, wherein stabilizing the variance of the detected signal strength includes inverse hyperbolic function scaling. 76. The non-transitory computer-readable storage medium of claim 73, wherein stabilizing the variance of the detected signal strengths includes solving an optimization problem having an objective function that is a similarity of the variances of different distributions of the detected signal strengths. 77. The non-transitory computer-readable storage medium of claim 73, wherein stabilizing the variance of the detected signal strengths includes determining an analytical relationship between the variance and mean of the detected signal strengths. 78. The non-transitory computer-readable storage medium of any one of Appendices 60 to 77, wherein aggregating the set of separability metrics into a panel score includes negating the value of the lowest separability score. 79. The non-transitory computer-readable storage medium of claim 62, wherein the method further comprises aggregating the set of separability metrics for each population-marker pair and quantitative pair separately.

[0210] 80. The non-transitory computer-readable storage medium of claim 79, wherein determining the panel score includes calculating a vector of a set of aggregated separability metrics for population-marker pairs and a set of aggregated separability metrics for quantitative pairs. 81. The non-transitory computer-readable storage medium of any one of Appendixes 60 to 77, wherein aggregating the set of separability metrics includes comparing each separability metric to a threshold value. 82. A non-transitory computer-readable storage medium described in any one of appendices 60 to 81, wherein the method further includes generating an optimized fluorochrome panel based on an evaluation of the suitability of the initial fluorochrome panel for use in a flow cytometry protocol. 83. The non-transitory computer-readable storage medium of claim 82, wherein generating an optimized fluorochrome panel includes determining a fluorochrome panel having an optimized panel number. 84. A non-transitory computer-readable storage medium as described in Appendix 82 or 83, wherein generating an optimized fluorochrome panel includes adjusting fluorochromes in the initial fluorochrome panel and evaluating the suitability of the adjusted fluorochrome panel for use in a flow cytometry protocol.

[0211] 85. The non-transitory computer-readable storage medium of claim 84, wherein generating an optimized fluorochrome panel includes iteratively adjusting the fluorochrome panel and evaluating the suitability of each iteratively adjusted fluorochrome panel for use in a flow cytometry protocol. 86. The method is receiving a gating strategy; and Determine the initial fluorochrome panel based on your gating strategy 86. The non-transitory computer-readable storage medium of any one of Clauses 60 to 85, further comprising: 87. The non-transitory computer-readable storage medium of any one of Appendices 60 to 85, wherein the initial fluorochrome panel is randomly determined.

[0212] 88. A method for assessing the suitability of a fluorochrome panel for use in a flow cytometry protocol for analyzing a biological sample, comprising: The method is: (a) a processor; an initial fluorochrome panel including a set of fluorochrome identifiers each referring to a fluorochrome in the set of fluorochromes and a set of biological marker identifiers each associated with a fluorochrome identifier in the set of fluorochrome identifiers; a plurality of population identifiers each referring to a particle population; and Device Identifier and a processor inputting: creating a set of population-marker pairs by associating each population identifier in the plurality of population identifiers with a biological marker identifier from the set of biological marker identifiers; generating a set of separability metrics each predicting a measure of statistical distance between particle populations in flow cytometer data space, wherein each measure of statistical distance relates to a detected signal intensity resulting from each fluorochrome associated with each population-marker pair employed in a flow cytometry protocol using an instrument associated with an instrument identifier; Aggregating the set of separability metrics into a panel score, A panel score is assessed to evaluate the suitability of the initial fluorochrome panel for use in the flow cytometry protocol. and inputting, (b) receiving from the processor an assessment of the suitability of the fluorochrome panel for use in the flow cytometry protocol; A method comprising: 89. The method of claim 88, wherein creating a set of population-marker pairs includes creating population-marker pairs for implicit particle populations that are not referenced in the received plurality of population identifiers but are present in the biological sample. 90. The method described in Appendix 88 or 89, wherein creating a set of population-marker pairs further includes defining one or more quantitative pairs of biological marker identifiers for assessing quantitative expression of particle populations. 91. The method of any one of appendixes 88 to 90, wherein generating a set of separability metrics includes predicting a statistical moment of each biological marker identifier in the set of biological marker identifiers based on detected signal strength. 92. The method of claim 91, wherein predicting the statistical moments includes predicting a covariance matrix of the detected signal strengths.

[0213] 93. The method of claim 91, wherein predicting the statistical moments includes predicting a variance-covariance matrix of the detected signal strengths. 94. The method of claim 91, wherein predicting the statistical moments includes predicting a mean matrix of detected signal strengths. 95. The method of any one of claims 91 to 94, wherein estimating the statistical moments includes performing a Monte Carlo simulation. 96. A method according to any one of appendices 91 to 95, wherein predicting the statistical moment of each biological marker identifier in the set of biological marker identifiers includes incorporating the effect of a noise model on the detected signal strength. 97. The method of claim 96, wherein the noise model is a Gaussian noise model.

[0214] 98. The method of claim 96, wherein the noise model is a Poisson noise model. 99. The method of any one of clauses 96 to 98, wherein incorporating the effect of the noise model on the detected signal strength includes obtaining an analytical expression relating the predicted statistical moments to the noise model. 100. The method of claim 99, wherein the processor is configured to incorporate the effect of a noise model into the detected signal strength based on a spillover diffusion matrix (SSM). 101. The method of any one of claims 88 to 100, wherein generating the set of separability metrics includes stabilizing the variance of the detected signal strengths. 102. The method of claim 101, wherein stabilizing the variance of the detected signal strength includes biexponential scaling.

[0215] 103. The method of claim 101, wherein stabilizing the variance of the detected signal strength includes inverse hyperbolic function scaling. 104. The method of claim 101, wherein stabilizing the variance of the detected signal strengths includes solving an optimization problem having an objective function that is a measure of the similarity of the variances of different distributions of the detected signal strengths. 105. The method of claim 101, wherein stabilizing the variance of the detected signal strength includes determining an analytical relationship between the variance and mean of the detected signal strength. 106. The method of any one of Appendices 88 to 105, wherein aggregating the set of separability metrics into a panel score includes negating the value of the lowest separability score. 107. The method of claim 90, wherein the processor is configured to aggregate the set of separability metrics separately for each population-marker pair and quantitative pair.

[0216] 108. The method of claim 107, wherein determining the panel score includes calculating a vector of a set of aggregated separability metrics for population-marker pairs and a set of aggregated separability metrics for quantitative pairs. 109. The method of any one of Appendices 88-105, wherein aggregating the set of separability metrics includes comparing each separability metric to a threshold. 110. The method of any one of claims 88 to 109, wherein the processor is configured to generate an optimized fluorochrome panel based on an evaluation of the suitability of the initial fluorochrome panel for use in the flow cytometry protocol. 111. The method of claim 110, wherein generating an optimized fluorochrome panel includes determining a fluorochrome panel having an optimized panel number. 112. The method described in appendix 110 or 111, wherein generating an optimized fluorochrome panel includes adjusting the fluorochromes in the initial fluorochrome panel and evaluating the suitability of the adjusted fluorochrome panel for use in a flow cytometry protocol.

[0217] 113. The method described in Appendix 112, wherein generating an optimized fluorochrome panel includes iteratively adjusting an initial fluorochrome panel and evaluating the suitability of each iteratively adjusted fluorochrome panel for use in a flow cytometry protocol. 114. The method of any one of clauses 110 to 113, further comprising receiving an optimized fluorochrome panel from the processor. 115. The method of any one of claims 88 to 114, further comprising inputting a gating strategy into a processor, the processor determining an initial fluorochrome panel based on the gating strategy. 116. The method of any one of appendices 88 to 114, wherein the initial fluorochrome panel is randomly determined.

[0218] Although the foregoing inventions have been described in some detail by way of illustration and example for purposes of clarity of understanding, it will be readily apparent to those skilled in the art that, in light of the teachings of the present invention, certain changes and modifications may be made thereto without departing from the spirit or scope of the appended claims.

[0219] Accordingly, the foregoing merely illustrates the principles of the present invention. It will be appreciated that those skilled in the art will be able to devise various arrangements, not explicitly described or shown herein, which embody the principles of the present invention and are within its spirit and scope. Furthermore, all examples and conditional language set forth herein are intended primarily to aid the reader in understanding the principles of the present invention and the concepts the inventors contributed to furthering the art, and should not be construed as being limited to such specifically described examples and conditions. Furthermore, all statements herein reciting principles, aspects, and embodiments of the present invention, as well as specific examples thereof, are intended to encompass both structural and functional equivalents thereof. Additionally, such equivalents are intended to include both currently known equivalents and future-developed equivalents, i.e., any elements developed to 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 recited in the claims.

[0220] 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 by the appended claims. In the claims, 35 U.S.C. 112(f) or 35 U.S.C. 112(6) is expressly defined as being invoked for a limitation in a claim only when the precise phrase "means for" or the precise phrase "step for" is recited at the beginning of such limitation in the claim; if such precise phrases are not used in a claim limitation, 35 U.S.C. 112(f) or 35 U.S.C. 112(6) is not invoked.

[0221] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims priority to the filing date of U.S. Provisional Patent Application No. 63 / 413,757, filed October 6, 2022, the disclosure of which is incorporated herein by reference.

Claims

1. 1. A method for assessing the suitability of a fluorochrome panel for use in a flow cytometry protocol for analyzing a biological sample, comprising: The method includes, using a processor: an initial fluorochrome panel including a set of fluorochrome identifiers each referring to a fluorochrome within the set of fluorochromes and a set of biological marker identifiers each associated with a fluorochrome identifier within said set of fluorochrome identifiers; a plurality of population identifiers each referring to a particle population; and Device Identifier receiving the creating a set of population-marker pairs by associating each population identifier in the plurality of population identifiers with a biological marker identifier from the set of biological marker identifiers; generating a set of separability metrics each predicting a measure of statistical distance between particle populations in flow cytometer data space, wherein each measure of statistical distance is related to detected signal intensity resulting from each fluorochrome associated with each population-marker pair used in a flow cytometry protocol using an instrument associated with the instrument identifier; aggregating said set of separability metrics into a panel score; assessing the panel score to evaluate the suitability of the initial fluorochrome panel for use in the flow cytometry protocol; and A method comprising:

2. 2. The method of claim 1 , wherein creating a set of population-marker pairs includes creating population-marker pairs for implicit particle populations that are present in the biological sample but that are not referenced in the received plurality of population identifiers.

3. The method of claim 1 or 2, wherein creating a set of population-marker pairs comprises defining one or more quantitative pairs of biological marker identifiers for assessing quantitative expression of particle populations.

4. 4. The method of claim 1, wherein generating a set of separability metrics comprises predicting a statistical moment of each biological marker identifier in the set of biological marker identifiers based on the detected signal strengths.

5. The method of claim 1 , wherein generating the set of separability metrics comprises stabilizing a variance of the detected signal strengths.

6. 6. The method of claim 1, wherein aggregating the set of separability metrics into a panel score comprises negating the value of the lowest separability score.

7. 7. The method of claim 1, further comprising generating an optimized fluorochrome panel based on an evaluation of the suitability of the initial fluorochrome panel for use in the flow cytometry protocol.

8. 8. The method of claim 7, wherein generating an optimized fluorochrome panel comprises determining a fluorochrome panel having an optimized panel number.

9. 9. The method of claim 7 or 8, wherein generating an optimized fluorochrome panel comprises adjusting the fluorochromes in the initial fluorochrome panel and evaluating the suitability of the adjusted fluorochrome panel for use in the flow cytometry protocol.

10. 10. The method of claim 9, wherein generating an optimized fluorochrome panel comprises iteratively adjusting the initial fluorochrome panel and evaluating the suitability of each iteratively adjusted fluorochrome panel for use in the flow cytometry protocol.

11. receiving a gating strategy; determining the initial fluorochrome panel based on the gating strategy; The method of claim 1 , further comprising:

12. The method of claim 1 , wherein the initial fluorochrome panel is determined randomly.

13. 13. The method of claim 1, further comprising determining the initial fluorochrome panel using the processor.

14. an initial fluorochrome panel including a set of fluorochrome identifiers each referring to a fluorochrome within the set of fluorochromes and a set of biological marker identifiers each associated with a fluorochrome identifier within said set of fluorochrome identifiers; a plurality of population identifiers each referring to a particle population; and Device Identifier Receive, creating a set of population-marker pairs by associating each population identifier in the plurality of population identifiers with a biological marker identifier from the set of biological marker identifiers; generating a set of separability metrics each predicting a measure of statistical distance between particle populations in flow cytometer data space, each measure of statistical distance relating to detected signal intensities resulting from each fluorochrome associated with each population-marker pair used in a flow cytometry protocol using an instrument associated with the instrument identifier; aggregating said set of separability metrics into a panel score; The panel score is assessed to evaluate the suitability of the initial fluorochrome panel for use in the flow cytometry protocol. A processor configured to Including, the system.

15. 1. A method for assessing the suitability of a fluorochrome panel for use in a flow cytometry protocol for analyzing a biological sample, comprising: The method comprises: (a) a processor; an initial fluorochrome panel including a set of fluorochrome identifiers each referring to a fluorochrome within the set of fluorochromes and a set of biological marker identifiers each associated with a fluorochrome identifier within said set of fluorochrome identifiers; a plurality of population identifiers each referring to a particle population; and Device Identifier and wherein the processor inputs: creating a set of population-marker pairs by associating each population identifier in the plurality of population identifiers with a biological marker identifier from the set of biological marker identifiers; generating a set of separability metrics each predicting a measure of statistical distance between particle populations in flow cytometer data space, each measure of statistical distance relating to detected signal intensities resulting from each fluorochrome associated with each population-marker pair used in a flow cytometry protocol using an instrument associated with the instrument identifier; aggregating said set of separability metrics into a panel score; The panel score is assessed to evaluate the suitability of the initial fluorochrome panel for use in the flow cytometry protocol. and inputting, (b) receiving from the processor an assessment of the suitability of the fluorochrome panel for use in the flow cytometry protocol; A method comprising:

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