Method and system for assessing applicability of fluorescent dye combination for flow cytometry regimen

Abstract

Methods of assessing the suitability of a fluorescent dye combination for a flow cytometry regimen for analyzing a biological sample are provided. A target method includes receiving, with a processor, an initial fluorochrome combination, a plurality of population identifiers, and an instrument identifier, wherein each population identifier refers to a population of particles. The method additionally includes creating a set of population-marker pairs, generating sets of separability metrics, where each separability metric predicts a metric of statistical distance between particle populations in a flow cytometer data space, aggregating the sets of separability metrics into a combined score, and evaluating the combined score. A system and a non-transitory computer-readable storage medium for assessing the suitability of a fluorescent dye combination for a flow cytometry regimen for analyzing a biological sample are also provided.

Description

[0001] Cross - Reference to Related Applications

[0002] This application claims the priority of U.S. Provisional Patent Application No. 63 / 413,757, filed on October 6, 2022; the disclosure of which is incorporated herein by reference. Background Art

[0003] Flow cytometry is a technique used to characterize and often to sort biological materials, such as cells in a blood sample or target particles 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 sheath fluid. The flow cytometer transports particles (including cells) in the fluid sample as a cell stream to a flow chamber while also directing the sheath fluid to the flow chamber. To characterize the components of the flowing stream, the flowing stream is irradiated with light. Changes in the material in the flowing stream, such as morphology or the presence of a fluorescent label, can cause changes in the observed light, which can be used for characterization and separation. For example, particles such as molecules, analyte - binding beads, or individual cells in a fluid suspension pass through a detection region where the particles are exposed to excitation light, which typically comes from one or more lasers, and the light - scattering and fluorescence characteristics of the particles are measured. The particles or their components are typically labeled with fluorescent dyes for ease of detection. By using spectrally distinct fluorescent dyes to label different particles or components, multiple different particles or components can be detected simultaneously. In some embodiments, the analyzer includes multiple detectors, one detector for each scattering parameter to be measured and one or more detectors for each different dye to be detected. For example, some embodiments include a spectral configuration that uses more than one sensor or detector for each dye. The data obtained includes signals measured for each light - scattering detector and fluorescence emission.

[0004] Parameters measured using a particle analyzer typically include light of the excitation wavelength scattered by the particle along a substantially forward narrow angle, called forward - scatter light (FSC), light scattered by the particle in a direction orthogonal to the excitation laser, called side - scatter light (SSC), and light emitted by a fluorescent molecule or fluorescent dye. Different cell types can be identified by their light - scattering characteristics and fluorescence emission, which is produced by labeling various cell proteins or other components with antibodies or other fluorescent probes labeled with fluorescent dyes. Forward - scatter light, side - scatter light, and fluorescence are detected by photodetectors located within the particle analyzer.

[0005] In the case of flow cytometry protocols that include fluorescence detection, experimental design typically involves the identification of a fluorescence dye panel, i.e., a set of fluorescence dyes that are used together in a given flow cytometry workflow. The process of fluorescence dye panel design is necessary because biological resolution (i.e., the ability to distinguish different target components within or between target particles) is directly affected by the measurement variance of the "raw" flow cytometry data and the mathematical process of spectral compensation or unmixing. Both of these factors depend largely on the choice of fluorescence dyes in the panel. First, measurement variance (i.e., noise) in a flow cytometer comes from a wide range of sources, including constant baseline measurement noise in the cytometer's electronics, optical shot noise that varies linearly with signal intensity, and multiplicative measurement noise that varies quadratically with signal intensity and is caused by random fluctuations in the cytometer's lasers and hydrodynamics. The measurement noise itself depends on the choice of fluorescence dyes. For example, brighter fluorescence dyes induce more shot noise than dimmer ones, and the signal amplitude values of dimmer fluorescence dyes are smaller compared to the constant "background noise" of the instrument's optics and electronics. Second, the raw measurement noise in the "detector space" (the number of dimensions of which is equal to the number of detectors in the instrument) is transferred to the final biological data in the "compensation space" or "unmixing space" (the number of dimensions of which is equal to the number of fluorescence dyes in the sample) through the mathematical operations of fluorescence compensation (in conventional flow cytometers) or spectral unmixing (in full-spectrum flow cytometers). The variance in the "unmixing space" is important because it is the space in which the final target biological analysis (e.g., gating, clustering, sorting, biomarker quantification, etc.) is performed. The mathematical mapping of noise into the biological space depends largely on the spectral characteristics of the fluorescence dyes themselves.

[0006] Traditional flow cytometry uses discrete photodetectors, where the discrete photodetectors are specifically designed to detect dye-specific fluorescence emission bands, imposing strict limitations on the number of fluorescent dyes that can be present simultaneously in a flow experiment: the number of fluorescent dyes cannot exceed the number of fluorescence detection channels on the instrument. In contrast, by definition, a spectral flow cytometer uses more detectors than the number of fluorescent dyes, and commercially available spectral flow cytometers have over 180 fluorescence channels. Considering current photonics technology (laser sources, optics, and detectors) and the availability of fluorescent dyes, flow cytometers typically operate in the ultraviolet (300 nm) to near-infrared (850 nm) range, which represents only an "effective range" of approximately 550 nm. Although it is possible to stack the operating wavelength ranges by using more lasers, it is often inevitable to use fluorescent dyes with overlapping emission spectra, thus requiring compensation or spectral unmixing to recover the true median fluorescent dye abundance. Spectral overlap adds additional noise to the detectors, thus increasing the spread of the compensated / unmixed data and reducing the ability to distinguish populations. The phenomenon of increased spread due to spectral overlap is known as spillover spread. Therefore, a combinatorial design approach, i.e., strategically pairing biomarkers with appropriate fluorescent dyes, is crucial for the success of flow experiments and becomes increasingly important as flow cytometry moves towards high-parameter spaces where the opportunity for spillover spread is greater. Summary of the Invention

[0007] The inventors have recognized that there are practical limitations on the number of fluorescent dyes and the number of biomarkers that can be used simultaneously in a flow cytometry experiment. Although nearly 100 different fluorescent dye molecules are commercially available for flow cytometry, the combinatorial space remains limited. This practical limitation stems from inevitable spectral overlap and the similarity of the fluorescent dyes used. Accordingly, methods and systems for evaluating and selecting appropriate fluorescent dye combinations are desirable. Embodiments of the present invention meet this need.

[0008] Aspects of the present invention include methods for assessing the suitability of fluorescence dye combinations for flow cytometry protocols for analyzing biological samples. The target method includes receiving, by a processor, an initial fluorescence dye combination that includes a group of fluorescence dye identifiers and a group of biomarker identifiers, where each fluorescence dye identifier refers to a fluorescence dye in the group of fluorescence dyes, and where each biomarker identifier is associated with a fluorescence dye identifier in the group of fluorescence dye identifiers; a plurality of population identifiers, where each population identifier refers to a population of particles; and an instrument identifier. In some cases, the processor also receives a gating strategy, and the initial fluorescence dye combination is determined based on the gating strategy. In some cases, the method includes receiving a randomly determined fluorescence dye combination. The method further includes creating a set of population-biomarker pairs by associating each population identifier in the plurality of population identifiers with a biomarker identifier in the group of biomarker identifiers. Subsequently, a set of separability metrics is generated, where each separability metric predicts a measure of the statistical distance between populations of particles in a flow cytometry data space. The measure of each statistical distance is related to the detected signal intensity produced by each fluorescence dye associated with each population-biomarker pair, the fluorescence dye being used in a flow cytometry protocol for an instrument associated with the instrument identifier. Next, the separability metrics can be aggregated into a panel score, and subsequently the panel score can be evaluated to assess the suitability of the initial fluorescence dye combination for the flow cytometry protocol.

[0009] In some cases, creating a set of population-marker pairs includes creating population-marker pairs for a population of latent particles that are not mentioned in the received plurality of population identifiers but are present in the biological sample. In other cases, creating a set of population-marker pairs also includes defining quantitative pairs of one or more biomarker identifiers for evaluating the quantitative expression of a population of particles. In an embodiment, generating a set of separability metrics can include predicting the statistical moments of each biomarker identifier in the set of biomarker identifiers based on the detected signal intensities. In such an embodiment, predicting the statistical moments can include predicting the covariance matrix of the detected signal intensities, the variance-covariance matrix of the detected signal intensities, or the mean matrix of the detected signal intensities. In some cases, predicting the statistical moments of each biomarker identifier in the set of biomarker identifiers includes incorporating the effects of a noise model (e.g., Gaussian noise model, Poisson noise model) into the detected signal intensities. In some examples, incorporating the effects of a noise model into the detected signal intensities includes running a Monte Carlo simulation and / or obtaining an analytical formula that relates the predicted statistical moments to the noise model (e.g., by incorporating the effects of the noise model into the detected signal intensities based on an overspill diffusion matrix). In some cases, generating a set of separability metrics includes stabilizing the variance of the detected signal intensities (e.g., by double-exponential scaling or inverse hyperbolic function scaling). In certain versions, stabilizing the variance of the detected signal intensities includes solving an optimization problem with the variance similarity of different distributions of the detected signal intensities as the objective function. In selected cases, stabilizing the variance of the detected signal intensities includes determining an analytical relationship between the variance and the mean of the detected signal intensities.

[0010] Aggregating the set of separability metrics into a combined score can include, for example, taking the inverse of the value of the lowest separability score. In certain embodiments, the method includes aggregating the set of separability metrics separately for each population-marker pair and quantitative pair. In some such embodiments, determining the combined score includes calculating a vector of the set of separability metrics aggregated for the population-marker pairs and the set of separability metrics aggregated for the quantitative pairs. Aggregating the set of separability metrics can include, for example, comparing each separability metric to a threshold.

[0011] The target method can also involve generating an optimized combination of fluorescent dyes based on an evaluation of the suitability of an initial combination of fluorescent dyes for a flow cytometry protocol, e.g., by determining a combination of fluorescent dyes with an optimized number of combinations. Generating an optimized combination of fluorescent dyes can include, for example, adjusting (e.g., iteratively adjusting) the fluorescent dyes in the initial combination of fluorescent dyes and evaluating the suitability of the adjusted (e.g., iteratively adjusted) combination of fluorescent dyes for the flow cytometry protocol.

[0012] Aspects of the present invention further include systems, where a target system is configured to perform the subject methods (e.g., as briefly described above). The subject system includes a processor configured to receive an initial fluorescent dye combination that includes a group of fluorescent dye identifiers and a group of biomarker identifiers, where each fluorescent dye identifier refers to a fluorescent dye in the group of fluorescent dyes, and where each biomarker identifier is associated with a fluorescent dye identifier in the group of fluorescent dye identifiers; a plurality of population identifiers, where each population identifier refers to a population of particles; and an instrument identifier. The processor of the present invention is configured to create a set of population-biomarker pairs by associating each population identifier in the plurality of population identifiers with a biomarker identifier in the group of biomarker identifiers, and generate a set of separability metrics, where each separability metric predicts a measure of the statistical distance between populations of particles in a flow cytometry data space, where each measure of the statistical distance is related to the detected signal intensity generated by each fluorescent dye associated with each population-biomarker pair, and the fluorescent dye is used in a flow cytometry protocol of an instrument associated with the instrument identifier. In an embodiment, the processor further aggregates the separability metrics into a combined score and evaluates the combined score to assess the suitability of the initial fluorescent dye combination for the flow cytometry protocol. In some cases, the system is a flow cytometer or includes a flow cytometer. The target system may also include a display configured to output an evaluation of the initial fluorescent dye combination and / or an optimized fluorescent dye combination. Aspects of the present invention also include a non-transitory computer-readable storage medium having instructions stored thereon that, when executed by a processor, generate an evaluation of the suitability of a fluorescent dye combination in a flow cytometry protocol for analyzing a biological sample. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The present invention may be best understood from the following detailed description when read in conjunction with the accompanying drawings. The accompanying drawings include the following figures:

[0014] Figures 1A to 1B A flowchart showing a method of evaluating the suitability of a fluorescent dye combination for a flow cytometry protocol for analyzing a biological sample, performed in accordance with certain embodiments of the present invention.

[0015] Figure 2 A functional block diagram of a flow cytometer system, in accordance with certain embodiments.

[0016] Figure 3 A control system, in accordance with certain embodiments.

[0017] Figures 4A to 4B A schematic diagram of a particle sorting system, in accordance with certain embodiments.

[0018] Figure 5 A block diagram of a computer system in accordance with certain embodiments is shown.

[0019] Figure 6 An exemplary gating strategy employed in the present invention is shown. Specific embodiments

[0020] A method for evaluating the suitability of a fluorescent dye combination for a flow cytometry protocol for analyzing a biological sample is provided. The target method includes receiving, by a processor, an initial fluorescent dye combination, 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 separability metric predicting a measure of the statistical distance between particle populations in a flow cytometer data space, aggregating the set of separability metrics into a combination score, and evaluating the combination score. A system and a non-transitory computer-readable storage medium for evaluating the suitability of a fluorescent dye combination for a flow cytometry protocol for analyzing a biological sample are also provided.

[0021] Before describing the present invention in more detail, it is to be understood that the invention is not limited to the particular embodiments described, as these may of course vary. It is also to be understood that, since the scope of the invention will be limited only by the appended claims, the terms used in the present invention are for the purpose of describing particular embodiments only and are not intended to be limiting.

[0022] Where numerical ranges are provided, it is to be understood that, unless the context clearly dictates otherwise, to the tenth of the unit of the lower limit, the invention includes each intermediate value between the upper and lower limits of such range as well as any other stated value or intermediate value within such stated range. The upper and lower limits of these smaller ranges may independently be included in the smaller ranges and are also included within the scope of the invention, subject to any specific excluded limitation within the stated range. When the stated range includes one or both of the limits, ranges excluding one or both of the included limits are also included in the invention.

[0023] Certain of the numerical ranges given herein are preceded by the term "about". The term "about" is used herein to provide literal support for the exact number that follows as well as a number that is close to or approximates the number that follows. When determining whether a number is close to or approximates a specifically recited number, the unrecited number that is close to or approximates the specifically recited number may be a number that provides a substantial equivalent of the specifically recited number in the context in which it occurs.

[0024] 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 to or equivalent to those described herein may also be used in the practice or testing of the present invention, representative illustrative methods and materials are described below.

[0025] All publications and patents cited in this specification are incorporated herein by reference as if each individual publication or patent were specifically and individually indicated to be incorporated herein by reference and are incorporated herein by reference to disclose and describe the methods and / or materials associated with the cited publications. 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. In addition, the provided publication dates may be different from the actual dates, which may require independent verification.

[0026] Note that, as used herein and in the appended claims, the singular forms “a,” “an,” and “the” before an element do not exclude the presence of a plurality of such elements unless the context clearly dictates otherwise. It should also be noted that the claims may be drafted to exclude any optional element. Thus, this statement is intended to serve as antecedent basis for the use of exclusive terms, such as “solely,” “only,” and the like, in connection with the recitation of claim elements, or the use of “negative” limitations.

[0027] After reading this disclosure, it will be apparent to those skilled in the art that the individual embodiments described and shown herein have discrete components and features that can be readily separated from or combined with the features of any one of a number of other embodiments without departing from the scope or spirit of the present invention. Any of the methods described herein can be performed in the order of events recited or in any other order that is logically possible.

[0028] Although the systems and methods have been or will be described for purposes of grammatical fluidity and functional explanation, it should be clearly understood that, unless expressly recited under 35 U.S.C. § 112, the claims should not be construed as necessarily limited in any way by the use of the terms “means” or “step,” but are to be given the full scope of the meaning and equivalents provided by the claim definitions under the doctrine of judicial equivalents, and, where the claims are expressly recited under 35 U.S.C. § 112, are to be given their full statutory equivalents under 35 U.S.C. § 112.

[0029] Method for Evaluating Fluorescent Dye Combinations

[0030] As described above, aspects of the present invention include methods for assessing the suitability of fluorescent dye combinations for flow cytometry protocols for analyzing biological samples. As used herein, a "fluorescent dye combination" refers to a group of different fluorescent molecular species (i.e., dyes) that can be used to identify particles in a sample or specific portions or components associated therewith. A "fluorescent dye combination" as used herein can also refer to a group of identifiers (e.g., numerical identifiers) that uniquely refer to and are associated with specific fluorescent molecular species. Such identifiers may be referred to herein as "fluorescent dye identifiers." In a fluorescent dye combination, distinguishable fluorescent dyes may differ in properties such as absorption spectrum, extinction coefficient, emission spectrum, and quantum efficiency (i.e., the number of photons emitted per photon absorbed), or combinations thereof. Thus, different or distinguishable fluorescent dyes may differ from one another in chemical composition and / or one or more properties of the dyes. For example, a given pair of fluorescent dyes can be considered distinguishable if they differ from one another in excitation and / or emission maxima, where, in some cases, the magnitude of such difference is 5 nm or more, such as 10 nm or more, including 15 nm or more, where, in some cases, the magnitude of the difference ranges from 5 nm to 400 nm, such as 10 nm to 200 nm, including 15 nm to 100 nm, such as 25 nm to 50 nm.

[0031] Assessing the suitability of a fluorescent dye combination for a flow cytometry protocol refers to predicting the quality of flow cytometer data that will be generated when the fluorescent dye combination is used in a flow cytometer. In other words, a fluorescent dye combination can be described as "suitable for" a flow cytometry protocol when the fluorescent dye combination produces intelligible flow cytometer data that reliably provides insights into the target features in the sample being studied. In some embodiments, a fluorescent dye combination is suitable for a flow cytometry protocol when the combination provides increased biological resolution. "Biological resolution" refers to the ability to distinguish different target entities (e.g., molecules, antigens, portions, epitopes, etc.) in a biological sample. In some cases, the fluorescent dye combinations identified by the present invention produce the highest biological resolution despite measurement variance and variance in the flow cytometer data space (e.g., flow cytometer data is subject to fluorescence compensation or spectral unmixing). In certain versions, the "highest" biological resolution is evaluated relative to the biological resolution achievable using one or more other sets of fluorescent dyes (i.e., containing one or more fluorescent dyes different from the optimized fluorescent dye combination determined by the present invention) that are different from the optimized fluorescent dye combination determined by the present invention.

[0032] The method of the present invention includes receiving an initial fluorescent dye combination. The initial fluorescent dye combination can be determined in any convenient manner. In some embodiments, the initial fluorescent dye combination is determined randomly (e.g., by a processor). In certain cases, the method includes receiving a gating strategy and determining the initial fluorescent dye combination based on the gating strategy. In other words, the processor can select the initial fluorescent dye combination based on fluorescent dyes typically used with particles having certain phenotypic characteristics of interest in the gating strategy. If desired, the fluorescent dyes available for a given fluorescent dye combination can vary. In some embodiments, the person skilled in the art implementing the method can limit the range of fluorescent dyes forming the initial fluorescent dye combination to, for example, those readily accessible to the person skilled in the art.

[0033] The fluorescent dye combination of the present invention can also include a set of biomarker identifiers, where each biomarker identifier is associated with a fluorescent dye identifier in the set of fluorescent dye identifiers. As described herein, a "biomarker" can refer to any distinguishable feature of a biological sample (e.g., an organ, tissue, cell, macromolecule, etc.) that can be associated with a fluorescent dye (e.g., as part of an antibody-dye conjugate) for analysis. In some embodiments, the biomarker includes one or more cell surface proteins. In certain cases, the biomarker includes cluster of differentiation (CD) molecules. A "biomarker identifier" refers to a set of identifiers (e.g., numerical identifiers) that uniquely refer to and are associated with a specific biomarker in a data space. An exemplary fluorescent dye combination having a fluorescent dye identifier and an associated biomarker identifier is shown in Table 5, which is given in the experimental section below. In certain cases, the person skilled in the art implementing the method can limit the range of fluorescent dyes forming the initial fluorescent dye combination to, for example, a set of fluorescent dyes for which antibody conjugates are available for all target biomarkers.

[0034] The method of the present invention may additionally include receiving a plurality of population identifiers, where each population identifier relates to a population of particles. As used herein, "population of particles" describes, in its conventional sense, a group of particles having the same or sufficiently similar characteristics for the purposes of a particular protocol. In some cases, a population of particles may be described based on the positivity or negativity of one or more biomarkers, as described above. In other words, a "population" or "subpopulation" of analytes, such as cells or other particles, generally refers to a group of analytes that have characteristics (e.g., optical, impedance, or temporal characteristics) with respect to one or more measured fluorescence parameters such that the measured parameter data forms a cluster in data space. Thus, a population is identified as a cluster in the data. Conversely, each data cluster is typically interpreted as corresponding to a population of a particular type of cell or analyte, although clusters corresponding to noise or background are also commonly observed. A cluster may be defined within a subset of dimensions, e.g., with respect to a subset of the measured fluorescence parameters (i.e., fluorescent dyes) that correspond to populations that differ only in a subset of the measured parameters or features extracted from the measurement results of the sample.

[0035] In some embodiments, the method further includes receiving a gating strategy, where the gating strategy provides rules for how to distinguish clusters (e.g., populations) of flow cytometry data having certain characteristics from one another. In certain cases, the gating strategy includes a gating hierarchy. The gating hierarchy described herein defines criteria for grouping fluorescence flow cytometry data into specific populations. In some embodiments, the hierarchy establishes how to group data points that are positive or negative for the same parameter (e.g., biomarker) together. In some versions, the gating hierarchy is a series of gating steps to identify a target population in a series of one-dimensional histograms or two-dimensional histograms. For example, a partial gating hierarchy for clustering T cells by determining whether cells are positive or negative for the presence of CD4 and CD8 is shown below:

[0036] CD4 + and CD8 - → CD4 T cells

[0037] CD4 - and CD8 + → CD8 T cells

[0038] CD4 + and CD8 + → double-positive T cells

[0039] CD4- and CD8- → double-negative T cells

[0040] As shown above, cells that are CD4 positive but CD8 negative are "CD4 T cells", and cells that are positive for both markers are "double-positive T cells", and so on.

[0041] The method of the present invention further includes receiving an instrument identifier. An "instrument identifier" refers to information or data related to a specific instrument (e.g., a particle analyzer, a flow cytometer). In some cases, the instrument identified by the instrument identifier is a flow cytometer. Any convenient flow cytometer configured to analyze fluorescence particle-modulated light can be used. In some cases, the target flow cytometer includes a flow cytometer produced by BD Biosciences. Exemplary flow cytometers include BD Biosciences FACSCanto TM Flow cytometer, BD Biosciences FACSCanto TM II Flow cytometer, BD Accuri TM Flow cytometer, BD Accuri TM C6 Plus Flow cytometer, BD Biosciences FACSCelesta TM Flow cytometer, BD Biosciences FACSLyric TM Flow cytometer, BD Biosciences FACSVerse TM Flow cytometer, BD Biosciences FACSymphony TM Flow cytometer, BD Biosciences LSRFortessa TM Flow cytometer, BD Biosciences LSRFortessa TM X-20 Flow cytometer, BD Biosciences FACSPresto TM Flow cytometer, BD Biosciences FACSVia TM Flow cytometer and BD Biosciences FACSCalibur TM Cell sorter, BD Biosciences FACSCount TM Cell sorter, BD Biosciences FACSLyric TM Cell sorter, BD Biosciences Via TM Cell sorter, BD Biosciences Influx TM Cell sorter, BDBiosciences Jazz TM Cell sorter, BD Biosciences Aria TM Cell sorter, BD BiosciencesFACSAria TMII Cell Sorter, BD Biosciences FACSAria TM III Cell Sorter, BD Biosciences FACSAria TM Fusion Cell Sorter and BD Biosciences FACSMelody TM Cell Sorter, BD Biosciences FACSymphony TM S6 Cell Sorter, BD FACSDiscover TM S8 Cell Sorter, etc.

[0042] In some cases, receiving an instrument identifier allows for an instrument - specific assessment of the fluorescent dye combinations used in a flow cytometry protocol. For example, differences in the number and arrangement of lasers and detection channels in separate instruments can result in different spectral characteristics associated with each instrument. A "spectral characteristic" refers to the characteristics of the fluorescence spectrum of a single fluorescent dye represented by one or more numerical values. Accordingly, embodiments of the method perform an analysis specific to a particular instrument (or instrument class / type), assuming that the fluorescent dyes in the fluorescent dye combination being evaluated are employed in the instrument. Due to differences between instruments, if a fluorescent dye is applied to two different types of instruments (e.g., flow cytometers), the same fluorescent dye combination may be more or less associated with variance in flow cytometer data. The method of the present invention can evaluate fluorescent dye combinations in the context of instrument - specificity, enabling those skilled in the art to more reliably gain insight into the quality of flow cytometer data when using the fluorescent dyes in a given combination with a particular instrument.

[0043] The method of the present invention further includes creating a set of population-biomarker pairs by associating each of a plurality of population identifiers with a biomarker identifier in a set of biomarker identifiers. In other words, biomarkers that can be used to distinguish one population from another are identified and associated with their respective populations. In cases where the method includes receiving a gating strategy, creating the set of population-biomarker pairs can include decomposing the gating strategy. For example, if the purpose of a flow cytometry experiment is to phenotype CD8+ and CD4+ T cell subsets and study the quantitative expression of CD27 and CD28 in these subsets, the biological hypothesis can be stated as a sample consisting of naïve CD8+ T cells (CD8N), central memory CD8+ T cells (CD8CM), effector memory CD8+ T cells (CD8EM), CD8+ effector cells (CD8Eff), corresponding cells of 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, and based on this, separately gate out the CD8+ and CD4+ populations. Among CD8+ or CD4+ cells, different subsets can be distinguished by examining the expression patterns of CCR7 and CD45RA. In addition, among CD4+ cells, Tregs can be found in the CD25+ and CD127- populations. Thus, in this example, the relevant biomarker pairs for gating are {CD3}, {CD8, CD4}, {CCR7, CD45RA}, and {CD25, CD127}.

[0044] In certain embodiments, the method includes creating population-biomarker pairs for populations of cryptic particles that are not mentioned in the plurality of population identifiers received but are present in the biological sample. In other words, populations that are not explicitly of interest in the experimental design but are still part of the biological sample are assigned to population-biomarker pairs (e.g., in the manner described above). For example, some populations such as CD3- cells (non-T cells) or CD8-CD4- cells (double-negative cells, T-dn) are not explicitly defined in the hypothesis (such as the hypothesis described above), but they may be present in the sample and affect the quality of gating of the explicitly defined cells. Thus, embodiments of the present invention include including these cryptic populations when formulating population-biomarker pairs.

[0045] In some cases, creating a set of population-marker pairs may also include defining a quantitative pair of one or more biomarker identifiers, which is used to evaluate the quantitative expression of a particle population. In other words, the method of the present invention may also include classifying population-marker pairs into gating pairs and quantitative pairs according to their purposes in a gating strategy. A gating pair refers to a pair used to gate out / classify a population, while a quantitative pair (e.g., {CD27, CD28}) is mainly used to study the quantitative expression level of a gated population. In cases where the quantitative expression of these markers needs to be measured, it is advantageous to be able to separate flow cytometry data populations having different expression profiles of these quantitative markers. Therefore, a version of the present invention includes considering population-marker pairs constructed with respect to quantitative markers and the populations defined above (i.e., those populations that are part of a biological hypothesis and / or gating strategy). Similar to the recessive gating populations defined above, recessive quantitative populations can also be defined, which together with the corresponding dominant populations constitute a complete set of four expression patterns for quantitative pairs, namely (+, +), (+, -), (-, +), (-, -), where "+" and "-" are qualitative symbols of abundance, which respectively represent a positive population and a negative population, and (+, -), for example, means that the first marker is positive and the second marker is negative. Therefore, the goal of having a narrow distribution of quantitative markers can be translated into separating the four dominant and recessive quantitative populations from each other, so that the logic is consistent with how gating pairs are evaluated. For example, the expression pattern of {CD27, CD28} for CD8N is (+, ++), based on which three additional recessive populations are formed, including CD8N*CD27*CD28*q2, CD8N*CD27*CD28*q3, and CD8N*CD27*CD28*q4, where the symbols are composed of {dominant cell population} * {the first marker} * {the second marker} * {quadrant number}, the quadrant number represents the relative abundance of the two markers. Specifically, q1 represents (+, ++), q2 (-, ++), q3 (-, -), and q4 (+, -). Therefore, the expression patterns of these recessive populations are (-, ++), (-, -), and (+, -). A good combination should separate these four populations from each other to ensure reliable quantitative analysis of {CD27, CD28} for CD8N.

[0046] Aspects of the present invention also include generating a set of separability metrics, where each separability metric predicts a measure of the statistical distance between particle populations in a flow cytometry data space. The "measure of statistical distance" as described herein is related to the detected signal intensity, which is generated by each fluorescent dye associated with each population-marker pair and is used in a flow cytometry protocol of an instrument associated with an instrument identifier. This results in a table of separability scores for all pairs defined in the gating hierarchy. Any distance metric that quantifies the separability between populations can be used. For example, the distance between two univariate distributions can be defined as the difference in means normalized by the square root of the sum of variances. For two bivariate distributions, the method can include projecting the bivariate distributions onto the direction in which the two distributions are most separated, and subsequently evaluating the distance between the projected distributions by taking the difference in means normalized by the square root of the sum of variances. "Predicting" a measure of statistical distance means estimating what the measure of statistical distance would be if the combination of fluorescent dyes being evaluated were used in a flow cytometry protocol. Thus, in some cases, the signal intensity associated with the statistical measure is an estimated value (e.g., a simulated value). In certain cases, generating a set of separability metrics, where each separability metric predicts a measure of the statistical distance between particle populations in a flow cytometry data space includes calculating the earth-mover distance (EMD) of the distributions.

[0047] In some embodiments, generating a set of separability metrics includes predicting the statistical moments of each biomarker identifier in a set of biomarker identifiers based on the detected signal intensity. "Statistical moments" in this context have their conventional meaning, which refers to quantitative measures related to the shape of the graph of a function. As understood in the art, the first moment is the mean, the second is the variance, the third is the skewness, and the fourth is the kurtosis. In some cases, predicting the statistical moments of each biomarker identifier in a set of biomarker identifiers based on the detected signal intensity includes calculating a matrix. In some cases, predicting the statistical moments includes predicting the covariance matrix of the detected signal intensity. In additional cases, predicting the statistical moments includes predicting the variance-covariance matrix of the detected signal intensity. In other cases, predicting the statistical moments includes predicting the mean matrix of the detected signal intensity.

[0048] The scheme for predicting statistical moments can vary. In some embodiments, predicting statistical moments includes running a simulation. Any suitable simulation scheme can be employed. In certain cases, the simulation is a Monte Carlo simulation. Monte Carlo simulation is described, for example, in Mooney, C. Z. (1997). Monte carlo simulation, the entire content of which is incorporated herein by reference. As understood in the art, Monte Carlo simulation uses repeated random sampling to obtain numerical results. Such a simulation can be used to generate a simulated data set of signal intensities, which can be expected (i.e., associated with an instrument identifier) on a given instrument. Statistical moments can then be calculated from the simulated data set. In certain cases, running the simulation involves parallel computing. In such cases, the methods of the present invention can use multiple processors in combination to perform the simulation. In the case of multiple processors, the number of processors can vary. For example, the simulation can be performed by 2 or more processors, 3 or more processors, 4 or more processors, 5 or more processors, 6 or more processors, 7 or more processors, 8 or more processors, 9 or more processors, and including 10 or more processors.

[0049] In certain versions, the methods of the present invention involve directly calculating the propagation of relevant statistical moments in the forward mode (i.e., from fluorophore to detector) and / or the backward mode (i.e., from detector to fluorophore). Specifically, in the forward mode, the distribution of fluorophore abundance, 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 in each detector. In the backward mode, the noise model is evaluated in the reverse order, i.e., the distribution of the signal in each detector and all other relevant information are used to predict the median and spread of the fluorophore abundance. In some embodiments, the propagation of relevant statistical moments is calculated in the forward mode. In other embodiments, the propagation of relevant statistical moments is calculated in the backward mode. In other embodiments, the propagation of relevant statistical moments is calculated in both the forward mode and the backward mode.

[0050] In an embodiment, predicting the statistical moments of each biomarker identifier in a group of biomarker identifiers includes incorporating the effect of a noise model into the (predicted) detected signal intensity. For example, in the case where predicting statistical moments involves a simulation, the target method includes incorporating an appropriate noise model into the simulation to capture the spread 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 the Gaussian distribution. In an embodiment, the Gaussian noise model may involve a probability function p of a Gaussian random variable z, as follows:

[0051]

[0052] Where z represents the gray level, μ represents the average gray value, and σ represents its standard deviation. In other embodiments, the noise model is a Poisson noise model. Poisson noise, sometimes referred to as "shot noise", describes the fluctuations in the number of detected photons. This light-current fluctuation is proportional to the square root of the average intensity, as follows:

[0053]

[0054] In some embodiments, the method includes incorporating a noise model (i.e., Gaussian noise or Poisson noise). In other embodiments, the method includes incorporating multiple noise models (i.e., Gaussian noise or Poisson noise).

[0055] In some cases, incorporating the effects of the noise model into the detected signal intensity includes obtaining an analytical formula that relates the target moments to the parameters of the noise model. In other words, errors related to spillover can be incorporated to more realistically simulate the noise effects caused by the simultaneous use of multiple fluorescent dyes in a single flow cytometry protocol. For example, after calculating the propagation of statistical moments in the forward and backward directions (e.g., as described above), an analytical model showing the relationship between noise and moments can be obtained. In some cases, the method includes using a characteristic noise tensor (i.e., describing the multilinear relationship between sets of algebraic objects related to a vector space) to predict the diffusion of certain markers. For example, in some cases, the effects of the noise model are incorporated into the detected signal intensity based on the spillover spread matrix (SSM). Spillover is the phenomenon in which the modulated light of particles indicating a specific fluorescent dye is received by one or more detectors not configured to measure that parameter. On the other hand, "spillover diffusion" refers to the error caused by spillover in fluorescence flow cytometry data. In some cases, the spillover diffusion noise is constructive, resulting in a signal intensity higher than the observed signal intensity, while in other cases, the noise is destructive, resulting in a lower intensity. In certain embodiments, the spillover spread matrix shows how the detection of a specific fluorescent dye through its corresponding detector is affected by spillover from other fluorescent dyes. U.S. Patent Application Publication Nos. 2021 / 0239592 and 2021 / 0349004 and Nguyen et al., Cytometry Part A, 83(3), 306-315 describe the spillover spread matrix and its calculation method; the entire disclosure of which is incorporated herein by reference.

[0056] In some embodiments, the spillover spread matrix is calculated by an auto-spreading algorithm. The AutoSpread algorithm was created by Becton Dickinson and is described in U.S. Patent Application Publication No. 2021 / 0349004, and is configured to create a spillover spread matrix (e.g., as described above) without the need to distinguish between flow cytometer populations that are positive and negative for a given fluorophore. AutoSpread characterizes the spread of the detection signal participating in the first fluorophore by including a second fluorophore in the same flow cytometer combination. AutoSpread generates a coefficient for each interaction between the fluorescence detector and the fluorophore, and arranges these coefficients into a matrix similar to the spillover spread matrix described above. In an embodiment, calculating the spillover spread coefficient includes assuming that the fluorescence intensity collected by the fluorescence detector for the negative population of the flow cytometer data is zero, and the corresponding standard deviation is an unknown quantity. In some embodiments, the spillover spread coefficient is calculated as follows:

[0057]

[0058] As shown in Equation 2, SS is the spillover spread coefficient, σ 2 is the standard deviation of the positive population of the fluorescence flow cytometer data, σ0 2 is the estimated standard deviation of the negative population of the fluorescence flow cytometer data, and d is the intensity of the light collected by the fluorescence detector. In some embodiments, when the fluorescence intensity collected by the fluorescence detector for the negative population of the flow cytometer data is assumed to be zero, in order to obtain an estimate of the standard deviation of the negative population of the fluorescence flow cytometer data (σ0 2 ), the spillover spread coefficient is calculated according to a series of linear regressions. The fluorescence flow cytometer data is first classified into quantiles according to the intensity values detected by the fluorescence detector. The number of quantiles defaults to 256, but is adjusted down to at least 8 to ensure that there are a sufficient number of data points in each quantile so that the standard deviation can be reliably estimated. Next, the robust standard deviation of the light emitted from the fluorophore is regressed against the square root of the median intensity of the light detected for each quantile. When the light intensity detected for the negative population is assumed to be zero, the y-intercept of the ordinary least squares fit is used as an estimate of the standard deviation of the negative population of the flow cytometer data. The estimated value of the standard deviation of the light emitted from the fluorophore is used to obtain a new zeroing standard deviation. The zeroing standard deviation of the fluorophore is regressed against the square root of the median fluorescence of each quantile detected by the fluorescence detector. The slope of the ordinary least squares fit is used as the spillover spread coefficient.

[0059] In a selected version, generating a separability metric group includes stabilizing the variance of the detected signal intensity. Compensated or unmixed flow data typically has a large dynamic range, which requires appropriate scaling before calculating statistical distances. Examples include double-exponential scaling or arcsinh (inverse hyperbolic function) scaling. The parameters used in these scaling methods can be determined by a data-driven or model-driven automated process. The data-driven method essentially solves an optimization problem where the objective function is the similarity of the variances of different distributions. The model-driven method bypasses the need for simulated fluorescence datasets and instead attempts to find an analytical relationship between the variance and mean of the compensated data, based on which the variance stabilization transformation can be determined.

[0060] After generating the separability metric group, the result is a table of separability scores for all pairs defined at the gating level. An example of such a table is Table 9, shown in the experimental section below. The method of the present invention further includes aggregating the separability metric group into a combined score. As described herein, a "combined score" is a metric that provides a measure of the suitability of a given combination of fluorescent dyes for a particular flow cytometry protocol, the particular flow cytometry protocol being performed using a particular instrument formulated with separability metrics. An aggregation procedure is needed to aggregate the table of separability scores into a combined score. In some embodiments, the combined score is a scalar (e.g., in embodiments involving single-objective optimization). In other cases, the combined score is a vector (e.g., in embodiments involving multi-objective optimization). In certain cases, aggregating the separability metric group into a combined score includes negating the value of the lowest separability score. In some such cases, the negated value can be used as the combined score. In some cases, the method includes using the number of pairs whose scores are below a particular threshold as the objective function. In some cases, determining the combined score includes calculating vectors of the separability metric groups aggregated for population-marker pairs and for quantitative pairs. In some such cases, the method includes separating quantitative markers and classification markers, using different aggregation strategies for each, and using a two-element vector as the combined score.

[0061] After defining the combined score, it can be used as an objective function in the process of combinatorial optimization. Thus, in an embodiment, the method includes optimizing a combination of fluorescent dyes based on an evaluation of the suitability of the combination of fluorescent dyes used to generate flow cytometry data, i.e., such that the combination of fluorescent dyes is suitable for a flow cytometry protocol. In some embodiments, optimizing the combination of fluorescent dyes includes the use of a combinatorial optimization algorithm. In some cases, the combinatorial optimization algorithm is a constrained optimization algorithm. "Constrained optimization" as referred to herein describes the process of optimizing variables where the variables are subject to constraints in their conventional sense. Any suitable constrained optimization method can be employed. Examples of constrained optimization techniques that can be employed include, but are not limited to, local search, local repair, backtracking and constraint propagation, stochastic restart hill climbing, and tabu search. In some cases, these can be combined with minimization techniques such as simulated annealing and genetic (evolutionary) algorithms. In some cases, the combination of fluorescent dyes described herein can be optimized in combination with the optimization scheme 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 an embodiment, the variable being optimized is the combined score. For problems with a small search space, techniques that intelligently traverse the search space can be used. Examples include, but are not limited to, dynamic programming and depth-first search or breadth-first search. For problems with a search space that is too large to traverse, heuristic search techniques such as greedy algorithms, genetic algorithms, and the algorithms described in U.S. Provisional Patent Application No. 63 / 305,010, filed January 31, 2022 (Attorney Docket No. BECT-310PRV (P-26714)) can be used.

[0062] In some cases, optimizing a fluorescent dye combination includes adjusting the fluorescent dyes in the fluorescent dye combination and evaluating the suitability of the adjusted fluorescent dye combination for generating flow cytometry data. "Adjusting" the fluorescent dyes in the fluorescent dye combination means swapping out a fluorescent dye or its associated fluorescent dye identifier for a different fluorescent dye. One or more than one fluorescent dye in the combination can be adjusted at any given time. In some cases, the method includes swapping out a single fluorescent dye in the combination at a given time. In certain cases, optimizing a fluorescent dye combination includes maintaining the fluorescent dye combination at a constant number. In other words, even if one or more than one fluorescent dye is adjusted, the number of fluorescent dyes in the resulting fluorescent dye combination does not change. For example, an evaluated fluorescent dye combination having N fluorescent dyes will continue to contain N fluorescent dyes after adjustment. In certain cases, the fluorescent dyes in the fluorescent dye combination are not replaced by fluorescent dyes already present in the fluorescent dye combination. After generating an adjusted fluorescent dye combination, the target method further includes evaluating the adjusted fluorescent dye combination (e.g., as described above). The target method also includes comparing the evaluation of the first fluorescent dye combination with the evaluation of the adjusted fluorescent dye combination. For example, the method can include determining which fluorescent dye combination, among the first fluorescent dye combination and the adjusted fluorescent dye combination, produces an optimized measure of resolvability through a representative of the combination score.

[0063] In some cases, the method includes iteratively adjusting the fluorescent dye combination and evaluating the suitability of each iteratively adjusted fluorescent dye combination. In an embodiment, any fluorescent dye combination that is evaluated as having a higher combination score, among the first fluorescent dye combination and the adjusted fluorescent dye combination, can be used as a seed for the next stage of the iterative process. A "seed" means a fluorescent dye combination that is determined to be associated with a higher combination score compared to one or more than one slightly modified fluorescent dye combinations in one iteration of the method. In some embodiments, the iterative process itself is repeated until a condition is met. Any suitable condition can be employed to terminate the iterative process. In some cases, the iterative process terminates after a certain runtime. In other cases, the iterative process terminates when the evaluations generated for each iteratively adjusted fluorescent dye combination converge. In other words, the iterative process terminates when only minor differences in the combination scores are observed between subsequent fluorescent dye combinations.

[0064] In some embodiments, the method further includes generating a visualization of the evaluated suitability of the fluorescent dye combination for generating flow cytometry data. Any suitable visualization can be employed. In some embodiments, the visualization includes a flow cytometry data plot simulated based on a given fluorescent dye combination. In other words, the visualization will include exemplary flow cytometry data that would be generated when a sample is run on a particular instrument with a particular fluorescent dye combination.

[0065] Figure 1A A flowchart depicting an embodiment of the described method is shown. In step 101, a biological hypothesis, an antibody panel (i.e., a list of biomarkers), a gating strategy, an initial fluorescent dye combination, and an instrument identifier (i.e., instrument configuration) are received. After receiving the initial fluorescent dye combination, the method includes evaluating the fluorescent dye combination (step 110) by defining target marker pairs in step 112, evaluating resolvability in step 113, and aggregating resolvability metrics into a combined score in step 114. In Figure 1A an embodiment, defining target marker pairs (step 112) includes decomposing the gating hierarchy in step 112a, adding latent populations in step 112b, and defining quantitative pairs in step 112c. Evaluating the resolvability of populations in step 113 also includes predicting statistical moments in step 113a, stabilizing variance in step 113b, and calculating statistical distances in 113c. After generating the combined score in step 114, an optimized fluorescent dye combination (step 120) can be obtained by running an optimization process (step 121) to create a combination with good resolvability performance, which is output in step 122.

[0066] Figure 1B An alternative description of the method is shown. The method begins with receiving an initial fluorescent dye combination in step 101. Next, a combined score is generated (refer to Figure 1A the above-described step 110). In the combination optimization process 121, it is determined in step 123 whether the combined score generated in step 110 is optimized. Whether the combination is optimized can be determined based on criteria provided by the user (e.g., a threshold). If it is optimized, the optimized combined score is output to the user. If not, the fluorescent dye combination is adjusted. Then the adjusted fluorescent dye combination is provided to the combined score generation process 110. This process can be repeated until an optimized fluorescent dye is determined.

[0067] The subject fluorescent dye combination can include any suitable combination of fluorescent dyes. According to certain embodiments, the target fluorescent dye has an excitation maximum in the range of 100 nm to 800 nm, such as 150 nm to 750 nm, such as 200 nm to 700 nm, such as 250 nm to 650 nm, such as 300 nm to 600 nm, and including 400 nm to 500 nm. According to certain embodiments, the target fluorescent dye has an excitation maximum in the range of, for example, 400 nm to 1000 nm, such as 450 nm to 950 nm, such as 500 nm to 900 nm, such as 550 nm to 850 nm, and including 600 nm to 800 nm. In some cases, the fluorescent dye is a luminescent dye, such as a fluorescent dye having a peak emission wavelength of 200 nm or more than 200 nm, such as 250 nm or more than 250 nm, such as 300 nm or more than 300 nm, such as 350 nm or more than 350 nm, such as 400 nm or more than 400 nm, such as 450 nm or more than 450 nm, such as 500 nm or more than 500 nm, such as 550 nm or more than 550 nm, such as 600 nm or more than 600 nm, such as 650 nm or more than 650 nm, such as 700 nm or more than 700 nm, such as 750 nm or more than 750 nm, such as 800 nm or more than 800 nm, such as 850 nm or more than 850 nm, such as 900 nm or more than 900 nm, such as 950 nm or more than 950 nm, such as 1000 nm or more than 1000 nm, and including 1050 nm or more than 1050 nm. For example, the fluorescent dye can be a fluorescent dye having a peak emission wavelength of 200 nm to 1200 nm, such as 300 nm to 1100 nm, such as 400 nm to 1000 nm, such as 500 nm to 900 nm, and including a fluorescent dye having a peak emission wavelength of 600 nm to 800 nm.

[0068] Target fluorescent dyes can include, but are not limited to: boron dipyrromethene (BODIPY) dyes, coumarin dyes, rhodamine dyes, acridine dyes, anthraquinone dyes, arylmethane dyes, diarylmethane dyes, chlorophyll-containing dyes, triarylmethane dyes, azo dyes, diazo dyes, nitro dyes, nitroso dyes, phthalocyanine dyes, cyanamide dyes, asymmetric cyanamide dyes, quinoneimine dyes, azine dyes, diaminoazine dyes, safranin dyes, indamin dyes, indophenol dyes, fluorine dyes, oxazine dyes, oxazone dyes, thiazine dyes, thiazole dyes, xanthene dyes, fluorene dyes, pyronin dyes, fluorine dyes, rhodamine dyes, phenanthridine dyes, squarylium dyes, boron dipyrromethene dyes, squarylium roxitanes dyes, naphthalene dyes, coumarin dyes, oxadiazole dyes, anthracene dyes, pyrene dyes, acridine dyes, arylmethylene dyes or tetrapyrrole dyes and combinations thereof. In certain embodiments, the conjugate can include two or more dyes, such as two or more dyes selected from the group consisting of: boron dipyrromethene dyes, coumarin dyes, rhodamine dyes, acridine dyes, anthraquinone dyes, arylmethane dyes, diarylmethane dyes, chlorophyll-containing dyes, triarylmethane dyes, azo dyes, diazo dyes, nitro dyes, nitroso dyes, phthalocyanine dyes, cyanamide dyes, asymmetric cyanamide dyes, quinoneimine dyes, azine dyes, diaminoazine dyes, safranin dyes, indamin dyes, indophenol dyes, fluorine dyes, oxazine dyes, oxazone dyes, thiazine dyes, thiazole dyes, xanthene dyes, fluorene dyes, pyronin dyes, fluorine dyes, rhodamine dyes, phenanthridine dyes, squarylium dyes, boron dipyrromethene dyes, squarylium roxitanes dyes, naphthalene dyes, coumarin dyes, oxadiazole dyes, anthracene dyes, pyrene dyes, acridine dyes, arylmethylene dyes or tetrapyrrole dyes and combinations thereof.

[0069] In certain embodiments, the target fluorescent dyes can include, but are not limited to, fluorescein isothiocyanate (FITC), phycoerythrin (PE) dyes, peridinin-chlorophyll-protein-cyanine dyes (such as PerCP-Cy5.5), phycoerythrin-cyanine (PE-Cy) dyes (PE-Cy7), allophycocyanin (APC) dyes (e.g., APC-R700), allophycocyanin-cyanine dyes (such as APC-Cy7), coumarin dyes (e.g., V450 or V500). In certain embodiments, the fluorescent dyes can include one or more of the following: 1,4-bis-(o-methylstyryl)-benzene (bis-MSB 1,4-bis[2-(2-methylphenyl)vinyl]-benzene), C510 dye, C6 dye, Nile Red dye, T614 dye (such as N-[7-(methylsulfonylamino)-4-oxo-6-phenoxybenzopyran-3-yl]formamide), LDS 821 dye ((2-(6-(p-dimethylaminophenyl)-2,4-neopentylglycol-1,3,5-hexatrienyl)-3-ethylbenzothiazolium perchlorate), mFluor dyes (e.g., mFluor Red dyes, e.g., mFluor780NS).

[0070] Target fluorescent dyes may include, but are not limited to, 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, SYTOX Green, TOTO-1, TO-PRO-1, thiazole orange, propidium iodide (PI), LDS 751, 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, Venus, 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 Fluor350, 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 HyPer, etc.

[0071] In some cases, the fluorescent dye combinations include one or more polymeric dyes (e.g., fluorescent polymeric dyes). The polymeric fluorescent dyes that can be used in the subject methods and systems are diverse. In some cases of the methods, the polymeric dyes include conjugated polymers. Conjugated polymers (CPs) are characterized by a delocalized electronic structure that includes a backbone with alternating unsaturated bonds (e.g., double bonds and / or triple bonds) and saturated bonds (e.g., single bonds), where π electrons can move from one bond to another. Thus, the conjugated backbone can give the polymeric dye an extended linear structure with restricted bond angles between the repeating units of the polymer. For example, proteins and nucleic acids are also polymeric, but in some cases do not form extended rod-like structures but instead fold into higher-order three-dimensional shapes. Additionally, CPs can form a "rigid rod" polymer backbone and experience limited torsional (e.g., twisting) angles between the monomer repeating units along the polymer backbone. In some cases, the polymeric dye includes a CP with a rigid rod structure. The structural features of the polymeric dye can affect the fluorescence properties of the molecule.

[0072] Any suitable polymeric dye can be employed in the subject devices and methods. In some cases, the polymeric dye is a multi-chromophore whose structure is capable of capturing light to amplify the fluorescence output of a fluorophore. In some cases, the polymeric dye is capable of capturing light and efficiently converting it to emitted light of a longer wavelength. In some cases, the polymeric dye has a light-capturing multi-chromophore system that can efficiently transfer energy to a nearby luminescent species (such as a "signal chromophore"). Energy transfer mechanisms include, for example, resonance energy transfer (such as Förster (or fluorescence) resonance energy transfer, FRET), quantum charge exchange (Dexter energy transfer), etc. In some cases, the distances for these energy transfer mechanisms are relatively short; that is, the close proximity of the light-capturing multi-chromophore system to the signal chromophore provides efficient energy transfer. Under conditions of efficient energy transfer, the emission of the signal chromophore is amplified when there are a relatively large number of individual chromophores in the light-capturing multi-chromophore system; that is, when the wavelength of the incident light ("excitation light") is absorbed by the light-capturing multi-chromophore system, the emission of the signal chromophore is stronger than when the signal chromophore is directly excited by the pump light.

[0073] The multi-chromophore can be a conjugated polymer. Conjugated polymers (CPs) are characterized by having a delocalized electronic structure and can be used as highly responsive optical reporters for chemical and biological targets. Due to the significant reduction in the effective conjugation length compared to the polymer chain length, the backbone contains a large number of closely linked conjugation segments. Thus, conjugated polymers have a high light-capturing efficiency and can achieve light amplification through Förster energy transfer.

[0074] Target polymeric dyes include, but are not limited to, the dyes described in U.S. Patent 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 entire disclosures of which are incorporated herein by reference; and Gaylord et 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, the entire contents of which are incorporated herein by reference. Specific polymeric dyes that can be employed include, but are not limited to, BD Horizon Brilliant TM Dyes, such as BD Horizon Brilliant TM Violet dyes (e.g., BV421, BV510, BV605, BV650, BV711, BV786); BD Horizon Brilliant TM Ultraviolet dyes (e.g., BUV395, BUV496, BUV737, BUV805); and BD Horizon Brilliant TM Blue dyes (e.g., BB515) (BD Biosciences, San Jose, CA). Any fluorescent dye known to those skilled in the art can be employed in the methods described herein, including but not limited to the fluorescent dyes described above or fluorescent dyes not yet discovered.

[0075] The fluorescent dyes in the subject fluorescent dye combination and / or the reference fluorescent dyes in the spectral matrix may or may not be coupled to a biomolecule (such as a biopolymer). The biopolymer can be a biopolymer. A "biopolymer" is a polymer of one or more types of repeating units. Biopolymers are typically present in biological systems and specifically include polysaccharides (such as carbohydrates), peptides (using this term to include polypeptides and proteins, whether or not attached to a polysaccharide), and polynucleotides and their analogs, such as compounds composed of amino acid analogs or non-amino acid groups or nucleotide analogs or non-nucleotide groups or compounds containing these substances. This includes polynucleotides in which the conventional backbone is replaced by a non-naturally occurring or synthetic backbone, and nucleic acids (or synthetic or naturally occurring analogs) in which one or more conventional bases are replaced by groups (natural or synthetic) capable of participating in Watson-Crick type hydrogen bond interactions. Polynucleotides include single-stranded or multi-stranded configurations, in which one or more strands may or may not be fully aligned with another strand. Specifically, regardless of source, "biopolymer" includes DNA (including cDNA), RNA, and oligonucleotides. Thus, biomolecules can include polysaccharides, nucleic acids, and polypeptides. For example, the nucleic acid can be an oligonucleotide, truncated DNA, or truncated RNA or full-length DNA or full-length RNA. In an embodiment, the oligonucleotide, truncated DNA, or truncated RNA, and full-length DNA or full-length RNA are composed of 10 or more nucleic acid monomers, such as 15 or more, such as 25 or more, such as 50 or more, such as 100 or more, such as 250 or more, and including 500 or more nucleotide monomers. For example, the length of the target oligonucleotide, truncated DNA, or truncated RNA, and full-length DNA or full-length RNA can be from 10 nucleotides to 10 8 nucleotides, such as from 10 2 nucleotides to 10 7 nucleotides, including from 10 3 nucleotides to 10 6 nucleotides. In an embodiment, the biopolymer is not a single nucleotide or a short-chain oligonucleotide (such as less than 10 nucleotides). "Full-length" means that the DNA or RNA is a nucleic acid polymer that is 70% or more of its own complete sequence (such as found in nature), 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, including 100% of the full-length sequence of the DNA or RNA (such as found in nature).

[0076] In some cases, the polypeptide can be a truncated protein, truncated enzyme or truncated antibody or a full-length protein, full-length enzyme or full-length antibody. In an embodiment, the polypeptide, truncated protein, truncated enzyme or truncated antibody or full-length protein, full-length enzyme or full-length antibody is composed of 10 or more than 10 amino acid monomers, such as composed of 15 or more than 15, such as 25 or more than 25, such as 50 or more than 50, such as 100 or more than 100, such as 250 or more than 250 and including 500 or more than 500 amino acid monomers. For example, the length of the target polypeptide, truncated protein, truncated enzyme or truncated antibody or full-length protein, full-length enzyme or full-length antibody can be from 10 amino acids to 10 8 amino acids, such as from 10 2 amino acids to 10 7 amino acids, including from 10 3 amino acids to 10 6 amino acids. In an embodiment, the biopolymer is not a single amino acid or a short-chain polypeptide (e.g., less than 10 amino acids). "Full-length" means that the protein, enzyme or antibody is a polypeptide polymer having 70% or more of its own complete sequence (e.g., as found in nature), 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 and including 100% of the full-length sequence of the protein, enzyme or antibody (e.g., as found in nature).

[0077] In some cases, the fluorescent dye is conjugated to a specific binding member. The specific binding member and the fluorescent dye can be conjugated to each other (e.g., covalently linked) at any suitable position of the two molecules through an optional linker. As used herein, the term "specific binding member" refers to one member of a pair of molecules that have binding specificity for each other. The surface of one member of the pair of molecules can have a region or cavity that specifically binds to the surface region or cavity of the other member of the pair of molecules. Thus, the members of this pair of molecules have the property of specifically binding to each other, thereby forming a binding complex. In some embodiments, the affinity between the specific binding members in the binding complex is characterized by a K d (dissociation constant) of 10 -6 M or less than 10 - 6 M, such as 10 -7 M or less than 10 -7 M, including 10 -8 M or less than 10 -8 M, such as 10 -9 M or less than 10 -9 M, 10 -10 M or less than 10- 10 M, 10 -11 M or less than 10 -11 M, 10 -12 M or less than 10 -12 M, 10 -13 M or less than 10 -13 M, 10 -14 M or less than 10 -14 M, including 10 -15 M or less than 10 -15 M. In certain embodiments, the specific binding member specifically binds with high affinity. High affinity means that the binding member is characterized by an apparent K d is 10×10 -9 M or less than 10×10 -9 M, such as 1×10 -9 M or less than 1×10 -9 M, 3×10 -10 M or less than 3×10 -10 M, 1×10 -10 M or less than 1×10 -10 M, 3×10 -11 M or less than 3×10 -11 M, 1×10 -11 M or less than 1×10 -11 M, 3×10 -12 M or less than 3×10 -12 M, or 1×10 -12 M or 1×10 -12 specifically binds with an apparent affinity of M.

[0078] The specific binding member can be proteinaceous. As used herein, the term "proteinaceous" refers to moieties composed of amino acid residues. The protein moiety can be a polypeptide. In some cases, the protein specific binding member is an antibody. In certain embodiments, the protein specific binding member is an antibody fragment, such as 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 composed of one or more polypeptides that are encoded substantially by all or part of the recognized immunoglobulin genes. For example, in humans, the recognized immunoglobulin genes include the kappa (κ), lambda (λ), and heavy chain loci, which together make up a large number of variable region genes as well as the constant region genes mu (μ), delta (δ), gamma (γ), epsilon (ε), and alpha (α), which encode the IgM isotype, IgD isotype, IgG isotype, IgE isotype, and IgA isotype, respectively. The immunoglobulin light chain variable region or heavy chain variable region consists of "framework" regions (FRs) interrupted by three hypervariable regions, which are also known as "complementary determining regions" or "CDRs". The ranges of the framework regions and CDRs have been precisely defined (see "Sequences of Proteins of Immunological Interest," E. Kabat et al., U.S. Department of Health and Human Services, (1991)). The framework region sequences of different light or heavy chains within a species are relatively conserved. The framework regions of an antibody, i.e., the framework regions that make up the combination of the light and heavy chains, are used to position and align the CDRs. The CDRs are primarily responsible for binding to the epitope of the antigen. The term "antibody" refers to including full-length antibodies and can refer to natural antibodies from any organism, engineered antibodies, or recombinantly produced antibodies for experimental, therapeutic, or other purposes further defined below. Target antibody fragments include, but are not limited to, Fab, Fab', F(ab')2, Fv, scFv, or other antigen-binding sequences of an antibody, and these antibody fragments can be produced by modifying the whole antibody or can be synthesized de novo using recombinant DNA techniques. Antibodies can be monoclonal or polyclonal and can have other specific activities against cells (such as antagonists, agonists, neutralizing antibodies, inhibitory antibodies, or stimulatory antibodies). It should be understood that antibodies can have additional conservative amino acid substitutions that have substantially no effect on 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 its binding fragment. In certain cases, the specific binding member is a recombinant antibody or its binding fragment.

[0079] As described above, the target biomarkers of the subject method 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, CD46, CD47, CD48, CD49a, CD49b, CD49c, CD49d, CD49e, CD49f, CD50, CD51, CD52, CD53, CD54, CD55, CD56, CD57, CD58, CD59, CD60a, CD60b, CD60c, CD61, CD62E, CD62L, CD62P, CD63, CD64a, CD65, CD65s, CD66a, CD66b, CD66c, CD66d, CD66e, CD66f, CD68, CD69, CD70, CD71, CD72, CD73, CD74, CD75, CD75s, CD77, CD79A, CD79B, CD80, CD81, CD82, CD83, CD84, CD85A, CD85B, CD85C, CD85D, CD85F, CD85G, CD85H, CD85I, CD85J, CD85K, CD85M, CD86, CD87, 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, CD255CD256, 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.

[0080] In an embodiment, the fluorescent dye combination is used to analyze a sample. In some cases, the sample being analyzed is a biological sample. The term "biological sample" in its conventional sense refers to a whole organism, plant, fungus, or a subset of animal tissue, cells, or components, which in some cases can be found in blood, mucus, lymph fluid, synovial fluid, cerebrospinal fluid, saliva, bronchoalveolar lavage fluid, amniotic fluid, amniotic cord blood, urine, vaginal fluid, and semen, etc. Thus, a "biological sample" refers to a natural organism or a subset of its tissues, as well as homogenates, lysates, or extracts prepared from a subset of an organism or its tissues, including but not limited to, for example, plasma, serum, spinal fluid, lymph fluid, skin sections, respiratory sections, gastrointestinal sections, cardiovascular sections, and urogenital tracts, tears, saliva, milk, blood cells, tumors, organs. A biological sample can be any type of organic tissue, including healthy tissue and diseased tissue (such as cancerous tissue, malignant tissue, necrotic tissue, etc.). In certain embodiments, the biological sample is a liquid sample such as blood or its derivatives, for example, plasma, tears, urine, semen, etc., where in some cases, the sample is a blood sample, which includes whole blood, such as blood obtained by venipuncture or finger prick (the blood is not necessarily mixed with any reagents such as preservatives, anticoagulants, etc. before analysis).

[0081] In certain embodiments, the source of the sample is a "mammal" or "mammalian", where these terms are widely used to describe organisms within the class Mammalia, including Carnivora (such as dogs and cats), Rodentia (such as mice, guinea pigs, and rats), and Primates (such as humans, chimpanzees, and monkeys). In some cases, the subject is a human. The method can be used for samples from human subjects of both genders and at any stage of development (i.e., neonate, infant, juvenile, adolescent, adult), where in certain embodiments, the human subject is a juvenile, adolescent, or adult. Although the present invention is applicable to samples from human subjects, it should be understood that the method is also applicable to samples from other animal subjects (i.e., "non-human subjects"), such as but not limited to birds, mice, rats, dogs, cats, livestock, and horses.

[0082] The fluorescent dyes in the fluorescent dye combination are configured to target different types of cells (e.g., via antibodies that target such cells). Multiple cell types can be characterized using the subject methods. Target cells 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 include cells having appropriate cell surface markers or antigens that can be captured or labeled for the antigen by an appropriate affinity reagent or its conjugate. For example, target cells can include 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 α / β, T cell receptor γ / δ, 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, Treg cells, antigen-specific T cell populations, tumor cells, or hematopoietic progenitor cells (CD34+) from whole blood, bone marrow, or cord blood.

[0083] In certain embodiments, the fluorescent dye combinations identified by the present method can be used in flow cytometry protocols (e.g., to analyze a sample as described above). In practicing this method, the sample (e.g., in the flow stream of a flow cytometer) is irradiated with light from a light source. In some embodiments, the light source is a broadband light source that emits light having a wide wavelength range, such as a wavelength span of 50 nm or greater than 50 nm, such as 100 nm or greater than 100 nm, such as 150 nm or greater than 150 nm, such as 200 nm or greater than 200 nm, such as 250 nm or greater than 250 nm, such as 300 nm or greater than 250 nm, such as 350 nm or greater than 350 nm, such as 400 nm or greater than 400 nm and including a span of 500 nm or greater than 500 nm. For example, a suitable broadband light source emits light having a wavelength of 200 nm to 1500 nm. Another example of a suitable broadband light source includes a light source that emits light having a wavelength of 400 nm to 1000 nm. When the method includes irradiation with a broadband light source, the target broadband light source protocols can include but are not limited to halogen lamps, deuterium arc lamps, xenon arc lamps, stabilized fiber-coupled broadband light sources, continuous spectrum broadband LEDs, superluminescent light-emitting diodes, semiconductor light-emitting diodes, broadband LED white light sources, multi-LED integrated white light sources, other broadband light sources, or any combination thereof.

[0084] In other embodiments, the method of the embodiments of the present invention includes irradiation using a narrowband light source that emits a specific wavelength or a narrow wavelength range, such as a light source that emits light within a narrow wavelength range, such as 50 nm or less than 50 nm, such as 40 nm or less than 40 nm, such as 30 nm or less than 30 nm, such as 25 nm or less than 25 nm, such as 20 nm or less than 20 nm, such as 15 nm or less than 15 nm, such as 10 nm or less than 10 nm, such as 5 nm or less than 5 nm, such as 2 nm or less than 2 nm and including a light source that emits light of a specific wavelength (i.e., monochromatic light). When the method includes irradiation using a narrowband light source, the target narrowband light source protocols can include but are not limited to narrow wavelength LEDs, laser diodes, or a broadband light source coupled with one or more optical bandpass filters, diffraction gratings, monochromators, or any combination thereof.

[0085] In some embodiments, the method includes irradiating a sample with one or more lasers. As described above, the type and number of lasers will vary depending on the sample and the desired collected light, and can be gas lasers such as helium-neon lasers, argon lasers, krypton lasers, xenon lasers, nitrogen lasers, CO2 lasers, CO lasers, argon fluoride (ArF) excimer lasers, krypton fluoride (KrF) excimer lasers, xenon chloride (XeCl) excimer lasers or xenon fluoride (XeF) excimer lasers or combinations thereof. In other cases, the method includes irradiating a flowing stream with a dye laser such as a stilbene laser, a coumarin laser or a rhodamine laser. In other cases, the method includes irradiating a flowing stream 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 other cases, the method includes irradiating a flowing stream 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 thulium YAG laser, a ytterbium YAG laser, a Yb2O3 laser or a cerium-doped laser and combinations thereof.

[0086] The sample can be irradiated with one or more of the above light sources, such as 2 or more light sources, such as 3 or more light sources, such as 4 or more light sources, such as 5 or more light sources and including 10 or more light sources. The light sources can include any combination of light source types. For example, in some embodiments, the method includes irradiating a sample in a flowing stream with a laser array, such as an array having one or more gas lasers, one or more dye lasers and one or more solid-state lasers.

[0087] The sample can be irradiated at wavelengths from 200 nm to 1500 nm, such as from 250 nm to 1250 nm, such as from 300 nm to 1000 nm, such as from 350 nm to 900 nm and including from 400 nm to 800 nm. For example, when the light source is a broadband light source, the sample can be irradiated at wavelengths from 200 nm to 900 nm. In other cases, when the light source includes a plurality of narrowband light sources, the sample can be irradiated at specific wavelengths from 200 nm to 900 nm. For example, the light source can be a plurality of narrowband LEDs (1 nm to 25 nm), each of which can independently emit light having a wavelength between 200 nm and 900 nm. In other embodiments, the narrowband light source includes one or more lasers (such as a laser array) and irradiates the sample at a specific wavelength from 200 nm to 700 nm, such as the laser array having a gas laser, an excimer laser, a dye laser, a metal vapor laser, and a solid-state laser as described above.

[0088] In the case of using more than one light source, the sample can be irradiated with the light source or a combination thereof simultaneously or sequentially. For example, each light source can be used to irradiate the sample simultaneously. In other embodiments, each light source is used to irradiate the flowing stream sequentially. When irradiating the sample sequentially with more than one light source, the irradiation time of each light source on the sample can independently be 0.001 microseconds or greater than 0.001 microseconds, such as 0.01 microseconds or greater than 0.01 microseconds, such as 0.1 microseconds or greater than 0.1 microseconds, such as 1 microseconds or greater than 1 microseconds, such as 5 microseconds or greater than 5 microseconds, such as 10 microseconds or greater than 10 microseconds, such as 30 microseconds or greater than 30 microseconds and including 60 microseconds or greater than 60 microseconds. For example, the method can include irradiating the sample with a light source (such as a laser) for a duration of 0.001 microseconds to 100 microseconds, such as 0.01 microseconds to 75 microseconds, such as 0.1 microseconds to 50 microseconds, such as 1 microseconds to 25 microseconds and including 5 microseconds to 10 microseconds. In embodiments where the sample is irradiated sequentially with two or more light sources, the irradiation duration of each light source on the sample can be the same or different.

[0089] The time interval between irradiations of each light source can also be varied as needed, with a delay of 0.001 microseconds or greater than 0.001 microseconds, such as 0.01 microseconds or greater than 0.01 microseconds, such as 0.1 microseconds or greater than 0.1 microseconds, such as 1 microsecond greater than 1 microsecond, such as 5 microseconds or greater than 5 microseconds, such as 10 microseconds or greater than 10 seconds, such as 15 microseconds or greater than 15 seconds, such as 30 microseconds or greater than 30 microseconds, and including 60 microseconds or greater than 60 microseconds, independently separated. For example, the time interval between irradiations of each light source can be from 0.001 microseconds to 60 microseconds, such as from 0.01 microseconds to 50 microseconds, such as from 0.1 microseconds to 35 microseconds, such as from 1 microseconds to 25 microseconds, and including from 5 microseconds to 10 microseconds. In certain embodiments, the time interval between irradiations of each light source is 10 microseconds. In embodiments where more than two (i.e., 3 or more) light sources are used for sequential irradiations, the delay between irradiations of each light source can be the same or different.

[0090] The sample can be irradiated continuously or at discontinuous intervals. In some cases, the method includes continuously irradiating the sample in the sample using a light source. In other cases, the sample is irradiated using a light source at discontinuous intervals, such as every 0.001 milliseconds, every 0.01 milliseconds, every 0.1 milliseconds, every 1 millisecond, every 10 milliseconds, every 100 milliseconds, including every 1000 milliseconds or at other intervals.

[0091] The distance for irradiating the sample can vary depending on the light source, such as a distance of 0.01 mm or greater than 0.01 mm, such as 0.05 mm or greater than 0.05 mm, such as 0.1 mm or greater than 0.1 mm, such as 0.5 mm or greater than 0.5 mm, such as 1 mm or greater than 1 mm, such as 2.5 mm or greater than 2.5 mm, such as 5 mm or greater than 5 mm, such as 10 mm or greater than 10 mm, such as 15 mm or greater than 15 mm, such as 25 mm or greater than 25 mm, and including 50 mm or greater than 50 mm. Additionally, the angle of irradiation can also be varied, with the angle being from 10° to 90°, such as from 15° to 85°, such as from 20° to 80°, such as from 25° to 75°, and including from 30° to 60°, such as 90°.

[0092] In an embodiment, the light from irradiating the sample is transmitted to a light detection system and measured using one or more photodetectors. When practicing the subject method, the light from the sample is transmitted to three or more wavelength separators, each of which is configured to pass light having a predetermined spectral range. The light of the spectral range from each wavelength separator is transmitted to one or more light detection modules, which are configured to have optical elements that can transmit light having a predetermined sub-spectral range to the photodetector.

[0093] Light can be measured continuously or at discrete intervals using an optical detection system. In some cases, the method includes continuously measuring the light. In other cases, the light is measured at discrete intervals, such as every 0.001 milliseconds, every 0.01 milliseconds, every 0.1 milliseconds, every 1 millisecond, every 10 milliseconds, every 100 milliseconds, and including every 1000 milliseconds, or at other intervals.

[0094] During implementation of the subject methods, one or more than one measurement may be made of the collected light, such as 2 or more than 2, 3 or more than 3, 5 or more than 5, and including 10 or more than 10. In certain embodiments, two or more than two measurements are made of the light propagation, and in some cases, the data is averaged.

[0095] In certain embodiments, the method includes conditioning the light prior to detecting the light using the subject optical detection system. For example, light from a sample source may pass through one or more than one lens, mirror, pinhole, slit, grating, light refractor, and any combination thereof. In some cases, the collected light passes through one or more than one focusing lens, such as to reduce the profile of the light directed to the optical detection system or the optical collection system as described above. In other cases, light emitted from the sample passes through one or more than one collimator to reduce the beam divergence transmitted to the optical detection system.

[0096] System for evaluating a combination of fluorescent dyes

[0097] Aspects of the present invention further include a system configured to perform the methods described above. The target system includes a processor configured to evaluate the suitability of a combination of fluorescent dyes for a flow cytometry protocol. In an embodiment, the subject processor is operated in conjunction with programmable logic, which can be implemented in hardware, software, firmware, or any combination thereof, to evaluate the combination of fluorochromes. For example, in the case where the programmable logic is implemented in software, the evaluation of the combination of fluorescent dyes can be implemented at least in part by a computer-readable data storage medium including program code, the program code including instructions that, when executed, can receive an initial combination of fluorescent dyes, the initial combination of fluorescent dyes including a set of fluorescent dye identifiers and a set of biomarker identifiers, where each fluorescent dye identifier refers to a fluorescent dye in the set of fluorescent dyes, where each biomarker identifier is associated with a fluorescent dye identifier in the set of fluorescent dye identifiers; a plurality of population identifiers, where each population identifier refers to a population of particles; and an instrument identifier. The processor is further configured to create a set of population-biomarker pairs by associating each population identifier in the plurality of population identifiers with a biomarker identifier from the set of biomarker identifiers, and generate a set of separability metrics, each separability metric predicting a measure of the statistical distance between populations of particles in a flow cytometer data space. As discussed above, the measure of each statistical distance is related to the detected signal intensity generated by each fluorescent dye associated with each population-biomarker pair, the fluorescent dye being used in a flow cytometry protocol for an instrument associated with the instrument identifier. The processor described herein is further configured to aggregate the separability metrics into a combined score and evaluate the combined score to evaluate the suitability of the initial combination of fluorescent dyes for a flow cytometry protocol.

[0098] The processor can also be configured to optimize the combination of fluorescent dyes based on an evaluation of the suitability of the combination of fluorescent dyes used to generate flow cytometer data. As discussed above, the combination optimization algorithm for optimizing the combination of fluorescent dyes includes, but is not limited to, constraint optimization methods. In some embodiments, the processor is configured to generate a visualization of the evaluated suitability of the combination of fluorescent dyes used to generate flow cytometer data. In some such embodiments, the system includes a display configured to depict the visualization. Any suitable display can be employed. The display can include, but is not limited to, a monitor, a tablet computer, a smartphone, or other electronic device configured to have a graphical interface.

[0099] The subject programmable logic can 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, or the like. In some embodiments, the programmable logic can be executed by a specially programmed processor, which can include one or more than one processor, such as one or more than one digital signal processor (DSP), a configurable microprocessor, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other equivalent integrated logic circuits or discrete logic circuits. A combination of computing devices, such as a combination of a DSP and a microprocessor, multiple microprocessors, a combination of one or more than one microprocessor and a DSP core, or any other such configuration in at least partial data communication, can implement one or more than one feature described in the present invention.

[0100] In some cases, the system is a particle analyzer or includes a particle analyzer. The target particle analyzer can include a flow chamber for transporting particles in a flowing stream, a light source for irradiating the flowing stream particles at an interrogation 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.

[0101] As described in the present invention, a "flow chamber" in its conventional sense refers to an element, such as a small glass tube, which contains a flow channel having a liquid flowing stream for transporting particles in a sheath fluid. The target small glass tube includes a container having a channel therethrough. The flowing stream can include a liquid sample injected from a sample tube. The target flow chamber includes a light-transmissive flow channel. In some cases, the flow chamber includes a transparent material (such as quartz) that allows light to pass through. In some embodiments, the flow chamber is an air-flow type flow chamber, in which the optical interrogation of particles occurs outside the flow chamber (i.e., within free space).

[0102] In some cases, the flowing stream is configured to be irradiated with light from a light source at an interrogation point. The flowing stream configured with a flow channel can include a liquid sample injected from a sample tube. In certain embodiments, the flowing stream can include a narrow and fast-flowing liquid stream, and its arrangement can enable linearly separated particles transported in the liquid stream to be separated from each other in a single-file manner. The "interrogation point" discussed herein refers to a region in the flow chamber where particles are irradiated with light from a light source, for example for analysis. The size of the interrogation point can vary as needed. For example, in the case where 0 μm represents the axis of the light emitted by the light source, the interrogation point can be -100 μm to 100 μm, such as -50 μm to 50 μm, such as -25 μm to 40 μm, and including -15 μm to 30 μm.

[0103] After the particles are irradiated in the flow cell, particle-modulated light can be observed. "Particle-modulated light" refers to the light received from the particles in the flowing stream after irradiating 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 the light refracted and reflected from the particle surface and internal structure. In additional embodiments, the particle-modulated light includes forward-scattered light (i.e., light that mainly passes through or around the particle along the forward direction). In other cases, the particle-modulated light includes fluorescence (i.e., light emitted from a fluorescent dye after irradiation with light at an excitation wavelength).

[0104] As described above, aspects of the present invention also include a light source configured to irradiate particles passing through the flow cell at an interrogation point. The light source described herein can employ any suitable light source. In some embodiments, the light source is a laser. In an embodiment, the laser can be any suitable 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 CO2 laser, a CO laser, an argon fluoride (ArF) excimer laser, a krypton fluoride (KrF) excimer laser, a xenon chloride (XeCl) excimer laser, or a xenon fluoride (XeF) excimer laser or a combination thereof. In other cases, the subject flow cytometer includes a dye laser, such as a stilbene, coumarin, or rhodamine laser. In other cases, the target laser includes 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 other cases, the flow cytometer includes 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 thulium YAG laser, a ytterbium YAG laser, a Yb2O3 laser, or a cerium-doped laser and combinations thereof.

[0105] The laser light source of certain embodiments may also include one or more optical adjustment elements. In certain embodiments, the optical adjustment element is located between the light source and the flow cell and may include any device capable of changing the spatial width of the illumination or some other characteristic of the illumination from the light source, such other characteristics as illumination direction, wavelength, beam width, beam intensity, and focus. The optical adjustment scheme may include any suitable device for adjusting one or more characteristics of the light source, including but not limited to lenses, mirrors, filters, optical fibers, wavelength splitters, pinholes, slits, collimation schemes, and combinations thereof. In certain embodiments, the target flow cytometer includes one or more focusing lenses. An example of a focusing lens may be a reducing lens. In other embodiments, the target flow cytometer includes an optical fiber.

[0106] In cases where the optical adjustment element is configured as a movable optical adjustment element, the optical adjustment element may be configured to move continuously or at discontinuous intervals, for example, in increments of 0.01 μm or greater than 0.01 μm, such as 0.05 μm or greater than 0.05 μm, such as 0.1 μm or greater than 0.1 μm, such as 0.5 μm or greater than 0.5 μm, such as 1 μm or greater than 1 μm, such as 10 μm or greater than 10 μm, such as 100 μm or greater than 100 μm, such as 500 μm or greater than 500 μm, such as 1 mm or greater than 1 mm, such as 5 mm or greater than 5 mm, such as 10 mm or greater than 10 mm, and including increments of 25 mm or greater than 25 mm.

[0107] Any displacement scheme may be employed to move the optical adjustment element structure, such as being coupled to a movable support stage or directly to a motor-driven translation stage, a lead screw translation assembly, a gear-type translation device, for example, using a stepper motor, a servo motor, a brushless motor, a brushed DC motor, a micro stepper drive motor, a high-resolution stepper motor, and other types of motors.

[0108] The light source may be positioned at any suitable distance from the flow cell, for example, the light source is separated from the flow cell by 0.005 mm or greater than 0.005 mm, such as 0.01 mm or greater than 0.01 mm, such as 0.05 mm or greater than 0.05 mm, such as 0.1 mm or greater than 0.1 mm, such as 0.5 mm or greater than 0.5 mm, such as 1 mm or greater than 1 mm, such as 5 mm or greater than 5 mm, such as 10 mm or greater than 10 mm, such as 25 mm or greater than 25 mm, and including a distance of 100 mm or greater than 100 mm. In addition, the light source may be positioned at any suitable angle relative to the flow cell, for example, an angle of 10 degrees to 90 degrees, such as 15 degrees to 85 degrees, such as 20 degrees to 80 degrees, such as 25 degrees to 75 degrees, and including an angle of 30 degrees to 60 degrees, such as 90 degrees.

[0109] In some embodiments, the target light source includes a plurality of lasers configured to provide discrete illumination for a flowing stream, such as 2 lasers or more than 2 lasers configured to provide discrete illumination for a flowing stream, such as 3 lasers or more than 3 lasers, such as 4 lasers or more than 4 lasers, such as 5 lasers or more than 5 lasers, such as 10 lasers or more than 10 lasers, and including 15 lasers or more than 15 lasers. Depending on the wavelength of the light required for illuminating the flowing stream, the specific wavelength of each laser can vary between 200 nm and 1500 nm, such as between 250 nm and 1250 nm, such as between 300 nm and 1000 nm, such as between 350 nm and 900 nm, and including between 400 nm and 800 nm. In certain embodiments, the target lasers can include one or more of a 405 nm laser, a 488 nm laser, a 561 nm laser, and a 635 nm laser.

[0110] As described above, the target particle analyzer can also include one or more particle modulation light detectors for detecting particle modulation light intensity data. In some embodiments, the one or more particle modulation light detectors include one or more forward scatter light detectors configured to detect forward scatter light. For example, the particle analyzer can include 1 forward scatter light detector or multiple forward scatter light detectors, such as 2 or more than 2, such as 3 or more than 3, such as 4 or more than 4, and including 5 or more than 5. In certain embodiments, the particle analyzer includes 1 forward scatter light detector. In other embodiments, the particle analyzer includes 2 forward scatter light detectors.

[0111] Any suitable detector for detecting the collected light can be employed in the forward scatter light detectors described herein. The target detector can include, but is not limited to, an optical sensor or an optical detector, such as an active pixel sensor (APS), an avalanche photodiode, an image sensor, a charge-coupled device (CCD), an intensified charge-coupled device (ICCD), a light-emitting diode, a photon counter, a bolometer, a pyroelectric detector, a photoresistor, a photovoltaic cell, a photodiode, a photomultiplier tube (PMT), a phototransistor, a quantum dot photoconductor or photodiode and combinations thereof, and other detectors. In certain embodiments, the collected light is measured using 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 photomultiplier tube, such as having an active detection surface area of 0.01 cm 2 to 10 cm 2 、such as 0.05 cm 2from 0 to 9 cm 2 e.g., 0.1 cm 2 from 0 to 8 cm 2 e.g., 0.5 cm 2 from 0 to 7 cm 2 and including 1 cm 2 from 1 to 5 cm 2 and a photomultiplier tube including from 1 cm

[0112] In an embodiment, the forward scatter light detector is configured to measure light continuously or at discontinuous time intervals. In some cases, the target detector is configured to continuously measure the collected light. In other cases, the target detector is configured to measure at discontinuous intervals, e.g., every 0.001 ms, every 0.01 ms, every 0.1 ms, every 1 ms, every 10 ms, every 100 ms, and including every 1000 ms or at other intervals to measure light.

[0113] In a further embodiment, the one or more particle modulated light detectors may include one or more side scatter light detectors for detecting the side scatter wavelength of light (i.e., light refracted and reflected from the particle surface and internal structure). In some embodiments, the particle analyzer includes a single side scatter light detector. In other embodiments, the particle analyzer may include a plurality of forward scatter light detectors, e.g., 2 or more, e.g., 3 or more, e.g., 4 or more, and including 5 or more.

[0114] Any suitable detector for detecting the collected light may be employed in the side scatter light detectors described herein. The target detector may include, but is not limited to, an optical sensor or optical detector, such as an active pixel sensor (APS), avalanche photodiode, image sensor, charge-coupled device (CCD), intensified charge-coupled device (ICCD), light emitting diode, photon counter, bolometer, pyroelectric detector, photoresistor, photovoltaic cell, photodiode, photomultiplier tube (PMT), phototransistor, quantum dot photoconductor or photodiode and combinations thereof, and other detectors. In certain embodiments, a charge-coupled device (CCD), semiconductor charge-coupled device (CCD), active pixel sensor (APS), complementary metal oxide semiconductor (CMOS) image sensor, or N-type metal oxide semiconductor (NMOS) image sensor is used to determine the collected light. In certain embodiments, the detector is a photomultiplier tube, e.g., the active detection surface area of each region is 0.01 cm 2 from 0.01 to 10 cm 2 e.g., 0.05 cm 2 from 0.05 to 9 cm 2 e.g., 0.1 cm 2 from 0.1 to 8 cm 2, for example, 0.5 cm 2 to 7 cm 2 and including 1 cm 2 to 5 cm 2 photomultiplier tubes

[0115] In an embodiment, the subject particle analyzer further includes a fluorescence detector configured to detect one or more fluorescence wavelengths of light. In other embodiments, the particle analyzer may include a plurality of fluorescence detectors, such as two or more, such as three or more, such as four or more, five or more, ten or more, fifteen or more, and including twenty or more.

[0116] Any suitable detector for detecting the collected light may be employed in the fluorescence detectors described herein. The target detector may include, but is not limited to, an optical sensor or an optical detector, such as an active pixel sensor (APS), an avalanche photodiode, an image sensor, a charge-coupled device (CCD), an intensified charge-coupled device (ICCD), a light-emitting diode, a photon counter, a bolometer, a pyroelectric detector, a photoresistor, a photovoltaic cell, a photodiode, a photomultiplier tube (PMT), a phototransistor, a quantum dot photoconductor or photodiode, and combinations thereof, and other detectors. In certain embodiments, the collected light is assayed using 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 photomultiplier tube, for example, the active detection surface area of each region is 0.01 cm 2 to 10 cm 2 , for example, 0.05 cm 2 to 9 cm 2 , for example, for example, 0.1 cm 2 to 8 cm 2 , for example, 0.5 cm 2 to 7 cm 2 and including 1 cm 2 to 5 cm 2 photomultiplier tubes

[0117] When the subject particle analyzer includes a plurality of fluorescence detectors, each fluorescence detector can be the same, or the set of fluorescence detectors can be a combination of different types of detectors. For example, when the subject particle analyzer includes two fluorescence detectors, in some embodiments, the first fluorescence detector is a CCD-type device and the second fluorescence detector (or imaging sensor) is a CMOS-type device. In other embodiments, both the first fluorescence detector and the second fluorescence detector are CCD-type devices. In other embodiments, both the first fluorescence detector and the second fluorescence detector are CMOS-type devices. In other embodiments, the first fluorescence detector is a CCD-type device and the second fluorescence detector is a photomultiplier tube (PMT). In other embodiments, the first fluorescence detector is a CMOS-type device and the second fluorescence detector is a photomultiplier tube. In other embodiments, both the first fluorescence detector and the second fluorescence detector are photomultiplier tubes.

[0118] In other embodiments of the present disclosure, the target fluorescence detector is configured to measure the collected light at one or more wavelengths, such as at 2 or more wavelengths, such as 5 or more different wavelengths, such as 10 or more different wavelengths, such as 25 or more different wavelengths, such as 50 or more different wavelengths, such as 100 or more different wavelengths, such as 200 or more different wavelengths, such as 300 or more different wavelengths, and including measuring the light emitted by the sample in the flowing stream at 400 or more different wavelengths. In some embodiments, 2 or more detectors in the particle analyzer described herein are configured to measure the same wavelength or overlapping wavelengths of the collected light.

[0119] In some embodiments, the target fluorescence detector is configured to measure light collected within a certain wavelength range (e.g., 200 nm to 1000 nm). In certain embodiments, the target detector is configured to collect the spectrum of light within a certain wavelength range. For example, a particle analyzer may include one or more detectors that are configured to collect the spectrum of light within one or more wavelength ranges from 200 nm to 1000 nm. In other embodiments, the target detector is configured to measure light emitted by a sample in a flowing stream at one or more specific wavelengths. For example, a particle analyzer may include one or more detectors that are configured to measure light at one or more of 450 nm, 518 nm, 519 nm, 561 nm, 578 nm, 605 nm, 607 nm, 625 nm, 650 nm, 660 nm, 667 nm, 670 nm, 668 nm, 695 nm, 710 nm, 723 nm, 780 nm, 785 nm, 647 nm, 617 nm, and any combination thereof. In certain embodiments, one or more detectors are configured to be paired with specific fluorophores, such as fluorophores used with a sample in fluorescence assays.

[0120] In some embodiments, the particle analyzer includes one or more wavelength separators located between the flow cell and the particle-modulated light detector. As used herein, the term "wavelength separator" in its conventional sense refers to an optical element 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 more than one wavelength separator, such as two or more wavelength separators, such as three or more, such as four or more, such as five or more, such as six or more, such as seven or more, such as eight or more, such as nine or more, such as ten or more, such as fifteen or more, such as twenty-five or more, such as fifty or more, such as seventy-five or more, and including one hundred or more wavelength separators. In some embodiments, the wavelength separator is configured to separate light collected from a sample into predetermined spectral ranges by passing light having a predetermined spectral range and reflecting light of one or more remaining spectral ranges. In other embodiments, the wavelength separator is configured to separate light collected from a sample into predetermined spectral ranges by passing light having a predetermined spectral range and absorbing light of one or more remaining spectral ranges. In additional embodiments, the wavelength separator is configured to diffract light collected from a sample spatially into predetermined spectral ranges. Each wavelength separator can be any suitable light separation scheme, 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 light detection system is a dichroic mirror.

[0121] Suitable flow cytometry systems can include, but are not limited to, those described in the following references: 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 Throm 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 some cases, the target flow cytometer includes a BD Biosciences FACSCanto TM Flow cytometer, BD Biosciences FACSCanto TM II Flow cytometer, BD Accuri TM Flow cytometer, BD Accuri TM C6 Plus Flow cytometer, BD Biosciences FACSCelesta TM Flow cytometer, BD Biosciences FACSLyric TM Flow cytometer, BD Biosciences FACSVerse TM Flow cytometer, BD Biosciences FACSymphony TM Flow cytometer, BD Biosciences LSRFortessa TM Flow cytometer, BD Biosciences LSRFortessa TM X-20 Flow cytometer, BD Biosciences FACSPresto TM Flow cytometer, BD Biosciences FACSVia TMFlow cytometers and BD Biosciences FACSCalibur TM Cell sorters, BD Biosciences FACSCount TM Cell sorters, BDBiosciences FACSLyric TM Cell sorters, BD Biosciences Via TM Cell sorters, BDBiosciences Influx TM Cell sorters, BD Biosciences Jazz TM Cell sorters, BD BiosciencesAria TM Cell sorters, BD Biosciences FACSAria TM II Cell sorters, BD BiosciencesFACSAria TM III Cell sorters, BD Biosciences FACSAria TM Fusion Cell sorters and BDBiosciences FACSMelody TM Cell sorters, BD Biosciences FACSymphony TM S6BD, BDFACSDiscover TM S8 Cell sorters, etc.

[0122] In some embodiments, the system is a flow cytometry system, such as the systems described in the following U.S. patents: U.S. Patent No. 10,663,476; U.S. Patent No. 10,620,111; U.S. Patent No. 10,613,017; U.S. Patent No. 10,605,713; U.S. Patent No. 10,585,031; U.S. Patent No. 10,578,542; U.S. Patent No. 10,578,469; U.S. Patent No. 10,481,074; U.S. Patent No. 10,302,545; U.S. Patent No. 10,145,793; U.S. Patent No. 10,113,967; U.S. Patent No. 10,006,852; U.S. Patent No. 9,952,076; U.S. Patent No. 9,933,341; U.S. Patent No. 9,726,527; U.S. Patent No. 9,453,789; U.S. Patent No. 9,200,334; U.S. Patent No. 9,097,640; U.S. Patent No. 9,095,494; U.S. Patent No. 9,092,034; U.S. Patent No. 8,975,595; U.S. Patent No. 8,753,573; U.S. Patent No. 8,233,146; U.S. Patent No. 8,140,300; U.S. Patent No. 7,544,326; U.S. Patent No. 7,201,875; U.S. Patent No. 7,129,505; U.S. Patent No. 6,821,740; U.S. Patent No. 6,813,017; U.S. Patent No. 6,809,804; U.S. Patent No. 6,372,506; U.S. Patent No. 5,700,692; U.S. Patent No. 5,643,796; U.S. Patent No. 5,627,040; U.S. Patent No. 5,620,842; U.S. Patent No. 5,602,039; U.S. Patent No. 4,987,086; U.S. Patent No. 4,498,766; the disclosures of which are hereby incorporated by reference in their entireties.

[0123] In some cases, the flow cytometry system of the present invention is configured to image particles in a flow cytometric stream using fluorescence imaging with radiofrequency tagging emission (FIRE), as described in the following references: Diebold et al., Nature Photonics Vol. 7(10); 806 - 810 (2013) and 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; 10,408,758; 10,451,538; 10,620,111; and U.S. Patent Publication Nos. 2017 / 0133857; 2017 / 0328826; 2017 / 0350803; 2018 / 0275042; 2019 / 0376895 and 2019 / 0376894, the disclosures of which are incorporated herein by reference. In such cases, flow cytometer data may include image data of the particles, such as cells present in a sample. See, e.g., Schraivogel et al., Science Vol. 375(6578); 315 - 320 (2022), the disclosure of which is incorporated herein in its entirety, and U.S. Provisional Patent Application No. 63 / 256,974, the disclosure of which is incorporated herein in its entirety. An example of such a system is the FACS Discover TM S8 Cell Sorter

[0124] Figure 2 FIG. 200 shows a system 200 for flow cytometry according to an exemplary embodiment of the present 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 cell 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 long pass (“LP”) filters 255a - 255b, and one or more fluorescence detectors 260a - 260f.

[0125] Excitation lasers 215a - c emit light in the form of laser beams. In Figure 2In the example system, the wavelengths of the laser beams emitted from the excitation lasers 215a to 215c are 488 nm, 633 nm, and 325 nm, respectively. The laser beams are first directed through one or more beam splitters 245a and 245b. Beam splitter 245a transmits light with a wavelength of 488 nm and reflects light with a wavelength of 633 nm. Beam splitter 245b transmits ultraviolet light (light with a wavelength in the range of 10 nm to 400 nm) and reflects light with wavelengths of 488 nm and 633 nm.

[0126] Subsequently, the laser beams are directed to a focusing lens 220, which focuses the beams onto the portion of the flowing stream in the flow chamber 225 where the sample particles are located. The flow chamber is part of a jet system that directs the particles in the particle stream to the focused laser beam for interrogation, typically one particle at a time. The flow chamber can include a flow chamber in a benchtop cytometer or a nozzle tip in an air-based flow cytometer.

[0127] Depending on the characteristics of the particles, such as size, internal structure, and the presence of one or more fluorescent molecules attached to or naturally occurring on or in the particles, light from one or more of the laser beams interacts with the particles in the sample at various different wavelengths through diffraction, refraction, reflection, scattering, absorption, and re-emission. Fluorescent emission as well as diffracted, refracted, reflected, and scattered light can be directed to a forward scatter detector 230 and a side scatter detector 235, and one or more of one or more fluorescent detectors 260a to 260f through one or more of beam splitters 245c to 245g, bandpass filters 250a to 250e, longpass filters 255a to 255b, and a fluorescence collection lens 240.

[0128] The fluorescence collection lens 240 collects the light emitted through the particle-laser beam interaction and directs it to one or more beam splitters and filters. Bandpass filters, such as bandpass filters 250a to 250e, allow a relatively narrow range of wavelengths 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, the 510 / 20 filter extends 10 nm on each side of the spectral band center, or from 500 nm to 520 nm. Shortpass filters transmit light equal to or less than a specific wavelength. Longpass filters, such as longpass filters 255a to 255b, transmit light with a wavelength equal to or greater than a specific wavelength. For example, longpass filter 255b, which is a 670 nm longpass filter, transmits light equal to or greater than 670 nm. Filters are typically selected to optimize the specificity of the detector for a particular fluorescent dye. The filters can be configured such that the spectral band of the light transmitted to the detector is close to the emission peak of the fluorescent dye.

[0129] The forward scatter detector 230 is slightly offset from the axis of the direct beam passing through the flow cell and is configured to detect diffracted light, i.e., excitation light that predominantly propagates forward through or around the particles. The intensity of the 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 may detect refracted and reflected light from the particle surface and internal structure, where the refracted and reflected light tends to increase with increasing particle structure complexity. One or more fluorescence detectors 260a to 260f may be used to detect fluorescence emissions from fluorescent molecules associated with the particles. The side scatter detector 235 and the fluorescence detectors may include photomultiplier tubes. Signals detected at the forward scatter detector 230, the side scatter detector 235, and the fluorescence detectors may be converted by the detectors into electrical signals (voltages). This data may provide information about the sample.

[0130] Those skilled in the art will recognize that the flow cytometer according to embodiments of the present invention is not limited to Figure 2 the flow cytometer shown, but may include any flow cytometer known in the art. For example, the flow cytometer may have any number, various wavelengths, and various different configurations of lasers, beam splitters, filters, and detectors.

[0131] During operation, the cytometer is controlled by a controller / processor 290, and measurement data from the detectors may be stored in a memory 295 and processed by the controller / processor 290. Although not explicitly shown, the controller / processor 290 is coupled to the detectors to receive output signals therefrom, and the controller / processor 290 may also be coupled to the electrical and electromechanical components of the flow cytometer 210 to control lasers, fluid flow parameters, etc. The system may also provide input / output (I / O) capabilities 297. The memory 295, the controller / processor 290, and the I / O 297 may all be part of the flow cytometer 210. In such an embodiment, the display may also form part of the I / O capabilities 297 for providing experimental data to the user of the cytometer 210. Alternatively, the memory 295, the controller / processor 290, and part or all of the I / O capabilities may be part of one or more external devices such as a general-purpose computer. In some embodiments, part or all of the memory 295 and the controller / processor 290 may communicate wirelessly or wired with the cytometer 210. The controller / processor 290, together with the memory 295 and the I / O 297, is configured to perform various functions related to the preparation and analysis of flow cytometer experiments.

[0132] Figure 2The system shown includes six different detectors that detect fluorescence in six different wavelength bands (which may be referred to in the present invention as "filter windows" for a given detector) as defined by the configuration of filters and / or beam splitters in the beam path from the flow cell 225 to each detector. Different fluorescent molecules in a fluorescent dye combination for a flow cytometry experiment will emit light in their respective characteristic wavelength bands. To generally match the filter windows of the detectors, specific fluorescent labels and their associated fluorescent emission bands for the experiment can be selected. The I / O 297 can be configured to receive data regarding a flow cytometry experiment, which includes a fluorescent label combination and multiple cell populations with multiple markers, each cell population having a subset of the multiple markers. The I / O 297 can also be configured to receive biological data, marker density data, emission spectral data, data assigning labels to one or more markers, and cytometer configuration data that assign one or more markers to one or more cell populations. Flow cytometry experiment data, such as label spectral characteristics and cytometer configuration data, can also be stored in the memory 295. The controller / processor 290 can be configured to evaluate one or more assignment results of labels to markers.

[0133] In some embodiments, the subject system is configured as a particle sorting system for sorting particles with an enclosed particle sorting module, such as the particle sorting module described in U.S. Patent Publication No. 2017 / 0299493, filed Mar. 28, 2017, the disclosure of which is incorporated herein by reference. In certain embodiments, particles (such as cells) of a sample are sorted using a sorting decision module having multiple sorting decision units, such as the sorting decision module described in U.S. Patent Publication No. 2020 / 0256781, filed Dec. 23, 2019, the disclosure of which is incorporated herein by reference. In some embodiments, the system for sorting sample components includes a particle sorting block having deflection plates, such as the particle sorting block described in U.S. Patent Publication No. 2017 / 0299493, filed Mar. 28, 2017, the disclosure of which is incorporated herein by reference.

[0134] Figure 3 A functional block diagram of an example of a control system, such as the processor 300, for analyzing and displaying biological events is shown. The processor 300 can be configured to implement various processes for controlling the graphical display of biological events.

[0135] The flow cytometer or sorting system 302 can be configured to acquire biological event data. For example, the flow cytometer can generate flow cytometer event data (e.g., particle-modulated light data). The flow cytometer 302 can be configured to provide the biological event data to the processor 300. A data communication channel can be included between the flow cytometer 302 and the processor 300. The biological event data can be provided to the processor 300 through the data communication channel.

[0136] The processor 300 can be configured to receive the biological event data from the flow cytometer 302. The biological event data received from the flow cytometer 302 can include flow cytometer event data. The processor 300 can be configured to provide a graphical display to the display device 306, which includes a first chart of the biological event data. The processor 300 can be further configured to present a target region as a gate (e.g., a first gate) around a population of biological event data displayed by the display device 306, e.g., overlaid on the first chart. In some embodiments, the gate can be a logical combination of one or more target graphical regions drawn on a one-parameter histogram or a bivariate plot. In some embodiments, the display can be used to display particle parameters or saturated detector data.

[0137] The processor 300 can be further configured to display the biological event data within the gate on the display device 306, which is different from other events in the biological event data outside the gate. For example, the processor 300 can be configured to present the color of the biological event data contained within the gate, which is different from the color of the biological event data outside the gate. The display device 306 can be implemented as a monitor, a tablet computer, a smart phone, or other electronic devices configured to present a graphical interface.

[0138] The processor 300 can be configured to receive a gate selection signal identifying the gate from a first input device. For example, the first input device can be implemented as the mouse 310. The mouse 310 can initiate a gate selection signal to the processor 300 to identify the gate to be displayed on or manipulated through the display device 306 (e.g., by clicking on or within the desired gate when the cursor is located on or within the desired gate). In some embodiments, the first device can be implemented as the keyboard 308 or other means for providing an input signal to the processor 300, such as a touch screen, a stylus, an optical detector, or a voice recognition system. Some input devices can include multiple input functions. In such an embodiment, each input function can be regarded as an input device. For example, as Figure 3 shown, the mouse 310 can include a right mouse button and a left mouse button, and each button can generate a triggering event.

[0139] A trigger event can cause the processor 300 to change the way data is displayed, where a portion of the data actually displayed on the display device 306, and / or provide input information for further processing, such as selecting a target population for particle sorting.

[0140] In some embodiments, the processor 300 can be configured to detect the time when a gate selection is initiated by the mouse 310. The processor 300 can further be configured to automatically modify the chart visualization to facilitate the gating process. The modification can be based on a specific distribution of the biological event data received by the processor 300. In some embodiments, the processor 300 extends the first gate to generate a second gate (e.g., as described above).

[0141] The processor 300 can be connected to the storage device 304. The storage device 304 can be configured to receive and store biological event data from the processor 300. The storage device 304 can also be configured to receive and store flow cytometry event data from the processor 300. The storage device 304 can further allow retrieval of biological event data, such as flow cytometry event data, through the processor 300.

[0142] The display device 306 can be configured to receive display data from the processor 300. The display data can include a chart of biological event data and gates outlining portions of the chart. The display device 306 can further be configured to change the presented information based on input information received from the processor 300, in combination with input information from the flow cytometer 302, the storage device 304, the keyboard 308, and / or the mouse 310.

[0143] In addition, the processor 300 can be configured to evaluate a fluorescent dye combination. In this case, the processor 300 is configured to receive an initial fluorescent dye combination that includes a group of fluorescent dye identifiers and a group of biomarker identifiers, where each fluorescent dye identifier refers to a fluorescent dye in the fluorescent dye group, where each biomarker identifier is associated with a fluorescent dye identifier in the group of fluorescent dye identifiers; a plurality of population identifiers, where each population identifier refers to a particle population, and an instrument identifier. The processor 300 can also be configured to generate a group of separability metrics by associating each population identifier in the plurality of population identifiers with a biomarker identifier in the group of biomarker identifiers to create a group of population-biomarker pairs, where each separability metric predicts a measure of the statistical distance between particle populations in the flow cytometry data space, aggregate the group of separability metrics into a combined score, and evaluate the combined score to assess the suitability of the initial fluorescent dye combination for a flow cytometry protocol. In some cases, the processor 300 is further configured to generate a visualization based on the evaluation of the fluorescent dye combination. In some cases, the visualization can be displayed on the display device 306.

[0144] In some embodiments, the processor 300 may generate a user interface to receive instance events for sorting. For example, the user interface may include mechanisms for receiving example events or example images. Exemplary events or images or exemplary gates may be provided before collecting sample event data or based on an initial set of events of a partial sample.

[0145] Figure 4A is a schematic diagram of a particle sorter system 400 (e.g., flow cytometer 302) according to an embodiment of the present invention. In some embodiments, the particle sorter system 400 is a cell sorter system. As Figure 4A shown, the droplet formation sensor 402 (e.g., piezoelectric oscillator) is coupled to the fluid conduit 401, where the fluid conduit 401 may be coupled to, may include, or may be the nozzle 403. Inside the fluid conduit 401, the sheath fluid 404 hydrodynamically focuses the sample fluid 406 containing the particles 409 into a moving fluid column 408 (e.g., a stream of liquid). Inside the moving fluid column 408, the particles 409 (e.g., cells) are arranged in a single file to cross the monitoring region 44 (e.g., the laser - flow intersection) and are irradiated by an irradiation source 412 (e.g., a laser). The droplet formation sensor 402 vibrates, causing the moving fluid column 408 to break into a plurality of droplets 410, some of which contain the particles 409.

[0146] During operation, the time when a target particle (or target cell) crosses the monitoring region 411 is identified by the detection station 414 (e.g., an event detector). The detection station 414 feeds into the timing circuit 428, and the timing circuit 428 then feeds into the flash charging circuit 430. At the droplet break point, upon reminder of the timed droplet delay (Δt), the moving fluid column 408 can be flash - charged so that the target droplets are charged. The target droplets may include one or more particles or cells to be sorted. Then, the charged droplets can be sorted by activating deflection plates (not shown) to deflect the charged droplets into containers such as collection tubes or porous or microwell sample plates, where the wells or microwells may be associated with specific target droplets. As Figure 4A shown, the droplets can be collected in the discharge container 438.

[0147] When the target particle passes through the monitoring area 411, the detection system 416 (such as a droplet boundary detector) functions to automatically determine the phase of the droplet drive signal. An exemplary droplet boundary detector is described in U.S. Patent No. 7,679,039, the entire content of which is incorporated herein by reference. The detection system 416 allows the instrument to accurately calculate the position of each detected particle within the droplet. The detection system 416 can feed an amplitude signal 420 and / or a phase signal 418, which in turn (through an amplifier 422) feeds an amplitude control circuit 426 and / or a frequency control circuit 424. The amplitude control circuit 426 and / or the frequency control circuit 424 in turn controls the droplet formation sensor 402. The amplitude control circuit 426 and / or the frequency control circuit 424 can be included in the control system.

[0148] In some embodiments, the sorting electronics (such as the detection system 416, the detection station 414, and the processor 440) can be coupled to a memory configured to store detected events and event-based sorting decisions. The sorting decisions can be included in the event data of the particles. In some embodiments, the detection system 416 and the detection station 414 can be implemented as a single detection unit or can be coupled in a communication manner so that event metrics can be collected by the detection system 416 or the detection station 414 and provided to non-collecting elements.

[0149] Figure 4B is a schematic diagram of a particle sorter system according to an embodiment of the present invention. Figure 4B The illustrated particle sorting system 400 includes deflection plates 452 and 454. Charge can be applied through a liquid flow charging line in the barbs. Thus, a liquid droplet stream 410 containing particles 409 for analysis is created. One or more light sources (such as lasers) can be used to irradiate the particles to generate light scattering and fluorescence information. The particle information is analyzed by sorting electronics or other detection systems ( Figure 4B not shown in the figure). The deflection plates 452 and 454 can be independently controlled to attract or repel charged droplets and direct the droplets to a destination collection container (such as one of 472, 474, 476, or 478). As Figure 4B shown, the deflection plates 452 and 454 can be controlled to direct the particles along a first path 462 to the container 474 or along a second path 468 to the container 478. If the particle is not a target particle (for example, does not show scattering information or illumination information within a specified sorting range), the deflection plates can allow the particle to continue flowing along the flow path 464. Such uncharged droplets can enter a waste container through a suction device 470 or the like.

[0150] Sorting electronics can be included to initiate metric collection, receive the fluorescence signal of the particles, and determine how to adjust the deflection plates to achieve particle sorting.Figure 4B Example embodiments of the illustrated embodiments include the BD FACSAria TM series of flow cytometers commercially available from Becton, Dickinson and Company (Franklin Lakes, NJ).

[0151] Computer control system

[0152] Aspects of the present disclosure further include a computer control system, wherein the system includes one or more computers implementing for full or partial automation. In some embodiments, the system includes a computer storing a computer program, wherein when loaded on the computer, the computer program includes instructions for evaluating the suitability of a combination of fluorescent dyes for a flow cytometry protocol. In some cases, the instructions cause the computer to receive an initial combination of fluorescent dyes, which includes a set of fluorescent dye identifiers and a set of biomarker identifiers, wherein each fluorescent dye identifier refers to a fluorescent dye in the set of fluorescent dyes, wherein each biomarker identifier is associated with a fluorescent dye identifier in the set of fluorescent dye identifiers, a plurality of population identifiers, wherein each population identifier refers to a population of particles, and an instrument identifier. The instructions also cause the processor to create a set of population-biomarker pairs by associating each population identifier in the plurality of population identifiers with a biomarker identifier in the set of biomarker identifiers, and generate a set of separability metrics, each separability metric predicting a measure of the statistical distance between particle populations in the flow cytometry data space, wherein each measure of the statistical distance is related to the detected signal intensity generated by each fluorescent dye associated with each population-biomarker pair, which is used in a flow cytometry protocol using the instrument associated with the instrument identifier. In an embodiment, the instructions also cause the processor to aggregate the separability metrics into a combined score and evaluate the combined score to assess the suitability of the initial combination of fluorescent dyes for a flow cytometry protocol. In some cases, the system is a flow cytometer or includes a flow cytometer.

[0153] The system may include a display and an operator input device. For example, the operator input device may be a keyboard, a mouse, etc. The processing module includes a processor accessible to a memory, the memory having instructions for performing 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 any other processor available now or in the future. The processor executes the operating system, which is connected to the firmware and hardware in a known manner through an interface, and helps the processor coordinate and execute the functions of various computer programs that can be written in various programming languages, such as Java, Perl, C++, Python, other high-level or low-level languages known in the art, and combinations thereof. The operating system typically cooperates with the processor to coordinate and execute the functions of 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 based on known techniques. In some embodiments, the processor includes analog electronic devices that provide feedback control, such as negative feedback control.

[0154] The system memory may be any of a variety of known memory storage devices or future memory storage devices. Examples include any conventionally purchased random access memory (RAM), magnetic media such as a resident hard disk or tape, optical media such as a read-write optical disc, a flash memory device, or other memory storage devices. The memory storage device may be any of a variety of known devices or future devices, including an optical disc drive, a tape drive, or a floppy disk drive. Such a memory storage device reads from and / or writes to a program storage medium (not shown), such as an optical disc. Any of these program storage media, or other program storage media currently in use or likely to be developed in the future, may be regarded as a computer program product. It should be understood that these program storage media generally store computer software programs and / or data. Computer software programs, also known as computer control logic, are typically stored in the system memory and / or in a program storage device used in conjunction with the memory storage device.

[0155] In some embodiments, the present invention describes a computer program product that includes a computer-usable medium having control logic (computer software program, including program code) stored therein. When executed by a processor and a computer, the control logic causes the processor to perform the functions described herein. In other embodiments, some functions are mainly implemented in hardware using, for example, a hardware state machine. It will be apparent to those skilled in the art that implementing a hardware state machine can perform the functions described in the present invention.

[0156] The memory can be any suitable device for a processor to store and retrieve data, such as a magnetic storage device, an optical storage device, or a solid-state storage device (including a disk, an optical disc, a magnetic tape, or RAM, or any other suitable stationary or portable device). The processor can include a general-purpose digital microprocessor that is appropriately programmed by a computer-readable medium carrying the necessary program code. The programming can be provided to the processor remotely via a communication channel or pre-stored in a computer program product, such as a memory or any other portable or stationary computer-readable storage medium utilizing any memory-related device. For example, a disk or an optical disc can carry the programming and can be read by a disk writer / reader. The system of the present invention also includes, for example, programming of the algorithms for practicing the above-described methods in the form of a computer program product. The programming according to the present invention can be recorded on a computer-readable medium, such as any medium directly readable and accessible by a computer. Such media include, but are not limited to: magnetic storage media, optical storage media, such as CD-ROM; electrical storage media, such as RAM and ROM; portable flash drives; and hybrid media of these categories, such as magnetic / optical storage media.

[0157] The processor can also access a communication channel to communicate with a user at a remote location. A remote location means that the user does not directly contact the system and transfers input information 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), to an input manager.

[0158] In some embodiments, the system according to the present disclosure can be configured to include a communication interface. In some embodiments, the communication interface includes a receiver and / or a transmitter for communicating with a network and / or another device. The communication interface can be used for wired or wireless communication, including but not limited to radio frequency (RF) communication (such as radio frequency identification (RFID)), Zigbee communication protocol, Wi-Fi, infrared, wireless universal serial bus (USB), ultra-wideband (UWB), communication protocols, and cellular communication, such as code division multiple access (CDMA) or global system for mobile communications (GSM).

[0159] In one embodiment, the communication interface can include one or more communication ports, such as physical ports or interfaces, such as a USB port, a USB-C port, an RS-232 port, or any other suitable electrical connection port, to enable data communication between the subject system and other external devices, such as computer terminals for similar supplementary data communication (e.g., in a doctor's office or a hospital environment).

[0160] In one embodiment, the communication interface is configured for infrared communication, A communication or any other suitable wireless communication protocol enables the subject system to communicate with computer terminals and / or networks, mobile phones supporting communication, personal digital assistants, and other devices, or any other communication devices available for users to use in combination.

[0161] In one embodiment, the communication interface provides a connection for data transfer using the 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 over a Wi-Fi hotspot.

[0162] In one embodiment, the subject system communicates wirelessly with a server device through the communication interface, for example, using common standards such as 802.11 or an RF protocol, or the IrDA infrared protocol. The server device can be another portable device, such as a smart phone, a personal digital assistant (PDA), or a laptop computer; or a larger device, such as a desktop computer, a household appliance, etc. In some embodiments, the server device has a display (such as a liquid crystal display (LCD)) and an input device (such as buttons, a keyboard, a mouse, or a touch screen).

[0163] In some embodiments, the communication interface is configured to automatically or semi-automatically transfer data stored in the subject system to a network device or a server device via a network or a server using one or more of the above-mentioned communication protocols and / or mechanisms. For example, data stored in an optional data storage unit.

[0164] 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. If one of the display devices provides visual information, this information can generally be organized logically and / or physically into a pixel array. 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 information. The functional elements of a computer may communicate with each other via a system bus. In alternative embodiments, some of this communication may be accomplished 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 according to known techniques, such as via the Internet, telephone, or satellite network. The presentation of data by the output manager may be based on a variety of known techniques. For example, the data may include SQL, HTML, or XML documents, e-mail, 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. Although one or more platforms present in the subject system generally fall within the broad category of computers commonly referred to as servers, they may also be any type of known computer platform or type to be developed in the future. However, they may also be mainframe computers, workstations, or other types of computers. These platforms may be connected in a networked or other fashion via any known or future type of wiring or other communication system, including wireless systems. They may collaborate co-located or be physically separated. A variety of operating systems may be applied to any computer platform, most likely depending on the type and / or model of the computer platform selected. Suitable operating systems include XP, 7, 8, 10, OS / i5 / IBM Android TM , SGI Oracle and so on.

[0165] Figure 5 FIG. shows a general architecture of an example computing device 500 in accordance with certain embodiments. Figure 5The general architecture of the computing device 500 shown includes an arrangement of computer hardware and software components. However, not all generally conventional components need to be shown for purposes of disclosure. As shown, the computing device 500 includes a processing unit 510, a network interface 520, a computer-readable media drive 530, an input / output device interface 540, a display 550, and an input device 560, all of which can communicate with each other via a communication bus. The network interface 590 can provide connectivity to one or more networks or computing systems. Thus, the processing unit 510 can receive information and instructions from other computing systems or services via the network. The processing unit 510 can also communicate with the memory 570 and further provide output information to the optional display 550 via the input / output device interface 540. For example, analysis software (such as etc., data analysis software or programs) stored as executable instructions in the non-transitory memory of the analysis system can display flow cytometry event data to the user. The input / output device interface 540 can also receive input information from the optional input device 560, such as a keyboard, mouse, digital pen, microphone, touch screen, gesture recognition system, voice recognition system, game pad, accelerometer, gyroscope, or other input devices.

[0166] To implement one or more embodiments, the memory 570 can include computer program instructions (grouped in modules or components in some embodiments) executed by the processing unit 510. The memory 570 generally includes RAM, ROM, and / or other permanent, auxiliary, or non-transitory computer-readable media. The memory 570 can store an operating system 572 that provides computer program instructions used by the processing unit 510 in the general management and operation of the computing device 500. Data can be stored in the data storage device 590. The memory 570 can further include computer program instructions and other information for implementing aspects of the present disclosure.

[0167] Computer-readable storage medium

[0168] Aspects of the present disclosure further include non-transitory computer-readable storage media having instructions for practicing the methods. The computer-readable storage media can be employed on one or more than one computer for full or partial automation of a system to implement the methods described herein. In certain embodiments, the instructions according to the methods described herein can be encoded onto the computer-readable media in the form of "programming", where the term "computer-readable media" as used herein refers to any non-transitory storage media that participates in the process of providing instructions and data to a computer for execution and processing. In some cases, when executed by a computer or a processor, the instructions cause the computer or processor to receive an initial fluorescent dye combination, which includes a group of fluorescent dye identifiers and a group of biomarker identifiers, where each fluorescent dye identifier refers to a fluorescent dye in the group of fluorescent dyes, where each biomarker identifier is associated with a fluorescent dye identifier in the group of fluorescent dye identifiers, each of a plurality of population identifiers refers to a population of particles, and an instrument identifier. The instructions further cause the processor to create a group of population-biomarker pairs by associating each of the plurality of population identifiers with a biomarker identifier in the group of biomarker identifiers, and generate a group of separability metrics, each separability metric predicting a metric value of a statistical distance between populations of particles in a flow cytometry data space, where each metric value of the statistical distance is related to a detected signal intensity generated by each fluorescent dye associated with each population-biomarker pair, and the fluorescent dye is used in a flow cytometry protocol of an instrument associated with the instrument identifier. In an embodiment, the instructions further cause the processor to aggregate the group of separability metrics into a combined score and evaluate the combined score to assess the suitability of the initial fluorescent dye combination for the flow cytometry protocol. In some cases, the system is a flow cytometer or includes a flow cytometer.

[0169] Examples of suitable non-transitory storage media include floppy disks, hard disks, optical disks, magneto-optical disks, CD-ROMs, CD-Rs, magnetic tapes, non-volatile memory cards, ROMs, DVD-ROMs, Blu-ray disks, solid state disks, flash drives, and network-attached storage (NAS), whether such devices are internal or external to the computer. A file containing information can be "stored" on the computer-readable media, where "storing" means recording the information so that the computer can later access and retrieve this information. When implementing the computer-implemented methods described herein, programming can be carried out in one or more than one of any number of computer programming languages. These languages include Java, Python, Visual Basic, and C++, etc.

[0170] Application

[0171] The methods, systems, and computer-readable media of the present invention may find use in situations where an automated determination of available fluorescent dye combinations for particle analysis (e.g., flow cytometry) is desired. In certain cases, the present invention is particularly applicable to the experimental design of full-spectrum (i.e., "spectral") flow cytometer combinations. In other words, as a first step in spectral combination design, the present invention determines whether dye sets can be used simultaneously. In some cases, the methods, systems, and computer-readable media described herein assist in determining which set or sets of fluorescent dyes may provide the best quality data (e.g., maximum biological resolution). The present invention achieves the above objects through an automated optimization algorithm, using the spectral characteristics of fluorescent dyes as easily obtainable and easily measurable inputs to the algorithm, and using a spectral matrix for computationally efficient exploration of the optimization.

[0172] Embodiments of the present invention can be used in applications where cells prepared from biological samples may be needed for research, laboratory testing, or for treatment. In some embodiments, the methods and devices can assist in obtaining individual cells prepared from a target fluid or tissue biological sample. For example, the methods and systems assist in obtaining cells from a fluid or tissue sample for research or diagnosis of diseases such as cancer. Similarly, the methods and systems can assist in obtaining cells from a fluid or tissue sample for treatment. Compared to traditional flow cytometry systems, the methods and devices described in the present disclosure allow for the separation and collection of cells from biological samples (e.g., organs, tissues, tissue fragments, fluids) with high efficiency and low cost.

[0173] Kit

[0174] Aspects of the present disclosure further include a kit, where the kit includes instructions and / or programmable logic for performing the claimed methods. For example, the kit includes programming that can be configured to evaluate and optionally optimize fluorescent dye combinations (e.g., as described in the method section above), such as in the form of a computer-readable medium (such as a flash drive, USB storage, compact disc, DVD, Blu-ray disc, etc.) or instructions for downloading the programming from an Internet network protocol or cloud server.

[0175] The kit may further include instructions for implementing the method. These instructions can exist in various forms in the subject kit, where one or more than one form may be present in the kit. One form in which these instructions can exist is information printed on a suitable medium or substrate (e.g., one or more sheets of paper with information printed thereon), in the packaging of the kit, in the packaging instructions, etc. Another form of these instructions is a computer-readable medium having information recorded thereon, such as a floppy disk, compact disc (CD), portable flash drive, etc. Another form in which these instructions may exist is a web address that can be used to access information on a removed site via the Internet.

[0176] The following embodiments are provided in an illustrative, not limiting, manner:

[0177] Embodiment

[0178] Question design

[0179] Search for a 9-color combination to phenotypically analyze CD8+ and CD4+ T cell subsets, study the quantitative expression levels of CD27 and CD28 in these subsets, and gate out regulatory T cell populations. The relevant markers are CD3, CD4, CD8, CCR7, CD45RA, CD27, CD28, CD25, and CD127. Use BD FACSLyric TM as a demonstration. This instrument has 3 lasers and 12 detectors. The three lasers are a purple laser, a blue laser, and a red laser.

[0180] Subsequently, a gating strategy was determined. After appropriate scatter gating, single lymphocytes were gated out. This gating strategy is as Figure 6 shown. Gate the CD3+ population (shown in Figure 601), and then display it in the CD4-CD8 bivariate scatter plot 602. For each of these populations, naive T cells, effector T cells, central memory T cells, and effector memory T cells are distinguished by the expression levels of CD45RA and CCR7 (Figure 604). For each of these subsets, their CD27 and CD28 expression levels can then be studied (Figure 605). For CD4+ T cells, the combination of CD25 and CD127 is used to gate out regulatory T cells (Figure 603).

[0181] A set of reagents from which to search for a combination of fluorescent dyes was determined. Although any reagent inventory can be used as the search space, 22 fluorescent dyes were selected, and it was assumed that their antibody conjugates were available for all target markers. In particular, the fluorescent dyes used in the search space are BV421, V450, BV480, PacificBlue, 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 combination is on the order of 5e5, which is unacceptable for brute-force screening.

[0182] Automatic combination design workflow

[0183] Most gating strategies boil down to using one or two features to compare the separability of two populations. The above gating strategy (i.e., regarding Figure 6)Subsequently, it can be decomposed into a list of cell and marker pairs, and the degree of separability of these cell and marker pairs is evaluated. Table 1 shows a partial view of such a list:

[0184] Table 1: Decomposed gating hierarchy

[0185] Cell pair Marker pair (CD8+, double negative T cell) (CD4, CD8) (CD4+, double negative T cell) (CD4, CD8) (CD8 - effector memory, CD8 - naive) (CCR7, CD45RA) … …

[0186] Subsequently, the latent population is then added. Although this biological problem is only related to T cells, there must be non-T cells stained simultaneously in the test tube, so they need to be gated out. To evaluate the separability of T cells, these non-T cells also need to be added. Essentially, any population that has no biological significance in this study but may appear in the gating hierarchy also needs to be defined. Some examples of such latent populations are shown in Table 2:

[0187] Table 2: Cell and marker pairs of exemplary latent populations

[0188] Cell pair Marker pair (non - T cell, T cell) (CD3) (non - regulatory T cell, regulatory T cell) (CD25, CD127)

[0189] Subsequently, quantitative pairs are added. In the gating hierarchy, some markers are used to classify populations, such as CD3, CD4, and CD8. However, some markers are used to study their quantitative expression levels, such as CD27 and CD28. It will be beneficial to distinguish these two types in the algorithm. To have a unified method to evaluate the ability to distinguish different expression levels, four populations are defined by constructing four populations for each pair of quantitative markers (i.e., (+,+), (+,-), (-,+), and (-,-)). Subsequently, the separability between these four pseudo-populations can be evaluated. In this embodiment, q1 (quadrant 1) is used to represent (+,+), q2 (quadrant 2) is used to represent (-,+), q3 (quadrant 3) is used to represent (-,-), and q4 (quadrant 4) is used to represent (+,-). The pseudo-populations synthesized for quantitative markers are shown in Table 3:

[0190] Table 3: Pseudo-populations for quantitative markers

[0191] Cell pair Marker pair (CD8Eff*CD27*CD28*q22,,(CD8Eff) (CD27, CD28) (CD8Eff*CD27*CD28*q22,,CD8Eff*CD27*CD28*q3) (CD27, CD28)

[0192] By performing these three steps, an overall list of cell / marker pairs (Table 4) is obtained. This list contains the biological problem and the corresponding gating hierarchy. A good combination should be able to separate these pairs well.

[0193] Table 4: Overall list of cell / marker pairs

[0194]

[0195]

[0196]

[0197]

[0198]

[0199]

[0200]

[0201]

[0202] Then, the statistical moments of different markers are determined. Essentially, a flow cytometer can be regarded as performing a linear transformation that converts a vector of fluorophore abundances into a vector of detector signals. Based on this model, by incorporating different noise sources (such as Poisson noise, baseline noise, and system noise) into this transformation, the mean value and variance-covariance matrix of the detector signals can be predicted. Next, the variance-covariance matrix in the detector space can be backpropagated to the variance-covariance matrix of fluorophore abundances through an unmixing or compensation process.

[0203] For example, Poisson noise, baseline noise of 30 statistical photoelectrons (spe) per detector, and a system coefficient of variation (cv) of 0.03 per detector are included in the model. Given the combinations listed in Table 5, the mean value and variance of the detector signals for each population are predicted in photon units.

[0204] Table 5: Exemplary Fluorescent Dye Combinations

[0205]

[0206] The variance of each detector for the selected cell populations is calculated and listed in Table 6:

[0207] Table 6: Signal Variances of Three Populations in Each Detector (Unit: Square of Photon Number)

[0208]

[0209] As shown in Table 7, the variance of the backpropagated fluorophore abundances is calculated:

[0210] Table 7: Variances of Each Marker for Three Populations (Unit: Square of Molecule Number)

[0211]

[0212] As can be clearly seen from Table 7, the differences in the variances of the same marker in different populations are significant. To facilitate subsequent separability evaluation, a variance stabilization step should be run to stabilize these variances.

[0213] In this embodiment, a transformation based on the inverse hyperbolic sine function with one parameter (arcsinh(x / c)) is used. The optimization process is run to find the optimal value of the parameter c to minimize the heteroscedasticity of the markers across different populations. The variances of the fluorophores after variance stabilization are listed in Table 8:

[0214] Table 8: Variances of each marker for three populations after auto-scaling.

[0215]

[0216] Given the processed statistical moments of different markers across different populations, the statistical distances between pairs defined at the gating hierarchy can be calculated. In this embodiment, for a one-dimensional histogram, such as the CD3 gating step, the Earth Mover's Distance (EMD) between two distributions is calculated. For a two-dimensional scatter plot, such as a CD4-CD8 bivariate plot, the two distributions are first projected onto their most separating direction, and then the EMD between the projected distributions is calculated. Table 9 shows an exemplary list of separability scores:

[0217] Table 9: List of exemplary separability scores

[0218]

[0219] The separability score defined in Table 9 above is a dimensionless quantity that describes the degree of separation between two univariate or bivariate distributions. Thus, an absolute threshold can be defined to give a binary classification on whether a pair is separable. It is found that the value 16 is a good threshold number and is used in this embodiment. Thus, in the combinatorial optimization process, the combinatorial score is used as the objective function to be minimized. Through all the above steps, the algorithm identifies the combination with zero non-separable pairs, as listed in Table 5.

[0220] Notwithstanding the appended claims, the present disclosure is also defined by the following clauses:

[0221] 1. A method for evaluating the suitability of a combination of fluorescent dyes for a flow cytometry protocol for analyzing a biological sample, the method comprising, using a processor:

[0222] Receiving:

[0223] An initial combination of fluorescent dyes, which includes a group of fluorescent dye identifiers and a group of biomarker identifiers, where each fluorescent dye identifier refers to a fluorescent dye in a group of fluorescent dyes, and where each biomarker identifier is associated with a fluorescent dye identifier in the group of fluorescent dye identifiers;

[0224] A plurality of population identifiers, where each population identifier refers to a population of particles; and

[0225] Instrument identifier;

[0226] Creating a group of population-biomarker pairs by associating each of a plurality of population identifiers with a biomarker identifier in a biomarker identifier group;

[0227] Generating a group of separability metrics, where each separability metric predicts a metric value of a statistical distance between particle populations in a flow cytometry data space, where each metric value of the statistical distance is related to a detected signal intensity generated by each fluorescent dye associated with each population-biomarker pair, and the fluorescent dye is used in a flow cytometry protocol of an instrument associated with the instrument identifier;

[0228] Aggregating the group of separability metrics into a combined score; and

[0229] Evaluating the combined score to assess the suitability of an initial fluorescent dye combination for a flow cytometry protocol.

[0230] 2. The method according to clause 1, wherein creating the group of population-biomarker pairs includes creating population-biomarker pairs for a latent particle population that is not mentioned in the received plurality of population identifiers but is present in the biological sample.

[0231] 3. The method according to clause 1 or 2, wherein creating the group of population-biomarker pairs further includes defining a quantitative pair of one or more biomarker identifiers for evaluating the quantitative expression of a particle population.

[0232] 4. The method according to any one of the preceding clauses, wherein generating the group of separability metrics includes predicting the statistical moments of each biomarker identifier in the biomarker identifier group based on the detected signal intensity.

[0233] 5. The method according to clause 4, wherein predicting the statistical moments includes predicting the covariance matrix of the detected signal intensity.

[0234] 6. The method according to clause 4, wherein predicting the statistical moments includes predicting the variance-covariance matrix of the detected signal intensity.

[0235] 7. The method according to clause 4, wherein predicting the statistical moments includes predicting the mean matrix of the detected signal intensity.

[0236] 8. The method according to any one of clauses 4 to 7, wherein predicting the statistical moments includes running a Monte Carlo simulation.

[0237] 9. The method according to any one of clauses 4 to 8, wherein the statistical moment of each biomarker identifier in the predicted biomarker identifier group includes incorporating the influence of a noise model into the detected signal intensity.

[0238] 10. The method according to clause 9, wherein the noise model is a Gaussian noise model.

[0239] 11. The method according to clause 9, wherein the noise model is a Poisson noise model.

[0240] 12. The method according to any one of clauses 9 to 11, wherein incorporating the influence of the noise model into the detected signal intensity includes obtaining an analytical formula that relates the predicted statistical moment to the noise model.

[0241] 13. The method according to clause 12, further comprising incorporating the influence of the noise model into the detected signal intensity based on a spillover spread matrix (SSM).

[0242] 14. The method according to any one of the preceding clauses, wherein generating the group of separability metrics includes stabilizing the variance of the detected signal intensity.

[0243] 15. The method according to clause 14, wherein stabilizing the variance of the detected signal intensity includes double-exponential scaling.

[0244] 16. The method according to clause 14, wherein stabilizing the variance of the detected signal intensity includes inverse hyperbolic function scaling.

[0245] 17. The method according to clause 14, wherein stabilizing the variance of the detected signal intensity includes solving an optimization problem with an objective function that is the similarity of the variances of different distributions of the detected signal intensity.

[0246] 18. The method according to clause 14, wherein stabilizing the variance of the detected signal intensity includes determining an analytical relationship between the variance and the mean of the detected signal intensity.

[0247] 19. The method according to any one of the preceding clauses, wherein aggregating the group of separability metrics into a combined score includes taking the inverse of the value of the lowest separability score.

[0248] 20. The method according to clause 3, further comprising aggregating the group of separability metrics separately for each population-biomarker pair and quantitative pair.

[0249] 21. The method according to clause 20, wherein determining the combined score includes calculating the vectors of the group of separability metrics aggregated for the population-biomarker pairs and the group of separability metrics aggregated for the quantitative pairs.

[0250] 22. The method according to any one of clauses 1 to 18, wherein the set of aggregation separability metrics includes comparing each separability metric with a threshold.

[0251] 23. The method according to any one of the preceding clauses, further comprising generating an optimized combination of fluorescent dyes based on an assessment of the suitability of an initial combination of fluorescent dyes for a flow cytometry protocol.

[0252] 24. The method according to clause 23, wherein generating the optimized combination of fluorescent dyes includes determining a combination of fluorescent dyes having an optimized number of combinations.

[0253] 25. The method according to any one of clauses 23 or 24, wherein generating the optimized combination of fluorescent dyes includes adjusting the fluorescent dyes in the initial combination of fluorescent dyes and assessing the suitability of the adjusted combination of fluorescent dyes for the flow cytometry protocol.

[0254] 26. The method according to clause 25, wherein generating the optimized combination of fluorescent dyes includes iteratively adjusting the initial combination of fluorescent dyes and assessing the suitability of each iteratively adjusted combination of fluorescent dyes for the flow cytometry protocol.

[0255] 27. The method according to any one of the preceding clauses, further comprising:

[0256] receiving a gating strategy; and

[0257] determining an initial combination of fluorescent dyes based on the gating strategy.

[0258] 28. The method according to any one of the preceding clauses, wherein the initial combination of fluorescent dyes is determined randomly.

[0259] 29. The method according to any one of the preceding clauses, further comprising determining the initial combination of fluorescent dyes with a processor.

[0260] 30. A system, comprising:

[0261] a processor configured to:

[0262] receive:

[0263] an initial combination of fluorescent dyes, comprising a set of fluorescent dye identifiers and a set of biomarker identifiers, wherein each fluorescent dye identifier refers to a fluorescent dye in a set of fluorescent dyes, and wherein each biomarker identifier is associated with a fluorescent dye identifier in the set of fluorescent dye identifiers;

[0264] a plurality of population identifiers, wherein each population identifier refers to a population of particles; and

[0265] Instrument identifier;

[0266] Creating a set of population-biomarker pairs by associating each of a plurality of population identifiers with a biomarker identifier in a biomarker identifier set;

[0267] Generating a set of separability metrics, where each separability metric predicts a metric value of the statistical distance between particle populations in a flow cytometry data space, where each metric value of the statistical distance is related to a detected signal intensity generated by each fluorescent dye associated with each population-biomarker pair and used in a flow cytometry protocol of an instrument associated with the instrument identifier;

[0268] Aggregating the set of separability metrics into a combined score; and

[0269] Evaluating the combined score to assess the suitability of an initial fluorescent dye combination for a flow cytometry protocol.

[0270] 31. The system according to clause 30, wherein creating the set of population-biomarker pairs includes creating population-biomarker pairs for latent particle populations that are not mentioned in the received plurality of population identifiers but are present in the biological sample.

[0271] 32. The system according to any one of clauses 30 or 31, wherein creating the set of population-biomarker pairs further includes defining a quantitative pair of one or more biomarker identifiers for evaluating the quantitative expression of a particle population.

[0272] 33. The system according to any one of clauses 30 to 32, wherein generating the set of separability metrics includes predicting the statistical moments of each biomarker identifier in the biomarker identifier set based on the detected signal intensity.

[0273] 34. The system according to clause 33, wherein predicting the statistical moments includes predicting the covariance matrix of the detected signal intensity.

[0274] 35. The system according to clause 33, wherein predicting the statistical moments includes predicting the variance-covariance matrix of the detected signal intensity.

[0275] 36. The system according to clause 33, wherein predicting the statistical moments includes predicting the mean matrix of the detected signal intensity.

[0276] 37. The system according to any one of clauses 33 to 36, wherein predicting the statistical moments includes running a Monte Carlo simulation.

[0277] 38. The system according to any one of clauses 33 to 37, wherein the statistical moment of each biomarker identifier in the predicted biomarker identifier group includes incorporating the influence of the noise model into the detected signal intensity.

[0278] 39. The system according to clause 38, wherein the noise model is a Gaussian noise model.

[0279] 40. The system according to clause 38, wherein the noise model is a Poisson noise model.

[0280] 41. The system according to any one of clauses 38 to 40, wherein incorporating the influence of the noise model into the detected signal intensity includes obtaining an analytical formula that relates the predicted statistical moment to the noise model.

[0281] 42. The system according to clause 41, wherein the processor is configured to incorporate the influence of the noise model into the detected signal intensity based on a spillover spread matrix (SSM).

[0282] 43. The system according to any one of clauses 30 to 42, wherein generating the group of separability metrics includes stabilizing the variance of the detected signal intensity.

[0283] 44. The system according to clause 43, wherein stabilizing the variance of the detected signal intensity includes double-exponential scaling.

[0284] 45. The system according to clause 43, wherein stabilizing the variance of the detected signal intensity includes inverse hyperbolic function scaling.

[0285] 46. The system according to clause 43, wherein stabilizing the variance of the detected signal intensity includes solving an optimization problem with an objective function that is the similarity of the variances of different distributions of the detected signal intensity.

[0286] 47. The system according to clause 43, wherein stabilizing the variance of the detected signal intensity includes determining an analytical relationship between the variance and the mean of the detected signal intensity.

[0287] 48. The system according to any one of clauses 30 to 47, wherein aggregating the group of separability metrics into a combined score includes taking the inverse of the value of the lowest separability score.

[0288] 49. The system according to clause 32, wherein the processor is configured to aggregate the group of separability metrics separately for each population-biomarker pair and quantification pair.

[0289] 50. The system according to clause 49, wherein determining the combined score includes calculating a vector of the set of separability metrics aggregated for the population-marker pairs and the set of separability metrics aggregated for the quantitative pairs.

[0290] 51. The system according to any one of clauses 30 to 47, wherein aggregating the set of separability metrics includes comparing each separability metric with a threshold.

[0291] 52. The system according to any one of clauses 30 to 51, wherein the processor is configured to generate an optimized combination of fluorescent dyes based on an evaluation of the suitability of an initial combination of fluorescent dyes for a flow cytometry protocol.

[0292] 53. The system according to clause 52, wherein generating the optimized combination of fluorescent dyes includes determining a combination of fluorescent dyes having an optimized number of combinations.

[0293] 54. The system according to clause 52 or 53, wherein generating the optimized combination of fluorescent dyes includes adjusting the fluorescent dyes in the initial combination of fluorescent dyes and evaluating the suitability of the adjusted combination of fluorescent dyes for the flow cytometry protocol.

[0294] 55. The system according to clause 54, wherein generating the optimized combination of fluorescent dyes includes iteratively adjusting the initial combination of fluorescent dyes and evaluating the suitability of each iteratively adjusted combination of fluorescent dyes for the flow cytometry protocol.

[0295] 56. The system according to any one of clauses 30 to 55, wherein the system is a flow cytometer.

[0296] 57. The system according to any one of clauses 30 to 56, further comprising a display configured to output an evaluation of the initial combination of fluorescent dyes.

[0297] 58. The method according to any one of clauses 30 to 57, wherein the processor is configured to:

[0298] receive a gating strategy; and

[0299] determine an initial combination of fluorescent dyes based on the gating strategy.

[0300] 59. The system according to any one of clauses 30 to 57, wherein the processor is configured to randomly determine the initial combination of fluorescent dyes.

[0301] 60. A non-transitory computer-readable storage medium comprising instructions stored thereon for evaluating the suitability of a combination of fluorescent dyes for a flow cytometry protocol, the instructions analyzing a biological sample by a method comprising the steps of:

[0302] Receiving:

[0303] An initial fluorescent dye combination, which includes a fluorescent dye identifier group and a biomarker identifier group, where each fluorescent dye identifier refers to a fluorescent dye in the fluorescent dye group, and where each biomarker identifier is associated with a fluorescent dye identifier in the fluorescent dye identifier group;

[0304] A plurality of population identifiers, where each population identifier refers to a population of particles; and

[0305] An instrument identifier;

[0306] By associating each population identifier in the plurality of population identifiers with a biomarker identifier in the biomarker identifier group to create a group of population-biomarker pairs;

[0307] Generating a group of separability metrics, where each separability metric predicts a metric value of the statistical distance between populations of particles in the flow cytometry data space, where each metric value of the statistical distance is related to the detected signal intensity, which is generated by each fluorescent dye associated with each population-biomarker pair and is used in the flow cytometry protocol of the instrument associated with the instrument identifier;

[0308] Aggregating the group of separability metrics into a combined score; and

[0309] Evaluating the combined score to assess the suitability of the initial fluorescent dye combination for the flow cytometry protocol.

[0310] 61. The non-transitory computer-readable storage medium according to clause 60, where creating the group of population-biomarker pairs includes creating population-biomarker pairs for a latent population of particles that is not mentioned in the received plurality of population identifiers but is present in the biological sample.

[0311] 62. The non-transitory computer-readable storage medium according to clause 60 or 61, where creating the group of population-biomarker pairs further includes defining a quantitative pair of one or more biomarker identifiers, which is used to evaluate the quantitative expression of the population of particles.

[0312] 63. The non-transitory computer-readable storage medium according to any one of clauses 60 to 62, where generating the group of separability metrics includes predicting the statistical moments of each biomarker identifier in the biomarker identifier group based on the detected signal intensity.

[0313] 64. The non-transitory computer-readable storage medium according to clause 63, where predicting the statistical moments includes predicting the covariance matrix of the detected signal intensity.

[0314] 65. The non-transitory computer-readable storage medium according to clause 63, wherein the predicted statistical moment includes a variance-covariance matrix of the predicted detected signal strength.

[0315] 66. The non-transitory computer-readable storage medium according to clause 63, wherein the predicted statistical moment includes a mean matrix of the predicted detected signal strength.

[0316] 67. The non-transitory computer-readable storage medium according to any one of clauses 63 to 66, wherein the predicted statistical moment includes running a Monte Carlo simulation.

[0317] 68. The non-transitory computer-readable storage medium according to any one of clauses 63 to 67, wherein the statistical moment of each biomarker identifier in the predicted biomarker identifier group includes incorporating the influence of a noise model into the detected signal strength.

[0318] 69. The non-transitory computer-readable storage medium according to clause 68, wherein the noise model is a Gaussian noise model.

[0319] 70. The non-transitory computer-readable storage medium according to clause 68, wherein the noise model is a Poisson noise model.

[0320] 71. The non-transitory computer-readable storage medium according to any one of clauses 68 to 70, wherein incorporating the influence of the noise model into the detected signal strength includes obtaining an analytical formula that correlates the predicted statistical moment with the noise model.

[0321] 72. The non-transitory computer-readable storage medium according to clause 71, wherein the method further includes incorporating the influence of the noise model into the detected signal strength based on a spillover diffusion matrix (SSM).

[0322] 73. The non-transitory computer-readable storage medium according to any one of clauses 60 to 72, wherein generating the group of separability metrics includes stabilizing the variance of the detected signal strength.

[0323] 74. The non-transitory computer-readable storage medium according to clause 73, wherein stabilizing the variance of the detected signal strength includes double-exponential scaling.

[0324] 75. The non-transitory computer-readable storage medium according to clause 73, wherein stabilizing the variance of the detected signal strength includes inverse hyperbolic function scaling.

[0325] 76. The non-transitory computer-readable storage medium according to clause 73, wherein stabilizing the variance of the detected signal strength includes solving an optimization problem with an objective function that is the similarity of the variances of different distributions of the detected signal strength.

[0326] 77. The non-transitory computer-readable storage medium according to clause 73, wherein stabilizing the variance of the detected signal strength includes determining an analytical relationship between the variance and the average value of the detected signal strength.

[0327] 78. The non-transitory computer-readable storage medium according to any one of clauses 60 to 77, wherein aggregating the separability metric groups into a combined score includes inverting the value of the lowest separability score.

[0328] 79. The non-transitory computer-readable storage medium according to clause 62, wherein the method further includes aggregating the separability metric groups for each population-marker pair and quantitative pair, respectively.

[0329] 80. The non-transitory computer-readable storage medium according to clause 79, wherein determining the combined score includes calculating vectors of the separability metric groups aggregated for the population-marker pairs and the separability metric groups aggregated for the quantitative pairs.

[0330] 81. The non-transitory computer-readable storage medium according to any one of clauses 60 to 77, wherein aggregating the separability metric groups includes comparing each separability metric with a threshold.

[0331] 82. The non-transitory computer-readable storage medium according to any one of clauses 60 to 81, wherein the method further includes generating an optimized combination of fluorescent dyes based on an assessment of the suitability of an initial combination of fluorescent dyes for a flow cytometry protocol.

[0332] 83. The non-transitory computer-readable storage medium according to clause 82, wherein generating the optimized combination of fluorescent dyes includes determining a combination of fluorescent dyes having an optimized number of combinations.

[0333] 84. The non-transitory computer-readable storage medium according to clause 82 or 83, wherein generating the optimized combination of fluorescent dyes includes adjusting the fluorescent dyes in the initial combination of fluorescent dyes and assessing the suitability of the adjusted combination of fluorescent dyes for the flow cytometry protocol.

[0334] 85. The non-transitory computer-readable storage medium according to clause 84, wherein generating the optimized combination of fluorescent dyes includes iteratively adjusting the combination of fluorescent dyes and assessing the suitability of each iteratively adjusted combination of fluorescent dyes for the flow cytometry protocol.

[0335] 86. The non-transitory computer-readable storage medium according to any one of clauses 60 to 85, wherein the method further includes:

[0336] receiving a gating strategy; and

[0337] Determine an initial fluorescent dye combination based on the gating strategy.

[0338] 87. The non-transitory computer-readable storage medium according to any one of clauses 60 to 85, wherein the initial fluorescent dye combination is determined randomly.

[0339] 88. A method for evaluating the suitability of a fluorescent dye combination for a flow cytometry protocol for analyzing a biological sample, the method comprising:

[0340] (a) Input into a processor:

[0341] An initial fluorescent dye combination, which includes a group of fluorescent dye identifiers and a group of biomarker identifiers, wherein each fluorescent dye identifier refers to a fluorescent dye in the group of fluorescent dyes, and wherein each biomarker identifier is associated with a fluorescent dye identifier in the group of fluorescent dye identifiers;

[0342] A plurality of population identifiers, wherein each population identifier refers to a population of particles; and

[0343] An instrument identifier, wherein the processor is configured to:

[0344] Create a group of population-biomarker pairs by associating each population identifier in the plurality of population identifiers with a biomarker identifier in the group of biomarker identifiers;

[0345] Generate a group of separability metrics, wherein each separability metric predicts a metric value of the statistical distance between populations of particles in the flow cytometry data space, and wherein each metric value of the statistical distance is related to the detected signal intensity, which is generated by each fluorescent dye associated with each population-biomarker pair and is used in the flow cytometry protocol of the instrument associated with the instrument identifier;

[0346] Aggregate the group of separability metrics into a combined score; and

[0347] Evaluate the combined score to evaluate the suitability of the initial fluorescent dye combination for the flow cytometry protocol; and

[0348] (b) Receive from the processor an evaluation of the suitability of the fluorescent dye combination for the flow cytometry protocol.

[0349] 89. The method according to clause 88, wherein creating the group of population-biomarker pairs includes creating population-biomarker pairs for latent particle populations that are not mentioned in the received plurality of population identifiers but are present in the biological sample.

[0350] 90. The method according to clause 88 or 89, wherein creating the group of population-marker pairs further comprises defining a quantitative pair of one or more biomarker identifiers for evaluating the quantitative expression of the particle population.

[0351] 91. The system according to any one of clauses 88 to 90, wherein generating the group of separability metrics comprises predicting the statistical moments of each biomarker identifier in the group of biomarker identifiers based on the detected signal intensities.

[0352] 92. The method according to clause 91, wherein predicting the statistical moments comprises predicting the covariance matrix of the detected signal intensities.

[0353] 93. The method according to clause 91, wherein predicting the statistical moments comprises predicting the variance-covariance matrix of the detected signal intensities.

[0354] 94. The method according to clause 91, wherein predicting the statistical moments comprises predicting the mean matrix of the detected signal intensities.

[0355] 95. The method according to any one of clauses 91 to 94, wherein predicting the statistical moments comprises running a Monte Carlo simulation.

[0356] 96. The method according to any one of clauses 91 to 95, wherein predicting the statistical moments of each biomarker identifier in the group of biomarker identifiers comprises incorporating the effect of the noise model into the detected signal intensities.

[0357] 97. The method according to clause 96, wherein the noise model is a Gaussian noise model.

[0358] 98. The method according to clause 96, wherein the noise model is a Poisson noise model.

[0359] 99. The method according to any one of clauses 96 to 98, wherein incorporating the effect of the noise model into the detected signal intensities comprises obtaining an analytical formula that relates the predicted statistical moments to the noise model.

[0360] 100. The system according to clause 99, wherein the processor is configured to incorporate the effect of the noise model into the detected signal intensities based on the spillover spread matrix (SSM).

[0361] 101. The method according to any one of clauses 88 to 100, wherein generating the group of separability metrics comprises stabilizing the variance of the detected signal intensities.

[0362] 102. The method according to clause 101, wherein stabilizing the variance of the detected signal intensities comprises double-exponential scaling.

[0363] 103. The method according to clause 101, wherein stabilizing the variance of the detected signal strength includes inverse hyperbolic function scaling.

[0364] 104. The method according to clause 101, wherein stabilizing the variance of the detected signal strength includes solving an optimization problem with an objective function that is the similarity of variances of different distributions of the detected signal strength.

[0365] 105. The method according to clause 101, wherein stabilizing the variance of the detected signal strength includes determining an analytical relationship between the variance and the mean of the detected signal strength.

[0366] 106. The system according to any one of clauses 88 to 105, wherein aggregating the separability metric groups into a combined score includes negating the value of the lowest separability score.

[0367] 107. The system according to clause 90, wherein the processor is configured to aggregate the separability metric groups for each population-marker pair and quantification pair respectively.

[0368] 108. The method according to clause 107, wherein determining the combined score includes calculating vectors of the separability metric groups aggregated for the population-marker pair and the separability metric groups aggregated for the quantification pair.

[0369] 109. The method according to any one of clauses 88 to 105, wherein aggregating the separability metric groups includes comparing each separability metric with a threshold.

[0370] 110. The system according to any one of clauses 88 to 109, wherein the processor is configured to generate an optimized fluorescent dye combination based on an evaluation of the suitability of an initial fluorescent dye combination for a flow cytometry protocol.

[0371] 111. The method according to clause 110, wherein generating the optimized fluorescent dye combination includes determining a fluorescent dye combination with an optimized number of combinations.

[0372] 112. The method according to clause 110 or 111, wherein generating the optimized fluorescent dye combination includes adjusting the fluorescent dyes in the initial fluorescent dye combination and evaluating the suitability of the adjusted fluorescent dye combination for the flow cytometry protocol.

[0373] 113. The method according to clause 112, wherein generating the optimized fluorescent dye combination includes iteratively adjusting the initial fluorescent dye combination and evaluating the suitability of each iteratively adjusted fluorescent dye combination for the flow cytometry protocol.

[0374] 114. The method according to any one of clauses 110 or 113 further comprises receiving an optimized fluorescent dye from the processor.

[0375] 115. The method according to any one of clauses 88 or 114 further comprises inputting a gating strategy into the processor, wherein the processor determines an initial fluorescent dye combination based on the gating strategy.

[0376] 116. The method according to any one of clauses 88 or 114, wherein the initial fluorescent dye combination is determined randomly.

[0377] Although the foregoing invention has been described in detail for purposes of clear understanding by way of illustrations and examples, it will be obvious to those of ordinary skill in the art that certain changes and modifications may be made thereto in accordance with the teachings of the present invention without departing from the spirit or scope of the appended claims.

[0378] Accordingly, the foregoing merely illustrates the principles of the invention. It is to be understood that those skilled in the art will be able to devise various arrangements which, although not explicitly described or shown herein, embody the principles of the invention and are included within its spirit and scope. In addition, all of the examples and conditional language recited herein are principally intended to aid the reader in understanding the principles of the invention and the concepts contributed by the inventor to further the art, and are to be construed as not being limited to such specifically recited examples and conditions. Moreover, all statements herein reciting principles, aspects, and embodiments of the invention and specific examples thereof are intended to cover both structural and functional equivalents thereof. In addition, such equivalents are intended to include both currently known equivalents and equivalents developed in the future, i.e., any elements developed that perform the same function. Furthermore, nothing disclosed herein is intended to be dedicated to the public regardless of whether such disclosure is explicitly recited in the claims.

[0379] 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 are 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 exact phrase "means" or the exact phrase "step" is recited at the beginning of the limitation in the claim; if such exact phrase is not used in the limitation of the claim, then 35 U.S.C. § 112(f) or 35 U.S.C. § 112(6) is not invoked.

Claims

1. A method for evaluating the suitability of a fluorescent dye combination for a flow cytometry protocol for analyzing biological samples, the method comprising, using a processor: Receiving: An initial fluorescent dye combination, which includes a group of fluorescent dye identifiers and a group of biomarker identifiers, wherein each fluorescent dye identifier refers to a fluorescent dye in the group of fluorescent dyes, and wherein each biomarker identifier is associated with a fluorescent dye identifier in the group of fluorescent dye identifiers; A plurality of population identifiers, wherein each population identifier refers to a population of particles; And An instrument identifier; By associating each population identifier in the plurality of population identifiers with a biomarker identifier in the group of biomarker identifiers to create a group of population-biomarker pairs, Generating a group of separability measures, wherein each separability measure predicts a measure of the statistical distance between populations of particles in a flow cytometry data space, and wherein each measure of the statistical distance is related to the detected signal intensity, the detected signal intensity being generated by each fluorescent dye associated with each population-biomarker pair, the fluorescent dye being used in a flow cytometry protocol of an instrument associated with the instrument identifier; Aggregating the group of separability measures into a combined score; and Evaluating the combined score to assess the suitability of the initial fluorescent dye combination for the flow cytometry protocol.

2. The method according to claim 1, wherein creating the group of population-biomarker pairs includes creating population-biomarker pairs for latent particle populations that are not mentioned in the received plurality of population identifiers but are present in the biological sample.

3. The method according to claim 1 or 2, wherein creating the group of population-biomarker pairs further includes defining a quantitative pair of one or more biomarker identifiers for evaluating the quantitative expression of a population of particles.

4. The method according to any one of the preceding claims, wherein generating the group of separability measures includes predicting the statistical moments of each biomarker identifier in the group of biomarker identifiers based on the detected signal intensity.

5. The method according to any one of the preceding claims, wherein generating the group of separability measures includes stabilizing the variance of the detected signal intensity.

6. The method according to any one of the preceding claims, wherein aggregating the group of separability measures into a combined score includes negating the value of the lowest separability score.

7. The method according to any one of the preceding claims, further comprising generating an optimized fluorescent dye combination based on the assessment of the suitability of the initial fluorescent dye combination for the flow cytometry protocol.

8. The method according to claim 7, wherein generating the optimized fluorescent dye combination includes determining a fluorescent dye combination having an optimized number of combinations.

9. The method according to claim 7 or 8, wherein generating the optimized fluorescent dye combination includes adjusting the fluorescent dyes in the initial fluorescent dye combination and evaluating the suitability of the adjusted fluorescent dye combination for the flow cytometry protocol.

10. The method according to claim 9, wherein generating an optimized fluorescent dye combination comprises iteratively adjusting an initial fluorescent dye combination and evaluating the suitability of each iteratively adjusted fluorescent dye combination for a flow cytometry protocol.

11. The method according to any one of the preceding claims, further comprising: receiving a gating strategy; and determining an initial fluorescent dye combination based on the gating strategy.

12. The method according to any one of the preceding claims, wherein the initial fluorescent dye combination is determined randomly.

13. The method according to any one of the preceding claims, further comprising determining the initial fluorescent dye combination with a processor.

14. A system, comprising: a processor configured to: receive: an initial fluorescent dye combination comprising a group of fluorescent dye identifiers and a group of biomarker identifiers, wherein each fluorescent dye identifier refers to a fluorescent dye in a group of fluorescent dyes, and wherein each biomarker identifier is associated with a fluorescent dye identifier in the group of fluorescent dye identifiers; a plurality of population identifiers, wherein each population identifier refers to a population of particles; and an instrument identifier; create a group of population-biomarker pairs by associating each population identifier in the plurality of population identifiers with a biomarker identifier in the group of biomarker identifiers; generate a group of separability metrics, wherein each separability metric predicts a metric value of a statistical distance between populations of particles in a flow cytometer data space, and wherein each metric value of the statistical distance is related to a detected signal intensity generated by each fluorescent dye associated with each population-biomarker pair, the fluorescent dye being used in a flow cytometry protocol for an instrument associated with the instrument identifier; aggregate the group of separability metrics into a combined score; and evaluate the combined score to assess the suitability of the initial fluorescent dye combination for a flow cytometry protocol.

15. A method for evaluating the suitability of a fluorescent dye combination for a flow cytometry protocol for analyzing a biological sample, the method comprising: (a) inputting into a processor: an initial fluorescent dye combination comprising a group of fluorescent dye identifiers and a group of biomarker identifiers, wherein each fluorescent dye identifier refers to a fluorescent dye in a group of fluorescent dyes, and wherein each biomarker identifier is associated with a fluorescent dye identifier in the group of fluorescent dye identifiers; a plurality of population identifiers, wherein each population identifier refers to a population of particles; and an instrument identifier, wherein the processor is configured to: create a group of population-biomarker pairs by associating each population identifier in the plurality of population identifiers with a biomarker identifier in the group of biomarker identifiers; generate a group of separability metrics, wherein each separability metric predicts a metric value of a statistical distance between populations of particles in a flow cytometer data space, and wherein each metric value of the statistical distance is related to a detected signal intensity generated by each fluorescent dye associated with each population-biomarker pair, the fluorescent dye being used in a flow cytometry protocol for an instrument associated with the instrument identifier; aggregate the group of separability metrics into a combined score; and Evaluate the combined scores to assess the suitability of an initial combination of fluorescent dyes for a flow cytometry protocol; and (b) Receive from the processor an assessment of the suitability of a combination of fluorescent dyes for a flow cytometry protocol.

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