Method and system for evaluating the suitability of a fluorescent dye panel for use in generating flow cytometer data

Abstract

A method is provided for evaluating the suitability of a fluorescent dye panel for use in generating flow cytometer data. The method in question includes obtaining a fluorescent dye panel, an instrument identifier, and a spectral matrix associated with the fluorescent dye panel and the instrument identifier. The method of the subject also includes calculating an inverse matrix from the obtained spectral matrix and analyzing the calculated inverse matrix to identify fluorescent dyes in the fluorescent dye panel that will be associated with the variance of flow cytometer data generated using the fluorescent dye panel, and evaluating the suitability of the fluorescent dye panel for use in generating flow cytometer data. A system and a non-transitory computer-readable storage medium for implementing the present invention are also provided.

Description

Background Art

[0001] The characterization of analytes in biological fluids is an important part of the biological research, medical diagnosis, and evaluation of a patient's overall health and wellness. Detecting analytes in biological fluids such as human blood or blood-derived products can provide results that can play a role in determining treatment protocols for patients with various disease states.

[0002] Particle analysis (e.g., flow cytometry) is a technique used to characterize biological materials, such as cells in a blood sample or target particles in another type of biological or chemical sample, and often to sort them. 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 transfers the particles (including cells) in the fluid sample as a cell stream to the flow cell while directing the sheath fluid to the flow cell. Light is directed at the flow stream to characterize the components of the flow stream.

[0003] Variations in the materials in the flow stream, such as the presence of morphological or fluorescent labels, can cause variations in the observed light, and these variations enable characterization and separation. To characterize the components in the flow stream, light must strike the flow stream and be collected. The light sources within a flow cytometer can vary and can include one or more broad-spectrum lamps, light-emitting diodes, and single-wavelength lasers. The light source is aligned with the flow stream such that the optical response from the illuminated particles is collected and quantified.

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

[0005] When a flow cytometry protocol involves the detection of fluorescence, the experimental design generally includes the identification of a fluorescence dye panel, i.e., a collection of fluorescence dyes that are used together in a given flow cytometry workflow. Biological resolution (i.e., the ability to distinguish different target components within or between the target particles) is directly affected by both the measurement variance of the "raw" flow cytometry data and the mathematical processes of spectral correction or decomposition. Therefore, a process for fluorescence dye panel design is required. Both of these factors strongly depend on the selection of the fluorescence dyes within the panel. First, the measurement variance (i.e., noise) in flow cytometry results from a wide range of causes, including a certain baseline measurement noise in the cytometer's electronics, optical shot noise that varies linearly with signal intensity, and multiplicative measurement noise resulting from random variations in the cytometer's laser and fluid that vary quadratically with signal intensity. The measurement noise itself depends on the selection of the fluorescence dye. For example, brighter fluorescence dyes induce more shot noise than dimmer fluorescence dyes, and dimmer fluorescence dyes have a smaller signal magnitude compared to a certain "noise floor" of the instrument's optical and electronic systems. Second, the raw measurement noise within the "detector space" (which has the same number of dimensions as the number of detectors within the instrument) propagates through the mathematical processes of fluorescence correction (in conventional cytometers) or spectral decomposition (in full-spectrum cytometers) to the final biological data within the "corrected space" or "unmixed space" (which has the same number of dimensions as the number of fluorescence dyes in the sample). The variance in the "unmixed space" is important because it is the space in which the final biological analysis of interest (e.g., gating, clustering, sorting, marker quantification, etc.) is performed. This mathematical mapping of noise to the biological space strongly depends on the spectral signature of the fluorescence dye itself.

[0006] The presence of significant spectral overlap within a flow cytometry panel leads to unacceptable levels of resolved data variance (aka, noise or spread), resulting in a decrease in resolved signal-to-noise and potentially rendering it impossible to resolve differences in biological marker expression in multicolor flow cytometry experiments. Resolved-dependence spread appears in three main ways: i) an increase in resolved variance for unstained particles, independent of fluorescent dye expression (i.e., “negative spread”), ii) an increase in resolved variance for one fluorescent dye due to intensity-dependent photon noise (i.e., “spillover spreading”) arising from the expression of one or more other fluorescent dyes, and iii) an increase in the co-variance between resolved fluorescent dyes (most prominent as “tilted diagonal double negativity” for the unstained population). For a given panel, resolved-dependence spread tends to affect certain fluorescent dyes much more than others. As the number of fluorophores in the panel and the overall degree of spectral overlap increase, resolved-dependence spread places an effective upper limit on the number of fluorescent dyes that can be used simultaneously in a single flow cytometry experiment. Practitioners must exercise great care in the design of fluorescent dye panels to avoid or mitigate this resolved-dependence spread.

[0007] Currently, there are two metrics for predicting resolved-dependence spread in a panel: cosine similarity (i.e., “similarity index”) and spectral matrix condition number (i.e., “complexity score”). Cosine similarity is a pairwise metric that describes the spectral overlap between two fluorescent dyes. This metric can predict pairs of fluorescent dyes that are most susceptible to the effects of spillover spreading and can be used to “exclude” pairs that are nearly identical and cannot be used together. Spectral matrix condition number is a single scalar metric that describes the numerical stability with which the spectral resolution problem can be solved for the fluorescent dyes of a given full panel. This metric can roughly predict panels that are expected to have a larger or smaller resolved-dependence spread.

[0008] Conventional flow cytometry, where individual photodetectors are dedicated to a dye's native fluorescence emission band, imposes a strict limit on the number of fluorescent dyes that can be used simultaneously in a flow experiment, and the number of fluorescent dyes may not exceed the number of fluorescence detection channels on the instrument. In contrast, full-spectrum flow cytometers, by definition, use more detectors than fluorescent dyes, and commercially available full-spectrum flow cytometers are available with over 180 fluorescence channels. Summary of the Invention

[0009] The inventors recognize that there are practical limits to the number of fluorescent dyes that can be used simultaneously in flow cytometry experiments. Despite nearly 100 distinct fluorescent dye molecules being commercially available for flow cytometry, the panel size remains limited. This practical limit results from the inevitable spectral overlap and similarity of the fluorescent dyes used. In addition, conventional methods for fluorescent dye analysis have been found to be inadequate. For example, cosine similarity does not consider the performance of fluorophores in the context of the full panel and does not provide a quantitative prediction of the severity of the spectral spread. In addition, the condition number does not provide an indicator of which fluorescent dyes are most affected by the spectral spread due to decomposition and does not indicate which fluorescent dyes are the source of the problem. The spectral spread can vary widely for the same fluorescent dye in two different panels with nearly the same condition number. In general, one panel may have a high degree of spectral spread "focused" on one or two fluorescent dyes, while another panel with nearly the same condition number may have a lower degree of spectral spread that is more evenly distributed across more fluorescent dyes in the panel. It is not possible to meaningfully compare the condition numbers across panels of different sizes. Alternative metrics such as the spillover spreading matrix (SSM) and the total spreading matrix (TSM) can be empirically calculated from measurement data such as singly stained particles. However, collecting such data is labor-intensive and requires repeating the spectral analysis for each candidate panel, leading to a high computational complexity that hinders rapid calculations (such as those required for an automated panel design algorithm).

[0010] In light of the above, there is a recognized need for a new panel performance metric that provides both information regarding panel-dependent spectral spread (such as the condition number) and information regarding dye-specific performance and dye-dye interactions (such as cosine similarity) while requiring minimal experimental overhead or computational complexity. Accordingly, methods and systems for evaluating and selecting a suitable fluorescent dye panel are desirable. Embodiments of the present invention meet this need.

[0011] Aspects of the present invention include a method for evaluating the suitability of a fluorescent dye panel for use in generating flow cytometer data. The method in question includes obtaining a fluorescent dye panel, an instrument identifier, and a spectral matrix associated with the fluorescent dye panel and the instrument identifier. Additionally, the method of the present invention includes calculating an inverse matrix from the obtained spectral matrix and analyzing the calculated inverse matrix to identify fluorescent dyes in the fluorescent dye panel that will be associated with the variance of the flow cytometer data generated using the fluorescent dye panel, and evaluating the suitability of the fluorescent dye panel for use in generating flow cytometer data. In some embodiments, the fluorescent dyes in the fluorescent dye panel that will be associated with the variance of the flow cytometer data will contribute to the variance of the flow cytometer data. In additional embodiments, the fluorescent dyes in the fluorescent dye panel that will be associated with the variance of the flow cytometer data will be affected by the variance of the flow cytometer data. The inverse matrix can include, for example, a pseudo-inverse matrix (e.g., a Moore-Penrose pseudo-inverse matrix). In some cases, the inverse matrix is a Gram inverse matrix. Analyzing the calculated inverse matrix can, in certain cases, include deriving a quantitative metric (e.g., a matrix norm, a vector norm) from the inverse matrix. Embodiments of the subject method also include optimizing the fluorescent dye panel based on an evaluation of the suitability of the fluorescent dye panel for use in generating flow cytometer data. In selected cases, optimizing the fluorescent dye panel includes using a panel optimization algorithm. In a particular version, optimizing the fluorescent dye panel includes adjusting the fluorescent dyes in the fluorescent dye panel and evaluating the suitability of the adjusted fluorescent dye panel for use in generating flow cytometer data. Flow cytometer data can, in embodiments, include a number of dimensions equal to the number of fluorescent dyes in a plurality of fluorescent dyes (e.g., spectral decomposition or corrected). In some cases, the variance includes noise in the flow cytometer data.The method may further include generating a visualization of the evaluated suitability of a fluorescent dye panel for use in generating flow cytometry data, such as highlighting the fluorescent dyes in the fluorescent dye panel where the visualization will be associated with the variance of flow cytometry data generated using the fluorescent dye panel.

[0012] Aspects of the invention also include a system for implementing the subject method (e.g., as described above). The system of interest includes a fluorescent dye panel, a device identifier, and a processor configured to obtain a spectral matrix associated with the fluorescent dye panel and the device identifier. The processor described herein is also configured to calculate an inverse matrix from the obtained spectral matrix and analyze the calculated inverse matrix to identify fluorescent dyes in the fluorescent dye panel that will be associated with the variance of flow cytometry data generated using the fluorescent dye panel, thereby evaluating the suitability of the fluorescent dye panel for use in generating flow cytometry data. The processor may be additionally configured to optimize the fluorescent dye panel based on the evaluation of the suitability of the fluorescent dye panel for use in generating flow cytometry data. In some embodiments, the processor is configured to generate a visualization of the evaluated suitability of a fluorescent dye panel for use in generating flow cytometry data, such as highlighting the fluorescent dyes in the fluorescent dye panel where the visualization will be associated with the variance of flow cytometry data generated using the fluorescent dye panel. In some such embodiments, the system includes a display configured to depict the visualization.

[0013] Aspects of the invention include a non-transitory computer-readable storage medium including instructions stored on the non-transitory computer-readable storage medium for evaluating the suitability of a fluorescent dye panel for use in generating flow cytometer data by a method of the invention (e.g., as described above). As discussed above, the method includes obtaining a fluorescent dye panel, an instrument identifier, and a spectral matrix associated with the fluorescent dye panel and the instrument identifier. The method executed by the non-transitory computer-readable storage medium also includes identifying fluorescent dyes in the fluorescent dye panel that will be associated with the variance of flow cytometer data generated using the fluorescent dye panel by analyzing the calculated inverse matrix, and evaluating the suitability of the fluorescent dye panel for use in generating flow cytometer data. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication and the color drawings are provided by the Patent Office upon request and payment of the necessary fee.

[0015] The present invention can be best understood from the following detailed description when read in conjunction with the accompanying drawings. The drawings include the following figures.

[0016]

Figure 1

Figure 2

Figure 3

Figure 4A

Figure 4B

Figure 5A

Figure 5B

Figure 5C

Figure 5D

Figure 6A

Figure 6B-1

Figure 6B-2

Figure 7A

Figure 7B

Figure 7C

Figure 7D

Figure 7E

Figure 8

Figure 9

Figure 10

Figure 11A

Figure 11B

Figure 12

Figure 13A

Figure 13B

Figure 14A

Figure 14B

Figure 15A

Figure 15B

Figure 16A

Figure 16B

Figure 16C

Figure 16D

Figure 16E

Figure 16F

Figure 16G

Figure 17A

Figure 17B

Figure 17C

Figure 17D

Figure 17E

Figure 17F

Figure 17G

Figure 18-1

Figure 18-2

Figure 19

Figure 20-1

Figure 20-2

Figure 21-1

Figure 21-2

Figure 22-1

Figure 22-2

Figure 23-1

Figure 23-2

Figure 24-1

Figure 24-2

Figure 25-1

Figure 25-2

Figure 25-3

Figure 26-1

Figure 26-2

Figure 26-3

Figure 27A

Figure 27B

Figure 27C

Figure 28A

Figure 28B

Figure 28C

Figure 28D

Figure 28E

Figure 28F

DETAILED DESCRIPTION OF THE INVENTION

[0017] A method is provided for evaluating the suitability of a fluorescent dye panel for use in generating flow cytometer data. The method in question includes obtaining a fluorescent dye panel, an instrument identifier, and a spectral matrix associated with the fluorescent dye panel and the instrument identifier. The method of the subject also includes calculating an inverse matrix from the obtained spectral matrix and analyzing the calculated inverse matrix to identify fluorescent dyes in the fluorescent dye panel that will be associated with the variance of the flow cytometer data generated using the fluorescent dye panel, and evaluating the suitability of the fluorescent dye panel for use in generating flow cytometer data. A system and a non-transitory computer-readable storage medium for implementing the present invention are also provided.

[0018] Before the present invention is described in more detail, it is to be understood that the invention is not limited to the specific embodiments described, and accordingly, can of course vary. Also, since the scope of the present invention will be limited only by the appended claims, it is to be understood that the terms used herein are for the purpose of describing only specific embodiments and are not intended to be limiting.

[0019] Where a range of values is provided, unless the context clearly dictates otherwise, each intervening value, to the tenth of the unit of the lower limit, is included between the upper and lower limits of that range and any other stated value or intervening value within that stated range. The upper and lower limits of these smaller ranges may independently be included in the smaller ranges and are also included in the present invention, subject to any specific excluded limitation within the stated range. Ranges excluding any one or both of those included limitations are also included in the present invention where the stated range includes one or both of those limitations.

[0020] A particular range is presented in this specification with the term "about" preceding a numerical value. The term "about" is used in this specification to provide literal support for the exact number that this term precedes, and for a number that is close to or approximately that number which this term precedes. When determining whether a number is close to or approximately a specifically recited number, a number that is close to or approximately that number which is not recited may, in the context presented, be a number that provides substantial equivalence to the specifically recited number.

[0021] Unless otherwise defined, all technical and scientific terms used in this specification have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Although any methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention, representative, exemplary methods and materials are described herein.

[0022] All publications and patents cited herein are hereby incorporated by reference as if each individual publication or patent were specifically and individually indicated to be incorporated by reference, and incorporated by reference herein to disclose and describe the methods and / or materials for which the publication is cited. The citation of any publication is for its disclosure prior to the filing date and should not be construed as an admission that the present invention has no right to antedate such publication by virtue of prior invention. Further, the dates of the publications provided may be different from the actual publication dates, which may need to be independently confirmed.

[0023] Note that, as used in this specification and the appended claims, the singular forms of "a", "an", and "the" include plural referents unless the context clearly dictates otherwise. Further note that the claims may be drafted to exclude any optional elements. Accordingly, this description is intended to serve as a basis for use of exclusive terms such as "solely", "only", etc. or the use of "negative" limitations in connection with the recitation of claim elements.

[0024] As will be apparent to those skilled in the art upon reading this disclosure, each of the separate embodiments described and illustrated herein has distinct components and features that can be readily separated from, or combined with, the features of any of the various other embodiments without departing from the scope or spirit of the invention. Any of the recited methods can be performed in the order of the recited events, or in any other order that is logically possible.

[0025] The systems and methods are described, or are to be described, with a grammatical fluidity that includes functional descriptions, but the claims are not to be construed as necessarily limited by the construction of "means" or "step" limitations, unless expressly set forth under 35 U.S.C. § 112, and are to be given the meaning ascribed by the claims under the judicially created doctrine of equivalents, including the full scope of equivalents thereof. It should be expressly understood that where the claims are expressly set forth under 35 U.S.C. § 112, full statutory equivalents under 35 U.S.C. § 112 are to be afforded.

[0026] Method for evaluating a fluorescent dye panel As discussed above, aspects of the present invention include methods for evaluating the suitability of a fluorescent dye panel for use in generating flow cytometer data. The method of interest includes receiving a fluorescent dye panel. As described herein, a "fluorescent dye panel" refers to a set of different fluorescent molecular substances (i.e., dyes) that can be used to identify particles in a sample or specific portions or components associated therewith. A "fluorescent dye panel" as described herein can also refer to a set of identifiers (e.g., digital identifiers) that uniquely reference a specific fluorescent molecular substance and are associated with the specific fluorescent molecular substance. Such identifiers may be referred to herein as "fluorescent dye identifiers". The different fluorescent dyes within a fluorescent dye panel may differ with respect to properties such as absorption spectrum, extinction coefficient, emission spectrum, and quantum efficiency (i.e., the number of photons emitted per photon absorbed), or combinations thereof. Thus, different or distinct fluorescent dyes may differ from one another with respect to chemical composition and / or with respect to one or more properties of the dyes. For example, a given pair of fluorescent dyes may be considered different if they differ from one another in terms of their point of maximum excitation and / or maximum emission, and the magnitude of such a difference is, in some cases, 5 nm or more, e.g., 10 nm or more (including 15 nm or more), and in some cases, the magnitude of the difference is in the range of 5 to 400 nm, e.g., 10 to 200 nm (including 15 to 100 nm), e.g., 25 to 50 nm.

[0027] In certain cases, the method is performed without evaluating antigen data. By "antigen data" is meant information regarding the antigens to be evaluated in a given flow cytometric protocol where a fluorochrome panel is desired. In other words, in contrast to methods of selecting fluorochromes taking into account and accordingly to the properties of the antigens to which they are associated, embodiments of the subject method instead aim to provide a set of fluorochromes (i.e., a "color palette") suitable for use in the same flow cytometric protocol. In these cases, the antigenicity of the sample is not considered during the identification and / or evaluation of the fluorochrome panel. In certain versions, after the subject fluorochrome panel has been identified, the method may also optionally include assigning the fluorochromes within the identified panel to specific antigens (e.g., via antibodies targeting the relevant antigens, etc.).

[0028] The method of the present invention also includes receiving a device identifier. The "device identifier" means information or data that refers to a specific device (e.g., a particle analyzer, a flow cytometer). In certain cases, the device identified via the device identifier is a flow cytometer. Any convenient flow cytometer configured to analyze fluorescent particle-modulated light can be used. In certain instances, the flow cytometer of interest includes those produced by BD Biosciences.Exemplary flow cytometers include the BD Biosciences FACSCanto™ flow cytometer, BD Biosciences FACSCanto™ II flow cytometer, BD Accuri™ flow cytometer, BD Accuri™ C6 Plus flow cytometer, BD Biosciences FACSCelesta™ flow cytometer, BD Biosciences FACSLyric™ flow cytometer, BD Biosciences FACSVerse™ flow cytometer, BD Biosciences FACSymphony™ flow cytometer, BD Biosciences LSRFortessa™ flow cytometer, BD Biosciences LSRFortessa™ X-20 flow cytometer, BD Biosciences FACSPresto™ flow cytometer, BD Biosciences FACSVia™ flow cytometer, and the BD Biosciences FACSCalibur™ cell sorter, BD Biosciences FACSCount™ cell sorter, BD Biosciences FACSLyric™ cell sorter, and BD Biosciences Via™ cell sorter, BD Biosciences Influx™ cell sorter, BD Biosciences Jazz™ cell sorter, BD Biosciences Aria™ cell sorter, BD Biosciences FACSAria™ II cell sorter, BD Biosciences FACSAria™ III cell sorter, BD Biosciences FACSAria™ Fusion cell sorter, and BD Biosciences FACSMelody™ cell sorter, BD Biosciences FACSymphony™ S6 cell sorter, and the like.

[0029] The method of the present invention includes obtaining a spectral matrix associated with a fluorescent dye panel and an instrument identifier. As described herein, a "spectral matrix" refers to a matrix that includes information regarding the spectral characteristics of a set of fluorescent dyes. In an embodiment, the spectral matrix of interest includes a set of spectral signatures associated with each fluorescent dye identifier in a set of fluorescent dye identifiers. A "spectral signature" refers to the characteristics of the fluorescence spectrum of an individual fluorescent dye represented as one or more numerical values. Two entities may be described as "associated" with each other if these entities are linked and / or related to each other within a data space. In other words, a spectral matrix associated with an instrument identifier (also referred to herein as the "input" or "original" spectral matrix) may represent a "palette" of possible fluorescent dyes or dyes from which the fluorescent dye panel of interest is selected. In a particular case, the spectral matrix includes information related to the spectral characteristics of each fluorophore on a target instrument (e.g., a flow cytometer). Such information may vary across different machines. For example, differences in the number and arrangement of lasers and detection channels between separate instruments may result in different spectral signatures associated with each instrument. Accordingly, embodiments of the subject method use a spectral matrix that includes spectral signatures that are specific to a particular instrument (or class / type of instrument) in which the fluorescent dyes in the evaluated fluorescent dye panel would hypothetically be used. Due to differences between instruments, if a fluorescent dye is applied on two different types of machines (e.g., flow cytometers), the same fluorescent dye panel may be associated, to some extent, with the dispersion of flow cytometer data. Obtaining a spectral matrix associated with an instrument identifier enables the fluorescent dye panel to be evaluated in an instrument-specific context, thereby allowing one skilled in the art to more reliably gain insight into the quality of flow cytometer data when the fluorescent dyes in a given panel are used on a particular instrument.

[0030] In some embodiments, the spectral signature is received from experimental data (i.e., the results of an experiment performed on a particular device). In other cases, the spectral signature is received from simulated data. The input spectral matrix can be associated with any suitable number of fluorescent dye identifiers. In some embodiments, the number of fluorescent dye identifiers in the input spectral matrix ranges from 2 to 150, such as from 2 to 140, such as from 2 to 130, such as from 2 to 120, such as from 2 to 110, such as from 2 to 100, such as from 2 to 90, such as from 2 to 80, such as from 2 to 70, such as from 2 to 60, and from 2 to 50. The collection of fluorescent dye identifiers used in a given embodiment of the present invention may be referred to as a fluorescent dye palette. This collectively refers to the fluorescent dye identifiers from which a given fluorescent dye panel can be selected.

[0031] In certain cases, the spectral signature includes one or more spillover values. A "spillover value" means the relative amount of signal that a given fluorescent dye emits into each detector band. In certain cases, the spillover value is normalized with respect to the detector at the maximum signal (i.e., the "peak" detector) of that fluorescent dye. In some cases, the particle-modulated light indicating a particular fluorescent dye is received by one or more detectors within a particle analyzer (e.g., a flow cytometer) that is not the peak detector of that fluorescent dye. Therefore, the light can be "spilled over" and detected by off-peak detectors. In other words, the particular fluorescent labels used in the experiment, and their associated fluorescent emission bands, can be selected to generally coincide with a particular detector. However, because more detectors are provided and more labels are utilized, a perfect correspondence between a particular detector and the fluorescent emission spectrum may not be possible. The peak of the emission spectrum of a particular fluorescent molecule may be within the window of one particular detector, but it is generally true that a portion of the emission spectrum of that label also overlaps with the windows of one or more other detectors. This can be referred to as spillover.

[0032] In some embodiments, the spectral matrix described herein may include one or more autofluorescence spectral signatures. Autofluorescence is the intrinsic fluorescence signal generated by particles such as cells when measured by a flow cytometer. It results from fluorescently active endogenous molecules such as metabolites within the cell. Different cells of the same type (e.g., lymphocytes) may have the same autofluorescence spectrum but can have different intensities. For example, larger cells typically tend to have larger autofluorescence signals. In certain cases, different types of particles are associated with different autofluorescence spectra. For example, different types of cells (e.g., lymphocytes vs. monocytes) can not only have various levels of autofluorescence but also different autofluorescence spectra (e.g., the spectral signature of lymphocyte autofluorescence may be different from that of monocyte autofluorescence). In some cases, for example, in spectral cytometry, the spectral signature of autofluorescence is measured by looking at unstained cells, and if multiple autofluorescence spectra are included, it is included in the spectral decomposition process as an additional "fluorescent dye" parameter or parameters.

[0033] As described above, the acquired spectral matrix is associated with a fluorescent dye panel and an instrument identifier. In other words, the acquired spectral matrix is a submatrix of the input spectral matrix (i.e., the spectral matrix associated with the instrument identifier) that includes spectral signatures only for the fluorescent dyes in the fluorescent dye panel. The term "submatrix" is considered herein in its conventional meaning and describes a matrix obtained by deleting some combination of rows and / or columns of another matrix.

[0034] After a spectral matrix associated with a fluorescent dye panel and an instrument identifier has been acquired, the method of the present invention includes calculating an inverse matrix from the acquired spectral matrix. As considered herein, the term "inverse matrix" can be used to describe the inverse of a matrix in its conventional meaning, i.e., the inverse of matrix A is AA -1 =A -1When A = I, A -1 where I is the identity matrix. For the purposes of the present disclosure, the term "inverse matrix" may also include other types of inverses, such as the inverse of a non-square matrix. For example, in some embodiments, the inverse matrix is a pseudo-inverse matrix. Generally, a "pseudo-inverse matrix" is a matrix that generalizes the inverse of a square invertible matrix for a non-square matrix. In some cases, the pseudo-inverse matrix is a Moore-Penrose pseudo-inverse matrix. In these cases, the pseudo-inverse matrix can be calculated as follows. A + =(A T A) -1 A T where A is an m×n matrix, A T is the transpose matrix of A, and A + is the pseudo-inverse. General discussions of pseudo-inverses (e.g., Moore-Penrose pseudo-inverses) can be found, for example, in U.S. Pat. Nos. 7,065,286 and 9,575,162.

[0035] Since the spectral matrix pseudo-inverse determines the mapping from the raw variance to the resolved variance, the pseudo-inverse is suitable for evaluating fluorescent dye panels. For example, both spectral flow cytometry and conventional flow cytometry can be described as linear mixing models. y = Mf where y is an [m×1] vector of detector signals, M is an [m×n] matrix of spectral signatures, and f is an [n×1] vector of fluorophore abundances. Spectral decomposition involves solving this system of linear equations for "f" via the method of least squares. For ordinary least squares, the solution can be described as follows. f = M † y where M † is the Moore-Penrose pseudo-inverse of M (or the inverse if M is square and in the case of correction).

[0036] In an additional embodiment, the inverse matrix is the Gram inverse matrix (also referred to as the "inverse moment matrix" of the spectral matrix). The Gram matrix is described, for example, in Horn, R.A., & Johnson, C.R. (2012). Matrix analysis, the disclosure of which is incorporated herein by reference. In some embodiments, the Gram inverse matrix is calculated according to the following formula. G=(M T M) -1 where G is the Gram inverse matrix, M is the spectral matrix, and M T is the transpose matrix of the spectral matrix.

[0037] Using the linear estimator theory, when the covariance matrix V y of the detector measurements is given, the covariance matrix V f of the decomposition solution can also be calculated. V f =M † V y (M † ) T where T represents the transpose operator. The diagonal elements of V y are the variances or noise terms for each detector, and the diagonal elements of V f are the decomposition variances for each fluorescent chromophore.

[0038] From this relationship, it is clear that the spectral matrix pseudo-inverse M † determines the mapping from the raw variance V y to the decomposition variance V f . This is demonstrated by the following proof. Let M represent the [n×m] spectral matrix and M † represent the [n×m] spectral matrix pseudo-inverse, where m is the number of detectors and n is the number of fluorescent dyes. Let V y represent the measurement variance of detector i

[0039]

Number

[0040] Let it be the [n×m] detector covariance matrix. The resolved fluorescence dye covariance matrix V of size [n×m] f has diagonal entries representing the resolved variance of fluorophore j

[0041]

Number

[0042] and off - diagonal entries representing the resolved covariance of fluorophores j and k

[0043]

Number

[0044] and has. Then, V f is defined as follows.

[0045]

Number

[0046] As shown above, regardless of the structure of V y the resolved variance of fluorophore j depends on the characteristics of its inverse spectrum

[0047]

Number

[0048] The resolved covariance of fluorophores j and k depends on both the inverse spectrum

[0049]

Number

[0050] of both. V y shows the case when it is diagonal (no covariance).

[0051] [Mathematics]

[0052] When making the simplifying assumption that the detector noises are uncorrelated, the decomposition variance depends only on the magnitude of the inverse spectrum, which can be summarized by the vector norm of the inverse spectrum. V y is

[0053] [Mathematics]

[0054] If it is homoscedastic as if it is

[0055] [Mathematics]

[0056] If it is also assumed that the detector variance is equal in all channels, V f is proportional to the Gram inverse matrix (M T M) -1 Therefore, it is shown that the Gram inverse matrix approximately predicts the true covariance matrix of the decomposition data.

[0057] Following the calculation of the inverse matrix, embodiments of the method include deriving a quantitative metric from the inverse matrix. In certain cases, the quantitative metric is a matrix norm. In some cases, the quantitative metric is a vector norm. In other cases, the quantitative metric is some combination of a matrix norm and a vector norm, for example, derived from the sum of the vector norms of some subset of columns or rows within the inverse matrix. Suitable norms include, but are not limited to, the L 2 norm, 1-norm, 2-norm, infinity norm, and Frobenius norm. In some cases, the norm is the L 2It is a norm. In some cases, the norm is the 1-norm. In some cases, the norm is the 2-norm. In some cases, the norm is the infinity norm. In some cases, the norm is the Frobenius norm. The Frobenius norm is described, for example, in Golub, G. H. and Van Loan, C. F. (1996) Matrix Computations, 3 rd ed. Baltimore, MD: Johns Hopkins, which is hereby incorporated by reference in its entirety. In a particular case, the Frobenius norm is calculated as follows (adapted from Golub and Van Loan).

[0058] [Number]

[0059] where A is an m×n matrix.

[0060] In some embodiments, the method also includes generating a visualization of the evaluated suitability of a fluorescent dye panel for use in generating flow cytometer data. Any suitable visualization may be used. In some embodiments, the visualization includes a plot of simulated flow cytometer data based on a given fluorescent dye panel. Put another way, the visualization includes exemplary flow cytometer data that would be generated if a sample were run on a particular instrument having a particular fluorescent dye panel. In some such embodiments, the visualization may highlight (e.g., by highlighting, color-coding, grouping into groups, pointing with an arrow, etc.) flow cytometer data that would be associated with dispersion if a particular fluorescent dye in the fluorescent dye panel were used to generate the flow cytometer data. In some embodiments, the visualization highlights flow cytometer data generated using fluorescent dyes that contribute to the dispersion of the data. In a particular case, the visualization highlights flow cytometer data generated using fluorescent dyes that are affected by the dispersion of the data. In an additional version, the visualization includes a table or matrix that quantifies the degree to which fluorescent dyes in the fluorescent dye panel are associated with dispersion (e.g., contribute to and / or are affected by the dispersion). For example, the table or matrix may be populated by the quantitative metrics discussed above. In some such versions, the cells of the table or matrix are color-coded based on the degree to which the fluorescent dye is associated with dispersion (e.g., contributes to and / or is affected by the dispersion). In a selected case, if the associated fluorescent dye is less associated with the dispersion, the cell is color-coded with a color having a lighter intensity, and if the associated fluorescent dye is more associated with the dispersion, the cell is color-coded with a color having a stronger intensity.

[0061] Figure 1 shows a flowchart for implementing a method for identifying a fluorescent dye panel according to a particular embodiment. As shown in Figure 1, step 101 includes inputting a fluorescent dye panel, a device identifier, and a spectral matrix associated with the fluorescent dye panel and the device identifier. Step 102 includes calculating an inverse matrix (e.g., a pseudo-inverse matrix). Step 103 includes calculating a quantitative metric, and step 104 includes optimizing the fluorescent dye panel based on the quantitative metric calculated in step 104. A visualization depicting the evaluation of the fluorescent dye panel can also be created in step 105.

[0062] Figures 2 and 3 illustrate the mapping from the "raw" space of flow cytometer data (Figure 2) and variance (Figure 3) (i.e., having the number of dimensions equal to the number of detectors in the device) to the "resolved space" (i.e., having the number of dimensions equal to the number of fluorescent dyes in the sample). As shown in Figure 2, spectral decomposition transforms data from a high-dimensional detector space (raw) to a lower-dimensional fluorophore space. As discussed in detail above, both spectral and conventional flow cytometry can be described by the linear mixing model f = M † y. As shown in Figure 3, the variance is also mapped from the detector space to the fluorophore space. Based on the solution V[f] = M † V[y] (M † ), it is clear that the spectral matrix pseudo-inverse M T † determines the mapping from the raw variance V[y] to the resolved variance V[f]. As shown in Figures 2 and 3, the spectral matrix pseudo-inverse maps the raw detector signal inversely to the resolved fluorophores. The spectral matrix pseudo-inverse also maps the raw detector space noise inversely to the resolved space noise, which is ultimately of interest in terms of the biological resolution in the experiment.

[0063] ​Figures 4A and 4B illustrate how the properties of the pseudo-inverse matrix determine the spread (noise) in the decomposition data. As shown in the forward problem (mixing) depicted in Figure 4A, column j of the spectral matrix M is the spectrum M j of fluorophore j. M j describes how the signal from fluorophore j is mapped to all detectors. As shown in the inverse problem (decomposition) depicted in Figure 4B, row j of the spectral matrix pseudo-inverse M † is the inverse spectrum of fluorophore j

[0064]

Number

[0065] is.

[0066]

Number

[0067] describes how the detector signal is inversely mapped to the decomposed fluorophore j.

[0068] In some embodiments, the method includes generating a panel hot spot matrix. As described herein, a "panel hot spot matrix" is a mechanism for mathematically describing and / or visualizing the effect of spread on fluorophores. Optionally, the panel hot spot matrix serves as the visualization described above. In some embodiments, the panel hot spot matrix is a diagonal matrix. The panel hot spot matrix can be calculated in some cases by taking the square root of the absolute value of the inverse matrix (e.g., the Gram inverse). In a particular case, the panel hot spot matrix can be calculated as follows.

[0069]

Number

[0070] The calculation of the panel hot spot matrix can result in two different metrics for panel fitness, the row norms of the pseudo-inverse matrix and the off-diagonal entries. The row norms of the pseudo-inverse matrix (i.e., the diagonal of the panel hot spot matrix) indicate which fluorophores are most affected by degradation-dependent spread in the full panel. In some cases, the off-diagonal entries are the two-norms of the rows of the pseudo-inverses of the respective fluorophores. In some versions, the row norms of the pseudo-inverse matrix can be represented on a scale corresponding to the factor by which the standard deviation of the degradation data in that fluorophore is amplified as a result of degradation in this panel. For example, 1 corresponds to no effect, while 2 corresponds to twice the spread, and so on. Examination of the off-diagonal entries within the full panel hot spot matrix reveals problematic combinations of fluorophores in the panel. The off-diagonal entries are the dot products of the corresponding row and the rows of the pseudo-inverses of the column fluorophores. The off-diagonal values indicate the magnitude of the covariance between two fluorophore pseudo-inverse matrix entries. For example, in some embodiments, an off-diagonal value of 0 represents no covariance, while higher values correspondingly represent higher levels of covariance.

[0071] Figures 5A-5D show a panel hot spot matrix according to an embodiment of the present invention, and an exemplary process for its calculation. Figure 5A presents a master database of single-stain spectra. The x-axis of the graph presented in Figure 5A includes different fluorophores, while the y-axis includes detectors. The degree to which light emitted by a given fluorophore is detected by a given detector is represented by color-coding. Figure 5B represents a subset of the master database of single-stain spectra shown in Figure 5A. The subset includes the fluorophores of interest for a given panel on the x-axis, while the remainder of the fluorophores in the master database are omitted. This subset constitutes the spectral matrix M. Figure 5C shows the calculation of the Gram matrix (equal to the Gramian of the pseudo-inverse matrix and proportional to the covariance of the pseudo-inverse matrix). Figure 5D presents a panel hot spot matrix that displays the square root of the absolute value of the Gram inverse. Diagonal 501 includes the row norms of the pseudo-inverse matrix that indicate which fluorophores are most affected by the decomposition-dependent spread in the full panel. The remaining part of the panel hot spot matrix (i.e., the off-diagonal entries) indicates the magnitude of the covariance between two fluorophore pseudo-inverse matrix entries.

[0072] In some embodiments, the method includes performing a separate analysis of the row norms (i.e., the diagonal) of the pseudo-inverse matrix of the panel hot spot matrix. In some such embodiments, the method includes generating a diagonal visualization. The diagonal visualization can be any representation (e.g., a graphic representation) of categorical data configured for the evaluation and / or comparison of factors that amplify the standard deviation of the decomposition data in the fluorophore as a result of decomposition in a particular panel. In some embodiments, the diagonal visualization is a bar graph, where each bar represents a factor that amplifies the standard deviation of the decomposition data in each fluorophore as a result of decomposition in the panel.

[0073] In some cases, the method includes generating a visualization of exemplary flow cytometer data that will be generated using specific fluorescent dyes based on a panel hot spot matrix. The exemplary flow cytometer data can be actual, i.e., flow cytometer data generated from a flow cytometry experiment. Alternatively, the data may be simulated. The visualization of the subject of the exemplary flow cytometer data illustrates the effect of using specific fluorescent dyes in the experiment. In some embodiments, the visualization is generated using specific pairs of fluorescent dyes, for example, to show how the covariance associated with those fluorescent dyes affects data quality. Alternatively, or in addition, the exemplary flow cytometer data can be simulated using a full fluorescent dye panel rather than just pairs of fluorescent dyes. Examination of such exemplary flow cytometer data can reveal problematic combinations of fluorescent dyes in the panel.

[0074] Figures 6A and 6B show visualizations created based on a panel hot spot matrix. Figure 6A shows a diagonal visualization according to a particular embodiment. The diagonal visualization in Figure 6A is a bar graph and corresponds to diagonal 501 in Figure 5D. The x-axis of the graph enumerates different fluorescent dyes, while the y-axis is a scale corresponding to the factor that amplifies the standard deviation of the decomposition data in the fluorophore as a result of the decomposition in the panel composed of the fluorescent dyes on the x-axis. In the example of Figure 6A, the fluorescent dyes identified by the arrows (VioR667, APC, SparkNIR685) are associated with particularly high factors that amplify the standard deviation of the decomposition data in the fluorophore. These can be identified as the fluorescent dyes most affected by the decomposition-dependent spread in the full panel. Figure 6B shows exemplary flow cytometer data demonstrating the effect of the inclusion of the fluorescent dyes identified in Figure 6A on data quality. The data can be represented using pairs of specific fluorescent dyes (right panel, upper row), or the entire panel (right panel, lower row).

[0075] In certain cases, the method includes generating a spread correlation matrix. As described herein, a "spread correlation matrix" is a mechanism for mathematically describing and / or visualizing the effect of a particular fluorescent dye on a particular data population, such as a double negative population. In embodiments, the spread correlation matrix can be used to predict the slope in the double negative population. "Slope" is referred to herein as a measure that describes the extent to which a population (e.g., a double negative population) is shifted in a particular direction (e.g., corresponding to a positive or negative correlation) due to the manner in which the data is collected and / or prepared. In some embodiments, preparing the spread correlation matrix includes treating the Gram inverse as the covariance matrix and normalizing each row and column by the square root of its diagonal element to calculate the correlation matrix. In some cases, the spread correlation matrix is calculated as follows.

[0076] [Number]

[0077] wherein diag indicates taking the diagonal of a 2D matrix or forming a diagonal matrix from a 1D vector. This operation is equivalent to dividing each row by the square root of its diagonal entry and dividing each column by the square root of its diagonal entry. The entry [i,j] of the spread correlation matrix corresponds to the correlation between the rows of the pseudo-inverse matrix corresponding to fluorophores i and j. In some cases, the method includes generating a visualization of exemplary flow cytometry data generated using a particular fluorescent dye based on the spread correlation matrix. Similar to the visualization related to the panel hot spot matrix, the visualization of exemplary flow cytometry data created for the spread correlation matrix may be real or simulated.

[0078] Figures 7A - 7E show the spread correlation matrix according to an embodiment of the present invention, and an exemplary process for its calculation. Figure 7A shows the calculation of the inverse of the Gram matrix (equal to the Gramian of the pseudo-inverse matrix and proportional to the covariance of the pseudo-inverse matrix). Figure 7B shows the spread correlation matrix calculated from the Gram inverse calculated in Figure 7A. Each cell of the spread correlation matrix is associated with a correlation measured between -1.00 and 1.00 between the rows of the pseudo-inverse matrix corresponding to the associated fluorophore. Cell 701 defines a pair of target fluorophores that are likely to result in a slope. Figures 7C and 7D show an exemplary visualization of exemplary flow cytometer data created using the pair identified in cell 701. In Figures 7C and 7D, the double-negative populations 702c and 702d are predicted to have a large negative correlation. On the other hand, the population 702e in Figure 7E is predicted to have a large positive correlation.

[0079] In an embodiment, the method includes optimizing a fluorescent dye panel based on an assessment of the suitability of the fluorescent dye panel for use in generating flow cytometer data, i.e., such that the fluorescent dye panel is suitable for use in a flow cytometric protocol. A fluorescent dye panel may be described as "suitable for use" in a flow cytometric protocol when the fluorescent dye panel generates understandable flow cytometer data that provides insights regarding the property of interest in the sample under investigation. In some embodiments, a fluorescent dye panel is suitable for use in a flow cytometric protocol when the panel provides increased biological resolution. "Biological resolution" refers to the ability to distinguish between different entities of interest in a biological sample (e.g., cells, molecules, antigens, moieties, epitopes, etc.). In some cases, the fluorescent dye panels identified herein generate the maximum biological resolution regardless of the measurement variance and variance in the flow cytometer data space (e.g., flow cytometer data that has undergone fluorescence correction or spectral decomposition). The "maximum" biological resolution is, in a particular version, evaluated against the biological resolution achieved using one or more other sets of fluorescent dyes that are different from the fluorescent dyes that are evaluated and / or identified as described herein (i.e., that include one or more different fluorescent dyes relative to these fluorescent dyes).

[0080] In some embodiments, optimizing a fluorescent dye panel includes using a panel optimization algorithm. In some cases, the panel optimization algorithm is a constrained optimization algorithm. "Constrained optimization" is referred to herein in its conventional sense to describe the process of optimizing variables in the presence of constraints on those variables. Any suitable constrained optimization method may be used. In certain cases, the constrained optimization method is a minimization algorithm. A "minimization algorithm" means a type of constrained optimization method where the method attempts to minimize a particular variable. Examples of constrained optimization techniques that may be used include, but are not limited to, local search, local repair, backtracking, and constraint propagation. These may, in certain cases, be combined with minimization techniques such as simulated annealing and genetic (evolutionary) algorithms. In some cases, the fluorescent dye panels described herein may be optimized in conjunction with the optimization protocol described in U.S. Patent Application No. 18 / 083,808, filed December 19, 2022 (Attorney Docket No. BECT-310 (P-26714.US02)), the disclosure of which is incorporated herein by reference.

[0081] In an embodiment, optimizing a fluorescent dye panel includes adjusting the fluorescent dyes in the fluorescent dye panel and evaluating the suitability of the adjusted fluorescent dye panel for use in generating flow cytometer data. "Adjusting" the fluorescent dyes in the fluorescent dye panel means switching the fluorescent dyes, i.e., the fluorescent dye identifiers associated with the fluorescent dyes, for different fluorescent dyes within the spectral matrix representing the palette of fluorescent dyes or dyes that could potentially be selected. One or more of the fluorescent dyes in the panel can be adjusted at any given time. In some cases, the method includes switching a single fluorescent dye in the panel at a given time. In certain instances, optimizing the fluorescent dye panel includes maintaining a fluorescent dye panel of a certain size. In other words, the number of fluorescent dyes in the obtained fluorescent dye panel does not change even when one or more fluorescent dyes are adjusted. For example, an evaluated fluorescent dye panel having N fluorescent dyes continues to have N fluorescent dyes after adjustment. In certain cases, the fluorescent dyes in the fluorescent dye panel are not exchanged with fluorescent dyes already in the fluorescent dye panel. After an adjusted fluorescent dye panel is generated, the method in question additionally includes evaluating the adjusted fluorescent dye panel, i.e., calculating the inverse matrix from the obtained spectral matrix and analyzing the calculated inverse matrix to identify the fluorescent dyes in the fluorescent dye panel that are associated with the variance of the flow cytometer data generated using the fluorescent dye panel and evaluating the suitability of the fluorescent dye panel for use in generating flow cytometer data, etc.

[0082] The method in question further involves comparing the evaluation of a first fluorescent dye panel with the evaluation of an adjusted fluorescent dye panel. For example, the method can include determining which of the first fluorescent dye panel and the adjusted fluorescent dye panel is associated with a smaller variance of the flow cytometry data. If either the first fluorescent dye panel or the adjusted fluorescent dye panel contains fewer fluorescent dyes and / or has fluorescent dyes that are associated with a smaller variance (as determined by a quantitative metric) and is thus associated with a smaller variance of the flow cytometry data than the other fluorescent dye panel, that fluorescent dye panel can be identified as being more suitable for generating the flow cytometry data. In some cases, the method includes discarding a fluorescent dye panel that has fluorescent dyes associated with a larger variance.

[0083] In certain cases, the method includes repeatedly adjusting a fluorescent dye panel and evaluating the suitability of each repeatedly adjusted fluorescent dye panel. In embodiments, either the first fluorescent dye panel or the adjusted fluorescent dye panel that is evaluated as being associated with a smaller variance of the fluorescent dye data can serve as a seed for the next part of the iterative process. By "seed" is meant a fluorescent dye panel that is determined in one iteration of the method to be associated with a smaller variance of the flow cytometry data compared to one or more slightly modified fluorescent dye panels. In some embodiments, the iterative process repeats itself until a condition is met. Any suitable condition can be used to end the iterative process. In some cases, the iterative process ends when a certain runtime has elapsed. In other cases, the iterative process ends when the evaluations generated for each repeatedly adjusted fluorescent dye panel converge. In other words, the iterative process ends when only a small variance difference is observed between subsequent fluorescent dye panels.

[0084] Figure 8 presents a flowchart for implementing the method of the present invention according to a particular embodiment. The method includes receiving an input 801, which may include an experiment / instrument-specific matrix from a single staining control 801a or a matrix obtained from a reference database 801b. In step 802, an inverse matrix is calculated along with a quantitative metric derived from the inverse matrix. These can include a gram inverse 802a, a vector norm 802b, or a matrix norm 802c. The metric calculated in step 802 can then be used in step 803 to improve or analyze the fluorophore panel. This can include visualizing spread using a panel hot spot matrix in step 803a, predicting slope in a double negative population using a spread correlation matrix in step 803b, optimizing the panel using a manually induced panel optimization by the metric in step 803c, or optimizing the panel using an automatically induced panel optimization by the metric in step 803d.

[0085] The fluorescent dye panel of the present invention can include any suitable set of fluorescent dyes. The fluorescent dyes targeted by certain embodiments have a maximum excitation in the range including 100 nm to 800 nm, for example, 150 nm to 750 nm, for example, 200 nm to 700 nm, for example, 250 nm to 650 nm, for example, 300 nm to 600 nm, and 400 nm to 500 nm. According to certain embodiments, the fluorescent dyes targeted have a maximum emission in the range including 400 nm to 1000 nm, for example, 450 nm to 950 nm, for example, 500 nm to 900 nm, for example, 550 nm to 850 nm, and 600 nm to 800 nm. In certain cases, the fluorescent dye is a luminescent dye such as a fluorescent dye having a peak emission wavelength including 200 nm or more, for example, 250 nm or more, for example, 300 nm or more, for example, 350 nm or more, for example, 400 nm or more, for example, 450 nm or more, for example, 500 nm or more, for example, 550 nm or more, for example, 600 nm or more, for example, 650 nm or more, for example, 700 nm or more, for example, 750 nm or more, for example, 800 nm or more, for example, 850 nm or more, for example, 900 nm or more, for example, 950 nm or more, for example, 1000 nm or more, and 1050 nm or more. For example, the fluorescent dye can be a fluorescent dye having a peak emission wavelength in the range of, for example, 200 nm to 1200 nm, for example, 300 nm to 1100 nm, for example, 400 nm to 1000 nm, for example, 500 nm to 900 nm (including a fluorescent dye having a peak emission wavelength of 600 nm to 800 nm).

[0086] The fluorescent dyes to be targeted may include, but are not limited to, bodipy dyes, coumarin dyes, rhodamine dyes, acridine dyes, anthraquinone dyes, arylmethane dyes, diarylmethane dyes, chlorophyll-containing dyes, triarylmethane dyes, azo dyes, diazonium dyes, nitro dyes, nitroso dyes, phthalocyanine dyes, cyanine dyes, asymmetric cyanine dyes, quinoneimine dyes, azine dyes, eurodin dyes, safranine dyes, indamine, indophenol dyes, fluorine dyes, oxazine dyes, oxazone dyes, thiazine dyes, thiazole dyes, xanthene dyes, fluorene dyes, pyronin dyes, fluorine dyes, rhodamine dyes, phenanthridine dyes, squaraine, bodipy, squaraine roxitane, naphthalene, coumarin, oxadiazole, anthracene, pyrene, acridine, arylmethine, or tetrapyrrole and combinations thereof. In certain embodiments, the conjugate may include two or more dyes selected from two or more dyes such as bodipy dyes, coumarin dyes, rhodamine dyes, acridine dyes, anthraquinone dyes, arylmethane dyes, diarylmethane dyes, chlorophyll-containing dyes, triarylmethane dyes, azo dyes, diazonium dyes, nitro dyes, nitroso dyes, phthalocyanine dyes, cyanine dyes, asymmetric cyanine dyes, quinoneimine dyes, azine dyes, eurodin dyes, safranine dyes, indamine, indophenol dyes, fluorine dyes, oxazine dyes, oxazone dyes, thiazine dyes, thiazole dyes, xanthene dyes, fluorene dyes, pyronin dyes, fluorine dyes, rhodamine dyes, phenanthridine dyes, squaraine, bodipy, squaraine roxitane, naphthalene, coumarin, oxadiazole, anthracene, pyrene, acridine, arylmethane, or tetrapyrrole and combinations thereof.

[0087] In certain embodiments, the fluorescent dyes of interest can include, but are not limited to, fluorescein isothiocyanate (FITC), phycoerythrin (PE) dyes, peridinin chlorophyll protein-cyanine dyes (e.g., PerCP-Cy5.5), phycoerythrin-cyanine (PE-Cy) dyes (PE-Cy7), allophycocyanin (APC) dyes (e.g., APC-R700), allophycocyanin-cyanine dyes (e.g., APC-Cy7), coumarin dyes (e.g., V450 or V500). In certain cases, the fluorescent dyes can include one or more of 1,4-bis-(o-methylstyryl)-benzene (bis-MSB 1,4-bis[2-(2-methylphenyl)ethenyl]-benzene), C510 dye, C6 dye, Nile Red dye, T614 dye (e.g., N-[7-(methanesulfonamido)-4-oxo-6-phenoxychromen-3-yl]formamide), LDS821 dye ((2-(6-(p-dimethylaminophenyl)-2,4-neopentylene-1,3,5-hexatrienyl)-3-ethylbenzothiazolium perchlorate), mFluor dyes (e.g., mFluor red dyes such as mFluor 780NS).

[0088] The target fluorescent dyes are fluorescein, hydroxycoumarin, aminocoumarin, methoxycoumarin, Cascade Blue, Pacific Blue, Pacific Orange, Lucifer yellow, NBD, R-phycoerythrin (PE), PE-Cy5 conjugate, PE-Cy7 conjugate, Red613, 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, mitomycin, YOYO-1, ethidium bromide, acridine orange, SYTOX Green, TOTO-1, TO-PRO-1, thiazole orange, propidium iodide (PI), LDS751, 7-AAD, SYTOXIncluding, but not limited to, Orange, TOTO-3, TO-PRO-3, DRAQ5, Indo-1, Fluo-3, DCFH, DHR, SNARF, Y66H, Y66F, EBFP, EBFP2, Azurite, GFPuv, T-Sapphire, TagBFP, Celestial, 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 Fluor 350, Alexa Fluor 405, Alexa Fluor 430, Alexa Fluor 488, Alexa Fluor 500, Alexa Fluor 514, Alexa Fluor 532, Alexa Fluor 546, Alexa Fluor 555, Alexa Fluor 568, Alexa Fluor 594, Alexa Fluor 610, Alexa Fluor 633, Alexa Fluor 647, Alexa Fluor 660, Alexa Fluor 680, Alexa Fluor 700, Alexa Fluor 750, Alexa Fluor 790, and HyPer.

[0089] In some cases, the fluorescent dye panel includes one or more polymeric dyes (e.g., fluorescent polymeric dyes). The fluorescent polymeric dyes found to be used in the subject methods and systems vary. In some cases of the present methods, the polymeric dye includes a conjugated polymer. A conjugated polymer (CP) is characterized by a delocalized electronic structure that includes a backbone of alternating unsaturated bonds (e.g., double bonds and / or triple bonds) and saturated (e.g., single bond) bonds, and the π electrons can move from one bond to the other. Thus, the conjugated backbone can limit the bond angle between the repeating units of the polymer and impart an extended linear structure to the polymeric dye. For example, proteins and nucleic acids are also macromolecules, but in some cases, they do not form an extended rod structure and rather fold into a higher-order three-dimensional shape. In addition to this, the CP can form a "rigid rod" polymer backbone, and the bending (e.g., torsion) angle between the monomer repeating units along the polymer backbone chain is restricted. In some cases, the polymeric dye includes a CP having a rigid rod structure. The structural features of the polymeric dye can affect the fluorescence properties of the molecule.

[0090] Any convenient polymeric dye can be utilized in the subject devices and methods. In some cases, the polymeric dye is a multi-chromophore having a structure capable of collecting light to amplify the fluorescence output of a fluorophore. In some cases, the polymeric dye can collect light and convert it efficiently to emission at a longer wavelength. In some cases, the polymeric dye has a light-harvesting multi-chromophore system capable of efficiently transferring energy to nearby emissive species (e.g., "signal-transducing chromophores"). Mechanisms for energy transfer include, for example, resonance energy transfer (e.g., Forster (or fluorescence) resonance energy transfer, FRET), quantum charge transfer (Dexter energy transfer), and the like. In some cases, these energy transfer mechanisms are relatively short-range, i.e., proximity of the light-harvesting multi-chromophore system to the signal-transducing chromophore provides efficient energy transfer. Under conditions for efficient energy transfer, amplification of emission from the signal-transducing chromophore occurs when the number of individual chromophores in the light-harvesting multi-chromophore system is large. That is, emission from the signal-transducing chromophore is stronger when the incident light ("excitation light") is at a wavelength absorbed by the light-harvesting multi-chromophore system than when the signal-transducing chromophore is directly excited by pump light.

[0091] The multi-chromophore can be a conjugated polymer. Conjugated polymers (CPs) are characterized by a delocalized electronic structure and can be used as highly responsive optical reporters for chemical and biological targets. Since the effective conjugation length is significantly shorter than the length of the polymer chain, the backbone contains a number of conjugated segments in close proximity. Thus, conjugated polymers are efficient at light harvesting and enable light amplification via Forster energy transfer.

[0092] Examples of the polymer dyes that can be used include, but are not limited to, 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 disclosures of which are incorporated herein by reference in their entireties; 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 disclosures of which are incorporated herein by reference in their entireties. Specific polymer dyes that can be used include BD Horizon Brilliant™ dyes, such as BD Horizon Brilliant™ Violet dyes (e.g., BV421, BV510, BV605, BV650, BV711, BV786), BD Horizon Brilliant™ UV dyes (e.g., BUV395, BUV496, BUV737, BUV805), and BD Horizon Brilliant™ Blue dyes (e.g., BB515) (BD Biosciences, San Jose, CA), but are not limited thereto. Any fluorescent dye known to those of ordinary skill in the art, including but not limited to those described above, or a fluorescent dye not yet discovered, can be used in the methods of the subject invention.

[0093] The fluorescent dye panel and / or fluorescent dyes of the subject matter referred to in the spectral matrix may or may not be bound to biomolecules such as biopolymers. The biopolymer may be a biopolymer. A "biopolymer" is a polymer of one or more types of repeating units. Biopolymers are typically found in biological systems and in particular include polysaccharides (e.g., carbohydrates), and peptides (the term is used to include proteins whether or not bound to polypeptides and polysaccharides), and polynucleotides, and their analogs (e.g., those compounds composed of or containing amino acid analogs or non-amino acid groups, or nucleotide analogs or non-nucleotide groups). This includes polynucleotides in which the conventional backbone is replaced by a backbone that does not occur naturally or a synthetic backbone, and nucleic acids (or synthetic or naturally occurring analogs) in which one or more of the conventional bases are replaced by groups (natural or synthetic) that can participate in Watson-Crick type hydrogen bonding interactions. Polynucleotides include single-stranded or multiple-stranded arrangements, and one or more of the strands may or may not be perfectly aligned with another strand. Specifically, "biopolymer" includes DNA (including cDNA), RNA, and oligonucleotides regardless of origin. Thus, biomolecules can include polysaccharides, nucleic acids, and polypeptides. For example, nucleic acids can be oligonucleotides, truncated or full-length DNA or RNA. In embodiments, oligonucleotides, truncated and full-length DNA or RNA consist of nucleotide monomers including 10 or more, e.g., 15 or more, e.g., 25 or more, e.g., 50 or more, e.g., 100 or more, e.g., 250 or more nucleotide monomers, and 500 or more nucleotide monomers. For example, the oligonucleotides, truncated and full-length DNA or RNA of interest are from 10 nucleotides to 10 8 nucleotides, e.g., 10 2 nucleotides to 10 7 nucleotide length range, and 10 3 nucleotides to 10 6It can be a range including the nucleotide length. In embodiments, the biopolymer is not a single base or a short-chain oligonucleotide (e.g., less than 10 bases). "Full-length" means that the DNA or RNA is a nucleic acid polymer having 70% or more, e.g., 75% or more, e.g., 80% or more, e.g., 85% or more, e.g., 90% or more, e.g., 95% or more, e.g., 97% or more, e.g., 99% or more of its complete sequence (e.g., as found in nature), and a nucleic acid polymer including 100% of the full-length sequence of the DNA or RNA (e.g., as found in nature).

[0094] The polypeptide can be, in certain cases, a truncated or full-length protein, enzyme, or antibody. In embodiments, the polypeptide, truncated and full-length proteins, enzymes, or antibodies consist of 10 or more, e.g., 15 or more, e.g., 25 or more, e.g., 50 or more, e.g., 100 or more, e.g., 250 or more amino acid monomers, and amino acid monomers including 500 or more amino acid monomers. For example, the polypeptide, truncated and full-length proteins, enzymes, or antibodies of interest are from 10 amino acids to 10 8 amino acids, e.g., 10 2 amino acids to 10 7 amino acid length range, and from 10 3 amino acids to 10 6 It can be a range including the amino acid length. In embodiments, 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, e.g., 75% or more, e.g., 80% or more, e.g., 85% or more, e.g., 90% or more, e.g., 95% or more, e.g., 97% or more, e.g., 99% or more of its complete sequence (e.g., as found in nature), and a polypeptide polymer including 100% of the full-length sequence of the protein, enzyme, or antibody (e.g., as found in nature).

[0095] In some cases, the fluorescent dye is conjugated to a specific binding member. The specific binding member and the fluorescent dye can be conjugated (e.g., covalently) to each other at any convenient position of the two molecules via an optional linker. As used herein, the term "specific binding member" refers to a member of a pair of molecules having binding specificity for each other. One member of the pair of molecules can have a surface region or depression that specifically binds to a region or depression on the surface of the other member of the pair of molecules. Thus, the pair of members has the property of specifically binding to each other to form a binding complex. In some embodiments, the affinity between the specific binding members in the binding complex is 10 -6 M or less, for example, 10 -7 M or less (including 10 -8 M or less), for example, 10 -9 M or less, 10 -10 M or less, 10 -11 M or less, 10 -12 M or less, 10 -13 M or less, 10 -14 M or less (including 10 -15 M or less), and is characterized by a K d (dissociation constant). In some embodiments, the specific binding member specifically binds with high avidity. High avidity means that the binding member specifically binds with an apparent affinity characterized by an apparent K -9 of 10×10 -9 M or less, for example, 1×10 -10 M or less, 3×10 -10 M or less, 1×10 -11 M or less, 3×10 -11 M or less, 1×10 -12 M or less, 3×10 -12 M or less, or 1×10 d M or less.

[0096] The specific binding member can be proteinaceous. As used herein, the term "proteinaceous" refers to a moiety composed of amino acid residues. The proteinaceous moiety can be a polypeptide. In certain cases, the protein-specific binding member is an antibody. In certain embodiments, the proteinaceous specific binding member is an antibody fragment, for example, a binding fragment of an antibody that specifically binds to a polymeric dye. As used herein, the terms "antibody" and "antibody molecule" are used interchangeably and refer to a protein consisting of one or more polypeptides substantially encoded by all or a portion of a recognized immunoglobulin gene. Recognized immunoglobulin genes include, for example, in humans, kappa (κ), lambda (λ), and heavy chain loci (which together contain numerous variable region genes), as well as constant region genes mu (μ), delta (δ), gamma (γ), epsilon (ε), and alpha (α) (encoding IgM, IgD, IgG, IgE, and IgA isotypes, respectively). The immunoglobulin light or heavy chain variable region consists of framework regions (FRs) interrupted by three hypervariable regions, also called "complementary determining regions" or "CDRs". The ranges of the framework regions and CDRs are precisely defined (see "Sequences of Proteins of Immunological Interest," E. Kabat et al., U.S. Department of Health and Human Services, (1991)). The numbering of all antibody amino acid sequences described herein conforms to the Kabat system. The sequences of the framework regions of different light or heavy chains are relatively conserved within a species. The framework region of an antibody, which is the combined framework region of the constituent light and heavy chains, serves to position and align the CDRs. The CDRs mainly contribute to the binding to the epitope of the antigen. The term "antibody" is meant to include full-length antibodies and can refer to natural antibodies from any organism, engineered antibodies, or antibodies recombinantly produced for experimental, therapeutic, or other purposes, as further defined below.The antibody fragments of interest include, but are not limited to, Fab, Fab’, F(ab’)2, Fv, scFv, or other antigen-binding subsequences of an antibody, produced by modification of a whole antibody, or newly synthesized using recombinant DNA techniques. The antibody may be monoclonal or polyclonal and may have other specific activities against cells (e.g., antagonist, agonist, neutralizing, inhibitory, or stimulatory antibodies). It is understood that the antibody may have additional conservative amino acid substitutions that do not substantially affect antigen binding or other antibody functions. In certain embodiments, the specific binding member is a Fab fragment, an F(ab’)2 fragment, an scFv, a diabody, or a triabody. In certain embodiments, the specific binding member is an antibody. In some cases, the specific binding member is a mouse antibody or a binding fragment thereof. In certain instances, the specific binding member is a recombinant antibody or a binding fragment thereof.

[0097] In an embodiment, the subject fluorescent dye panel is used to analyze a sample. In some cases, the sample being analyzed is a biological sample. The term "biological sample" is used in its conventional meaning to refer to whole organisms, plants, fungi, or, in certain cases, a subset of animal tissues, cells, or component parts that can be found in, for example, blood, mucus, lymph, synovial fluid, cerebrospinal fluid, saliva, bronchoalveolar lavage, amniotic fluid, amniotic cord blood, urine, vaginal fluid, and semen. Thus, a "biological sample" refers to both natural organisms or subsets of their tissues, as well as, without limitation, for example, plasma, serum, cerebrospinal fluid, lymph, skin biopsies, respiratory, gastrointestinal, cardiovascular, and urinary organs, tears, saliva, milk, blood cells, tumors, homogenates, lysates, or extracts prepared from a biological or subset of its tissues. A biological sample can be any type of biological tissue, including both healthy and diseased tissues (e.g., cancerous, malignant, necrotic, etc.). In certain embodiments, the biological sample is a blood or its derivative, such as a liquid sample like plasma, tears, urine, semen, etc., and in some cases, the sample is a blood sample, including whole blood, such as blood obtained by venipuncture or finger stick (the blood may or may not be combined with any reagents such as preservatives, anticoagulants, etc. prior to the assay).

[0098] In certain embodiments, the sample source is a "mammal" or "mammalian animal", terms that are widely used to describe organisms within the class of mammals, including carnivores (e.g., dogs and cats), rodents (e.g., mice, guinea pigs, and rats), and primates (e.g., humans, chimpanzees, and monkeys). In some cases, the subject is a human. The method can be applied to samples obtained from human subjects of either sex at any stage of development (i.e., neonate, infant, juvenile, adolescent, adult), and in certain embodiments, the human subject is a juvenile, adolescent, or adult. It should be understood that the present invention can be applied to samples from human subjects, but also, without limitation, to samples from other animal subjects (i.e., "non-human subjects") such as birds, mice, rats, dogs, cats, livestock, and horses.

[0099] The fluorescent dyes within the fluorescent dye panel may be configured to target different types of cells (e.g., via an antibody that targets the cell). Various cells can be characterized using the methods of the subject matter. Target 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 erythrocyte cells. Target target cells include cells having a convenient cell surface marker or cell surface antigen that can be captured or labeled by a convenient affinity agent or complex thereof. For example, target cells may 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 alpha / beta, T cell receptor gamma / delta, CD253, CD95, CD20, CD105, CD117, CD120b, Notch4, Lgr5 (N-terminus), SSEA-3, TRA-1-60 antigen, disialoganglioside GD2, and CD71. In some embodiments, the target cells are selected from HIV-containing cells, Treg cells, antigen-specific T cell populations, tumor cells, or hematopoietic progenitor cells (CD34+) from whole blood, bone marrow, or umbilical cord blood.

[0100] In certain embodiments, the fluorescent dye panel identified via the present method can be used in a flow cytometric protocol (e.g., for analyzing a sample such as those described above). When implementing such a 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 emits light having a broad range of wavelengths, for example, wavelengths ranging from 100 nm or more, for example, 150 nm or more, for example, 200 nm or more, for example, 250 nm or more, for example, 300 nm or more, for example, 350 nm or more, for example, 400 nm or more, and 500 nm or more, for example, wavelengths ranging from 50 nm or more. For example, a suitable broad-band light source emits light having wavelengths in the range of 200 nm to 1500 nm. Another example of a suitable broad-band light source includes a light source that emits light having wavelengths in the range of 400 nm to 1000 nm. When the method includes irradiating with a broad-band light source, examples of the broad-band light source protocols that may be used include, but are not limited to, among broad-band light sources, halogen lamps, deuterium arc lamps, xenon arc lamps, stabilized fiber-coupled broad-band light sources, broad-band LEDs having a continuous spectrum, ultra-high-brightness light-emitting diodes, semiconductor light-emitting diodes, broad-spectrum LED white light sources, multi-LED integrated white light sources, or any combination thereof.

[0101] In other embodiments, the method of the embodiments of the present invention includes irradiating with a narrow-band light source that emits light at a specific wavelength or a narrow range of wavelengths, such as light having a wavelength range of 50 nm or less, for example, 40 nm or less, for example, 30 nm or less, for example, 25 nm or less, for example, 20 nm or less, for example, 15 nm or less, for example, 10 nm or less, for example, 5 nm or less, for example, 2 nm or less, including a light source that emits light at a specific wavelength (i.e., monochromatic light). When the method includes irradiating with a narrow-band light source, examples of the narrow-band light source protocols that may be used include, but are not limited to, narrow-wavelength LEDs, laser diodes, or a broad-band light source combined with one or more optical band-pass filters, diffraction gratings, monochromators, or any combination thereof.

[0102] In certain embodiments, the method includes irradiating the sample with one or more lasers. As described above, the type and number of lasers can vary depending on the sample and the desired light to be collected, and can be gas lasers such as 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, xenon-fluoride (XeF) excimer lasers, or combinations thereof. In other cases, the method includes irradiating the flow stream with a dye laser such as a stilbene laser, a coumarin laser, or a rhodamine laser. In still other cases, the method includes irradiating the flow 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 still other cases, the method includes irradiating the flow stream with a solid laser such as a ruby laser, a Nd:YAG laser, a NdCrYAG laser, an Er:YAG laser, a Nd:YLF laser, a Nd:YVO4 laser, a Nd:YCa4O(BO3)3 laser, a Nd:YCOB laser, a titanium sapphire laser, a slim YAG laser, a ytterbium YAG laser, a Yb2O3 laser, or a cerium-doped laser, and combinations thereof.

[0103] The sample can be irradiated with one or more of the light sources mentioned above, such as two or more light sources, three or more light sources, four or more light sources, five or more light sources, including ten or more light sources. The light sources can include any combination of types of light sources. For example, in some embodiments, the method includes irradiating the sample in the flow stream with an array of lasers, such as an array having one or more gas lasers, one or more dye lasers, and one or more solid lasers.

[0104] The sample can be irradiated with wavelengths in the range of 200 nm to 1500 nm, including 400 nm to 800 nm, such as 250 nm to 1250 nm, 300 nm to 1000 nm, 350 nm to 900 nm. For example, when the light source is a broadband light source, the sample can be irradiated with wavelengths in the range of 200 nm to 900 nm. In other cases, when the light source includes a plurality of narrowband light sources, the sample can be irradiated with specific wavelengths in the range of 200 nm to 900 nm. For example, the light source can be a plurality of narrowband LEDs (1 nm to 25 nm) that independently emit light having wavelengths in the range of 200 nm to 900 nm. In other embodiments, the narrowband light source includes one or more lasers (such as a laser array), and the sample is irradiated with specific wavelengths in the range of 200 nm to 700 nm by a laser array having a gas laser, an excimer laser, a dye laser, a metal vapor laser, and a solid laser, as described above.

[0105] When two or more light sources are used, the sample can be irradiated simultaneously, sequentially, or in a combination thereof by the light sources. For example, the sample can be irradiated simultaneously by each of the light sources. In other embodiments, the flow stream is irradiated sequentially by each of the light sources. When two or more light sources are used to irradiate the sample sequentially, the time for each light source to irradiate the sample can independently be 0.001 microseconds or more, including 60 microseconds or more, such as 0.01 microseconds or more, 0.1 microseconds or more, 1 microsecond or more, 5 microseconds or more, 10 microseconds or more, 30 microseconds or more. For example, the method can include irradiating the sample with a light source (such as a laser) for a duration in the range of 0.001 microseconds to 100 microseconds, including 5 microseconds to 10 microseconds, such as 0.01 microseconds to 75 microseconds, 0.1 microseconds to 50 microseconds, 1 microseconds to 25 microseconds. In embodiments where the sample is irradiated sequentially by two or more light sources, the duration for which the sample is irradiated by each light source can be the same or different.

[0106] Also, the periods between irradiations by each light source can be independently separated by delays of 0.001 microseconds or more, such as 0.01 microseconds or more, 0.1 microseconds or more, 1 microsecond or more, 5 microseconds or more, 10 microseconds or more, 15 microseconds or more, 30 microseconds or more, including 60 microseconds or more, and can be different as desired. For example, the periods between irradiations by each light source can be in the range of 0.001 microseconds to 60 microseconds, such as 0.01 microseconds to 50 microseconds, 0.1 microseconds to 35 microseconds, 1 microseconds to 25 microseconds, including 5 microseconds to 10 microseconds. In a certain specific embodiment, the period between irradiations by each light source is 10 microseconds. In embodiments where the sample is sequentially irradiated by more than two (i.e., three or more) light sources, the delays between irradiations by each light source can be the same or different.

[0107] The sample can be irradiated continuously or at discrete intervals. In some cases, the method includes continuously irradiating the sample within the sample with a light source. In other cases, the sample is irradiated at discrete intervals with a light source, 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 some other interval.

[0108] Depending on the light source, the sample can be irradiated from different distances, such as 0.01 mm or more, 0.05 mm or more, 0.1 mm or more, 0.5 mm or more, 1 mm or more, 2.5 mm or more, 5 mm or more, 10 mm or more, 15 mm or more, 25 mm or more, including 50 mm or more. Also, the angle or irradiation can also be different in the range of 10° to 90°, such as 15° to 85°, 20° to 80°, 25° to 75°, including 30° to 60°, for example, at an angle of 90°.

[0109] In an embodiment, light from the irradiated sample is transmitted to a light detection system and measured by one or more photodetectors. When implementing the method of the subject matter, light from the sample is transmitted to three or more wavelength separators, each configured to pass light having a predetermined spectral range. The spectral range of light from each of the wavelength separators is transmitted to one or more light detection modules having optical components configured to transmit light having a predetermined sub-spectral range to the photodetector.

[0110] Light can be measured continuously or at discrete intervals in the light 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, every 1000 milliseconds, or at some other interval.

[0111] Measurements of the collected light can be made one or more times during the method of the subject matter, such as two or more times, three or more times, five or more times, including ten or more times. In certain embodiments, the light propagation is measured two or more times, and in certain cases, the data is averaged.

[0112] In some embodiments, the method includes conditioning the light before detecting the light with the light detection system of the subject matter. For example, light from the sample source can pass through one or more lenses, mirrors, pinholes, slits, gratings, photorefractive devices, and any combination thereof. In some cases, the collected light passes through one or more focusing lenses to reduce the profile of the light directed towards the light detection system or the optical collection system, as described above. In other cases, light emitted from the sample passes through one or more collimators to reduce the light beam divergence of the light transmitted to the light detection system.

[0113] System Aspects of the present invention additionally include a system configured to implement the methods described above. The system in question includes a fluorescent dye panel, a device identifier, and a processor configured to obtain a spectral matrix associated with the fluorescent dye panel and the device identifier. The processor of the system of the subject matter is also configured to identify fluorescent dyes in the fluorescent dye panel that will be associated with the variance of flow cytometer data generated using the fluorescent dye panel by calculating an inverse matrix from the obtained spectral matrix and analyzing the calculated inverse matrix, and to evaluate the suitability of the fluorescent dye panel for use in the generation of flow cytometer data. In an embodiment, the processor of the subject matter operates in conjunction with programmable logic that can be implemented in hardware, software, firmware, or any combination thereof to evaluate the fluorescent dye panel. For example, when the programmable logic is implemented in software, the fluorescent dye panel evaluation can be at least partially realized by a computer-readable data storage medium including program code including instructions configured to obtain the fluorescent dye panel, the device identifier, and the spectral matrix associated with the fluorescent dye panel and the device identifier when executed. Additionally, the instructions are configured to identify fluorescent dyes in the fluorescent dye panel that will be associated with the variance of flow cytometer data generated using the fluorescent dye panel by calculating an inverse matrix from the obtained spectral matrix and analyzing the calculated inverse matrix, and to evaluate the suitability of the fluorescent dye panel for use in the generation of flow cytometer data.

[0114] The processor may be additionally configured to optimize the fluorescent dye panel based on an evaluation of the suitability of the fluorescent dye panel for use in generating flow cytometer data. As described above, the panel optimization algorithm for use in optimizing the fluorescent dye panel includes, but is not limited to, constrained optimization methods. In some embodiments, the processor is configured to generate a visualization of the evaluated suitability of the fluorescent dye panel for use in generating flow cytometer data. For example, an embodiment of the visualization highlights the fluorescent dyes in the fluorescent dye panel that will be associated with the dispersion of the flow cytometer data generated using that fluorescent dye panel. In some embodiments, the visualization includes a plot of simulated flow cytometer data based on a given fluorescent dye panel. Put another way, the visualization includes exemplary flow cytometer data that would be generated if a sample were run on a particular instrument with a particular fluorescent dye panel. In an additional version, the visualization includes a table or matrix that quantifies the extent to which the fluorescent dyes in the fluorescent dye panel are associated with the dispersion (e.g., contribute to and / or are affected by the dispersion). In some such embodiments, the system includes a display configured to depict the visualization. Any suitable display may be used. Examples of suitable devices include, but are not limited to, a monitor, a tablet computer, a smartphone, or other electronic device configured to present a graphical interface.

[0115] The programmable logic of the subject matter 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, etc. In some embodiments, the programmable logic can be executed by a specially programmed processor that can include one or more processors such as one or more digital signal processors (DSPs), configurable microprocessors, application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), or other equivalent integrated circuits or discrete logic circuits. Combinations of computing devices, such as a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration that is at least partially in a data-connected state, can implement one or more of the described features.

[0116] In certain instances, the system is or includes a particle analyzer. The particle analyzer of interest can include a flow cell for transporting particles in a flow stream, a light source for irradiating particles in the flow stream at an inspection point, and a particle-modulated light detector for detecting 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.

[0117] As described herein, a "flow cell" is described in its conventional sense as referring to a component such as a cuvette that includes a flow channel having a liquid flow stream for transporting particles in a sheath fluid. The cuvette of interest includes a container having a passage extending therein. The flow stream can include a liquid sample injected from a sample tube. The flow cell of interest includes a flow channel that is optically accessible. In some cases, the flow cell includes a transparent material (e.g., quartz) that allows light to pass through. In some embodiments, the flow cell is a stream-in air flow cell in which optical inspection of the particles occurs outside the flow cell (i.e., within free space).

[0118] In some cases, the flow stream is configured to be irradiated with light from a light source at an inspection point. The flow stream in which the flow channel is configured can include a liquid sample injected from a sample tube. In certain embodiments, the flow stream can include a narrow, rapidly flowing liquid stream in which linearly separated particles being transported therein are separated from each other in a row-like manner. The "inspection point" described herein refers to, for example, a region within the flow cell where particles are irradiated with light from a light source for analysis. The size of the inspection point can vary as desired. For example, if 0 μm represents the axis of the light emitted by the light source, the inspection point can range from -15 μm to 30 μm, including -50 μm to 50 μm, such as -25 μm to 40 μm, such as -100 μm to 100 μm.

[0119] After the particles are irradiated in the flow cell, particle-modulated light can be observed. "Particle-modulated light" means the light received from the particles in the flow 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 light diffracted and reflected from the surface and internal structure of the particles. In additional embodiments, the particle-modulated light includes forward-scattered light (i.e., light that travels mostly in the forward direction through or around the particles). In still other cases, the particle-modulated light includes fluorescence (i.e., light emitted from a fluorescent dye after irradiation with excitation wavelength light).

[0120] As described above, aspects of the present invention also include a light source configured to irradiate particles passing through the flow cell at the inspection point. Any convenient light source can be used as the light source described herein. In some embodiments, the light source is a laser. In an embodiment, the laser can be any convenient laser such as a continuous wave laser. For example, the laser can be a diode laser such as an ultraviolet diode laser, a visible diode laser, and a near-infrared diode laser. In other embodiments, the laser can be a helium neon (HeNe) laser. In some cases, the laser is a gas laser such as a helium neon laser, an argon laser, a krypton laser, a xenon laser, a nitrogen laser, a 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 flow cytometer of the subject includes a dye laser such as a stilbene laser, a coumarin laser, or a rhodamine laser. In still other cases, the laser of interest 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 still other cases, the flow cytometer includes a solid laser such as a ruby laser, a Nd:YAG laser, a NdCrYAG laser, an Er:YAG laser, a Nd:YLF laser, a Nd:YVO4 laser, a Nd:YCa4O(BO3)3 laser, a Nd:YCOB laser, a titanium sapphire laser, a slim YAG laser, a ytterbium YAG laser, a Yb2O3 laser, or a cerium-doped laser, and combinations thereof.

[0121] A laser light source according to certain embodiments may also include one or more optical adjustment components. In certain embodiments, the optical adjustment component is located between the light source and the flow cell and can change the spatial width of the irradiation or some other characteristics of the irradiation from the light source, such as, for example, the irradiation direction, wavelength, beam width, beam intensity, and focus. The optical adjustment protocol may include any convenient device for adjusting one or more characteristics of the light source, including but not limited to lenses, mirrors, filters, optical fibers, wavelength separators, pinholes, slits, collimating protocols, and combinations thereof. In certain embodiments, the flow cytometer of interest includes one or more focusing lenses. The focusing lens may be, in one example, a non-magnifying lens. In still other embodiments, the flow cytometer of interest includes an optical fiber.

[0122] When the optical adjustment component is configured to move, the optical adjustment component may be configured to move continuously or at discrete intervals, including increments of 25 mm or more, such as increments of 0.05 μm or more, 0.1 μm or more, 0.5 μm or more, 1 μm or more, 10 μm or more, 100 μm or more, 500 μm or more, 1 mm or more, 5 mm or more, 10 mm or more, for example, increments of 0.01 μm or more.

[0123] Any displacement protocol can be used to move the optical adjustment component structure, such as being coupled to a movable support stage or directly coupled to a geared translation device using a motor type, such as a stepping motor, servo motor, brushless electric motor, brushed DC motor, microstepping drive motor, high-resolution stepping motor.

[0124] The light source and the flow cell can be positioned at any suitable distance and angle relative to the flow cell. For example, the light source and the flow cell can be separated by a distance of 0.01 mm or more, such as 0.05 mm or more, 0.1 mm or more, 0.5 mm or more, 1 mm or more, 5 mm or more, 10 mm or more, 25 mm or more, including distances of 100 mm or more. In addition, the light source can be positioned at an angle within a range of 10 degrees to 90 degrees, such as 15 degrees to 85 degrees, 20 degrees to 80 degrees, 25 degrees to 75 degrees, including 30 degrees to 60 degrees, for example, at an angle of 90 degrees, at any suitable angle relative to the flow cell.

[0125] In some embodiments, the light source of interest includes two or more lasers, such as three or more lasers, four or more lasers, five or more lasers, ten or more lasers, including fifteen or more lasers configured to provide laser light for discrete illumination of the flow stream. Depending on the desired wavelength of the light for irradiating the flow stream, each laser can have a specific wavelength that varies from 250 nm to 1250 nm, such as 300 nm to 1000 nm, 350 nm to 900 nm, 200 nm to 1500 nm, including 400 nm to 800 nm. In certain embodiments, the laser of interest can include one or more of a 405 nm laser, a 488 nm laser, a 561 nm laser, and a 635 nm laser.

[0126] As described above, the particle analyzer of interest can further include one or more particle-modulated light detectors for detecting particle-modulated light intensity data. In some embodiments, the particle-modulated light detector includes one or more forward-scattered light detectors configured to detect forward-scattered light. For example, the particle analyzer of the subject matter can include one forward-scattered light detector, or a plurality (e.g., two or more, such as three or more, such as four or more, and including five or more) forward-scattered light detectors. In certain embodiments, the particle analyzer includes one forward-scattered light detector. In other embodiments, the particle analyzer includes two forward-scattered light detectors.

[0127] Any convenient detector for detecting the collected light can be used in the forward scattered light detector described herein. Suitable detectors include, but are not limited to, optical sensors or detectors such as active pixel sensors (APS), avalanche photodiodes, image sensors, charge coupled devices (CCD), intensified charge coupled devices (ICCD), light emitting diodes, photon counters, bolometers, pyroelectric detectors, photoresistors, solar cells, photodiodes, photomultiplier tubes (PMT), phototransistors, quantum dot photoconductors, or photodiodes, and combinations thereof. In certain embodiments, the collected light is measured by a charge coupled device (CCD), a semiconductor charge coupled device (CCD), an active pixel sensor (APS), a complementary metal oxide semiconductor (CMOS) image sensor, or an N-type metal oxide semiconductor (NMOS) image sensor. In certain embodiments, the detector has an active detection surface area in each region in the range of 0.01 cm 2 ~5 cm 2 including, 0.05 cm 2 ~9 cm 2 such as, 0.1 cm 2 ~8 cm 2 such as, 0.5 cm 2 ~7 cm 2 such as, 0.01 cm 2 ~10 cm 2 and is a photomultiplier tube such as a photomultiplier tube having an active detection surface area in each region of the range.

[0128] In embodiments, the forward scattered light detector is configured to measure light continuously or at discrete intervals. In some cases, the detector in question is configured to continuously measure the collected light. In other cases, the detector in question is configured to measure light at discrete intervals, such as at 0.001 milliseconds per, 0.01 milliseconds per, 0.1 milliseconds per, 1 millisecond per, 10 milliseconds per, 100 milliseconds per, or some other interval, including every 1000 milliseconds.

[0129] In additional embodiments, one or more particle-modulated light detectors may include one or more side-scattered light detectors for detecting the side-scattered wavelengths of light (i.e., light refracted and reflected from the surface and internal structure of the particles). In some embodiments, the particle analyzer includes a single side-scattered light detector. In other embodiments, the particle analyzer includes a plurality of (e.g., two or more, e.g., three or more, e.g., four or more, and including five or more) side-scattered light detectors.

[0130] Any convenient detector for detecting the collected light may be used for the side-scattered light detectors described herein. Suitable detectors include, among others, active pixel sensors (APS), avalanche photodiodes, image sensors, charge-coupled devices (CCD), intensified charge-coupled devices (ICCD), light-emitting diodes, photon counters, bolometers, pyroelectric detectors, photoresistors, solar cells, photodiodes, photomultiplier tubes (PMT), phototransistors, quantum dot photoconductors, or photodiodes, and optical sensors or detectors such as combinations thereof, but are not limited thereto. In certain embodiments, the collected light is measured with a charge-coupled device (CCD), a semiconductor charge-coupled device (CCD), an active pixel sensor (APS), a complementary metal-oxide-semiconductor (CMOS) image sensor, or an N-type metal-oxide-semiconductor (NMOS) image sensor. In certain embodiments, the detector is a photomultiplier tube such as a photomultiplier tube having an active detection surface area in each region in the range of 2 ~5 cm 2 including 0.05 cm 2 ~9 cm 2 such as 0.1 cm 2 ~8 cm 2 such as 0.5 cm 2 ~7 cm 2 such as 0.01 cm 2 ~10 cm 2 of the photomultiplier tube.

[0131] In an embodiment, the subject particle analyzer also includes a fluorescence detector configured to detect one or more fluorescence wavelengths of light. In other embodiments, the particle analyzer includes a plurality (e.g., two or more, e.g., three or more, e.g., four or more, five or more, ten or more, fifteen or more, and twenty or more) of fluorescence detectors.

[0132] Any convenient detector for detecting the collected light can be used for the fluorescence detectors described herein. Suitable detectors include, among others, active pixel sensors (APS), avalanche photodiodes, image sensors, charge-coupled devices (CCD), intensified charge-coupled devices (ICCD), light-emitting diodes, photon counters, bolometers, pyroelectric detectors, photoresistors, solar cells, photodiodes, photomultiplier tubes (PMT), phototransistors, quantum dot photoconductors, or photodiodes, and combinations thereof, but are not limited thereto. In certain embodiments, the collected light is measured with a charge-coupled device (CCD), a semiconductor charge-coupled device (CCD), an active pixel sensor (APS), a complementary metal-oxide-semiconductor (CMOS) image sensor, or an N-type metal-oxide-semiconductor (NMOS) image sensor. In certain embodiments, the detector has an active detection surface area in the range of 0.01 cm 2 to 5 cm 2 including, for example, 0.05 cm 2 to 9 cm 2 such as, for example, 0.1 cm 2 to 8 cm 2 such as, for example, 0.5 cm 2 to 7 cm 2 such as, for example, 0.01 cm 2 to 10 cm 2 of a photomultiplier tube, such as a photomultiplier tube having an active detection surface area in each of these ranges.

[0133] When the particle analyzer of the subject includes a plurality of fluorescence detectors, each fluorescence detector may be the same, or the collection of fluorescence detectors may be a combination of different types of detectors. For example, when the particle analyzer of the subject includes two fluorescence detectors, in some embodiments, the first fluorescence detector is a CCD-type device, and the second fluorescence detector (or image sensor) is a CMOS-type device. In other embodiments, both the first fluorescence detector and the second fluorescence detector are CCD-type devices. In still other embodiments, both the first fluorescence detector and the second fluorescence detector are CMOS-type devices. In still other embodiments, the first fluorescence detector is a CCD-type device, and the second fluorescence detector is a photomultiplier tube (PMT). In still other embodiments, the first fluorescence detector is a CMOS-type device, and the second fluorescence detector is a photomultiplier tube. In still other embodiments, both the first fluorescence detector and the second fluorescence detector are photomultiplier tubes.

[0134] In embodiments of the present disclosure, the fluorescence detector of interest is configured to measure the collected light at one or more wavelengths, such as five or more different wavelengths, ten or more different wavelengths, twenty-five or more different wavelengths, fifty or more different wavelengths, one hundred or more different wavelengths, two hundred or more different wavelengths, three hundred or more different wavelengths, including measuring light emitted by particles in the flow stream at 400 or more different wavelengths. In some embodiments, two or more detectors within the particle analyzer described herein are configured to measure the same or overlapping wavelengths of the collected light.

[0135] In some embodiments, the fluorescence detector of interest is configured to measure light collected over a range of wavelengths (e.g., 200 nm to 1000 nm). In certain embodiments, the detector of interest is configured to collect the spectrum of light over a range of wavelengths. For example, a particle analyzer may include one or more detectors configured to collect the spectrum of light over one or more of the wavelength ranges from 200 nm to 1000 nm. In yet other embodiments, the detector of interest is configured to measure light emitted by a sample in a flow stream at one or more specific wavelengths. For example, a particle analyzer may include one or more detectors configured to measure one or more of the light at 450 nm, 518 nm, 519 nm, 561 nm, 578 nm, 605 nm, 607 nm, 625 nm, 650 nm, 660 nm, 667 nm, 670 nm, 668 nm, 695 nm, 710 nm, 723 nm, 780 nm, 785 nm, 647 nm, 617 nm, and any combination thereof. In certain embodiments, one or more detectors may be configured to pair with a specific fluorophore such as those used with a sample in a fluorescence assay.

[0136] In some embodiments, the particle analyzer includes one or more wavelength separators disposed between the flow cell and the particle modulation photodetector. The term "wavelength separator" is used herein in its conventional sense to refer to an optical component configured to separate light collected from a sample into a predetermined spectral range. In some embodiments, the particle analyzer includes a single wavelength separator. In other embodiments, the particle analyzer includes more than 100 wavelength separators, for example, two or more wavelength separators, for example, three or more, for example, four or more, for example, five or more, for example, six or more, for example, seven or more, for example, eight or more, for example, nine or more, for example, ten or more, for example, fifteen or more, for example, twenty-five or more, for example, fifty or more, for example, seventy-five or more wavelength separators. In some embodiments, the wavelength separator is configured to separate light collected from a sample into a predetermined spectral range by passing light having a predetermined spectral range and reflecting light in one or more remaining spectral ranges. In other embodiments, the wavelength separator is configured to separate light collected from a sample into a predetermined spectral range by passing light having a predetermined spectral range and absorbing light in one or more remaining spectral ranges. In still other embodiments, the wavelength separator is configured to spatially diffract light collected from a sample into a predetermined spectral range. Each wavelength separator can be any convenient light separation protocol, such as one or more dichroic mirrors, bandpass filters, diffraction gratings, beam splitters, or prisms. In some embodiments, the wavelength separator is a prism. In other embodiments, the wavelength separator is a diffraction grating. In a particular embodiment, the wavelength separator in the subject light detection system is a dichroic mirror.

[0137] Suitable flow cytometry systems may include, but are not limited to, those described in Ormerod (ed.), Flow Cytometry: A Practical Approach, Oxford Univ. Press (1997); Jaroszeski et al. (eds.), Flow Cytometry Protocols, Methods in Molecular Biology No. 91, Humana Press (1997); Practical Flow Cytometry, 3rd ed., Wiley-Liss (1995); Virgo, et al. (2012) Ann Clin Biochem. Jan; 49(pt 1):17-28; Linden, et.al., Semin 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 hereby incorporated by reference into this specification.In certain cases, the flow cytometry systems of interest include the BD Biosciences FACSCanto™ flow cytometer, BD Biosciences FACSCanto™ II flow cytometer, BD Accuri™ flow cytometer, BD Accuri™ C6 Plus flow cytometer, BD Biosciences FACSCelesta™ flow cytometer, BD Biosciences FACSLyric™ flow cytometer, BD Biosciences FACSVerse™ flow cytometer, BD Biosciences FACSymphony™ flow cytometer, BD Biosciences LSRFortessa™ flow cytometer, BD Biosciences LSRFortessa™ X-20 flow cytometer, BD Biosciences FACSPresto™ flow cytometer, BD Biosciences FACSVia™ flow cytometer, and the BD Biosciences FACSCalibur™ cell sorter, BD Biosciences FACSCount™ cell sorter, BD Biosciences FACSLyric™ cell sorter, and BD Biosciences Via™ cell sorter, BD Biosciences Influx™ cell sorter, BD Biosciences Jazz™ cell sorter, BD Biosciences Aria™ cell sorter, BD Biosciences FACSAria™ II cell sorter, BD Biosciences FACSAria™ III cell sorter, BD Biosciences FACSAria™ Fusion cell sorter, and BD Biosciences FACSMelody™ cell sorter, BD Biosciences FACSymphony™ S6 cell sorter, etc.

[0138] In some embodiments, the subject system is a flow cytometry system such as those described in U.S. Patent Nos. 10,663,476, 10,620,111, 10,613,017, 10,605,713, 10,585,031, 10,578,542, 10,578,469, 10,481,074, 10,302,545, 10,145,793, 10,113,967, 10,006,852, 9,952,076, 9,933,341, 9,726,527, 9,453,789, 9,200,334, 9,097,640, 9,095,494, 9,092,034, 8,975,595, 8,753,573, 8,233,146, 8,140,300, 7,544,326, 7,201,875, 7,129,505, 6,821,740, 6,813,017, 6,809,804, 6,372,506, 5,700,692, 5,643,796, 5,627,040, 5,620,842, 5,602,039, 4,987,086, 4,498,766, the disclosures of which are hereby incorporated by reference in their entireties.

[0139] In certain instances, the flow cytometry system of the present invention is configured to image particles in a flow stream by fluorescence imaging using high-frequency tagged emission (FIRE), such as those described in Diebold, et al. Nature Photonics Vol.7(10);806-810(2013), 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 Publications Nos. 2017 / 0133857, 2017 / 0328826, 2017 / 0350803, 2018 / 0275042, 2019 / 0376895, and 2019 / 0376894. These disclosures are incorporated herein by reference.

[0140] FIG. 9 shows a system 900 for flow cytometry according to an exemplary embodiment of the present invention. The system 900 includes a flow cytometer 910, a controller / processor 990, and a memory 995. The flow cytometer 910 includes one or more excitation lasers 915a-915c, a focusing lens 920, a flow chamber 925, a forward scatter detector 930, a side scatter detector 935, a fluorescence collection lens 940, one or more beam splitters 945a-945g, one or more bandpass filters 950a-950e, one or more long pass (“LP”) filters 955a-955b, and one or more fluorescence detectors 960a-960f.

[0141] The excitation lasers 915a - c emit light in the form of laser beams. The wavelengths of the laser beams emitted from the excitation lasers 915a - 915c are 488 nm, 633 nm, and 325 nm, respectively, in the exemplary system of FIG. 9. The laser beams are first directed through one or more of the beam splitters 945a and 945b. The beam splitter 945a transmits light at 488 nm and reflects light at 633 nm. The beam splitter 945b transmits UV light (light having wavelengths in the range of 10 - 400 nm) and reflects light at 488 nm and 633 nm.

[0142] Next, the laser beams are directed to the focusing lens 920, which focuses the beams onto the portion of the fluid stream in the flow chamber 925 where the particles of the sample are located. The flow chamber is part of a fluidics system that directs, typically one at a time for investigation, the particles in the stream into the focused laser beam. The flow chamber can comprise a flow cell within a benchtop flow cytometer or a nozzle tip within a stream - in - air cytometer.

[0143] The light from the laser beams interacts with the particles in the sample through diffraction, refraction, reflection, scattering, and absorption with re - emission at various different wavelengths depending on particle properties such as particle size, internal structure, and the presence of one or more fluorescent molecules attached to or naturally present on or in the particles. Fluorescent emission, as well as diffracted light, refracted light, reflected light, and scattered light can be routed through one or more of the beam splitters 945c - 945g, band - pass filters 950a - 950e, long - pass filters 955a - 955b, and fluorescence collection lens 940 to one or more of the forward scatter detector 930, side scatter detector 935, and one or more fluorescence detectors 960a - 960f.

[0144] The fluorescence collection lens 940 collects the light emitted from the interaction between the particle laser beams and routes the light towards one or more beam splitters and filters. Bandpass filters such as bandpass filters 950a - 950e allow a narrow wavelength range to pass through the filter. For example, bandpass filter 950a 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 center of the spectral band, or from 500 nm to 520 nm. A short - pass filter transmits the wavelengths of light below a specified wavelength. Long - pass filters such as long - pass filters 955a - 955b transmit the wavelengths of light above a specified wavelength of light. For example, long - pass filter 955b, which is a 670 nm long - pass filter, transmits light of 670 nm and above. Filters are often selected to optimize the specificity of the detector for a particular fluorescent dye. Those 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.

[0145] The forward scatter detector 930 is positioned slightly off - axis from the direct beam passing through the flow cell and is configured to detect diffracted light, excitation light that moves mostly in the forward direction through or around the particles. The intensity of the light detected by the forward scatter detector depends on the overall size of the particles. The forward scatter detector can include a photodiode. The side scatter detector 935 is configured to detect diffracted and reflected light from the surface and internal structure of the particles, which tends to increase as the particle structure becomes more complex. Fluorescent emission from fluorescent molecules associated with the particles can be detected by one or more fluorescence detectors 960a - 960f. The side scatter detector 935 and the fluorescence detectors can include photomultiplier tubes. The signals detected by the forward scatter detector 930, the side scatter detector 935, and the fluorescence detectors can be converted by the detectors into electrical signals (voltages). This data can provide information about the sample.

[0146] One skilled in the art will recognize that the flow cytometer according to an embodiment of the present invention is not limited to the flow cytometer shown in FIG. 9 and may include any flow cytometer known in the art. For example, the flow cytometer may have any number of lasers, beam splitters, filters, and detectors of various wavelengths and various different configurations.

[0147] During operation, the operation of the flow cytometer is controlled by a controller / processor 990, and measurement data from the detector can be stored in a memory 995 and processed by the controller / processor 990. Although not explicitly shown, the controller / processor 990 is coupled to the detector to receive an output signal therefrom and is also coupled to the electrical and electromechanical components of the flow cytometer 910 to control lasers, fluid flow parameters, etc. An input / output (I / O) function unit 997 may also be provided within the system. The memory 995, the controller / processor 990, and the I / O 997 may be provided as an integral part of the flow cytometer 910. In such an embodiment, a display may also form part of the I / O function unit 997 for presenting experimental data to the user of the cytometer 910. Alternatively, some or all of the memory 995, the controller / processor 990, and the I / O function unit may be part of one or more external devices such as a general-purpose computer. In some embodiments, some or all of the memory 995 and the controller / processor 990 can communicate wirelessly or wired with the cytometer 910. Together with the memory 995 and the I / O 997, the controller / processor 990 may be configured to perform various functions related to the preparation and analysis of flow cytometer experiments.

[0148] The system illustrated in FIG. 9 includes six different detectors that detect fluorescence within six different wavelength bands (which may be referred to herein as "filter windows" for a given detector), as defined by the configuration of filters and / or splitters in the beam path from the flow cell 925 to each detector. Different fluorescent molecules in the fluorescent dye panel used in a flow cytometry experiment emit light in their own characteristic wavelength bands. The particular fluorescent labels used in the experiment, and their associated fluorescence emission bands, can be selected to generally coincide with the filter windows of the detectors. I / O 997 can be configured to receive data related to flow cytometry experiments having a panel of fluorescent labels and multiple cell populations having multiple markers, where each cell population has a subset of the multiple markers. I / O 997 can also be configured to receive biological data that assigns one or more markers to one or more cell populations, marker concentration data, emission spectrum data, data that assigns labels to one or more markers, and cytometer configuration data. Flow cytometry experiment data such as label spectral characteristics and flow cytometer configuration data can also be stored in the memory 995. The controller / processor 990 can be configured to evaluate one or more assignments of labels to markers.

[0149] In some embodiments, the subject system is a particle sorting system configured to sort particles using a sealed particle sorting module, such as that 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, the particles of a sample (e.g., cells) are sorted using a sort determination module having a plurality of sort determination units, such as that 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 components of a sample includes a particle sorting module having a deflector plate, such as that described in U.S. Patent Publication No. 2017 / 0299493, filed Mar. 28, 2017, the disclosure of which is incorporated herein by reference.

[0150] FIG. 10 shows a functional block diagram of an example of a control system, such as processor 1000, for analyzing and displaying a biological event. Processor 1000 may be configured to perform various processes for controlling a graphic display of a biological event.

[0151] A flow cytometer or sorting system 1002 may be configured to acquire biological event data. For example, a flow cytometer can generate flow cytometric event data (e.g., particle-modulated light data). Flow cytometer 1002 may be configured to provide biological event data to processor 1000. A data communication channel may be included between flow cytometer 1002 and processor 1000. The biological event data may be provided to processor 1000 via the data communication channel.

[0152] Processor 1000 can be configured to receive biological event data from flow cytometer 1002. The biological event data received from flow cytometer 1002 can include flow cytometric event data. Processor 1000 can be configured to provide a graphical display including a first plot of the biological event data to display device 1006. Processor 1000 can be further configured to render a target region, for example, on the first plot, as a gate (e.g., a first gate) around a population of biological event data indicated by display device 1006. In some embodiments, the gate can be a logical combination of one or more graphical regions of interest drawn on a histogram of a single parameter or a bivariate plot. In some embodiments, the display can be used to display particle parameters or saturated detector data.

[0153] Processor 1000 can be further configured to display the biological event data on display device 1006 within the gate differently from other events in the biological event data outside the gate. For example, processor 1000 can be configured to render the color of the biological event data included within the gate to be distinguishable from the color of the biological event data outside the gate. Display device 1006 can be implemented as a monitor, a tablet computer, a smartphone, or other electronic device configured to present a graphical interface.

[0154] Processor 1000 may be configured to receive a gate selection signal that identifies a gate from a first input device. For example, the first input device may be implemented as mouse 1010. This mouse 1010 can initiate a gate selection signal to processor 1000 that identifies a gate to be displayed or manipulated via display device 1006 (e.g., by clicking on the desired gate when positioning a cursor there). In some implementations, the first device may be implemented as a keyboard 1008, or other means for providing an input signal to processor 1000, such as a touch screen, input pen, light detector, or voice recognition system. Some input devices may include multiple input functions. In such implementations, each input function may be considered an input device. For example, as shown in FIG. 10, mouse 1010 may include a right mouse button and a left mouse button, each of which may generate an activation event.

[0155] This activation event may cause processor 1000 to change how data is displayed, which portions of the data are actually displayed on display device 1006, and / or provide input to further processing such as selection of a population to be particle sorted.

[0156] In some embodiments, processor 1000 may be configured to detect when a gate selection is initiated by mouse 1010. Processor 1000 may be further configured to automatically modify the visualization of the plot to facilitate the gate control process. This modification may be based on a particular distribution of the biological event data received by processor 1000. In some embodiments, processor 1000 expands the first gate such that a second gate is generated (e.g., as described above).

[0157] Processor 1000 can be connected to memory device 1004. This memory device 1004 can be configured to receive and store biological event data from processor 1000. Memory device 1004 can also be configured to receive and store flow cytometric event data from processor 1000. Memory device 1004 can be further configured by processor 1000 to enable retrieval of biological event data such as flow cytometric event data.

[0158] Display device 1006 can be configured to receive display data from processor 1000. The display data can include plots of biological event data and gates depicting the outlines of the plots' compartments. Display device 1006 can be further configured to change the information presented according to the input received from processor 1000 in conjunction with inputs from flow cytometer 1002, memory device 1004, keyboard 1008, and / or mouse 1010.

[0159] Processor 1000 can be additionally configured to evaluate a fluorescent dye panel. In such a case, processor 1000 is configured to obtain a fluorescent dye panel, a device identifier, and a spectral matrix associated with the fluorescent dye panel and the device identifier. Processor 1000 is also configured to calculate an inverse matrix from the obtained spectral matrix and analyze the calculated inverse matrix to identify fluorescent dyes in the fluorescent dye panel that will be associated with the variance of the flow cytometer data generated using the fluorescent dye panel, and to evaluate the suitability of the fluorescent dye panel for use in generating flow cytometer data. In certain cases, processor 1000 is also configured to generate a visualization based on the evaluation of the fluorescent dye panel. This visualization can, in some cases, be shown on display device 1006.

[0160] In some implementations, processor 1000 can generate a user interface and receive exemplary events for sorting. For example, this user interface can include a mechanism for receiving exemplary events or exemplary images. Exemplary events or images, or exemplary gates, can be provided before collection of event data for a sample, or based on an initial set of events for a portion of the sample.

[0161] FIG. 11A is a schematic diagram of a particle sorter system 1100 (e.g., flow cytometer 502) according to one embodiment presented herein. In some embodiments, particle sorter system 1100 is a cell sorter system. As shown in FIG. 11A, a droplet formation transducer 1102 (e.g., a piezoelectric oscillator) is coupled to a fluid conduit 1101, which can be coupled to, include, or be nozzle 1103. Within fluid conduit 1101, sheath fluid 1104 hydrodynamically focuses sample fluid 1106 containing particles 1109 into a moving fluid column 1108 (e.g., a stream). Within moving fluid column 1108, particles 1109 (e.g., cells) are lined up in a single file and cross a monitoring area 1111 (e.g., where a laser stream intersects), and are irradiated by an irradiation source 1112 (e.g., a laser). Due to the vibration of droplet formation transducer 1102, moving fluid column 1108 splits into a plurality of droplets 1110, some of which contain particles 1109.

[0162] During operation, the detection station 1114 (e.g., an event detector) identifies when a target particle (or target cell) crosses the monitored area 1111. The detection station 1114 supplies an input to the timing circuit 1128, which then supplies an input to the flash charge circuit 1130. At the droplet splitting point, upon notification by the timed droplet delay (Δt), flash charge can be applied to the moving fluid column 1108, and thus the target droplet can carry a charge. The target droplet can contain one or more particles or cells to be sorted. The charged droplet is then sorted by activating a deflection plate (not shown) to deflect the droplet into a container such as a collection tube, or a multiwell or microwell sample plate where wells or micro-wells can be associated with specific target droplets. As shown in FIG. 11A, the droplets can be collected in the drain container 1138.

[0163] The detection system 1116 (e.g., a droplet boundary detector) serves to automatically determine the phase of the droplet drive signal when a target particle passes through the monitored area 1111. An exemplary droplet boundary detector is described in U.S. Patent No. 7,679,039, which is hereby incorporated by reference in its entirety. The detection system 1116 enables the device to accurately calculate the position of each detected particle in the droplet. The detection system 1116 can supply an input to the amplitude signal 1120 and / or the phase signal 1118, which are then supplied (via the amplifier 1122) to the amplitude control circuit 1126 and / or the frequency control circuit 1124. The amplitude control circuit 1126 and / or the frequency control circuit 1124 then controls the droplet formation transducer 1102. The amplitude control circuit 1126 and / or the frequency control circuit 1124 can be included within the control system.

[0164] In some implementations, the sorting electronics (e.g., detection system 1116, detection station 1114, and processor 1140) can be coupled to a memory configured to store detected events and sorting decisions based thereon. Sorting decisions can be included in the event data of the particles. In some implementations, detection system 1116 and detection station 1114 can be implemented as a single detection unit, or communicatively coupled such that event measurements are collected by one of detection system 1116 or detection station 1114 and provided to non-collecting elements.

[0165] FIG. 11B is a schematic diagram of a particle sorter system according to one embodiment presented herein. The particle sorter system 1100 shown in FIG. 11B includes deflection plates 1152 and 1154. Charge can be applied via a stream charging wire within the barb. Thereby, a stream of droplets 1110 containing particles 1109 is created for analysis. The particles can be irradiated with one or more light sources (e.g., lasers) to generate light scattering and fluorescence information. Information about the particles is analyzed, such as by sorting electronics or other detection systems (not shown in FIG. 11B). Deflection plates 1152 and 1154 are independently controlled to attract or repel charged droplets and direct the droplets towards a desired collection container (e.g., one of 1172, 1174, 1176, or 1178). As shown in FIG. 11B, deflection plates 1152 and 1154 are controlled to direct the particles along a first path 1162 towards container 1174 or along a second path 1168 towards container 1178. If the particles are not of interest (e.g., do not exhibit scattering or illumination information within a specified sorting range), the deflection plates can allow the particles to continue to travel along flow path 1164. Such uncharged droplets can be transferred into a waste container, such as via aspirator 1170.

[0166] Sorting electronics may be included to initiate collection of measurement values, receive fluorescence signals regarding particles, and determine how to adjust deflection plates to cause sorting of the particles. As an exemplary implementation of the embodiment shown in FIG. 11B, a BD FACSAria™ flow cytometer commercially available from Becton, Dickinson and Company (Franklin Lakes, NJ) may be mentioned.

[0167] Computer-readable storage medium Aspects of the present disclosure further include a non-transitory computer-readable storage medium having instructions for implementing the subject methods. The computer-readable storage medium can be used on one or more computers for complete or partial automation of a system for implementing the methods described herein. In certain embodiments, the instructions according to the methods described herein can be encoded in a computer-readable medium in the form of a “programming,” where the term “computer-readable medium,” as used herein, refers to any non-transitory storage medium involved in providing instructions and data to a computer for execution and processing. In some cases, the instructions, when executed, are configured to obtain a fluorescence dye panel, an instrument identifier, and a spectral matrix associated with the fluorescence dye panel and the instrument identifier. Additionally, the instructions are configured to identify fluorescence dyes in the fluorescence dye panel associated with the variance of flow cytometer data generated using the fluorescence dye panel by calculating an inverse matrix from the obtained spectral matrix and analyzing the calculated inverse matrix, and to evaluate the suitability of the fluorescence dye panel for use in the generation of flow cytometer data.

[0168] Examples of suitable non-transitory memory media include magnetic disks, hard disks, optical disks, magneto-optical disks, CD-ROMs, CD-Rs, magnetic tapes, non-volatile memory cards, ROMs, DVD-ROMs, Blue-ray disks, solid state disks, flash drives, and network-attached storage (NAS) devices, whether such devices are internal or external to the computer. A file containing information can be "stored" on a computer-readable medium, where "stored" means recording the information such that it is accessible and searchable by a computer at a later time. The computer-implemented methods described herein can be executed using programming written in one or more of any number of computer programming languages. Such languages include, for example, Java, Python, Visual Basic, and C++, as well as many others.

[0169] In some embodiments, the computer-readable storage medium of interest includes a computer program stored on the computer-readable storage medium, the computer program including instructions for obtaining a fluorescent dye panel, a device identifier, and a spectral matrix associated with the fluorescent dye panel and the device identifier when loaded onto a computer. By calculating an inverse matrix from the obtained spectral matrix and analyzing the calculated inverse matrix, instructions are additionally included for identifying fluorescent dyes in the fluorescent dye panel that will be associated with the variance of flow cytometer data generated using the fluorescent dye panel and for evaluating the suitability of the fluorescent dye panel for use in generating flow cytometer data.

[0170] Computer control system Aspects of the present disclosure further include a computer control system, the system including one or more computers for full automation or partial automation. In some embodiments, the system includes a computer in which a computer program is stored, the computer program including instructions for obtaining a fluorescent dye panel, a device identifier, and a spectral matrix associated with the fluorescent dye panel and the device identifier when loaded onto the computer. By calculating an inverse matrix from the obtained spectral matrix and analyzing the calculated inverse matrix, instructions are additionally included for identifying fluorescent dyes in the fluorescent dye panel that will be associated with the variance of flow cytometer data generated using the fluorescent dye panel and for evaluating the suitability of the fluorescent dye panel for use in the generation of flow cytometer data.

[0171] The system may include a display and an operator input device. The operator input device may be, for example, a keyboard, a mouse, etc. The processing module includes a processor that accesses a memory having instructions stored therein for performing the steps of the subject method. The processing module may include an operating system, a graphical user interface (GUI) controller, system memory, a memory storage device, and an input / output controller, cache memory, a data backup unit, and many other devices. The processor may be a commercially available processor or one of other processors that are available or will become available. The processor executes an operating system, which interfaces with firmware and hardware in a well-known manner and facilitates the processor to cooperate and execute the functions of various computer programs that may be described in various programming languages such as Java, Perl, C++, Python, other high-level or low-level languages, and combinations thereof, as known in the art. The operating system typically cooperates with the processor to coordinate and execute 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, all in accordance with well-known techniques. In some embodiments, the processor includes analog electronics that provide feedback control such as, for example, negative feedback control.

[0172] The system memory can be any of a variety of known or future memory storage devices. Examples include any commonly available random access memory (RAM), magnetic media such as resident hard disks or tapes, optical media such as read / write compact disks, flash memory devices, or other memory storage devices. The memory storage device can be any of a variety of known or future devices, including a compact disk drive, tape drive, or disk drive. Memory storage devices of such types typically read from and / or write to a program storage medium (not shown), such as a compact disk. Any of these program storage media, or other ones currently in use or that may be developed later, can be considered a computer program product. As is understood, these program storage media typically store computer software programs and / or data. A computer software program, also referred to as computer control logic, is typically stored in the system memory and / or a program storage device used in conjunction with the memory storage device.

[0173] In some embodiments, a computer program product is described that comprises a computer-usable medium having control logic (a computer software program including program code) stored therein. The control logic, when executed by a processor, causes the computer, the processor, to perform the functions described herein. In other embodiments, some of the functions are implemented primarily in hardware, using, for example, a hardware state machine. Implementations of a hardware state machine to perform the functions described herein will be apparent to those skilled in the relevant art.

[0174] The memory can be any suitable device such as a magnetic, optical, or solid-state storage device (including magnetic or optical disks, or tapes, or RAM, or any other suitable device, either fixed or portable) in which a processor can store and retrieve data. The processor can include a general-purpose digital microprocessor suitably programmed from a computer-readable medium carrying the necessary program code. The programming can be provided remotely to the processor via a communication channel, or can be pre-stored using any of those devices, together with the memory, in a computer program product such as a memory or some other portable or fixed computer-readable storage medium. For example, a magnetic or optical disk can carry the programming and can be read by a disk writer / reader. The system of the present invention also includes, for example, programming in the form of a computer program product, and algorithms for use in implementing the above methods. The programming according to the present invention can be recorded on a computer-readable medium, such as any medium that can be directly read and accessed by a computer. Such media include, but are not limited to, magnetic storage media, optical storage media such as CD-ROMs, electrical storage media such as RAM and ROM, portable flash drives, and hybrids of these categories such as magnetic / optical storage media.

[0175] The processor can also have access to a communication channel for communicating with a user located at a remote location. A remote location means that the user does not have direct contact with the system and relays input information to the input manager from an external device such as a computer connected to any other suitable communication channel including a wide area network ("WAN"), a telephone network, a satellite network, or a mobile phone (i.e., a smartphone).

[0176] In some embodiments, the system according to the present disclosure may be configured to include a communication interface. In some embodiments, the communication interface includes a receiver and / or a transmitter for communicating with a network and / or another device. The communication interface may be configured for wired or wireless communication, including, but not limited to, radio frequency (RF) communication (e.g., radio frequency identification (RFID), ZigBee communication protocol, Wi-Fi, infrared, wireless universal serial bus (USB), ultra-wideband (UWB), Bluetooth® communication protocol, and cellular communication such as code division multiple access (CDMA) or global system for mobile communications (GSM) for mobile communications).

[0177] In one embodiment, the communication interface is configured to 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 a computer terminal configured for similar complementary data communication (e.g., in a clinic or hospital environment).

[0178] In one embodiment, the communication interface is configured for infrared communication, Bluetooth® communication, or any other suitable wireless communication protocol, enabling the subject system to communicate with other devices, such as a computer terminal and / or a network, a communicable mobile phone, a personal digital assistant, or any other communication device that the user may use in combination.

[0179] In one embodiment, the communication interface is configured to provide a connection for data transfer using the Internet protocol (IP) via a cellular phone network, short message service (SMS), a wireless connection to a personal computer (PC) on a local area network (LAN) connected to the Internet, or a Wi-Fi connection to the Internet at a Wi-Fi hotspot.

[0180] In one embodiment, the subject system is configured to wirelessly communicate with a server device via a communication interface using a common standard such as, for example, the 802.11 or Bluetooth® RF protocol, or the IrDA infrared protocol. The server device may be another portable device such as a smartphone, a personal digital assistant (PDA) or a notebook computer, or a larger device such as a desktop computer, an 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.

[0181] In some embodiments, the communication interface is configured to automatically or semi-automatically communicate data stored within the subject system, e.g., within an optional data storage unit, with a network or a server device using one or more of the communication protocols and / or mechanisms described above.

[0182] The output controller may include a controller for any of a variety of known display devices for presenting information to a user, whether human or machine, local or remote. When one of the display devices provides visual information, this information can typically be logically and / or physically organized as an array of pixels. The graphical user interface (GUI) controller may include any of a variety of known or future software programs for providing a graphical input and output interface between the system and the user and for processing user input. The functional elements of the computer may communicate with each other via a system bus. Some of these communications may be achieved in alternative embodiments using a network or other type of remote communication. The output manager may also provide information generated by the processing module to the user at a remote location, for example, via the Internet, telephone, or satellite network, according to known techniques. The presentation of data by the output manager can be realized according to a variety of known techniques. As some examples, the data may include SQL, HTML, or XML documents, email or other files, or other forms of data. The data may include an Internet URL address so that the user can retrieve additional SQL, HTML, XML, or other documents or data from a remote source. One or more platforms present within the subject system are typically of a class of computers commonly referred to as servers, but can be of any type of known computer platform or a type to be developed in the future. However, they can be mainframe computers, workstations, or other computer types. They can be connected via other communication systems, including any known or future type of cable wiring, or a wireless system, whether networked or not. They can be located in the same place or physically separated.Depending on the type and / or configuration of the selected computer platform, various operating systems may be used on any of the computer platforms. Suitable operating systems include Windows® NT®, Windows® XP, Windows® 7, Windows® 8, Windows® 10, iOS®, macOS®, Linux®, Ubuntu®, Fedora®, OS / 400®, i5 / OS®, IBM i®, Android™, SGI IRIX®, Oracle Solaris® and others.

[0183] FIG. 12 shows a general architecture of an exemplary computing device 1200 according to a particular embodiment. The general architecture of the computing device 1200 shown in FIG. 12 includes an arrangement of computer hardware and software components. However, in order to provide an effective disclosure, not all of these generally traditional elements necessarily need to be shown. As illustrated, the computing device 1200 includes a processing unit 1210, a network interface 1220, a computer-readable media drive 1230, an input / output device interface 1240, a display 1250, and an input device 1260, all of which can communicate with each other via a communication bus. The network interface 1220 can provide a connection to one or more networks or computing systems. Thus, the processing unit 1210 can receive information and instructions from other computing systems or services via the network. The processing unit 1210 can also communicate with a memory 1270 and can further provide output information for an optional display 1250 via the input / output device interface 1240. For example, analysis software (e.g., data analysis software or programs such as FlowJo®) stored as executable instructions in the non-transitory memory of an analysis system can display flow cytometry event data to a user. The input / output device interface 1240 can also receive input from an optional input device 1260 such as a keyboard, mouse, digital pen, microphone, touch screen, gesture recognition system, voice recognition system, game pad, accelerometer, gyroscope, or other input device.

[0184] Memory 1270 may contain computer program instructions (grouped as modules or components in some embodiments) that the processing unit 1210 executes in sequence to implement one or more embodiments. Memory 1270 generally includes RAM, ROM, and / or other persistent, auxiliary, or non-transitory computer-readable media. Memory 1270 may store an operating system 1272 that provides computer program instructions for use by the processing unit 1210 in the general management and operation of computing device 1200. Data may be stored in data storage device 1290. Memory 1270 can further include computer program instructions and other information for implementing aspects of the present disclosure.

[0185] Usefulness The methods, systems, and computer-readable media of the present invention may find uses that desire to automatically determine a set of fluorescent dyes usable for particle analysis (e.g., flow cytometry). In certain cases, the present invention particularly finds use in experimental design for full-spectrum (i.e., "spectral") flow cytometry panels. In other words, the present invention serves as the first step in spectral panel design by specifying whether a set of dyes can be used simultaneously. In some cases, the methods, systems, and computer-readable media described herein help determine which set of fluorescent dyes is likely to provide the best quality data (e.g., maximum biological resolution). The present invention can achieve the above through the use of an automatic optimization algorithm, the use of the spectral signatures of fluorescent dyes as easily available and measurable inputs to the algorithm, and the use of spectral matrices as computationally efficient heuristics for optimization.

[0186] Embodiments of the present invention find use in applications where cells prepared from a biological sample may be desirable for research, laboratory testing, or therapeutic use. In some embodiments, the subject methods and devices may facilitate obtaining individual cells prepared from a target fluid or tissue biological sample. For example, the subject methods and systems facilitate obtaining cells from a fluid or tissue sample that is used as a specimen for research or diagnosis of a disease such as cancer. Similarly, the subject methods and systems may facilitate obtaining cells from a fluid or tissue sample that is used in therapy. The disclosed methods and devices enable separating and collecting cells from a biological sample (e.g., an organ, tissue, tissue fragment, body fluid) with improved efficiency and at low cost compared to conventional flow cytometry systems.

[0187] Kit Aspects of the present disclosure further include a kit, the kit including instructions and / or programmable logic for implementing the claimed method. For example, the kit may include programming configured to evaluate and optionally optimize a fluorescence panel (e.g., as described above in the section on the method) in the form of a computer-readable medium (e.g., a flash drive, USB storage, compact disk, DVD, Blu-ray (registered trademark) disk, etc.), or instructions for downloading programming from an Internet web protocol or cloud server.

[0188] The kit may further include instructions for carrying out the subject method. These instructions may be present in the subject kit in a variety of forms, one or more of which may be present in the kit. One form in which these instructions may be present is as printed information on a suitable medium or substrate, such as a piece of paper on which the information is printed, within the package of the kit, within an accompanying document, etc. Yet another form of these instructions is a computer-readable medium on which the information is recorded, such as a diskette, a compact disk (CD), a portable flash drive, etc. Still another form of these instructions that may be present is a website address that may be used via the Internet to access the information at a remote site.

[0189] The following are provided by way of illustration and not by way of limitation.

[0190] Experiment Example 1 Raw flow cytometer data (VioBrightR667 single chromosome control) was investigated for three different types of resolution-dependent spreads: 1) unexpected negative spread, 2) unexpected spillover spread, and 3) skewed double negativity. The appearance of all of these spreads leads to a decrease in the biological resolution within the panel. With respect to 1), the raw data shows substantially higher dispersion of negative cells in the resolved VioBrightR667 channel when resolved using the 40C matrix (Figure 13B) versus the 25C matrix (Figure 13A). With respect to 2), the same raw data shows substantially higher spillover spreading from R718 to VioBrightR667 when resolved with the 40C panel (Figure 14B) versus the 25C panel (Figure 14A). With respect to 3), the double negative population shows an extreme degree of negative co-dispersion in the full 40C panel (Figures 15A - 15B) and does not reflect the true underlying biological expression.

[0191] Example 2 The fluorescent dye panel was examined for fluorescent dyes that, when used together in a "hot spot", i.e., have an emission spectrum that produces the type of degradation-dependent spread described above. A hot spot can be located within and around a particular "spectral neighborhood". For example, if two fluorescent dyes have similar spectra such that there is a possibility or likelihood of overlap, the fluorescent dyes can be considered to be in the spectral neighborhood of another fluorescent dye. Two hot spots were evaluated. One was in the vicinity of the APC spectrum (hot spot 1), and the other was in the vicinity of the BB700 spectrum (hot spot 2). SparkNIR685 and VioR667 are considered to be part of the vicinity of the APC spectrum, while PerCP and PerCP-Cy5.5 are considered to be part of the vicinity of the BB700 spectrum. Flow cytometer data plots were obtained using combinations of these fluorescent dyes. For hot spot 1, flow cytometer data for SparkNIR685 / APC, SparkNIR685 / VioR667, and APC / VioR667 were acquired. For hot spot 2, flow cytometer data for BB700 / PerCP, BB700 / PerCP-Cy5.5, and PerCP / PerCP-Cy5.5 were acquired. This data was obtained using the effects of each pair combined with autofluorescence, a first full panel (Panel A of 39C), or a second full panel (Panel B of 39C). These panels are provided in Table 1 below.

[0192] Figures 16A - 16D show flow cytometer data collected for hot spot 1. Figures 16A - 16C show flow cytometer data for the SparkNIR685 / APC, SparkNIR685 / VioR667, and APC / VioR667 fluorophore pairs, respectively, combined with autofluorescence. This data reveals the baseline degree of resolution - dependent spread in the absence of a full panel. Figures 16D - 16F show flow cytometer data for SparkNIR685 / APC, SparkNIR685 / VioR667, and APC / VioR667, respectively, in the context of panel 39C. As shown in Figures 16D - 16F, severe spread hot spots are present in the vicinity of the APC spectrum. Figure 16G shows flow cytometer data collected from SparkNIR685 / APC in the context of panel 39C B. Since VioR667 is not present in panel 39C B, flow cytometer data from this fluorochrome was not generated.

[0193] Figures 17A - 17D show flow cytometer data collected for hot spot 2. Figures 17A - 17C show flow cytometer data from the BB700 / PerCP, BB700 / PerCP - Cy5.5, and PerCP / PerCP - Cy5.5 fluorophore pairs, respectively, combined with autofluorescence. This data reveals the baseline degree of resolution - dependent spread in the absence of a full panel. Figure 17D shows flow cytometer data from the BB700 / PerCP fluorophore pair in the context of panel 39C A. As shown in Figure 17D, the minimum spread is present in the vicinity of the BB700 spectrum. Figures 17E - 17G show flow cytometer data for the BB700 / PerCP, BB700 / PerCP - Cy5.5, and PerCP / PerCP - Cy5.5 fluorophore pairs in the context of panel 39C B.

[0194] When comparing the results from 39C panel A with those from 39C panel B, it is shown that the spread hot spots shift from the vicinity of the APC spectrum to the vicinity of the BB700 spectrum. Thus, it was exemplified that the spread depends not simply on what fluorescent dyes are used in pairs, but rather on the full set of dyes in the panel.

[0195] Example 3 A similarity index was calculated for the fluorescent dyes in the 39C panel. This index compares how similar the spectra of two fluorophores are on a scale of 0 to 1. The calculation results are presented in Figure 18. In the left chart of Figure 18, each cell is color-coded based on the similarity index, with darker colors representing higher similarity. A subset of high-similarity pairs is enlarged from the chart. As shown in this enlarged portion of the chart, BB515 / cFluorB532 and BB700 / PerCP-Cy5.5 have significantly similar spectra as measured by the similarity index. The BB515 / cFluorB532 pair has a similarity index of 0.91, while the BB700 / PerCP-Cy5.5 pair has a similarity index of 0.90. Flow cytometer data was collected for the fluorescent dye pairs and their associated autofluorescence only, as well as for the same pairs in the context of the full fluorescent dye panel. A plot of the resulting flow cytometer data is shown on the right side of Figure 18. As shown in the flow cytometer data collected for BB515 / cFluorB532 (top), there was no change in the full panel spread for the pair only. However, as shown in the flow cytometer data collected for BB700 / PerCP-Cy5.5 (bottom), there was a significant spread change in the full panel spread for the pair only. Therefore, it was shown that Pair 1 (BB515 and cFluorB532) and Pair 2 (BB700 and PerCP-Cy5.5) have equivalent similarity indices but dramatically different spreads in the full panel. Thus, it was illustrated that the similarity index does not predict differences in spread in the context of the full panel. In other words, the similarity index indicates whether two fluorophores may cause problems when used together, but does not indicate whether there are actually problems.

[0196] In addition, the condition numbers were calculated for two different fluorophore panels, Panel A and Panel B, which differed only in a single fluorophore. The fluorophores in each of these panels are presented in Table 1 below. Panel A was found to have a condition number of 69.8, while Panel B was found to have a condition number of 67.0. Flow cytometer data were developed for each of the APC / SparkNIR685 and BB700 / PerCP fluorophore pairs in the context of Panel A and Panel B. The results are shown in Figure 19. The plot for the pair in Panel A is shown on the left, while the plot for Panel B is shown on the right. As shown in Figure 19, Panel A and Panel B differ by only one fluorophore and have comparable condition numbers, but the same dye pair has a dramatically different spread when comparing Panel A and Panel B. Thus, it was concluded that the condition number does not indicate which fluorophore is involved in the full panel spread. In other words, the condition number indicates that a given panel may have a problem, but does not indicate which fluorophore is causing the problem or how to correct it.

[0197] Similarity analysis was performed for Panel A (Figure 20) and Panel B (Figure 21) described above. This analysis involves plotting a similarity index against the average full matrix spillover spreading (SS). As shown in Figures 20 and 21, the similarity analysis does not predict where the spread occurs in two different panels.

[0198] Example 4 As shown in Table 1 below, for each of Panel A and Panel B, a panel hot spot matrix was generated. The panel hot spot matrix was described above with respect to FIGS. 5A - 5D and was created using the illustrated method. The hot spot matrix for Panel A is shown in FIG. 22, while the hot spot matrix for Panel B is shown in FIG. 23. As shown in FIGS. 22 and 23, unlike the conventional methods of fluorescent dye panel analysis, the panel hot spot matrix correctly identifies which fluorescent dyes are most affected by the degradation - dependent spread in Panels A and B. Thus, panel hot spot analysis has been found to correctly identify the spread problem areas in different panels.

[0199] To further illustrate this ability of the panel hot spot matrix, for each of Panel A (FIG. 24) and Panel B (FIG. 25), flow cytometer data plots associated with the fluorescent dyes identified as being affected by spread were generated by the panel hot spot matrix. As shown in FIGS. 24 and 25, the fluorescent dyes identified as being affected by spread by the panel hot spot matrix are, in fact, affected by spread. In the panel hot spot matrix, high - magnitude diagonal entries indicate the fluorescent dyes most affected, while high - magnitude off - diagonal entries indicate pairs of problematic dyes.

[0200] Example 5 The ability of the panel hot spot matrix to estimate spread severity was examined. To achieve this, the hot spot matrix of Panel A described above was analyzed. The results are shown in FIG. 26. The magnitude of the quantitative metric indicated by the color intensity within the cells of the hot spot matrix of Panel A tracks the severity of the hot spots. Hot spots in regions of the matrix with a higher magnitude of the quantitative metric were shown to have significant spread, while hot spots with a relatively lower magnitude of the quantitative metric had correspondingly lower spread. This information indicates cases where decomposition-dependent spread is most problematic in the panel and may be used in panel design when assigning fluorescent dyes to markers (e.g., ensuring that "hot spot" fluorescent dyes are not co-expressed and do not affect their biological resolution, and are not weak / sensitive markers in the panel).

[0201] Example 6 The hot spot matrix of Panel A was used to improve Panel A. The improvement of the panel can be achieved by manual trial and error using the panel hot spot matrix as a guide, or through an automatic algorithm that minimizes an objective function derived from the matrix (e.g., the magnitude of the maximum entry). The hot spot matrix of Panel A is shown in FIG. 27A. The hot spots in the vicinity of the APC spectrum were then resolved by replacing VioR667 with QDot800. This replacement resulted in Panel A-1 presented in Table 1 below. The panel hot spot matrix of Panel A-1 is shown in FIG. 27B. As shown in the Panel A-1 hot spot matrix, the hot spots in the vicinity of the APC spectrum were eliminated as a result of replacing VioR667 with QDot800. Also, the condition number was reduced from 69.8 to 32.5. However, hot spots were still visible in the Panel A-1 hot spot matrix in the vicinity of the BV510 spectrum. This hot spot was resolved by replacing BV510 with QDot705, resulting in Panel A-2 also presented in Table 1 below. The panel hot spot matrix of Panel A-2 is shown in FIG. 27C. As shown in the Panel A-2 hot spot matrix, the hot spots in the vicinity of the BV510 spectrum were eliminated as a result of replacing BV510 with QDot705. This swap also had the effect of reducing the condition number of the fluorophore panel from 32.5 to 24.8. Thus, it was shown that panel hot spot analysis can facilitate improving or expanding existing panels.

[0202] Example 7 Flow cytometer data was generated using fluorescent dyes in Panel A. The resulting data was used to populate the spread correlation matrix shown in FIG. 28A. The cells within the matrix are color-coded such that the color of the cell is associated with the degree to which the double-negative population is skewed, i.e., the degree to which it has a positive or negative correlation. This matrix was compared to the spread correlation matrix generated as described above with respect to FIGS. 7A and 7B. The resulting matrix predicts the correlations derived from the Gram inverse and is shown in FIG. 28B. The target regions 1-4 are identified in FIGS. 28A and 28B. Flow cytometer data for the fluorescent dye pairs identified in target regions 1-4 are shown in FIGS. 28C-28F, respectively. As illustrated in FIGS. 28C-28F, the spread correlation matrix (FIG. 28B) accurately predicts the measured correlation data (FIG. 28A).

[0203]

Table 1

[0204] Notwithstanding the appended claims, the present disclosure is also defined by the following appendices. 1. A method for evaluating the suitability of a fluorescent dye panel for use in generating flow cytometer data, comprising: a fluorescent dye panel, an instrument identifier, and obtaining a spectral matrix associated with the fluorescent dye panel and the instrument identifier; calculating an inverse matrix from the obtained spectral matrix; identifying fluorescent dyes in the fluorescent dye panel that will be associated with the variance of flow cytometer data generated using the fluorescent dye panel by analyzing the calculated inverse matrix, and evaluating the suitability of the fluorescent dye panel for use in generating flow cytometer data; and a method comprising the steps of: 2. The method according to Appendix 1, wherein the fluorescent dyes in the fluorescent dye panel, which are to be associated with the dispersion of the flow cytometer data, contribute to the dispersion of the flow cytometer data. 3. The method according to Appendix 1 or 2, wherein the fluorescent dyes in the fluorescent dye panel, which are to be associated with the dispersion of the flow cytometer data, are affected by the dispersion of the flow cytometer data. 4. The method according to any one of Appendices 1 to 3, wherein the inverse matrix is a pseudo-inverse matrix. 5. The method according to Appendix 4, wherein the pseudo-inverse matrix is a Moore-Penrose pseudo-inverse matrix.

[0205] 6. The method according to any one of Appendices 1 to 3, wherein the inverse matrix is a Gram inverse matrix. 7. The inverse matrix is calculated according to the following formula: G=(M T M) -1 wherein G is a Gram inverse matrix, M is a spectral matrix, M T is the transposed matrix of the spectral matrix, the method according to Appendix 6. 8. The method according to any one of Appendices 1 to 7, wherein analyzing the calculated inverse matrix includes deriving a quantitative metric from the inverse matrix. 9. The method according to Appendix 8, wherein the quantitative metric is a matrix norm. 10. The method according to Appendix 8, wherein the quantitative metric is a vector norm.

[0206] 11. The method according to any one of Appendices 1 to 10, further comprising optimizing the fluorescent dye panel based on an evaluation of the suitability of the fluorescent dye panel for use in generating flow cytometer data. 12. The method according to Appendix 11, wherein optimizing the fluorescent dye panel includes using a panel optimization algorithm. 13. The method according to appendix 11 or 12, wherein optimizing a fluorescent dye panel includes adjusting the fluorescent dyes in the fluorescent dye panel and evaluating the suitability of the adjusted fluorescent dye panel for use in generating flow cytometer data. 14. The method according to appendix 13, wherein optimizing a fluorescent dye panel includes repeatedly adjusting the fluorescent dye panel and evaluating the suitability of each repeatedly adjusted fluorescent dye panel. 15. The method according to any one of appendices 1 to 14, wherein the flow cytometer data includes a number of dimensions equal to the number of fluorescent dyes among a plurality of fluorescent dyes.

[0207] 16. The method according to appendix 15, wherein the flow cytometer data is spectrally resolved flow cytometer data. 17. The method according to appendix 15, wherein the flow cytometer data is corrected flow cytometer data. 18. The method according to any one of appendices 1 to 17, wherein the variance includes noise in the flow cytometer data. 19. The method according to any one of appendices 1 to 18, further comprising generating a visualization of the evaluated suitability of the fluorescent dye panel for use in generating flow cytometer data. 20. The method according to appendix 19, wherein the visualization highlights the fluorescent dyes in the fluorescent dye panel, which will be associated with the variance of the flow cytometer data generated using the fluorescent dye panel.

[0208] 21. The method according to appendix 19 or 20, wherein the visualization includes a panel hot spot matrix. 22. The method according to appendix 21, wherein the visualization includes a diagonal visualization of the panel hot spot matrix. 23. The method according to appendix 19 or 20, wherein the visualization includes a spread correlation matrix.

[0209] 24. A system, comprising a processor, and the processor a fluorescent dye panel, an instrument identifier, and Obtain a spectral matrix associated with a fluorescent dye panel and an instrument identifier, Calculate the inverse matrix from the obtained spectral matrix, and By analyzing the calculated inverse matrix, identify the fluorescent dyes in the fluorescent dye panel that will be associated with the variance of flow cytometer data generated using the fluorescent dye panel, and evaluate the suitability of the fluorescent dye panel for use in generating flow cytometer data. A system configured as such. 25. The system according to appendix 24, wherein the fluorescent dyes in the fluorescent dye panel that will be associated with the variance of flow cytometer data contribute to the variance of flow cytometer data. 26. The system according to appendix 24 or 25, wherein the fluorescent dyes in the fluorescent dye panel that will be associated with the variance of flow cytometer data are affected by the variance of flow cytometer data. 27. The system according to any one of appendices 24 to 26, wherein the inverse matrix is a pseudo-inverse matrix. 28. The system according to appendix 27, wherein the pseudo-inverse matrix is a Moore-Penrose pseudo-inverse matrix.

[0210] 29. The system according to any one of appendices 24 to 26, wherein the inverse matrix is a Gram inverse matrix. 30. The inverse matrix is given by the following formula: G=(M T M) -1 Wherein, G is the Gram inverse matrix, M is the spectral matrix, M T is the transpose matrix of the spectral matrix, calculated according to the formula of appendix 29. The system according to appendix 29. 31. The system according to any one of appendices 24 to 30, wherein analyzing the calculated inverse matrix includes deriving a quantitative metric from the inverse matrix. 32. The system according to appendix 31, wherein the quantitative metric is a matrix norm. 33. The system according to appendix 31, wherein the quantitative metric is a vector norm.

[0211] 34. The system according to any one of appendices 24 to 33, wherein the processor is configured to optimize a fluorescent dye panel based on an evaluation of the suitability of the fluorescent dye panel for use in generating flow cytometer data. 35. The system according to appendix 34, wherein optimizing the fluorescent dye panel includes using a panel optimization algorithm. 36. The system according to appendix 34 or 35, wherein optimizing the fluorescent dye panel includes adjusting the fluorescent dyes in the fluorescent dye panel and evaluating the suitability of the adjusted fluorescent dye panel for use in generating flow cytometer data. 37. The system according to appendix 36, wherein optimizing the fluorescent dye panel includes iteratively adjusting the fluorescent dye panel and evaluating the suitability of each iteratively adjusted fluorescent dye panel. 38. The system according to any one of appendices 24 to 37, wherein the flow cytometer data includes a number of dimensions equal to the number of fluorescent dyes among a plurality of fluorescent dyes.

[0212] 39. The system according to appendix 38, wherein the flow cytometer data is spectrally resolved flow cytometer data. 40. The system according to appendix 38, wherein the flow cytometer data is corrected flow cytometer data. 41. The system according to any one of appendices 24 to 40, wherein the variance includes noise in the flow cytometer data. 42. The system according to any one of appendices 24 to 41, wherein the processor is configured to generate a visualization of the evaluated suitability of a fluorescent dye panel for use in generating flow cytometer data. 43. The system according to appendix 42, wherein the visualization highlights the fluorescent dyes in the fluorescent dye panel, which will be associated with the variance of the flow cytometer data generated using the fluorescent dye panel.

[0213] 44. The system according to appendix 42 or 43, wherein the visualization includes a panel hot spot matrix. 45. The system according to appendix 44, wherein the visualization includes diagonal visualization of the panel hot spot matrix. 46. The system according to appendix 42 or 43, wherein the visualization includes a spread correlation matrix. 47. The system according to any one of appendices 42 to 46, further comprising a display configured to depict the visualization.

[0214] 48. A non-transitory computer-readable storage medium, a method, a fluorescent dye panel, a device identifier, and obtaining a spectral matrix associated with the fluorescent dye panel and the device identifier; calculating an inverse matrix from the obtained spectral matrix; identifying fluorescent dyes in the fluorescent dye panel that will be associated with the variance of flow cytometry data generated using the fluorescent dye panel by analyzing the calculated inverse matrix, and evaluating the suitability of the fluorescent dye panel for use in generating flow cytometry data. A non-transitory computer-readable storage medium storing instructions for evaluating the suitability of a fluorescent dye panel for use in generating flow cytometry data, the instructions including the method described above. 49. The non-transitory computer-readable storage medium according to appendix 48, wherein the fluorescent dyes in the fluorescent dye panel that will be associated with the variance of flow cytometry data contribute to the variance of the flow cytometry data. 50. The non-transitory computer-readable storage medium according to appendix 48 or 49, wherein the fluorescent dyes in the fluorescent dye panel that will be associated with the variance of flow cytometry data are affected by the variance of the flow cytometry data. 51. The non-transitory computer-readable storage medium according to any one of appendices 48 to 50, wherein the inverse matrix is a pseudo-inverse matrix. 52. The non-transitory computer-readable storage medium according to appendix 51, wherein the pseudo-inverse matrix is a Moore-Penrose pseudo-inverse matrix.

[0215] 53. The non-transitory computer-readable storage medium according to any one of appendices 48 to 50, wherein the inverse matrix is a Gram inverse matrix. 54. The inverse matrix is calculated according to the following formula: G = (M T M) -1 wherein, G is a Gram inverse matrix, M is a spectral matrix, M T is the transposed matrix of the spectral matrix, and is calculated according to the formula described in appendix 53. 55. The non-transitory computer-readable storage medium according to any one of appendices 48 to 54, wherein analyzing the calculated inverse matrix includes deriving a quantitative metric from the inverse matrix. 56. The non-transitory computer-readable storage medium according to appendix 55, wherein the quantitative metric is a matrix norm. 57. The non-transitory computer-readable storage medium according to appendix 55, wherein the quantitative metric is a vector norm.

[0216] 58. The method further includes optimizing a fluorescent dye panel based on an evaluation of the suitability of the fluorescent dye panel for use in the generation of flow cytometer data, in the non-transitory computer-readable storage medium according to any one of appendices 48 to 57. 59. The non-transitory computer-readable storage medium according to appendix 58, wherein optimizing the fluorescent dye panel includes using a panel optimization algorithm. 60. The non-transitory computer-readable storage medium according to appendix 58 or 59, wherein optimizing the fluorescent dye panel includes adjusting the fluorescent dyes in the fluorescent dye panel and evaluating the suitability of the adjusted fluorescent dye panel for use in the generation of flow cytometer data. 61. The non - transitory computer - readable storage medium according to appended note 60, wherein optimizing a fluorescent dye panel includes iteratively adjusting the fluorescent dye panel and evaluating the suitability of each iteratively adjusted fluorescent dye panel. 62. The non - transitory computer - readable storage medium according to any one of appended notes 48 to 61, wherein the flow cytometer data includes a number of dimensions equal to the number of fluorescent dyes among a plurality of fluorescent dyes.

[0217] 63. The non - transitory computer - readable storage medium according to appended note 62, wherein the flow cytometer data is spectrally resolved flow cytometer data. 64. The non - transitory computer - readable storage medium according to appended note 62, wherein the flow cytometer data is corrected flow cytometer data. 65. The non - transitory computer - readable storage medium according to any one of appended notes 48 to 64, wherein the variance includes noise in the flow cytometer data. 66. The non - transitory computer - readable storage medium according to any one of appended notes 48 to 65, wherein the method further includes generating a visualization of the evaluated suitability of a fluorescent dye panel for use in generating flow cytometer data. 67. The non - transitory computer - readable storage medium according to appended note 66, wherein the visualization highlights fluorescent dyes in a fluorescent dye panel that will be associated with the variance of flow cytometer data generated using the fluorescent dye panel.

[0218] 68. The non - transitory computer - readable storage medium according to appended note 66 or 67, wherein the visualization includes a panel hot - spot matrix. 69. The non - transitory computer - readable storage medium according to appended note 68, wherein the visualization includes a diagonal visualization of the panel hot - spot matrix. 70. The non - transitory computer - readable storage medium according to appended note 66 or 67, wherein the visualization includes a spread correlation matrix.

[0219] 71. A method for evaluating the suitability of a fluorescent dye panel for use in generating flow cytometer data, (a) A fluorescent dye panel, an instrument identifier, and a spectral matrix associated with the fluorescent dye panel and the instrument identifier to a processor configured to: calculate an inverse matrix from the acquired spectral matrix and input to the processor to identify fluorescent dyes in the fluorescent dye panel that will be associated with the variance of flow cytometry data generated using the fluorescent dye panel by analyzing the calculated inverse matrix; (b) receiving, from the processor, an evaluation of the suitability of the fluorescent dye panel for use in generating flow cytometry data; A method comprising. 72. The method according to appendix 71, wherein the fluorescent dyes in the fluorescent dye panel that will be associated with the variance of flow cytometry data contribute to the variance of the flow cytometry data. 73. The method according to appendix 71 or 72, wherein the fluorescent dyes in the fluorescent dye panel that will be associated with the variance of flow cytometry data are affected by the variance of the flow cytometry data. 74. The method according to any one of appendices 71 to 73, wherein the inverse matrix is a pseudo-inverse matrix. 75. The method according to appendix 74, wherein the pseudo-inverse matrix is a Moore-Penrose pseudo-inverse matrix.

[0220] 76. The method according to any one of appendices 71 to 73, wherein the inverse matrix is a Gram inverse matrix. 77. The inverse matrix is given by the following formula: G = (M T M) -1 wherein G is the Gram inverse matrix, M is the spectral matrix, M T is the transposed matrix of the spectral matrix, and is calculated according to appendix 76. 78. The method according to any one of appendices 71 to 77, wherein analyzing the calculated inverse matrix includes deriving a quantitative metric from the inverse matrix. 79. The method according to appendix 78, wherein the quantitative metric is a matrix norm. 80. The method according to appendix 78, wherein the quantitative metric is a vector norm.

[0221] 81. The method according to any one of appendices 71 to 80, wherein the processor is configured to optimize a fluorescent dye panel based on an evaluation of the suitability of the fluorescent dye panel for use in the generation of flow cytometer data. 82. The method according to appendix 81, wherein optimizing the fluorescent dye panel includes using a panel optimization algorithm. 83. The method according to appendix 81 or 82, wherein optimizing the fluorescent dye panel includes adjusting the fluorescent dyes in the fluorescent dye panel and evaluating the suitability of the adjusted fluorescent dye panel for use in the generation of flow cytometer data. 84. The method according to appendix 83, wherein optimizing the fluorescent dye panel includes iteratively adjusting the fluorescent dye panel and evaluating the suitability of each iteratively adjusted fluorescent dye panel. 85. The method according to any one of appendices 71 to 84, wherein the flow cytometer data includes a number of dimensions equal to the number of fluorescent dyes among a plurality of fluorescent dyes.

[0222] 86. The method according to appendix 85, wherein the flow cytometer data is spectrally resolved flow cytometer data. 87. The method according to appendix 85, wherein the flow cytometer data is corrected flow cytometer data. 88. The method according to any one of appendices 71 to 87, wherein the variance includes noise in the flow cytometer data. 89. The method according to any one of appendices 71 to 88, wherein the processor is configured to generate a visualization of the evaluated suitability of a fluorescent dye panel for use in the generation of flow cytometer data. 90. The method according to appendix 89, which highlights fluorescent dyes in a fluorescent dye panel, where visualization is associated with the dispersion of flow cytometer data generated using the fluorescent dye panel.

[0223] 91. The method according to appendix 89 or 90, where visualization includes a panel hot spot matrix. 92. The method according to appendix 91, where visualization includes diagonal visualization of the panel hot spot matrix. 93. The method according to appendix 89 or 90, where visualization includes a spread correlation matrix. 94. The method according to any one of appendices 89 to 93, further including receiving the generated visualization.

[0224] The above inventions have been described in some detail by way of illustration and example for the sake of clear understanding. However, it will be readily apparent to those skilled in the art that, in light of the teachings of the present invention, some changes and modifications can be made to those inventions without departing from the spirit or scope of the appended claims.

[0225] Accordingly, the foregoing is merely illustrative of the principles of the present invention. It is to be understood that those skilled in the art can devise various arrangements that, although not explicitly described or illustrated herein, embody the principles of the present invention and are within its spirit and scope. Further, all examples and conditional language recited herein are principally intended to aid the reader in understanding the principles of the present invention and the concepts contributed by the inventors to further the art and are to be construed as not being limited to such specifically recited examples and conditions. Moreover, all descriptions in this specification listing the principles, aspects, and embodiments of the present invention, as well as specific examples thereof, are intended to encompass both their structural and functional equivalents. Additionally, such equivalents are intended to include both currently known equivalents and equivalents developed in the future, i.e., any elements developed to perform the same function regardless of structure. Further, what is disclosed herein is not intended to be dedicated to the public whether or not such disclosure is expressly recited in the claims.

[0226] Accordingly, the scope of the present invention is not intended to be limited to the illustrative 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 triggered for limitations in the claims only when the exact phrase "means for" or the exact phrase "step for" is recited at the beginning of such limitations in the claims, and 35 U.S.C. § 112(f) or 35 U.S.C. § 112(6) is not triggered when such exact phrases are not used for limitations in the claims.

[0227] Cross - Reference to Related Applications This application claims the benefit of the filing date of U.S. Provisional Patent Application No. 63 / 347,848, filed on June 1, 2022, the disclosure of which is incorporated herein by reference.

Claims

1. A method for evaluating the suitability of a fluorescent dye panel for use in generating flow cytometer data, comprising: a fluorescent dye panel, a device identifier, and obtaining a spectral matrix associated with the fluorescent dye panel and the device identifier; calculating an inverse matrix from the obtained spectral matrix; identifying fluorescent dyes in the fluorescent dye panel that will be associated with the variance of flow cytometer data generated using the fluorescent dye panel by analyzing the calculated inverse matrix, and evaluating the suitability of the fluorescent dye panel for use in generating the flow cytometer data; A method comprising the above steps.

2. The method according to claim 1, wherein the fluorescent dyes in the fluorescent dye panel that will be associated with the variance of the flow cytometer data either contribute to the variance of the flow cytometer data, or are affected by the variance of the flow cytometer data.

3. The method according to any one of the preceding claims, wherein the inverse matrix is a pseudo-inverse matrix.

4. The method according to claim 1 or 2, wherein the inverse matrix is a Gram inverse matrix.

5. The method according to any one of the preceding claims, wherein analyzing the calculated inverse matrix includes deriving a quantitative metric from the inverse matrix.

6. The method according to claim 5, wherein the quantitative metric is selected from a matrix norm or a vector norm.

7. The method according to any one of the preceding claims, further comprising optimizing the fluorescent dye panel based on the evaluation of the suitability of the fluorescent dye panel for use in generating the flow cytometer data.

8. The method according to any one of the preceding claims, wherein the flow cytometer data includes a number of dimensions equal to the number of fluorescent dyes among a plurality of fluorescent dyes.

9. The method according to any one of the preceding claims, further comprising generating a visualization based on the evaluation of the suitability of the fluorescent dye panel for use in generating the flow cytometer data.

10. The method according to claim 9, wherein the visualization highlights fluorescent dyes in the fluorescent dye panel that will be associated with the variance of the flow cytometer data generated using the fluorescent dye panel.

11. The method according to claim 9 or 10, wherein the visualization includes a panel hot spot matrix.

12. The method according to claim 11, wherein the visualization includes diagonal visualization of the panel hot spot matrix.

13. The method according to claim 9 or 10, wherein the visualization includes a spread correlation matrix.

14. A system comprising a processor, the processor being configured to obtain a fluorescent dye panel, an instrument identifier, and a spectral matrix associated with the fluorescent dye panel and the instrument identifier, calculate an inverse matrix from the obtained spectral matrix, and analyze the calculated inverse matrix to identify fluorescent dyes in the fluorescent dye panel that will be associated with the variance of flow cytometer data generated using the fluorescent dye panel, and to evaluate the suitability of the fluorescent dye panel for use in the generation of the flow cytometer data.

15. A method for evaluating the suitability of a fluorescent dye panel for use in the generation of flow cytometer data, the method comprising: (a) inputting into a processor, which is configured to obtain a fluorescent dye panel, an instrument identifier, and a spectral matrix associated with the fluorescent dye panel and the instrument identifier, calculate an inverse matrix from the obtained spectral matrix, and analyze the calculated inverse matrix to identify fluorescent dyes in the fluorescent dye panel that will be associated with the variance of flow cytometer data generated using the fluorescent dye panel, (b)receiving from the processor an evaluation of the suitability of the fluorescent dye panel for use in the generation of the flow cytometer data and.

Images