Method and system for assessing applicability of fluorescent dye combination for generating flow cytometer data

Abstract

The present invention provides a method of assessing the applicability of a fluorescent dye combination for generating flow cytometry data. A target method includes obtaining a fluorochrome combination, an instrument identifier, and a spectral matrix associated with the fluorochrome combination and the instrument identifier. The subject method further includes calculating an inverse matrix from the obtained spectral matrix, and identifying a fluorescent dye in the fluorescent dye combination associated with a flow cytometer data variance generated using the fluorescent dye combination by analyzing the calculated inverse matrix, the applicability of the fluorescent dye combination for generating flow cytometry data is evaluated. Meanwhile, the invention further provides a system for implementing the method and a non-transient computer readable storage medium.

Description

[0001] Cross-references to related patent applications

[0002] 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 U.S. Provisional Patent Application No. 63 / 347,848 becomes a part of the present invention by reference to the present invention. Background Art

[0003] Characterization of analytes in biofluids has become an important part of biological research, medical diagnostics, and overall patient health assessment. Testing for analytes in biofluids, such as human blood or blood-derived products, can provide results that can play a role in determining treatment options for patients with a variety of disease conditions.

[0004] Particle analysis, such as flow cytometry, is a technique used to characterize and sometimes sort biological material, such as cells in a blood sample or target particles in another biological or chemical sample. A flow cytometer typically includes a sample reservoir for receiving a fluid sample (e.g., a blood sample) and a sheath reservoir containing a sheath fluid. The flow cytometer delivers particles (including cells) in the fluid sample as a cell stream to a flow chamber, while also directing a sheath fluid into the flow chamber. To characterize the components of the flowing stream, the flowing stream is irradiated with light. Changes in the materials in the flowing stream, such as morphology or the presence of fluorescent tags, can result in changes in the observed light, which can be characterized and separated. To characterize the components of the flowing stream, light must be directed onto the flowing stream and collected. The light sources in a flow cytometer vary and can include one or more broad spectrum lamps, light emitting diodes, and single wavelength lasers. The light source is aligned with the flowing stream, and the optical response from the irradiated particles is collected and quantified.

[0005] The parameters measured using a particle analyzer typically include the excitation wavelength light scattered by the particles along a narrow angle in a substantially forward direction (called forward scattered light (FSC)), the excitation light scattered by the particles in a direction orthogonal to the excitation laser (called side scattered light (SSC)), and the light emitted by fluorescent molecules or fluorescent dyes. Different cell types can be identified by the light scattering characteristics and fluorescence emission generated by labeling various cellular proteins or other components with fluorescent dye-labeled antibodies or other fluorescent probes. Forward scattered light, side scattered light, and fluorescence are detected using a photodetector placed in the particle analyzer.

[0006] In cases where the flow cytometry protocol includes fluorescence detection, experimental design typically involves identifying a fluorochrome panel, i.e., a set of fluorochromes to be used together in a given flow cytometry workflow. The fluorochrome panel design process is necessary because biological resolution (i.e., the ability to distinguish between different target components within or between particles) is directly affected by the measurement variance of the “raw” flow cytometry data and the mathematical process of spectral compensation or unmixing. Both of these factors are largely dependent on the choice of fluorochromes in the panel. First, measurement variance (i.e., noise) in flow cytometry comes from a variety of sources, including a constant baseline measurement noise in the cytometer electronics, optical shot noise that varies linearly with signal intensity, and multiplicative measurement noise that varies quadratically with signal intensity due to random fluctuations in the cytometer lasers and fluid dynamics. The measurement noise itself depends on the choice of fluorochrome. For example, brighter fluorochromes will induce more shot noise than dimmer fluorochromes, which will have smaller signal amplitudes compared to the constant “noise floor” of the instrument optics and electronics. Second, the raw measurement noise in the "detector space" (whose number of dimensions is equal to the number of detectors in the instrument) is propagated through the mathematical process of fluorescence compensation (in conventional cytometers) or spectral unmixing (in full-spectral cytometers) to the final biological data in the "compensation space" or "unmixing space" (whose number of dimensions is equal to the number of fluorescent dyes in the sample). Since the final target biological analysis (e.g., gating, clustering, sorting, marker quantification, etc.) is performed in the "unmixing space", the variance of the "unmixing space" is very important. This mathematical mapping of noise to biological space depends heavily on the spectral characteristics of the fluorescent dyes themselves.

[0007] Significant spectral overlap in a flow cytometry panel can result in unacceptable variance (also known as noise or spread) in the unmixed data, reducing the unmixed signal-to-noise ratio and making it impossible to resolve differences in biomarker expression in multicolor flow cytometry experiments. Unmixing-dependent spread manifests itself in three main ways: i) increased unmixing variance on unstained particles that is independent of fluorochrome expression (i.e., “negative spread”); ii) increased unmixing variance on one fluorochrome due to intensity-dependent photon noise generated by the expression of one or more other fluorochromes (i.e., “spillover spread”); and iii) increased covariance between unmixed fluorochromes (most evident on the unstained population as “tilted diagonal double negatives”). Unmixing-dependent spread tends to have a much greater impact on some fluorochromes than on others in a given panel. As the number of fluorophores in a panel increases and the total spectral overlap increases, unmixing-dependent spread places an effective upper limit on the number of fluorophores that can be used simultaneously in a single flow cytometry experiment. Experimental operators must take great care to avoid or mitigate this unmixing-dependent spread when designing fluorochrome panels.

[0008] There are two metrics currently used to predict the spread of unmixing dependencies in a combination: cosine similarity (i.e., "similarity index") and the spectral matrix condition number (i.e., "complexity score"). Cosine similarity is a pairwise metric that describes the spectral overlap between two fluorochromes. It predicts the pairs of fluorochromes most likely to experience spillover spread, and can be used to "rule out" pairs of nearly identical fluorochromes that cannot be used together. The spectral matrix condition number is a single scalar metric that describes the numerical stability required to solve the spectral unmixing problem in a given full set of fluorochromes. It can roughly predict which combinations are expected to have more or less unmixing dependency spread.

[0009] Conventional flow cytometry, which uses discrete photodetectors that are designed to detect specific fluorescence emission bands of the dye, places a hard limit on the number of fluorescent dyes that can be used simultaneously in a flow experiment: the number of fluorescent dyes must not exceed the number of fluorescent 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 have more than 180 fluorescent channels available. Summary of the invention

[0010] The inventors have realized that there is a practical limit to the number of fluorescent dyes that can be used simultaneously in a flow cytometry experiment. Although nearly a hundred different flow cytometry fluorescent dye molecules are commercially available, the size of the combination is still limited. This practical limit stems from the inevitable spectral overlap and similarity of the fluorescent dyes used. In addition, it has been found that conventional fluorescent dye analysis methods also have shortcomings. For example, cosine similarity cannot take into account the performance of the fluorescent groups in the full combination, nor can it quantitatively predict the severity of unmixed diffusion. In addition, the condition number cannot indicate which fluorescent groups are most affected by unmixed diffusion, nor can it indicate which fluorescent groups are the root of the problem. The unmixed diffusion of the same fluorescent dye in two different combinations with almost identical condition numbers can be very different. In general, one combination can have a higher unmixed diffusion that is "focused" on one or two fluorescent dyes, while another combination with almost identical condition numbers can have a lower unmixed diffusion that is more evenly distributed over more fluorescent dyes in the combination. The condition numbers on combinations of different sizes cannot be meaningfully compared. Alternative metrics such as the spillover diffusion matrix (SSM) and the total diffusion matrix (TSM) can be calculated empirically from measured data (such as single-stained particles). However, collecting such data is labor-intensive, and the unmixing analysis must be repeated for each candidate combination, which is computationally complex and cannot be computed quickly (such as is required for automated combinatorial design algorithms).

[0011] In view of the above, the inventors realized that a new combination performance metric is needed that can provide information about the combination dependency unmixing diffusion (such as the condition number) and information about the specific properties of the fluorescent dyes and the fluorescent dye-fluorescent dye interactions (such as the cosine similarity), while requiring minimal experimental overhead or computational complexity. Accordingly, a method and system for evaluating and selecting suitable fluorescent dye combinations is needed. Embodiments of the present invention meet this need.

[0012] Various aspects of the present invention include methods for evaluating the suitability of a fluorescent dye combination for generating flow cytometer data. The target method includes obtaining a fluorescent dye combination, an instrument identifier, and a spectral matrix associated with the fluorescent dye combination and the instrument identifier. In addition, the method of the present invention also includes calculating an inverse matrix based on the obtained spectral matrix, and identifying the fluorescent dyes in the fluorescent dye combination associated with the variance of the flow cytometer data generated using the fluorescent dye combination by analyzing the calculated inverse matrix to evaluate the suitability of the fluorescent dye combination for generating flow cytometer data. In some embodiments, the fluorescent dyes in the fluorescent dye combination associated with the variance of the flow cytometer data will produce the variance of the flow cytometer data. In additional embodiments, the fluorescent dyes in the fluorescent dye combination associated with the variance of the flow cytometer data will be affected by the variance of the flow cytometer data. For example, the inverse matrix may include a pseudo-inverse matrix (such as a Moore-Penrose pseudo-inverse matrix). In some instances, the inverse matrix is ​​a Gram inverse matrix. In some instances, analyzing the calculated inverse matrix may include deriving a quantitative indicator (such as a matrix norm, a vector norm) from the inverse matrix. Embodiments of the subject method also include optimizing the fluorescent dye combination based on an assessment of the suitability of the fluorescent dye combination for generating flow cytometer data. In some instances, the optimizing the fluorescent dye combination includes using a combinatorial optimization algorithm. In some versions, the optimizing the fluorescent dye combination includes adjusting the fluorescent dyes in the fluorescent dye combination and assessing the suitability of the adjusted fluorescent dye combination for generating flow cytometer data. In an embodiment, the flow cytometer data may include a number of dimensions equal to the number of fluorescent dyes in a plurality of fluorescent dyes (e.g., after spectral unmixing or compensation). In some cases, the variance includes noise in the flow cytometer data. The method may further include generating a visualization of the assessed suitability of the fluorescent dye combination for generating flow cytometer data, such as a visualization highlighting the fluorescent dyes in the fluorescent dye combination that are associated with the variance of the flow cytometer data generated using the fluorescent dye combination.

[0013] Various aspects of the present invention also include systems for implementing the subject methods (e.g., as described above). The target system includes a processor that can obtain a fluorescent dye combination, an instrument identifier, and a spectral matrix associated with the fluorescent dye combination and the instrument identifier. The processor of the present invention can also calculate an inverse matrix based on the obtained spectral matrix, and identify the fluorescent dyes in the fluorescent dye combination that are associated with the variance of the flow cytometer data generated using the fluorescent dye combination by analyzing the calculated inverse matrix to evaluate the suitability of the fluorescent dye combination for generating flow cytometer data. In addition, the processor can optimize the fluorescent dye combination based on the evaluation of the suitability of the fluorescent dye combination for generating flow cytometer data. In some embodiments, the processor can generate a visualization of the evaluated suitability of the fluorescent dye combination for generating flow cytometer data, such as a visualization that highlights the fluorescent dyes in the fluorescent dye combination that are associated with the variance of the flow cytometer data generated using the fluorescent dye combination. In some such embodiments, the system includes a display for depicting the visualization.

[0014] In addition, various aspects of the present invention also include a non-transitory computer-readable storage medium, including instructions stored thereon, the instructions being used to employ the method of the present invention (e.g., as described above) to evaluate the suitability of a fluorescent dye combination for generating a flow cytometer. As described above, the method includes obtaining a fluorescent dye combination, an instrument identifier, and a spectral matrix associated with the fluorescent dye combination and the instrument identifier. The method performed by the non-transitory computer-readable storage medium also includes: identifying the fluorescent dyes in the fluorescent dye combination that are associated with the variance of the flow cytometer data generated using the fluorescent dye combination by analyzing the calculated inverse matrix to evaluate the suitability of the fluorescent dye combination for generating flow cytometer data. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The patent or application file shall contain at least one drawing executed in color. Copies of the patent or patent application publication and color drawings will be provided by the Patent Office upon request and payment of the necessary fee.

[0016] The best understanding of the present invention can be obtained by reading the following specific embodiments in conjunction with the accompanying drawings. The accompanying drawings include the following figures:

[0017] Figure 1 A flow chart is shown for implementing a method for evaluating a fluorescent dye combination according to certain embodiments.

[0018] Figure 2 The basic principles of the invention are shown according to some embodiments.

[0019] Figure 3 The basic principles of the invention are shown according to some embodiments.

[0020] Figure 4A-4B It is shown to what extent the properties of the pseudo-inverse matrix determine the diffusion (noise) in the unmixed data.

[0021] Figures 5A-5D The combined hotspot matrix and its exemplary calculation process according to an embodiment of the present invention are shown.

[0022] Figure 6A-6B A visualization created based on the combined hotspot matrix is ​​shown.

[0023] Figures 7A-7E The diffusion correlation matrix and an exemplary calculation process thereof according to an embodiment of the present invention are shown.

[0024] Figure 8 A flow chart is shown for implementing methods of the present invention according to certain embodiments.

[0025] Fig. 9 A functional block diagram of a flow cytometry system according to certain embodiments is shown.

[0026] Fig.10 A control system according to certain embodiments is shown.

[0027] Figures 11A-11B A schematic diagram of a particle sorter system according to certain embodiments is shown.

[0028] Fig.12 A block diagram of a computing system according to some embodiments is shown.

[0029] Figures 13A-13B Unexpected negative spread in the raw flow cytometry data is shown.

[0030] Figures 14A-14B Unexpected spillover diffusion in raw flow cytometry data is shown.

[0031] Figures 15A-15B The skewed double-negative population in the raw flow cytometry data is shown.

[0032] Figures 16A-16G Diffuse "hot spots" in the APC spectral neighborhood are shown, which depend on the full set of dyes in the fluorochrome panel.

[0033] Figures 17A-17G Diffuse "hot spots" in the BB700 spectral neighborhood are shown, which depend on the full set of dyes in the fluorochrome panel.

[0034] Fig.18 We show the extent to which similarity indices fail to predict diffusion differences in the context of the full assemblage.

[0035] Fig.19demonstrated the extent to which the condition number does not indicate which fluorophores are involved in the full combinatorial diffusion.

[0036] Fig. 20 demonstrated the extent to which similarity analysis fails to predict where diffusion will occur in a combination of fluorescent dyes.

[0037] Fig.21 demonstrated the extent to which similarity analysis fails to predict where diffusion will occur in a combination of fluorescent dyes.

[0038] Fig. 22 Demonstrates how combined hot spot analysis can correctly identify areas of diffusion problems in a combination of fluorescent dyes.

[0039] Fig.23 Demonstrates how combined hot spot analysis can correctly identify areas of diffusion problems in a combination of fluorescent dyes.

[0040] Fig.24 Demonstrates how combined hot spot analysis can correctly identify areas of diffusion problems in a combination of fluorescent dyes.

[0041] Fig.25 Demonstrates how combined hot spot analysis can correctly identify areas of diffusion problems in a combination of fluorescent dyes.

[0042] Fig.26 It is demonstrated how combined hot spot analysis can provide a semi-quantitative indication of the severity of spread.

[0043] Figures 27A-27C It shows the extent to which combined hotspot analysis can be beneficial for improving or extending existing combinations.

[0044] Figures 28A-28F Supporting data for the diffusion correlation matrix are shown. DETAILED DESCRIPTION

[0045] The present invention provides a method for evaluating the suitability of a fluorescent dye combination for generating flow cytometer data. The target method includes obtaining a fluorescent dye combination, an instrument identifier, and a spectral matrix associated with the fluorescent dye combination and the instrument identifier. The subject method also includes calculating an inverse matrix based on the obtained spectral matrix, and identifying the fluorescent dyes in the fluorescent dye combination that are associated with the variance of the flow cytometer data generated using the fluorescent dye combination by analyzing the calculated inverse matrix to evaluate the suitability of the fluorescent dye combination for generating flow cytometer data. At the same time, a system and a non-transient computer-readable storage medium for implementing the present invention are also provided.

[0046] Before describing the present invention in more detail, it should be understood that the present invention is not limited to the specific embodiments described, which may vary. It should also be understood that since the scope of the present invention will be limited only by the appended claims, the terms in the present invention are only used to describe specific embodiments and are not intended to be limiting.

[0047] If the present invention provides a range of values, it should be understood that the present invention includes each intermediate value between the upper and lower limits of this range (unless the context clearly stipulates otherwise, it is accurate to one tenth of the unit of the lower limit), as well as any other specified values ​​or intermediate values ​​within this specified range. The upper and lower limits of these smaller ranges may be independently included in the smaller ranges and are included in the scope of the present invention at the same time, but are subject to any explicitly excluded limits in the specified range. If the specified range includes one or two of the limits, the present invention also includes a range excluding any or both of these included limits.

[0048] Certain ranges in the present invention are expressed as numerical values ​​preceded by the term "about". The term "about" as used in the present invention is used to provide literal support for the exact number that follows it, as well as numbers that are close to or approximately the number that follows it. In determining whether a number is close to or approximately a number that is explicitly recited, the close or approximate unrecited number can be a number that is substantially equivalent to the explicitly recited number in the context in which it is presented.

[0049] Unless otherwise defined, all technical and scientific terms used in the present invention have the same meaning as commonly understood by those of ordinary skill in the art to which the present invention belongs. Although any methods and materials similar or equivalent to the methods or materials described herein may also be used in the practice or testing of the present invention, representative illustrative methods and materials are described below.

[0050] All publications and patents cited in this specification are incorporated herein by reference as if each individual publication or patent was specifically and individually indicated to be incorporated herein by reference as a part of the present invention and is intended to disclose and describe the methods and / or materials in connection with the cited publication. Citation of any publication is for the purpose of disclosure prior to the filing date and should not be construed as an admission that the present invention is not entitled to antedate such publication by virtue of prior invention. Furthermore, the dates of publication provided may be different from the actual publication dates which need to be independently confirmed.

[0051] It is noted that, as used in the present disclosure and the appended claims, the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise. It should also be noted that any optional elements may be excluded when drafting the claims. Therefore, this statement is intended to serve as a precondition for using exclusive terminology such as "only," "only" and the like in connection with the recitation of claim elements or using a "negative" limitation.

[0052] After reading this disclosure, it will be apparent to those skilled in the art that each individual embodiment described and illustrated in the present invention has discrete components and features that are readily separable or combined with the features of any of the other multiple embodiments without departing from the scope or spirit of the present invention. Any method recited may be implemented in the order of events recited or in any other order that is logically possible.

[0053] Although the systems and methods have been or will be described for the sake of grammatical fluency and functional explanation, it should be expressly understood that, except as expressly provided in 35 U.S. Patent Act § 112, the claims should not be construed as necessarily being limited in any way to “means” or “steps” but rather be given the full meaning and scope of equivalents provided by the definitions in the claims in accordance with the doctrine of judicial equivalents, and to the extent that a claim is expressly provided in 35 U.S. Patent Act § 112, it should be given its full legal equivalent pursuant to 35 U.S. Patent Act § 112.

[0054] Fluorescent dye combination evaluation method

[0055] As described above, various aspects of the present invention include methods for evaluating the suitability of a fluorescent dye combination for generating flow cytometer data. The target method includes receiving a fluorescent dye combination. As described in the present invention, a "fluorescent dye combination" refers to a group of different fluorescent molecular substances (i.e., dyes) that can be used to identify particles in a sample or specific parts or components associated with particles. The "fluorescent dye combination" described in the present invention may also refer to a group of identifiers (e.g., digital identifiers) that uniquely refer to and are associated with specific fluorescent molecular substances. Such identifiers may be referred to as "fluorescent dye identifiers" in the present invention. In the fluorescent dye combination, the distinctive fluorescent dyes may differ in properties such as absorption spectra, extinction coefficients, emission spectra, and quantum efficiencies (i.e., the number of photons emitted per absorbed photon) or combinations thereof. Therefore, different or distinctive fluorescent dyes may differ from each other in terms of chemical composition and / or one or more properties of the dyes. For example, a given pair of fluorescent dyes can be considered to be distinguished if they differ from each other in excitation and / or emission maxima, wherein, in some instances, the magnitude of such difference is 5 nm or more, such as 10 nm or more, including 15 nm or more, wherein, in some instances, the magnitude of the difference ranges from 5-400 nm, such as 10-200 nm, including 15-100 nm, such as 25-50 nm.

[0056] In some cases, the method can be performed without evaluating antigen data. By "antigen data" is meant information about an antigen being evaluated in a given flow cytometry protocol for which a combination of fluorescent dyes is to be used. In other words, unlike methods that take into account the characteristics of the antigens associated with the fluorescent dyes and select the fluorescent dyes accordingly, embodiments of the subject method are intended to provide a set of fluorescent dyes (i.e., a "palette") suitable for use in the same flow cytometry protocol. In these cases, sample antigenicity is not considered when identifying and / or evaluating the fluorescent dye combinations. In some versions, after identifying the subject fluorescent dye combination, the method may also optionally include assigning the fluorescent dyes in the identified combination to specific antigens (e.g., by antibodies to the relevant antigens, etc.).

[0057] The method of the present invention also includes receiving an instrument identifier. The so-called "instrument identifier" refers to information or data related to a specific instrument (such as a particle analyzer, a flow cytometer). In some cases, the instrument identified by the instrument identifier is a flow cytometer. Any suitable flow cytometer can be used to analyze the fluorescent particle modulated light. In some instances, the target flow cytometer includes a flow cytometer produced by BD Biosciences. Exemplary flow cytometers include BD Biosciences FACSCanto TM Flow cytometer, BD Biosciences FACSCanto TM II flow cytometer, BD Accuri TM Flow cytometer, BD Accuri TM C6 Plus flow cytometer, BD Biosciences FACSCelesta TM Flow cytometer, BD Biosciences FACSLyric TM Flow cytometer, BD Biosciences FACSVerse TM Flow cytometer, BD Biosciences FACSymphony TM Flow cytometer, BD Biosciences LSRFortessa TM Flow cytometer, BD Biosciences LSRFortessa TM X-20 flow cytometer, BD Biosciences FACSPresto TM Flow cytometer, BD Biosciences FACSVia TM Flow cytometer and BD Biosciences FACSCalibur TMCell sorter, BD Biosciences FACSCount TM Cell sorter, BD Biosciences FACSLyric TM Cell sorter, BD Biosciences Via TM Cell sorter, BD Biosciences Influx TM Cell sorter, BD Biosciences Jazz TM Cell sorter, BD Biosciences Aria TM Cell sorter, BD Biosciences FACSAria TM II cell sorter, BD Biosciences FACSAria TM III cell sorter, BD Biosciences FACSAria TM Fusion Cell Sorter and BD Biosciences FACSMelody TM Cell sorter, BD Biosciences FACSymphony TM S6 cell sorter, etc.

[0058] In addition, the method of the present invention also includes obtaining a spectral matrix associated with the fluorescent dye combination and the instrument identifier. As described in the present invention, a "spectral matrix" refers to a matrix containing information about the spectral characteristics of a group of fluorescent dyes. In an embodiment, the target spectral matrix includes a set of spectral features associated with each fluorescent dye identifier in a set of fluorescent dye identifiers. "Spectral features" refers to the characteristics of the fluorescence spectrum of a single fluorescent dye represented by one or more numerical values. If two entities are related and / or related to each other in the data space, they can be described as being "associated" with each other. In other words, the spectral matrix associated with the instrument identifier (also referred to as the "input" or "original" spectral matrix in the present invention) can represent a "palette" of possible fluorescent dyes or dyes, from which the subject fluorescent dye combination can be selected. In some cases, the spectral matrix contains information about the spectral characteristics of each fluorescent group on the target instrument (such as a flow cytometer). Such information may be different on different machines. For example, differences in the number and arrangement of lasers and detection channels between separate instruments may result in different spectral features associated with each instrument. Accordingly, embodiments of the subject methods employ spectral matrices that include spectral signatures for a particular instrument (or class / type of instrument) in which the fluorochromes in the evaluated fluorochrome combination are assumed to be employed. Due to differences between instruments, the same fluorochrome combination may be associated with greater or lesser variance in flow cytometric data if the fluorochromes are employed in two different types of instruments (e.g., flow cytometers). Having a spectral matrix associated with an instrument identifier allows for the evaluation of fluorochrome combinations in an instrument-specific context, thereby providing those skilled in the art with more reliable insight into the quality of flow cytometric data if the fluorochromes in a given combination are employed in a particular instrument when practicing the subject methods.

[0059] In some embodiments, the spectral features are received from experimental data (i.e., the results of experiments performed on a specific instrument). In other cases, the spectral features are received from simulation 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 2-150, such as 2-140, such as 2-130, such as 2-120, such as 2-110, such as 2-100, such as 2-90, such as 2-80, such as 2-70, such as 2-60, including 2-50. The series of fluorescent dye identifiers used in a given embodiment of the present invention can be referred to as a fluorescent dye palette, collectively referring to fluorescent dye identifiers, from which a given fluorescent dye combination can be selected.

[0060] In some instances, the spectral signature includes one or more spillover values. By "spilling over" is meant the relative amount of signal emitted by a given fluorescent dye into each detector band. In some cases, spillover values ​​are normalized to the maximum signal detector for that fluorescent dye (i.e., the "peak" detector). In some cases, one or more detectors in a particle analyzer (e.g., a flow cytometer) receive particle modulated light indicative of a particular fluorescent dye, and the detector is not a peak detector for that fluorescent dye. As a result, light may "spilling over" and be detected by a non-peak detector. In other words, specific fluorescent labels and their associated fluorescence emission bands for use in an experiment may be selected to be generally consistent with certain detectors. However, as more detectors are provided and more labels are used, there may not be an exact correspondence between certain detectors and fluorescence emission spectra. In general, although the peak of the emission spectrum of a particular fluorescent molecule may be within the window of a particular detector, a portion of the emission spectrum of that label will also overlap with the windows of one or more other detectors. This situation may be referred to as spillover.

[0061] In certain embodiments, the spectral matrix described herein may include one or more autofluorescence spectral features. Autofluorescence is an endogenous fluorescent signal generated by particles such as cells when they are measured in a flow cytometer. Autofluorescence is generated by endogenous molecules in cells that are fluorescently active, such as metabolites. Different cells of the same type (such as lymphocytes) may have the same autofluorescence spectrum, but with different intensities, for example, larger cells generally tend to have larger autofluorescence signals. In certain instances, different types of particles are associated with different autofluorescence spectra. For example, different types of cells (such as lymphocytes and monocytes) may not only have different autofluorescence levels, but also different autofluorescence spectra (for example, the spectral features of lymphocyte autofluorescence may be different from the spectral features of monocyte autofluorescence). In certain cases, such as in spectral cytometry, the spectral features of autofluorescence are measured by observing unstained cells, and if multiple autofluorescence spectra are included, the spectral features are included in the spectral unmixing process as an additional "fluorochrome" parameter.

[0062] As described above, the obtained spectral matrix is ​​associated with the fluorescent dye combination and the instrument identifier. In other words, the obtained spectral matrix is ​​a sub-matrix of the input spectral matrix (i.e., the spectral matrix associated with the instrument identifier), which only includes the spectral characteristics of the fluorescent dyes in the fluorescent dye combination. The "sub-matrix" described in the present invention is a matrix in the conventional sense, which describes a matrix obtained by deleting certain row and / or column combinations of another matrix.

[0063] After obtaining the spectral matrix associated with the fluorescent dye combination and the instrument identifier, the method of the present invention includes calculating the inverse matrix based on the obtained spectral matrix. The term "inverse matrix" in the present invention can be used to describe the inverse of a matrix in the conventional sense, that is, when When the matrix The inverse of ,in is a matrix unit. However, for the purposes of this 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. Broadly speaking, a "pseudo-inverse matrix" is a matrix that generalizes the inverse of a square invertible matrix to 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:

[0064]

[0065] in, yes matrix, yes Transpose, is the pseudoinverse. For a general discussion of pseudoinverses (eg, Moore-Penrose pseudoinverses), see U.S. Patent Nos. 7,065,286 and 9,575,162.

[0066] Since the spectral matrix pseudoinverse determines the mapping of the original variance to the unmixed variance, the pseudoinverse is suitable for evaluating combinations of fluorescent dyes. For example, both spectral and conventional flow cytometry can be described by a linear mixed model:

[0067]

[0068] in, is the [m-by-1] vector of detector signals, is the [m-by-n] matrix of spectral features, is an [n × 1] vector of fluorophore abundances. Spectral unmixing involves solving the “ " is a linear system of equations. For the ordinary least squares method, the solution can be described as:

[0069]

[0070] in, yes The Moore–Penrose pseudoinverse (or inverse in the compensated case, where is a square).

[0071] In additional embodiments, the inverse matrix is ​​an inverse Gram matrix (also known as the "inverse moment matrix" of the spectral matrix). The Gram matrix is ​​described in Horn, RA and Johnson, CR (2012) in "Matrix Analysis", the disclosure of which is incorporated herein by reference. In certain embodiments, the inverse Gram matrix is ​​calculated according to the following formula:

[0072]

[0073] in, is the inverse Gram matrix, is the spectral matrix, is the transposed spectral matrix.

[0074] Linear estimator theory can also be used to calculate the variance-covariance matrix of the unmixed solution , assuming the variance-covariance matrix of the detector measurements is for:

[0075]

[0076] in, T Represents the transpose operator. The diagonal elements of are the variance or noise terms for each detector, The diagonal elements of are the unmixed variances for each fluorochrome.

[0077] It is obvious from this relationship that the spectral matrix pseudo-inverse Determines the original variance To unmixing variance The following proof can prove this conclusion: Let express Spectral matrix and express The pseudo-inverse of the spectral matrix is is the number of detectors, is the amount of fluorescent dye. for The detector covariance matrix, Indicates the detector The measurement variance of . The unmixed phosphor covariance matrix With diagonal elements , indicating the fluorescent group The unmixed variance of , indicating the fluorescent group and The unmixed covariance of The definition is as follows:

[0078]

[0079]

[0080]

[0081]

[0082]

[0083]

[0084] As shown above, regardless of Regardless of the structure of The unmixed covariance of fluorophores j and k depends on the inverse spectrum and .if is diagonal (no covariance):

[0085]

[0086]

[0087] If we make the simplifying assumption that the detector noise is uncorrelated, then the unmixing variance depends only on the magnitude of the inverse spectrum and can be summarized by the vector norm of the inverse spectrum. are of the same variance, then :

[0088]

[0089]

[0090] If we also assume that the detector variances are equal for all channels, then and the inverse Gram matrix Therefore, it can be seen that the inverse Gram matrix approximately predicts the true covariance matrix of the unmixed data.

[0091] After calculating the inverse matrix, embodiments of the method include deriving a quantitative indicator from the inverse matrix. In some cases, the quantitative indicator is a matrix norm. In some instances, the quantitative indicator is a vector norm. In other instances, the quantitative indicator is derived from some combination of a matrix norm and a vector norm, for example, the sum of the vector norms of a subset of columns or rows in the inverse matrix. Suitable norms include, but are not limited to, L 2 norm, 1-norm, 2-norm, infinity norm, and Frobenius norm. In some cases, the norm is L 2norm. In some cases, the norm is a 1-norm. In some cases, the norm is a 2-norm. In some cases, the norm is an infinity norm. In some cases, the norm is a Frobenius norm. For example, the Frobenius norm is described in Golub, GH and Van Loan, CF (1996), Matrix Computation, 3rd Edition (Baltimore, MD, USA: Johns Hopkins), which publication is incorporated herein by reference in its entirety. In some cases, the Frobenius norm is calculated as follows (adapted from Golub and Van Loan):

[0092]

[0093] in, yes matrix.

[0094] In some embodiments, the method further comprises generating a visualization of the suitability of the evaluation of the fluorescent dye combination for generating flow cytometric data. Any suitable visualization may be used. In some embodiments, the visualization comprises a flow cytometric data graph simulated based on a given fluorescent dye combination. In other words, the visualization comprises exemplary flow cytometric data that would be generated if the sample were run on a specific instrument equipped with a specific fluorescent dye combination. In some such embodiments, the visualization may emphasize (e.g., by highlighting, color coding, grouping, pointing out with arrows, etc.) flow cytometric data that would be associated with variance if certain fluorescent dyes in the fluorescent dye combination were used to generate the data. In some embodiments, the visualization emphasizes the use of fluorescent dyes that generate data variance to generate flow cytometric data. In some instances, the visualization emphasizes the use of fluorescent dyes that are affected by data variance to generate flow cytometric data. In other versions, the visualization comprises a table or matrix for quantifying the extent to which the fluorescent dyes in the fluorescent dye combination are associated with variance (e.g., generate variance and / or are affected by variance). For example, the table or matrix may be populated with the quantitative indicators described above. In some such versions, cells of the table or matrix are color-coded based on the degree to which the fluorescent dye is associated with the variance (e.g., causes the variance and / or is affected by the variance). In selected instances, if the degree to which the associated fluorescent dye is associated with the variance is low, the cell is color-coded with a lighter color; if the degree to which the associated fluorescent dye is associated with the variance is high, the cell is color-coded with a higher color.

[0095] Figure 1 A flow chart of a method for implementing a fluorescent dye combination marking method according to certain embodiments is shown. Figure 1As shown, step 101 includes inputting a fluorescent dye combination, an instrument identifier, and a spectral matrix associated with the fluorescent dye combination and the instrument identifier. Step 102 includes calculating an inverse matrix (e.g., a pseudo-inverse matrix). Step 103 includes calculating a quantitative index, and step 104 includes optimizing the fluorescent dye combination based on the quantitative index calculated in step 104. A visualization may also be created in step 105 to depict the evaluation of the fluorescent dye combination.

[0096] Figure 2-3 Flow cytometry data are shown ( Figure 2 ) and variance ( Figure 3 ) from a "raw" space (i.e., a number of dimensions equal to the number of detectors in the instrument) to an "unmixed space" (i.e., a number of dimensions equal to the number of fluorescent dyes in the sample). Figure 2 As shown, spectral unmixing transforms the data from the high-dimensional detector space (raw) to the low-dimensional fluorophore space. As detailed above, both spectral and conventional flow cytometry can be described as linear mixed models. .like Figure 3 As shown in , the variance is also mapped from the detector space to the fluorophore space. It can be clearly seen that the pseudo-inverse of the spectral matrix Determines the original variance To unmixing variance The mapping of Figure 2-3 As shown, the spectral matrix pseudo-inversion maps the original detector signal back to the unmixed fluorophores. The spectral matrix pseudo-inversion can also map the original detector spatial noise back to the unmixed spatial noise, ultimately affecting the biological resolution in the experiment.

[0097] Figure 4A-4B It describes to what extent the properties of the pseudo-inverse matrix determine the diffusion (noise) in the unmixed data. Figure 4A As shown in the positive problem (mixing) depicted in the figure, the spectral matrix The jth column is the spectrum of fluorescent group j . describes how the signal from fluorophore j is mapped to all detectors. Figure 4B As shown in the inverse problem (unmixing) depicted in , the spectral matrix pseudo-inverse The jth row is the inverse spectrum of fluorescent group j . Describes how the detector signal is mapped back to the unmixed fluorophore j.

[0098] In some embodiments, the method includes generating a combined hotspot matrix. As described herein, the "combined hotspot matrix" is a mechanism for mathematically describing and / or visualizing the effects of diffusion on fluorescent groups. In selected cases, the combined hotspot matrix is ​​used as the above-mentioned visualization. In some embodiments, the combined hotspot matrix is ​​a diagonal matrix. In some instances, the combined hotspot matrix can be calculated by taking the square root of the absolute value of an inverse matrix (such as an inverse Gram matrix). In some cases, the combined hotspot matrix can be calculated as follows:

[0099]

[0100]

[0101]

[0102] Computing the combination hotspot matrix yields two different combination suitability metrics: the pseudo-inverse row norm and the off-diagonal elements. The pseudo-inverse row norm (i.e., the diagonal of the combination hotspot matrix) describes which fluorochromes in the entire combination are most affected by the unmixing-dependent diffusion. In some cases, the diagonal elements are the 2-norms of the pseudo-inverse rows of each fluorophore. In some versions, the pseudo-inverse row norm can be represented by a scale that corresponds to the factor by which the standard deviation of the unmixed data for that fluorophore is amplified due to the unmixing in the combination. For example, 1 corresponds to no effect, 2 corresponds to a 2-fold diffusion, and so on. Examining the off-diagonal elements in the full combination hotspot matrix can reveal problematic combinations of fluorophores in the combination. The off-diagonal elements are the dot products of the corresponding row and column fluorophore pseudo-inverse rows. The off-diagonal values ​​represent the magnitude of the covariance between two fluorophore pseudo-inverse matrix elements. For example, in some embodiments, an off-diagonal value of 0 indicates no covariance, while a higher value indicates a correspondingly higher level of covariance.

[0103] Figures 5A-5D The combined hotspot matrix and its exemplary calculation process according to an embodiment of the present invention are shown. Figure 5A A master database of single-stain spectra is shown. Figure 5A The x-axis of the graph shown in FIG. 4 includes different fluorescent groups, while the y-axis includes detectors. The degree to which light emitted by a given fluorescent group is detected by a given detector is represented by color coding. Figure 5B express Figure 5A A subset of the master database of single stain spectra is shown. The subset includes the target fluorophores for a given combination on the x-axis, while omitting the remaining fluorophores in the master database. This subset constitutes the spectral matrix . Figure 5C Depicts the computation of the inverse Gram matrix (which is equal to the Gram of the pseudoinverse matrix and proportional to the covariance of the pseudoinverse matrix). Figure 5DThe combined hotspot matrix is ​​shown, showing the square root of the absolute value of the Gram's inverse. The diagonal 501 includes the pseudo-inverse matrix row norms, indicating which fluorochromes in the entire combination are most affected by unmixing-dependent diffusion. The remainder of the combined hotspot matrix (i.e., the off-diagonal elements) represents the magnitude of the covariance between the elements of the pseudo-inverse matrices for the two fluorophores.

[0104] In some embodiments, the method includes performing a separate analysis of the row norms (i.e., diagonals) of the pseudo-inverse matrix of the combined hotspot matrix. In some such embodiments, the method includes generating a diagonal visualization. The diagonal visualization can be any representation (e.g., a graphical representation) of the categorical data for evaluating and / or comparing the factor by which the standard deviation of the unmixed data in the fluorophores is magnified by the unmixing in a particular combination. In some embodiments, the diagonal visualization is a bar graph, wherein each bar represents the factor by which the standard deviation of the unmixed data in each fluorophore is magnified by the unmixing in the combination.

[0105] In some cases, the method includes generating a visualization of exemplary flow cytometer data using specific fluorescent groups based on a combined hotspot matrix. The exemplary flow cytometer data can be actual flow cytometer data, i.e., data generated from a flow cytometry experiment. Alternatively, the data can be simulated data. The subject visualization of the exemplary flow cytometer data shows the effect of using certain fluorescent dyes in the experiment. In certain embodiments, the visualization shows exemplary flow cytometer data generated using a pair of specific fluorescent dyes to indicate the extent to which the covariance associated with these fluorescent dyes affects the data quality. Alternatively or in addition, the exemplary flow cytometer data can also be simulated using an entire combination of fluorescent dyes, rather than just a pair of fluorescent dyes. By examining such exemplary flow cytometer data, it is possible to find problematic combinations of fluorescent dyes in the combination.

[0106] Figure 6A-6B A visualization created based on the combined hotspot matrix is ​​shown. Fig. 6A A diagonal visualization is shown in accordance with certain embodiments. Fig. 6A The diagonal visualization shown is a bar chart, with Figure 5D The x-axis of the graph lists the different fluorophores, while the y-axis is a scale corresponding to the factor by which the standard deviation of the unmixed data in the fluorophore is magnified due to the unmixing in the combination of fluorophores on the x-axis. Fig. 6A In the example above, the fluorophores indicated by the arrows (VioR667, APC, SparkNIR685) are associated with particularly high coefficients when the standard deviation of the unmixed data is amplified among the fluorophores. These fluorophores can be identified as the ones that are most affected by unmixing-dependent diffusion in the entire panel. Figure 6B Exemplary flow cytometry data are shown, demonstrating that Fig. 6A The data can be presented for a specific pair of fluorescent dyes (right panel, top row) or for the entire panel (right panel, bottom row).

[0107] In some cases, the method includes generating a diffusion correlation matrix. As described in the present invention, the "diffusion correlation matrix" is a mechanism for mathematically describing and / or visualizing the effects of a specific fluorescent dye on certain data populations (such as a double negative population). In an embodiment, the diffusion correlation matrix can be used to predict the tilt in a double negative population. In the present invention, "tilt" refers to the degree to which a population (such as a double negative population) is offset in a specific direction (such as corresponding to a positive or negative correlation) due to the way data is collected and / or prepared. In certain embodiments, preparing a diffusion correlation matrix includes treating the Gram inverse as a covariance matrix and normalizing each row and column to the square root of its diagonal elements to calculate a correlation matrix. In some cases, the diffusion correlation matrix is ​​calculated as follows:

[0108]

[0109] in, represents taking the diagonal of a two-dimensional matrix, or forming a diagonal matrix from a one-dimensional vector. This operation is equivalent to dividing each row by the square root of its diagonal elements and each column by the square root of its diagonal elements. The diffusion correlation matrix element [i, j] corresponds to the correlation between the pseudo-inverse matrix rows corresponding to fluorophores i and j. In some cases, the method includes generating a visualization of exemplary flow cytometry data using specific fluorophores based on the diffusion correlation matrix. As with the combined hotspot matrix correlation visualization, the exemplary flow cytometry data visualization created with respect to the diffusion correlation matrix can be an actual visualization or a simulated visualization.

[0110] Figures 7A-7E The diffusion correlation matrix and an exemplary calculation process thereof according to an embodiment of the present invention are shown. Fig. 7A The computation of the inverse Gram matrix (which is equal to the Gram of the pseudoinverse matrix and proportional to the covariance of the pseudoinverse matrix) is shown. Figure 7B Shows the Fig. 7A The diffusion correlation matrix is ​​calculated from the inverse Gram matrix calculated in . Each cell of the diffusion correlation matrix is ​​associated with a correlation between the pseudo-inverse matrix rows corresponding to the correlated fluorophores with a measurement range between -1.00 and 1.00. Cell 701 defines the target fluorophore pairs that may cause a tilt. Figure 7C-7D An exemplary visualization of exemplary flow cytometry data created using a pair of fluorophores identified in cell 701 is depicted. Figure 7C-7D In the double negative group 702c and 702d, it is expected that they will have a larger negative correlation. Fig. 7E Group 702e is expected to have a larger positive correlation.

[0111] In embodiments, methods include optimizing the fluorescent dyes based on the evaluation of the suitability of the fluorescent dye combination for generating flow cytometric data, i.e., making the fluorescent dye combination suitable for flow cytometric protocols. A fluorescent dye combination can be described as "suitable for" flow cytometric protocols when it produces understandable flow cytometric data and the data reliably provides the target characteristics of the sample under investigation. In certain embodiments, a fluorescent dye combination is suitable for flow cytometric protocols when the fluorescent dye combination provides higher biological resolution. "Biological resolution" refers to the ability to distinguish different target entities (such as cells, molecules, antigens, parts, epitopes, etc.) in a biological sample. In some cases, despite the measurement variance and the variance of the flow cytometric data space (e.g., the flow cytometric data has been processed by fluorescence compensation or spectral unmixing), the fluorescent dye combination identified in the present invention can still produce the highest biological resolution. In some versions, the "highest" biological resolution is evaluated relative to the biological resolution that can be achieved using one or more sets of other fluorescent dyes that are different from (i.e., include one or more sets of other fluorescent dyes that are different from) the evaluated and / or identified fluorescent dyes described in the present invention.

[0112] In certain embodiments, the optimization of the fluorescent dye combination includes the use of a combinatorial optimization algorithm. In some cases, the combinatorial optimization algorithm is a constrained optimization algorithm. The "constrained optimization" mentioned in the present invention describes in its conventional sense the process of optimizing these variables when the variables are constrained. Any suitable constrained optimization method can be used. In some cases, the constrained optimization method is a minimization algorithm. The so-called "minimization algorithm" refers to a constrained optimization method, wherein the method strives to minimize specific variables. Examples of constrained optimization techniques that can be used include, but are not limited to, local search, local repair, backtracking, and constraint propagation. In some cases, these techniques can be combined with minimization techniques such as simulated annealing and genetic (evolutionary) algorithms. In some cases, the fluorescent dye combination described in the present invention can be optimized in combination with the optimization scheme described in U.S. Patent Application No. 18 / 083,808 (Agent Case No. BECT-310 (P-26714.US02) filed on December 19, 2022, and the disclosure of U.S. Patent Application No. 18 / 083,808 becomes a part of the present invention by reference to the present invention.

[0113] In an embodiment, the optimization of the fluorescent dye combination includes adjusting the fluorescent dyes in the fluorescent dye combination, and evaluating the suitability of the adjusted fluorescent dye combination for generating flow cytometer data. The so-called "adjustment" of the fluorescent dyes in the fluorescent dye combination refers to exchanging the fluorescent dye or the fluorescent dye identifier associated therewith with different fluorescent dyes in the spectrum matrix, and the spectrum matrix represents a palette of possible fluorescent dyes or dyes that can be selected. One or more fluorescent dyes in the combination can be adjusted at any given time. In some instances, the method includes exchanging a single fluorescent dye in the combination at a given time. In some cases, optimizing the fluorescent dye combination includes keeping the size of the fluorescent dye combination unchanged. In other words, even if one or more fluorescent dyes are adjusted, the number of fluorescent dyes in the resulting fluorescent dye will not change. For example, after adjustment, a fluorescent dye combination having N fluorescent dyes will continue to have N fluorescent dyes after evaluation. In some instances, the fluorescent dyes in the fluorescent dye combination will not be replaced by the fluorescein already present in the fluorescent dye combination. After generating the adjusted fluorescent dye combination, the target method also includes evaluating the adjusted fluorescent dye combination, that is, calculating an inverse matrix based on the obtained spectral matrix, and identifying fluorescent dyes in the fluorescent dye combination that are associated with the variance of flow cytometer data generated using the fluorescent dye combination by analyzing the calculated inverse matrix to evaluate the suitability of the fluorescent dye combination for generating flow cytometer data.

[0114] The subject method further includes comparing the evaluation of the first fluorescent dye combination with the evaluation of the adjusted fluorescent dye combination. For example, the method may include determining which fluorescent dye of the first and adjusted fluorescent dye combinations is associated with a smaller variance in the flow cytometer data. If the first or adjusted fluorescent dye combination includes fewer fluorescent dyes associated with a variance in the flow cytometer data than the other fluorescent dye combination, and / or has a fluorescent dye associated with a smaller variance (e.g., as determined by a quantitative indicator), then the fluorescent dye combination can be determined to be more suitable for generating flow cytometer data. In some cases, the method includes discarding the fluorescent dye combination in which the fluorescent dye is associated with a larger variance.

[0115] In some cases, the method includes iteratively adjusting the fluorescent dye combination and evaluating the suitability of each iteratively adjusted fluorescent dye combination. In an embodiment, the fluorescent dye combination that has been evaluated as being associated with a smaller variance of the fluorescent dye data in the first and adjusted fluorescent dye combinations can be used as a seed for the next part of the iterative process. The so-called "seed" refers to a fluorescent dye combination that has been determined to be associated with a smaller variance of the flow cytometer data compared to one or more slightly modified fluorescent dye combinations in an iteration of the method. In certain embodiments, the iterative process itself is repeated until a condition is met. Any suitable condition can be used to terminate the iterative process. In certain instances, the iterative process terminates after a certain running time. In other instances, the iterative process terminates when the evaluation generated for each iteratively adjusted fluorescent dye combination converges. In other words, the iterative process terminates when only a small variance difference occurs between subsequent fluorescent dye combinations.

[0116] Figure 8 A flow chart for implementing the method of the present invention according to certain embodiments is shown. The method includes receiving input 801, which may include an experimental / instrument-specific matrix 801a from a single-stained control group, or a matrix 801b derived from a reference database. In step 802, an inverse matrix and quantitative indicators derived therefrom are calculated. These indicators may include a Gram inverse 802a, a vector norm 802b, or a matrix norm 802c. The indicators calculated in step 802 can be subsequently used to improve or analyze the fluorescent dye combination in step 803. It may include using a combined hot spot matrix for diffusion visualization in step 803a, using a diffusion correlation matrix to predict the tilt of a double-negative population in step 803b, optimizing the combination using an indicator-oriented manual combination optimization in step 803c, or optimizing the combination using an indicator-oriented automatic combination optimization in step 803d.

[0117] The subject fluorescent dye combination may include any suitable fluorescent dye. According to certain embodiments, the target fluorescent dye has an excitation maximum in the range of 100 nm-800 nm (e.g., 150 nm-750 nm, e.g., 200 nm-700 nm, e.g., 250 nm-650 nm, e.g., 300 nm-600 nm, including 400 nm-500 nm). According to certain embodiments, the target fluorescent dye has an emission maximum in the range of 400 nm-1000 nm (e.g., 450 nm-950 nm, e.g., 500 nm-900 nm, e.g., 550 nm-850 nm, including 600 nm-800 nm). In some examples, the fluorescent dye is a luminescent dye, such as a fluorescent dye having a peak emission wavelength of 200 nm or more (e.g., 250 nm or more, such as 300 nm or more, such as 350 nm or more, such as 400 nm or more, such as 450 nm or more, such as 500 nm or more, such as 550 nm or more, such as 600 nm or more, such as 650 nm or more, such as 700 nm or more, such as 750 nm or more, such as 800 nm or more, such as 850 nm or more, such as 900 nm or more, such as 950 nm or more, such as 1000 nm or more, including 1050 nm or more). For example, the fluorescent dye can be a fluorescent dye having a peak emission wavelength in the range of 200 nm-1200 nm, such as 300 nm-1100 nm, such as 400 nm-1000 nm, such as 500 nm-900 nm, including a fluorescent dye having a peak emission wavelength of 600 nm-800 nm.

[0118] Target fluorescent dyes may include, but are not limited to, fluoroboron dipyrrole dyes, coumarin dyes, rhodamine dyes, acridine dyes, anthraquinone dyes, arylmethane dyes, diarylmethane dyes, chlorophyll-containing dyes, triarylmethane dyes, azo dyes, diazo dyes, nitro dyes, nitroso dyes, phthalocyanine dyes, cyanamide dyes, asymmetric cyanamide dyes, quinoneimine dyes, azine dyes, diaminoacazine dyes, safranin dyes, indamin, indophenol dyes, fluorine dyes, oxazine dyes, oxadiazole dyes, thiazine dyes, thiazole dyes, xanthene dyes, fluorene dyes, pyronine dyes, fluorine dyes, rhodamine dyes, phenanthridine dyes, squaryl cyanines, fluorine boron complexes, squarine roxitanes, naphthalenes, coumarins, oxadiazoles, anthracenes, pyrenes, acridines, arylmethines or tetrapyrroles and combinations thereof. In certain embodiments, the conjugate may include two or more dyes, such as two or more dyes selected from the following: fluoroboron dipyrrole dyes, coumarin dyes, rhodamine dyes, acridine dyes, anthraquinone dyes, arylmethane dyes, diarylmethane dyes, chlorophyll-containing dyes, triarylmethane dyes, azo dyes, diazo dyes, nitro dyes, nitroso dyes, phthalocyanine dyes, cyanamide dyes, asymmetric cyanamide dyes, quinoneimine dyes, azine dyes, diaminoacazine dyes, safranin dyes, indamin, indophenol dyes, fluorine dyes, oxazine dyes, oxadiazole dyes, thiazine dyes, thiazole dyes, xanthene dyes, fluorene dyes, pyronine dyes, fluorine dyes, rhodamine dyes, phenanthridine dyes, squaryl cyanines, fluoroboron complexes, squarine roxitanes, naphthalenes, coumarins, oxadiazoles, anthracenes, pyrenes, acridines, arylmethines or tetrapyrroles and combinations thereof.

[0119] In some embodiments, the target fluorescent dye may include but is not limited to fluorescein isothiocyanate (FITC), phycoerythrin (PE) dye, peridinin-chlorophyll-protein-cyan dye (such as PerCP-Cy5.5), phycoerythrin-cyan (PE-Cy) dye (PE-Cy7), allophycocyanin (APC) dye (such as APC-R700), allophycocyanin-cyan dye (such as APC-Cy7), coumarin dye (such as V450 or V500). In some examples, the fluorescent dye may include one or more of the following: 1,4-bis-(o-methylphenylvinyl)-benzene (bis-MSB 1,4-bis[2-(2-methylphenyl)vinyl]-benzene), C510 dye, C6 dye, Nile Red dye, T614 dye (such as N-[7-(methylsulfonylamino)-4-oxo-6-phenoxybenzopyran-3-yl]carboxamide), LDS 821 dye ((2-(6-(p-dimethylaminophenyl)-2,4-neopentyl glycol-1,3,5-hexatrienyl)-3-ethylbenzothiazole perchlorate), mFluor dye (for example, mFluor Red dye, such as mFluor 780NS).

[0120] Target fluorescent dyes may include, but are not limited to, fluorescein, hydroxycoumarin, aminocoumarin, methoxycoumarin, fast blue lake, Pacific Blue, Pacific Orange, Roche Yellow, NBD, phycoerythrin (PE), PE-Cy5 conjugate, PE-Cy7 conjugate, Red 613, PerCP, TruRed, FluorX, BODIPY-FL, TRITC, X-Rhodamine, Lissamine basic rhodamine B, Texas Red, allophycocyanin (APC), APC-Cy7 conjugate, Cy2, Cy3, Cy3B, Cy3.5, Cy5, Cy5.5, Cy7, Hoechst 33342, DAPI, Hoechst 33258, SYTOX Blue, chromomycin A3, mithramycin, YOYO-1, ethidium bromide, acridine orange, SYTOX Green, TOTO-1, TO-PRO-1, Thiazole Orange, Propidium Iodide (PI), LDS751, 7-AAD, SYTOX Orange, TOTO-3, TO-PRO-3, DRAQ5, Indo-1, Fluo-3, DCFH, DHR, SNARF, Y66H, Y66F, EBFP, EBFP2, Azurite, GFPuv, T-Sapphire, TagBFP, Cerulean, mCFP, ECFP, CyPet, Y66W, dKeima-Red, mKeima-Red, TagCFP, AmCyan1, mTFP1 (Teal), S65A, Midoriishi-Cyan, wild-type GFP, S65C, TurboGFP, TagGFP, TagGFP2, AcGFP1, S65L, Emerald, S65T, EGFP, Azami-Green, ZsGreen1, Dronpa-Green, TagYFP, EYFP, Topaz, V enus, mCitrine, YPet, TurboYFP, PhiYFP, PhiYFP-m, ZsYellow1, mBanana, Kusabira-Orange, mOrange, mOrange2, mKO, TurboRFP, tdTomato, DsRed-Express2, TagRFP, DsRedmonomer, DsRed2 (“RFP”), mStrawberry, TurboFP602, AsRed2, mRFP1, J-Red, mCherry, HcRed1, mKate2, Katushk a (TurboFP635), mKate (TagFP635), TurboFP635, mPlum, mRaspberry, mNeptune, E2-Crimson, monochlorodiamine, calcein, Alexa Fluor 350, Alexa Fluor 405, Alexa Fluor 430, Alexa Fluor488, 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 Fluor750, Alexa Fluor 790 and HyPer, etc.

[0121] In some instances, the fluorescent dye combination includes one or more polymeric dyes (e.g., polymeric fluorescent dyes). There are many polymeric fluorescent dyes that can be used in the subject methods and systems. In some instances of the method, the polymeric dye includes a coupled polymer. A coupled polymer (CP) is characterized by a delocalized electronic structure, which includes a main chain with alternating unsaturated bonds (e.g., double bonds and / or triple bonds) and saturated bonds (e.g., single bonds), wherein π electrons can move from one bond to another. Therefore, the coupled main chain can give the polymeric dye an extended linear structure with limited bond angles between polymer repeating units. For example, although proteins and nucleic acids are also polymerized, in some cases they do not form extended rod-like structures, but fold into higher-order three-dimensional shapes. In addition, the CP may form a "rigid rod" polymer main chain and experience limited twisting (e.g., torsion) angles between monomer repeating units along the polymer main chain. In some instances, the polymeric dye includes a CP with a rigid rod structure. The structural characteristics of the polymeric dye will affect the fluorescence properties of the molecule.

[0122] Any suitable polymer dye may be used in the subject apparatus and methods. In some instances, the polymer dye is a polychromophore whose structure is capable of capturing light to amplify the fluorescent output of the fluorescent group. In some instances, the polymer dye is capable of capturing light and efficiently converting it into emitted light with a longer wavelength. In some cases, the polymer dye has a light-harvesting multichromophore system that is capable of efficiently transferring energy to a nearby luminescent species (such as a "signal chromophore"). Energy transfer mechanisms include resonance energy transfer (such as Foster (or fluorescence) resonance energy transfer, FRET), quantum charge exchange (Dexter energy transfer), etc. In some instances, the distance of these energy transfer mechanisms is relatively short; that is, the light-harvesting multichromophore system is close to the signal chromophore, and efficient energy transfer can be achieved. Under conditions of efficient energy transfer, the emission of the signal chromophore is amplified when a larger number of individual chromophores are present in the light-harvesting multichromophore system; that is, when the wavelength of incident light ("excitation light") is absorbed by the light-harvesting multichromophore system, the emission of the signal chromophore is more intense than when the signal chromophore is directly excited by the pump light.

[0123] The multichromophore can be a coupled polymer. Coupled polymers (CPs) are characterized by a nonlocalized electronic structure and can be used as highly responsive optical reporters for chemical and biological targets. Since the effective coupling length is significantly shorter than the polymer chain length, the backbone contains a large number of tightly connected coupled segments. Therefore, coupled polymers have high light capture efficiency and can achieve light amplification through Förster energy transfer.

[0124] The target polymer dyes include but are not limited to No. 7,270,956; No. 7,629,448; No. 8,158,444; No. 8,227,187; No. 8,455,613; No. 8,575,303; No. 8,802,450; No. 8,969,509; No. 9,139,869; No. 9,371,559; No. 9,547,008; No. 10,094,838; No. 10,302,648; No. 10,458,989; No. 10,641,775 and No. 10,962, The dyes described in U.S. Pat. No. 546, the disclosure of which is incorporated herein by reference in its entirety, become a part of the present invention; Gaylord et al., Journal of the American Chemical Society, 2001, 123(26), pp. 6417-6418; Feng et al., Reviews of the Chemical Society, 2010, 39, 2411-2419; Traina et al., Journal of the American Chemical Society, 2011, 133(32), pp. 12600-12607, the disclosure of which is incorporated herein by reference in its entirety, become a part of the present invention. Specific polymer dyes that can be used include, but are not limited to, BD HorizonBrilliant™ dyes, such as BD Horizon Brilliant™ violet dyes (e.g., BV421, BV510, BV605, BV650, BV711, BV786); BD Horizon Brilliant™ ultraviolet dyes (e.g., BUV395, BUV496, BUV737, BUV805); BD Horizon Brilliant™ blue dyes (e.g., BB515) (BD Biosciences, San Jose, CA). The subject method can employ any fluorescent dye known to those skilled in the art, including but not limited to the fluorescent dyes described above or fluorescent dyes yet to be discovered.

[0125] The fluorescent dyes in the subject fluorescent dye combination and / or the reference fluorescent dyes in the spectral matrix may or may not be coupled to a biomolecule (e.g., a biomacromolecule). The biomacromolecule may be a biopolymer. A "biopolymer" is a polymer composed of one or more repeating units. Biopolymers are commonly found in biological systems and include, in particular, polysaccharides (e.g., carbohydrates), peptides (this term is used to include polypeptides and proteins, whether or not attached to a polysaccharide), polynucleotides, and analogs thereof, such as compounds composed of or containing amino acid analogs or non-amino acid groups or nucleotide analogs or non-nucleotide groups. These include polynucleotides in which the conventional backbone is replaced by a non-naturally occurring backbone or a synthetic backbone; and nucleic acids (or synthetic or naturally occurring analogs) in which one or more conventional bases are replaced by a group (natural or synthetic) that is capable of participating in Watson-Crick type hydrogen bonding interactions. Polynucleotides include single-stranded or multi-stranded configurations in which one or more chains may or may not be completely aligned with another chain. In particular, "biopolymers" include DNA (including cDNA), RNA, and oligonucleotides (regardless of origin). Thus, biomacromolecules may include polysaccharides, nucleic acids, and polypeptides. For example, the nucleic acid can be an oligonucleotide, a truncated or full-length DNA or RNA. In an embodiment, the oligonucleotide, truncated and full-length DNA or RNA is composed of 10 or more nucleotide monomers (e.g., 15 or more, such as 25 or more, such as 50 or more, such as 100 or more, such as 250 or more, including 500 or more nucleotide monomers). For example, the length of the target oligonucleotide, truncated and full-length DNA or RNA can range from 10 nucleotides to 10 8 nucleotides, for example 10 2 Nucleotides -10 7 nucleotides, including 10 3 Nucleotides -10 6 nucleotides. In an embodiment, the biopolymer is not a single nucleotide or a short oligonucleotide (e.g., less than 10 nucleotides). The so-called "full length" means that the DNA or RNA is a nucleic acid polymer having 70% or more complete sequence (e.g., complete sequence found in nature) (e.g., 75% or more, such as 80% or more, such as 85% or more, such as 90% or more, such as 95% or more, such as 97% or more, such as 99% or more, including 100% of the full length sequence of the DNA or RNA (e.g., full length sequence found in nature)).

[0126] In some instances, the polypeptide may be a truncated or full-length protein, enzyme, or antibody. In an embodiment, the polypeptide, truncated and full-length protein, enzyme, or antibody is composed of 10 or more amino acid monomers (e.g., 15 or more, such as 25 or more, such as 50 or more, such as 100 or more, such as 250 or more, including 500 or more amino acid monomers). For example, the length of the target polypeptide, truncated and full-length protein, enzyme, or antibody may range from 10 amino acids to 10 8 amino acids, such as 10 2 Amino Acids -10 7 amino acids, including 10 3 Amino Acids -10 6 amino acids. In an embodiment, the biopolymer is not a single amino acid or a short-chain polypeptide (e.g., less than 10 amino acids). The so-called "full length" means that the protein, enzyme or antibody is a polypeptide polymer having 70% or more of the complete sequence (e.g., the complete sequence found in nature) (e.g., 75% or more, such as 80% or more, such as 85% or more, such as 90% or more, such as 95% or more, such as 97% or more, such as 99% or more, including 100% of the full-length sequence of the protein, enzyme or antibody (e.g., the full-length sequence found in nature)).

[0127] In some instances, the fluorescent dye is coupled to a specific binding member. The specific binding member and the fluorescent dye may be coupled to each other (e.g., covalently linked) at any suitable position of the two molecules via an optional linker. The term "specific binding member" as used in the present invention refers to a member of a pair of molecules that have binding specificity to each other. The surface of one member of the pair of molecules may have an area or cavity that specifically binds to a surface area or cavity of the other member of the pair of molecules. Thus, the members of the pair of molecules have the property of specifically binding to each other, thereby producing a binding complex. In certain embodiments, the affinity between the specific binding members in the binding complex is characterized by K d (dissociation constant) is 10 -6 M or less, e.g. 10 -7 M or below, including 10 -8 M or less, e.g. 10 -9 M or below, 10 -10 M or below, 10 -11 M or below, 10 -12 M or below, 10 -13 M or below, 10 -14 M or below, including 10 -15 M or less. In certain embodiments, the specific binding member specifically binds with high affinity. The so-called "high affinity" refers to a binding member with an apparent Kd is 10×10 -9 M or less (e.g. 1×10 -9 M or below, 3×10 -10 M or less, 1×10 -10 M or below, 3×10 -11 M or less, 1×10 -11 M or below, 3×10 -12 M or less, or 1×10 -12 M or less) with an apparent affinity for specific binding.

[0128] The specific binding member can be a protein member. The term "protein" used in the present invention refers to a portion consisting of amino acid residues. The protein portion can be a polypeptide. In some cases, the protein-specific binding member is an antibody. In certain embodiments, the protein-specific binding member is an antibody fragment, such as an antibody binding fragment that specifically binds to a polymer dye. The terms "antibody" and "antibody molecule" used in the present invention are used interchangeably to refer to a protein consisting of one or more polypeptides that are substantially encoded by all or part of an identified immunoglobulin gene. Taking human immunoglobulin genes as an example, the identified immunoglobulin genes include kappa (k), lambda (l) and heavy chain loci (which together constitute a large number of variable region genes), as well as constant region genes mu (u), delta (d), gamma (g), sigma (e) and alpha (a) (encoding IgM, IgD, IgG, IgE and IgA isotypes, respectively). The variable region of an immunoglobulin light chain or heavy chain consists of a "framework" region (FR) interrupted by three hypervariable regions (also known as "complementarity determining regions" or "CDRs"). The scope of the framework region and CDR has been precisely defined (see "Protein Sequences of Immunological Importance", E. Kabat et al., U.S. Department of Health and Human Services, (1991)). The numbering of all antibody amino acid sequences described in the present invention conforms to the Kabat system. The framework region sequences of different light chains or heavy chains within a species are relatively conserved. The framework region of an antibody (i.e., the combined framework region of the component light chain and heavy chain) is used to position and align the CDR. The CDR is primarily responsible for binding to the epitope of the antigen. "Antibodies" include full-length antibodies and may refer to natural antibodies from any organism, engineered antibodies, or antibodies recombinantly generated for experimental, therapeutic, or other purposes further defined below. Target antibody fragments include, but are not limited to, Fab, Fab', F(ab')2, Fv, scFv, or other antibody antigen-binding subsequences, which can be produced by modifying the entire antibody or synthesized de novo using DNA recombinant technology. Antibodies can be monoclonal or polyclonal antibodies and can have other specific activities against cells (such as antagonists, agonists, neutralizing antibodies, inhibitory antibodies, or stimulatory antibodies). It should be understood that antibodies may have additional conservative amino acid substitutions that have little effect on antigen binding or other antibody functions. In certain embodiments, the specific binding member is a Fab fragment, a F(ab')2 fragment, a scFv, a di-antibody or a tri-antibody. In certain embodiments, the specific binding member is an antibody. In some cases, the specific binding member is a murine antibody or a binding fragment thereof. In some instances, the specific binding member is a recombinant antibody or a binding fragment thereof.

[0129] In an embodiment, the subject fluorescent dye combination is used to analyze the sample. In some instances, the sample analyzed is a biological sample. The term "biological sample" refers to a whole organism, plant or fungus, or a subset of animal tissues, cells or components in its conventional sense, and in some instances, can be found in blood, mucus, lymph, synovial fluid, cerebrospinal fluid, saliva, bronchoalveolar lavage fluid, amniotic fluid, amniotic umbilical blood, urine, vaginal fluid and semen, etc. Therefore, "biological sample" refers to a subset of a protist or its tissue, and a homogenate, lysate or extract prepared from an organism or a subset of its tissue, including but not limited to plasma, serum, cerebrospinal fluid, lymph, skin slices, respiratory tract, gastrointestinal tract, cardiovascular, urogenital tract, tears, saliva, milk, blood cells, tumors, organs, etc. A biological sample can be any type of organism tissue, including healthy tissue and diseased tissue (e.g., cancer tissue, malignant tissue, necrotic tissue, etc.). In certain embodiments, the biological sample is a liquid sample, such as blood or its derivatives, such as plasma, tears, urine, semen, etc., wherein in some cases, the sample is a blood sample, including whole blood, such as blood obtained by venipuncture or finger stick (the blood is not necessarily mixed with any reagents such as preservatives and anticoagulants before analysis).

[0130] In certain embodiments, the source of the sample is a "mammal" or "mammalian", where these terms are widely used to describe organisms within the class Mammalia, including Carnivora (e.g., dogs and cats), Rodentia (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 used for samples from human subjects of both sexes and at any stage of development (i.e., newborns, infants, juveniles, adolescents, adults), where in certain embodiments, the human subject is a juvenile, adolescent, or adult. Although the present invention is applicable to samples from human subjects, it should be understood that the method can also be applied to samples from other animal subjects (i.e., "non-human subjects", such as, but not limited to, birds, mice, rats, dogs, cats, livestock, and horses).

[0131] The fluorescent dyes in the fluorescent dye combination can target different types of cells (e.g., through antibodies targeting such cells, etc.). A variety of cells can be characterized using the above-mentioned target methods. Target cells include, but are not limited to, stem cells, T cells, dendritic cells, B cells, granulocytes, leukemia cells, lymphoma cells, viral cells (such as HIV cells), NK cells, macrophages, monocytes, fibroblasts, epithelial cells, endothelial cells, and erythroid cells. Target cells include cells with appropriate cell surface markers or antigens, which can be captured or labeled using appropriate affinity agents or their conjugates. For example, the target cell 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 α / β, T cell receptor γ / δ, CD253, CD95, CD20, CD105, CD117, CD120b, Notch4, Lgr5 (N-terminal), SSEA-3, TRA-1-60 antigen, disialylganglioside GD2, and CD71. In certain embodiments, the target cell is 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.

[0132] In certain embodiments, the fluorescent dye combination identified by the present method can be used in a flow cytometry protocol (e.g., analyzing a sample, as 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 certain embodiments, the light source is a broadband light source, emitting light with a wide wavelength range, such as a wavelength span of 50 nm or more, such as 100 nm or more, such as 150 nm or more, such as 200 nm or more, such as 250 nm or more, such as 300 nm or more, such as 350 nm or more, such as 400 nm or more, including a span of 500 nm or more. For example, a suitable broadband light source emits light with a wavelength of 200 nm-1500 nm. For another example, a suitable broadband light source includes a light source that emits light with a wavelength of 400 nm-1000 nm. When the method includes irradiating with a broadband light source, the target broadband light source scheme may include but is not limited to halogen lamps, deuterium arc lamps, xenon arc lamps, stabilized fiber-coupled broadband light sources, continuous spectrum broadband LEDs, superluminescent diodes, semiconductor light emitting diodes, wide spectrum LED white light sources, multi-LED integrated white light sources and other broadband light sources or any combination thereof.

[0133] In other embodiments, the method of the present embodiment includes irradiating with a narrowband light source emitting a specific wavelength or a narrow wavelength range, such as using a light source emitting light in a narrow wavelength range (e.g., 50 nm or less, such as 40 nm or less, such as 30 nm or less, such as 25 nm or less, such as 20 nm or less, such as 15 nm or less, such as 10 nm or less, such as 5 nm or less, such as 2 nm or less), including a light source emitting light of a specific wavelength (i.e., monochromatic light). When the method includes irradiating with a narrowband light source, the target narrowband light source scheme may include, but is not limited to, a narrow wavelength LED, a laser diode, or a broadband light source coupled to one or more optical bandpass filters, a diffraction grating, a monochromator, or any combination thereof.

[0134] In some embodiments, the method includes irradiating the sample with one or more lasers. As described above, the type and number of lasers will vary depending on the sample and the desired collection light, and can be 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 method includes irradiating the flow stream with a dye laser, such as a stilbene, coumarin, or rhodamine laser. In 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 other cases, the method includes irradiating the flow stream with a solid-state laser (e.g., a ruby ​​laser, a Nd:YAG laser, a NdCrYAG laser, an Er:YAG laser, a Nd:YLF laser, a Nd:YVO4 laser, a Nd:YCa4O(BO3)3 laser, a Nd:YCOB laser, a titanium sapphire laser, a thulium YAG laser, an ytterbium YAG laser, a Yb2O3 laser, or a cerium-doped laser, and combinations thereof).

[0135] The sample may be irradiated with one or more of the above light sources, such as 2 or more light sources, such as 3 or more light sources, such as 4 or more light sources, such as 5 or more light sources, including 10 or more light sources. The light sources may include any combination of various types of light sources. For example, in some embodiments, the method includes irradiating the sample in the flow stream with a laser array (e.g., an array having one or more gas lasers, one or more dye lasers, and one or more solid-state lasers).

[0136] The sample may be irradiated at a wavelength in the range of 200 nm-1500 nm (e.g., 250 nm-1250 nm, e.g., 300 nm-1000 nm, e.g., 350 nm-900 nm, including 400 nm-800 nm). For example, when the light source is a broadband light source, the sample may be irradiated at a wavelength in the range of 200 nm-900 nm. In other cases, if the light source includes a plurality of narrow-band light sources, the sample may be irradiated at a specific wavelength in the range of 200 nm-900 nm. For example, the light source may be a plurality of narrow-band LEDs (1 nm-25 nm), each of which may independently emit light having a wavelength in the range of 200 nm-900 nm. In other embodiments, the narrow-band light source includes one or more lasers (e.g., a laser array) to irradiate the sample at a specific wavelength in the range of 200 nm-700 nm, such as the above-mentioned laser array having a gas laser, an excimer laser, a dye laser, a metal vapor laser, and a solid-state laser.

[0137] In the case of using more than one light source, the light sources or combinations thereof may be used to irradiate the sample simultaneously or sequentially. For example, each light source may be used to irradiate the sample simultaneously. In other embodiments, each light source is used sequentially to irradiate the flow stream. In the case of using more than one light source sequentially to irradiate the sample, the time for each light source to irradiate the sample may be independently 0.001 microseconds or more, such as 0.01 microseconds or more, such as 0.1 microseconds or more, such as 1 microsecond or more, such as 5 microseconds or more, such as 10 microseconds or more, such as 30 microseconds or more, including 60 microseconds or more. For example, the method may include irradiating the sample using a light source (such as a laser) for a duration ranging from 0.001 microseconds to 100 microseconds, such as 0.01 microseconds to 75 microseconds, such as 0.1 microseconds to 50 microseconds, such as 1 microsecond to 25 microseconds, including 5 microseconds to 10 microseconds. In embodiments where two or more light sources are used sequentially to irradiate the sample, the duration for each light source to irradiate the sample may be the same or different.

[0138] The time period between the irradiation of each light source can also be changed as needed, and can be separated independently by a delay of 0.001 microseconds or more (e.g., 0.01 microseconds or more, such as 0.1 microseconds or more, such as 1 microsecond or more, such as 5 microseconds or more, such as 10 microseconds or more, such as 15 microseconds or more, such as 30 microseconds or more, including 60 microseconds or more). For example, the time period between the irradiation of each light source can range from 0.001 microseconds to 60 microseconds, such as 0.01 microseconds to 50 microseconds, such as 0.1 microseconds to 35 microseconds, such as 1 microsecond to 25 microseconds, including 5 microseconds to 10 microseconds. In some embodiments, the time period between the irradiation of each light source is 10 microseconds. In embodiments where two or more (i.e., 3 or more) light sources are used sequentially to irradiate the sample, the delay between the irradiation of each light source can be the same or different.

[0139] The sample may be irradiated continuously or at discrete intervals. In some cases, the method includes irradiating the sample in the sample continuously using the light source. In other cases, the sample is irradiated at discrete intervals (e.g., every 0.001 milliseconds, every 0.01 milliseconds, every 0.1 milliseconds, every 1 millisecond, every 10 milliseconds, every 100 milliseconds, including every 1000 milliseconds, or at other intervals) using the light source.

[0140] Depending on the light source, the distance of the irradiated sample can be different, for example, a distance of 0.01 mm or more, for example, 0.05 mm or more, for example, 0.1 mm or more, for example, 0.5 mm or more, for example, 1 mm or more, for example, 2.5 mm or more, for example, 5 mm or more, for example, 10 mm or more, for example, 15 mm or more, for example, 25 mm or more, including 50 mm or more. In addition, the irradiation angle can also be changed, and the angle range is 10°-90°, for example, 15°-85°, for example, 20°-80°, for example, 25°-75°, including 30°-60°, for example, 90°.

[0141] In an embodiment, light from the irradiated sample is transmitted to a light detection system and measured using one or more photodetectors. In implementing the subject method, light from the sample is transmitted to three or more wavelength separators, each of which allows light having a predetermined spectral range to pass through. Light from the spectral range of each wavelength separator is transmitted to one or more light detection modules, which have optical elements that can transmit light having a predetermined sub-spectral range to the photodetector.

[0142] The light detection system can be used to measure light continuously or at discrete intervals. In some cases, the method includes measuring light continuously. 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, including every 1000 milliseconds, or at other intervals.

[0143] During implementation of the subject method, the collected light may be measured one or more times (e.g., 2 or more, 3 or more, 5 or more, including 10 or more). In some embodiments, 2 or more light propagation measurements are taken, and in some instances, the data are averaged.

[0144] In some embodiments, the method includes conditioning the light before detecting the light using the target light detection system. For example, the light from the sample source may pass through one or more lenses, reflectors, pinholes, slits, gratings, light refractors, and any combination thereof. In some cases, for example, to reduce the profile of the light directed to the above-mentioned light detection system or optical collection system, the collected light passes through one or more focusing lenses. In other cases, in order to reduce the divergence of the light beam transmitted to the light detection system, the light emitted from the sample passes through one or more collimators.

[0145] system

[0146] In addition, various aspects of the present invention also include a system that can perform the above method. The target system includes a processor that can obtain a fluorescent dye combination, an instrument identifier, and a spectral matrix associated with the fluorescent dye combination and the instrument identifier. The processor of the subject system can also calculate an inverse matrix based on the obtained spectral matrix, and identify the fluorescent pigments in the fluorescent pigment combination that are associated with the variance of the flow cytometer data generated using the fluorescent pigment combination by analyzing the calculated inverse matrix to evaluate the suitability of the fluorescent pigment combination for generating flow cytometer data. In an embodiment, the target processor is operated in conjunction with programmable logic, and the programmable logic can be implemented in hardware, software, firmware, or any combination thereof to evaluate the fluorescent pigment combination. For example, in the case of implementing programmable logic in software, the fluorescent pigment combination evaluation can be implemented at least in part by a computer-readable data storage medium including program code, and the program code includes instructions that, when executed, can obtain the fluorescent pigment combination, the instrument identifier, and the spectral matrix associated with the fluorescent pigment combination and the instrument identifier. In addition, the instructions may calculate an inverse matrix based on the obtained spectral matrix, and identify fluorescent pigments in the fluorescent pigment combination that are associated with the variance of flow cytometer data generated using the fluorescent pigment combination by analyzing the calculated inverse matrix to evaluate the suitability of the fluorescent pigment combination for generating flow cytometer data.

[0147] In addition, the processor may optimize the fluorescent dye combination based on the evaluation of the suitability of the fluorescent dye combination for generating flow cytometer data. As described above, the combinatorial optimization algorithm for optimizing the fluorescent dye combination includes but is not limited to the constrained optimization method. In some embodiments, the processor may generate a visualization of the evaluated suitability of the fluorescent dye combination for generating flow cytometer data. For example, the visualization embodiment highlights the fluorescent dyes in the fluorescent dye combination that are associated with the variance of the flow cytometer data generated using this fluorescent dye combination. In some embodiments, the visualization includes a flow cytometer data diagram simulated based on a given fluorescent dye combination. In other words, the visualization includes exemplary flow cytometer data that would be generated if the sample was run on a specific instrument equipped with a specific fluorescent dye combination. In other versions, the visualization includes a table or matrix for quantifying the degree of association (e.g., generating variance and / or being affected by variance) of the fluorescent dyes in the fluorescent dye combination. In some such embodiments, the system includes a display for depicting the visualization. Any suitable display can be used. The subject display may include but is not limited to a monitor, a tablet computer, a smart phone, or other electronic device that provides a graphical interface.

[0148] The subject programmable logic may be implemented in any of a variety of devices, such as a specially programmed event processing computer, a wireless communication device, an integrated circuit device, or the like. In certain embodiments, the programmable logic may be executed by a specially programmed processor, which may include one or more processors, such as one or more digital signal processors (DSPs), configurable microprocessors, application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), or other equivalent integrated or discrete logic circuits. A combination of computing devices (e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, a combination of one or more microprocessors and a DSP core, or any other such configuration with at least partial data connectivity) may implement one or more features described herein.

[0149] In some instances, the system is or includes a particle analyzer. The target particle analyzer may include a flow chamber for transporting particles in a flow stream, a light source for irradiating the flow stream particles at an interrogation point, and a particle modulated light detector for detecting particle modulated light. In some 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.

[0150] As used herein, a "flow cell" in its conventional sense refers to an element, such as a small glass tube, comprising a flow channel with a liquid flow stream for transporting particles in a sheath fluid. The target small glass tube includes a container having a channel extending therethrough. The flow stream may include a liquid sample injected from a sample tube. The target flow chamber includes a flow channel that is permeable to light. In some cases, the flow chamber includes a transparent material (e.g., quartz) that allows light to pass through. In some embodiments, the flow chamber is an air flow flow chamber in which optical interrogation of the particles occurs outside the flow chamber (i.e., in free space).

[0151] In some cases, the flow stream can be irradiated with light from a light source at the interrogation point. The flow stream configured with a flow channel may include a liquid sample injected from a sample tube. In some embodiments, the flow stream may include a narrow and fast-flowing liquid stream, which is arranged in a manner that linearly separated particles transported in the liquid stream are separated from each other in a single column. The "interrogation point" discussed in the present invention refers to the area in the flow chamber in which the particles are irradiated with light from a light source (e.g., for analysis). The size of the interrogation point can vary as needed. For example, where 0 μm represents the axis of light emitted by the light source, the range of the interrogation point can be -100 μm-100 μm, such as -50 μm-50 μm, such as -25 μm-40 μm, including -15 μm-30 μm.

[0152] Particle modulated light is observed after the particles are irradiated in the flow chamber. By "particle modulated light" is meant light received from particles in a flow stream after the particles are irradiated 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 refracted and reflected from the surface and internal structure of a particle. In additional embodiments, the particle modulated light includes forward scattered light (i.e., light that passes through or around the particle primarily in a forward direction). In other cases, the particle modulated light includes fluorescence (i.e., light emitted from a fluorescent pigment after exposure to light of an excitation wavelength).

[0153] As described above, various aspects of the present invention also include a light source that can irradiate particles passing through the flow chamber at the interrogation point. The light source described in the present invention can be any suitable light source. In certain embodiments, the light source is a laser. In embodiments, the laser can be any suitable laser, such as a continuous wave laser. For example, the laser can be a diode laser, such as an ultraviolet diode laser, a visible diode laser, and a near-infrared diode laser. In other embodiments, the laser can be a helium-neon (HeNe) laser. In certain instances, 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-fluorine (ArF) excimer laser, a krypton-fluorine (KrF) excimer laser, a xenon-chlorine (XeCl) excimer laser, or a xenon-fluorine (XeF) excimer laser, or a combination thereof. In other instances, the flow cytometer of the present invention includes a dye laser, such as a stilbene, coumarin, or rhodamine laser. In other examples, the target laser includes a metal vapor laser, such as a helium cadmium (HeCd) laser, a helium mercury (HeHg) laser, a helium selenium (HeSe) laser, a helium silver (HeAg) laser, a strontium laser, a neon copper (NeCu) laser, a copper laser or a gold laser and combinations thereof. In other examples, the flow cytometer of the present invention includes a solid-state laser, such as a ruby ​​laser, a Nd:YAG laser, a NdCrYAG laser, an Er:YAG laser, a Nd:YLF laser, a Nd:YVO4 laser, a Nd:YCa4O(BO3)3 laser, a Nd:YCOB laser, a titanium sapphire laser, a thulium YAG laser, an ytterbium YAG laser, a Yb2O3 laser or a cerium-doped laser and combinations thereof.

[0154] According to some embodiments, the laser light source may further include one or more optical adjustment elements. In some embodiments, the optical adjustment element is located between the light source and the flow chamber and may include any device capable of changing the spatial width of the irradiation or some other characteristic of the light source irradiation (such as irradiation direction, wavelength, beam width, beam intensity and focus). The optical adjustment scheme may include any suitable device for adjusting one or more characteristics of the light source, including but not limited to lenses, reflectors, filters, optical fibers, wavelength separators, pinholes, slits, collimation schemes and combinations thereof. In some embodiments, the target flow cytometer includes one or more focusing lenses. In one example, the focusing lens can be a magnifying lens. In other embodiments, the target flow cytometer includes an optical fiber.

[0155] When the optical adjustment element is configured as a movable optical adjustment element, the optical adjustment element can be moved continuously or in discrete intervals, for example, in increments of 0.01 µm or greater, for example, in increments of 0.05 µm or greater, for example, in increments of 0.1 µm or greater, for example, in increments of 0.5 µm or greater, for example, in increments of 1 µm or greater, for example, in increments of 10 µm or greater, for example, in increments of 100 µm or greater, for example, in increments of 500 µm or greater, for example, in increments of 1 mm or greater, for example, in increments of 5 mm or greater, for example, in increments of 10 mm or greater, including increments of 25 mm or greater.

[0156] Any displacement scheme can be used to move the light adjustment element structure, such as coupling with a movable support table, or directly using a motor-driven translation stage, a guide rod translation assembly, a gear-type translation device, such as a stepper motor, a servo motor, a brushless motor, a brushed DC motor, a micro stepper drive motor, a high-resolution stepper motor and other types of motors.

[0157] The light source can be positioned at any suitable distance from the flow chamber, for example, the light source is separated from the flow chamber by 0.005 mm or more, for example, 0.01 mm or more, for example, 0.05 mm or more, for example, 0.1 mm or more, for example, 0.5 mm or more, for example, 1 mm or more, for example, 5 mm or more, for example, 10 mm or more, for example, 25 mm or more, including a distance of 100 mm or more. In addition, the light source can be positioned at any suitable angle relative to the flow chamber, for example, an angle in the range of 10°-90°, for example, 15°-85°, for example, 20°-80°, for example, 25°-75°, including 30°-60°, for example, 90° angle.

[0158] In some embodiments, the target light source includes multiple (e.g., 2 lasers or more, e.g., 3 lasers or more, e.g., 4 lasers or more, e.g., 5 lasers or more, e.g., 10 lasers or more, including 15 lasers or more) lasers that provide discrete irradiation lasers to the flow stream. Depending on the wavelength of light required to irradiate the flow stream, the specific wavelength of each laser can vary between 200 nm-1500 nm (e.g., 250 nm-1250 nm, 300 nm-1000 nm, 350 nm-900 nm, including 400 nm-800 nm). In some embodiments, the target laser may include one or more of a 405 nm laser, a 488 nm laser, a 561 nm laser, and a 635 nm laser.

[0159] As described above, the target particle analyzer may further include one or more particle modulated light detectors for detecting particle modulated light intensity data. In certain embodiments, the particle modulated light detector includes one or more forward scattered light detectors, which can detect forward scattered light. For example, the subject particle analyzer may include 1 forward scattered light detector or multiple forward scattered light detectors, such as 2 or more, such as 3 or more, such as 4 or more, including 5 or more. In certain embodiments, the particle analyzer includes 1 forward scattered light detector. In other embodiments, the particle analyzer includes 2 forward scattered light detectors.

[0160] The forward scattered light detector described in the present invention may use any suitable detector for detecting the collected light. The target detector may include, but is not limited to, an optical sensor or detector, such as an active pixel sensor (APS), an avalanche photodiode, an image sensor, a charge coupled device (CCD), an enhanced charge coupled device (ICCD), a light emitting diode, a photon counter, a bolometer, a pyroelectric detector, a photoresistor, a photovoltaic cell, a photodiode, a photomultiplier tube (PMT), a phototransistor, a quantum dot photoconductor or a photodiode and a combination thereof, and other detectors. In some embodiments, 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 is used to measure the collected light. In some embodiments, the detector is a photomultiplier tube, for example, the active detection surface area of ​​each region ranges from 0.01 cm 2 -10 cm 2 (e.g. 0.05 cm 2 -9 cm 2 , for example 0.1 cm 2 -8 cm 2 , for example 0.5 cm 2 -7 cm 2 , including 1cm 2 -5 cm 2 ) of the photomultiplier tube.

[0161] In embodiments, the forward scattered light detector may measure light continuously or at discrete time intervals. In some cases, the target detector measures the collected light continuously. In other cases, the target detector may measure light at discrete intervals, such as every 0.001 ms, every 0.01 ms, every 0.1 ms, every 1 ms, every 10 ms, every 100 ms (including every 1000 ms), or at other intervals.

[0162] In additional embodiments, the one or more particle modulated light detectors may include one or more side scattered light detectors for detecting the wavelength of side scattered light (i.e., light refracted and reflected from the surface and internal structure of the particle). In some embodiments, the particle analyzer includes a single side scattered light detector. In other embodiments, the particle analyzer includes multiple side scattered light detectors, such as 2 or more, such as 3 or more, such as 4 or more, including 5 or more.

[0163] The side scattered light detector described in the present invention may use any suitable detector for detecting the collected light. The target detector may include, but is not limited to, an optical sensor or detector, such as an active pixel sensor (APS), an avalanche photodiode, an image sensor, a charge coupled device (CCD), an enhanced charge coupled device (ICCD), a light emitting diode, a photon counter, a bolometer, a pyroelectric detector, a photoresistor, a photovoltaic cell, a photodiode, a photomultiplier tube (PMT), a phototransistor, a quantum dot photoconductor or a photodiode and a combination thereof, and other detectors. In some embodiments, 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 is used to measure the collected light. In some embodiments, the detector is a photomultiplier tube, for example, the active detection surface area of ​​each region ranges from 0.01 cm 2 -10 cm 2 (e.g. 0.05 cm 2 -9 cm 2 , for example 0.1 cm 2 -8 cm 2 , for example 0.5 cm 2 -7 cm 2 , including 1cm 2 -5 cm 2 ) of the photomultiplier tube.

[0164] In an embodiment, the subject particle analyzer further comprises a fluorescence detector, which can detect one or more fluorescence wavelengths. In other embodiments, the particle analyzer comprises a plurality of fluorescence detectors, such as 2 or more, such as 3 or more, such as 4 or more, 5 or more, 10 or more, 15 or more, including 20 or more.

[0165] The fluorescence detector described in the present invention may use any suitable detector for detecting the collected light. The target detector may include, but is not limited to, an optical sensor or detector, such as an active pixel sensor (APS), an avalanche photodiode, an image sensor, a charge coupled device (CCD), an enhanced charge coupled device (ICCD), a light emitting diode, a photon counter, a bolometer, a pyroelectric detector, a photoresistor, a photovoltaic cell, a photodiode, a photomultiplier tube (PMT), a phototransistor, a quantum dot photoconductor or a photodiode and a combination thereof, and other detectors. In some embodiments, 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 is used to measure the collected light. In some embodiments, the detector is a photomultiplier tube, for example, the active detection surface area of ​​each region ranges from 0.01 cm 2 -10 cm 2 (e.g. 0.05 cm 2 -9 cm 2 , for example 0.1 cm 2 -8 cm 2 , for example 0.5 cm 2 -7 cm 2 , including 1 cm 2 -5cm 2 ) of the photomultiplier tube.

[0166] When the subject particle analyzer includes multiple fluorescence detectors, each fluorescence detector can be identical, or the collection of fluorescence detectors can be a combination of different types of detectors. For example, when the subject particle analyzer includes two fluorescence detectors, in some embodiments, the first fluorescence detector is a CCD type device and the second fluorescence detector (or imaging sensor) is a CMOS type device. In other embodiments, the first and second fluorescence detectors are both CCD type devices. In other embodiments, the first and second fluorescence detectors are both CMOS type devices. In other embodiments, the first fluorescence detector is a CCD type device and the second fluorescence detector is a photomultiplier tube (PMT). In other embodiments, the first fluorescence detector is a CMOS type device and the second fluorescence detector is a photomultiplier tube. In other embodiments, the first and second fluorescence detectors are both photomultiplier tubes.

[0167] In embodiments of the present disclosure, the fluorescence detector measures the collected light at one or more wavelengths (e.g., 2 or more wavelengths, 5 or more different wavelengths, 10 or more different wavelengths, 25 or more different wavelengths, 50 or more different wavelengths, 100 or more different wavelengths, 200 or more different wavelengths, 300 or more different wavelengths), including measuring light emitted by a sample in a flow stream at 400 or more different wavelengths. In certain embodiments, two or more detectors in a particle analyzer according to the present invention may measure the same or overlapping wavelengths of collected light.

[0168] In some embodiments, the target fluorescence detector can measure light collected within a certain wavelength range (e.g., 200 nm-1000 nm). In some embodiments, the target detector can collect a spectrum of light within a certain wavelength range. For example, a particle analyzer may include one or more detectors that can collect one or more spectra of light within a wavelength range of 200 nm-1000 nm. In other embodiments, the target detector can 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 that can measure light at wavelengths of 450 nm, 518 nm, 519 nm, 561 nm, 578 nm, 605 nm, 607 nm, 625 nm, 650 nm, 660 nm, 667 nm, 670 nm, 668 nm, 695 nm, 710 nm, 723 nm, 780 nm, 785 nm, 647 nm, 617 nm, and any combination thereof. In some embodiments, one or more detectors can be paired with a specific fluorescent group, such as a fluorescent group used with a sample in a fluorescence analysis method.

[0169] In some embodiments, the particle analyzer includes one or more wavelength separators, which are located between the flow chamber and the particle modulated light detector. The term "wavelength separator" used in the present invention refers to an optical element in its conventional sense that can separate the light collected from the sample into a predetermined spectral range. In some embodiments, the particle analyzer includes a single wavelength separator. In other embodiments, the particle analyzer includes a plurality of wavelength separators, such as 2 or more wavelength separators, such as 3 or more, such as 4 or more, such as 5 or more, such as 6 or more, such as 7 or more, such as 8 or more, such as 9 or more, such as 10 or more, such as 15 or more, such as 25 or more, such as 50 or more, such as 75 or more, including 100 or more wavelength separators. In some embodiments, the wavelength separator can separate the light collected from the sample into a predetermined spectral range by allowing light with a predetermined spectral range to pass and reflecting light of one or more remaining spectral ranges. In other embodiments, the wavelength separator can separate the light collected from the sample into a predetermined spectral range by allowing light with a predetermined spectral range to pass and absorbing light of one or more remaining spectral ranges. In other embodiments, the wavelength separator can diffract the light collected from the sample into a predetermined spectral range in space. Each wavelength separator can be any suitable light separation scheme, such as one or more dichroic mirrors, bandpass filters, diffraction gratings, beam splitters or prisms. In some embodiments, the wavelength separator is a prism. In other embodiments, the wavelength separator is a diffraction grating. In certain embodiments, the wavelength separator in the optical detection system of the present invention is a dichroic mirror.

[0170] Suitable flow cytometry systems may include, but are not limited to, those described in Ormerod (ed.), Flow Cytometry: A Practical Approach, Oxford University Press (1997); Jaroszeski et al. (eds.), Flow Cytometry Protocols, Methods in Molecular Biology 91, Humana Press (1997); Practical Flow Cytometry, 3rd ed., Wiley-Liss (1995); Virgo et al. (2012), Annals of Clinical Biochemistry Jan;49(pt 1):17-28; Linden et al., Seminars in Thrombosis and Hemostasis, Oct 2004;30(5):502-11; Alison et al., J Pathol, Dec 2010;222(4):335-344; and Herbig et al. (2007), Critical Reviews of Therapeutic Drug Delivery Systems 24(3):203-255; the disclosures of the above references are incorporated herein by reference.In certain examples, the flow cytometry systems of interest include BD Biosciences FACSCanto™ flow cytometer, BD Biosciences FACSCanto™ II flow cytometer, BD Accuri™ flow cytometer, BD Accuri™ C6Plus 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 BD Biosciences FACSCalibur™ cell sorter, BD Biosciences FACSCount™ cell sorter, BD Biosciences FACSLyric™ cell sorter, 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.

[0171] In certain embodiments, the system of the invention is a flow cytometric system, such as the systems described in the following U.S. Patents: 10,663,476; 10,620,111; 10,613,017; 10,605,713; 10,585,031; 10,578,542; 10,578,469; 10,48 No. 1,074; No. 10,302,545; No. 10,145,793; No. 10,113,967; No. 10,006,852; No. 9,952,076; No. 9,933,341; No. 9,726,527; No. 9,453,789; No. 9,200,334; No. 9,097,640; No. 9, No. 095,494; No. 9,092,034; No. 8,975,595; No. 8,753,573; No. 8,233,146; No. 8,140,300; No. 7,544,326; No. 7,201,875; No. 7,129,505; No. 6,821,740; No. 6,813,017; No. 6,80 No. 9,804; No. 6,372,506; No. 5,700,692; No. 5,643,796; No. 5,627,040; No. 5,620,842; No. 5,602,039; No. 4,987,086; No. 4,498,766; the disclosures of the aforementioned U.S. patents are incorporated herein by reference in their entirety and become a part of the present invention.

[0172] In some cases, the flow cytometry system of the present invention can image particles in the flow cell stream by using fluorescence imaging of radio frequency labeled emission (FIRE), as described in the following references: Diebold et al., Nature Photonics 7(10); 806-810 (2013) and 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; 10,451,5 No. 38; No. 10,620,111; and U.S. Patent Publications Nos. 2017 / 0133857; 2017 / 0328826; 2017 / 0350803; 2018 / 0275042; 2019 / 0376895 and 2019 / 0376894; the disclosures of the said U.S. Patent Publications are incorporated herein by reference.

[0173] Fig. 9A system 900 for flow cytometry according to an illustrative embodiment of the invention is shown. 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.

[0174] The excitation lasers 915a-c emit light in the form of laser beams. Fig. 9 In the example system of FIG. 1 , the wavelengths of the laser beams emitted from the excitation lasers 915a-915c are 488 nm, 633 nm, and 325 nm, respectively. The laser beams are first directed through one or more beam splitters 945a and 945b. Beam splitter 945a transmits light having a wavelength of 488 nm and reflects light having a wavelength of 633 nm. Beam splitter 945b transmits ultraviolet light (light having a wavelength in the range of 10-400 nm) and reflects light having a wavelength of 488 nm and 633 nm.

[0175] The laser beam is then directed to a focusing lens 920 which focuses the beam onto the portion of the fluid stream where the sample particles are located in a flow chamber 925. The flow chamber is the portion of the fluid system that directs the particles of the flow stream (usually one at a time) to the focused laser beam for interrogation. The flow chamber may include a flow chamber in a benchtop cytometer or a nozzle tip in an air flow cytometer.

[0176] Light from the laser beam interacts with particles in the sample at various wavelengths by diffraction, refraction, reflection, scattering, and absorption and re-emission, depending on the characteristics of the particles, such as size, internal structure, and the presence or absence of one or more fluorescent molecules attached to or naturally present on or in the particles. Fluorescent emission, as well as diffracted, refracted, reflected, and scattered light, can be directed to one or more of a forward scatter detector 930 and a side scatter detector 935, and one or more fluorescence detectors 960a-960f, via one or more of beam splitters 945c-945g, bandpass filters 950a-950e, longpass filters 955a-955b, and fluorescence collection lens 940.

[0177] Fluorescence collection lens 940 collects light emitted by the interaction of the particles with the laser beam and directs it to one or more beam splitters and filters. Bandpass filters (such as bandpass filters 950a-950e) allow a narrow range of wavelengths 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. Therefore, a 510 / 20 filter extends 10 nm on each side of the center of the spectral band, or extends from 500 nm to 520 nm. Short-pass filters transmit light less than or equal to a specified wavelength. Long-pass filters (such as long-pass filters 955a-955b) transmit light with a wavelength greater than or equal to a specified light wavelength. For example, long-pass filter 955b (i.e., a 670 nm long-pass filter) transmits light greater than or equal to 670 nm. Filters are typically selected to optimize the specificity of the detector for a particular fluorescent dye. The filter can cause the spectral band of light transmitted to the detector to be close to the emission peak of the fluorescent dye.

[0178] The forward scatter detector 930 is slightly offset from the axis of the direct beam through the flow chamber, detecting diffracted light and excitation light that mainly propagates through or around the particles in the forward direction. The intensity of the light detected by the forward scatter detector depends on the overall size of the particle. The forward scatter detector may include a photodiode. The side scatter detector 935 can detect refracted light and reflected light from the surface and internal structure of the particle, and the refracted light and reflected light tend to increase with the increase of the complexity of the particle structure. One or more fluorescence detectors 960a-960f can be used to detect the fluorescent emission from the fluorescent molecules associated with the particles. The side scatter detector 935 and the fluorescence detector may include a photomultiplier tube. The signals detected at the forward scatter detector 930, the side scatter detector 935 and the fluorescence detector can be converted into electronic signals (voltage) by the detector. This data can provide information about the sample.

[0179] Those skilled in the art will appreciate that the flow cytometer according to the embodiments of the present invention is not limited to Fig. 9 The flow cytometer shown, but can include any flow cytometer known in the art.For example, the flow cytometer can have any number of lasers, beam splitters, filters and detectors of various wavelengths and various different configurations.

[0180] During operation, the flow cytometer is controlled by a controller / processor 990, and measurement data from the detector may 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 output signals therefrom, and may also be coupled to the electrical and electromechanical components of the flow cytometer 910 to control lasers, fluid flow parameters, etc. The system may also have input / output (I / O) capabilities 997. The memory 995, the controller / processor 990, and the I / O 997 may all be integral parts of the flow cytometer 910. In such embodiments, the display may also constitute part of the I / O capabilities 997 for providing experimental data to a user of the cytometer 910. Alternatively, some or all of the memory 995 and the controller / processor 990 and the I / O capabilities may be part of one or more external devices (e.g., a general purpose computer). In some embodiments, some or all of the memory 995 and the controller / processor 990 may communicate wirelessly or wired with the cytometer 910. Controller / processor 990, together with memory 995 and I / O 997, performs various functions related to the preparation and analysis of flow cytometric experiments.

[0181] Fig. 9 The system shown includes six different detectors that detect fluorescence in six different wavelength bands (which may be referred to as "filter windows" for a given detector in the present invention) as defined by the configuration of filters and / or beam splitters in the beam path from the flow chamber 925 to each detector. Different fluorescent molecules in the panel of fluorochromes used in the flow cytometer experiment will emit light in their own unique wavelength bands. In order to be generally consistent with the filter windows of the detectors, the specific fluorescent labels and their associated fluorescence emission bands used in the experiment can be selected. I / O 997 can receive data about a flow cytometer experiment, which includes a set of fluorescent labels and multiple cell populations with multiple labels, each cell population having a subset of the multiple labels. I / O 997 can also receive biological data that assigns one or more labels to one or more cell populations, label density data, emission spectrum data, data that assigns labels to one or more labels, and cytometer configuration data. Flow cytometer experiment data, such as label spectral characteristics and flow cytometer configuration data, can also be stored in memory 995. Controller / processor 990 can evaluate one or more assignment results of labels in markers.

[0182] In some embodiments, the subject system is a particle sorting system that can sort particles using a closed particle sorting module, such as the particle sorting module described in U.S. Patent Publication No. 2017 / 0299493 filed on March 28, 2017, the disclosure of which is incorporated by reference in this invention and becomes a part of this invention. In some embodiments, a sorting decision module having multiple sorting decision units is used to sort particles (e.g., cells) of a sample, such as the sorting decision module described in U.S. Patent Publication No. 2020 / 0256781 filed on December 23, 2019, the disclosure of which is incorporated by reference in this invention and becomes a part of this invention. In some embodiments, the sample component sorting system includes a particle sorting module having a deflection plate, such as described in U.S. Patent Publication No. 2017 / 0299493 filed on March 28, 2017, the disclosure of which is incorporated by reference in this invention and becomes a part of this invention.

[0183] Fig.10 1 is a functional block diagram of one example of a control system, such as processor 1000, for analyzing and displaying biological events. Processor 1000 may implement various processes for controlling a graphical display of biological events.

[0184] The flow cytometer or sorting system 1002 can collect biological event data. For example, the flow cytometer can generate flow cytometric event data (e.g., particle modulated light data). The flow cytometer 1002 can provide the biological event data to the processor 1000. A data communication channel can be provided between the flow cytometer 1002 and the processor 1000. The biological event data can be provided to the processor 1000 through the data communication channel.

[0185] Processor 1000 may receive biological event data from flow cytometer 1002. The biological event data received from flow cytometer 1002 may include flow cytometric event data. Processor 1000 may provide a graphical display to display device 1006, including a first graph of the biological event data. Processor 1000 may further present a target region as a gate (e.g., a first gate) around a set of biological event data overlaid on the first graph as shown by display device 1006. In some embodiments, the gate may be a logical combination of one or more target graphical regions plotted on a single parameter histogram or a bivariate graph. In some embodiments, the display may be used to display particle parameters or saturated detector data.

[0186] Processor 1000 may further display the biological event data inside the gate on display device 1006, which is different from other events in the biological event data outside the gate. For example, processor 1000 may present the biological event data contained inside the gate in a color different from the color of the biological event data outside the gate. Display device 1006 may be implemented as a monitor, a tablet computer, a smart phone, or other electronic device that presents a graphical interface.

[0187] The processor 1000 may receive a door selection signal from a first input device identifying a door. For example, the first input device may be implemented as a mouse 1010. The mouse 1010 may send a door selection signal to the processor 1000 to identify a door to be displayed on the display device 1006 or manipulated through the display device (e.g., when the cursor is on or in the desired door, just click here). In some embodiments, the first device may be implemented as a keyboard 1008 or other device for providing input signals to the processor 1000, such as a touch screen, a stylus, an optical detector, or a voice recognition system. Some input devices may include multiple input functions. In such embodiments, each input function may be considered an input device. For example, if Fig.10 As shown, the mouse 1010 may include a right mouse button and a left mouse button, each of which may generate a trigger event.

[0188] The triggering event may cause the processor 1000 to change the manner in which the data is displayed and the portion of the data actually displayed on the display device 1006, and / or provide input information for further processing, such as selecting a target population for particle sorting.

[0189] In some embodiments, processor 1000 may detect when gate selection is initiated by mouse 1010. Processor 1000 may further automatically modify the chart visualization to facilitate the gating process. The modification may be based on a particular distribution of bio-event data received by processor 1000. In some embodiments, processor 1000 expands the first gate to generate a second gate (e.g., as described above).

[0190] Processor 1000 may be connected to storage device 1004. Storage device 1004 may receive and store bio-event data from processor 1000. Storage device 1004 may also receive and store flow cytometry event data from processor 1000. Storage device 1004 may further allow retrieval of bio-event data, such as flow cytometry event data, by processor 1000.

[0191] The display device 1006 can receive display data from the processor 1000. The display data can include a graph of the biological event data and gates outlining portions of the graph. The display device 1006 can further change the information presented based on input information received from the processor 1000, in combination with input information from the flow cytometer 1002, the storage device 1004, the keyboard 1008, and / or the mouse 1010.

[0192] In addition, the processor 1000 can also evaluate the fluorescent dye combination. In this case, the processor 1000 can obtain the fluorescent dye combination, the instrument identifier, and the spectrum matrix associated with the fluorescent dye combination and the instrument identifier. The processor 1000 can also calculate the inverse matrix based on the obtained spectrum matrix, and identify the fluorescent pigments in the fluorescent pigment combination that are associated with the flow cytometer data variance generated using the fluorescent pigment combination by analyzing the calculated inverse matrix to evaluate the applicability of the fluorescent pigment combination for generating flow cytometer data. In some cases, the processor 1000 can also generate visualization based on the fluorescent pigment combination evaluation. In some cases, this visualization can be displayed on the display device 1006.

[0193] In some embodiments, the processor 1000 may generate a user interface to receive sample events for sorting. For example, the user interface may include a mechanism for receiving sample events or sample images. Sample events or images or sample gates may be provided before collecting event data for a sample, or based on an initial set of events for a portion of the sample.

[0194] Fig.11A is a schematic diagram of a particle sorter system 1100 (eg, flow cytometer 502) according to one embodiment of the present invention. In some embodiments, the particle sorter system 1100 is a cell sorter system. Fig.11A As shown, a droplet formation sensor 1102 (e.g., a piezoelectric oscillator) is coupled to a fluid conduit 1101, which may be coupled to, may include, or may be a nozzle 1103. Within the fluid conduit 1101, a sheath fluid 1104 fluidically focuses a sample fluid 1106 containing particles 1109 into a moving liquid column 1108 (e.g., a liquid stream). Within the moving liquid column 1108, particles 1109 (e.g., cells) are arranged in a single file, traverse a monitoring region 1111 (e.g., where a laser and a liquid stream meet), and are irradiated by an irradiation source 1112 (e.g., a laser). The droplet formation sensor 1102 vibrates, causing the moving liquid column 1108 to break into a plurality of droplets 1110, some of which contain particles 1109.

[0195] During operation, the time at which a target particle (or target cell) traverses the monitoring region 1111 is determined by a detection station 1114 (e.g., an event detector). The detection station 1114 feeds into a timing circuit 1128, which in turn feeds into a flash charging circuit 1130. At the droplet breakoff point, the moving liquid column 1108 can be flash charged, prompted by a timed droplet delay (Δt), to charge the target droplet. The target droplet may include one or more particles or cells to be sorted. The charged droplets may then be sorted by activating a deflection plate (not shown) to deflect the charged droplets into a container such as a collection tube or a multi-well or micro-porous sample plate, in which a well or micro-well may be associated with a particular target droplet. As Fig.11A As shown, the droplets can be collected in a discharge container 1138.

[0196] As target particles pass through monitoring zone 1111, a detection system 1116 (e.g., a droplet boundary detector) functions to automatically determine the phase of the droplet drive signal. An exemplary droplet boundary detector is described in U.S. Patent No. 7,679,039, which is incorporated herein by reference in its entirety. The detection system 1116 allows the instrument to accurately calculate the position of each detected particle in the droplet. The detection system 1116 can feed an amplitude signal 1120 and / or a phase signal 1118, which in turn (via amplifier 1122) is fed into an amplitude control circuit 1126 and / or a frequency control circuit 1124. The amplitude control circuit 1126 and / or the frequency control circuit 1124 in turn controls the droplet formation sensor 1102. The amplitude control circuit 1126 and / or the frequency control circuit 1124 can be included in the control system.

[0197] In some embodiments, the sorting electronics (e.g., detection system 1116, detection station 1114, and processor 1140) may be coupled to a memory that can store detected events and sorting decisions based on the events. The sorting decision can be included in the event data of the particle. In some embodiments, the detection system 1116 and the detection station 1114 can be implemented as a single detection unit or can be coupled in a communication manner so that event measurements can be collected by the detection system 1116 or the detection station 1114 and provided to a non-collecting component.

[0198] Fig. 11B is a schematic diagram of a particle sorter system according to an embodiment of the present invention. Fig. 11B The particle sorter system 1100 shown includes deflection plates 1152 and 1154. Charge can be applied through the liquid flow charging wires in the barbs. This forms a droplet stream 1110 containing particles to be analyzed 1109. The particles can be irradiated with one or more light sources (such as lasers) to generate light scattering and fluorescence information. The particles can be detected by sorting electronics or other detection systems ( Fig. 11B The deflector plates 1152 and 1154 can be independently controlled to attract or repel charged droplets and direct the droplets to a destination collection container (e.g., one of 1172, 1174, 1176, or 1178). Fig. 11B As shown, the deflection plates 1152 and 1154 can be controlled to direct the particles to flow along the first path 1162 to the container 1174 or along the second path 1168 to the container 1178. If the particle is not a target particle (e.g., does not show scattering or illumination information within a specified sorting range), the deflection plates can allow the particle to continue to flow along the flow path 1164. Such uncharged droplets can enter a waste container via an aspirator 1170 or the like.

[0199] Sorting electronics may be included to initiate measurement collection, receive the fluorescence signal from the particles, and determine how to adjust the deflection plates to achieve particle sorting. Fig. 11B Example implementations of the illustrated embodiments include BD FACSAria™ series flow cytometers available from BD Biosciences (Franklin Lakes, NJ).

[0200] Computer readable storage medium

[0201] Various aspects of the present disclosure further include a non-transient computer-readable storage medium, which stores instructions for implementing the subject method. The computer-readable storage medium can be used to implement one or more computers for the full automation or partial automation of the system for implementing the method of the present invention. In certain embodiments, the instructions described in the method according to the present invention can be encoded in the form of "programming" on a computer-readable medium, wherein the term "computer-readable medium" refers to any non-transient storage medium that participates in the process of providing instructions and data to a computer for execution and processing. In some cases, the instructions can obtain a fluorescent dye combination, an instrument identifier, and a spectral matrix associated with the fluorescent dye combination and the instrument identifier when executed. In addition, the instructions can calculate an inverse matrix based on the obtained spectral matrix, and identify the fluorescent pigments in the fluorescent pigment combination that are associated with the variance of the flow cytometer data generated using the fluorescent pigment combination by analyzing the calculated inverse matrix to evaluate the applicability of the fluorescent pigment combination for generating flow cytometer data.

[0202] Examples of suitable non-transitory storage media include floppy disks, hard disks, optical disks, magneto-optical disks, CD-ROMs, CD-Rs, magnetic tapes, non-volatile memory cards, ROMs, DVD-ROMs, Blu-ray disks, solid-state disks, flash drives, and network attached storage (NAS) (whether such devices are internal or external to the computer). Files containing information may be "stored" on computer-readable media, where "storing" means recording the information so that the computer can later access and retrieve the information. When executing the computer-implemented methods described in the present invention, programming may be performed in one or more of any number of computer programming languages. Such languages ​​include Java, Python, Visual Basic, and C++, among others.

[0203] In some embodiments, the target computer-readable storage medium includes a computer program stored thereon, wherein, when loaded on a computer, the computer program includes instructions for obtaining a fluorescent dye combination, an instrument identifier, and a spectral matrix associated with the fluorescent dye combination and the instrument identifier. In addition, the instructions also include calculating an inverse matrix based on the obtained spectral matrix, and identifying fluorescent dyes in the fluorescent dye combination that are associated with the variance of flow cytometer data generated using the fluorescent dye combination by analyzing the calculated inverse matrix to evaluate the suitability of the fluorescent dye combination for generating flow cytometer data.

[0204] Computer control system

[0205] Various aspects of the present disclosure further include a computer control system, wherein the system includes one or more computers for implementing full automation or partial automation. In certain embodiments, the system includes a computer storing a computer program, wherein, when loaded on the computer, the computer program includes instructions for obtaining a fluorescent dye combination, an instrument identifier, and a spectral matrix associated with the fluorescent dye combination and the instrument identifier. In addition, the instructions also include calculating an inverse matrix based on the obtained spectral matrix, and identifying the fluorescent pigments in the fluorescent pigment combination that are associated with the variance of the flow cytometer data generated using the fluorescent pigment combination by analyzing the calculated inverse matrix to evaluate the suitability of the fluorescent pigment combination for generating flow cytometer data.

[0206] The system may include a display and an operator input device. For example, the operator input device may be a keyboard, a mouse, etc. The processing module includes a processor that can access a memory containing instructions for executing the steps of the subject method. The processing module may include an operating system, a graphical user interface (GUI) controller, a system memory, a memory storage device, an input and output controller, a cache memory, a data backup unit, and many other devices. The processor may be a commercially available processor or may be another processor that is available now or in the future. The processor executes an operating system that interfaces with firmware and hardware in a known manner to help the processor coordinate and execute the functions of various computer programs, which may be in various programming languages, such as Java, Perl, C++, Python, other high-level or low-level languages ​​known in the art, and combinations thereof. The operating system typically cooperates with the processor to coordinate and execute the functions of other components of the computer. The operating system also provides scheduling, input and output control, file and data management, memory management, and communication control and related services, all of which are based on known technologies. In some embodiments, the processor includes analog electronics that provide feedback control (e.g., negative feedback control).

[0207] The system memory may be any of a variety of known or future memory storage devices. Examples include any commonly used random access memory (RAM), magnetic media (such as a resident hard disk or tape), optical media (such as a read-write optical disk), flash memory devices, or other memory storage devices. The memory storage device may be any of a variety of known or future devices, including an optical disk drive, a tape drive, or a floppy disk drive. Such memory storage devices typically read and / or write to program storage media (not shown) such as optical disks. Any of these program storage media, or other program storage media currently in use or that may be developed in the future, may be considered a computer program product. It should be understood that these program storage media typically store computer software programs and / or data. Computer software programs (also known as computer control logic) are typically stored in system memory and / or program storage devices used in conjunction with memory storage devices.

[0208] In some embodiments, the present invention describes a computer program product including a computer usable medium containing control logic (computer software program, including program code). When executed by a processor and a computer, the control logic causes the processor to perform the functions described in the present invention. In other embodiments, some functions are implemented in hardware, for example, primarily through a hardware state machine. It will be apparent to a person skilled in the relevant art that implementing a hardware state machine can perform the functions described in the present invention.

[0209] The memory may be any suitable device for storing and retrieving data from the processor, such as a magnetic, optical or solid-state storage device (including a disk, optical disk, tape or RAM, or any other suitable fixed or portable device). The processor may include a general-purpose digital microprocessor, which is appropriately programmed by a computer-readable medium carrying the necessary program code. The programming may be provided to the processor remotely via a communication channel, or previously stored in a computer program product (such as a memory or other portable or fixed computer-readable storage medium using any memory-related device). For example, a disk or optical disk may carry the programming and can be read by a disk writer / reader. The system of the present invention also includes (for example) programming of the algorithm for implementing the above-mentioned method in the form of a computer program product. The programming according to the present invention may 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-ROM; electrical storage media, such as RAM and ROM; portable flash drives; and hybrid media of these categories, such as magnetic / optical storage media.

[0210] The processor may also access a communication channel to communicate with a user at a remote location. A remote location is one where the user is not directly interacting with the system and relays input information to the input manager from an external device such as a computer connected to a wide area network ("WAN"), telephone network, satellite network, or any other suitable communication channel, including a mobile phone (i.e., smart phone).

[0211] In some embodiments, the system according to the present disclosure may include a communication interface. In some embodiments, the communication interface includes a receiver and / or a transmitter for communicating with a network and / or another device. The communication interface can be used for wired or wireless communication, including but not limited to radio frequency (RF) communication (such as radio frequency identification (RFID)), Zigbee communication protocol, Wi-Fi, infrared, wireless universal serial bus (USB), ultra-wideband (UWB), Bluetooth ® communication protocols, and cellular communications, such as Code Division Multiple Access (CDMA) or Global System for Mobile Communications (GSM).

[0212] In one embodiment, the communication interface may 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 (e.g., in a doctor's office or hospital environment) and other external devices such as a computer terminal for similar supplemental data communications.

[0213] In one embodiment, the communication interface is used for infrared communication, Bluetooth ®communications or any other suitable wireless communication protocol that enables the subject system to communicate with computer terminals and / or networks, other devices such as mobile phones that support communications, personal digital assistants, or any other communication devices available to users for use in conjunction with them.

[0214] In one embodiment, the communication interface provides a connection for data transfer using the Internet Protocol (IP) through a cellular network, a 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 on a Wi-Fi hotspot.

[0215] In one embodiment, the subject system communicates via a communication interface (e.g., using 802.11 or Bluetooth ® The server device may be another portable device, such as a smart phone, a personal digital assistant (PDA), or a laptop computer; or a larger device, such as a desktop computer, a home appliance, etc. In some embodiments, the server device has a display, such as a liquid crystal display (LCD), and an input device, such as a button, a keyboard, a mouse, or a touch screen.

[0216] In certain embodiments, the communication interface automatically or semi-automatically transfers data stored in the subject system (eg, optional data storage unit) via a network or server using one or more of the communication protocols and / or mechanisms described above.

[0217] The output controller may include a controller for any of various known display devices, which are used to present information to a user (whether a person or a machine, whether local or remote). If one of the display devices provides visual information, the information can usually be logically and / or physically organized into a pixel array. A graphical user interface (GUI) controller may include any of various known or future software programs for providing a graphical input and output interface between the system and the user and processing user input information. The functional components of the computer can communicate with each other through a system bus, and in alternative embodiments, some of the communications can be completed using a network or other types of remote communications. The output manager can also provide information generated by the processing module to a user at a remote location according to known techniques, such as through the Internet, telephone or satellite network. The presentation of data by the output manager can be based on various known techniques. For example, the data may include SQL, HTML or XML documents, emails or other files or other forms of data. The data may include an Internet URL address so that the user can retrieve more SQL, HTML, XML or other documents or data from a remote source. Although one or more platforms present in the subject system generally belong to the general category of computers commonly known as servers, they can also be any type of known computer platform or a type developed in the future. However, they may also be rack computers, workstations, or other computer types. These platforms may be networked or otherwise connected by any known or future type of wiring or other communication system, including wireless systems. They may be co-located or physically separate. A variety of operating systems may be applied to any computer platform, which may depend on the type and / or model of computer platform selected. Suitable operating systems include Windows ® NT ® , Windows ® XP, Windows ® 7. Windows ® 8. Windows ® 10. iOS ® , macOS ® , Linux ® , Ubuntu ® , Fedora ® 、OS / 400 ® 、i5 / OS ® 、IBM i ® 、Android™、SGI IRIX ® , Oracle Solaris ® wait.

[0218] Fig.12The general architecture of an example computing device 1200 is shown in accordance with certain embodiments. Fig.12 The general architecture of the computing device 1200 shown includes an arrangement of computer hardware and software components. However, it is not necessary to show all general conventional components for public purposes. As shown, the computing device 1200 includes a processing unit 1210, a network interface 1220, a computer-readable medium drive 1230, an input / output device interface 1240, a display 1250 and an input device 1260, all of which can communicate with each other in the form of a communication bus. The network interface 1220 can provide connectivity with one or more networks or computing systems. Therefore, the processing unit 1210 can receive information and instructions from other computing systems or services through the network. The processing unit 1210 can also communicate with the memory 1270 and further provide output information to the optional display 1250 through the input / output device interface 1240. For example, analysis software (such as FlowJo) stored as executable instructions in the non-transient memory of the analysis system ® The input / output device interface 1240 may also receive input information from an optional input device 1260 (e.g., a keyboard, mouse, digital pen, microphone, touch screen, gesture recognition system, voice recognition system, game controller, accelerometer, gyroscope, or other input device).

[0219] Memory 1270 may contain computer program instructions (grouped in modules or components in some embodiments) executed by processing unit 1210 to implement one or more embodiments. Memory 1270 generally includes RAM, ROM, and / or other permanent, secondary, or non-transitory computer-readable media. Memory 1270 may store an operating system 1272 that provides computer program instructions for use by processing unit 1210 in general management and operation of computing device 1200. Data may be stored in data storage device 1290. Memory 1270 may further include computer program instructions and other information for implementing various aspects of the present disclosure.

[0220] Useful functions

[0221] The methods, systems, and computer-readable media of the present invention can be used in situations where it is necessary to automatically determine several groups of usable fluorescent dye combinations for particle analysis (such as flow cytometry). In some cases, the present invention is particularly suitable for experimental design of full-spectrum (i.e., "spectral") flow cytometry combinations. In other words, by specifying whether a group of dyes can be used at the same time, the present invention can be used as the first step in spectral combination design. In some instances, the methods, systems, and computer-readable media described in the present invention help determine which group (groups) of fluorescent dyes are likely to provide the best quality data (e.g., maximum biological resolution). The present invention can achieve the above purposes by automatically optimizing the algorithm, using the spectral characteristics of the fluorescent dyes as a ready-made and easily measured input to the algorithm, and using the spectral matrix as a heuristic optimization with high computational efficiency.

[0222] Embodiments of the present invention can be used in applications where it may be necessary to use cells made from biological samples for research, laboratory testing, or treatment. In some embodiments, the subject methods and apparatus can help obtain single cells made from target fluid or tissue biological samples. For example, the subject methods and systems help obtain cells from fluid or tissue samples for use as research or diagnostic samples for diseases such as cancer. Similarly, the subject methods and systems can help obtain cells from fluid or tissue samples for treatment. Compared to traditional flow cytometry systems, the methods and apparatus described in the present disclosure allow cells to be separated and collected from biological samples (e.g., organs, tissues, tissue fragments, fluids) with high efficiency and low cost.

[0223] Kits

[0224] Various aspects of the present disclosure further include kits, wherein the kits include instructions and / or programmable logic for performing the claimed methods. For example, the kits include programming that can evaluate and optionally optimize fluorescent dye combinations (e.g., as described in the methods section above), such as in the form of a computer-readable medium (e.g., a flash drive, USB storage, optical disk, DVD, Blu-ray disk, etc.) or downloading the programming instructions from an Internet network protocol or cloud server.

[0225] The kit may further include instructions for implementing the subject method. These instructions may be present in the subject kit in various forms, wherein one or more forms may be present in the kit. One form of these instructions is information printed on a suitable medium or substrate (e.g., one or more sheets of paper with information printed on them), in the kit packaging, in a package insert, etc. Another form of these instructions is a computer-readable medium containing information, such as a floppy disk, a compact disk (CD), a portable flash drive, etc. Another form of these instructions is a URL that can be used to access the information on the removed site via the Internet.

[0226] The following examples are provided by way of illustration and not limitation:

[0227] Experimental Examples

[0228] Example 1

[0229] The raw flow cytometry data (VioBright R667 single stain control) were investigated to understand three different types of unmixing-dependent diffusion: 1) unexpected negative diffusion; 2) unexpected overflow diffusion; and 3) skewed double negatives. Each of these diffusion manifestations reduces the biological resolution in the composite. Regarding 1), the raw data showed that when using the 40C matrix ( Fig. 13B ) and 25C matrix ( Fig.13A ) unmixed, the variance of negative cells in the unmixed VioBrightR667 channel was significantly higher. Regarding 2), the same raw data showed that in the 40C combination ( Fig. 14B ) and 25C combination ( Fig.14A ) when unmixed in R718, the spillover diffusion from R718 to VioBright R667 was significantly higher. Regarding 3), the double negative population was significantly higher in the complete 40C combination ( Figures 15A-15B ) show an extreme degree of negative covariance, which does not reflect the true underlying biological expression.

[0230] Example 2

[0231] Fluorochrome combinations were investigated for "hot spots," i.e., fluorochromes with emission spectra that, when used together in a combination, produce an unmixing-dependent spread of the type described above. Hot spots can be located in and around certain "spectral neighborhoods." For example, if one fluorochrome and another have similar spectra such that overlap is possible, one can be considered to be in the spectral neighborhood of the other. Two hot spots were evaluated: one in the APC spectral neighborhood (hot spot 1) and the other in the BB700 spectral neighborhood (hot spot 2). SparkNIR685 and VioR667 can be considered part of the APC spectral neighborhood, while PerCP and PerCP-Cy5.5 can be considered part of the BB700 spectral neighborhood. Flow cytometric data were plotted using these combinations of fluorochromes. For hot spot 1, flow cytometric data were obtained for SparkNIR685 / APC, SparkNIR685 / VioR667, and APC / VioR667. Flow cytometry data were obtained for BB700 / PerCP, BB700 / PerCP-Cy5.5, and PerCP / PerCP-Cy5.5 for hotspot 2. These data were obtained using the effect of each fluorescent dye pair in combination with autofluorescence, the first full combination (39C combination A), or the second full combination (39C combination B). These combinations are listed in Table 1 below.

[0232] Figures 16A-16D Flow cytometry data collected for hotspot 1 are shown. Figures 16A-16C Flow cytometry data for combined autofluorescence of the SparkNIR685 / APC, SparkNIR685 / VioR667, and APC / VioR667 fluorophore pairs are shown, respectively. These data indicate a baseline degree of unmixing-dependent diffusion in the absence of full combination. Figures 16D-16F Flow cytometry data of SparkNIR685 / APC, SparkNIR685 / VioR667, and APC / VioR667 in the 39C combination A background are shown. Figures 16D-16F As shown, there are severe diffusion hot spots in the spectral neighborhood of APC. Figure 16G Shown are flow cytometry data collected from SparkNIR685 / APC in the context of 39C Combination B. Since VioR667 was not present in 39C Combination B, flow cytometry data for this fluorescent dye were not generated.

[0233] Figures 17A-17D Flow cytometry data collected for hotspot 2 are shown. Figures 17A-17C Flow cytometry data (combined with autoluminescence) from BB700 / PerCP, BB700 / PerCP-Cy5.5, and PerCP / PerCP-Cy5.5, respectively, are shown. These data demonstrate a baseline degree of unmixing-dependent diffusion in the absence of full assembly. Fig.17D Flow cytometry data from the BB700 / PerCP fluorophore pair in the context of 39C combination A are shown. Fig.17D As shown, there is minimal diffusion in the BB700 spectral neighborhood. Figures 17E-17G Flow cytometry data for BB700 / PerCP, BB700 / PerCP-Cy5.5, and PerCP / PerCP-Cy5.5 fluorophore pairs in the 39C combination B background are shown.

[0234] Comparing the results of 39C combination A with those of 39C combination B, the results show that the diffusion hotspot shifts from the APC spectral neighborhood to the BB700 spectral neighborhood. Therefore, the results indicate that amplification is not solely dependent on the fluorochrome used in the fluorophore pair, but rather on the full set of dyes in the combination.

[0235] Example 3

[0236] Calculate the similarity index of the fluorescent dyes in the 39C combination. In the range of 0-1, this index compares the similarity of the spectra of two fluorescent groups. The calculation results are as follows Fig.18 As shown. Fig.18In the chart on the left, each cell is color-coded based on a similarity index, with darker colors representing higher similarity. Zoom in on a subset of high similarity pairs from the chart. As shown in the zoomed-in portion of the chart, BB515 / cFluorB532 and BB700 / PerCP-Cy5.5 have significantly similar spectra when 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 cytometry data was collected only from the fluorochrome pairs and their associated autofluorescence, as well as the same fluorochrome pairs in the context of the full fluorochrome combination. The resulting flow cytometry data is shown in the figure below. Fig.18 Shown on the right. Flow cytometric data collected for BB515 / cFluorB532 (top) show no change in diffusion across the full panel relative to data collected from the fluorochrome pair alone. However, flow cytometric data collected for BB700 / PerCP-Cy5.5 (bottom) show a significant change in diffusion across the full panel relative to data collected from the fluorochrome pair alone. Accordingly, the results show that the Similarity Index for fluorochrome pair 1 (BB515 and cFluorB532) and fluorochrome pair 2 (BB700 and PerCP-Cy5.5) are comparable, but have significantly different diffusion across the full panel. Therefore, the results show that the Similarity Index cannot predict differences in diffusion in the context of the full panel. In other words, the Similarity Index indicates whether there is a potential problem when two fluorophores are used together, but does not indicate whether there is a problem.

[0237] In addition, the condition numbers for two different fluorochrome combinations (combination A and combination B) were calculated, which differed only in one fluorochrome. The fluorochromes for each combination are listed in Table 1 below. The results show that the condition number for combination A is 69.8, while the condition number for combination B is 67.0. Flow cytometry data for each fluorochrome pair of APC / SparkNIR685 and BB700 / PerCP fluorochrome pairs in the context of combination A and combination B were developed. The results are shown in Fig.19 The graph of the fluorescent dye pair in combination A is shown on the left, while the graph of the fluorescent dye pair in combination B is shown on the right. Fig.19 As shown, combinations A and B differ by one fluorophore and have comparable condition numbers, but the diffusion of the same dye pair in combinations A and B is significantly different. Therefore, the conclusion is that the condition number does not indicate which fluorophores are responsible for the diffusion of the entire combination. In other words, the condition number can indicate that a given combination may have problems, but it does not indicate which fluorophores are causing the problem or how to solve it.

[0238] For the above combination A ( Fig. 20 ) and combination B ( Fig.21) were subjected to a similarity analysis. This analysis involved plotting the similarity index against the average full matrix spillover spread (SS). Fig. 20 and Fig.21 As shown, similarity analysis cannot predict where diffusion will occur in two different combinations.

[0239] Example 4

[0240] Generate the combined hotspot matrix of combination A and combination B respectively, as shown in Table 1 below. Figures 5A-5D The combined hotspot matrix is ​​created by the method shown in the figure. Fig. 22 As shown, the hotspot matrix of combination B is as follows Fig.23 As shown. Fig. 22 and Fig.23 As shown, unlike the previous fluorescent dye combination analysis, the combination hotspot matrix correctly identifies which fluorescent dyes are most affected by unmixing diffusion in combination A and combination B. Therefore, the results show that the combination hotspot analysis can correctly identify diffusion problem areas in different combinations.

[0241] To further prove that the combined hotspot matrix has this capability, we analyze the combined hotspot matrix of combination A (Figure 24) and combination B ( Fig.25 ) were plotted against the flow cytometry data for each combination of the fluorescent dyes affected by diffusion as identified by the combined hotspot matrix. Fig.24 and Fig.25 As shown, the fluorescent dyes identified as being affected by diffusion by the combined hotspot matrix are indeed affected by diffusion. In the combined hotspot matrix, high-magnitude diagonal elements represent the most affected fluorescent dyes, while high-magnitude off-diagonal elements represent problematic dye pairs.

[0242] Example 5

[0243] The ability of the combined hotspot matrix to estimate the severity of diffusion is studied. To this end, the above combined A hotspot matrix is ​​analyzed. The results are shown in Fig.26 As shown. The magnitude of the quantifier represented by color density in the cells of the hotspot matrix for combination A tracks the severity of the hotspot. Hotspots have more severe diffusion in areas of the matrix where the quantifier magnitude is high, whereas hotspots with relatively low quantifier magnitude have correspondingly lower diffusion. This information indicates where unmixing-dependent diffusion is most problematic in the panel and can be used in panel design when assigning fluorochromes to markers (e.g., ensuring that “hotspot” fluorochromes are not co-expressed in the panel or are not dimmer / sensitive markers, as biological resolution of the “hotspot” fluorochromes will be compromised).

[0244] Example 6

[0245] The combination A hotspot matrix is ​​used to improve the combination A. The combination improvement can be achieved through manual trial and error guided by the combination hotspot matrix, or through an automatic algorithm that minimizes the objective function derived from the matrix (such as the size of the largest element). The combination A hotspot matrix is ​​as follows Fig.27A As shown. Subsequently, VioR667 was replaced by QDot 800 to resolve the hotspot in the APC spectral neighborhood. This exchange resulted in combination A-1, see Table 1 below. Fig.27B The combined hotspot matrix of combination A-1 is shown. As shown in the hotspot matrix of combination A-1, the hotspot in the APC spectral neighborhood is eliminated due to the exchange of VioR667 with QDot 800. The condition number is also reduced from 69.8 to 32.5. However, the hotspot can still be seen in the hotspot matrix of combination A-1 in the spectral neighborhood of BV510. By replacing BV510 with QDot 705, the hotspot is resolved, resulting in combination A-2, as shown in Table 1 below. Fig.27C The combined hotspot matrix for combination A-2 is shown. As shown in the hotspot matrix for combination A-2, the hotspot in the spectral neighborhood of BV510 is eliminated due to the exchange of BV510 with QDot705. This exchange also has the effect of reducing the condition number of the fluorescent dye combination from 32.5 to 24.8. Therefore, the results show that the combined hotspot analysis is beneficial for improving or expanding existing combinations.

[0246] Example 7

[0247] The fluorescent dyes in panel A were used to generate flow cytometry data. The resulting data were used to populate Fig.28A The diffusion correlation matrix is ​​shown in Figure 2. The matrix cells are color coded so that the cell color relates to the degree to which the double negative population is tilted, i.e., having a positive or negative correlation. Figure 7A-7B The resulting diffusion correlation matrix is ​​compared with that shown in Figure 1. The resulting matrix is ​​able to predict the correlations derived by Gram's inverse, as Fig.28B shown. Fig.28A and Fig.28B Target regions 1-4 are identified in FIG. The flow cytometric data associated with the fluorescent dye pairs identified in target regions 1-4 are shown in FIG. Figures 28C-28F As shown. Figures 28C-28F As shown, the diffusion correlation matrix ( Fig.28B ) can accurately predict the measured relevant data ( Fig.28A ).

[0248] Table 1: Reference combinations

[0249] 39A 39A-1 39A-2 39B 1 BUV395 BUV395 BUV395 BUV395 2 BUV496 BUV496 BUV496 BUV496 3 Autofluorescence Autofluorescence Autofluorescence Autofluorescence 4 BUV563 BUV563 BUV563 BUV563 5 BUV615 BUV615 BUV615 BUV615 6 BUV661 BUV661 BUV661 BUV661 7 BUV737 BUV737 QDot705 BUV737 8 BUV805 QDot800 BUV737 BUV805 9 BV421 BUV805 QDot800 BV421 10 VioBlue BV421 BUV805 VioBlue 11 BV480 VioBlue BV421 BV480 12 BV510 BV480 VioBlue BV510 13 AmethystOrange BV510 BV480 AmethystOrange 14 BV570 AmethystOrange AmethystOrange BV570 15 BV605 BV570 BV570 BV605 16 BV650 BV605 BV605 BV650 17 BV711 BV650 BV650 BV711 18 BV750 BV711 BV711 BV750 19 BV786 BV750 BV750 BV786 20 BB515 BV786 BV786 BB515 21 cFluorB532 BB515 BB515 cFluorB532 22 BB630 cFluorB532 cFluorB532 BB630 23 PerCP BB630 BB630 PerCP 24 BB660 PerCP PerCP BB660 25 BB700 BB660 BB660 PerCP-Cy5.5 26 BB755 BB700 BB700 BB700 27 RB780 BB755 BB755 BB755 28 PE RB780 RB780 RB780 29 RY586 PE PE PE 30 PE-CF594 RY586 RY586 RY586 31 PE-Cy5 PE-CF594 PE-CF594 PE-CF594 32 PE-Fire700 PE-Cy5 PE-Cy5 PE-Cy5 33 NovaFluorY730 PE-Fire700 PE-Fire700 PE-Fire700 34 PE-Cy7 NovaFluorY730 NovaFluorY730 NovaFluorY730 35 VioR667 PE-Cy7 PE-Cy7 PE-Cy7 36 APC APC APC APC 37 SparkNIR685 SparkNIR685 SparkNIR685 SparkNIR685 38 R718 R718 R718 R718 39 APC-Cy7 APC-Cy7 APC-Cy7 APC-Cy7 40 APC-Fire810 APC-Fire810 APC-Fire810 APC-Fire810

[0250] Notwithstanding the appended claims, the present disclosure is defined by the following terms:

[0251] 1. A method for evaluating the suitability of a combination of fluorescent dyes for generating flow cytometric data, the method comprising:

[0252] get:

[0253] Fluorescent dye combinations;

[0254] Instrument identifier;

[0255] a spectral matrix associated with fluorochrome combinations and instrument identifiers;

[0256] Calculate the inverse matrix based on the obtained spectral matrix;

[0257] The calculated inverse matrix is ​​analyzed to identify fluorescent dyes in the fluorescent dye combination that are associated with the variance of flow cytometry data generated using the fluorescent dye combination to assess the suitability of the fluorescent dye combination for generating flow cytometry data.

[0258] 2. The method of clause 1, wherein the fluorescent dyes in the fluorescent dye combination that are associated with flow cytometer data variance will produce flow cytometer data variance.

[0259] 3. A method according to clause 1 or 2, wherein the fluorescent dyes in the fluorescent dye combination that are associated with the variance of the flow cytometer data will be affected by the variance of the flow cytometer data.

[0260] 4. A method according to any of the preceding clauses, wherein the inverse matrix is ​​a pseudo-inverse matrix.

[0261] 5. The method of clause 4, wherein the pseudo-inverse matrix is ​​a Moore-Penrose pseudo-inverse matrix.

[0262] 6. A method according to any of clauses 1-3, wherein the inverse matrix is ​​an inverse Gram matrix.

[0263] 7. The method according to clause 6, wherein the inverse matrix is ​​calculated according to the following formula:

[0264]

[0265] in:

[0266] is the inverse Gram matrix;

[0267] is the spectral matrix;

[0268] is the transposed spectral matrix.

[0269] 8. A method according to any of the preceding clauses, wherein analyzing the calculated inverse matrix comprises deriving quantitative indicators from the inverse matrix.

[0270] 9. The method of clause 8, wherein the quantitative indicator is a matrix norm.

[0271] 10. The method of clause 8, wherein the quantitative indicator is a vector norm.

[0272] 11. The method of any of the preceding clauses, further comprising optimizing the fluorescent dye combination based on an assessment of the suitability of the fluorescent dye combination for generating flow cytometric data.

[0273] 12. The method of clause 11, wherein optimizing the fluorescent dye combination comprises using a combinatorial optimization algorithm.

[0274] 13. The method according to clause 11 or 12, wherein optimizing the fluorescent dye combination comprises adjusting the fluorescent dyes in the fluorescent dye combination and evaluating the suitability of the adjusted fluorescent dye combination for generating flow cytometric data.

[0275] 14. The method according to clause 13, wherein the optimizing the fluorescent dye combination comprises iteratively adjusting the fluorescent dye combination and evaluating the suitability of each fluorescent dye combination after the iterative adjustment.

[0276] 15. A method according to any of the preceding clauses, wherein the flow cytometer data comprises a number of dimensions equal to the number of fluorescent dyes in the plurality of fluorescent dyes.

[0277] 16. The method of clause 15, wherein the flow cytometer data is spectrally unmixed flow cytometer data.

[0278] 17. The method of clause 15, wherein the flow cytometer data is flow cytometer compensation data.

[0279] 18. A method according to any of the preceding clauses, wherein the variance comprises noise in the flow cytometer data.

[0280] 19. A method according to any of the preceding clauses, further comprising generating a visualization of the assessed suitability of a fluorescent dye combination for generating flow cytometry data.

[0281] 20. The method of clause 19, wherein the visualization highlights fluorescent dyes in the fluorescent dye combination that are associated with variance in flow cytometric data generated using the fluorescent dye combination.

[0282] 21. A method according to clause 19 or 20, wherein the visualization comprises a combined hotspot matrix.

[0283] 22. The method of clause 21, wherein the visualization comprises a diagonal visualization of a combined hotspot matrix.

[0284] 23. A method according to clause 19 or 20, wherein the visualization comprises a diffusion correlation matrix.

[0285] 24. A system comprising:

[0286] Processors that can:

[0287] get:

[0288] Fluorescent dye combinations;

[0289] Instrument identifier;

[0290] a spectral matrix associated with fluorochrome combinations and instrument identifiers;

[0291] Calculate the inverse matrix based on the obtained spectral matrix;

[0292] The calculated inverse matrix is ​​analyzed to identify fluorescent dyes in the fluorescent dye combination that are associated with the variance of flow cytometry data generated using the fluorescent dye combination to assess the suitability of the fluorescent dye combination for generating flow cytometry data.

[0293] 25. The system of clause 24, wherein the fluorescent dyes in the fluorescent dye combination that are associated with flow cytometer data variance will produce flow cytometer data variance.

[0294] 26. A system according to clause 24 or 25, wherein the fluorescent dyes in the fluorescent dye combination that are associated with the variance of the flow cytometer data will be affected by the variance of the flow cytometer data.

[0295] 27. The system of any of clauses 24-26, wherein the inverse matrix is ​​a pseudo-inverse matrix.

[0296] 28. The system of clause 27, wherein the pseudo-inverse matrix is ​​a Moore-Penrose pseudo-inverse matrix.

[0297] 29. The system of any of clauses 24-26, wherein the inverse matrix is ​​an inverse Gram matrix.

[0298] 30. The system of clause 29, wherein the inverse matrix is ​​calculated according to the following formula:

[0299]

[0300] in:

[0301] is the inverse Gram matrix;

[0302] is the spectral matrix;

[0303] is the transposed spectral matrix.

[0304] 31. The system of any of clauses 24-30, wherein analyzing the calculated inverse matrix comprises deriving a quantitative indicator from the inverse matrix.

[0305] 32. The system of clause 31, wherein the quantitative indicator is a matrix norm.

[0306] 33. A system according to clause 31, wherein the quantitative indicator is a vector norm.

[0307] 34. A system according to any of clauses 24-33, wherein the processor is operable to optimize the fluorescent dye combination based on an assessment of the suitability of the fluorescent dye combination for generating flow cytometric data.

[0308] 35. The system of clause 34, wherein optimizing the fluorescent dye combination comprises using a combinatorial optimization algorithm.

[0309] 36. A system according to clause 34 or 35, wherein optimizing the fluorescent dye combination includes adjusting the fluorescent dyes in the fluorescent dye combination and evaluating the suitability of the adjusted fluorescent dye combination for generating flow cytometer data.

[0310] 37. The system of clause 36, wherein the optimizing the fluorescent dye combination comprises iteratively adjusting the fluorescent dye combination and evaluating the suitability of each fluorescent dye combination after the iterative adjustment.

[0311] 38. A system according to any of clauses 24-37, wherein the flow cytometer data comprises a number of dimensions equal to the number of fluorescent dyes in the plurality of fluorescent dyes.

[0312] 39. A system according to clause 38, wherein the flow cytometer data is spectrally demixed flow cytometer data.

[0313] 40. The system of clause 38, wherein the flow cytometer data is flow cytometer compensation data.

[0314] 41. A system according to any of clauses 24-40, wherein the variance comprises noise in the flow cytometer data.

[0315] 42. A system according to any of clauses 24-41, wherein the processor is operable to generate a visualization of the assessed suitability of a combination of fluorescent dyes for generating flow cytometry data.

[0316] 43. A system according to clause 42, wherein the visualization highlights fluorescent dyes in the fluorescent dye combination that are associated with the variance of flow cytometer data generated using the fluorescent dye combination.

[0317] 44. A system according to clause 42 or 43, wherein the visualization includes a combined hotspot matrix.

[0318] 45. The system of clause 44, wherein the visualization comprises a diagonal visualization of a combined hotspot matrix.

[0319] 46. ​​A system according to clause 42 or 43, wherein the visualization comprises a diffusion correlation matrix.

[0320] 47. The system of any of clauses 42-46, further comprising a display for depicting the visualization.

[0321] 48. A non-transitory computer readable storage medium comprising instructions stored thereon for evaluating the suitability of a combination of fluorescent dyes for use in generating a flow cytometer using a method comprising:

[0322] get:

[0323] Fluorescent dye combinations;

[0324] Instrument identifier;

[0325] a spectral matrix associated with fluorochrome combinations and instrument identifiers;

[0326] Calculate the inverse matrix based on the obtained spectral matrix;

[0327] The calculated inverse matrix is ​​analyzed to identify fluorescent dyes in the fluorescent dye combination that are associated with the variance of flow cytometry data generated using the fluorescent dye combination to assess the suitability of the fluorescent dye combination for generating flow cytometry data.

[0328] 49. The non-transitory computer readable storage medium of clause 48, wherein the fluorescent dyes in the fluorescent dye combination that are associated with flow cytometer data variance will produce flow cytometer data variance.

[0329] 50. The non-transitory computer-readable storage medium of clause 48 or 49, wherein the fluorescent dyes in the fluorescent dye combination that are associated with the variance of the flow cytometer data will be affected by the variance of the flow cytometer data.

[0330] 51. The non-transitory computer-readable storage medium of any of clauses 48-50, wherein the inverse matrix is ​​a pseudo-inverse matrix.

[0331] 52. The non-transitory computer-readable storage medium of clause 51, wherein the pseudo-inverse matrix is ​​a Moore-Penrose pseudo-inverse matrix.

[0332] 53. The non-transitory computer-readable storage medium of any of clauses 48-50, wherein the inverse matrix is ​​an inverse Gram matrix.

[0333] 54. The non-transitory computer-readable storage medium of clause 53, wherein the inverse matrix is ​​calculated according to the following formula:

[0334]

[0335] in:

[0336] is the inverse Gram matrix;

[0337] is the spectral matrix;

[0338] is the transposed spectral matrix.

[0339] 55. The non-transitory computer-readable storage medium of any of clauses 48-54, wherein analyzing the calculated inverse matrix comprises deriving a quantitative metric from the inverse matrix.

[0340] 56. The non-transitory computer-readable storage medium of clause 55, wherein the quantitative metric is a matrix norm.

[0341] 57. The non-transitory computer-readable storage medium of clause 55, wherein the quantitative metric is a vector norm.

[0342] 58. The non-transitory computer-readable storage medium of any of clauses 48-57, wherein the method further comprises optimizing the fluorescent dye combination based on an assessment of the suitability of the fluorescent dye combination for generating flow cytometric data.

[0343] 59. The non-transitory computer-readable storage medium of clause 58, wherein optimizing the fluorescent dye combination comprises using a combinatorial optimization algorithm.

[0344] 60. A non-transitory computer-readable storage medium according to clause 58 or 59, wherein optimizing the fluorescent dye combination includes adjusting the fluorescent dyes in the fluorescent dye combination and evaluating the suitability of the adjusted fluorescent dye combination for generating flow cytometer data.

[0345] 61. The non-transitory computer-readable storage medium of clause 60, wherein optimizing the fluorescent dye combination comprises iteratively adjusting the fluorescent dye combination and evaluating the suitability of each fluorescent dye combination after iterative adjustment.

[0346] 62. The non-transitory computer-readable storage medium of any of clauses 48-61, wherein the flow cytometer data comprises a number of dimensions equal to a number of fluorescent dyes in the plurality of fluorescent dyes.

[0347] 63. The non-transitory computer-readable storage medium of clause 62, wherein the flow cytometer data is spectrally demixed flow cytometer data.

[0348] 64. The non-transitory computer-readable storage medium of clause 62, wherein the flow cytometer data is flow cytometer compensation data.

[0349] 65. The non-transitory computer-readable storage medium of any of clauses 48-64, wherein the variance comprises noise in the flow cytometer data.

[0350] 66. The non-transitory computer-readable storage medium of any of clauses 48-65, wherein the method further comprises generating a visualization of the assessed suitability of fluorescent dye combinations for generating flow cytometry data.

[0351] 67. The non-transitory computer-readable storage medium of clause 66, wherein the visualization highlights fluorescent dyes in the fluorescent dye combination that are associated with a variance in flow cytometer data generated using the fluorescent dye combination.

[0352] 68. The non-transitory computer-readable storage medium of clause 66 or 67, wherein the visualization comprises a combined hotspot matrix.

[0353] 69. The non-transitory computer-readable storage medium of clause 68, wherein the visualization comprises a diagonal visualization of a combined hotspot matrix.

[0354] 70. The non-transitory computer-readable storage medium of clause 66 or 67, wherein the visualization comprises a diffusion correlation matrix.

[0355] 71. A method for evaluating the suitability of a combination of fluorescent dyes for use in generating flow cytometric data, the method comprising:

[0356] (a) Input to the processor:

[0357] Fluorescent dye combinations;

[0358] Instrument identifier;

[0359] a spectral matrix associated with fluorescent dye combinations and instrument identifiers, wherein the processor may:

[0360] Calculate the inverse matrix based on the obtained spectral matrix;

[0361] identifying fluorescent dyes in the fluorescent dye combination that are associated with variance in flow cytometric data generated using the fluorescent dye combination by analyzing the calculated inverse matrix;

[0362] (b) Receiving, from a processor, an assessment of the suitability of a fluorescent dye combination for generating flow cytometry data.

[0363] 72. A method according to clause 71, wherein the fluorescent dyes in the fluorescent dye combination that are associated with flow cytometer data variance will produce flow cytometer data variance.

[0364] 73. A method according to clause 71 or 72, wherein the fluorescent dyes in the fluorescent dye combination that are associated with the variance of the flow cytometer data will be affected by the variance of the flow cytometer data.

[0365] 74. A method according to any of clauses 71-73, wherein the inverse matrix is ​​a pseudo-inverse matrix.

[0366] 75. The method of clause 74, wherein the pseudo-inverse matrix is ​​a Moore-Penrose pseudo-inverse matrix.

[0367] 76. A method according to any of clauses 71-73, wherein the inverse is a Gram inverse.

[0368] 77. The method according to clause 76, wherein the inverse matrix is ​​calculated according to the following formula:

[0369]

[0370] in:

[0371] is the inverse Gram matrix;

[0372] is the spectral matrix;

[0373] is the transposed spectral matrix.

[0374] 78. A method according to any of clauses 71-77, wherein analyzing the calculated inverse matrix includes deriving quantitative indicators from the inverse matrix.

[0375] 79. A method according to clause 78, wherein the quantitative indicator is a matrix norm.

[0376] 80. A method according to clause 78, wherein the quantitative indicator is a vector norm.

[0377] 81. A method according to any of clauses 71-80, wherein the processor is operable to optimize the fluorescent dye combination based on an assessment of the suitability of the fluorescent dye combination for generating flow cytometer data.

[0378] 82. The method of clause 81, wherein optimizing the fluorescent dye combination comprises using a combinatorial optimization algorithm.

[0379] 83. A method according to clause 81 or 82, wherein optimizing the fluorescent dye combination includes adjusting the fluorescent dyes in the fluorescent dye combination and evaluating the suitability of the adjusted fluorescent dye combination for generating flow cytometer data.

[0380] 84. The method according to clause 83, wherein the optimizing the fluorescent dye combination comprises iteratively adjusting the fluorescent dye combination and evaluating the suitability of each fluorescent dye combination after the iterative adjustment.

[0381] 85. A method according to any of clauses 71-84, wherein the flow cytometer data comprises a number of dimensions equal to the number of fluorescent dyes in the plurality of fluorescent dyes.

[0382] 86. A method according to clause 85, wherein the flow cytometer data is spectrally demixed flow cytometer data.

[0383] 87. A method according to clause 85, wherein the flow cytometer data is flow cytometer compensation data.

[0384] 88. A method according to any of clauses 71-87, wherein the variance comprises noise in the flow cytometer data.

[0385] 89. A method according to any of clauses 71-88, wherein the processor is operable to generate a visualization of the assessed suitability of a combination of fluorescent dyes for generating flow cytometry data.

[0386] 90. The method of clause 89, wherein the visualization highlights fluorescent dyes in the fluorescent dye combination that are associated with variance in flow cytometer data generated using the fluorescent dye combination.

[0387] 91. A method according to clause 89 or 90, wherein the visualization includes a combined hotspot matrix.

[0388] 92. A method according to clause 91, wherein the visualization includes a diagonal visualization of the combined hot spot matrix.

[0389] 93. A method according to clause 89 or 90, wherein the visualization includes a diffusion correlation matrix.

[0390] 94. The method of any of clauses 89-93, further comprising receiving the generated visualization.

[0391] Although the above invention has been described in some detail by way of illustration and example for purposes of clarity of understanding, it will be apparent to one of ordinary skill in the art, based on the teachings of the present invention, that certain changes and modifications may be made thereto without departing from the spirit or scope of the appended claims.

[0392] Therefore, the above content only illustrates the principles of the present invention. It should be understood that those skilled in the art will be able to design various arrangements, which, although not explicitly described or shown in the present invention, embody the principles of the present invention and are included in its spirit and scope. In addition, all embodiments and conditional language described in the present invention are mainly intended to help readers understand the principles of the present invention and the concepts provided by the inventors to deepen the field, and should be interpreted as not being limited to these specifically listed examples and conditions. In addition, all statements recording the principles, aspects and embodiments of the present invention and their specific examples in the present invention are intended to cover their structural and functional equivalents. In addition, it is intended that such equivalents include currently known equivalents and equivalents developed in the future, that is, any part of the developed performance of the same function (regardless of its structure). In addition, any content disclosed by the present invention, whether or not it is explicitly listed in the claims, is not intended to be open to the public exclusively.

[0393] Therefore, the scope of the present invention is not intended to be limited to the exemplary embodiments shown and described herein. Instead, the appended claims embody the scope and spirit of the present invention. In the claims, 35 U.S. Patent Act Section 112(f) or 35 U.S. Patent Act Section 112(6) is expressly defined as a limitation in a claim only if such limitation begins with the exact phrase "means" or the exact phrase "step" in the claim; if such exact phrase is not used in the claim, 35 U.S. Patent Act Section 112(f) or 35 U.S. Patent Act Section 112(6) is not cited.

Claims

1. A method for evaluating the suitability of a combination of fluorescent dyes for generating flow cytometric data, the method comprising: get: Fluorescent dye combinations; Instrument identifier; and a spectral matrix associated with fluorochrome combinations and instrument identifiers; Calculate the inverse matrix based on the obtained spectral matrix; and The calculated inverse matrix is ​​analyzed to identify fluorescent dyes in the fluorescent dye combination that are associated with the variance of flow cytometry data generated using the fluorescent dye combination to assess the suitability of the fluorescent dye combination for generating flow cytometry data.

2. The method according to claim 1, wherein: Fluorochromes in the fluorochrome panel that are associated with variance in flow cytometry data are: Generate flow cytometry data variance; or Affected by the variance of flow cytometric data.

3. A 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 ​​an inverse Gram matrix.

5. A method according to any one of the preceding claims, wherein: Analyzing the calculated inverse matrix includes deriving quantitative indicators from the inverse matrix.

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

7. The method of any of the preceding claims, further comprising optimizing the fluorescent dye combination based on evaluating the suitability of the fluorescent dye combination for generating flow cytometric data.

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

9. The method of any of the preceding claims, further comprising generating a visualization based on an assessment of the suitability of a combination of fluorescent dyes for generating flow cytometric data.

10. The method according to claim 9, wherein: The visualization highlights the fluorescent dyes in the fluorescent dye combination that are associated with the variance of the flow cytometry data generated using the fluorescent dye combination.

11. The method according to claim 9 or 10, wherein: The visualization includes a combined hotspot matrix.

12. The method according to claim 11, wherein: The visualization includes a diagonal visualization of the combined hotspot matrix.

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

14. A system comprising: The processor is configured as: get: Fluorescent dye combinations; Instrument identifier; and a spectral matrix associated with fluorochrome combinations and instrument identifiers; Calculating an inverse matrix based on the obtained spectral matrix; and The calculated inverse matrix is ​​analyzed to identify fluorescent dyes in the fluorescent dye combination that are associated with the variance of flow cytometry data generated using the fluorescent dye combination to assess the suitability of the fluorescent dye combination for generating flow cytometry data.

15. A method for evaluating the suitability of a combination of fluorescent dyes for generating flow cytometric data, the method comprising: (a) Input to the processor: Fluorescent dye combinations; Instrument identifier; and A spectral matrix associated with fluorescent dye combinations and instrument identifiers, wherein the processor is configured to: Calculating an inverse matrix based on the obtained spectral matrix; and identifying fluorescent dyes in the fluorescent dye combination that are associated with variance in flow cytometric data generated using the fluorescent dye combination by analyzing the calculated inverse matrix; and (b) Receiving, from a processor, an assessment of the suitability of a fluorescent dye combination for generating flow cytometry data.

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