Pigment data acquisition method, pigment data acquisition device, and pigment data acquisition program

Abstract

To determine a monochromatic object including a single fluorescent dye even when an observation condition is changed.SOLUTION: The dye data acquisition system 1 processes first spectrum data that is a distribution of intensity values of fluorescence for each of C (C is an integer of 2 or more) detection wavelengths generated from an analysis target in response to irradiation with excitation light of a first wavelength band, and second spectrum data that is a distribution of intensity values of fluorescence for each of C detection wavelengths generated from the analysis target in response to irradiation with excitation light of a second wavelength band. The index parameter is calculated based on the intensity value for each of the C detection wavelengths in the first spectrum data and the intensity value for each of the C detection wavelengths in the second spectrum data, and the degree of monochromaticity of the analysis object is determined based on the index parameter.SELECTED DRAWING: Figure 4

Description

[Technical Field]

[0001] One aspect of the embodiment relates to a dye data acquisition method, a dye data acquisition device, and a dye data acquisition program. [Background technology]

[0002] Flow cytometry has been known as a technique for counting, selecting, and analyzing the characteristics of samples such as cells using laser light. For example, Patent Document 1 below discloses clustering of cells based on fluorescence data of each color light from the cells output from a flow cytometer. Furthermore, Non-Patent Document 2 below discloses unmixing fluorescence data output from a flow cytometer to generate fluorescence spectra for each fluorescent dye, and then performing clustering processing on the fluorescence spectra. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2020-87465 [Patent Document 2] Patent Publication No. 2021-36224 Summary of the Invention [Problem to be solved by the invention]

[0004] In conventional methods such as those described above, it tends to be difficult to determine whether a sample is monochromatic and contains a single fluorescent dye when the observation conditions change due to changes in the type of dye being observed, the type of excitation light used, etc.

[0005] Therefore, one aspect of the embodiment has been made in consideration of such problems, and aims to provide a dye data acquisition method, a dye data acquisition device, and a dye data acquisition program that are capable of determining a monochromatic object containing a single fluorescent dye even when the observation conditions change. [Means for solving the problem]

[0006] A dye data acquisition method according to a first aspect of the embodiment includes a data acquisition step of irradiating an object with excitation light having a first wavelength distribution to acquire first spectral data, which is a distribution of intensity values ​​of fluorescence for each of C (C is an integer of 2 or more) detection wavelengths generated from the object, and irradiating the object with excitation light having a second wavelength distribution to acquire second spectral data, which is a distribution of intensity values ​​of fluorescence for each of the C detection wavelengths generated from the object; a parameter calculation step of calculating an index parameter based on the intensity values ​​for each of the C detection wavelengths in the first spectral data and the intensity values ​​for each of the C detection wavelengths in the second spectral data; and a determination step of determining the degree of monochromaticity of the object based on the index parameter.

[0007] Alternatively, a pigment data acquisition device according to a second aspect of the embodiment processes first spectral data, which is a distribution of intensity values ​​of fluorescence at each of C (C is an integer of 2 or more) detection wavelengths generated from an object in response to irradiation with excitation light having a first wavelength distribution, and second spectral data, which is a distribution of intensity values ​​of fluorescence at each of C detection wavelengths generated from an object in response to irradiation with excitation light having a second wavelength distribution, and calculates an index parameter based on the intensity values ​​at each of the C detection wavelengths in the first spectral data and the intensity values ​​at each of the C detection wavelengths in the second spectral data, and determines the degree of monochromaticity of the object based on the index parameter.

[0008] Alternatively, a dye data acquisition program according to a third aspect of the embodiment is a dye data acquisition program for processing first spectral data, which is a distribution of intensity values ​​of fluorescence for each of C (C is an integer of 2 or more) detection wavelengths generated from an object in response to irradiation with excitation light having a first wavelength distribution, and second spectral data, which is a distribution of intensity values ​​of fluorescence for each of C detection wavelengths generated from an object in response to irradiation with excitation light having a second wavelength distribution, and causes a computer to execute a parameter calculation step of calculating an index parameter based on the intensity values ​​for each of the C detection wavelengths in the first spectral data and the intensity values ​​for each of the C detection wavelengths in the second spectral data, and a determination step of determining the degree of monochromaticity of the object based on the index parameter.

[0009] According to the first, second, or third aspect, first and second spectral data of the fluorescence distribution at C detection wavelengths generated from an object irradiated with excitation light of two wavelength distributions are processed. Specifically, an index parameter is calculated from the intensity values ​​for each of the C detection wavelengths in the first spectral data and the intensity values ​​for each of the C detection wavelengths in the second spectral data, and the degree of monochromaticity of the object is determined based on the calculated index parameter. This makes it possible to accurately determine whether the object is monochromatic, even if the type of dye to be observed or the type of excitation light used for observation changes, or even if a bias in the number of each dye occurs.

[0010] In the first aspect, it is preferable that the parameter calculation step calculates the index parameter based on a ratio between an intensity value for each of the C detected wavelengths in the first spectral data and an intensity value for each of the C detected wavelengths in the second spectral data. In this case, by making a determination based on the ratio of the intensity values ​​for each of the detected wavelengths, it is possible to accurately determine whether the object is monochromatic.

[0011] In the first aspect, it is also preferable that the parameter calculation step calculates the index parameter based on a relationship between first vector data represented by intensity values ​​for each of the C detected wavelengths in the first spectral data and second vector data represented by intensity values ​​for each of the C detected wavelengths in the second spectral data. In this case, by making a determination based on the relationship between the two vectors represented by the intensity values ​​for each of the detected wavelengths, it is possible to accurately determine whether the object is monochromatic.

[0012] In addition, in the first aspect, it is also preferable that the determination step further comprises a clustering step of determining the monochromaticity of N objects (N is an integer of 2 or more) based on index parameters calculated for the N objects, and clustering the N objects into L object groups (L is an integer of 2 or more and N-1 or less) based on the monochromaticity of the N objects. In this case, the N objects can be clustered accurately into L object groups based on the monochromaticity of the N objects.

[0013] Furthermore, in the first aspect, it is preferable that the method further comprises a data generation step of calculating statistical values ​​of intensity values ​​of object groups at C detection wavelengths for each of the L clustered objects, performing unmixing for the C detection wavelengths using the statistical values ​​at the C detection wavelengths for each of the L object groups, and generating K pieces of dye data indicating distributions for each of K fluorescent dyes (K is an integer between 2 and C). In this case, statistical values ​​of intensity values ​​at the C detection wavelengths are calculated for each of the L clustered object groups, and unmixing is performed based on the statistical values ​​for each of the L cluster groups, thereby generating K pieces of dye data indicating distributions for each of the K fluorescent dyes. As a result, even if the type of dye observed or the type of excitation light used for observation changes, or if a bias in the number of each dye occurs, unmixing appropriate to the observation conditions can be performed, and dye data that is highly accurate fluorescence spectrum data can be obtained.

[0014] The pigment data acquisition method of the embodiment is [1] "a pigment data acquisition method comprising: a data acquisition step of irradiating an object with excitation light having a first wavelength distribution, and acquiring first spectral data which is a distribution of intensity values ​​of fluorescence for each of C detection wavelengths (C is an integer of 2 or more) generated from the object; and irradiating the object with excitation light having a second wavelength distribution, and acquiring second spectral data which is a distribution of intensity values ​​of fluorescence for each of the C detection wavelengths generated from the object; a parameter calculation step of calculating an index parameter based on the intensity values ​​for each of the C detection wavelengths in the first spectral data and the intensity values ​​for each of the C detection wavelengths in the second spectral data; and a determination step of determining the degree of monochromaticity of the object based on the index parameter."

[0015] The pigment data acquisition method of the embodiment may be [2] "the pigment data acquisition method described in [1] above, wherein in the parameter calculation step, the index parameter is calculated based on the ratio between the intensity value for each of the C detection wavelengths in the first spectral data and the intensity value for each of the C detection wavelengths in the second spectral data."

[0016] The pigment data acquisition method of the embodiment may be [3] "the pigment data acquisition method described in [1] above, wherein in the parameter calculation step, the index parameter is calculated based on the relationship between first vector data represented by the intensity values ​​for each of the C detection wavelengths in the first spectral data and second vector data represented by the intensity values ​​for each of the C detection wavelengths in the second spectral data."

[0017] The pigment data acquisition method of the embodiment may be [4] "a pigment data acquisition method described in any of [1] to [3] above, further comprising a clustering step in which, in the determination step, the degree of monochromaticity of N objects (N is an integer of 2 or more) is determined based on the index parameters calculated for the N objects, and the N objects are clustered into L object groups (L is an integer of 2 or more and N-1 or less) based on the degree of monochromaticity of the N objects."

[0018] The dye data acquisition method of the embodiment may be the dye data acquisition method described in [4] above, further comprising a data generation step of calculating statistical values ​​of the intensity values ​​of the object group at C detection wavelengths for each of the L clustered objects, and using the statistical values ​​at the C detection wavelengths for each of the L object groups to perform unmixing for the C detection wavelengths, and generating the K dye data items showing the distribution for each of K fluorescent dyes (K is an integer between 2 and C).

[0019] The pigment data acquisition device of the embodiment is [6] "a pigment data acquisition device that processes first spectral data, which is a distribution of intensity values ​​of fluorescence for each of C (C is an integer greater than or equal to 2) detection wavelengths generated from an object in response to irradiation with excitation light of a first wavelength distribution, and second spectral data, which is a distribution of intensity values ​​of fluorescence for each of the C detection wavelengths generated from the object in response to irradiation with excitation light of a second wavelength distribution, and calculates an index parameter based on the intensity values ​​for each of the C detection wavelengths in the first spectral data and the intensity values ​​for each of the C detection wavelengths in the second spectral data, and determines the degree of monochromaticity of the object based on the index parameter."

[0020] The pigment data acquisition program of the embodiment is [7] "a pigment data acquisition program for processing first spectral data, which is a distribution of intensity values ​​of fluorescence for each of C (C is an integer greater than or equal to 2) detection wavelengths generated from an object in response to irradiation with excitation light of a first wavelength distribution, and second spectral data, which is a distribution of intensity values ​​of fluorescence for each of the C detection wavelengths generated from the object in response to irradiation with excitation light of a second wavelength distribution, the pigment data acquisition program causing a computer to execute a parameter calculation step of calculating an index parameter based on the intensity values ​​for each of the C detection wavelengths in the first spectral data and the intensity values ​​for each of the C detection wavelengths in the second spectral data, and a determination step of determining the degree of monochromaticity of the object based on the index parameter." [Effects of the Invention]

[0021] According to one aspect of the embodiment, it is possible to determine a monochromatic object containing a single fluorescent dye even when the observation conditions change. [Brief explanation of the drawings]

[0022] [Figure 1] 1 is a schematic configuration diagram of a pigment data acquisition system 1 according to an embodiment. [Figure 2] FIG. 2 is a schematic configuration diagram of the data acquisition device 3 of FIG. [Figure 3] 2 is a block diagram showing an example of a hardware configuration of a data processing device 5 in FIG. 1. FIG. [Figure 4] FIG. 2 is a block diagram showing the functional configuration of a data processing device 5 in FIG. [Figure 5] 10 is a graph showing distributions of first and second spectral data acquired for a single-color cell. [Figure 6] 10 is a graph showing distributions of first and second spectral data acquired for a single-color cell. [Figure 7] 1 is a graph showing the distribution of first and second spectral data obtained from co-expressing cells. [Figure 8] 10 is a graph showing the distribution of intensity ratios calculated for a plurality of types of cells. [Figure 9] 5 is a diagram showing an image of matrix data Y' regenerated by the statistical value calculation unit 205 in FIG. 4 and dye matrix data X' derived by the matrix estimation unit 206 in FIG. [Figure 10] 1 is a flowchart showing the procedure of a pigment data acquisition method according to an embodiment. [Figure 11] FIG. 10 is a schematic diagram of a data acquisition device 3A according to a modified example. [Figure 12] 10 is a graph showing dot plots that are simulation results of determination processing for a single-color object by a data processing device 5 according to the embodiment. [Figure 13]10 is a graph showing dot plots that are simulation results of determination processing for a single-color object by a data processing device 5 according to the embodiment. [Figure 14] 10 is a graph showing dot plots that are simulation results of determination processing for a single-color object by a data processing device 5 according to the embodiment. [Figure 15] 10 is a graph showing dot plots that are simulation results of determination processing for a single-color object by a data processing device 5 according to the embodiment. [Figure 16] 10 is a graph showing dot plots that are simulation results of determination processing for a single-color object by a data processing device 5 according to the embodiment. [Figure 17] 10 is a graph showing dot plots that are simulation results of determination processing for a single-color object by a data processing device 5 according to the embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0023] Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings. In the description, the same elements or elements having the same functions will be denoted by the same reference numerals, and redundant description will be omitted.

[0024] FIG. 1 is a schematic diagram of a dye data acquisition system 1, which is a dye data acquisition device according to an embodiment. The dye data acquisition system 1 is a device for generating dye data for identifying the amount of dye (fluorescent dye) contained in a sample, such as a cell or particle, that is an analyte. The dye data generated by the dye data acquisition system 1 is used for purposes such as counting, sorting, and characteristic analysis of cells through analysis of the data. Therefore, the dye data acquisition system 1 is required to generate dye data for a large amount of analytes with high throughput. The dye data acquisition system 1 includes a data acquisition device 3 that performs flow cytometry analysis of the analytes, and a data processing device 5 that processes the data acquired by the data acquisition device 3. The data acquisition device 3 and the data processing device 5 may be configured to be able to send and receive data between them using wired or wireless communication, or may be configured to be able to input and output data via a recording medium. Alternatively, the data acquisition device 3 and the data processing device 5 may be configured as an integrated device.

[0025] Figure 2 is a schematic diagram of the data acquisition device 3 in Figure 1. The data acquisition device 3 is a system for performing flow cytometry, generally called a flow cytometer, and is composed of a fluid system 52, an optical system (optical system) 53, and an electronic system (signal processing device) 54.

[0026] The fluid system 52 is configured to include a flow cell 56 into which a sample fluid containing analytes such as cells or particles is injected, and which allows the analytes contained in the sample fluid to be aligned and passed through a narrow channel 55. The flow cell 56 is also provided with a function (not shown) for sorting (classifying and distributing) the gated analytes by controlling an electric field or the like.

[0027] The optical system 53 optically analyzes an analyte passing through a flow cell 56 by flow cytometry. The optical system 53 includes light sources 7a, 7b, 7c, and 7d, dichroic mirrors 57a, 57b, and 57c, a mirror 57d, a lens 8, filters 9a, 9b, 9c, and 9d, dichroic mirrors 10b and 10c, and photodetectors 11a, 11b, 11c, and 11d. The light sources 7a, 7b, 7c, and 7d are light source devices that generate light (excitation light) having different center wavelengths (excitation wavelengths). The light sources 7a, 7b, 7c, and 7d are, for example, laser light sources, light-emitting diodes, or superluminescent diodes. The dichroic mirror 57a transmits light emitted from the light source 7a toward the lens 8 and reflects light emitted from the other light sources 7b, 7c, and 7d toward the lens 8. The dichroic mirror 57b reflects light emitted from the light source 7b toward the dichroic mirror 57a and transmits light emitted from the light sources 7c and 7d toward the dichroic mirror 57a. The dichroic mirror 57c reflects light emitted from the light source 7c toward the dichroic mirror 57b and transmits light emitted from the light source 7d toward the dichroic mirror 57b. The mirror 57d reflects light emitted from the light source 7d toward the dichroic mirror 57c. The lens 8 focuses the light emitted from the light sources 7a, 7b, 7c, and 7d onto the channel 55 in the flow cell 56. Filter 9a transmits forward scattered light generated from the sample fluid by irradiation with light (i.e., at least one of the lights from light sources 7a, 7b, 7c, and 7d). Dichroic mirror 10b reflects fluorescence (first fluorescence) in a first wavelength band (first detection wavelength) generated from the sample fluid by irradiation with light emitted from light sources 7a, 7b, 7c, and 7d, and transmits the remaining fluorescence generated from the sample fluid.Dichroic mirror 10c reflects fluorescence (second fluorescence) of a second wavelength band (second detection wavelength) from the fluorescence that has passed through dichroic mirror 10b, and transmits fluorescence (third fluorescence) of the remaining wavelength band (third detection wavelength) from the fluorescence that has passed through dichroic mirror 10b. Filter 9b transmits the first fluorescence of the first wavelength band reflected by dichroic mirror 10b, and filter 9c transmits the second fluorescence of the second wavelength band reflected by dichroic mirror 10c. Filter 9d transmits the third fluorescence of a third wavelength band from the fluorescence that has passed through dichroic mirror 10c. Photodetectors 11a, 11b, 11c, and 11d are provided on the optical axes of the forward scattered light, the first fluorescence, the second fluorescence, and the third fluorescence, respectively, and measure the intensities of the forward scattered light, the first fluorescence, the second fluorescence, and the third fluorescence, respectively. The photodetectors 11a, 11b, 11c, and 11d are, for example, photomultiplier tubes, avalanche photodiodes, HPDs (Hybrid Photo Detectors), or SiPMs (Silicon Photomultipliers). Alternatively, the photodetectors 11a, 11b, 11c, and 11d may be configured as a single photodetector having multiple detection ports. An example of such a single photodetector is a multi-anode photomultiplier tube. The optical system 53 can measure the intensity of each of the fluorescence at C (C is an integer equal to or greater than 2) detection wavelengths, depending on the number of combinations of dichroic mirrors, filters, and photodetectors.

[0028] The optical system 53 is capable of measuring the intensities of the forward scattered light, the first fluorescence, the second fluorescence, and the third fluorescence while switching between irradiating light in multiple wavelength bands from the light sources 7a, 7b, 7c, and 7d. This allows for efficient measurement of the intensities of fluorescence in multiple wavelength bands. Alternatively, the optical system 53 may be capable of measuring the intensities of the forward scattered light, the first fluorescence, the second fluorescence, and the third fluorescence while continuously irradiating excitation light in multiple wavelength bands from the light sources 7a, 7b, 7c, and 7d. For example, the optical system 53 may be capable of measuring the intensities of the forward scattered light, the first fluorescence, the second fluorescence, and the third fluorescence generated from the analyte in response to the irradiation of excitation light in each wavelength band while sequentially switching between irradiating excitation light in a first wavelength band (wavelength distribution) from the light sources 7a, 7b, 7c, and 7d, the second fluorescence, the third fluorescence, and the third fluorescence. Furthermore, the optical system 53 may have a configuration similar to that described above that allows observation of side scattered light.

[0029] The electronic system 54 is a device for collecting data on the light intensity measured by the optical system 53. Specifically, the electronic system 54 is electrically connected to the multiple photodetectors 11a, 11b, 11c, and 11d, and performs A / D conversion on intensity signals indicating the intensity detected by each channel of the multiple photodetectors 11a, 11b, 11c, and 11d to generate array data in which the A / D-converted intensity values ​​for each analyte are linearly arrayed, and transmits the array data to an external device. Here, the electronic system 54 may generate the array data by directly A / D-converting the intensity signals detected by the multiple photodetectors 11a, 11b, 11c, and 11d, or may correct the intensity signals or the A / D-converted intensity values ​​using various parameters and generate array data based on the corrected intensity signals or the corrected intensity values.

[0030] Next, the configuration of the data processing device 5 will be described with reference to Fig. 3 and Fig. 4. Fig. 3 is a block diagram showing an example of the hardware configuration of the data processing device 5, and Fig. 4 is a block diagram showing the functional configuration of the data processing device 5.

[0031] 3, the data processing device 5 is physically a computer or the like including a processor such as a CPU (Central Processing Unit) 101, a recording medium such as a RAM (Random Access Memory) 102 or a ROM (Read Only Memory) 103, a communication module 104, and an input / output module 106, all of which are electrically connected to one another. Note that the data processing device 5 may include input / output devices such as a display, a keyboard, a mouse, a touch panel display, or a data recording device such as a hard disk drive or semiconductor memory. The data processing device 5 may also be composed of multiple computers.

[0032] As shown in FIG. 4 , the data processing device 5 includes, as functional components, a data acquisition unit 201, a parameter calculation unit 202, a determination unit 203, a clustering unit 204, a statistical value calculation unit 205, a matrix estimation unit 206, and a data generation unit 207. The functional units of the data processing device 5 shown in FIG. 4 are realized by loading a program (a dye data acquisition program according to an embodiment) onto hardware such as the CPU 101 and RAM 102, thereby operating the communication module 104 and the input / output module 106 under the control of the CPU 101 and reading and writing data from and to the RAM 102. The CPU 101 of the data processing device 5 executes the computer program to cause the functional units of FIG. 4 to function and sequentially execute processes corresponding to the dye data acquisition method described below. The CPU 101 may be a standalone piece of hardware or may be implemented in a programmable logic device such as an FPGA, like a software processor. The RAM and ROM may also be standalone pieces of hardware or may be incorporated into a programmable logic device such as an FPGA. All of the various data required for executing this computer program and the various data generated by executing this computer program are stored in built-in memories such as ROM 103 and RAM 102, or in storage media such as a hard disk drive. The functions of the functional components of the data processing device 5 will be described in detail below.

[0033] The data acquisition unit 201 acquires, from the data acquisition device 3, array data (spectral data) indicating the distribution of fluorescence intensity values ​​at pre-specified C (C is an integer of 2 or greater) detection wavelengths for N (N is an integer of 2 or greater) analytes. These C array data are spectral data obtained by measuring the distribution of fluorescence intensity values ​​for each of the C detection wavelengths from the N analytes in the data acquisition device 3. Specifically, the data acquisition unit 201 acquires first spectral data by irradiating the N analytes with excitation light in a first wavelength band and measuring the distribution of fluorescence intensity values ​​for each of the C detection wavelengths from the N analytes, and acquires second spectral data by irradiating the N analytes with excitation light in a second wavelength band and measuring the distribution of fluorescence intensity values ​​for each of the C detection wavelengths from the N analytes. At this time, the number C of spectral data to be acquired (the number C of fluorescence detection wavelengths observed) is specified in advance to be equal to or greater than the maximum number of dyes that can be contained in the analyte.

[0034] The parameter calculation unit 202 calculates an index parameter indicating the degree to which the analyte contains only a single dye (degree of monochromaticity) for each of the N analytes based on the first spectral data and the second spectral data. That is, the parameter calculation unit 202 calculates the index parameter based on the ratio between the intensity values ​​of each of the C detection wavelengths in the first spectral data and the intensity values ​​of each of the C detection wavelengths in the second spectral data. More specifically, the parameter calculation unit 202 calculates the index parameter based on the ratio r of the intensity values ​​of each of the C detection wavelengths between the two spectral data for the N analytes. i (i is an integer between 1 and C) and calculate the ratio r i The average value of r ave and calculates the index parameter metric1 for each of the N analytes using the following formula (1). ave may be obtained by calculating the ratio between the sum of the intensity values ​​of the C detected wavelengths in the first spectral data and the sum of the intensity values ​​of the C detected wavelengths in the second spectral data, or by calculating a weighted average.

number

[0035] Here, the meaning of the index parameters calculated by the parameter calculation unit 202 will be explained.

[0036] 5 is a graph showing the distribution of the first spectral data SD1 and the second spectral data SD2 obtained from a monochromatic cell containing only the dye "Dye A." As shown in FIG. 5, in the spectral data obtained from a monochromatic cell, the ratio r between the first spectral data SD1 and the second spectral data SD2 at each of the three detection wavelengths, "Detection Wavelength 1," "Detection Wavelength 2," and "Detection Wavelength 3," is i tends to be a nearly constant value α regardless of the detection wavelength. Also, FIG. 6 is a graph showing the distribution of the first spectral data SD1 and the second spectral data SD2 acquired from a monochromatic cell containing only "dye B," which is different from "dye A." In this way, in the spectral data acquired from a monochromatic cell containing different types of dyes, the ratio r between the first spectral data SD1 and the second spectral data SD2 at each of the three detection wavelengths, "detection wavelength 1," "detection wavelength 2," and "detection wavelength 3," is i is a nearly constant value β (≠α) regardless of the detection wavelength, and the value β tends to take on different values ​​depending on the type of dye contained in the monochrome cell.

[0037] On the other hand, Figure 7 is a graph showing the distribution of the first spectral data SD1 and the second spectral data SD2 obtained from a co-expression cell containing two types of pigments, "Pigment A" and "Pigment B." In this way, in the spectral data obtained from a co-expression cell containing multiple types of pigments, the ratio r between the first spectral data SD1 and the second spectral data SD2 at each of the three detection wavelengths, "Detection Wavelength 1," "Detection Wavelength 2," and "Detection Wavelength 3," is i tend to have different values ​​γ1, γ2, and γ3.

[0038] As shown in Figure 8, the ratio r iThe distribution of D is the distribution of monochrome cells containing "dye A". A is almost constant at the value α, and the distribution D of the monochromatic cells containing "dye B" B is almost constant at the value β, and the distribution of co-expressing cells containing "pigment A" and "pigment B" D A+B The distribution fluctuates between values ​​γ1 to γ3. Due to this property, the smaller the value of the index parameter metric1, the higher the monochromaticity. In this case, the average value r ave It is understood that indicates the type of dye contained in the monochrome cell.

[0039] The determination unit 203 determines the degree of monochromaticity of the N analytes based on the index parameters calculated for each of the N analytes. That is, the determination unit 203 determines whether each of the N analytes is a monochromatic object (e.g., a monochromatic cell) containing only a monochromatic dye based on the index parameters. As an example, an analyte whose index parameter is equal to or less than a threshold is determined as a monochromatic object, and an analyte whose index parameter is greater than the threshold is determined as not a monochromatic object. The threshold may be a value preset by a user or may be determined by the determination unit 203. Note that the case in which the threshold is determined by the determination unit 203 is not limited to being automatically determined by the determination unit 203 (e.g., determined according to a histogram of index parameters), and the determination unit 203 may determine the threshold according to parameters input by the user.

[0040] The clustering unit 204 performs clustering on O (O is an integer between 1 and N) analysis targets (hereinafter referred to as "multiple monochromatic targets") that have been determined to be monochromatic targets by the determination unit 203. Prior to the clustering process, the clustering unit 204 reads the C array data acquired by the data acquisition unit 201 and performs clustering on the O monochromatic targets that make up the C array data based on the intensity values ​​of each monochromatic target for each of the C detection wavelengths. Prior to the clustering process, the clustering unit 204 generates matrix data Y in which the intensity values ​​of the O monochromatic targets that make up each of the C array data are arranged in parallel in a one-dimensional manner. The C fluorescence intensity values ​​may be a compilation of C intensity values ​​acquired by the data acquisition device 3 by irradiating the target with excitation light of a first wavelength band and excitation light of a second wavelength band, or may be C intensity values ​​acquired by the data acquisition device 3 by irradiating the target with excitation light of either wavelength band. Alternatively, the C intensity values ​​may be obtained in a different optical state by irradiating excitation light in a different wavelength band from the excitation light in the first and second wavelength bands.

[0041] Then, the clustering unit 204 clusters the O single-color objects into L object groups (L is an integer between 2 and O-1) based on the distribution information of the intensity values ​​of each detection wavelength. For example, the clustering unit 204 clusters the O single-color objects into L object groups (L is an integer between 2 and O-1) based on the distribution information of the intensity values ​​of each detection wavelength. ave Then, a histogram of the average value r is created, and a threshold value is set between the two peaks based on the histogram. aveBy comparing the intensity values ​​of the C array data, the analysis target can be clustered into two object groups. The clustering unit 204 can classify monochromatic objects into three or more object groups using a similar function. Here, the number L of object groups to be clustered by the clustering unit 204 is set in advance as a parameter stored in the data processing device 5, corresponding to the number of types of dyes that may be present in the analysis target. The clustering unit 204 then divides and regenerates matrix data Y, in which the intensity values ​​of monochromatic objects of C array data are arranged in parallel in one dimension, into cluster matrices for each of the L object groups. Note that the number L of object groups to be clustered by the clustering unit 204 may be set in advance depending on the type of detection wavelength or the number (C) of detection wavelengths. Alternatively, the number L of object groups to be clustered by the clustering unit 204 may be set independently of these factors.

[0042] The statistical value calculation unit 205 and the matrix estimation unit 206 obtain a mixing matrix A for generating K pieces of dye data (K is an integer between 2 and C) indicating the distribution of each of K dyes from C pieces of array data, based on L cluster matrices obtained for a single-color object. Generally, according to the non-negative matrix factorization (NMF) calculation method, the relationship between matrix data Y, which is an observation matrix, and dye matrix data X, in which K dye data are arranged in parallel in one dimension for each analysis object, is expressed by the following formula using the mixing matrix A: Y=AX Here, Y is matrix data with C rows and O columns, A is matrix data with C rows and K columns, and X is matrix data with K rows and O columns. Conversely, once the value of mixing matrix A is determined, the dye matrix data X can be calculated by the inverse matrix A of mixing matrix A. -1 and matrix data Y, the following equation: X=A -1 Y (This process is called unmixing.)

[0043] Here, the statistical value calculation unit 205 regenerates matrix data Y' by compressing the matrix data Y generated by the clustering unit 204 in units of object groups clustered by the clustering unit 204. In detail, the statistical value calculation unit 205 calculates a statistical value for each object group of the clustered cluster matrix using the intensity values ​​of each row of the matrix data Y as a target, and compresses the object group of each row into a single analysis object having the calculated statistical value. In this way, the statistical value calculation unit 205 regenerates matrix data Y', which is matrix data with C rows and L columns. As the statistical value, the statistical value calculation unit 205 may calculate an average value based on the integrated value of the intensity values, may calculate the most frequent value of the intensity values, or may calculate the median value of the intensity values.

[0044] The matrix estimation unit 206 calculates the matrix data Y′ reproduced by the statistical value calculation unit 205 and the dye matrix data X′ compressed in the same manner from the dye matrix data X by the following equation including the mixing matrix A: Y'=AX' Using the property that the following holds, the mixing matrix A is derived based on the matrix data Y'. FIG. 9 shows an image of the matrix data Y' regenerated by the statistical value calculation unit 205 and the corresponding dye matrix data X'. One square shown in FIG. 9 represents one element of the matrix data. For example, if three object groups PGr 01 ~PGr 03 The pigment matrix data X and matrix data Y are divided into the object group PGr 01 ~PGr 03 The statistical values ​​for each pixel are used as representative values ​​and compressed into three columns of dye matrix data X' and matrix data Y'.

[0045] The matrix estimation unit 206 derives the mixing matrix A based on the matrix data Y' as follows. That is, the matrix estimation unit 206 sets an initial value to the mixing matrix A, calculates the following loss function (loss value) Loss while sequentially changing the value of the mixing matrix A, and derives the mixing matrix A that reduces the value of the loss function Loss. Note that a regularization term such as the L1 norm λ|A| (λ is a coefficient indicating the degree to which importance is attached to the regularization term) may be added to this loss function.

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[0046] As described above, the matrix estimation unit 206 calculates a loss function for each of the L cluster matrices divided by the clustering unit 204 by referring to the statistical values ​​of the C pieces of matrix data Y', calculates the loss function Los based on the sum of the L loss functions, and obtains the mixing matrix A based on the loss function Los. In this case, the matrix estimation unit 206 corrects the loss function calculated for each of the L cluster matrices by dividing it by the average values ​​a, b, and c of the statistical values ​​of the C pieces of matrix data Y', and then obtains the loss function Los by calculating the sum of the corrected loss functions. Note that the matrix estimation unit 206 may calculate the loss function for each of the L cluster matrices by correcting the row components of the difference value Y'-AX' for each fluorescence wavelength band by the C statistical values ​​corresponding to each fluorescence wavelength band.

[0047] The above equation can also be generalized as follows. That is, the matrix estimation unit 206 derives the mixing matrix A and the dye matrix data X' based on the matrix data Y' as follows. That is, the matrix estimation unit 206 sets initial values ​​for the mixing matrix A and the dye matrix data X', calculates a loss function (loss value) Loss using the equation below while sequentially changing the values ​​of the mixing matrix A and the dye matrix data X', and derives the mixing matrix A and the dye matrix data X' that reduce the value of the loss function Loss. Note that a regularization term such as the L1 norm λ|A| (λ is a coefficient indicating the degree to which the regularization term is emphasized) may be added to this loss function. Furthermore, the calculation may be performed with a constraint that the mixing matrix A and the dye matrix data X' become non-negative values.

number

[0048] As described above, the matrix estimation unit 206 calculates a loss function for each of the L cluster matrices divided by the clustering unit 204 by referring to the statistical values ​​of the C matrix data Y′, and calculates the L loss functions Los i The matrix estimation unit 206 calculates the loss function Los based on the sum of the L cluster matrices and obtains the mixing matrix A based on the loss function Los. i may be calculated by correcting the row components of the difference values ​​Y'-AX' for each wavelength band of fluorescence by dividing them by the C statistical values ​​corresponding to each wavelength band of fluorescence.

[0049] The data generation unit 207 obtains K pieces of dye data by unmixing C pieces of sequence data obtained for the analyte using the mixing matrix A derived by the matrix estimation unit 206. Specifically, the data generation unit 207 obtains K pieces of dye data by applying the inverse matrix A of the mixing matrix A to the matrix data Y generated by the clustering unit 204 based on the C pieces of sequence data. -1to calculate the dye matrix data X. Then, the data generation unit 207 reproduces K pieces of dye data from the dye matrix data X and outputs the reproduced K pieces of dye data. The output destination at this time may be an output device of the data processing device 5, such as a display or a touch panel display, or may be an external device connected to the data processing device 5 so as to be able to communicate data. Furthermore, the dye data generated by the data generation unit 207 may be used in data analysis for purposes such as counting, selecting, and analyzing the characteristics of the analyte.

[0050] Next, the procedure of the observation process for an analysis target object using the pigment data acquisition system 1 according to this embodiment, i.e., the flow of the pigment data acquisition method according to this embodiment, will be described. Fig. 10 is a flowchart showing the procedure of the observation process by the pigment data acquisition system 1.

[0051] First, the data acquisition device 3 performs an observation process on a plurality of analytes, resulting in the generation and transmission of sequence data (step S1). Next, the data acquisition unit 201 of the data processing device 5 acquires sequence data of C fluorescence wavelength bands from the data acquisition device 3 (step S2; data acquisition step).

[0052] Furthermore, the parameter calculation unit 202 of the data processing device 5 calculates an index parameter for each of the N analytes (step S3; parameter calculation step). Thereafter, the determination unit 203 of the data processing device 5 determines the degree of monochromaticity of the N analytes based on the index parameter, and determines whether the N analytes are monochromatic objects (step S4; determination step).

[0053] Furthermore, the clustering unit 204 of the data processing device 5 performs clustering on the C array data for each single-color object, and the O single-color objects of the array data are clustered into L object groups (step S5; clustering step). Next, the statistical value calculation unit 205 of the data processing device 5 calculates statistical values ​​of the L object groups, thereby regenerating matrix data Y' based on the matrix data Y generated by the clustering unit 204 (step S6; calculation step).

[0054] Then, the matrix estimation unit 206 of the data processing device 5 derives a mixing matrix A based on the matrix data Y' (step S7; data generation step). Finally, the data generation unit 207 of the data processing device 5 unmixes the matrix data Y generated based on the C pieces of sequence data for the analyte using the mixing matrix A, thereby acquiring and outputting K pieces of dye data (step S8; data generation step). This completes the observation process for multiple analytes.

[0055] According to the dye data acquisition system 1 described above, first and second spectral data of the fluorescence distribution at C detection wavelengths generated from an analyte irradiated with excitation light of two wavelength bands are processed. Specifically, index parameters are calculated from the intensity values ​​for each of the C detection wavelengths in the first spectral data and the intensity values ​​for each of the C detection wavelengths in the second spectral data, and the degree of monochromaticity of the analyte is determined based on the calculated index parameters. This allows for accurate determination of monochromaticity of the analyte even when the type of dye being observed or the type of excitation light used for observation changes, or when a bias in the number of each dye occurs. Furthermore, the determination results can be used to cluster the spectral data, and a mixing matrix can be calculated based on the clustering results, allowing for accurate determination of the expression level of each dye in the analyte.

[0056] In this embodiment, the index parameter is calculated based on the ratio between the intensity value for each of the C detected wavelengths in the first spectral data and the intensity value for each of the C detected wavelengths in the second spectral data. In this case, by making a determination based on the ratio of the intensity values ​​for each detected wavelength, it is possible to accurately determine whether the object is monochromatic.

[0057] Furthermore, in this embodiment, the degree of monochromaticity of the N analytes is determined based on index parameters calculated for the N analytes, and the N analytes are clustered into L object groups based on the degree of monochromaticity of the N analytes. In this case, the N analytes can be clustered into L object groups with high accuracy based on the degree of monochromaticity of the N analytes.

[0058] Furthermore, in this embodiment, for each of the L clustered object groups, statistical values ​​of the object group intensity values ​​at C detection wavelengths are calculated, and unmixing is performed for the C detection wavelengths using the statistical values ​​at the C detection wavelengths for each of the L object groups, to generate K items of dye data showing the distribution for each of the K fluorescent dyes. As a result, even if the type of dye being observed or the type of excitation light used for observation changes, or if a bias in the number of each dye occurs, unmixing appropriate to the observation conditions can be performed, and dye data that is highly accurate fluorescence spectrum data can be obtained.

[0059] Flow cytometry is a technique that enables the identification or quantification of cell populations by staining cells, cell surface proteins, and intracellular proteins, and can be used for cell sorting, immunophenotyping, etc. According to this embodiment, by analyzing the expression combination of antibodies used on blood cells, for example, based on the acquired dye data, it is possible to accurately identify which cells have become tumorigenic.

[0060] Various embodiments of the present invention have been described above, but the present invention is not limited to the above embodiments, and may be modified or applied to other things within the scope that does not change the gist of the claims.

[0061] As a method for clustering a group of objects in the data processing device 5, a method using machine learning such as the K-means method, a method using deep learning, or the like may be adopted.

[0062] Furthermore, as a clustering method for a group of objects in the data processing device 5, in addition to the K-means algorithm, methods using machine learning such as decision trees, support vector machines, KNN (K nearest neighbors), self-organizing maps, spectral clustering, Gaussian mixture models, DBSCAN, affinity propagation, meanshift, Ward, agglomerative clustering, OPTICS, and BIRCH, and methods using deep learning may be adopted. Furthermore, preprocessing may be performed on the matrix data Y before applying clustering. For example, the dimension of the C-dimensional data of each analysis object may be reduced by phasor analysis, principal component analysis, singular value decomposition, independent component analysis, linear discriminant analysis, t-SNE, UMAP, or other machine learning methods.

[0063] The clustering unit 204 of the data processing device 5 may further generate M cluster matrices in which the intensity values ​​of each detection target substance are arranged for each wavelength group by clustering the C fluorescence wavelength bands of the matrix data Y (M is an integer between 2 and C-1) based on the intensity values ​​of each wavelength band. These M cluster matrices may be generated before or after the generation of the L cluster matrices described above. In this case, the statistical value calculation unit 205 regenerates matrix data Y′ by compressing the matrix data Y for each clustered wavelength group using statistical values, using a method similar to that described above. In this way, clustering fluorescence wavelength bands can improve the S / N ratio of the dye data. Furthermore, the amount of calculation required for unmixing can be further reduced. Furthermore, performing unmixing using statistical values ​​of the intensity values ​​for each clustered wavelength group can maintain the accuracy of dye data separation.

[0064] The data acquisition device 3 of the above embodiment may be modified to the configuration shown in FIG. 11. The data acquisition device 3A according to the modified example shown in FIG. 11 differs from the data acquisition device 3A in that it includes a spectrometer 58 and a detector 59 as the optical system 53. The spectrometer 58 separates the fluorescence emitted from the sample fluid into multiple wavelength bands (e.g., 1024 wavelength bands). The detector 59 is a one-dimensional or two-dimensional detector having multiple pixels (e.g., 1024 pixels), and measures the intensity of the fluorescence of each wavelength band incident on each pixel from the spectrometer 58 and outputs the measured intensity to the electronic system 54. Such a data acquisition device 3A can generate array data of multiple detection wavelengths. For example, the 1024-channel array data generated by the data acquisition device 3A is divided into 512 cluster matrices by the data processing device 5, with two adjacent channels being clustered into one wavelength group.

[0065] The parameter calculation unit 202 of the data processing device 5 may calculate the index parameter metric2 for N analytes using the following formula (2).

number

[0066] Furthermore, the parameter calculation unit 202 of the data processing device 5 may calculate the index parameter cos θ for N analytes using the following formula (3).

number

[0067] Furthermore, the parameter calculation unit 202 may calculate the angle θ between the two vectors as an index parameter using the following formula (4): In this case, the determination unit 203 determines that an analysis target whose index parameter θ is equal to or smaller than a threshold value is a monochromatic object, and determines that an analysis target whose index parameter θ is greater than the threshold value is not a monochromatic object.

number

[0068] Furthermore, when calculating the statistical values ​​for each group of objects in the clustered cluster matrix, the statistical value calculation unit 205 of the data processing device 5 may calculate the statistical values ​​by weighting the intensity values ​​of the objects to be analyzed (for example, using an index parameter) according to the degree of monochromaticity determined for the objects to be analyzed. In this case, the mixing matrix can be obtained using the determination result of the degree of monochromaticity, and the expression amount of each pigment in the objects to be analyzed can be obtained with higher accuracy.

[0069] Below are shown the results of a simulation of the determination process for a monochromatic object using the data processing device 5. Here, first and second spectral data for nine channels of detection wavelengths were prepared, assuming an analysis object containing two types of dyes, "dye 1" and "dye 2," in various mixture ratios, and the nine channels of first and second spectral data were subjected to principal component analysis to reduce the dimensions to two channels of spectral data, which was then used in the determination process.

[0070] Figure 12 shows the results of determining whether an object is a monochromatic object, using the index parameter metric1 by the data processing device 5 for first and second spectral data in which the mixture ratio of two types of dyes is evenly distributed. In the graph shown in Figure 12, the horizontal axis represents the intensity value at the first detection wavelength, and the vertical axis represents the intensity value at the second detection wavelength. Two intensity values ​​for each object are plotted at a single point, and objects determined to be monochromatic objects are indicated by a cross mark. This result demonstrates that the index parameter can be used to accurately determine whether an object contains only one of the dyes, "Dye 1" or "Dye 2."

[0071] 13 shows the results of determination of a monochromatic object performed by the data processing device 5 using the index parameter metric 1 for the first and second spectral data in which the mixture ratios of two types of dyes are distributed with variation (close to the actual measurement data). From this result, it was found that although there was a bias in the determination accuracy between dyes, monochromatic objects could be determined with high accuracy.

[0072] 14 shows the results of determining whether a monochromatic object is present, using the index parameter metric2 with the data processing device 5, for first and second spectral data in which the mixture ratio of two types of dyes is evenly distributed and which contain shot noise. These results demonstrate that even when noise is present, the index parameter metric2 can accurately determine whether a monochromatic object contains only "dye 1" or "dye 2." In particular, because the index parameter is calculated taking noise into account, even when the sample or measurement conditions change, the threshold can be determined probabilistically from the distribution of the index parameter, enabling accurate determination of a monochromatic object.

[0073] 15 shows the results of determination of a monochromatic object performed by the data processing device 5 using the index parameter metric2 for the first and second spectral data in which the mixture ratios of two types of dyes are distributed with variation (close to the actual measurement data).The results show that monochromatic objects containing only "dye 1" and "dye 2" can be determined with high accuracy without bias in determination accuracy between the dyes.

[0074] 16 shows the results of determining whether a target object is a monochromatic object, using the index parameter θ with the data processing device 5, for first and second spectral data in which the mixture ratios of two types of dyes are evenly distributed and which contain shot noise. These results demonstrate that even when noise is present, the index parameter θ can be used to accurately determine whether a target object contains only one of the dyes, "dye 1" or "dye 2." In particular, in this case, targets with high intensity values ​​compared to the noise are more likely to be determined to be monochromatic objects, improving the accuracy of estimating the mixing matrix.

[0075] 17 shows the results of determination of a monochromatic object performed by the data processing device 5 using the index parameter θ for the first and second spectral data in which the mixture ratios of two types of dyes are distributed with variation (close to the actual measurement data).The results show that monochromatic objects containing only "dye 1" and "dye 2" can be determined with high accuracy without bias in determination accuracy between the dyes. [Explanation of symbols]

[0076] 1...pigment data acquisition system, 3, 3A...data acquisition device, 5...data processing device, 201...data acquisition unit, 202...parameter calculation unit, 203...judgment unit, 204...clustering unit, 205...statistical value calculation unit, 206...matrix estimation unit, 207...data generation unit, A...mixing matrix.

Claims

1. a data acquisition step of irradiating an object with excitation light having a first wavelength distribution, and acquiring first spectral data which is a distribution of intensity values ​​of fluorescence for each of C (C is an integer of 2 or more) detection wavelengths generated from the object, and irradiating the object with excitation light having a second wavelength distribution, and acquiring second spectral data which is a distribution of intensity values ​​of fluorescence for each of the C detection wavelengths generated from the object; a parameter calculation step of calculating an index parameter based on the intensity values ​​for each of the C detected wavelengths in the first spectrum data and the intensity values ​​for each of the C detected wavelengths in the second spectrum data; a determining step of determining a degree of monochromaticity of the object based on the index parameters; A dye data acquisition method comprising:

2. the parameter calculation step calculates the index parameter based on a ratio between the intensity value for each of the C detection wavelengths in the first spectrum data and the intensity value for each of the C detection wavelengths in the second spectrum data; The method for obtaining dye data according to claim 1 .

3. the parameter calculation step calculates the index parameter based on a relationship between first vector data represented by the intensity values ​​for each of the C detected wavelengths in the first spectrum data and second vector data represented by the intensity values ​​for each of the C detected wavelengths in the second spectrum data; The method for obtaining dye data according to claim 1 .

4. In the determination step, the monochromaticity of N objects (N is an integer of 2 or more) is determined based on the index parameters calculated for the N objects; further comprising a clustering step of clustering the N objects into L object groups (L is an integer of 2 or more and N-1 or less) based on the monochromaticity of the N objects; The method for obtaining dye data according to claim 1 .

5. a data generation step of calculating a statistical value of the intensity values ​​of the object group at C detection wavelengths for each of the L clustered objects, performing unmixing for the C detection wavelengths using the statistical value at the C detection wavelengths for each of the L object groups, and generating K dye data items indicating distributions for K fluorescent dyes (K is an integer of 2 or more and C or less); The method for obtaining dye data according to claim 4.

6. A dye data acquisition device that processes first spectral data, which is a distribution of intensity values ​​of fluorescence for each of C (C is an integer of 2 or more) detection wavelengths generated from an object in response to irradiation with excitation light having a first wavelength distribution, and second spectral data, which is a distribution of intensity values ​​of fluorescence for each of the C detection wavelengths generated from the object in response to irradiation with excitation light having a second wavelength distribution, calculating an index parameter based on the intensity values ​​for each of the C detection wavelengths in the first spectrum data and the intensity values ​​for each of the C detection wavelengths in the second spectrum data; determining the degree of monochromaticity of the object based on the index parameter; Dye data acquisition device.

7. A dye data acquisition program for processing first spectral data which is a distribution of intensity values ​​of fluorescence at each of C (C is an integer of 2 or more) detection wavelengths generated from an object in response to irradiation with excitation light having a first wavelength distribution, and second spectral data which is a distribution of intensity values ​​of fluorescence at each of the C detection wavelengths generated from the object in response to irradiation with excitation light having a second wavelength distribution, On the computer, a parameter calculation step of calculating an index parameter based on the intensity values ​​for each of the C detected wavelengths in the first spectrum data and the intensity values ​​for each of the C detected wavelengths in the second spectrum data; a determining step of determining a degree of monochromaticity of the object based on the index parameters; Execute the dye data acquisition program.

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