Information processing apparatus, information processing system, information processing method, and program

Abstract

The main object of the present technology is to provide a technology for automatically proposing a good combination of fluorescent dye-labeled antibodies. The present technology provides an information processing apparatus including a processing unit that generates a combination list of fluorescent dyes with respect to a biological molecule based on an expression level category that classifies a plurality of biological molecules for analyzing a sample according to respective expression levels in the sample, a brightness category that classifies a plurality of fluorescent dyes for analyzing the sample according to brightness, and correlation information between the plurality of fluorescent dyes. The processing unit selects fluorescent dyes to be assigned to the biological molecule in the combination list from among the fluorescent dyes belonging to the brightness category associated with the expression level category to which the biological molecule belongs.

Description

TECHNICAL FIELD

[0001] The present technology relates to an information processing apparatus, an information processing system, an information processing method, and a program. More specifically, the present technology relates to an information processing apparatus, an information processing system, an information processing method, and a program that propose how to assign a phosphor to a biomolecule. BACKGROUND

[0002] For example, a population of particles such as cells, microorganisms, and liposomes is labeled with a fluorescent dye, and the intensity and / or pattern of fluorescence generated by the fluorescent dye excited by irradiating each particle of the population of particles with laser light is measured, thereby measuring a characteristic of the particle. Typical examples of a particle analyzer that performs the measurement include a flow cytometer.

[0003] A flow cytometer is a device that irradiates particles flowing in a line in a flow path with laser light of a specific wavelength (excitation light) and detects fluorescence and / or scattered light emitted from each particle to analyze a plurality of particles one by one. The flow cytometer can convert light detected by a photodetector into an electrical signal, quantize the electrical signal, and perform statistical analysis to determine a characteristic of each particle, such as type, size, and structure.

[0004] Several technologies have been proposed so far regarding technologies for selecting a fluorescent dye to be used for labeling a population of particles to be analyzed by a flow cytometer. For example, Patent Literature 1 below describes a method for designing a probe panel of a flow cytometer, the method including: determining a strain factor that quantifies an influence of leakage to a second channel caused by luminescence of a first label intended to be measured in a first channel;

[0005] inputting a predicted maximum signal of a first probe-label combination including a first label and a first probe;

[0006] calculating an increase in a detection limit in the second channel based on the strain factor and the predicted maximum signal of the first probe-label combination; and

[0007] selecting a probe-label combination to be included in the probe panel based on the calculated increase in the detection limit.

[0008] LIST OF CITATIONS

[0009] PATENT LITERATURE

[0010] Patent Literature 1: Japanese Unexamined Patent Application Publication (Translation of the PCT Application) No. 2016-517000. SUMMARY

[0011] PROBLEMS TO BE SOLVED BY THE INVENTION

[0012] In order to label a population of particles to be analyzed by a flow cytometer, a plurality of fluorescent dye-labeled antibodies are often used. The process of determining a combination of fluorescent dye-labeled antibodies used for analysis is also called panel design. The number of fluorescent dye-labeled antibodies used in analysis tends to increase, and as the number increases, panel design becomes more difficult.

[0013] Accordingly, a main object of the present technology is to provide a technology that automatically proposes a better combination of fluorescent dye-labeled antibodies.

[0014] Solution to the problem

[0015] The present technology provides an information processing apparatus including a processing unit that generates a combination list of fluorescent bodies for biological molecules based on an expression level category that classifies a plurality of biological molecules to be used for analysis of a sample based on an expression level in the sample, a brightness category that classifies a plurality of fluorescent bodies usable for analysis of the sample based on brightness, and correlation information between the plurality of fluorescent bodies, wherein the processing unit selects, from among the fluorescent bodies belonging to the brightness category associated with the expression level category to which the biological molecule belongs, a fluorescent body to be assigned to the biological molecule in the combination list.

[0016] The expression level category is associated with the brightness category such that the expression level category obtained by classifying biological molecules exhibiting a lower expression level corresponds to the brightness category classifying brighter fluorescent bodies.

[0017] The processing unit can select each of the fluorescent bodies by using the correlation information.

[0018] The correlation information can be a correlation coefficient between fluorescent spectra of the plurality of fluorescent bodies.

[0019] The correlation information can be a value obtained by squaring a correlation coefficient between fluorescent spectra of the plurality of fluorescent bodies.

[0020] The processing unit can calculate the correlation coefficient by using two or more fluorescent spectra respectively obtained in a case where the fluorescent body is irradiated with excitation light beams of two or more different wavelengths.

[0021] The correlation information can be a staining index between the plurality of fluorescent bodies.

[0022] The correlation information can be an overflow diffusion matrix between the plurality of fluorescent bodies.

[0023] The plurality of fluorescent bodies can be identified based on data on whether a complex of a fluorescent body and a biological molecule is usable.

[0024] The processing unit can adjust the number of fluorescent bodies belonging to each of the brightness categories in accordance with the number of biological molecules belonging to each of the expression level categories.

[0025] The plurality of fluorophores can be identified based on information about priorities associated with the fluorophores.

[0026] The processing unit can perform evaluation of the separation ability in relation to the combined list.

[0027] The processing unit can further generate a modified combined list in which at least one fluorophore of the set of fluorophores included in the combined list is changed to another fluorophore, according to a result of the evaluation of the separation ability, and further perform evaluation of the separation ability in relation to the modified combined list.

[0028] The processing unit can further generate the combined list based on cost information about complexes of the biomolecules and the fluorophores.

[0029] In a case where the fluorophore to be assigned to at least one of the plurality of biomolecules is identified, the processing unit can further generate the combined list based on correlation information between the plurality of fluorophores and the pre-identified fluorophore.

[0030] In a case where the light-producing substance to be used for analyzing the sample is identified, the processing unit can further generate the combined list based on correlation information between the plurality of fluorophores and the light-producing substance.

[0031] Each of the plurality of biomolecules can be an antigen or an antibody.

[0032] In a case where each of the plurality of biomolecules is an antigen, the expression level can be an expression level of the antigen, and in a case where each of the plurality of biomolecules is an antibody, the expression level can be an expression level of an antigen captured by the antibody.

[0033] Further, the present technology also provides an information processing system including: an input unit that accepts input of data about expression levels of a plurality of biomolecules to be used for analyzing a sample; and a processing unit that generates a combined list of fluorophores for the biomolecules, based on an expression level category in which the plurality of biomolecules are classified based on the expression levels in the sample, a brightness category in which a plurality of fluorophores that can be used for analyzing the sample are classified based on brightness, and correlation information between the plurality of fluorophores, wherein the processing unit selects a fluorophore to be assigned to the biomolecule in the combined list from among fluorophores belonging to the brightness category associated with the expression level category to which the biomolecule belongs.

[0034] Further, the present technology provides an information processing method including: a list generation step of generating a combination list of fluorescent bodies with respect to a biological molecule, based on an expression level category that classifies a plurality of biological molecules to be used for analyzing a sample based on expression levels in the sample, a luminance category that classifies a plurality of fluorescent bodies usable for analyzing the sample based on luminance, and relevant information between the plurality of fluorescent bodies, wherein, in the list generation step, a fluorescent body to be assigned to the biological molecule in the combination list is selected from among fluorescent bodies belonging to a luminance category associated with an expression level category to which the biological molecule belongs.

[0035] Further, the present technology provides a program that causes an information processing apparatus to execute: a list generation step of generating a combination list of fluorescent bodies with respect to a biological molecule, based on an expression level category that classifies a plurality of biological molecules to be used for analyzing a sample based on expression levels in the sample, a luminance category that classifies a plurality of fluorescent bodies usable for analyzing the sample based on luminance, and relevant information between the plurality of fluorescent bodies, wherein, in the list generation step, a fluorescent body to be assigned to the biological molecule in the combination list is selected from among fluorescent bodies belonging to a luminance category associated with an expression level category to which the biological molecule belongs. BRIEF DESCRIPTION OF DRAWINGS

[0036] Figure 1 is a schematic diagram of a configuration of a flow cytometer.

[0037] Figure 2 is a diagram showing an example of an experimental procedure in a case where the present technology is applied to flow cytometry.

[0038] Figure 3 is a diagram showing an example of a configuration of an information processing apparatus according to the present technology.

[0039] Figure 4 is a flowchart of processing performed by an information processing apparatus according to the present technology.

[0040] Figure 5A is a diagram for explaining information processing according to the present technology.

[0041] Figure 5B is a diagram for explaining information processing according to the present technology.

[0042] Figure 6 is a diagram showing a matrix of correlation coefficient square values.

[0043] Figure 7 is a conceptual diagram showing how to assign fluorescent bodies to biological molecules.

[0044] Figure 8 is a diagram showing a fluorescent spectrum.

[0045] Figure 9is a diagram for explaining adjustment of brightness category.

[0046] Figure 10 is a diagram showing an example of a list related to phosphors.

[0047] Figure 11 is a diagram for explaining a dyeing index.

[0048] Figure 12 is a diagram showing an example of a calculation result of inter-phosphor dyeing index.

[0049] Figure 13 is a flowchart of processing performed by the information processing apparatus according to the present technology.

[0050] Figure 14 is a flowchart of separation ability evaluation processing.

[0051] Figure 15 is a diagram showing an example of data of inter-phosphor SI.

[0052] Figure 16 is a diagram showing an example of a window that displays candidate phosphors that replace phosphors with poor separation performance.

[0053] Figure 17A is a diagram showing a calculation result of inter-phosphor SI.

[0054] Figure 17B is a diagram showing a calculation result of inter-phosphor SI.

[0055] Figure 18 is a flowchart of processing performed by the information processing apparatus according to the present technology.

[0056] Figure 19 is a flowchart of processing performed by the information processing apparatus according to the present technology.

[0057] Figure 20 is a diagram showing an example of an input acceptance window.

[0058] Figure 21 is a flowchart of processing performed by the information processing apparatus according to the present technology.

[0059] Figure 22 is a diagram showing an example of an input acceptance window.

[0060] Figure 23A is a diagram showing an example of a standardized inter-phosphor SI list.

[0061] Figure 23B is a diagram for explaining a method for calculating a standardized inter-phosphor SI.

[0062] Figure 24A is a diagram showing an example of inter-phosphor SSM.

[0063] Figure 24B is a diagram for explaining a method for calculating inter-phosphor SSM.

[0064] Figure 25A is a diagram showing inter-phosphor dyeing indices obtained through experiments.

[0065] Figure 25B is a diagram showing a two-dimensional map obtained through experiments.

[0066] Figure 25C is a diagram showing inter-phosphor dyeing indices obtained through experiments.

[0067] Figure 25D is a diagram showing a two-dimensional map obtained through experiments.

[0068] Figure 26A is a diagram showing a two-dimensional map obtained through experiments.

[0069] Figure 26B is a diagram showing a two-dimensional map obtained through experiments.

[0070] Figure 27 is a diagram showing a two-dimensional map obtained through experiments.

[0071] Figure 28 is a diagram showing processing time required for generating a combination list.

[0072] Figure 29A is a diagram showing inter-phosphor dyeing indices and a two-dimensional map obtained through experiments.

[0073] Figure 29B is a diagram showing inter-phosphor dyeing indices and a two-dimensional map obtained through experiments.

[0074] Figure 30A is a diagram showing inter-phosphor dyeing indices and a two-dimensional map obtained through experiments.

[0075] Figure 30B is a diagram showing inter-phosphor dyeing indices and a two-dimensional map obtained through experiments.

[0076] Figure 31A is a diagram showing inter-phosphor dyeing indices and a two-dimensional map obtained through experiments.

[0077] Figure 31B is a diagram showing inter-phosphor dyeing indices and a two-dimensional map obtained through experiments. DETAILED DESCRIPTION

[0078] The modes for implementing this technology will now be described. Note that the embodiments described below illustrate representative embodiments of this technology, and the scope of this technology is not limited to these embodiments. Note that this technology will be described in the following order.

[0079] 1. First Embodiment (Information Processing Device)

[0080] (1) Details of the problem of the present invention

[0081] (2) Example of the procedure for performing experiments using this technology

[0082] (3) Description of the first embodiment

[0083] (3-1) Example of information processing device configuration

[0084] (3-2) Example of processing by the processing unit (processing flow)

[0085] (Example 1: Evaluation using simulated data)

[0086] (Example 2: Evaluation using cells)

[0087] (3-3) Example of processing in the processing unit (example of fluorescence spectrum used to calculate correlation coefficient)

[0088] (3-4) Example of processing in the processing unit (refer to the example in the database on reagent availability)

[0089] (3-5) Example of processing in the processing unit (example of adjusting the number of phosphors classified into each brightness category)

[0090] (3-6) Examples of processing by the processing unit (examples of prioritization information associated with each phosphor and limitations on the number of phosphors used in optimization based on the number of phosphors selected)

[0091] (Example 3: Whether to use comparisons of information about priority)

[0092] (3-7) Example of processing by the processing unit (example of performing a separation capability assessment)

[0093] (Example 4: Comparison of the presence and absence of separation capability assessment)

[0094] (3-8) Example of processing in the processing unit (processing flow considering reagent costs)

[0095] (3-9) Examples of processing in the processing unit (including processing flow when the phosphor to be used is predetermined)

[0096] (3-10) Examples of processing by the processing unit (including processing flow when the light-generating material to be used is predetermined)

[0097] (3-11) Example of processing in the processing unit (processing flow where staining index between fluorophores or spillover diffusion matrix between fluorophores is used as relevant information)

[0098] (3-11-1) Cases involving the use of staining indices between fluorescent bodies

[0099] (Example 5: Evaluation using simulated data - normalized inter-fluorophore SI case)

[0100] (3-11-2) Case of using the spillover diffusion matrix between phosphors

[0101] (Example 6: Evaluation using simulated data - SSM between fluorescent cells)

[0102] 2. Second Embodiment (Information Processing System)

[0103] 3. Third Embodiment (Information Processing Method)

[0104] 4. Fourth Implementation Example (Program)

[0105] 1. First Embodiment (Information Processing Device)

[0106] (1) Details of the problem of the present invention

[0107] For example, from the perspective of the optical system used in fluorescence measurement, flow cytometry can be roughly classified into filter type and spectral type. Filter flow cytometry can employ, for example... Figure 1 The configuration shown in Figure 1 is designed to extract target light information only from the target fluorescent dye. Specifically, the light generated by irradiating the particles is branched into multiple branches by a wavelength separation device DM (such as a dichroic mirror), allowing it to pass through different filters, and then the light of each branch is measured by multiple detectors (e.g., photomultiplier tubes, PMTs, etc.). That is, in a filter flow cytometer, fluorescence detection is performed for each wavelength band corresponding to each fluorescent dye by using a detector corresponding to each fluorescent dye to perform fluorescence detection of multiple colors. In this case, when using multiple fluorescent dyes that all have similar fluorescence wavelengths, fluorescence correction processing can be performed to calculate a more accurate fluorescence amount. However, when using multiple fluorescent dyes that all have very similar fluorescence spectra, leakage of fluorescence to detectors other than the detector used to detect fluorescence increases, and therefore, events that prevent fluorescence correction from being performed may also occur.

[0108] Spectroscopic flow cytometry utilizes the spectral information of fluorescent dyes used for staining to perform deconvolution (demixing) on ​​fluorescence data obtained by detecting the light generated when particles are illuminated, in order to analyze the fluorescence level of each particle. For example... Figure 1 As shown in Figure 2, spectroscopic flow cytometry disperses fluorescence using a prism-based spectroscopic optical element P. Furthermore, to detect both the fluorescence in spectroscopic flow cytometry and the dispersed fluorescence, an array-type detector, such as an array-type photomultiplier PMT, is used instead of the numerous photodetectors found in filter-based flow cytometry. Compared to filter-based flow cytometry, spectroscopic flow cytometry more easily avoids the effects of fluorescence leakage and is more suitable for analysis using a variety of fluorescent dyes.

[0109] In basic and clinical medicine, multicolor analysis using multiple fluorescent dyes has become common in flow cytometry to advance comprehensive interpretation. However, when a large number of fluorescent dyes are used in a single measurement, as in multicolor analysis, fluorescence leakage from dyes other than the target dye can occur in filter flow cytometry, as mentioned above, leading to reduced analytical accuracy. With a large number of colors, fluorescence leakage can be mitigated to some extent by using spectral flow cytometry. However, to perform more appropriate multicolor analysis, a suitable panel design (combination design of fluorescent dyes and antibodies) is required, taking into account the fluorescence spectral shape, antibody expression level, and the brightness of the fluorescent dyes.

[0110] Panel design has traditionally relied heavily on user experience and trial-and-error adjustments. However, as the number of colors increases, especially when the number of colors is around 20 or more, the number of combinations of fluorescent dyes to consider increases rapidly, making it very difficult to find the optimal combination of dyes with sufficient decomposition performance.

[0111] Manufacturers of flow cytometers and reagents that sell antibodies with fluorescent dyes have released web tools to facilitate panel design for their products. However, these web tools may not demonstrate sufficient usability as the number of colors increases.

[0112] When the number of colors is, for example, 10 or more, significant overlap between fluorescence spectra cannot be avoided, and it is difficult for humans to predict actual fluorescence leakage from the apparent overlap of spectra. If the number of parameters is 1, humans can manually adjust them to some extent; however, in panel design for multicolor analysis, there are multiple parameters that need to be adjusted independently. Key examples of parameters to consider include fluorescence spectral shape, antigen expression level, and the brightness of the fluorescent dye. Furthermore, the excitation characteristics, availability, and cost of the fluorescent dye are also expected to be considered. Therefore, it is very difficult to determine which fluorescent dye should be prioritized and to predict the overall impact by changing the combination of some fluorescent dyes. Basic principles of fluorescence correction and independent information about each fluorescent dye and antigen are insufficient for proper panel design, and manually finding the optimal combination is extremely difficult. Because the number of panel candidates generated when considering the aforementioned multiple parameters is enormous, it is believed that automatically presenting better panels could significantly reduce the burden on users.

[0113] (2) Example of the procedure for performing experiments using this technology

[0114] As described above, this technique can be used to generate lists associated with combinations of antibodies and fluorescent agents in particle analyses such as flow cytometry. (See reference...) Figure 2 This describes an example of an experimental procedure for applying this technique in flow cytometry.

[0115] The experimental procedure using flow cytometry generally consists of: experimental planning steps involving examining the cells as the experimental target and preparing antibody reagents with fluorescent indicators for cell detection. Figure 2 "1: Plan" in the text; sample preparation steps for actual staining and preparing cells in conditions suitable for measurement ( Figure 2 (2: Preparation); Flow cytometry (FCM) measurement steps for measuring the fluorescence intensity of each stained cell using flow cytometry (FCM). Figure 2 (3: FCM); and data analysis steps that perform various data processing to obtain the desired analytical results from the data recorded by FCM measurement ( Figure 2 (See "4: Data Analysis" in the document). Then, these steps can be repeated as needed.

[0116] In the experimental planning steps, the first step is to determine which molecules (e.g., antigens, cytokines, etc.) are expressed to identify the microparticles (primarily cells) to be detected using flow cytometry; that is, the markers used to detect the microparticles. This can be determined, for example, based on information such as past experimental results and publications. Next, the choice of fluorescent dye to detect the markers is examined. Information such as the number of markers to be detected simultaneously, the specifications of available FCM equipment, commercially available fluorescently labeled reagents, and the spectrum, intensity, price, and delivery date of the fluorescent dyes are comprehensively determined, and the combination of fluorescently labeled antibody reagents required for the actual experiment is determined. The process of determining the reagent combination is often referred to as panel design in FCM. Here, reagents that are insufficient in the set determined by the panel design are ordered from the reagent manufacturer and purchased. However, fluorescently labeled antibody reagents are expensive, and relatively rare reagents may take a month or more from order to delivery. Therefore, performing trial and error by repeatedly repeating the above four steps is impractical. The desired results are expected with fewer experimental planning steps.

[0117] In the sample preparation step, the experimental target is first prepared to a state suitable for FCM measurement. For example, cell isolation and purification can be performed. For instance, for immune cells derived from blood, red blood cells are removed from the blood by hemolysis and density gradient centrifugation, and white blood cells are extracted. The extracted cell population, which is the target, is then stained using fluorescently labeled antibodies. At this point, in addition to the sample to be analyzed, which is stained with multiple fluorescent dyes simultaneously, it is generally recommended to prepare a single-stained sample stained with only one fluorescent dye used as a standard for analysis and a completely unstained sample.

[0118] In the FCM measurement procedure, when optically analyzing microparticles, firstly, excitation light is emitted from the light source of the flow cytometer's illumination unit, and the excitation light illuminates the microparticles flowing in the flow path. Next, the fluorescence emitted from the microparticles is detected by the flow cytometer's detection unit. Specifically, using a dichroic mirror, bandpass filter, etc., only light of a specific wavelength (target fluorescence) is separated from the light emitted from the microparticles, and the separated light is detected by a detector such as a 32-channel PMT. At this point, for example, prisms, diffraction gratings, etc., are used to disperse the fluorescence, and different wavelengths of light are detected in each channel of the detector. This facilitates obtaining spectral information of the detected light (fluorescence). There are no particular limitations on the microparticles to be analyzed, and examples include cells, microbeads, etc.

[0119] Flow cytometers can record fluorescence information for each particle acquired via FCM measurements, as well as scattered light, time, and location information in addition to fluorescence. This recording function can be primarily performed using a computer's memory or disk. In normal cell analysis, the analysis of thousands to millions of particles is performed under a single experimental condition, thus necessitating the recording of multiple data points in tissue condition for each experimental condition.

[0120] In the data analysis step, the light intensity data detected in each wavelength region during the FCM measurement step is equivalently quantified using a computer, and the fluorescence amount (intensity) for each fluorescent dye used is obtained. For this analysis, a calibration method is used, employing a standard calculated from the experimental data. The standard is calculated through statistical processing of two types of measurement data—one for particles stained with only one fluorescent dye and the other for unstained particles. The calculated fluorescence amount can be recorded along with information such as the name of the fluorescent molecule, the measurement date, and the type of particle in a data recording unit set up in the computer. The fluorescence amount (fluorescence spectral data) of the sample estimated through data analysis is stored and displayed as a graph, and the fluorescence distribution of the particles is analyzed. For example, the proportion of cells to be detected contained in the measured sample can be calculated by analyzing the fluorescence distribution.

[0121] This technology can be used for panel design in the experimental planning step. For example, the information processing device according to this technology can accept user input on biomolecules and their expression levels in the target measurement, and can automatically generate optimized FCM experimental panels using the input data. That is, the information processing device of this technology can be described as a device with an optimization algorithm for panel generation.

[0122] Furthermore, as another example of particle analysis applying this technology, a particle sorter for separating particles in an enclosed space can be mentioned. For example, to determine whether to sort particles, the device may include: a chip having a flow path that allows particles to flow through and sorts the particles; a light irradiation unit that irradiates the particles flowing through the flow path with light; a detection unit that detects the light generated by the light irradiation; and a determination unit that determines whether to sort the particles based on information about the detected light. Examples of particle sorters include the device described in Japanese Patent Application Publication No. 2020-041881.

[0123] Furthermore, the analysis using this technique is not limited to particle analysis. That is, this technique can be used for various types of processing that require the allocation of fluorophores to biomolecules. For example, in the microscopic analysis or observation of cell or tissue samples, such as multicolor fluorescence imaging, the process of allocating fluorophores to biomolecules according to this technique can be performed to stain these samples. In recent years, the number of fluorophores used in fluorescence imaging has also tended to increase, and this technique can also be used for such analysis or observation.

[0124] (3) Description of the first embodiment

[0125] The information processing apparatus according to this technology includes a processing unit for generating a combination list of fluorophores for biomolecules. The processing unit generates the combination list based on expression level categories obtained by classifying multiple biomolecules used to analyze a sample according to their expression levels in the sample, brightness categories classifying multiple fluorophores that can be used to analyze the sample based on their brightness, and relevant information between the multiple fluorophores. During generation, the processing unit selects fluorophores to be assigned to the biomolecules from among the fluorophores belonging to the brightness categories associated with the expression level categories to which the biomolecules belong, from the combination list.

[0126] By performing selections and generating a list of combinations, a more appropriate list of combinations can be produced, and the processing used for generation can be performed more efficiently. This allows for the automatic execution of optimized panel designs.

[0127] (3-1) Example of information processing device configuration

[0128] Reference Figure 3 An example of an information processing apparatus according to the present technology is described. Figure 3 This is a block diagram of an information processing device. Figure 3 The information processing apparatus 100 shown may include a processing unit 101, a storage unit 102, an input unit 103, an output unit 104, and a communication unit 105. The information processing apparatus 100 may be configured, for example, by a general-purpose computer.

[0129] Processing unit 101 is configured to generate a list of combinations of fluorophores for biomolecules. The process of generating the combination list will be described in detail below. Processing unit 101 may include, for example, a central processing unit (CPU) and random access memory (RAM). The CPU and RAM may be connected to each other, for example, via a bus. Input / output interfaces may be further connected to the bus. Input unit 103, output unit 104, and communication unit 105 may be connected to the bus via the input / output interfaces.

[0130] Storage unit 102 stores various types of data. Storage unit 102 may be configured, for example, to store data acquired in the processing described later, data generated in the processing described later, etc. Examples of such data include, but are not limited to, various types of data received by input unit 103 (e.g., biomolecular data and expression level data), various types of data received by communication unit 105 (e.g., lists related to fluorophores), various types of data generated by processing unit 101 (e.g., expression level categories, brightness categories, related information, combination lists, etc.), etc. Furthermore, storage unit 102 may store operating systems (e.g., WINDOWS, UNIX, LINUX, etc.), programs for causing the information processing apparatus or information processing system to execute the information processing method according to the present technology, and various other programs.

[0131] Input unit 103 may include an interface configured to receive various types of data input. For example, input unit 103 may be configured to accept various data inputs in the processing described later. Examples of such data include biomolecular data, expression level data, etc. As a device for receiving such operation, input unit 103 may include, for example, a mouse, keyboard, touch panel, etc.

[0132] Output unit 104 may include an interface configured to output various types of data. For example, output unit 104 may be configured to output various types of data generated in the processing described later. Examples of such data include, but are not limited to, various types of data generated by processing unit 101 (e.g., level categories, brightness categories, related information, combination lists, etc.). Output unit 104 may, for example, include a display device as a device for outputting data.

[0133] The communication unit 105 can be configured to connect the information processing device 100 to a network via a wired or wireless means. Through the communication unit 105, the information processing device 100 can acquire various data (e.g., lists related to phosphors, etc.) via the network. For example, the acquired data can be stored in the storage unit 102. The configuration of the communication unit 105 can be appropriately selected by those skilled in the art.

[0134] The information processing device 100 may include, for example, a driver (not shown). The driver can read data (e.g., the various types of data described above) or programs (e.g., the programs described above) recorded on a recording medium, and output the read data or programs to RAM. The recording medium is, for example, a micro-secure digital (SD) memory card, an SD memory card, or flash memory, but is not limited thereto.

[0135] (3-2) Example of processing by the processing unit (processing flow)

[0136] The following will refer to Figure 4Describes the processing performed by the processing unit. Figure 4 This is a flowchart of the process. The following description illustrates an application example of this technique in optimizing the combination of antibodies and fluorescent dyes used in flow cytometry.

[0137] exist Figure 4 In step S101, the information processing device 100 (specifically, the input unit 103) accepts inputs of multiple biomolecules and their corresponding expression levels.

[0138] Biomolecules can be antigens to be measured in flow cytometry (e.g., surface antigens, cytokines, etc.), or they can be antibodies that capture the antigens to be measured. When multiple biomolecules are antigens, the expression level can be the expression level of the antigens themselves. When each of the multiple biomolecules is an antibody, the expression level can be the expression level of the antigen captured by the antibody.

[0139] The processing unit 101 can cause the output unit 104 (specifically, the display device) to display an input receiving window for accepting input, thereby facilitating user input. The input receiving window may, for example, include a biomolecular input receiving field and an expression level receiving field, such as... Figure 5A The “Antibody” and “Expression Level” columns are shown in section a.

[0140] The biomolecule input field can be, for example, a list box LB1 that facilitates the selection of biomolecules, such as... Figure 5A As shown in the "Antibody" section. Figure 5A In section 'a', for ease of description, nine list boxes are described, but the number of list boxes is not limited to this. The number of list boxes can be, for example, 5 to 300 or 10 to 200.

[0141] In response to a user activating each list box via an action such as clicking or touching, the processing unit 101 displays a list of biomolecule options above or below the list box. In response to a user selecting a biomolecule from the list, the list is closed and the selected biomolecule is displayed.

[0142] exist Figure 5A In section 'a', a screen is displayed after the user selects a biomolecule. In response to the selection of an antigen captured by the antibody, as shown in the figure, for example, "CD1a", "CD2", etc., are displayed.

[0143] Furthermore, the expression level acceptance field can be, for example, a list box LB2 that facilitates the selection of expression levels, as in... Figure 5A The "Expression Level" column in section a is shown. The number of list boxes LB2 that promote the selection of expression levels can be the same as the number of list boxes LB1 that promote the selection of biomolecules. Figure 5AIn section 'a', for ease of description, nine list boxes are described, but the number of list boxes is not limited to this. The number of list boxes can be, for example, 5 to 300 or 10 to 200.

[0144] In response to a user activating each list box via an action such as clicking or touching, the processing unit 101 displays a list of expression level options above or below the list box. In response to a user selecting a biomolecule from the list, the list closes and displays the selected expression level.

[0145] exist Figure 5A In section a, a screen is displayed showing the expression level selected by the user. Responding to the selected expression level, as shown in the figure, for example, "+", "++", and "+++". Figure 5A In the 'a' group, for example, "+" is selected for the expression level of the biomolecule "CD1a". Additionally, "++" is selected for the expression level of the biomolecule "CD4". The symbols "+", "++", and "+++" indicate that the expression level increases in this order.

[0146] In this specification, "expression level" may refer to, for example, a grade of expression level or a specific numerical value of expression level. Preferably, as described above... Figure 5A As shown in a, the level of expression refers to the grade of expression level. The grade of expression level is preferably from 2 to 20, more preferably from 2 to 15, even more preferably from 2 to 10, and can be, for example, divided into 3 to 10 grades.

[0147] After the selection of biomolecules and expression levels is completed as described above, for example, in response to the user clicking the selection completion button (not shown) in the input acceptance window, the processing unit 101 accepts the input of the selected biomolecules and expression levels.

[0148] In step S102, processing unit 101 classifies the multiple biomolecules selected in step S101 based on the expression levels selected for the corresponding biomolecules, and generates one or more expression level categories, particularly multiple expression level categories. The number of expression level categories can be, for example, a value corresponding to the number of expression level grades, and can preferably be two or more, and more preferably three or more. This number can preferably be 2 to 20, preferably 3 to 15, and even more preferably 3 to 10.

[0149] exist Figure 5AIn step a, for each of the multiple biomolecules, an expression level grade "+", "++", or "+++" is selected. Processing unit 101 classifies the biomolecules with the selected expression level grade "+" into expression level category "+". Similarly, processing unit 101 classifies the biomolecules with the selected expression level grades "++" or "+++" into expression level category "++" or expression level category "+++", respectively. In this way, processing unit 101 generates three expression level categories. Each expression level category includes biomolecules with the corresponding selected expression level grade. Figure 5A In 'a', input three biomolecules at the expression level "+", four biomolecules at the expression level "++", and two biomolecules at the expression level "+++".

[0150] In step S103, processing unit 101 acquires a list of fluorophores associated with the biomolecules input in step S101 that can be labeled. The list of fluorophores may be acquired, for example, from a database located outside the information processing device 100 via communication unit 105, or from a database stored inside the information processing device 100 (e.g., storage unit 102).

[0151] The list associated with a fluorophore may include, for example, the name and brightness of each fluorophore. Furthermore, the list preferably also includes the fluorescence spectrum of each fluorophore. The fluorescence spectrum of each fluorophore can be obtained from a database as data distinct from this list.

[0152] Preferably, the list may selectively include fluorophores that are available in devices (e.g., particle analyzers) where a combination of biomolecules and fluorophores is used to analyze samples. By removing fluorophores that cannot be used in the device from the list, the burden on the device can be reduced in the processing described later (particularly the computational processing of relevant information).

[0153] In step S104, the processing unit 101 classifies the phosphors included in the list associated with the phosphors obtained in step S103 based on the brightness of each phosphor, and generates one or more brightness categories, in particular multiple brightness categories.

[0154] In step S104, preferably, processing unit 101 generates a brightness category with reference to the expression level category generated in step S102. Therefore, the generated brightness category can be more effectively associated with the expression level category to generate a combination of biomolecules and fluorescent particles. The specific details of the reference will be described below.

[0155] Brightness-based classification can be based on fluorescence amount or fluorescence intensity. To perform the classification, for example, a numerical range of fluorescence amount or fluorescence intensity can be associated with each brightness category. The processing unit 101 can then refer to the fluorescence amount or fluorescence intensity to classify each of the phosphors included in the list into a brightness category associated with the numerical range of fluorescence amount or fluorescence intensity included with each phosphor.

[0156] Preferably, in step S104, processing unit 101 generates brightness categories with reference to the number of expression level categories generated in step S102. Particularly preferably, in step S104, processing unit 101 generates the same number of brightness categories as the number of expression level categories generated in step S102. Thus, expression level categories and brightness categories can be associated one-to-one. Furthermore, the generation of phosphors not considered in the generation of the combination list described later can be prevented, and better combinations can be generated. The number of brightness categories can be, for example, a value corresponding to the number of expression level categories, and can preferably be two or more, more preferably three or more. This number can preferably be 2 to 20, preferably 3 to 15, and even more preferably 3 to 10.

[0157] For example, such as Figure 5A As shown in b, three brightness categories (bright, normal, and dim) can be generated. Within these three brightness categories, the brightness decreases in this order: any phosphor included in the bright category is brighter than any phosphor included in the normal category, and any phosphor included in the normal category is brighter than any phosphor included in the dim category.

[0158] Preferably, in step S104, processing unit 101 generates brightness categories with reference to the number of biomolecules included in each expression level category generated in step S102. Particularly preferably, in step S104, processing unit 101 classifies fluorophores into individual brightness categories such that fluorophores with a number of biomolecules equal to or greater than that included in the expression level categories generated in step S102 are included in the associated brightness category. This prevents the generation of biomolecules that do not assign fluorophores during the generation of the combination list described later.

[0159] In step S105, processing unit 101 associates the expression level category generated in step S102 with the brightness category generated in step S104. Preferably, processing unit 101 associates one expression level category with one brightness category. Alternatively, processing unit 101 can perform the association such that there is a one-to-one correspondence between expression level categories and brightness categories. That is, the association can be performed such that two or more expression level categories are not associated with a brightness category.

[0160] In a particularly preferred embodiment of this technology, processing unit 101 may perform associations such that expression level categories with lower expression levels are associated with higher brightness categories. For example, processing unit 101 may associate the expression level category with the lowest expression level with the brightness category with the highest brightness, then associate the expression level category with the second lowest expression level with the brightness category with the second highest brightness, and similarly repeat this association until no expression level category exists. Conversely, processing unit 101 may associate the expression level category with the highest expression level with the lowest brightness, then associate the expression level category with the second highest expression level with the brightness category with the second lowest brightness, and similarly repeat this association until no expression level category exists.

[0161] In this embodiment, for example, such as Figure 5A As shown by the arrows between a and b in the diagram, the processing unit 101 associates the expression level categories “+”, “++”, and “+++” with the brightness categories “bright”, “normal”, and “dim”, respectively.

[0162] As described above, regarding the expression level categories generated in this technology, preferably, the expression level category for classifying biomolecules exhibiting lower expression levels can be associated with a brightness category so as to correspond to the brightness category for classifying brighter fluorescent molecules.

[0163] In step S106, processing unit 101 identifies the optimal combination of fluorophores by using relevant information between the fluorophores. The optimal combination of fluorophores can be determined, for example, from the perspective of the correlation between fluorescence spectra, more specifically, from the perspective of the correlation coefficient between fluorescence spectra, and even more specifically, from the perspective of the square of the correlation coefficient between fluorescence spectra. The correlation coefficient can be, for example, any one of the Pearson correlation coefficient, Spearman correlation coefficient, and Kendall correlation coefficient, and is preferably the Pearson correlation coefficient.

[0164] The relevant information between phosphors is preferably the relevant information between fluorescence spectra. That is, in a preferred embodiment of this technology, the processing unit 101 identifies the optimal combination of phosphors by using the relevant information between fluorescence spectra.

[0165] For example, the Pearson correlation coefficient between two fluorescence spectra X and Y can be calculated as follows.

[0166] First, for example, fluorescence spectra X and Y can be represented as follows.

[0167] Fluorescence spectrum X = (X1, X2, ..., X 320 ), average value = μ x Standard deviation = σ x (where X1 to X320 The fluorescence intensity at 320 different wavelengths, with an average value of μ. x This is the average of these fluorescence intensities, and the standard deviation σ x (This is the standard deviation of these fluorescence intensities.)

[0168] Fluorescence spectrum Y = (Y1, Y2, ..., Y) 320 ), average value = μ y Standard deviation = σ y

[0169] (where Y1 to Y) 320 (Fluorescence intensity at different wavelengths of 320 nm). Average value μ y This is the average of these fluorescence intensities. Standard deviation σ y This is the standard deviation of these fluorescence intensities.

[0170] Note that the value "320" is a value set for ease of description, and the value used in calculating the correlation coefficient is not limited to this. This value can be appropriately changed according to the configuration of the fluorescence detector, such as the number of photomultiplier tubes (PMTs) used for fluorescence detection.

[0171] The Pearson correlation coefficient R between fluorescence spectra X and Y is obtained through the following expression 1.

[0172] [Mathematical Expression 1]

[0173]

[0174] In the formula of mathematical expression 1, Z Xn (n is 1 to 320) is the normalized fluorescence intensity and is expressed as follows.

[0175] Z x1 =(X1-μ) x )÷σ x Z x2 =(X2-μ) x )÷σ x , ...Z x320 =(X 320 -μ x )÷σ x

[0176] Similarly, Z Yn (n is from 1 to 320) is also represented as follows.

[0177] Z yl =(Y1-μ y )÷σ y Z y2 =(Y2-μ) y )÷σy , ...Z y320 =(Y 320 -μ y )÷σ y

[0178] Furthermore, in the formula of expression 1, N is the number of data points.

[0179] The following will describe an example of how to determine the optimal combination of phosphors.

[0180] Processing unit 101 selects the same number of fluorophores from a specific brightness category as the number of biomolecules belonging to the expression level category associated with that specific brightness category. Fluorophores are selected for all brightness categories. Thus, the same number of fluorophores as the number of multiple biomolecules to be used to analyze the sample are selected, and in this way, a candidate fluorophore combination is obtained.

[0181] Next, processing unit 101 calculates the square of the correlation coefficient (e.g., Pearson correlation coefficient) between the fluorescence spectra of any two combinations of fluorophores included in the fluorophore combination candidate. Processing unit 101 calculates the square of the correlation coefficients for all combinations. For example, through this calculation process, processing unit 101 obtains... Figure 6 The matrix of squared correlation coefficient values ​​is shown. Then, processing unit 101 identifies the maximum squared correlation coefficient value based on the matrix of squared correlation coefficient values. For example, in... Figure 6 In the figure, the correlation coefficient between the fluorescence spectrum of Alexa Fluor 647 and the fluorescence spectrum of APC is 0.934, and the processing unit 101 identifies this value as the maximum correlation coefficient squared value (the part surrounded by quadrilaterals in the upper left of the figure).

[0182] Note that the smaller the squared correlation coefficient, the less similar the spectra of the two fluorophores. That is, the two fluorophores with the largest squared correlation coefficient can be the two fluorophores with the most similar fluorescence spectra among the fluorophore candidates included in the fluorophore combination.

[0183] Through the above processing, the processing unit 101 identifies the maximum squared correlation coefficient of a candidate fluorescent combination.

[0184] Here, when the number of fluorophores belonging to a specific brightness category is greater than the number of biomolecules belonging to an expression level category associated with that specific brightness category, there are multiple combinations of fluorophores selected from that specific brightness category. For example, when two fluorophores are selected from four fluorophores, there are six fluorophore combinations (=4C2). Therefore, for example, when there are three brightness categories, four fluorophores belong to any one of the three brightness categories, and two fluorophores are selected from each brightness category, there are 216 candidate fluorophore combinations (6×6×6).

[0185] In this technology, processing unit 101 identifies the maximum squared correlation coefficient value for each possible candidate phosphor combination, as described above. For example, in the case of 216 candidate phosphor combinations, processing unit 101 identifies the maximum squared correlation coefficient value for each of the 216 candidate phosphor combinations. Then, processing unit 101 identifies the candidate phosphor combination with the minimum identified maximum squared correlation coefficient value. Processing unit 101 identifies the candidate phosphor combination identified in this way as the optimal phosphor combination.

[0186] Figure 5A c shows the identification results for the optimal combination of fluorophores. Figure 5A In c, the fluorophores that constitute the optimal combination of fluorophores identified are marked with a star.

[0187] Note that when there are two or more candidate fluorophore combinations with the smallest maximum squared correlation coefficient, processing unit 101 can compare the next maximum squared correlation coefficient values ​​of the two or more candidate fluorophore combinations and identify the candidate fluorophore combination with the smaller next maximum squared correlation coefficient value as the optimal fluorophore combination. When the next maximum squared correlation coefficient values ​​are the same, their next maximum squared correlation coefficient values ​​can be compared.

[0188] In the above description, the optimal fluorophore combination is determined by referring to the squared value of the maximum correlation coefficient; however, the references used to determine the optimal fluorophore combination are not limited to this. For example, the total value of the average or maximum value of the squared correlation coefficients up to the nth maximum value (here, n can be any positive number, for example, n can be 2 to 10, particularly 2 to 8, and even more particularly 2 to 5) can be referenced. Processing unit 101 can identify fluorophore combination candidates with the minimum average or minimum total value as the optimal fluorophore combination.

[0189] In step S107, processing unit 101 assigns the fluorophores constituting the optimal fluorophore combination identified in step S106 to a plurality of biomolecules. More specifically, processing unit 101 assigns each fluorophore constituting the optimal fluorophore combination to a biomolecule belonging to an expression level category associated with the brightness category to which the fluorophore belongs.

[0190] When two or more fluorophores are included in a brightness category, two or more biomolecules can be included in an associated expression level category. In this case, a fluorophore with higher brightness can be assigned to a biomolecule with a lower expression level (or one expected to have a lower expression level). Figure 7 A conceptual diagram related to this allocation is shown.

[0191] Processing unit 101 generates a combination of fluorophore and biomolecule for each biomolecule through the above-described allocation process. In this way, processing unit 101 generates a list of combinations of fluorophores for biomolecules.

[0192] exist Figure 5A Example of the generated result of the combined list is shown in d. Processing unit 101 can output the generated result of the combined list, and in this case, for example, as in Figure 5A In addition to the combination of biomolecules and fluorescent agents, processing unit 101 can display the fluorescence spectrum of each fluorescent dye. By displaying the fluorescence spectrum, the user can visually confirm whether there is spectral overlap.

[0193] In step S108, for example, processing unit 101 may cause output unit 104 to output the combination list generated in step S107. For example, the combination list may be displayed on a display device.

[0194] In step S108, the processing unit 101 may further display reagent information corresponding to the combination of antibody (or antigen) and fluorescent dye on the output unit 104. The reagent information may include, for example, the reagent name, product number, manufacturer name, price, etc. For example, to display the reagent information, the processing unit 101 may retrieve the reagent information from a database located outside the information processing device 100, or from a database stored inside the information processing device 100 (e.g., storage unit 102).

[0195] In step S108, processing unit 101 may further display simulation results (e.g., various graphs, etc.) regarding separation capability when using a combination list. Processing unit 101 may further display the expected separation performance when using a combination list.

[0196] Figure 5B An example of the output results is shown. In this example, in addition to the name of the antibody (or antigen), the name of the fluorescent dye, the name of the reagent, the product number, the name of the manufacturer, the price, etc., the simulation results are also shown.

[0197] The above processing can optimize the combination of biomolecules and fluorophores, and the optimized combination list can be presented to the user.

[0198] (Example 1: Evaluation using simulated data)

[0199] By performing the processing described above using the information processing apparatus according to this technology, 20 fluorophores are assigned to 20 biomolecules, and a list of combinations of biomolecules and fluorophores is generated. To perform this generation, the squared values ​​of the aforementioned correlation coefficients are used as relevant information. The fluorescence separation performance of the combination list is confirmed using simulated data. The fluorescence separation performance is evaluated using the interfluorophore staining index. Figure 25A A list of interspheric staining indices is shown. In addition, Figure 25B A two-dimensional diagram obtained when using a combination list is also shown.

[0200] In addition, 20 fluorophores were manually assigned to 20 biomolecules, generating a list of biomolecule-fluorophore combinations. Simulated data were used to validate the fluorescence separation performance of the combinations in a similar manner to that described above. The fluorescence separation performance was assessed using the interfluorophore staining index. Figure 25C A list of interspheric staining indices is shown. In addition, Figure 25D The diagram shown is a two-dimensional graph obtained when using a combination list.

[0201] For the combination list generated by manual allocation, the inter-fluorophore staining index is calculated, and the minimum value among the calculated inter-fluorophore staining indices is 3.36. Figure 25C On the other hand, for the combination list generated according to this technique, the inter-fluorophore staining index is calculated, and the minimum value among the calculated inter-fluorophore staining indices is 9.98. Figure 25A Therefore, it can be seen that the separation performance can be improved by using an information processing device according to this technology.

[0202] Figure 25B and Figure 25D The improved separation performance can also be confirmed by comparing two-dimensional graphs.

[0203] Furthermore, the maximum correlation coefficient between the fluorescence spectra of any two fluorophores constituting the combination list generated by manual allocation is 0.957. On the other hand, the maximum correlation coefficient between the fluorescence spectra of any two fluorophores constituting the combination list generated according to this technology is 0.351. Moreover, it can be seen from these results that the separation performance can be performed by the information processing device according to this technology.

[0204] (Example 2: Evaluation using cells)

[0205] By performing the processing described above using an information processing apparatus according to this technology, 28 fluorophores are assigned to 28 biomolecules, and a list of combinations of biomolecules and fluorophores is generated. A cluster of fluorescently labeled antibodies according to the combination list is prepared. A sample containing leukocytes is stained using the fluorescently labeled antibody cluster. The stained sample is analyzed by flow cytometry. Figure 26A andFigure 27 A shows a set of two-dimensional plots obtained through analysis.

[0206] In addition, 28 fluorophores were manually assigned to 28 biomolecules, and a list of combinations of biomolecules and fluorophores was generated. Fluorescently labeled antibody populations were prepared according to the combination list. Samples containing leukocytes were stained using the fluorescently labeled antibody populations. The stained samples were analyzed by flow cytometry. Figure 26B and Figure 27 B shows a set of two-dimensional curves obtained through analysis.

[0207] from Figure 26A , Figure 26B and Figure 27 A comparison of A and B in the diagram shows that, compared to manual operation, the fluorescently labeled antibody population generates a two-dimensional map with improved separation performance based on the combination list generated by the information processing device according to the present technology. Furthermore, it can be seen that, based on the combination list generated by the information processing device according to the present technology, the fluorescently labeled antibody population can more clearly classify each cell population.

[0208] (3-3) Example of processing in the processing unit (example of fluorescence spectrum used to calculate correlation coefficient)

[0209] As described in (3-2) above, the processing unit 101 may acquire the fluorescence spectrum of each phosphor in step S103, and then may use the relevant information between the fluorescence spectra in step S106 to identify the optimal combination of phosphors. The fluorescence spectrum used to acquire the relevant information may have a horizontal axis representing the wavelength or the photodetector number corresponding to the wavelength and a vertical axis representing the fluorescence intensity (specifically, the fluorescence intensity normalized by the maximum fluorescence intensity value).

[0210] The fluorescence spectrum used in this technique can be a fluorescence spectrum generated when the phosphor is irradiated by an excitation beam of one wavelength, or it can be a combination of multiple fluorescence spectra obtained when the phosphor is irradiated by excitation beams of two or more different wavelengths, particularly a fluorescence spectrum obtained by combining two or more fluorescence spectra. Reference will be made below to... Figure 7 These embodiments are described.

[0211] Figure 8 A and B show examples of fluorescence spectra of fluorescence generated when a phosphor is irradiated by an excitation beam of one wavelength. Figure 8 Figure A shows the fluorescence spectrum obtained by irradiating PE-Cy5 with a 561 nm excitation laser. The horizontal axis of the fluorescence spectrum corresponds to the photodetector number for each wavelength; that is, 30 photodetectors were used to obtain the fluorescence spectrum. The vertical axis of the fluorescence spectrum is the fluorescence intensity normalized to the maximum fluorescence intensity value.Figure 8 B is the fluorescence spectrum obtained by irradiating APC-Cy5 with a 628 nm excitation laser. The vertical and horizontal axes of the fluorescence spectrum are... Figure 8 The same as in A.

[0212] In this technology, the following can be used: Figure 8 A or Figure 8 The fluorescence spectrum shown by B. However, Figure 8 A and Figure 8 The fluorescence spectrum of B also appears to have a similar spectral waveform, and it may be impossible to distinguish between the two phosphors.

[0213] Therefore, in this technology, by using a combination of multiple fluorescence spectra obtained when a phosphor is irradiated by multiple excitation beams of different wavelengths, especially by combining multiple fluorescence spectra, two phosphors can be distinguished more reliably.

[0214] Figure 8 C illustrates an example of data obtained by combining multiple fluorescence spectra obtained when a phosphor is irradiated with multiple excitation beams of different wavelengths. Figure 8 The data shown in C is obtained by combining five fluorescence spectra generated under conditions of excitation laser beams at five different wavelengths. The five different wavelengths are 355 nm, 405 nm, 488 nm, 561 nm, and 638 nm. The horizontal axis of the combined five fluorescence spectra represents the photodetector numbers corresponding to each wavelength; that is, 64 photodetectors were used to acquire the fluorescence spectra. The vertical axis represents the fluorescence intensity, and the five fluorescence spectra were normalized to a maximum value of 1 for the fluorescence intensity of the five spectra.

[0215] exist Figure 8 In Figure C, the thin line represents the fluorescence spectrum of the combined PE-Cy5 data, and the thick line represents the fluorescence spectrum of the combined APC-Cy5 data. Comparing these two sets of data reveals that the waveforms of the fluorescence spectra obtained under irradiation with excitation light at, for example, 488 nm, 561 nm, and 638 nm are completely different. Therefore, using these two sets of data allows for a more reliable differentiation of the fluorescence of the two fluorescent dyes.

[0216] As described above, in this technology, the processing unit 101 can easily identify the optimal combination of phosphors by using combined data of multiple fluorescence spectra, particularly by using combined data of multiple fluorescence spectra, to obtain relevant information. The combination data and combining data can be, for example, data obtained by performing a predetermined normalization process on multiple fluorescence spectra as described above.

[0217] Furthermore, by using fluorescence spectroscopy as described above to obtain relevant information, the same processing procedure can be applied to various particle analyzers with different optical systems.

[0218] (3-4) Example of processing in the processing unit (refer to the example in the database on reagent availability)

[0219] In step S103, processing unit 101 obtains a list associated with fluorophores capable of labeling the biomolecules input in step S101. Then, in step S104, the fluorophores included in the list are classified based on brightness, and one or more brightness categories, particularly multiple brightness categories, are generated. When generating brightness categories, processing unit 101 may preferably refer to a reagent database containing information about whether reagents obtained by combining biomolecules and fluorophores are available (e.g., can be purchased). For example, in step S103, processing unit 101 may refer to the reagent database and form a list containing only fluorophores constituting usable reagents. Alternatively, in step S104, processing unit 101 may refer to the reagent database and form each brightness category containing only fluorophores constituting usable reagents. In this way, processing unit 101 can prevent fluorophores constituting unusable reagents from being classified into each brightness category. This allows for a reduction in the computational load in processing according to the art (especially processing for calculating relevant information).

[0220] In step S107, processing unit 101 performs a process of dispensing a fluorophore to a biomolecule. Processing unit 101 may refer to a reagent database during the dispensing process. For example, in the dispensing process, processing unit 101 may refer to a reagent database and generate only combinations of fluorophores and biomolecules constituting usable reagents through the dispensing process.

[0221] More specifically, after assigning a specific fluorophore to a specific biomolecule, processing unit 101 refers to a reagent database and, if a reagent obtained by combining the specific fluorophore and the specific biomolecule exists in the database, includes that combination in the combination list. Furthermore, if a reagent obtained by combining the specific fluorophore and the specific biomolecule does not exist in the reagent database, the combination is not included in the combination list, and then processing unit 101 can perform the process of assigning the specific fluorophore to another biomolecule and perform a similar reagent database reference process.

[0222] By referencing a reagent database containing information on the availability of the aforementioned reagents, the information presented to the user can be limited to the available reagents. As described above, in this technique, multiple fluorophores constituting a brightness category can be identified based on data regarding the availability of complexes between fluorophores and biomolecules.

[0223] (3-5) Example of processing in the processing unit (example of adjusting the number of phosphors classified into each brightness category)

[0224] In step S104, the processing unit 101 classifies the phosphors based on the brightness of each phosphor and generates multiple brightness categories. The information processing device of this technology can be configured to change the classification criteria.

[0225] For example, as shown in (3-2) above, the numerical range of fluorescence amount or fluorescence intensity can be associated with each brightness category, and the phosphor can be classified into brightness categories according to the fluorescence amount or fluorescence intensity of the phosphor.

[0226] However, depending on the biomolecules and their expression levels input by the user, the number of biomolecules belonging to the expression level category can be greater than the number of fluorophores belonging to the brightness category associated with that expression level category. Furthermore, it is desirable to eliminate biases in the number of combined candidates for each brightness category. Therefore, by adjusting the number of fluorophores classified for each brightness category, it is difficult for the error of not finding candidates to occur. See below for reference. Figure 9 Describes the adjustment of the number of phosphors categorized for each brightness class.

[0227] Figure 9 Example A shows a window displaying the results of classifying phosphors according to a fixed brightness threshold. Figure 9 In A, the fluorophores “8”, “23”, and “13” are classified into three brightness categories: “bright”, “normal”, and “dim” (“Number of Candidate Dyes” column). Here, the number of biomolecules belonging to the expression level category associated with the brightness category “bright” is “9” (“Number of Extracted” column), the number of biomolecules is larger, and the number of candidates for combinations associated with assigning fluorophores to biomolecules is 0.

[0228] Therefore, for example, processing unit 101 may respond to a user clicking the "Auto-adjust Classification" checkbox displayed in the above window to change the classification criteria for the brightness category. In one embodiment of the present technology, processing unit 101 may change the numerical range of fluorescence amount or fluorescence intensity associated with each brightness category such that "the difference between the number of phosphors classified as a brightness category and the number of biomolecules belonging to the expression level category associated with the brightness category" is comparable across all brightness categories. For example, processing unit 101 may change the numerical range of fluorescence amount or fluorescence intensity associated with each brightness category such that all combinations of the two brightness categories for the total brightness category satisfy the condition that "the difference between the difference of one brightness category and the difference of the other brightness category is, for example, 3 or less, preferably 2 or less, and more preferably 1 or less".

[0229] Figure 9 Figure B shows an example of the window after the classification criteria have been changed. As shown, the number of fluorescent molecules classified as the brightness category "bright" has become "17", and the difference between this number and the number of biomolecules belonging to the expression level category associated with the brightness category "bright" is "8". For other brightness categories, the difference is 8 or 7. Therefore, the bias in the number of combined candidates used to assign biomolecules to fluorescent molecules can be eliminated.

[0230] As described above, in this technology, the processing unit 101 can adjust the number of phosphors belonging to each brightness category according to the number of biomolecules belonging to each expression level category.

[0231] Note that in this technology, the processing unit 101 can accept changes from the user to the numerical range associated with each brightness category, and based on the changes, the processing unit 101 can change the number of phosphors classified into the changed brightness category.

[0232] (3-6) Examples of processing by the processing unit (examples of prioritization information associated with each phosphor and limitations on the number of phosphors used in optimization based on the number of phosphors selected)

[0233] In a preferred embodiment of this technology, priority information can be associated with each fluorophore included in the fluorophore data acquired in step S103, or priority information can be associated with each fluorophore in any step after acquiring the fluorophore data in step S103. For example, based on the number of biomolecules assigned to the fluorophores, processing unit 101 can refer to priority information to limit the selectable fluorophores to those with high priority to the user. This limitation reduces the processing burden on processing unit 101 and enables optimization to be performed at a higher speed.

[0234] This implementation is also advantageous from the following perspective: For example, among commercial fluorescent dyes, there are rare fluorescent dyes that are not processed by multiple manufacturers. Such fluorescent dyes are generally more expensive and tend to take longer to deliver after being ordered. Therefore, users performing analyses such as flow cytometry tend to use a master fluorescent dye as the fluorescent dye used in the staining process. Therefore, as mentioned above, in the panel design, the phosphors used are limited to those with high priority, thereby preventing rare fluorescent dyes from being easily included in the panel.

[0235] In this embodiment, for example, information about priority may be a value indicating priority order (hereinafter also referred to as a "priority order value"), a code indicating priority order, etc. Those skilled in the art can appropriately set the values ​​and reference numerals.

[0236] In this embodiment, the list associated with phosphors may include, for example, information about the name, brightness, and priority of each phosphor. Figure 10 A shows an example of a list in this embodiment. Figure 10 The list of fluorophores shown in A may have a name for each fluorophore (“Name” column) and a priority order value (“Value” column).

[0237] In this embodiment, processing unit 101 may refer to the number of biomolecules (e.g., the number of biomolecules input in step S101) and restrict the phosphors that can be used to generate brightness categories based on a list associated with phosphors. For this restriction, processing unit 101 may process the data with a priority order that defines the relationship between the number of biomolecules and information about priority. Figure 10 B illustrates an example of how data is processed in a priority order. For example... Figure 10 As shown in B, priority order processing associates the quantity (specifically, the range of quantities) of biomolecules with a priority order value (specifically, the range of priority order values). For example, the quantity of biomolecules "0 to 10" is associated with a priority order value "1000".

[0238] Processing unit 101 can process data with reference to priority order, and in the processing of step S 103 and subsequent steps, only fluorophores with priority order values ​​corresponding to the number of biomolecules are used.

[0239] For example, in the classification based on the brightness of the fluorophores in step S104, processing unit 101 can process the data with reference to a priority order, classifying only fluorophores with priority-related information corresponding to the number of biomolecules based on brightness, and generating brightness categories that include only fluorophores with priority-related information. For example, when the number of biomolecules is 0 to 10, processing unit 101 can process the data with reference to the aforementioned priority order in step S104, classifying only fluorophores with a priority order value of 1000 based on the corresponding brightness, and generating brightness categories.

[0240] As described above, in this technology, the processing unit 101 can identify multiple phosphors included in the brightness category based on priority-related information associated with the phosphors.

[0241] The information processing apparatus 100 according to this technology can be configured to switch between processing using information about priority and processing without using information about priority. The processing unit 101 can cause the output unit 104 to display a window enabling the switching. Furthermore, the processing unit 101 can cause the output unit 104 to display a window showing the change in the phosphor selectable during processing according to the switching. Figure 10 C shows an example of this type of window. Figure 10 To the left of C, all checkboxes for the listed fluorophores have been checked, indicating that all fluorophores are selectable. On the other hand, in Figure 10 To the right of C, some checkboxes for the listed fluorophores (the parts surrounded by quadrilaterals) are not checked. This allows you to identify which fluorophores can be selected and which cannot be selected during processing.

[0242] (Example 3: Whether to use comparisons of information about priority)

[0243] The information processing apparatus according to this technology is used to assign 15 fluorophores to 15 antibodies and generate a combination list using information regarding the priorities described above. The number of fluorophores present as assignment candidates is 44. The time required to generate the combination list is measured.

[0244] Similarly, the information processing apparatus according to this technology is used to assign 20 fluorophores to 20 antibodies and generate a combination list using information about the priorities described above. The number of fluorophores present as assignment candidates is 44. The time required to generate the combination list is measured.

[0245] The time measured is Figure 28 It is shown in the "Restriction Enabled" section.

[0246] A combination list was generated by assigning 15 fluorophores to 15 antibodies in a similar manner, except that information about priority was not used. The time required to generate the combination list was measured.

[0247] A combination list was generated by assigning 20 fluorophores to 20 antibodies in a similar manner, except that information about priority was not used. The time required to generate the combination list was measured.

[0248] The time measured is Figure 28 It is shown in the "Restriction Off" section.

[0249] like Figure 28 As shown, by using information about priorities, the processing time required to generate a list of combinations is significantly reduced. Therefore, it can be seen that a list of combinations can be generated much faster by using information about priorities.

[0250] (3-7) Example of processing by the processing unit (example of performing a separation capability assessment)

[0251] In a preferred embodiment of this technology, processing unit 101 can evaluate the separation capability of the generated combination list. For example, processing unit 101 can generate simulated data related to the generated combination list and use the simulated data to evaluate the separation capability related to the combination list. By performing the separation capability evaluation, the accuracy of optimization can be improved. For example, by performing the separation capability evaluation, it can be confirmed whether the combination list generated in step S107 exhibits the expected separation performance, or a combination list exhibiting better separation performance can be generated based on the confirmation result.

[0252] In this embodiment, for example, the processing unit 101 can further generate a modified combination list based on the results of the separation capability assessment, and further perform a separation capability assessment related to the modified combination list, in which at least one fluorophore in a group of fluorophores included in the modified combination list is changed to another fluorophore. By generating the modified combination list and then performing the separation capability assessment, a combination list exhibiting better separation performance can be generated.

[0253] Separation capability can be assessed, for example, using a staining index, and more preferably, using an assessment of the staining index between phosphors. In the art, the staining index is an indicator of the performance of the phosphor (fluorescent dye) itself, and is defined by the fluorescence intensity of stained and unstained particles, as well as the standard deviation of the unstained particle data, for example, as... Figure 11 As shown on the left. The data for unstained particles replaced by particles stained with another fluorophore is the staining index between fluorophores, for example, as... Figure 11 As shown on the right. By using the staining index between phosphors, the leakage, fluorescence intensity, and noise caused by the overlap of fluorescence spectra can be taken into account to evaluate the separation performance between phosphors. Figure 12 An example is shown showing the results of calculating the interfluorophore staining index for all combinations of two fluorophores in the group of fluorophores that constitute the generated combination list.

[0254] Note that the processing unit 101 of this technology can enable the output unit 104 to output the calculation results for all combinations of two fluorophores in the fluorophore group constituting the combination list generated by the processing unit. This facilitates the user's evaluation of separation performance.

[0255] For example, in such Figure 12In the table of interfluorophore staining indices shown, the smaller the number of regions with small interfluorophore staining index values, the better the separation performance. In this technique, firstly, a list of combinations can be generated based on expression level category, brightness category, and related information, and then a separation capability assessment can be performed using indicators such as staining indices. For example, in the generated list of combinations, a separation capability assessment can identify fluorophore combinations with poor separation performance, and panels with better separation performance can be designed by changing the fluorophore combinations.

[0256] Furthermore, performing panel design by generating a list of combinations based on the aforementioned categories and performing a separation capability assessment (and panel correction as needed) can reduce computation time significantly compared to performing panel design by performing a separation capability assessment for each combination.

[0257] In the following text, reference will be made to Figure 13 and Figure 14 An example describing the processing flow in this embodiment. Figure 13 In the processing flow shown, steps S201 to S207 and S209 are the same as those in the reference. Figure 4 The steps S101 to S107 and S108 are described in the same way, and their description also applies to steps S201 to S207 and S209.

[0258] In step S208, processing unit 101 evaluates the separation capability of the fluorophore groups used to constitute the combination list generated by the allocation process in step S207. (Refer to...) Figure 14 An example describing a more detailed processing flow for step S208.

[0259] exist Figure 14 In step S301, the processing unit 101 begins the separation capability assessment process.

[0260] In step S302, processing unit 101 calculates the staining index between phosphors (in this specification, the staining index is also referred to as "SI"). The SI can be obtained, for example, by generating simulated data using the combination list generated in step S207 and performing demixing processing on the simulated data using a spectral reference.

[0261] Here, simulated data can be, for example, a set of data measured by a device (e.g., a flow cytometer) that performs analysis using reagents according to a combination list. In the case where the device is a particle analyzer (such as a flow cytometer), for example, the set of data obtained in the case of actually measuring 100 to 1000 particles. For the generation of the data set, conditions such as device noise, staining bias, and the amount of data generated can be considered.

[0262] In step S302, for example, processing unit 101 may obtain such as Figure 15 The SI data shown is for fluorophores. The data includes all SIs between two different fluorophores in the fluorophore group that makes up the combination list.

[0263] In step S303, processing unit 101 identifies one or more fluorophores with poor separation performance based on the calculated inter-fluorophore SI, particularly a fluorophore with poor separation performance. For example, processing unit 101 may identify the fluorophore considered positive among the two fluorophores with the calculated minimum inter-fluorophore SI as a fluorophore with poor separation performance.

[0264] For example, regarding Figure 15 In step S303, the processing unit 101 identifies the fluorophore "PerCP-Cy5.5", which is considered positive among the two fluorophores with the minimum inter-fluorophore SI "2.8", as a fluorophore with poor separation performance.

[0265] In step S304, processing unit 101 identifies candidate phosphors to replace the phosphors with poor separation performance identified in step S303. For example, candidate phosphors can be identified as follows: First, processing unit 101 refers to the brightness category to which the phosphor with poor separation performance belongs, and can identify phosphors belonging to that brightness category that are not used in the combination list as candidate phosphors. Alternatively, processing unit 101 can select candidate phosphors from the brightness category whose brightness is closest to that of the phosphor with poor separation performance. Processing unit 101 can identify phosphors belonging to the closest brightness category that are not used in the combination list as candidate phosphors.

[0266] For example, in Figure 16 In this process, processing unit 101 identifies six fluorophores, such as "Alexa Fluor 647," as candidate fluorophores to replace the poorly separated fluorophore "PerCP-Cy5.5." In this way, multiple candidate fluorophores can be identified, or only one candidate fluorophore can be identified.

[0267] In step S305, the processing unit 101 calculates the inter-fluorophore separation index (SI) when the poorly separated fluorophores identified in step S304 are changed to candidate fluorophores. This calculation can be performed for each of all candidate fluorophores.

[0268] Figure 17A and Figure 17B An example of the calculation results is shown. Figure 17A and Figure 17B In the middle, for reference Figure 16Each of the six fluorophores mentioned is shown in the inter-fluorophore separation index (SI) when a fluorophore with poor separation performance is replaced with a candidate fluorophore.

[0269] In step S306, the processing unit 101 selects the candidate phosphor with the largest minimum SI value among the phosphors that has been obtained from the calculation results in step S305 as the phosphor to replace the phosphor with poor separation performance.

[0270] For example, regarding Figure 17A and Figure 17B The calculation results show that the minimum inter-fluorophore SI value associated with "BV650" is the largest among the minimum inter-fluorophore SI values ​​calculated for the six candidate fluorophores. Therefore, processing unit 101 selects "BV650" as the fluorophore to replace "PerCP-Cy5.5".

[0271] In step S307, processing unit 101 determines whether there exists a better combination of fluorophores than the combination list obtained by replacing the poorly separating fluorophores with the fluorophores selected in step S306. For this determination, steps S303 to S306 may be repeated, for example.

[0272] If there is a combination where the minimum SI value between phosphors increases due to repeated steps S303 to S306, the processing unit 101 determines that a better phosphor combination exists. In cases where this determination is made, the processing unit 101 returns to step S303.

[0273] If, as a result of repeating steps S303 to S306, there is no combination where the minimum SI value of the phosphors increases, the processing unit 101 determines that there is no better phosphor combination. In determining that there is no better phosphor combination, the processing unit 101 identifies the phosphor combinations from the stages immediately preceding repeating steps S303 to S306 as an optimized combination list and proceeds the processing to step S308.

[0274] In step S308, the processing unit 101 ends the separation capability evaluation process and proceeds to step S209.

[0275] Through the processing described above, a list of combinations of biomolecules and fluorophores optimized for separation capabilities can be presented.

[0276] (Example 4: Comparison of the presence and absence of separation capability assessment)

[0277] The combination list generated in step S208 of the process according to the present technology without performing a separation capability assessment (hereinafter referred to as the "combination list without separation capability assessment") is compared with the combination list generated in step S208 with performing a separation capability assessment (hereinafter referred to as the "combination list with separation capability assessment").

[0278] Simulated data were used to confirm the fluorescence separation performance of each of these combination lists. The interfluorophoretic staining index was used to assess fluorescence separation performance. Furthermore, two-dimensional plots were generated using these combination lists. Figure 29A The inter-fluorophoretic staining index of the combination list without separation capability assessment is shown, along with a two-dimensional plot generated from the list. Figure 29B The interfluorophore staining index of the combination list with separation ability assessment is shown, and a two-dimensional plot generated from the list is presented.

[0279] Figure 29A and Figure 29B The comparison shows that the list of combinations with separation capability assessment outperformed the list of combinations without it. This demonstrates that separation performance can be further improved by assessing separation capability.

[0280] (3-8) Example of processing in the processing unit (processing flow considering reagent costs)

[0281] As shown in (3-2) above, in step S107, processing unit 101 allocates the fluorophores constituting the optimal fluorophore combination identified in step S106 to multiple biomolecules. Reagent costs may be considered in this allocation process. That is, in a preferred embodiment of the present technology, processing unit 101 may generate a combination list based on cost information regarding the complexes of biomolecules and fluorophores. The complex may be, for example, an antibody labeled with a fluorescent dye. For example, processing unit 101 may refer to the cost information to calculate the cost required to prepare a set of reagents (e.g., a set of fluorescently labeled antibodies) according to the combination list. In step S108, processing unit 101 may further output the cost along with the output of the combination list.

[0282] Figure 18 An example of the processing flow for reference cost information is shown. Figure 18 In the processing flow shown, steps S401 to S406 and S408 are the same as those in the reference. Figure 4 The steps S101 to S106 and S108 are described in the same way, and their description also applies to steps S401 to S406 and S408.

[0283] In step S407, processing unit 101 assigns fluorophores constituting the optimal fluorophore combination identified in step S406 to multiple biomolecules based on cost information regarding the complexes of biomolecules and fluorophores. To utilize the cost information, in step S407, processing unit 101 may, for example, refer to a database external to information processing device 100. This database may, for example, use price data for complexes containing biomolecules and fluorophores (e.g., antibodies labeled with fluorescent dyes) as cost information. In step S407, in addition to cost information, expression levels, etc., as described in step S107 above, may also be considered.

[0284] (3-9) Examples of processing in the processing unit (including processing flow when the phosphor to be used is predetermined)

[0285] In a preferred embodiment of this technology, when identifying a fluorophore to be assigned to at least one of a plurality of biomolecules, the processing unit 101 further generates a combination list based on relevant information between the plurality of fluorophores and the pre-identified fluorophores. This embodiment can be applied to situations where there are antibodies labeled with fluorescent dyes that the user has already decided to use from past experiments, or where the user expects to add one or more antibodies to an already established panel (also referred to as an "existing panel"), for example, when performing experiments using a flow cytometer or the like. In the case where some fluorophores are pre-identified as described above, the fluorophores are treated as fixed values ​​in the processing according to this technology.

[0286] In the following text, reference will be made to Figure 19 The process according to this embodiment is described. Figure 19 This is an example of the processing flow according to this embodiment. Figure 19 In the processing flow shown, apart from the pre-dispensed fluorophores for recognizing certain biomolecules, steps S502 to S507 and S509 are similar to those in the reference. Figure 4 Steps S102 to S107 and S109 are described, and their description also applies to steps S102 to S107 and S109. In addition to the fluorophores pre-dispensed for some biomolecule recognition, step S508 is similar to the reference... Figure 13 The description of step S208 is similar, and its description also applies to step S508.

[0287] exist Figure 19 In step S501, the information processing device 100 receives input of multiple biomolecules and their corresponding expression levels. Furthermore, for some of the multiple biomolecules, it receives input of assigned fluorophores.

[0288] For example, in step S501, the processing unit 101 may cause the output unit 104 (specifically, the display device) to display an input receiving window for accepting input and prompt the user to make input.

[0289] Figure 20 A shows an example of an input acceptance window. In addition to columns in a list box for accepting biomolecules and expression level grades, Figure 20 The window shown in Figure A also includes columns for a fluorophore identification list box that identifies the fluorophores to be assigned to the biomolecule. The processing unit 101 accepts the input of the assigned fluorophores by having the user select one or more fluorophores from the fluorophore identification list box to identify the fluorophores. Figure 20 In A, “PE”, “APC”, “FITC” and “AlexaFluor700” have been identified as fluorophores assigned to the biomolecules “CD27”, “CD5”, “CD4” and “CD45”, respectively.

[0290] In step S502, processing unit 101 classifies the multiple biomolecules selected in step S501 based on the expression level selected for each biomolecule, and generates one or more expression level categories, specifically multiple expression level categories. Biomolecules that were identified as fluorophores in step S501 can also be classified based on the expression levels in step S502.

[0291] The number of expression level categories can be, for example, a value corresponding to the number of expression level grades, and can preferably be 2 to 20, more preferably 2 to 15, even more preferably 2 to 10, and for example 3 to 10.

[0292] In step S503, processing unit 101 acquires a list associated with fluorophores capable of labeling the biomolecules input in step S501. Furthermore, processing unit 101 also acquires the information about the fluorophores input in step S501. For example, the list of fluorophores can be acquired from a database external to information processing device 100, or from a database stored internal to information processing device 100 (e.g., storage unit 102).

[0293] In step S504, the processing unit 101 classifies the fluorophores in the list of fluorophores that can label biomolecules, which are included in the list of fluorophores associated with the fluorophores obtained in step S503, based on the brightness of each fluorophore, and generates one or more brightness categories, in particular multiple brightness categories.

[0294] In the list of phosphors obtained in step S503, the phosphors input in step S501 are also classified based on the brightness of each phosphor and placed into an arbitrary brightness category.

[0295] In step S505, processing unit 101 associates the expression level category generated in step S502 with the brightness category generated in step S504. Preferably, processing unit 101 associates one expression level category with one brightness category. Alternatively, processing unit 101 can perform the association such that there is a one-to-one correspondence between expression level categories and brightness categories. That is, it can perform the association such that two or more expression level categories are not associated with a brightness category.

[0296] In step S506, the processing unit 101 identifies the optimal combination of fluorophores by using relevant information between the fluorophores. The optimal combination of fluorophores can be, for example, from the perspective of the correlation between the fluorophore spectra, more specifically, from the perspective of the correlation coefficient between the fluorophore spectra, and even more specifically, from the perspective of the square of the correlation coefficient between the fluorophore spectra.

[0297] The relevant information between phosphors can preferably be the relevant information between phosphor spectra. That is, in a preferred embodiment of this technology, the processing unit 101 identifies the optimal combination of phosphors by using the relevant information between phosphor spectra.

[0298] The following will describe an example of how to identify the optimal combination of fluorophores.

[0299] Processing unit 101 selects the same number of fluorophores from a specific brightness category as "the number of biomolecules belonging to the expression level category associated with the specific brightness category". However, if the fluorophores input in step S501 include those in the specific brightness category, processing unit 101 selects the same number of fluorophores from the specific brightness category as ("the number of biomolecules belonging to the expression level category associated with the specific brightness category" - "the fluorophores input in step S501").

[0300] The above fluorophore selection is performed for all brightness categories. Therefore, the sum of "number of selected fluorophores" and "number of fluorophores entered in step S501" becomes the same as "number of multiple biomolecules to be used to analyze the sample", and thus a candidate fluorophore combination is obtained.

[0301] Next, processing unit 101 calculates the square of the correlation coefficient between the fluorescence spectra of any two fluorophores included in the fluorophore combination candidates. Processing unit 101 calculates the square of the correlation coefficient for all combinations. For example, through this calculation process, processing unit 101 obtains... Figure 6 The matrix of squared correlation coefficient values ​​is shown. Then, the processing unit 101 identifies the maximum squared correlation coefficient value based on the matrix of squared correlation coefficient values.

[0302] Here, when the number of fluorophores belonging to a specific brightness category is greater than the number of biomolecules belonging to an expression level category associated with that specific brightness category, there are combinations of multiple fluorophores selected from that specific brightness category. For example, when two fluorophores are selected from four fluorophores, there are six fluorophore combinations (=4C2). Therefore, for example, when there are three brightness categories, four fluorophores belong to any one of the three brightness categories, and two fluorophores are selected from each brightness category, there are 216 fluorophore combination candidates (6×6×6). However, when the fluorophores input in step S501 include those in a specific brightness category, the number of fluorophore combination candidates decreases.

[0303] In this technology, processing unit 101 identifies the maximum squared correlation coefficient value for each possible candidate phosphor combination as described above. Then, processing unit 101 identifies the candidate phosphor combination with the minimum identified maximum squared correlation coefficient value. Processing unit 101 identifies the candidate phosphor combination identified in this manner as the optimal phosphor combination.

[0304] In step S507, processing unit 101 assigns the fluorophores constituting the optimal fluorophore combination identified in step S506 to a plurality of biomolecules. More specifically, processing unit 101 assigns each fluorophore constituting the optimal fluorophore combination to a biomolecule belonging to an expression level category associated with the brightness category to which the fluorophore belongs.

[0305] When two or more fluorophores are included in a brightness category, two or more biomolecules can be included in an associated expression level category. In this case, a fluorophore with higher brightness can be assigned to a biomolecule with a lower expression level (or one expected to have a lower expression level).

[0306] Processing unit 101 generates a combination of fluorophores and biomolecules for each biomolecule through the above-described allocation process. In this way, processing unit 101 generates a list of combinations of fluorophores for biomolecules.

[0307] In step S508, processing unit 101 evaluates the separation capability of the fluorophore groups constituting the combination list generated by the allocation process in step S507. A more detailed processing flow for step S508 is provided in reference [reference needed]. Figure 14 As described.

[0308] However, in Figure 14 In step S303, the processing unit 101 does not identify the phosphor input in step S501 as a phosphor with poor separation performance. This prevents the phosphor input in step S501 from being altered.

[0309] In step S509, the processing unit 101 may, for example, cause the output unit 104 to output the combination list generated in step S508. For example, the combination list may be displayed on a display device.

[0310] For example, such as Figure 20 As shown in B, the output results confirm that the biomolecules are recognized by fluorescent markers marked with circles.

[0311] Furthermore, in step S509, information about the complex of the fluorophore and the biomolecule (e.g., information about the fluorescently labeled antibody) can be displayed for the biomolecule that has been allocated the fluorophore through optimized selection. Figure 20 Figure C illustrates an example of how this information is displayed. As shown, the information includes the name of the biomolecule (the name of the antigen captured by the antibody, "Antibody" column), the name of the fluorescent agent labeling the biomolecule ("Fluorescent Dye" column), the name of the clone ("Clonal" column), the ISO type ("ISO Type" column), the species of the antigen captured by the derived biomolecule (antibody) ("Target Species" column), the species of the derived biomolecule (antibody) ("Host Species" column), the catalog number ("Catalog" column), the size of the reagent ("Size" column), the name of the company that manufactures the complex (reagent) ("Company" column), the price ("Price" column), the webpage associated with the antibody ("URL" column), and the product name of the complex ("Product Name" column). In this technology, information about the complex may include one or more of the listed information, and preferably includes the name of the biomolecule and the name of the fluorescent agent labeling the biomolecule. In a preferred embodiment, the information may also include the price.

[0312] (3-10) Examples of processing by the processing unit (including processing flow when the light-generating material to be used is predetermined)

[0313] In one embodiment of this technology, upon identifying the light-generating substance to be used for analyzing a sample, the processing unit further generates a combination list based on relevant information between multiple phosphors and the light-generating substance. For example, in order to perform experiments on fluorescently labeled samples by flow cytometry or the like, this embodiment can be applied to situations where light-generating substances other than these biomolecules are used for analysis, in addition to determining the amount of normal surface antibodies or cytokines. Examples of light-generating substances include fluorescent proteins and cell viability / death determination reagents. For example, in experiments, fluorescence originating from fluorescent proteins contained in the cells themselves can be determined and / or light induced by reagents used to determine cell viability / death can be determined. Such fluorescent proteins and / or viability / death determination reagents can also be treated as fixed values ​​in the processing according to this technology, similar to the processing described above (3-9), and the dye can then be optimized for other biomolecules.

[0314] In the following text, reference will be made to Figure 21 The process according to this embodiment is described. Figure 21 This is an example of the processing flow according to this embodiment. Figure 21 In the processing flow shown, apart from the prior identification of light-generating substances other than biomolecules, steps S602 to S607 and S609 are the same as in the reference. Figure 4 Steps S102 to S107 and S109 are described similarly, and their description also applies to steps S102 to S107 and S109. Except for the prior identification of light-generating substances other than biomolecules, step S608 is similar to the reference. Figure 13 The description of step S208 also applies to step S608.

[0315] exist Figure 21 In step S601, the information processing device 100 receives input of multiple biomolecules and their corresponding expression levels. Furthermore, the information processing device 100 also receives input of light-generating substances other than the biomolecules for which fluorophores need to be dispensed.

[0316] For example, in step S601, the processing unit 101 may cause the output unit 104 (specifically, the display device) to display an input receiving window for accepting input, prompting the user to perform input.

[0317] Figure 22 A shows an example of an input receiving window for a light-generating substance. Figure 22 Window A, as shown, accepts input of fluorescent proteins and reagents for determining cell viability / death. Figure 22 In section A, the fluorescent protein selection field 1 ("Fluorescent Protein 1"), where "EGFP" has been selected, has been checked, and "+" has been selected for the expression level. Similarly, the cell viability / death determination reagent selection field ("Viability / Death"), where "PI" has been selected, has been checked, and "++" has been selected for the expression level. Note that the term "expression level" is not initially used for the cell viability / death determination reagent, but is used for convenience in treating the reagent equally with other substances. In response to the selection of the fluorescent protein and cell viability / death determination reagent and their expression levels, and the clicking of the close button ("Close"), the results of the selection are reflected in the input acceptance window for accepting input of biomolecules and expression level grades, as described in step S102 of (3-2) above, and as... Figure 22 As shown in Figure B. In the... Figure 22 In row B, the "▲" indicates the selection result in the input accepting window for the substance that produces reflected light. Since the input accepting windows for accepting biomolecules and expression level levels are as described above (3-2), they are omitted.

[0318] In step S601, in response to the selection of the light-generating substance, the processing unit 101 can acquire information about the selected light-generating substance. Therefore, as... Figure 22 As shown in A, the processing unit 101 can, for example, display the spectrum of light generated from the light-generating substance in the input receiving window. Information about the light-generating substance may include, for example, the brightness and spectrum of the light generated from the light-generating substance.

[0319] Steps S602 and S603 can be performed similarly to steps S102 and S103 described above (3-2). Note that in step S603, in addition to information about the phosphor, the processing unit 101 can also obtain information about the light-generating substance from the database. In this case, information about the light-generating substance may not be obtained in step S601.

[0320] In step S604, the processing unit 101 classifies the fluorophores in the list of fluorophores that can label biomolecules, which are included in the list of fluorophores associated with the fluorophores obtained in step S603, based on the brightness of each fluorophore, and generates one or more brightness categories, in particular multiple brightness categories.

[0321] In step S604, the processing unit 101 further classifies the light-generating substances selected in step S601 based on the brightness of each light-generating substance, and places the light-generating substances into arbitrary brightness categories.

[0322] In step S605, processing unit 101 associates the expression level category generated in step S602 with the brightness category generated in step S604. Preferably, processing unit 101 associates one expression level category with one brightness category. Alternatively, processing unit 101 can perform the association such that there is a one-to-one correspondence between expression level categories and brightness categories. That is, processing unit 101 can perform the association such that two or more expression level categories are not associated with a brightness category.

[0323] In step S606, in addition to the relevant information between phosphors, processing unit 101 also identifies the optimal phosphor combination by using the relevant information between phosphors and light-generating substances and / or the relevant information between light-generating substances. The optimal phosphor combination can be determined, for example, from the perspective of the correlation between the spectra of these substances, more specifically from the perspective of the correlation coefficient between the spectra of these substances, and even more specifically from the perspective of the square of the correlation coefficient between the spectra of these substances. Note that in this embodiment, "spectrum" includes the fluorescence spectrum of the phosphors and the spectrum of light generated from the light-generating substances.

[0324] The relevant information between these substances is preferably the relevant information between spectra. That is, in a preferred embodiment of this technology, the processing unit 101 identifies the optimal combination of phosphors by using the relevant information between spectra.

[0325] The method for identifying the optimal combination of phosphors is as described above (3-2), and this description also applies to this embodiment.

[0326] Step S607 can be performed similarly to step S107. Processing unit 101 generates a combination of fluorophores and biomolecules for each biomolecule through the allocation process in step S607, and generates a list of combinations of fluorophores for the biomolecules.

[0327] In step S608, processing unit 101 evaluates the separation capability of the phosphor group and the group including the photogenerating substance constituting the combination list generated by the allocation process in step S607. (Reference) Figure 14 A more detailed description of the processing flow for step S608 is provided.

[0328] However, in Figure 14 In step S303, the processing unit 101 does not identify the light-generating material input in step S601 as a phosphor with poor separation performance. This prevents the light-generating material input in step S601 from being altered.

[0329] In step S609, for example, processing unit 101 may cause output unit 104 to output the combination list generated in step S608. For example, the combination list may be displayed on a display device.

[0330] For example, such as Figure 22 As shown in C, the output results confirm that the biomolecules are recognized by fluorescent markers marked with circles.

[0331] (3-11) Example of processing in the processing unit (processing flow where staining index between fluorophores or spillover diffusion matrix between fluorophores is used as relevant information)

[0332] In step S106 of (3-2) above, the correlation between phosphor spectra has been mentioned as an example of relevant information between phosphors. However, in another embodiment of this technology, the relevant information between phosphors is not limited to this. For example, the staining index or the spillover diffusion matrix between phosphors can be used as relevant information between phosphors. Apart from the different relevant information used, another embodiment here is as described in (3-2) above. For example, in the processing flow described in (3-2) above, step S106 is different, but the other steps are the same. Therefore, step S106 will be described below.

[0333] (3-11-1) Cases involving the use of staining indices between fluorescent bodies

[0334] In this case, a standardized list of interfluorophoretic staining indices can be prepared in advance to perform the processing in step S106. In this specification, the standardized interfluorophoretic staining indices are also referred to as "standardized interfluorophoretic SI".

[0335] A standardized inter-fluorophore staining index (SI) list can be a list having inter-fluorophore staining indices for all combinations of two fluorophores in a fluorophore group, which includes at least all fluorophores included in the list associated with the fluorophores obtained in step S103. For example, a standardized inter-fluorophore staining index list can be a list having inter-fluorophore staining indices for all combinations of two fluorophores in a fluorophore group, which includes all fluorophores available in the device using a list of combinations of fluorophores for the biomolecules generated by processing unit 101.

[0336] Figure 23A An example of a standardized interfluorophore SI list is shown. Figure 23A As shown, the row headings in the list indicate positive fluorophores, and the column headings indicate negative fluorophores. For example, all fluorophores available in the device are listed in both the row and column headings. Note that in Figure 23A In this context, “…” indicates that a portion of the table is omitted.

[0337] Reference Figure 23B A method for calculating the normalized inter-fluorophore index (SI) is described. First, simulated data is generated for all fluorophores appearing in the normalized inter-fluorophore SI list, assuming the fluorescence intensity of each fluorophore is the same. Then, the simulated data is used to calculate the normalized inter-fluorophore SI between two different fluorophores. Figure 23B An example of calculating the normalized interfluorophoretic SI for PE relative to FITC is shown. The normalized interfluorophoretic SI represents the separation performance of FITC in the case of PE positivity.

[0338] Figure 23B The circled number 1 indicates the number used to calculate the normalized interfluorometric SI (SI) in both PE-positive and FITC-negative cases. PE SI FITC The formula is as shown in the formula. PE SI FITC Depend on{ PE F PE - PE F FITC}÷{ PE σ FITC ×2} represents. Each term in this formula is as follows: Figure 23BAs shown on the left, and indicating the following.

[0339] PE F PE It represents the average fluorescence intensity of PE-positive and FITC-negative particles at the fluorescence wavelength of PE.

[0340] PE F FITC It represents the average fluorescence intensity of PE-negative and FITC-positive particles at the fluorescence wavelength of PE.

[0341] PE σ FITC It is the standard deviation of the average fluorescence intensity of PE-negative and FITC-positive particles at the fluorescence wavelength of PE.

[0342] Perform the above calculations for all combinations of the two phosphors and generate results as follows: Figure 23A The standardized inter-fluorophore SI list is shown.

[0343] In step S106, the processing unit 101 identifies the optimal combination of fluorophores as relevant information between fluorophores by using the standardized inter-fluorophore SI list prepared as described above. An example of how to determine the optimal combination of fluorophores will be described below.

[0344] Processing unit 101 selects the same number of fluorophores from a specific brightness category as the number of biomolecules belonging to the expression level category associated with that specific brightness category. Fluorophores are selected for all brightness categories. Thus, the same number of fluorophores as the number of multiple biomolecules to be used to analyze the sample are selected, and in this way, a candidate fluorophore combination is obtained.

[0345] Next, for any combination of two fluorophores included in the fluorophore combination candidates, processing unit 101 refers to a standardized inter-fluorophore SI list and identifies the standardized inter-fluorophore SI corresponding to the combination of the two fluorophores. Here, for each combination, standardized inter-fluorophore SIs are identified for the case where one is positive and the other is negative, and for the case where one is negative and the other is positive. Processing unit 101 identifies two such standardized inter-fluorophore SIs for all combinations of two fluorophores included in the fluorophore combination candidates. Then, processing unit 101 determines the minimum value among all standardized inter-fluorophore SIs determined for the fluorophore combination candidates.

[0346] Note that the larger the normalized inter-fluorophore index (SI), the better the separation performance. Therefore, it is believed that the larger the minimum value identified as described above, the better the separation performance of the candidate fluorophore combinations from which the minimum value is obtained.

[0347] Here, as described above (3-2), when the number of fluorophores belonging to a specific brightness category is greater than the number of biomolecules belonging to the expression level category associated with that specific brightness category, there are multiple combinations of fluorophores selected from that specific brightness category. Therefore, in this technology, processing unit 101 identifies the minimum value of the normalized inter-fluorophore SI as described above for all possible fluorophore combination candidates. Then, processing unit 101 identifies the fluorophore combination candidate with the minimum value that has been identified to the maximum extent. Processing unit 101 identifies the fluorophore combination candidate identified in this way as the optimal fluorophore combination.

[0348] (Example 5: Evaluation using simulated data - normalized inter-fluorophore SI case)

[0349] In addition to using the standardized inter-fluorophore staining index (SI) instead of the squared correlation coefficient as relevant information, a list of biomolecule-fluorophore combinations was generated by assigning 20 fluorophores to 20 biomolecules in a manner similar to Example 1. The fluorescence separation performance of the combination list was confirmed using simulated data. The fluorescence separation performance was evaluated using the inter-fluorophore staining index. Figure 30A Above this is a list of interspheric staining indices. Additionally, in Figure 30A Below is also a two-dimensional diagram obtained when using a combination list.

[0350] Additionally, 20 fluorophores were manually assigned to 20 biomolecules, generating a list of biomolecule-fluorophore combinations. Simulated data were used to validate the fluorescence separation performance of these combinations in a similar manner to the above. The fluorescence separation performance was assessed using the interfluorophore staining index. Figure 30B Above this is a list of interspheric staining indices. Additionally, in Figure 30B Below is also a two-dimensional diagram obtained when using a combination list.

[0351] For the combination list generated by manual allocation, the inter-fluorophore staining index was calculated, and the minimum value among the calculated inter-fluorophore staining indices was 2.14. Figure 30B Above). On the other hand, for the combination list generated according to this technique, the inter-fluorophore staining index is calculated, and the minimum value among the calculated inter-fluorophore staining indices is 14.41 ( Figure 30A (above). Therefore, it can be seen that the separation performance can be improved by using the information processing device according to this technology.

[0352] Furthermore, for a combination list generated by manual allocation, the minimum normalized inter-fluorophore SI value is 7.8, while for a combination list generated according to this technology, the minimum normalized inter-fluorophore SI value is 23.2. This demonstrates that the information processing apparatus according to this technology can improve separation performance.

[0353] You can also compare Figure 30A Below and Figure 30B The improved separation performance is confirmed by the two-dimensional graph shown below.

[0354] (3-11-2) Case of using the spillover diffusion matrix between phosphors

[0355] In this case, in order to perform the process in step S106, an inter-fluorophore spillover diffusion matrix can be prepared in advance. In this specification, the inter-fluorophore spillover diffusion matrix is ​​also referred to as "inter-fluorophore SSM". In addition, inter-fluorophore spillover diffusion is also referred to as "inter-fluorophore SS".

[0356] The interfluorophore SSM can be an interfluorophore SSM that includes at least all combinations of two fluorophores from a fluorophore group that includes all fluorophores in the list associated with the fluorophores obtained in step S103. For example, an interfluorophore SSM can be an interfluorophore SSM that includes all combinations of two fluorophores in a fluorophore group that includes all fluorophores available in the device using a list of combinations of fluorophores for the biomolecules generated by processing unit 101.

[0357] Figure 24A An example of interfluorophoretic SSM is shown. For example... Figure 24A As shown, the row headers (filters) of the matrix represent the detectors in the wavelength band corresponding to each fluorophore, and the column headers (samples) represent the particles labeled with the fluorophores. The inter-fluorophore SS is an index related to the degree of light leakage from the fluorophore shown in the sample to the detector corresponding to the fluorophore shown in the filter. Note that in Figure 24A In this context, “…” indicates that a portion of the table is omitted.

[0358] Reference Figure 24B This paper describes a method for calculating the inter-fluorophore SS (Self-Solution). First, simulated data are generated for all fluorophores appearing in a standardized inter-fluorophore SI (Self-Solution) list, assuming identical fluorescence intensity for each fluorophore. Then, the simulated data are used to calculate the inter-fluorophore SS between two different fluorophores. Note that actual measurements of data for fluorescently labeled particles can be used instead of the simulated data. Figure 24B An example of calculating the interphosphor SS of FITC relative to PE is shown. Interphosphor SS refers to the degree of fluorescence leakage from the light generated by FITC to the detector corresponding to PE.

[0359] Figure 24B The circled number 1 indicates the value used to calculate the interfluorophoretic SS (FITC relative to PE). FITC SS PE The formula is as shown in the formula. FITC SS PE Depend on{( PE σ FITC ) 2 -( PE σ Nega ) 2} 0.5 ÷{ FITC F FITC FITC F Nega} 0.5 This is represented as follows. Each term in the formula is as follows: Figure 24B As shown on the left, and indicating the following.

[0360] PE σ FITC It is the standard deviation of the average fluorescence intensity of PE-negative and FITC-positive particles at the fluorescence wavelength of PE.

[0361] PE σ Nega It is the standard deviation of the average fluorescence intensity of PE-negative and FITC-negative particles at the fluorescence wavelength of PE.

[0362] FITC F FITC It represents the average fluorescence intensity of PE-negative and FITC-positive particles at the fluorescence wavelength of FITC.

[0363] FITC F Nega It represents the average fluorescence intensity of PE-negative and FITC-negative particles at the fluorescence wavelength of FITC.

[0364] Perform the above calculations for all combinations of the two phosphors and produce results such as Figure 24A The SSM between the fluorescent cells is shown.

[0365] In step S106, the processing unit 101 identifies the optimal combination of fluorophores as relevant information between fluorophores by using the inter-fluorophore SSM prepared as described above. An example of how to determine the optimal combination of fluorophores will be described below.

[0366] Processing unit 101 selects the same number of fluorophores from a specific brightness category as the number of biomolecules belonging to the expression level category associated with that specific brightness category. Fluorophores are selected for all brightness categories. Thus, the same number of fluorophores as the number of multiple biomolecules to be used to analyze the sample are selected, and in this way, a candidate fluorophore combination is obtained.

[0367] Next, for any combination of two fluorophores included in the fluorophore combination candidates, processing unit 101 refers to the inter-fluorophore SSM and identifies the inter-fluorophore SS corresponding to the combination of the two fluorophores. Here, for each combination, inter-fluorophore SS is identified when one is positive and the other is negative, and inter-fluorophore SS when one is negative and the other is positive. Processing unit 101 identifies such inter-fluorophore SS for all combinations of two fluorophores included in the fluorophore combination candidates. Then, processing unit 101 determines the maximum value among all inter-fluorophore SS determined for the fluorophore combination candidates.

[0368] Note that the smaller the SS between fluorophores, the better the separation performance. Therefore, it is believed that the smaller the maximum value identified as described above, the better the separation performance of the candidate fluorophore combination from which the maximum value is obtained.

[0369] Here, as described above (3-2), when the number of fluorophores belonging to a specific brightness category is greater than the number of biomolecules belonging to the expression level category associated with that specific brightness category, there are multiple combinations of fluorophores selected from that specific brightness category. Therefore, in this technology, processing unit 101 identifies the maximum value of the SS among fluorophores as described above for all possible fluorophore combination candidates. Then, processing unit 101 identifies the fluorophore combination candidate with the minimum identified maximum value. Processing unit 101 identifies the fluorophore combination candidate identified in this way as the optimal fluorophore combination.

[0370] (Example 6: Evaluation using simulated data - SSM between fluorescent cells)

[0371] In addition to using the squared value of the correlation coefficient instead of the inter-fluorophore staining index (SSM) as relevant information, a list of biomolecule-fluorophore combinations was generated by assigning 20 fluorophores to 20 biomolecules in a manner similar to Example 1. The fluorescence separation performance of the combination list was confirmed using simulated data. The fluorescence separation performance was evaluated using the inter-fluorophore staining index. Figure 31A Above this is a list of interspheric staining indices. Additionally, in Figure 31A Below is also a two-dimensional diagram obtained when using a combination list.

[0372] Additionally, 20 fluorophores were manually assigned to 20 biomolecules, generating a list of biomolecule-fluorophore combinations. Simulated data were used to validate the fluorescence separation performance of these combinations in a similar manner to the above. The fluorescence separation performance was assessed using the interfluorophore staining index. Figure 31B Above this is a list of interspheric staining indices. Additionally, in Figure 31B Below is also a two-dimensional diagram obtained when using a combination list.

[0373] For the combination list generated by manual allocation, the inter-fluorophore staining index was calculated, and the minimum value among the calculated inter-fluorophore staining indices was 4.77. Figure 31B Above). On the other hand, for the combination list generated according to this technique, the inter-fluorophore staining index is calculated, and the minimum value among the calculated inter-fluorophore staining indices is 19.8 ( Figure 31A (above). Therefore, it can be seen that the separation performance can be improved by using the information processing device according to this technology.

[0374] Furthermore, for the combination list generated by manual allocation, the minimum SS value in the SSM between fluorophores is 9.38, while for the combination list generated according to this technology, the minimum SS value in the SSM between fluorophores is 4.43. This demonstrates that the information processing device according to this technology can improve separation performance.

[0375] You can also compare Figure 31A Below and Figure 31B The two-dimensional graph shown below confirms the improved separation performance.

[0376] 2. Second Embodiment (Information Processing System)

[0377] This technology also provides an information processing system that includes the processing unit described in "1. First Embodiment (Information Processing Apparatus)" above. In addition to the processing unit, the information processing system may include the storage unit, input unit, output unit, and communication unit described in "1. First Embodiment (Information Processing Apparatus)" above. These components may be disposed in one device or may be distributed across multiple devices. For example, in addition to the processing unit, the information processing system of this technology may also include an input unit that accepts input data regarding the expression levels of multiple biomolecules to be used in the analysis of a sample.

[0378] Furthermore, by using the information processing system according to this technology, as described in "1. First Embodiment (Information Processing Apparatus)" above, a more appropriate combination list can be generated, and the processing for generating it can be performed more efficiently. This enables the automatic execution of optimized panel designs.

[0379] 3. Third Embodiment (Information Processing Method)

[0380] This technology also relates to an information processing method. The information processing method includes: a list generation step, which generates a combination list of fluorophores for biomolecules based on expression level categories obtained by classifying multiple biomolecules to be used for sample analysis based on their expression levels in a sample, brightness categories for classifying multiple fluorophores that can be used for sample analysis based on their brightness, and relevant information between the multiple fluorophores. In the list generation step, fluorophores to be assigned to biomolecules in the combination list are selected from fluorophores belonging to brightness categories associated with the expression level categories to which the biomolecules belong.

[0381] By generating a list of combinations of fluorophores for biomolecules using the information processing method of this technology, a more appropriate list of combinations can be produced, and the processing for generation can be performed more efficiently. This enables the automatic execution of optimized panel designs.

[0382] The list generation step included in the information processing method according to the present technology can be performed according to any of the processes described in "1. First Embodiment (Information Processing Apparatus)" above.

[0383] The list generation step may include, for example, an expression level category generation step, which obtains expression level categories by classifying multiple biomolecules to be used for sample analysis based on expression levels in the sample; a brightness category generation step, which classifies multiple fluorophores that can be used for sample analysis based on brightness; and an allocation step, which performs a process of assigning fluorophores to biomolecules based on the expression level categories, brightness categories, and relevant information between the multiple fluorophores.

[0384] The expression level category generation step may, for example, include performing step S102 as described in "1. First Embodiment (Information Processing Apparatus)" above. In addition to performing step S102, the expression level category generation step may also include performing step S101. These steps are as described in "1. First Embodiment (Information Processing Apparatus)" above, and their description also applies to this embodiment.

[0385] The brightness category generation step may, for example, include performing step S104 as described in "1. First Embodiment (Information Processing Apparatus)" above. In addition to performing step S104, the brightness category generation step may also include performing step S103. These steps are as described in "1. First Embodiment (Information Processing Apparatus)" above, and their description also applies to this embodiment.

[0386] The allocation step may, for example, include performing step S107 as described in "1. First Embodiment (Information Processing Apparatus)" above. In addition to performing step S107, the allocation step may also include performing step S105 and / or performing step S106. These steps are as described in "1. First Embodiment (Information Processing Apparatus)" above, and their description also applies to this embodiment.

[0387] The information processing method according to this technology may further include a separation capability assessment step, performing a separation capability assessment on the combination list generated in the allocation step. The separation capability assessment step may be performed as described in “(3-7) Example of processing of the processing unit (Example of performing separation capability assessment)” above 1.

[0388] Furthermore, the relevant information between phosphors used in the information processing method according to this technology can be relevant information between fluorescence spectra. Alternatively, the relevant information between phosphors can be a normalized inter-phosphor SI or inter-phosphor SSM. For details regarding this relevant information, please refer to "1. First Embodiment (Information Processing Apparatus)" above.

[0389] 4. Fourth Implementation Example (Program)

[0390] This technology also provides a program for causing an information processing apparatus to execute the information processing method described in section 3 above. This information processing method is as described in sections 1 and 3 above, and the description also applies to this embodiment. The program according to this technology can, for example, be recorded in the aforementioned recording medium, or can be stored in the aforementioned information processing apparatus or in a storage unit included in the aforementioned information processing apparatus.

[0391] Note that this technology can also have the following configurations.

[0392] [1] An information processing apparatus includes: a processing unit that generates a combination list of fluorophores for biomolecules based on expression level categories for classifying multiple biomolecules to be used for analysis of the sample based on expression levels in the sample, brightness categories for classifying multiple fluorophores that can be used for analysis of the sample based on brightness, and related information between the multiple fluorophores, wherein the processing unit selects fluorophores to be assigned to biomolecules from the combination list of fluorophores belonging to the brightness categories associated with the expression level categories to which the biomolecules belong.

[0393] [2] According to the information processing apparatus of [1], the expression level category is associated with the brightness category, such that the expression level category obtained by classifying biomolecules exhibiting lower expression levels corresponds to the brightness category for classifying brighter fluorescent molecules.

[0394] [3] The information processing apparatus according to [1] or [2], wherein the processing unit selects each of the phosphors by using relevant information.

[0395] [4] An information processing apparatus according to any one of [1] to [3], wherein the relevant information is the correlation coefficient between the fluorescence spectra of multiple phosphors.

[0396] [5] An information processing apparatus according to any one of [1] to [4], wherein the relevant information is a value obtained by squaring the correlation coefficients between the fluorescence spectra of a plurality of phosphors.

[0397] [6] According to the information processing apparatus of [4] or [5], the processing unit calculates the correlation coefficient by using two or more fluorescence spectra obtained when the phosphor is irradiated by two or more excitation beams of different wavelengths.

[0398] [7] An information processing apparatus according to any one of [1] to [3], wherein the relevant information is a staining index among multiple fluorescent cells.

[0399] [8] An information processing apparatus according to any one of [1] to [3], wherein the relevant information is an overflow diffusion matrix among a plurality of phosphors.

[0400] [9] An information processing apparatus according to any one of [1] to [8], wherein multiple fluorophores are identified based on data regarding the availability of complexes of fluorophores and biomolecules.

[0401]

[10] An information processing apparatus according to any one of [1] to [9], wherein the processing unit adjusts the number of fluorescent molecules belonging to the brightness category based on the number of biomolecules belonging to the expression level category.

[0402]

[11] An information processing apparatus according to any one of [1] to

[10] , wherein multiple phosphors are identified based on information relating to priority associated with the phosphors.

[0403]

[12] An information processing apparatus according to any one of [1] to

[11] , wherein the processing unit performs an evaluation of the separation capability related to the combination list.

[0404]

[13] According to the information processing apparatus of

[12] , the processing unit further generates a modified combination list based on the results of the separation capability assessment, and further performs a separation capability assessment related to the modified combination list, wherein at least one fluorophore included in a group of fluorophores in the modified combination list is changed to another fluorophore.

[0405]

[14] An information processing apparatus according to any one of [1] to

[13] , wherein the processing unit further generates a combination list based on cost information about the complex of biomolecules and fluorophores.

[0406]

[15] An information processing apparatus according to any one of [1] to

[14] , wherein, in the case of identifying a fluorophore to be assigned to at least one of a plurality of biomolecules, the processing unit further generates a combination list based on relevant information between the plurality of fluorophores and the pre-identified fluorophores.

[0407]

[16] An information processing apparatus according to any one of [1] to

[15] , wherein, in the case of identifying a light-generating substance to be used for analyzing a sample, the processing unit further generates a combination list based on relevant information between multiple phosphors and the light-generating substance.

[0408]

[17] An information processing apparatus according to any one of [1] to

[16] , wherein each of a plurality of biomolecules is an antigen or an antibody.

[0409]

[18] An information processing apparatus according to any one of [1] to

[17] , wherein,

[0410] When each of the multiple biomolecules is an antigen, the expression level is the antigen expression level, and

[0411] In the case where each of the multiple biomolecules is an antibody, the expression level is determined by the expression level of the antigen captured by the antibody.

[0412]

[19] An information processing system, comprising:

[0413] The input unit accepts input data regarding the expression levels of multiple biomolecules to be used in the sample for analysis; and

[0414] The processing unit generates a list of combinations of fluorophores for biomolecules based on expression level categories for classifying multiple biomolecules according to their expression levels in the sample, brightness categories for classifying multiple fluorophores that can be used to analyze the sample according to their brightness, and relevant information between the multiple fluorophores.

[0415] The processing unit selects the fluorophore to be assigned to the biomolecule from a list of fluorophores belonging to the brightness category associated with the expression level category to which the biomolecule belongs.

[0416]

[20] An information processing method includes: a list generation step, which generates a list of combinations of fluorophores for biomolecules based on expression level categories for classifying multiple biomolecules to be used to analyze the sample, brightness categories for classifying multiple fluorophores that can be used to analyze the sample based on brightness, and relevant information between the multiple fluorophores.

[0417] In the list generation step, the fluorescent fluorescent to be assigned to the biomolecule is selected from the fluorescent fluorescents belonging to the brightness category associated with the expression level category to which the biomolecule belongs.

[0418]

[21] A program that causes an information processing device to perform: a list generation step, generating a list of combinations of fluorophores for biomolecules based on expression level categories for classifying multiple biomolecules to be used to analyze the sample, brightness categories for classifying multiple fluorophores that can be used to analyze the sample based on brightness, and relevant information between the multiple fluorophores.

[0419] In the list generation step, the fluorescent fluorescent to be assigned to the biomolecule in the combination list is selected from the fluorescent fluorescents belonging to the brightness category associated with the expression level category to which the biomolecule belongs.

[0420] Reference tag list

[0421] 100 Information Processing Device

[0422] 101 Processing Unit

[0423] 102 storage units

[0424] 103 Input Unit

[0425] 104 output units

[0426] 105 Communication Unit.

Claims

1. An information processing apparatus, comprising: The processing unit generates a list of combinations of fluorophores for biomolecules based on expression level categories for classifying multiple biomolecules to be used to analyze the sample, brightness categories for classifying multiple fluorophores that can be used to analyze the sample based on brightness, and relevant information between the multiple fluorophores. The processing unit selects the fluorophore to be assigned to the biomolecule from the list of combinations of fluorophores belonging to a brightness category associated with the expression level category to which the biomolecule belongs. The processing unit adjusts the number of fluorescent molecules belonging to each of the brightness categories based on the number of biomolecules belonging to each of the expression level categories.

2. The information processing apparatus according to claim 1, wherein, The expression level category is associated with the brightness category such that the expression level category obtained by classifying biomolecules exhibiting lower expression levels corresponds to the brightness category for classifying brighter phosphors.

3. The information processing apparatus according to claim 1, wherein, The processing unit selects each of the plurality of phosphors by using the relevant information.

4. The information processing apparatus according to claim 1, wherein, The relevant information is the correlation coefficient between the fluorescence spectra of the plurality of phosphors.

5. The information processing apparatus according to claim 1, wherein, The relevant information is obtained by squaring the correlation coefficients between the fluorescence spectra of the plurality of phosphors.

6. The information processing apparatus according to claim 4, wherein, The processing unit calculates the correlation coefficient using two or more fluorescence spectra obtained when the phosphor is irradiated by two or more excitation beams of different wavelengths.

7. The information processing apparatus according to claim 1, wherein, The relevant information is the staining index among the plurality of fluorescent cells.

8. The information processing apparatus according to claim 1, wherein, The relevant information is the spillover diffusion matrix among the plurality of phosphors.

9. The information processing apparatus according to claim 1, wherein, The plurality of fluorophores are identified based on data regarding the usability of complexes between fluorophores and biomolecules.

10. The information processing apparatus according to claim 1, wherein, The plurality of phosphors are identified based on priority information associated with them.

11. The information processing apparatus according to claim 1, wherein, The processing unit performs an evaluation of the separation capability associated with the combination list.

12. The information processing apparatus according to claim 11, wherein, The processing unit further generates a modified combination list based on the results of the separation capability assessment, and further performs a separation capability assessment related to the modified combination list, wherein at least one fluorophore in a group of fluorophores in the modified combination list is changed to another fluorophore.

13. The information processing apparatus according to claim 1, wherein, The processing unit further generates the combination list based on cost information regarding the complexes of biomolecules and fluorophores.

14. The information processing apparatus according to claim 1, wherein, In the case of identifying a fluorophore to be assigned to at least one of the plurality of biomolecules, the processing unit further generates the combination list based on relevant information between the plurality of fluorophores and the pre-identified fluorophores.

15. The information processing apparatus according to claim 1, wherein, Upon identifying the light-generating substance to be used for analyzing the sample, the processing unit further generates a combination list based on relevant information between the plurality of phosphors and the light-generating substance.

16. The information processing apparatus according to claim 1, wherein, Each of the plurality of biomolecules is an antigen or an antibody.

17. The information processing apparatus according to claim 1, wherein, In the case where each of the plurality of biomolecules is an antigen, the expression level is the expression level of the antigen, and In the case where each of the plurality of biomolecules is an antibody, the expression level is the expression level of the antigen captured by the antibody.

18. An information processing system, comprising: The input unit accepts input data on the expression levels of multiple biomolecules to be used in the sample for analysis. as well as The processing unit generates a list of combinations of fluorophores for biomolecules based on expression level categories for classifying the plurality of biomolecules according to their expression levels in the sample, brightness categories for classifying the plurality of fluorophores that can be used to analyze the sample according to their brightness, and relevant information between the plurality of fluorophores. The processing unit selects the fluorophore to be assigned to the biomolecule from a list of fluorophores belonging to a brightness category associated with the expression level category to which the biomolecule belongs. The processing unit adjusts the number of fluorescent molecules belonging to each of the brightness categories based on the number of biomolecules belonging to each of the expression level categories.

19. An information processing method, comprising: The list generation step generates a combined list of fluorophores for biomolecules based on expression level categories for classifying multiple biomolecules to be used to analyze the sample, brightness categories for classifying multiple fluorophores that can be used to analyze the sample based on brightness, and relevant information between the multiple fluorophores. In the list generation step, fluorescent fluorescent cells belonging to the brightness category associated with the expression level category to which the biomolecule belongs are selected from the combined list to be assigned to the biomolecule, and the number of fluorescent fluorescent cells belonging to each of the brightness categories is adjusted according to the number of biomolecules belonging to each of the expression level categories.

20. A computer-readable storage medium storing a program, which, when executed, causes an information processing apparatus to perform: a list generation step, generating a list of combinations of fluorophores for biomolecules based on expression level categories for classifying a plurality of biomolecules to be used to analyze the sample, brightness categories for classifying a plurality of fluorophores that can be used to analyze the sample based on brightness, and relevant information between the plurality of fluorophores. in, In the list generation step, fluorophores to be assigned to the biomolecule are selected from the list of fluorophores belonging to the brightness category associated with the expression level category to which the biomolecule belongs, and the number of fluorophores belonging to each of the brightness categories is adjusted according to the number of biomolecules belonging to each of the expression level categories.

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