Information processing system, information processing method, and information processing device

Abstract

The present technology relates to an information processing system, an information processing method, and an information processing device that make it possible to facilitate panel design for a flow cytometer or the like. An information processing system according to the present invention comprises a fluorochrome selection unit that selects a fluorochrome to combine with a biomolecule to be measured by a second device on the basis of expression level information about expression levels of biomolecules measured by a first device, panel information about combinations of the biomolecules and fluorochromes used for the measurement of the expression levels by the first device, first reagent data about the characteristics of the fluorochromes at the first device, and second reagent data about the characteristics of fluorochromes at the second device. The present technology can be applied, for example, to panel design for a flow cytometer or the like.

Description

Information Processing System, Information Processing Method, and Information Processing Apparatus

[0001] The present technology relates to an information processing system, an information processing method, and an information processing apparatus, and particularly relates to an information processing system, an information processing method, and an information processing apparatus suitable for use in panel design for an FCM (flow cytometer) or the like.

[0002] In recent years, along with the multicolorization of FCM (flow cytometers), it has become possible to simultaneously measure more markers, and various cell types have been identified. In addition, in order to identify novel cell types or comprehensively identify cell types, panel design is performed such that each marker can be comprehensively measured. That is, markers to be measured are selected comprehensively, and the combination of the selected markers and fluorescent dyes used for each marker is designed.

[0003] In contrast, devices that support panel design have been conventionally proposed (see, for example, Patent Document 1).

[0004] Japanese Unexamined Patent Publication No. 2022-44213

[0005] On the other hand, after identifying a cell type using a comprehensive panel, there is a demand for enabling cell type identification using a simpler and less expensive panel. To this end, it is necessary to redesign the panel so as to reduce the number of markers to be measured and the types of fluorescent dyes used for measuring the markers.

[0006] However, proper panel design requires experience and a considerable amount of time.

[0007] The present technology has been made in view of such circumstances, and facilitates panel design for FCM and the like.

[0008] One aspect of this technology is an information processing system which includes a fluorescent dye selection unit that selects a fluorescent dye to be combined with a biomolecule to be measured in the second device, based on expression level information relating to the expression levels of each biomolecule measured in the first device, panel information relating to the combination of biomolecule and fluorescent dye used to measure the expression levels in the first device, first reagent data relating to the characteristics of each fluorescent dye in the first device, and second reagent data relating to the characteristics of each fluorescent dye in the second device.

[0009] One aspect of this technology is an information processing method in which an information processing system selects a fluorescent dye to be combined with a biomolecule to be measured in the second device, based on expression level information relating to the expression levels of each biomolecule measured in the first device, panel information relating to the combination of biomolecule and fluorescent dye used to measure the expression levels in the first device, first reagent data relating to the characteristics of each fluorescent dye in the first device, and second reagent data relating to the characteristics of each fluorescent dye in the second device.

[0010] An information processing device representing one aspect of this technology includes a fluorescent dye selection unit that selects a fluorescent dye to be combined with a biomolecule to be measured in the second device, based on expression level information relating to the expression levels of each biomolecule measured in the first device, panel information relating to the combination of biomolecule and fluorescent dye used to measure the expression levels in the first device, first reagent data relating to the characteristics of each fluorescent dye in the first device, and second reagent data relating to the characteristics of each fluorescent dye in the second device.

[0011] In one aspect of this technology, a fluorescent dye to be combined with the biomolecule to be measured in the second device is selected based on expression level information regarding the expression levels of each biomolecule measured in the first device, panel information regarding the combination of biomolecule and fluorescent dye used to measure the expression levels in the first device, first reagent data regarding the characteristics of each fluorescent dye in the first device, and second reagent data regarding the characteristics of each fluorescent dye in the second device.

[0012] This is a diagram illustrating the background of this technology. This is a block diagram showing an example of the configuration of an information processing system. This is a block diagram showing an example of the configuration of a biological sample analyzer. This is a block diagram showing an example of the configuration of a server. This is a block diagram showing an example of the configuration of an information processing unit. This is a diagram showing an example of the data structure of a reagent DB. This is a flowchart illustrating the flow of an experiment. This is a flowchart illustrating the panel design process. This is a diagram showing an example of a marker selection screen. This is a diagram showing an example of a histogram of marker expression levels. This is a diagram illustrating the details of the fluorescent dye selection process. This is a diagram showing an example of a combination list. This is a diagram illustrating the method of calculating SI. This is a diagram illustrating the method of calculating SI between fluorescent dyes. This is a diagram showing an example of a screen showing the results of panel design. This is a diagram illustrating the first embodiment of the marker selection process. This is a diagram illustrating the method of selecting a marker. This is a diagram showing an example of a screen showing the marker selection results. This is a diagram illustrating the second embodiment of the marker selection process. This is a diagram illustrating the method of selecting a marker. This is a diagram illustrating the third embodiment of the marker selection process. This is a diagram illustrating the method of selecting a marker.

[0013] The following describes the embodiments for implementing this technology. The explanation will proceed in the following order: 0. Background of this technology 1. First embodiment (Example where the user specifies the measurement marker) 2. Second embodiment (First example where the marker used for gating is selected as the measurement marker) 3. Third embodiment (Second example where the marker used for gating is selected as the measurement marker) 4. Fourth embodiment (Example where the measurement marker is selected by recursive feature reduction) 5. Modifications 6. Others

[0014] <<0. Background of this Technology>> First, we will explain the background of this technology with reference to Figure 1.

[0015] As mentioned above, in FCM, there is a desire to be able to identify cell types using a simpler and less expensive panel after identifying them using a comprehensive panel. To achieve this, it is necessary to redesign the panel to reduce the number of markers to be measured and the types of fluorescent dyes used to measure the markers, but panel design requires experience and considerable time.

[0016] Here, a marker refers to a biomolecule such as an antigen or immunoglobulin that is characteristic of the measurement.

[0017] Hereinafter, the marker to be measured, that is, the marker whose expression level is measured for purposes such as identifying cell types, will be referred to as the measurement marker. Hereinafter, the fluorescent dye used to measure the measurement marker, that is, the fluorescent dye used in combination with an antibody that binds to the measurement marker, will be referred to as the dye used.

[0018] For example, the simplest method, as shown in Figure 1, is to exclude markers that are not involved in cell type identification from the measurement target and simply remove the fluorescent dyes used in the excluded markers. In this example, CD2 is excluded from the measurement target and the PE used in CD2 is removed.

[0019] For example, if measurements using 40 types of markers and 40 fluorescent dyes reveal that cell types can be identified using only 10 types of markers, then a method can be considered in which markers other than those 10 are excluded from the measurement, and the 30 fluorescent dyes used for the excluded markers are removed.

[0020] However, simply reducing the number of fluorescent dyes in this way may not necessarily result in an appropriate combination of the 10 markers being measured and the remaining fluorescent dyes. For example, the spectral shapes of the remaining 10 fluorescent dyes may be similar, or there may be an extreme imbalance between the brightness of the fluorescent dyes and the expression levels of the markers, which could lead to a decrease in marker separation performance and a reduction in the accuracy of cell identification.

[0021] Furthermore, the same equipment may not necessarily be used. For example, a high-performance device might be used for experiments using a comprehensive range of markers, while a lower-performance device might be used for experiments with a reduced number of marker types.

[0022] Generally, different devices have different optical characteristics. For example, the type and number of photodetectors (e.g., PMT (photomultiplier tube), APD (avalanche photodiode), etc.), spectral patterns, and light source configurations may differ. Therefore, if the device is different, the suitability of the method of simply removing the fluorescent dye described above may be further reduced.

[0023] In contrast, this technology facilitates the design of panels such as FCM. More specifically, it enables the appropriate reduction of markers and fluorescent dyes to be measured when, for example, cell types can be identified using simpler and less expensive panels.

[0024] <<1. First Embodiment>> Next, embodiments of the present technology will be described with reference to Figures 2 to 14.

[0025] <Example Configuration of Information Processing System 1> Figure 2 shows an example configuration of information processing system 1 to which this technology is applied.

[0026] The information processing system 1 comprises biological sample analyzers 11-1 to 11-n, server 12-1, and server 12-2.

[0027] The biological sample analyzers 11-1 to 11-m and server 12-2 are connected via a network (not shown). The biological sample analyzers 11-1 to 11-m and server 12-1 are owned by a single organization.

[0028] Furthermore, the organizational unit that owns the biological sample analyzers 11-1 to 11-m and the server 12-1 is not particularly limited. For example, the organization may be a single company, school, or group, or it may be a department of a company, school, or group.

[0029] The biological sample analyzers 11-m+1 to 11-n, server 12-1, and server 12-2 are connected via a network (not shown).

[0030] Hereafter, when it is not necessary to distinguish between biological sample analyzers 11-1 to 11-n individually, they will simply be referred to as biological sample analyzer 11. Hereafter, when it is not necessary to distinguish between servers 12-1 and 12-2 individually, they will simply be referred to as server 12.

[0031] The biological sample analysis device 11 is comprised of, for example, a flow cytometer or an imaging cytometer.

[0032] The server 12 controls, for example, each biological sample analysis device 11. The server 12 manages and processes, for example, the data and programs used by each biological sample analysis device 11, as well as the data obtained from the processing performed by each biological sample analysis device 11.

[0033] Furthermore, the server 12 uses the data obtained from the processing of each biological sample analysis device 11 to perform processing that supports panel design.

[0034] Furthermore, the server 12-1 prevents data obtained by the biological sample analysis device 11 within the tissue from leaking outside the tissue.

[0035] Note that the number of servers 12 and organizations is not limited to the example shown in this diagram. Also, for example, one organization may own multiple servers 12.

[0036] <Example of the configuration of the biological sample analyzer 11> Figure 3 shows an example of the configuration of the biological sample analyzer 11 shown in Figure 2.

[0037] The biological sample analyzer 11 includes a light irradiation unit 111 that irradiates light onto a biological sample S flowing through a flow path C, a detection unit 112 that detects the light generated by irradiating the biological sample S, and an information processing unit 113 that processes information related to the light detected by the detection unit 112. Examples of the biological sample analyzer 11 include a flow cytometer and an imaging cytometer. The biological sample analyzer 11 may also include a sorting unit 114 that sorts specific biological particles P within the biological sample. An example of the biological sample analyzer 11 including the sorting unit 114 is a cell sorter.

[0038] (Biological Sample) The biological sample S may be a liquid sample containing biological particles. These biological particles are, for example, cells or noncellular biological particles. The cells may be living cells, and more specifically, blood cells such as red blood cells and white blood cells, and germ cells such as sperm and fertilized eggs. The cells may be directly collected from a sample such as whole blood, or they may be cultured cells obtained after culturing. Examples of noncellular biological particles include extracellular vesicles, particularly exosomes and microvesicles.

[0039] The biological particles may be labeled with one or more labeling substances (e.g., dyes (particularly fluorescent dyes) and fluorescently labeled antibodies). For example, the biological particles may be labeled (stained) with one or more types of fluorescent dyes. Labeling of the biological particles with fluorescent dyes may be carried out by known methods. Specifically, if the biological particles are cells, the cells to be measured can be labeled with a fluorescent dye by mixing a fluorescently labeled antibody that selectively binds to antigens present on the cell surface with the cells to be measured, thereby binding the fluorescently labeled antibody to the antigens on the cell surface. Alternatively, the cells to be measured can be labeled with a fluorescent dye by mixing a fluorescent dye that is selectively taken up by specific cells with the cells to be measured.

[0040] Fluorescently labeled antibodies are antibodies to which a fluorescent dye has been conjugated as a label. Fluorescently labeled antibodies may be antibodies to which the fluorescent dye has been directly conjugated. Alternatively, fluorescently labeled antibodies may be biotin-labeled antibodies to which a fluorescent dye conjugated with avidin has been conjugated via the avidin-biodin reaction. Both polyclonal and monoclonal antibodies can be used.

[0041] The fluorescent dye for labeling cells is not particularly limited, and at least one or more known dyes used for staining cells or the like can be used. Examples of usable fluorescent dyes include phycoerythrin (PE), fluorescein isothiocyanate (FITC), PE-Cy5, PE-Cy7, PE-Texas Red (registered trademark), allophycocyanin (APC), APC-Cy7, ethidium bromide, propidium iodide, Hoechst (registered trademark) 33258, Hoechst (registered trademark) 33342, DAPI (4',6-diamidino-2-phenylindole), acridine orange, chromomycin, mithramycin, olivomycin, pyronin Y, thiazole orange, rhodamine 101, isothiocyanate, BCECF, BCECF-AM, C. SNARF-1, C. SNARF-1-AMA, aequorin, Indo-1, Indo-1-AM, Fluo-3, Fluo-3-AM, Fura-2, Fura-2-AM, oxonol, Texas Red (registered trademark), rhodamine 123, 10-N-nonyl-acridine orange, fluorescein, fluorescein diacetate, carboxyfluorescein, carboxyfluorescein diacetate, carboxydichlorofluorescein, and carboxydichlorofluorescein diacetate. Derivatives of the above-mentioned fluorescent dyes can also be used.

[0042] (Flow channel) The flow channel C is configured to allow a biological sample S to flow through it. In particular, the flow channel C may be configured to form a flow in which biological particles contained in the biological sample are arranged in a substantially straight line. The flow channel structure including the flow channel C may be designed to form a laminar flow. In particular, the flow channel structure is designed to form a laminar flow in which the flow of the biological sample (sample flow) is surrounded by the flow of the sheath liquid. The design of the flow channel structure may be appropriately selected by those skilled in the art, and known designs may be adopted. The flow channel C may be formed in a flow channel structure such as a microchip (a chip having a flow channel on the order of micrometers) or a flow cell. The width of the flow channel C is 1 mm or less, and in particular may be 10 μm or more and 1 mm or less. The flow channel C and the flow channel structure including it may be formed from materials such as plastic or glass.

[0043] The biological sample analyzer according to this disclosure is configured such that light from the light irradiation unit 111 is irradiated onto a biological sample, particularly biological particles, flowing through the channel C. The biological sample analyzer according to this disclosure may be configured such that the light irradiation point (interrogation point) for the biological sample is located within the channel structure in which the channel C is formed, or it may be configured such that the light irradiation point is located outside the channel structure. An example of the former is a configuration in which the light is irradiated onto the channel C in a microchip or flow cell. In the latter case, the light may be irradiated onto biological particles after they have exited the channel structure (particularly its nozzle), for example, a Jet-in-Air type flow cytometer.

[0044] (Light Irradiation Unit) The light irradiation unit 111 includes a light source unit that emits light, and a light guiding optical system that guides the light to an irradiation point. The light source unit includes one or more light sources. The type of the light source is, for example, a laser light source or an LED. The wavelength of light emitted from each light source may be any wavelength of ultraviolet light, visible light, or infrared light. The light guiding optical system includes optical components such as, for example, a beam splitter group, a mirror group, or an optical fiber. Further, the light guiding optical system may include a lens group for condensing light, for example, an objective lens. There may be one or more irradiation points where light intersects the biological sample. The light irradiation unit 111 may be configured to condense light emitted from one or a plurality of different light sources onto one irradiation point.

[0045] (Detection Unit) The detection unit 112 includes at least one photodetector that detects light generated by irradiating biological particles with light. The light to be detected is, for example, fluorescence or scattered light (for example, any one or more of forward scattered light, back scattered light, and side scattered light). Each photodetector includes one or more light receiving elements, and has, for example, a light receiving element array. Each photodetector may include, as light receiving elements, one or more PMTs (photomultiplier tubes) and / or photodiodes such as APD and MPPC. The photodetector includes, for example, a PMT array in which a plurality of PMTs are arranged in a one-dimensional direction. Further, the detection unit 112 may include an imaging element such as a CCD or a CMOS. The detection unit 112 can acquire images of biological particles (for example, bright-field images, dark-field images, fluorescence images, etc.) using the imaging element.

[0046] The detection unit 112 includes a detection optical system that directs light of a predetermined detection wavelength to a corresponding photodetector. The detection optical system includes a spectroscopic unit such as a prism or diffraction grating, or a wavelength separation unit such as a dichroic mirror or optical filter. The detection optical system is configured to spectrally analyze light generated by, for example, irradiation of biological particles, and to detect the spectrally analyzed light using a plurality of photodetectors, more than the number of fluorescent dyes on which the biological particles are labeled. A flow cytometer including such a detection optical system is called a spectral flow cytometer. The detection optical system is also configured to separate light corresponding to the fluorescence wavelength range of a specific fluorescent dye from light generated by, for example, irradiation of biological particles, and to detect the separated light using a corresponding photodetector.

[0047] Furthermore, the detection unit 112 may include a signal processing unit that converts the electrical signal obtained by the photodetector into a digital signal. The signal processing unit may include an A / D converter as the device that performs the conversion. The digital signal obtained by the conversion by the signal processing unit may be transmitted to the information processing unit 113. The digital signal may be treated by the information processing unit 113 as data related to light (hereinafter also referred to as "light data"). The light data may be, for example, light data including fluorescence data. More specifically, the light data may be light intensity data, and the light intensity may be light intensity data of light including fluorescence (which may include feature quantities such as Area, Height, Width, etc.).

[0048] (Information Processing Unit) The information processing unit 113 includes, for example, a processing unit that performs processing of various data (e.g., optical data) and a storage unit that stores various data. When the processing unit obtains optical data corresponding to a fluorescent dye from the detection unit 112, it may perform fluorescence leakage correction (compensation processing) on ​​the optical intensity data. In the case of a spectral flow cytometer, the processing unit also performs fluorescence separation processing on the optical data to obtain optical intensity data corresponding to a fluorescent dye. The fluorescence separation processing may be performed, for example, according to the unmixing method described in Japanese Patent Application Publication No. 2011-232259. If the detection unit 112 includes an image sensor, the processing unit may obtain morphological information of biological particles based on the image obtained by the image sensor. The storage unit may be configured to store the acquired optical data. The storage unit may further be configured to store spectral reference data used in the unmixing processing.

[0049] If the biological sample analyzer 11 includes a sorting unit 114 described later, the information processing unit 113 may determine whether to sort biological particles based on optical data and / or morphological information. Based on the result of this determination, the information processing unit 113 controls the sorting unit 114, and the sorting unit 114 may sort biological particles.

[0050] The information processing unit 113 may be configured to output various types of data (e.g., optical data and images). For example, the information processing unit 113 may output various types of data (e.g., two-dimensional plots, spectral plots, etc.) generated based on the optical data. The information processing unit 113 may also be configured to accept input of various types of data, for example, to accept gating processing on a plot by a user. The information processing unit 113 may include an output unit (e.g., a display) or an input unit (e.g., a keyboard) for executing such output or input.

[0051] The information processing unit 113 may be configured as a general-purpose computer, for example, as an information processing device equipped with a CPU, RAM, and ROM. The information processing unit 113 may be contained within the housing that houses the light irradiation unit 111 and the detection unit 112, or it may be located outside of the housing. Furthermore, various processing or functions performed by the information processing unit 113 may be implemented by a server computer or cloud connected via a network.

[0052] (Separation Unit) The separation unit 114 performs separation of biological particles according to the determination result by the information processing unit 113. The separation method may be a method in which droplets containing biological particles are generated by vibration, an electric charge is applied to the droplets to be separated, and the direction of movement of the droplets is controlled by electrodes. The separation method may also be a method in which the direction of movement of biological particles is controlled and separation is performed within a flow channel structure. The flow channel structure is provided with, for example, a control mechanism by pressure (injection or suction) or electric charge. An example of such a flow channel structure is a chip (for example, the chip described in Japanese Patent Application Publication No. 2020-76736) in which a flow channel C has a flow channel structure in which a recovery flow channel and a waste liquid flow channel branch downstream thereof, and specific biological particles are recovered into the recovery flow channel.

[0053] <Example of Server 12 Configuration> Figure 4 is a block diagram showing an example of the configuration of Server 12.

[0054] In server 12, the processing circuit 201, ROM (Read Only Memory) 202, and RAM (Random Access Memory) 203 are interconnected by a bus 204.

[0055] An input / output interface 205 is further connected to the bus 204. An input / output interface 205 is connected to an input unit 206, an output unit 207, a storage unit 208, a communication unit 209, and a drive 210.

[0056] The input unit 206 may include physical or virtual operating means that the user operates to input information, such as a keyboard, mouse, or touch panel, as well as means that the user inputs information through voice, eye gaze, etc. Furthermore, the input unit 206 may include sensors for inputting various physical quantities to the server 12. For example, the input unit 206 may include sensors that acquire physical quantities such as light (including infrared light other than visible light) or sound, such as a camera or microphone. Also, for example, the input unit 206 may include sensors that acquire other physical quantities such as temperature, moisture content, acceleration, and distance.

[0057] The output unit 207 may include means for presenting information to the user by stimulating the user's perception, such as a display, speaker, or haptic device.

[0058] The storage unit 208 consists of a hard disk, non-volatile or volatile memory, etc., and stores various types of information (including programs).

[0059] The communication unit 209 is a network interface, etc., and performs wired or wireless communication with the outside world.

[0060] The drive 210 drives removable media 211 such as a magnetic disk, optical disk, magneto-optical disk, or semiconductor memory.

[0061] The processing circuit 201 includes a processor that executes programs such as a CPU (Central Processing Unit) and a DSP (Digital Signal Processor). The processing circuit 201 (its processor) performs the series of processes described above by loading the program stored in the memory unit 208 into the RAM 203 via the input / output interface 205 and the bus 204 and executing it. The processing circuit 201 can output the processing results of the series of processes from the output unit 207, for example, via the bus 204 and the input / output interface 205, as needed. The processing circuit 201 can also store the processing results in the memory unit 208 or transmit them from the communication unit 209.

[0062] The program executed by the server 12 (processing circuit 201) can be provided by recording it on a removable medium 211, such as a package medium. The program can also be provided via wired or wireless transmission media, such as a local area network, the internet, or digital satellite broadcasting.

[0063] On server 12, programs can be installed in the storage unit 208 via the input / output interface 205 by inserting the removable media 211 into the drive 210. Alternatively, programs can be received by the communication unit 209 from other devices such as servers via wired or wireless transmission media and installed in the storage unit 208. Furthermore, programs can be pre-installed in ROM 202 or the storage unit 208.

[0064] The program executed by server 12 may be a program that is processed chronologically in the order described herein, or it may be a program that is processed in parallel or at necessary times, such as when a call is made.

[0065] The processes that server 12 performs according to the program do not necessarily have to be performed chronologically in the order described in the flowchart. In other words, the processes that server 12 performs according to the program include processes that are executed in parallel or individually (for example, parallel processing or processing by objects).

[0066] The program may be processed by a single server 12 (processor), or it may be processed in a distributed manner by multiple servers 12. Furthermore, the program may be transferred to a remote server 12 for execution.

[0067] In the following, when each part of the server 12 exchanges data via the bus 204 and the input / output interface 205, the bus 204 and the input / output interface 205 will not be mentioned. For example, when the processing circuit 201 exchanges data with the communication unit 209 via the bus 204 and the input / output interface 205, it will simply be stated that the processing circuit 201 exchanges data with the communication unit 209.

[0068] <Example of the configuration of the information processing unit 113> Figure 5 shows an example of the configuration of the information processing unit 251 realized by the processing circuit 201 of the server 12 in Figure 4.

[0069] The information processing unit 251 includes a panel design unit 261 and an output control unit 262.

[0070] The panel design unit 261 performs processing to support the panel design of the biological sample analyzer 11. For example, the panel design unit 261 performs processing to support the panel design of a new experiment to be performed on the biological sample analyzer 11 (hereinafter referred to as the planned experiment) based on experimental data (hereinafter referred to as reference experiment data) related to experiments previously performed on the biological sample analyzer 11 (hereinafter referred to as reference experiments) and reagent data stored in the reagent DB (Data Base) 252.

[0071] Hereinafter, the biological sample analysis device 11 on which the reference experiment was conducted will be referred to as the reference device, and the biological sample analysis device 11 on which the planned experiment will be conducted will be referred to as the implementation device. The reference device and the implementation device may be different biological sample analysis devices 11, or they may be the same biological sample analysis device 11.

[0072] The panel design unit 261 includes a marker selection unit 271, an expression level calculation unit 272, and a fluorescent dye selection unit 273.

[0073] The marker selection unit 271 selects the markers (measurement markers) to be measured in the planned experiment in the implementation device. For example, the marker selection unit 271 selects the measurement markers based on instructions input by the user via the input unit 206 or the communication unit 209, or based on reference experimental data. The marker selection unit 271 supplies measurement marker information related to the measurement markers to the expression level calculation unit 272 and the fluorescent dye selection unit 273.

[0074] The expression level calculation unit 272 calculates the true expression level of each measurement marker based on reference experimental data and reagent data from the reference device stored in the reagent DB 252. The true expression level is the expression level obtained by reducing the influence of differences in the brightness of the fluorescent dyes used for each measurement marker in the reference device, compared to the expression level of each measurement marker measured by the reference device. The expression level calculation unit 272 supplies information regarding the true expression level of each measurement marker to the fluorescent dye selection unit 273.

[0075] The fluorescent dye selection unit 273 selects the fluorescent dye (dye used) to be used for each measurement marker based on reference experimental data, reagent data of the implementation device stored in the reagent DB 252, and the true expression level of the measurement marker. The fluorescent dye selection unit 273 generates panel information regarding each combination of measurement marker and dye used. The fluorescent dye selection unit 273 supplies the panel information to the output control unit 262 or transmits it to the implementation device via the communication unit 209.

[0076] The output control unit 262 controls the output of various types of information by the output unit 207.

[0077] The reagent DB252 is a database that stores reagent data for each biological sample analyzer 11, and is stored, for example, in the storage unit 208 or the removable media 211.

[0078] Figure 6 shows an example of the reagent data structure.

[0079] The reagent data includes data on the characteristics of each fluorescent dye in each biological sample analyzer 11. The reagent data also includes brightness data and spectral data.

[0080] The brightness data includes data on the brightness of each fluorescent dye in each biological sample analyzer 11. The brightness of each fluorescent dye is normalized, for example, so that the maximum value is 100, based on the value actually measured in each biological sample analyzer 11.

[0081] The spectral data includes data relating to the spectrum of each fluorescent dye in each biological sample analyzer 11. The spectrum of each fluorescent dye is normalized, for example, so that the maximum value becomes 1, based on the value actually measured by the PMT equipped in the detection unit 112 of each biological sample analyzer 11. Since each PMT detects light of a different wavelength, the intensity of fluorescence at each wavelength emitted from each fluorescent dye is indicated by the data from each PMT.

[0082] Note that the composition of spectral data differs depending on the number of PMTs and the wavelengths detected by each biological sample analyzer 11. For example, in example A of Figure 6, spectral data is represented based on the measurement values ​​of 320 PMTs, while in example B of Figure 6, spectral data is represented based on the measurement values ​​of 100 PMTs.

[0083] Furthermore, if each biological sample analyzer 11 is equipped with a photodetector other than a PMT, spectral data is displayed based on the values ​​actually measured by each photodetector.

[0084] <Experimental Procedure> Figure 7 shows an example of the experimental procedure using the biological sample analyzer 11.

[0085] In step S1, a hypothesis to be tested experimentally is established.

[0086] In step S2, the experimental protocol is created. At this time, the panel design for the biological sample analyzer 11 is performed. For example, the optimal combination of markers and labeling substances (fluorescent dyes) to be used in the experiment is designed. More specifically, the optimal combination of antibodies that bind to the markers to be used in the experiment and labeling substances (fluorescent dyes) is designed.

[0087] In step S3, the equipment to be used in the experiment (biological sample analysis device 11) is set up.

[0088] In step S4, the light generated by the irradiation of biological particles with light is measured. This allows experimental data to be obtained.

[0089] In step S5, the experimental data is analyzed. For example, this may involve analysis by a regular user, analysis using machine learning, or support for regular analysis using machine learning.

[0090] In step S6, the experimental data is compiled. For example, an experimental report including the results of the analysis of the experimental data is edited.

[0091] In step S7, experimental data is shared. For example, the experimental report may be published on server 12, or the experimental report may be presented.

[0092] This technology primarily targets the processing in step S2.

[0093] <Panel Design Process> Next, the panel design process executed by the server 12 will be explained with reference to the flowchart in Figure 8.

[0094] In step S101, the panel design unit 261 acquires reference experimental data.

[0095] For example, the user inputs reference experimental data to the server 12 via the input unit 206. The panel design unit 261 acquires the reference experimental data from the input unit 206.

[0096] Alternatively, for example, the panel design unit 261 receives reference experimental data from the reference device via the communication unit 209.

[0097] Reference experimental data includes, for example, information about the reference equipment used in the reference experiment, panel information, experimental data, and the results of the analysis of the experimental data.

[0098] Information regarding the reference device includes, for example, information regarding the model, specifications, and settings of the reference device.

[0099] Panel information includes, for example, information about the combination of markers and fluorescent dyes used in the reference experiment.

[0100] The experimental data includes, for example, expression level information regarding the expression levels of each marker in each cell within each sample, measured by a reference device using the marker and fluorescent dye combinations shown in the panel information. For example, the expression level information includes data showing the expression level of each marker per cell, and data showing the distribution of expression levels of each marker for one or more samples (e.g., a histogram). The expression level of each marker is indicated, for example, by the signal intensity, which represents the intensity of fluorescence emitted by the fluorescent dye used to label each marker.

[0101] The results of the experimental data analysis include, for example, the clustering results of each cell within each sample, the determination of the cell type of each cell, a gate tree showing the gating results, and labels assigned to each sample.

[0102] In step S102, the marker selection unit 271 selects a measurement marker according to the user's instructions.

[0103] For example, the output unit 207 displays the marker selection screen shown in Figure 9 under the control of the output control unit 252. The marker selection screen displays a list of marker and fluorescent dye combinations used in the reference experiment.

[0104] In response, the user selects the measurement marker by checking the column for the marker to be measured in the planned experiment via the input unit 206.

[0105] The marker selection unit 271 acquires information from the input unit 206 indicating the selection result of the measurement marker. The marker selection unit 271 selects the marker selected by the user as the measurement marker. The marker selection unit 271 supplies measurement marker information related to the selected measurement marker to the expression level calculation unit 272 and the fluorescent dye selection unit 273.

[0106] In step S103, the expression level calculation unit 272 calculates the true expression level of each marker based on the brightness of the fluorescent dye in the reference device.

[0107] Specifically, the expression level calculation unit 272 detects a positive population, which is a group of positive cells in the histogram of each marker in the reference experimental data, where the target marker is expressed and the signal intensity indicating the expression level of the marker is above a predetermined threshold.

[0108] There are no particular limitations on the detection method for the positive group; for example, flowDensity can be used.

[0109] For example, Figure 10 shows an example of a histogram obtained by bi-exponential conversion of the signal intensity indicating the expression level of a certain marker. The horizontal axis represents the bi-exponential converted signal intensity, and the vertical axis represents the frequency (number of cells). The signal intensity represents the intensity of fluorescence emitted from the fluorescent dye to which the target marker is labeled.

[0110] For example, if a group of cells to the right of the auxiliary line L1 is detected as a positive group, the expression level calculation unit 272 calculates a representative value of the marker's signal intensity (hereinafter referred to as representative intensity) based on the distribution of signal intensity (expression level) of the positive group. For example, the expression level calculation unit 272 calculates the representative intensity as the mode, mean, or median of the signal intensity distribution of the positive group.

[0111] For example, in the example shown in Figure 10, the signal intensity indicated by the auxiliary line L2 becomes the mode of the positive population, and this signal intensity is set as the representative intensity.

[0112] Next, the expression level calculation unit 272 calculates the true expression level of the marker based on the brightness and representative intensity of the fluorescent dye in the reference device.

[0113] For example, if the characteristic intensity of CD1 is 10 ５ If the brightness of the FITC used in CD1 is 100, then the true emission level of CD1 = 10 ５ ÷100 = 1000. For example, if the characteristic intensity of CD3 is 10 ５ If the brightness of the APC used in CD3 is 10, then the true emission level of CD3 = 10 ５ ÷10 = 10000. For example, if the representative intensity of CD5 is 10 ４ If the brightness of the AF488 used in CD5 is 40, then the true emission level of CD5 = 10 ４÷40 = 250.

[0114] The expression level calculation unit 272 supplies information indicating the true expression level of each marker to the fluorescent dye selection unit 273.

[0115] In step S104, the fluorescent dye selection unit 273 acquires information about the implementation device. For example, the user inputs information about the implementation device via the input unit 206. The input unit 206 supplies the information about the implementation device to the fluorescent dye selection unit 273. The fluorescent dye selection unit 273 acquires reagent data for the implementation device from the reagent DB 252.

[0116] In step S105, the information processing unit 251 performs a fluorescent dye selection process.

[0117] Here, we will explain the details of the fluorescent dye selection process with reference to the flowchart in Figure 11.

[0118] In step S151, the fluorescent dye selection unit 273 classifies the measurement markers based on the true expression level. For example, the fluorescent dye selection unit 273 classifies the measurement markers into several marker categories using a predetermined threshold. For example, the marker categories are classified into three categories: "low expression level (+)", "medium expression level (++)", and "high expression level (+++)". For example, a measurement marker with a true expression level of less than 500 is classified into the low expression level category. For example, a measurement marker with a true expression level of 500 or more and less than 1200 is classified into the medium expression level category. For example, a measurement marker with a true expression level of 1200 or more is classified into the high expression level category.

[0119] The number of marker categories is set to prevent an explosion in the number of marker and fluorescent dye combinations. Therefore, the number of categories is not limited to three, and can be set to any number of two or more, for example, taking into consideration the computing resources of server 12.

[0120] In step S152, the fluorescent dye selection unit 273 classifies the fluorescent dyes based on their brightness. For example, based on the brightness data of the apparatus, the fluorescent dye selection unit 273 classifies the fluorescent dyes usable for the planned experiment into several dye categories using predetermined thresholds. For example, the dye categories are classified into three categories: "dark," "intermediate," and "bright." For example, fluorescent dyes with a brightness of 33 or less are classified into the dark dye category. For example, fluorescent dyes with a brightness in the range of 34 to 67 are classified into the intermediate dye category. For example, fluorescent dyes with a brightness of 68 or more are classified into the bright dye category.

[0121] The number of classifications for the pigment category, like the number of classifications for the marker category, is set to prevent an explosion in the number of marker and fluorescent pigment combinations. Therefore, the number of classifications is not limited to 3, and can be set to any number of 2 or more, taking into consideration the computing resources of server 12. However, it is desirable that the number of classifications for the marker category and the pigment category be set to the same value.

[0122] In step S153, the fluorescent dye selection unit 273 associates marker categories with dye categories. For example, the fluorescent dye selection unit 273 selects marker categories one by one in order of decreasing expression level, and dye categories one by one in order of decreasing intensity, and associates the selected marker categories with dye categories.

[0123] This allows for the correspondence between, for example, high expression levels and dark pigment categories, medium expression levels and intermediate pigment categories, and low expression levels and light pigment categories. In other words, marker categories with higher expression levels are associated with dark pigment categories, and marker categories with lower expression levels are associated with light pigment categories.

[0124] In step S154, the fluorescent dye selection unit 273 selects the fluorescent dyes to be used (dyes to be used) for each dye category based on correlation information of spectral shapes between fluorescent dyes. Specifically, the fluorescent dye selection unit 273 selects the same number of fluorescent dyes to be used for the markers in the marker category corresponding to each dye category, from among the fluorescent dyes in each dye category, based on correlation information of spectral shapes between fluorescent dyes.

[0125] For example, the fluorescent dye selection unit 273 selects a dye category (hereinafter referred to as the target dye category) from which to select fluorescent dyes. The fluorescent dye selection unit 273 calculates the squared value of the correlation coefficient between the spectral data of each fluorescent dye within the target dye category in the implementation device.

[0126] The type of correlation coefficient is not particularly limited, but for example, the Pearson correlation coefficient, Spearman correlation coefficient, or Kendall correlation coefficient may be used.

[0127] Next, the fluorescent dye selection unit 273 calculates a representative value of the correlation coefficient between fluorescent dyes for each combination of fluorescent dyes selected from the fluorescent dyes within the target dye category, in order to select the same number of fluorescent dyes as the number of measurement markers within the marker category corresponding to the target dye category. For example, if there are 10 types of fluorescent dyes within the target dye category and 3 types of measurement markers within the marker category corresponding to the target dye category, a representative value of the correlation coefficient between fluorescent dyes is calculated for each combination of fluorescent dyes selected from the 10 types of fluorescent dyes.

[0128] For example, the maximum or average value of the squared correlation coefficients between each fluorescent dye in a combination of fluorescent dyes is calculated as a representative value of the correlation coefficient.

[0129] The fluorescent dye selection unit 273 then selects the combination of fluorescent dyes that minimizes the representative value of the correlation coefficient as the fluorescent dye to be used in the target dye category (dye to be used).

[0130] The fluorescent dye selection unit 273 performs this fluorescent dye selection process for all dye categories.

[0131] This allows for the selection of dyes for each dye category. Specifically, from among the fluorescent dyes within each dye category, a combination of fluorescent dyes with low correlation in the spectral data of the instrument is selected. In addition, from among the fluorescent dyes within each dye category, the same number of dyes are selected as the number of measurement markers in the corresponding marker category.

[0132] In step S155, the fluorescent dye selection unit 273 evaluates the separation ability between each measurement marker when each fluorescent dye is used in the implementation device by simulation.

[0133] For example, the fluorescent dye selection unit 273 generates a combination list for each combination pattern of the measurement marker and the dye used. Each combination list represents one combination pattern of the measurement marker and the dye used.

[0134] Figure 12 shows an example of a combination list. The vertical and horizontal axes of Figure 12 represent pairs of measurement markers and dyes used (hereinafter referred to as marker-dye pairs). In this example, for example, the measurement marker CD1 and the dye used PE, and the measurement marker CD2 and the dye used BV421 are shown as combinations.

[0135] Furthermore, the combination list is a matrix showing all possible combinations of marker-dye pairs for every two sets of marker-dye pairs in a given combination pattern of a single measurement marker and dye used. The numerical values ​​in the matrix represent the evaluation values ​​of the separation efficiency between the corresponding two sets of marker-dye pairs, as will be explained later.

[0136] The fluorescent dye selection unit 273 generates a combination list for all combination patterns of measurement markers and dyes used.

[0137] Furthermore, dyes used within each dye category can only be combined with measurement markers within the corresponding marker category. Therefore, a combination list containing pairs of measurement markers and dyes belonging to categories that do not correspond to each other will not be generated.

[0138] Next, the fluorescent dye selection unit 273 calculates, for example, the fluorescent dye-to-fluorescent dye SI (Stain Index) as an evaluation value of the separation ability between two marker-dye pairs in each combination list.

[0139] Here, with reference to Figures 13 and 14, the method for calculating the inter-fluorescent dye SI will be explained.

[0140] First, we will explain how to calculate the SI of a single fluorescent dye, referring to Figure 13.

[0141] Figure 13 schematically shows an example of a histogram illustrating the distribution of signal intensity of a marker labeled with a fluorescent dye to be evaluated (hereinafter referred to as the "dye to be evaluated") (hereinafter referred to as the "marker to be evaluated").

[0142] The SI of this fluorescent dye is calculated by the following equation (1).

[0143] SI=(MFIp-MFIn) / 2SDn...(1)

[0144] MFIp is the median signal intensity within the positive population of the marker being evaluated. MFIin is the median signal intensity within the negative population of the marker being evaluated. SDn is the standard deviation of signal intensity within the negative population.

[0145] Therefore, SI indicates the ability to separate the positive and negative populations of the target marker using the target dye.

[0146] In contrast, the inter-fluorescent dye SI is calculated based on a two-dimensional distribution of signal intensities indicating the expression levels of the two markers.

[0147] Now, with reference to Figure 14, we will explain how to calculate the inter-fluorescent dye SI.

[0148] Figure 14 is a histogram showing the two-dimensional distribution of signal intensity indicating the expression levels of marker 1 labeled with fluorescent dye 1 and marker 2 labeled with fluorescent dye 2. The horizontal axis shows the signal intensity of bi-exponentially converted marker 1, and the vertical axis shows the signal intensity of bi-exponentially converted marker 2.

[0149] Population G1 in the histogram represents a group of cells that are positive for marker 1 and negative for marker 2. Population G2 in the histogram represents a group of cells that are positive for marker 2 and negative for marker 1.

[0150] For example, the interfluorescence SI1 between marker 2 labeled with fluorescent dye 2 and marker 1 labeled with fluorescent dye 1 is calculated by the following equation (2).

[0151] SI1=(MFI1p-MFI1n) / 2SD1n...(2)

[0152] MFI1p is the median signal intensity of marker 1 within population G1. MFI1n is the median signal intensity of marker 1 within population G2. SD1n is the standard deviation of the intensity of marker 1 within population G2.

[0153] The inter-fluorescent dye SI1 indicates the separation ability between population G1 and population G2 based on the expression levels of marker 1, labeled with fluorescent dye 1, and marker 2, labeled with fluorescent dye 2, in the distribution of expression levels of marker 1. In other words, the inter-fluorescent dye SI1 indicates the separation ability of population G1 (the population positive for marker 1) and population G2 (the population positive for marker 2) based on the expression levels of marker 1, using fluorescent dyes 1 and 2. To put it another way, the inter-fluorescent dye SI1 indicates the extent to which population G1 (the population positive for marker 1) and population G2 (the population positive for marker 2) can be separated from marker 1.

[0154] For example, the interfluorescence SI2 between marker 1 stained with fluorescent dye 1 and marker 2 stained with fluorescent dye 2 is calculated by the following equation (3).

[0155] SI2=(MFI2p-MFI2n) / 2SD2n...(3)

[0156] MFI2p is the median signal intensity of marker 2 within population G2. MFI2n is the median signal intensity of marker 2 within population G1. SD2n is the standard deviation of the intensity of marker 2 within population G1.

[0157] The inter-fluorescent dye SI2 indicates the separation ability between population G2 and population G1 based on the expression level of marker 2 in the distribution of expression levels of marker 1 labeled with fluorescent dye 1 and marker 2 labeled with fluorescent dye 2. In other words, the inter-fluorescent dye SI2 indicates the separation ability of population G2 (the population positive for marker 2) and population G1 (the population positive for marker 1) based on the expression level of marker 2, as determined by fluorescent dyes 1 and 2. To put it another way, the inter-fluorescent dye SI2 indicates the extent to which population G2 (the population positive for marker 2) and population G1 (the population positive for marker 1) can be separated from the perspective of marker 2 by using fluorescent dyes 1 and 2.

[0158] For example, the fluorescent dye selection unit 273 performs a simulation of the distribution of the expression level of marker 1 measured using fluorescent dye 1 in the implementation device, based on the true expression level of marker 1 and the spectral data of fluorescent dye 1 in the implementation device. The fluorescent dye selection unit 273 performs a simulation of the distribution of the expression level of marker 2 measured using fluorescent dye 2 in the implementation device, based on the true expression level of marker 2 and the spectral data of fluorescent dye 2 in the implementation device. Based on the simulation results, the fluorescent dye selection unit 273 generates a two-dimensional distribution of the expression levels of marker 1 and marker 2, and calculates the inter-fluorescent dye SI1 and inter-fluorescent dye SI2.

[0159] The fluorescent dye selection unit 273 then calculates all the inter-fluorescent dye SIs between the two marker-dye pairs shown in each combination list. This allows the separation ability between each measurement marker when each measurement marker is combined with each dye used in the implementation device to be evaluated.

[0160] In step S156, the fluorescent dye selection unit 273 selects a combination of measurement marker and fluorescent dye based on the results of the separation performance evaluation.

[0161] For example, the fluorescent dye selection unit 273 calculates a representative value of the inter-fluorescent dye SI for each combination list. For example, the fluorescent dye selection unit 273 calculates the minimum or average value of the inter-fluorescent dye SI between each marker-dye pair in the combination list as the representative value of the inter-fluorescent dye SI for that combination list.

[0162] Then, for example, the fluorescent dye selection unit 273 selects a combination list that maximizes the representative value of the inter-fluorescent dye SI. The fluorescent dye selection unit 273 selects the combination of measurement marker and dye used from the selected combination list to be used in the planned experiment. In this way, the dye used for each measurement marker is selected so that the separation ability between each measurement marker is maximized.

[0163] After that, the fluorescent dye selection process is completed.

[0164] Returning to Figure 8, in step S106, the output unit 207, under the control of the output control unit 262, presents the panel design results. For example, as shown in Figure 15, the pairs of measurement markers and dyes selected by the panel design process are displayed in a list.

[0165] After that, the panel design process is completed.

[0166] As described above, the panel can be appropriately redesigned based on reference experimental data and reagent data for the reference and planned devices. In other words, when reducing the number of markers measured in the reference experiment, an appropriate fluorescent dye can be selected for the remaining measurement markers.

[0167] For example, combinations of markers and fluorescent dyes are designed so that interference between fluorescent dyes is reduced, the separation between positive populations of markers is increased, and the positive population of each marker is appropriately extracted.

[0168] <<2. Second Embodiment>> Next, a second embodiment of the present technology will be described with reference to Figures 16 to 18.

[0169] The second embodiment of this technology differs from the first embodiment in its marker selection process. Specifically, the process in step S102 of the flowchart in Figure 8 is different. In particular, in the second embodiment of this technology, the marker selection unit 271 automatically selects the measurement marker based on reference experimental data.

[0170] Now, referring to the flowchart in Figure 16, we will explain the marker selection process performed by the server 12.

[0171] In step S201, the marker selection unit 271 selects a target marker from among the undetermined markers. Specifically, the marker selection unit 271 selects one marker as a target marker from among the markers measured in the reference experiment for which a determination process has not yet been performed on whether or not to select it as a measurement marker.

[0172] In step S202, the marker selection unit 271 determines whether the target marker is used for gating. Specifically, if the marker selection unit 271 detects the target marker in the gate tree within the reference experimental data, it determines that the target marker is used for gating, and the process proceeds to step S203.

[0173] Figure 17 shows an example of a gate tree. In this gate tree, gate G11 is selected using CD1 and CD2, gate G12 is selected from the cells within gate G11 using CD3 and CD4, and gate G13 is selected from the cells within gate G11 using CD5 and CD7.

[0174] In this case, if the target marker is one of CD1 to CD5 or CD7, it is determined that the target marker is being used for gating.

[0175] In step S203, the marker selection unit 271 selects the target marker as the measurement marker.

[0176] The process then proceeds to step S204.

[0177] On the other hand, if it is determined in step S202 that the target marker is not used for gating, the process in step S203 is skipped, and the process proceeds to step S204.

[0178] In step S204, the marker selection unit 271 determines whether the determination of all markers has been completed. If there are markers among those measured in the reference experiment for which the determination process of whether or not to select them as measurement markers has not yet been performed, the marker selection unit 271 determines that the determination of all markers has not yet been completed, and the process returns to step S201.

[0179] Subsequently, the processes from steps S201 to S204 are repeatedly executed until it is determined in step S204 that the determination of all markers has been completed.

[0180] On the other hand, in step S204, if there are no markers among those measured in the reference experiment for which a determination process of whether or not to select them as measurement markers has not been performed, the marker selection unit 271 determines that the determination of all markers has been completed, and the process proceeds to step S205.

[0181] In step S205, the output unit 207, under the control of the output control unit 262, presents the marker selection result.

[0182] Figure 18 shows an example of a screen that displays the marker selection results. This screen is similar to the screen in Figure 9.

[0183] After that, the marker selection process ends.

[0184] As described above, it becomes possible to automatically select the markers necessary for the planned experiment, for example, the markers necessary for identifying the cell tumors identified in the reference experiment.

[0185] <<3. Third Embodiment>> Next, a third embodiment of the present technology will be described with reference to Figures 19 to 20.

[0186] The third embodiment of this technology differs from the second embodiment in its method of selecting the marker.

[0187] Now, referring to the flowchart in Figure 19, we will explain the marker selection process performed by the server 12.

[0188] In step S251, the marker selection unit 271 sets the gate specified by the user as the target gate.

[0189] For example, the output unit 207, under the control of the output control unit 262, presents the gate tree in the reference experiment.

[0190] In response, the user specifies the lowest-level gate among the gates to be used in the planned experiment via the input unit 206.

[0191] The input unit 206 supplies information about the gate specified by the user to the marker selection unit 271. The marker selection unit 271 sets the gate specified by the user as the target gate.

[0192] Figure 20 shows an example of a gate tree similar to Figure 17. Below, we will explain an example where gate G13, indicated by the diagonal lines, is specified by the user.

[0193] In step S252, the marker selection unit 271 selects the marker used for gating the target gate as the measurement marker.

[0194] For example, in the example shown in Figure 20, CD5 and CD7, which are used for gating gate G13, are selected as measurement markers.

[0195] In step S253, the marker selection unit 271 determines whether the target gate is the root of the gate tree. If it is determined that the target gate is not the root of the gate tree, the process proceeds to step S254.

[0196] In step S254, the marker selection unit 271 sets the gate one level above as the target gate.

[0197] For example, in the case of the example in Figure 20, gate G11, which is one level above gate G13, is set as the target gate.

[0198] Subsequently, the process returns to step S251, and steps S251 to S254 are repeatedly executed until it is determined in step S253 that the target gate is the root of the gate tree.

[0199] On the other hand, if it is determined in step S253 that the target gate is the root of the gate tree, the process proceeds to step S255.

[0200] For example, in the case of the example in Figure 20, if gate G11 is set as the target gate, then since gate G11 is the root of the gate tree, the process proceeds to step S255.

[0201] In step S255, the marker selection result is presented, similar to the process in step S205 in Figure 16.

[0202] After that, the gate selection process ends.

[0203] In this way, the markers used for gating from the root of the gate tree to the gate specified by the user are selected as measurement markers. For example, in the example in Figure 20, CD1, CD2, CD5, and CD7 are selected as measurement markers. This automatically selects the markers necessary for analyzing the gate specified by the user as measurement markers.

[0204] Although the above explanation showed an example where only one gate was specified, it is also possible to specify multiple gates. Even when multiple gates are specified, the same process is used to select the markers necessary to identify each gate.

[0205] <<4. Fourth Embodiment>> Next, a fourth embodiment of the present technology will be described with reference to Figures 21 to 22.

[0206] The fourth embodiment of this technology differs from the second and third embodiments in that it uses machine learning to select markers.

[0207] For example, if the samples used in the reference experiment are labeled as "disease" or "healthy," it is possible to select measurement markers based on the sample labels. For instance, the marker selection unit 271 of server 12 uses a recursive feature reduction method to remove markers that have a low contribution to the estimation of the sample labels from among the markers measured in the reference experiment, and selects markers with a high contribution as measurement markers.

[0208] Now, referring to the flowchart in Figure 20, we will explain the marker selection process performed by the server 12.

[0209] In step S301, the marker selection unit 271 selects all markers as measurement markers. That is, all markers measured in the reference experiment are selected as measurement markers.

[0210] In step S302, the marker selection unit 271 separates the samples into training samples and test samples. For example, the marker selection unit 271 selects some of the samples measured in the reference experiment as training samples and the rest as test samples. For example, if there are a total of 100 samples, 90 are classified as training samples and the remaining 10 are classified as test samples.

[0211] In step S303, the marker selection unit 271 generates a learning model for estimating labels using the measurement marker and the training sample.

[0212] For example, the marker selection unit 271 combines the training samples and uses a measurement marker to perform clustering of the cells within the combined samples. As a result, the cells within the training samples are classified into multiple clusters.

[0213] Here, the clustering method is not particularly limited. For example, methods such as the K-means method and FlowSOM can be used for clustering. The number of clusters may be set by the user or by the marker selection unit 271.

[0214] Next, the marker selection unit 271 performs a learning process that uses the number of cells in each cluster within the sample as an explanatory variable and a label as the result. This generates a learning model that estimates the sample label based on the number of cells in each cluster within the sample.

[0215] Here, the learning method is not particularly limited as long as it is supervised learning. For example, methods such as RandomForest, XGBost, deep learning, and SVM (Support Vector Machine) can be used.

[0216] In step S304, the marker selection unit 271 uses the generated learning model to estimate the labels of the test samples and calculates the accuracy rate.

[0217] In step S305, the marker selection unit 271 selects one of the measurement markers for which the contribution rate has not been calculated as the target marker.

[0218] In step S306, the marker selection unit 271 generates a learning model for estimating the label of a sample using the markers obtained by removing the target marker from the measurement markers and the training sample. That is, the marker selection unit 271 performs the same processing as in step S303 using the markers obtained by removing the target marker from the current measurement markers.

[0219] In step S307, similar to the process in step S304, the generated learning model is used to estimate the labels of the test samples and calculate the accuracy rate. That is, the accuracy rate of the labels of the test samples is calculated when the target marker is excluded from the measurement markers.

[0220] In step S308, the marker selection unit 271 calculates the contribution rate of the target marker. For example, the marker selection unit 271 calculates the difference between the accuracy rate of the test sample labels by the learning model before removing the target marker and the accuracy rate of the test sample labels by the learning model after removing the target marker as the contribution rate of the target marker in the sample label estimation.

[0221] In step S309, the marker selection unit 271 determines whether or not the contribution rate of all measurement markers has been calculated. If it is determined that the contribution rate of all measurement markers has not yet been calculated, the process returns to step S305.

[0222] Subsequently, the processes from steps S305 to S309 are repeatedly executed until it is determined in step S309 that the contribution rate of all measurement markers has been calculated. This calculates the contribution rate of each measurement marker in estimating the label of the sample.

[0223] On the other hand, if it is determined in step S309 that the contribution rate of all measurement markers has been calculated, the process proceeds to step S310.

[0224] In step S310, the marker selection unit 271 determines whether the accuracy rate when the marker with the lowest contribution rate is excluded is above a threshold. For example, the marker selection unit 271 sets the accuracy rate of the sample labels when all markers measured in the reference experiment are used as the reference value, and sets the accuracy rate at a predetermined percentage of the reference value as the threshold.

[0225] For example, Figure 22 shows an example of selecting measurement markers from CD1 to CD10 measured in a reference experiment. In Figure 22, "Y" indicates markers that have not been excluded from the measurement markers, and "N" indicates markers that have been excluded from the measurement markers.

[0226] In this case, the accuracy rate of the sample labels when all markers are used is 90%, so the baseline is set to 90%. Then, if the baseline accuracy rate of 90% is set as the threshold, then 81% is set as the threshold.

[0227] Then, if the marker selection unit 271 determines that the accuracy rate when the marker with the lowest contribution rate is removed is above a threshold, the process proceeds to step S311.

[0228] For example, in the case of the example in Figure 22, the correct answer rate when CD1, which has the lowest contribution rate, is excluded is 89%, which is above the threshold of 81%. Therefore, it is determined that the correct answer rate when the marker with the lowest contribution rate is excluded is above the threshold, and the process proceeds to step S311.

[0229] In step S311, the marker selection unit 271 removes the marker with the lowest contribution rate from the measurement markers. This reduces the number of measurement markers by one.

[0230] Subsequently, the process returns to step S305, and steps S305 to S311 are repeatedly executed until it is determined in step S310 that the accuracy rate after removing the marker with the lowest contribution rate is below a threshold. In this way, markers are removed one by one in order of lowest contribution rate until the accuracy rate of the sample labels falls below a threshold.

[0231] On the other hand, if it is determined in step S311 that the accuracy rate when the marker with the lowest contribution rate is removed is below the threshold, the process proceeds to step S312.

[0232] For example, in the case of the example in Figure 22, the accuracy rate when CD1, CD3, and CD10 are removed becomes 80%, which is lower than the threshold of 81%. Therefore, it is determined that the accuracy rate when the marker with the lowest contribution rate is removed is below the threshold, and the process proceeds to step S312.

[0233] In step S312, the marker selection result is presented, similar to the process in step S205 of Figure 15.

[0234] For example, in the case of the example in Figure 21, CD2 and CD4 to CD10 are selected as measurement markers.

[0235] After that, the marker selection process ends.

[0236] In this way, markers that contribute highly to sample label estimation are automatically selected as measurement markers from among the markers measured in the reference experiment.

[0237] Furthermore, machine learning methods other than recursive feature reduction may be used to select measurement markers. For example, methods such as X-AI (Explainable AI), SHAP (SHapley Additive exPlanations) analysis, and Lime's method may be used to calculate the contribution rate and importance of each marker in the sample label estimation, and measurement markers may be selected based on the calculated contribution rate and importance.

[0238] <<5. Modified Examples>> Below, modified examples of the embodiments of the present technology described above will be explained.

[0239] <Modifications regarding the division of processing> For example, the processing described above may be divided among multiple servers 12.

[0240] For example, a user may remotely access the functions of the server 12 described above using the information processing unit 113 of the biological sample analysis device 11, or an information processing device such as a smartphone, tablet terminal, or personal computer. In this case, the server 12 may be implemented via the cloud.

[0241] For example, some or all of the processing performed by the server 12 described above may be performed by a device other than the server 12. For example, the information processing unit 113 of the biological sample analysis device 11 or various other information processing devices may be used to perform some or all of the processing performed by the server 12.

[0242] For example, the server 12 may receive reagent data from each biological sample analyzer 11 without storing the reagent data from each biological sample analyzer 11.

[0243] <Other variations> For example, indicators other than the above-mentioned inter-fluorescent dye SI may be used to evaluate the separation ability between markers using fluorescent dyes.

[0244] As described above, the implementing device may be the same biological sample analyzer 11 as the reference device. In this case, the server 12 uses the same reagent data as the reference device to perform the panel design process described above.

[0245] Furthermore, if the device being used is the same model as the reference device but is a different device, the reagent data may be common or different. In the latter case, individual differences will be reflected in the reagent data even among biological sample analyzers 11 of the same model.

[0246] For example, instead of classifying measurement markers and fluorescent dyes into multiple categories, the panel design may be carried out by considering all combinations of measurement markers and fluorescent dyes usable in the implementation device.

[0247] This technology can be applied not only to FCM but also, for example, to the panel design of imaging cytometers.

[0248] This technology can also be applied to the analysis of biological particles other than cells.

[0249] This technology can also be applied to measuring biomolecules other than markers using fluorescent dyes.

[0250] <<6. Others>> The series of processes described above can be executed by hardware or by software.

[0251] Furthermore, in this specification, a system means a collection of multiple components (devices, modules (parts), etc.), regardless of whether all components are located in the same enclosure or not. Therefore, multiple devices housed in separate enclosures and connected via a network, and a single device in which multiple modules are housed in one enclosure, are both considered systems.

[0252] Furthermore, the embodiments of this technology are not limited to those described above, and various modifications are possible without departing from the spirit of this technology.

[0253] For example, this technology can be configured as cloud computing, where a single function is shared and processed collaboratively by multiple devices via a network.

[0254] Furthermore, each step described in the flowchart above can be performed by a single device, or it can be divided and performed by multiple devices.

[0255] Furthermore, if a single step includes multiple processes, those processes can be executed by a single device or shared among multiple devices.

[0256] <Examples of configuration combinations> This technology can also be configured as follows:

[0257] (1) An information processing system comprising a fluorescent dye selection unit that selects a fluorescent dye to be combined with a biomolecule to be measured in the second device, based on expression level information relating to the expression level of each biomolecule measured in the first device, panel information relating to the combination of biomolecule and fluorescent dye used to measure the expression level in the first device, first reagent data relating to the characteristics of each fluorescent dye in the first device, and second reagent data relating to the characteristics of each fluorescent dye in the second device. (2) The information processing system according to (1), wherein the second reagent data includes spectral data of each fluorescent dye in the second device. (3) The information processing system according to (2), wherein the first reagent data includes data relating to the brightness of each fluorescent dye in the first device, and the second reagent data further includes data relating to the brightness of each fluorescent dye in the second device. (4) The information processing system according to (3), wherein the expression level information includes data showing the distribution of the expression levels of each biomolecule measured by the first device, and further comprises an expression level calculation unit that calculates a true expression level from the expression level of each measured biomolecule, reducing the influence of differences in the brightness of the fluorescent dyes, based on the brightness of each fluorescent dye in the first device and the distribution of the expression levels of each biomolecule measured by the first device, and the fluorescent dye selection unit selects a fluorescent dye to be combined with each measured biomolecule based on the true expression level of the measured biomolecule. (5) The information processing system according to (4), wherein the fluorescent dye selection unit performs a simulation of the distribution of the expression levels of each measured biomolecule measured using each fluorescent dye in the second device, based on the brightness and spectral data of each fluorescent dye in the second device and the true expression level of each measured biomolecule, and selects a fluorescent dye to be combined with each measured biomolecule based on the results of the simulation. (6) The information processing system according to (5), wherein the fluorescent dye selection unit evaluates the separation ability between each of the measured biomolecules when each of the measured biomolecules and each of the fluorescent dyes are combined in the second apparatus based on the results of the simulation, and selects a fluorescent dye to be combined with each of the measured biomolecules based on the results of the evaluation of the separation ability.(7) The information processing system according to (6), wherein the fluorescent dye selection unit evaluates the separation ability by the stain index between fluorescent dyes. (8) The information processing system according to any one of (4) to (7), wherein the fluorescent dye selection unit classifies each of the measured biomolecules into a plurality of biomolecule categories based on the true expression amount, classifies each fluorescent dye into a plurality of dye categories based on the brightness in the second device, associates each of the biomolecule categories with each of the dye categories based on the true expression amount and brightness, and selects a fluorescent dye to be combined with each of the measured biomolecules in each of the biomolecule categories from among the fluorescent dyes in the dye category corresponding to each of the biomolecule categories. (9) The information processing system according to (8), wherein the fluorescent dye selection unit selects a fluorescent dye to be used for each dye category based on correlation information between fluorescent dyes based on spectral data of each fluorescent dye in the second device. (10) The information processing system according to any one of (4) to (9), wherein the expression level calculation unit calculates the true expression level of the target biomolecule based on a representative value of the expression level of the positive population in the distribution of the expression levels of the target biomolecule, and the brightness of the fluorescent dye used to label the target biomolecule. (11) The information processing system according to any one of (2) to (10), wherein the fluorescent dye selection unit selects the fluorescent dye to be used based on correlation information between fluorescent dyes based on spectral data of each fluorescent dye in the second device. (12) The information processing system according to any one of (1) to (11), further comprising a biomolecule selection unit that selects the biomolecule to be measured from among the biomolecule measured by the first device. (13) The information processing system according to (12), wherein the biomolecule selection unit selects a biomolecule specified by the user from among the biomolecule measured by the first device to be measured. (14) The information processing system according to (12), wherein the expression level information includes a gate tree, and the biomolecule selection unit selects the biomolecules used in the gate tree in the first apparatus as the biomolecules to be measured.(15) The information processing system according to (14), wherein the biomolecule selection unit selects the biomolecules used between the root of the gate tree and the gate specified by the user as the biomolecules to be measured. (16) The information processing system according to (12), wherein the biomolecule selection unit selects the biomolecules to be measured by using machine learning to remove biomolecules that have a low contribution to the estimation of the sample label from the biomolecules measured by the first device. (17) The information processing system according to any one of (1) to (16), wherein the first device and the second device are the same device or of the same model, and the reagent data for the first and the reagent data for the second are the same data. (18) The information processing system according to any one of (1) to (17), further comprising the second device which measures each biomolecule using a combination of the biomolecule to be measured and a fluorescent dye selected by the fluorescent dye selection unit. (19) An information processing method comprising an information processing system selecting a fluorescent dye to be combined with a biomolecule to be measured in the second device, based on expression level information relating to the expression level of each biomolecule measured in the first device, panel information relating to combinations of biomolecules and fluorescent dyes used to measure the expression level in the first device, first reagent data relating to the characteristics of each fluorescent dye in the first device, and second reagent data relating to the characteristics of each fluorescent dye in the second device. (20) An information processing apparatus comprising a fluorescent dye selection unit that selects a fluorescent dye to be combined with a biomolecule to be measured in the second device, based on expression level information relating to the expression level of each biomolecule measured in the first device, panel information relating to combinations of biomolecules and fluorescent dyes used to measure the expression level in the first device, first reagent data relating to the characteristics of each fluorescent dye in the first device, and second reagent data relating to the characteristics of each fluorescent dye in the second device.

[0258] Furthermore, the effects described herein are merely illustrative and not limiting; other effects may also occur.

[0259] 1. Information processing system, 11-1 to 11-n. Biological sample analyzer, 12-1, 12-2. Server, 111. Light irradiation unit, 112. Detection unit, 113. Information processing unit, 114. Sorting unit, 201. Processing circuit, 207. Output unit, 251. Information processing unit, 252. Reagent DB, 271. Marker selection unit, 272. Expression level calculation unit, 273. Fluorescent dye selection unit, 262. Output control unit.

Claims

1. An information processing system comprising a fluorescent dye selection unit that selects a fluorescent dye to be combined with a biomolecule to be measured in the second device, based on expression level information relating to the expression levels of each biomolecule measured in the first device, panel information relating to the combination of biomolecule and fluorescent dye used to measure the expression levels in the first device, first reagent data relating to the characteristics of each fluorescent dye in the first device, and second reagent data relating to the characteristics of each fluorescent dye in the second device.

2. The information processing system according to claim 1, wherein the second reagent data includes spectral data of each fluorescent dye in the second apparatus.

3. The information processing system according to claim 2, wherein the first reagent data includes data relating to the brightness of each fluorescent dye in the first apparatus, and the second reagent data further includes data relating to the brightness of each fluorescent dye in the second apparatus.

4. The information processing system according to claim 3, wherein the expression level information includes data showing the distribution of the expression levels of each biomolecule measured by the first device, and further comprises an expression level calculation unit that calculates a true expression level from the expression level of each measured biomolecule, reducing the influence of differences in the brightness of the fluorescent dyes, based on the brightness of each fluorescent dye in the first device and the distribution of the expression levels of each biomolecule measured by the first device, and the fluorescent dye selection unit selects a fluorescent dye to be combined with each measured biomolecule based on the true expression level of the measured biomolecule.

5. The information processing system according to claim 4, wherein the fluorescent dye selection unit performs a simulation of the distribution of the expression levels of each biomolecule measured using each fluorescent dye in the second device, based on the brightness and spectral data of each fluorescent dye in the second device and the true expression level of each biomolecule, and selects a fluorescent dye to be combined with each biomolecule based on the results of the simulation.

6. The information processing system according to claim 5, wherein the fluorescent dye selection unit evaluates the separation ability between each of the measured biomolecules when each of the measured biomolecules and each of the fluorescent dyes are combined in the second apparatus based on the results of the simulation, and selects a fluorescent dye to be combined with each of the measured biomolecules based on the results of the evaluation of the separation ability.

7. The information processing system according to claim 6, wherein the fluorescent dye selection unit evaluates the separation ability by the stain index between fluorescent dyes.

8. The information processing system according to claim 4, wherein the fluorescent dye selection unit classifies each of the measured biomolecules into a plurality of biomolecule categories based on the true expression level, classifies each fluorescent dye into a plurality of dye categories based on the brightness in the second device, associates each of the biomolecule categories with each of the dye categories based on the true expression level and brightness, and selects a fluorescent dye to be combined with each of the measured biomolecules in each of the biomolecule categories from among the fluorescent dyes in the dye category corresponding to each of the biomolecule categories.

9. The information processing system according to claim 8, wherein the fluorescent dye selection unit selects a fluorescent dye to be used for each dye category based on correlation information between fluorescent dyes based on spectral data of each fluorescent dye in the second apparatus.

10. The information processing system according to claim 4, wherein the expression level calculation unit calculates the true expression level of the target biomolecule based on the representative value of the expression level of the positive population in the distribution of the expression levels of the target biomolecule, and the brightness of the fluorescent dye used to label the target biomolecule.

11. The information processing system according to claim 2, wherein the fluorescent dye selection unit selects a fluorescent dye to be used based on correlation information between fluorescent dyes, which is based on spectral data of each fluorescent dye in the second apparatus.

12. The information processing system according to claim 1, further comprising a biomolecule selection unit for selecting the biomolecules to be measured from among the biomolecules measured by the first device.

13. The information processing system according to claim 12, wherein the biomolecule selection unit selects a biomolecule specified by the user from among the biomolecule measured by the first device to be measured.

14. The information processing system according to claim 12, wherein the expression level information includes a gate tree, and the biomolecule selection unit selects the biomolecules used in the gate tree in the first apparatus as the biomolecules to be measured.

15. The information processing system according to claim 14, wherein the biomolecule selection unit selects the biomolecules used between the root of the gate tree and the gate specified by the user as the biomolecules to be measured.

16. The information processing system according to claim 12, wherein the biomolecule selection unit selects the measured biomolecule by using machine learning to remove biomolecules that have a low contribution to the estimation of the sample label from the biomolecule measured by the first device.

17. The information processing system according to claim 1, wherein the first apparatus and the second apparatus are the same apparatus or of the same model, and the first reagent data and the second reagent data are the same data.

18. The information processing system according to claim 1, further comprising the second apparatus for measuring each biomolecule using a combination of the biomolecule to be measured and a fluorescent dye selected by the fluorescent dye selection unit.

19. An information processing method comprising an information processing system selecting a fluorescent dye to be combined with a biomolecule to be measured in the second device, based on expression level information relating to the expression levels of each biomolecule measured in the first device, panel information relating to combinations of biomolecules and fluorescent dyes used to measure the expression levels in the first device, first reagent data relating to the characteristics of each fluorescent dye in the first device, and second reagent data relating to the characteristics of each fluorescent dye in the second device.

20. An information processing device comprising a fluorescent dye selection unit that selects a fluorescent dye to be combined with a biomolecule to be measured in the second device, based on expression level information relating to the expression levels of each biomolecule measured in the first device, panel information relating to the combination of biomolecule and fluorescent dye used to measure the expression levels in the first device, first reagent data relating to the characteristics of each fluorescent dye in the first device, and second reagent data relating to the characteristics of each fluorescent dye in the second device.

Images