Server system, information processing system, data acquisition client terminal, data analysis client terminal, and information processing method

Abstract

The present disclosure relates to a server system, an information processing system, a data acquisition client terminal, a data analysis client terminal, and an information processing method. The main object is to provide an information processing system capable of coping with an increase in the amount of information processing involved in the analysis of biological samples. The present disclosure provides a server system including: an automatic analysis processing unit that generates output data by performing analysis processing on light intensity data or fluorescent marker intensity data obtained by irradiating light on a biological sample; an analysis result data storage unit that stores output data generated based on the light intensity data or the fluorescent marker intensity data; and an interactive analysis processing unit that analyzes the fluorescent marker intensity data based on an analysis command related to the output data output to an output device, and outputs analysis result data. The present disclosure also provides an information processing system including the server system.

Description

[0001] This application is a divisional application of the Chinese national phase application of PCT application filed on September 22, 2021, with international application number PCT / JP2021 / 034791 and invention title "Server System, Information Processing System, Data Acquisition Client Terminal, Data Analysis Client Terminal and Information Processing Method". The entry date of the Chinese national phase application was April 3, 2023, with application number 202180068088.9. Technical Field

[0002] This disclosure relates to server systems, information processing systems, data acquisition client terminals, data analysis client terminals, and information processing methods, and more specifically, to server systems that perform processing on light intensity data acquired by irradiating biological samples with light, information processing systems including the server systems, data acquisition client terminals and data analysis client terminals included in the information processing systems, and information processing methods related to the processing. Background Technology

[0003] For example, a process has been performed to measure particle characteristics by labeling a swarm of cells, microorganisms, liposomes, etc., with a fluorescent dye, irradiating individual particles within the swarm with a laser beam, and measuring the intensity and / or pattern of fluorescence generated from the excited fluorescent dye. Representative examples of particle analysis devices for performing these measurements include flow cytometry.

[0004] As a technique related to the processing of data acquired via flow cytometry (specifically, light intensity data), for example, PTL 1 discloses a computer program product for processing scientific data according to a model independent of a particular dataset. This computer program product includes a data discovery node data structure residing on a non-transient computer-readable storage medium and a plurality of processor-executable commands residing on the same non-transient computer-readable storage medium, and the data discovery node data structure includes a specific specification.

[0005] Additionally, PTL 2 discloses a sample analysis system using a flow cytometer. The sample analysis system includes: a measurement data acquisition unit that measures particles contained in a sample prepared by adding reagents to the sample, thereby acquiring particle measurement data; an output mode information acquisition unit that acquires output mode information representing the output mode of the measurement data; and an output unit that outputs the measurement data in an output mode corresponding to the output mode information.

[0006] [List of Citations]

[0007] [Patent Literature]

[0008] [PTL 1]

[0009] JP 2018-527674 T

[0010] [PTL 2]

[0011] JP 2020-051838 A Summary of the Invention

[0012] [Technical Issues]

[0013] In flow cytometry, there is a trend of increasing amounts of fluorescent dyes used for single measurements, and along with this, there is also a trend of increasing information processing volume in the data analysis steps. Therefore, performing data analysis steps in these information processing devices requires higher specifications from those devices, but such specifications are often impractical and undesirable for users.

[0014] Furthermore, examples of analysis systems for data obtained through flow cytometry include those employing a client-server approach. However, in some cases, client-server analysis systems incur high operating costs. This could be because, for example, the server needs to be active at all times, even when no users are using it, or analytical processing can be performed via the server at any time.

[0015] Furthermore, analysis systems employing a client-server approach also suffer from speed-related issues. For example, as the number of users performing analysis simultaneously increases, computational resources become exhausted, and processing can be delayed. Additionally, in some cases, it is difficult to achieve high speeds for processing tasks requiring significant computational resources, such as dimensionality compression or clustering.

[0016] Therefore, the primary objective of this disclosure is to provide a technique for solving at least one of these problems. The purpose of this disclosure is not limited thereto; for example, any one or more of the problems described below can be solved within this specification.

[0017] [Protocol for Problem Resolution]

[0018] This disclosure provides a server system, including: an automatic analysis and processing unit that generates output data by analyzing and processing light intensity data or fluorescent labeling intensity data obtained by irradiating a biological sample with light; an analysis result data storage unit that stores the output data generated based on the light intensity data or fluorescent labeling intensity data; and an interactive analysis and processing unit that analyzes the fluorescent labeling intensity data based on analysis commands for the output data to be output to an output device and outputs the analysis result data.

[0019] The automatic analysis and processing unit can calculate the fluorescence label intensity data from the light intensity data.

[0020] The processing of the automatic analysis and processing unit and the processing of the interactive analysis and processing unit can be performed on different computing resources.

[0021] In response to receiving the analysis start command, the server system can reserve computing resources for the processing of the automatic analysis processing unit and / or the processing of the interactive analysis processing unit.

[0022] The server system may further include a database on which analysis setting data used in the processing of the automatic analysis processing unit and / or the processing of the interactive analysis processing unit is stored.

[0023] The server system may further include an optical data storage unit for storing light intensity data and / or fluorescent marker intensity data.

[0024] The optical data storage unit may include two or more types of memory with different access speeds, and

[0025] Under predetermined conditions, the server system can perform the process of migrating light intensity data and / or fluorescent label intensity data stored in a higher-speed memory to a lower-speed memory.

[0026] The output data may include at least one of the following: a two-dimensional plot image, a spectral plot image, a one-dimensional histogram image, a two-dimensional contour plot image, a dimensionally compressed image, and a clustering result display view.

[0027] This disclosure provides an information processing system, comprising: a data acquisition client terminal, which acquires light intensity data obtained by irradiating a biological sample with light or acquires fluorescent labeling intensity data by calculating and processing the light intensity data; and a server system, comprising an optical data storage unit that stores the light intensity data or fluorescent labeling intensity data sent from the data acquisition client terminal; an automatic analysis processing unit that calculates the fluorescent labeling intensity data based on the light intensity data; an analysis result data storage unit that stores output data generated based on the light intensity data or fluorescent labeling intensity data; and an interactive analysis processing unit that analyzes the fluorescent labeling intensity data based on analysis commands for the output data to be output to an output device and outputs analysis result data.

[0028] In response to the acquisition of light intensity data or fluorescent label intensity data, the data acquisition client terminal can send the light intensity data or fluorescent label intensity data to the server system.

[0029] In response to the acquisition of light intensity data or fluorescent label intensity data, the data acquisition client terminal can perform predetermined processing on the light intensity data or fluorescent label intensity data, and then send the processed light intensity data or processed fluorescent label intensity data to the server system.

[0030] The server system can pre-save the analysis settings data used in the automatic analysis and processing unit, and

[0031] The automatic analysis and processing unit can calculate the fluorescence label intensity data from the light intensity data by using the analysis setting data.

[0032] In response to the light intensity data being stored in the optical data storage unit, the automatic analysis and processing unit can perform processing to calculate the fluorescence label intensity data based on the light intensity data.

[0033] The information processing system may further include a data analysis client terminal, which includes an output device.

[0034] The data analysis client terminal can send analysis commands for the data to be output to the output device to the server system.

[0035] The data analysis client terminal can cause the output device to output a window on which the data to be output is displayed, and accept analysis commands input on the window.

[0036] Multiple data analysis client terminals may be able to share any one or more of the light intensity data, fluorescence intensity data, and analysis setting data from the server system.

[0037] Multiple data acquisition client terminals can share and analyze configuration data within the server system.

[0038] Furthermore, this disclosure provides a data acquisition client terminal, including: a data acquisition unit for acquiring light intensity data obtained by irradiating a biological sample with light; and a transmission unit for transmitting the light intensity data to a server system in response to the acquisition of the light intensity data. In the server system, fluorescent labeling intensity data is calculated from the light intensity data.

[0039] Furthermore, this disclosure provides a data analysis client terminal, including: a communication unit that receives output data created by the server system based on fluorescence marker intensity data from a server system; and a processing unit that performs processing to cause an output device to output the output data.

[0040] The data analysis client terminal can cause the output device to output a window on which the data to be output is displayed, and accept input on the window for analysis commands on the data to be output.

[0041] Furthermore, this disclosure provides an information processing method, comprising: an automatic analysis processing step, performing analysis processing on light intensity data obtained by irradiating a biological sample with light or on fluorescent label intensity data calculated from the light intensity data and generating output data; an analysis result data storage step, storing the output data generated based on the light intensity data or the fluorescent label intensity data; and an interactive analysis processing step, analyzing the fluorescent label intensity data based on an analysis command on the output data to be output to an output device and outputting analysis result data. Attached Figure Description

[0042] Figure 1 This is a schematic diagram of the configuration of a flow cytometer.

[0043] Figure 2 This is a diagram illustrating an example of an experimental procedure when this technique is applied to a flow cytometer.

[0044] Figure 3 This is a diagram illustrating an instance of a door setting.

[0045] Figure 4 It is a diagram used to illustrate surface markings.

[0046] Figure 5 A diagram illustrating a configuration example of an information processing system.

[0047] Figure 6 It is a diagram illustrating an example of the functional configuration of a server system.

[0048] Figure 7 It is a diagram depicting a configuration instance of metadata.

[0049] Figure 8 It is a diagram depicting a configuration instance of metadata.

[0050] Figure 9 It is a diagram depicting an example of a hardware configuration of a server device included in a server system.

[0051] Figure 10 This is a diagram illustrating an example of the functional configuration of a data analysis client terminal.

[0052] Figure 11 This is a diagram illustrating an example of the hardware configuration of a data analytics client terminal.

[0053] Figure 12 This is a diagram illustrating an example of the functional configuration of a client terminal for data acquisition.

[0054] Figure 13 This is a diagram illustrating an example configuration of a biological sample analysis device.

[0055] Figure 14It is a diagram illustrating an example of the automated analysis and processing flow of an information processing system.

[0056] Figure 15 It is a diagram illustrating an example of the automated analysis and processing flow of an information processing system.

[0057] Figure 16 It is a diagram illustrating an example of the process of acquiring and processing output data in an information processing system.

[0058] Figure 17 It is a diagram illustrating an example of the interactive analysis and processing flow of an information processing system.

[0059] Figure 18 This is a diagram used to explain the interactive analysis and processing flow.

[0060] Figure 19 This is a diagram used to explain the interactive analysis and processing flow.

[0061] Figure 20 This is a diagram illustrating an example of a window that outputs data from a data analysis client terminal.

[0062] Figure 21 This is a diagram illustrating an example of a window that outputs data from a data analysis client terminal.

[0063] Figure 22 This is a diagram illustrating an example of a window that outputs data from a data analysis client terminal.

[0064] Figure 23 This is a diagram illustrating an example of a window that outputs data from a data analysis client terminal.

[0065] Figure 24 It is a diagram illustrating an instance of a view displaying clustering results.

[0066] Figure 25 It is a diagram depicting an instance of a marker-selection window.

[0067] Figure 26 It is a diagram depicting an instance of a marker-selection window.

[0068] Figure 27 It is a diagram illustrating an instance of a view displaying clustering results.

[0069] Figure 28 It is a diagram depicting an instance of a marker-selection window.

[0070] Figure 29 This is a diagram showing an instance of a view displaying clustering results.

[0071] Figure 30It is a diagram depicting an instance of a marker-selection window.

[0072] Figure 31 This is a diagram showing an instance of a view displaying clustering results.

[0073] Figure 32 This is a diagram illustrating an instance of a view image displaying clustering results.

[0074] Figure 33 This is a graph showing an instance of a meta-clustering graph.

[0075] Figure 34 This is a graph showing an instance of a meta-clustering graph.

[0076] Figure 35 It is a graph that describes an instance of the clustering results display view and an instance of its corresponding two-dimensional plot.

[0077] Figure 36 This is a diagram used to illustrate meta-clustering in the clustering results display view.

[0078] Figure 37 It is a diagram depicting instances of user actions on the clustering results display view and instances of two-dimensional drawings color-coded by those actions.

[0079] Figure 38 This is a diagram used to illustrate the pie chart displayed in the node.

[0080] Figure 39 This is an illustration showing an example of two-dimensional drawing data and a drawing settings window used to set the display of the drawing data. Detailed Implementation

[0081] The following describes suitable modes for carrying out the invention. It should be noted that the embodiments described below are representative embodiments of this disclosure, and the scope of this disclosure is not limited to these embodiments. It should be noted that this disclosure is described in the following order.

[0082] 1. Information processing system

[0083] (1) Description of related technologies

[0084] (2) Overview of information processing systems

[0085] (3) Components included in the information processing system

[0086] (3-1) Server System

[0087] (3-2) Data Analysis Client Terminal

[0088] (3-3) Data Acquisition Client Terminal

[0089] (3-4) Biological sample analysis device

[0090] (4) Examples of the processing flow of information processing systems

[0091] (4-1) Automatic analysis and processing

[0092] (4-2) Examples of automated analysis and processing

[0093] (4-3) Data Acquisition and Processing

[0094] (4-4) Interactive Analysis and Processing

[0095] (4-5) Examples of interactive analysis and processing

[0096] (5) Setting up the area

[0097] (6) Data migration

[0098] (7) Use external storage or computing resources

[0099] (8) Analysis of the division of object data

[0100] (9) Data sharing

[0101] (10) Standardization of the output data

[0102] (11) Example 1 of output control for clustering result display view

[0103] (12) Example 2 of output control for clustering result display view

[0104] (13) Relationship between meta-clustering in clustering result display view and 2D plot

[0105] (14) Control of the pie chart axis in the star diagram

[0106] 2. Information Processing Methods

[0107] 1. Information processing system

[0108] (1) Description of related technologies

[0109] For example, from the perspective of fluorescence measurement optical systems, flow cytometers can be broadly classified into filter-type flow cytometers and spectral flow cytometers. To extract only the target light information from the target fluorescent dye, filter-type flow cytometers can employ... Figure 1The structure described in section 1 is similar to that described in the section 1. Specifically, for example, light generated by applying light to a particle is divided into multiple beams by a wavelength separation device (DM) such as a dichroic mirror, and the beams are passed through different filters. Then, measurements of the corresponding beams generated by the division are performed by multiple sensors (e.g., photomultiplier tubes, PMTs, etc.). That is, in a filter-type flow cytometer, multicolor fluorescence sensing is performed by using a sensor corresponding to a fluorescent dye to perform fluorescence sensing corresponding to each wavelength band of that fluorescent dye. In this case, when using multiple fluorescent dyes with fluorescence wavelengths close to each other, fluorescence correction processing can be performed to calculate a more accurate fluorescence amount.

[0110] Spectroscopic flow cytometry analyzes the fluorescence level of each particle by performing deconvolution (demixing) on ​​fluorescence data obtained by sensing light generated when light is applied to the particles, using the spectral information of the fluorescent dyes used in staining. For example... Figure 1 As shown in Figure 2, spectral flow cytometry uses a prism dispersive optical element (P-dispersive fluorescence). Furthermore, to sense the dispersive fluorescence, instead of the numerous optical sensors included in filter-type flow cytometry, spectral flow cytometry incorporates array-type sensors, such as array-type photomultiplier (PMT) sensors. Compared to filter-type flow cytometry, spectral flow cytometry easily avoids the effects of fluorescence leakage and is more suitable for analysis using a variety of fluorescent dyes.

[0111] In recent years, the increasing use of multicolor analysis with multiple fluorescent dyes in flow cytometry has been observed in both basic medical science and clinical medicine for comprehensive interpretation. There is a growing trend in the number of fluorescent dyes used in a single multicolor analysis process. As mentioned above, if a large number of fluorescent dyes are used in a single measurement using a filter-type flow cytometer, fluorescence leakage from dyes other than the target fluorescent dye can occur in the individual sensors, reducing analytical accuracy. In cases involving a large number of colors, the problem of fluorescence leakage can be addressed by using a spectral flow cytometer.

[0112] The following is for reference Figure 2 Explain an example of the experimental procedure using a flow cytometer.

[0113] The procedures for experiments using flow cytometry are broadly categorized as follows: experimental planning steps (… Figure 2 The "1: Plan" in the document refers to the examination of cells as experimental subjects and methods for sensing cells, and the preparation of fluorescently labeled antibody reagents; sample preparation steps ( Figure 2 "2: Preparation" in the text refers to the preparation of cells using actual stained cells to make them suitable for measurement; the FCM measurement step ( Figure 2The “3:FCM” in the figure refers to the measurement of fluorescence intensity of each stained cell using flow cytometry; and the data analysis steps ( Figure 2 The section “4: Data Analysis” involves performing various types of data processing to obtain the desired analytical results from the data recorded in the FCM measurements. These steps can then be repeated as needed.

[0114] In the experimental planning steps, the first step is to decide which molecules (e.g., antigens, cytokines, etc.) will be used as indicators of the presence of microparticles (primarily cells) to be sensed using flow cytometry. That is, the labeling for sensing the microparticles is determined. This decision can be based, for example, on information such as past experimental results or research papers. Next, it is determined which fluorescent dye will be used to sense the markers. Simultaneously, information such as the number of markers to be sensed, the specifications of the available FCM apparatus, the available fluorescently labeled reagents, the spectrum, intensity, price, and delivery date of the fluorescent dyes is considered, and the combination of fluorescently labeled antibody reagents necessary for the actual experiment is determined. This process of determining the reagent combination is often referred to as panel design in FCM.

[0115] In the sample preparation step, the experimental subjects are first processed to make them suitable for FCM measurement. For example, cell isolation and refinement can be performed. For instance, regarding immune cells derived from blood, red blood cells are removed from the blood through hemolysis and density gradient centrifugation, and white blood cells are extracted. A set of extracted target cells is then stained using fluorescently labeled antibodies.

[0116] In the FCM measurement procedure, when optically analyzing particles, firstly, excitation light is emitted from the light source of the flow cytometer's light application unit, illuminating the particles flowing in the flow channel. Next, the fluorescence emitted from the particles is sensed by the flow cytometer's sensing unit. Specifically, for example, light with only a specific wavelength (target fluorescence) is separated from the emitted light by the particles using a dichroic mirror, bandpass filter, etc., and sensed by a sensor such as a PMT. At this time, for example, the fluorescence is dispersed using a prism, diffraction grating, etc., and light with different wavelengths is sensed in different channels using a sensor such as a 32-channel PMT. As a result, spectral information related to the sensed light (fluorescence) can be easily obtained.

[0117] Flow cytometers can record fluorescence information for each particle obtained through FCM measurements, as well as information other than fluorescence (such as scattered light information, time information, or position information). The recording function is primarily performed by the computer's memory or disk. Because thousands to millions of particles are analyzed under typical cell analysis conditions, it is necessary to record multiple data points organized for each experimental condition.

[0118] In the data analysis step, the light intensity data for each wavelength region obtained through sensing in the FCM measurement step are quantified by computer, and the fluorescence amount (intensity) of each fluorescent dye used is determined. This analysis uses a calibration method that employs a baseline calculated from the experimental data. The baseline is calculated through statistical processing using two types of data: measurement data of particles stained with only one fluorescent dye and measurement data of unstained particles. The calculated fluorescence amounts, along with information such as the name of the fluorescent molecule, the measurement date, or the type of particle, can be recorded on a data recording unit included in the computer. The fluorescence amounts (fluorescence spectral data) of the samples estimated by the data analysis are stored and displayed in graphs as needed, and the fluorescence distribution of the particles is analyzed.

[0119] For example, to analyze fluorescence distribution, gating can typically be performed, allowing the calculation of the ratio of target cells in the sample. For example, as... Figure 3 As shown, by generating a two-dimensional plot related to forward scattered light (FSC) and side scattered light (SSC) and selecting a predetermined range in the plot, the ratio of monocytes and lymphocytes among the blood cells included in PBMCs can be identified. Furthermore, gating and amplification of lymphocytes expressing predetermined surface markers allows for the calculation of the ratios of B cells, T cells, and NK cells in lymphocytes. Additionally, the ratio of memory B cells in B cells, the ratio of cytotoxic T cells and helper T cells in T cells, and the ratio of primitive T cells and memory T cells in T cells can also be identified. For example, as... Figure 4 As shown, the surface markers expressed by corresponding cell types are known to differ between cell types. Therefore, cells in a sample can be examined by appropriately selecting antibodies that bind to the surface markers and fluorescent dyes that label each antibody, followed by analysis using flow cytometry.

[0120] The information processing system and its constituent elements according to this disclosure can be used for analysis in the data analysis step.

[0121] (2) Overview of information processing systems

[0122] For example, information processing in the data analysis step is performed by an information processing device attached to the flow cytometer or by the user's information processing device using the flow cytometer. However, as mentioned above, there is a trend of increasing amounts of fluorescent dyes used for single measurements in flow cytometers, and along with this, there is also a trend of increasing information processing volume in the data analysis step. Therefore, in order to perform data analysis steps in these information processing devices, higher specifications are required. This trend becomes particularly evident when performing what are commonly referred to as advanced analytical techniques, such as dimensionality compression or clustering, which require significant computational loads. However, it is often impractical for these information processing devices to possess such specifications, and this is also undesirable for users.

[0123] In view of this, it is considered desirable for users to be that information processing in the data analysis steps can be performed by an analysis system that adopts a client-server approach, specifically by an analysis system that uses a cloud-based client-server approach.

[0124] Furthermore, as mentioned above, analysis systems employing a client-server approach typically require significant operational costs. Additionally, there are issues related to the processing speed of the analysis system.

[0125] The inventors have discovered that a specific server system can solve at least one of these problems. Specifically, this disclosure provides a server system and an information processing system including the server system, the server system comprising: an automatic analysis processing unit that generates output data by analyzing and processing light intensity data or fluorescent labeling intensity data obtained by irradiating a biological sample with light; an analysis result data storage unit that stores the output data generated based on the light intensity data or the fluorescent labeling intensity data; and an interactive analysis processing unit that analyzes the fluorescent labeling intensity data based on analysis commands for the output data to be output to an output device and outputs analysis result data.

[0126] Since the server system includes an automatic analysis processing unit and an interactive analysis processing unit, data analysis steps can be performed within the server system. Furthermore, because the interactive analysis processing unit allows analysis processing to be performed or analysis settings to be adjusted while the user is examining the data to be output on the output device, the desired analysis results can be easily obtained.

[0127] Furthermore, since the server system performs processing only when needed through the automatic analysis and processing unit and the interactive analysis and processing unit, operating costs can be reduced. In addition, the configurations described below in this specification also allow for increased processing speed of the server system.

[0128] The following reference Figure 5This section describes a configuration example of the information processing system according to the present invention.

[0129] Figure 5 The information processing system 1 described herein includes a server system 10, a data analysis client terminal 20, a data acquisition client terminal 30, and a biological sample analysis device 40.

[0130] Server system 10 can connect to data analysis client terminal 20 and data acquisition client terminal 30 via network 50. Network 50 can be a communication network used to perform data sending / receiving. For example, network 50 can be the Internet, satellite communication network, telephone network, or mobile communication network (e.g., 4G network, 5G network, etc.) or a combination thereof.

[0131] For example, data analysis client terminal 20 and data acquisition client terminal 30 can be connected to each other via cable or wirelessly, or via network 50.

[0132] For example, the bioparticle analyzer 40 can be connected to the data acquisition client terminal 30 via cable or wireless means.

[0133] (3) Components included in the information processing system

[0134] The following explains each element included in the information processing system according to this disclosure.

[0135] (3-1) Server System

[0136] Reference Figure 6 Description of server system 10. Figure 6 This is a block diagram illustrating an example of the functional configuration of the system. The server system 10 may include an automatic analysis and processing unit 11, an interactive analysis and processing unit 12, a data generation unit 13, a connection unit 14, an optical data storage unit 15, an analysis result data storage unit 16, and a database 17.

[0137] In embodiments of this disclosure, the automatic analysis processing unit 11 can calculate fluorescent label intensity data from light intensity data acquired by illuminating a biological sample. The light intensity data can be light intensity data sent from the data acquisition client terminal 30 to the server system 10. The light intensity data can be stored on the optical data storage unit 15, from which the automatic analysis processing unit 11 can acquire the light intensity data.

[0138] In another embodiment of this disclosure, the automatic analysis and processing unit 11 may not perform calculation processing. That is, the data acquisition client terminal 30 may perform processing to calculate the fluorescence marker intensity data from the light intensity data, and then the data acquisition client terminal 30 may send the fluorescence marker intensity data to the server system 10.

[0139] The automatic analysis and processing unit 11 can perform the processing of calculating fluorescence marker intensity data from light intensity data. The automatic analysis and processing unit 11 can perform the calculation processing using analysis setting data pre-saved by the server system 10. For example, the analysis setting data can be analysis setting data sent in advance (specifically, before performing the calculation processing) from the data analysis client terminal 20 or the data acquisition client terminal 30.

[0140] For example, the automatic analysis and processing unit 11 calculates fluorescent label intensity data by performing fluorescence correction processing or demixing processing on the light intensity data. Demixing processing is also known as fluorescence separation processing.

[0141] Preferably, the automatic analysis and processing unit 11 performs the demixing process using spectral reference data. The spectral reference data used in the demixing process includes spectral data of fluorescence generated when the fluorescent dye-labeled particles are irradiated with a predetermined excitation light. The spectral reference data used in the demixing process may include spectral data of fluorescence generated when the fluorescent dye-labeled particles are irradiated with light having a predetermined wavelength and spectral data of fluorescence generated when the fluorescent dye-labeled particles are irradiated with light having a different wavelength.

[0142] The spectral reference data can be pre-stored in either the storage unit or the database in the server system 10, and for example, it can be stored in the database 17. Specifically, the spectral reference data can be stored as a piece of metadata, which will be described later. The automatic analysis processing unit 11 can, for example, retrieve the spectral reference data from the database 17 and then use the retrieved spectral reference data to perform demixing processing.

[0143] For example, the automatic analysis processing unit 11 can perform unmixing processing using the least squares method (LSM), more preferably using the weighted least squares method (WLSM). For example, the unmixing processing using the least squares method can be performed using the fluorescence intensity correction method described in Japanese Patent No. 5985140. For example, the fluorescence intensity correction method can be performed using the following mathematical formula (1) of WLSM.

[0144] [Mathematical Expression 1]

[0145] In the above mathematical formula (1), x n [S] represents the fluorescence intensity of the nth fluorescent dye. T [] denotes the transpose of the spectral reference, [L] denotes the weighting matrix, [S] denotes the matrix of the spectral reference, and y i Let λ represent the measurement value of the i-th optical sensor. i Represents the weight of the i-th optical sensor, max(y i,0) represents the larger of the sensed value of the i-th sensor and zero, and offset' represents the value determined based on the sensed value of each sensor.

[0146] In some cases, fluorescent dyes exhibit a broad fluorescence wavelength distribution. Therefore, for example, a photodetector (PMT) used to sense fluorescence from one fluorescent dye can also sense fluorescence from another. That is, the optical data acquired by each PMT can be data in which fluorescence data from multiple fluorescent dyes are superimposed. Given this, correction is necessary to separate the optical data into fluorescence data from each fluorescent dye. Demixing is a technique used for correction; demixing separates the superimposed fluorescence labeling intensity data from multiple fluorescent dyes into fluorescence labeling intensity data from each fluorescent dye, thus obtaining fluorescence labeling intensity data from each individual fluorescent dye.

[0147] The automatic analysis and processing unit 11 analyzes and processes the fluorescence label intensity data. The automatic analysis and processing unit 11 generates analysis result data through the analysis and processing. Output data can be generated based on the analysis result data. The output data is sent to the data analysis client terminal 20, and then the data analysis client terminal 20 causes an output device to output the output data. For example, the output device can be a display device. The output device can be configured to allow the user to input analysis commands, which will be described later.

[0148] Preferably, in response to the light intensity data being stored in the optical data storage unit 15, the automatic analysis processing unit 11 performs processing to calculate the fluorescent label intensity data from the light intensity data. Then, the automatic analysis processing unit 11 can analyze the fluorescent label intensity data and generate analysis result data. Additionally, the automatic analysis processing unit 11 can generate output data based on the analysis result data. For example, in response to the light intensity data being stored in the optical data storage unit 15, the automatic analysis processing unit 11 can begin automatic analysis processing. That is, the automatic analysis processing unit 11 can perform event-driven analysis processing triggered by storage.

[0149] In response to the light intensity data being stored in the optical data storage unit 15, the automatic analysis and processing unit 11 reserves computing resources for automatic analysis and processing. That is, the automatic analysis and processing unit 11 can receive the stored data as an analysis start command, and in response to this reception, reserve computing resources in the server system 10. Using the reserved computing resources, the automatic analysis and processing unit 11 can perform fluorescence marker intensity data calculation processing, fluorescence marker intensity data analysis processing, and output data generation processing using the analysis result data.

[0150] Based on the analysis command of the data to be output on the output device, the interactive analysis processing unit 12 can analyze the fluorescence label intensity data and generate analysis result data. The data to be output can be generated by the automatic analysis processing unit 11, or it can be acquired or generated by the data to be output generation unit 13, which will be described later.

[0151] The interactive analysis processing unit 12 can perform processing to calculate fluorescent label intensity data from light intensity data. Then, the interactive analysis processing unit 12 can analyze the calculated fluorescent label intensity data based on analysis commands and generate analysis result data. Then, the interactive analysis processing unit 12 can generate output data based on the generated analysis result data.

[0152] In response to receiving an analysis start command, the interactive analysis processing unit 12 can reserve computing resources for its processing. After reservation, the interactive analysis processing unit 12 waits until an analysis command is sent from the data analysis client terminal 20.

[0153] As described above, the automatic analysis processing unit 11 can reserve computing resources for its processing in response to receiving an analysis start command, and the interactive analysis processing unit 12 can also reserve computing resources for its processing in response to receiving an analysis start command. Therefore, in this disclosure, the processing of the automatic analysis processing unit and the processing of the interactive analysis processing unit can be executed on different computing resources.

[0154] In response to receiving an analysis command from the data analysis client terminal 20, the interactive analysis processing unit 12 can perform fluorescence label intensity data calculation processing, fluorescence label intensity data analysis processing, and output data generation processing using the analysis result data. These processes can be event-driven analysis processes triggered by receiving the analysis command. That is, the interactive analysis processing according to this technology can be event-driven analysis processing.

[0155] Similar to these processes in the automatic analysis and processing unit 11, the interactive analysis and processing unit 12 can use the analysis result data to perform fluorescence label intensity data calculation processing, fluorescence label intensity data analysis processing, and output data generation processing.

[0156] The connection unit 14 is a functional element for executing RPC (Remote Procedure Call) in the interactive processing between the server system 10 (specifically the interactive analysis processing unit 12) and the data analysis client terminal 20. For example, the connection unit 14 enables the server system 10 to execute analysis commands input on the data analysis client terminal 20.

[0157] The output data generation unit 13 can generate output data based on the analysis result data stored in the analysis result data storage unit 16. Then, the output data generation unit 13 sends the output data to the data analysis client terminal 20. The processing required by the output data generation unit 13 is relatively small. Therefore, the operating cost of the server system can be reduced.

[0158] The processing of the output data generation unit 13 can be performed by a virtual server that is active at any time in the server system 10. Since the virtual server is active at all times, the output data acquisition process can be performed at high speed without the waiting time associated with server activation.

[0159] Furthermore, the output data generation unit 13 can be configured as a serverless architecture.

[0160] The optical data storage unit 15 stores light intensity data and / or fluorescent marker intensity data. The optical data storage unit 15 may include two storage units: a light intensity data storage unit for storing light intensity data and a fluorescent marker intensity data storage unit for storing fluorescent marker intensity data.

[0161] The analysis result data storage unit 16 stores analysis result data generated by the automatic analysis processing unit 11 based on fluorescence label intensity data and / or analysis result data generated by the interactive analysis processing unit 12 based on fluorescence label intensity data. Additionally, the analysis result data storage unit 16 stores output data generated from these analysis result data.

[0162] Database 17 can store various types of metadata. Metadata may include analysis setting data. Specifically, database 17 can store analysis setting data that will be used in the processing of the automatic analysis processing unit and / or the processing of the interactive analysis processing unit.

[0163] For example, the analysis setup data may include additional data to be referenced or used in the processing of the analyzed light intensity data. For example, the additional data may include data related to the biological sample itself and / or data related to the biological sample analysis setup.

[0164] In addition, for example, the analysis setup data may include data used to calculate fluorescent label intensity data from light intensity data (e.g., data including spectral reference data) and / or data used in the data analysis processing of fluorescent intensity labels (e.g., data including analysis commands).

[0165] refer to Figure 7 and Figure 8 This describes an example of metadata configuration.

[0166] like Figure 7 As shown, for example, project data included in metadata ( Figure 7 The items in the project list can be data that identifies the project units set by the user according to their expectations.

[0167] Metadata may include one or more of the following: project data, preset fluorescent dye data, fluorescent dye data set according to expectations, spectral reference data, autofluorescence data, and instrument setting data.

[0168] For example, a piece of project data can be compared with preset fluorescent dye data ( Figure 7 The fluorescent dye (preset) is associated with one or more of the following data: fluorescent dye data (customized) set as needed, spectral reference data (spectral reference), autofluorescence data (autofluorescence), and instrument setting data (instrument setting).

[0169] The preset fluorescent dye data includes data related to one or more fluorescent dyes commonly used in project data analysis (e.g., fluorescent dye name data, etc.).

[0170] For example, for each experiment or for each sample in the data analysis used for project data, the fluorescent dye data set according to expectations includes data related to the fluorescent dye selected by the user according to expectations (e.g., fluorescent dye name data, etc.).

[0171] The spectral reference data includes: spectral reference data for each fluorescent dye included in the preset fluorescent dye data and the fluorescent dye data set according to expectations.

[0172] Autofluorescence data includes data related to the autofluorescence of biological samples analyzed using project data.

[0173] The instrument setting data includes data related to the analysis settings of the biological sample analysis device, which has acquired light intensity data of the biological sample being analyzed using the project data.

[0174] The number of data items associated with a single item data item can be one or more. For example, a single item data item can be associated with one, two, or more pre-defined fluorescent dye data items.

[0175] Metadata can include experimental data. For example, one piece of project data can be associated with one, two, or more pieces of experimental data. Figure 7 This is related to the experiments (in the experiment). For example, each piece of experiment data may include one or more of the following: experiment name data, data about the name of the user who created the experiment data, and data about the date / time the experiment data was created.

[0176] Metadata can include board data. For example, one experimental data point can be associated with one, two, or more board data points. Figure 7 The data is associated with the plate(s) in the analysis apparatus. For example, each plate data includes data identifying the plate (specifically, a well plate or microtiter plate) as the analyte of the biosample analysis apparatus, and may include, for example, one or more of the following: plate name, data about the name of the user who created the plate data, data about the date / time the plate data was created, and data about the type of plate.

[0177] Metadata can include sample group data. For example, a single piece of board data can be associated with one, two, or more pieces of sample group data. Figure 7 The sample group data is associated with each plate data set. For example, each sample group data set includes data identifying a group of biological samples included in each plate data set, and may include one or more of the following: sample group name data, data about the name of the user who created the sample group data set, and data about the date / time when the sample group data set was created.

[0178] Metadata can include protocol data.

[0179] For example, each sample group of data can be compared with protocol data ( Figure 7 This is related to the protocol (in the protocol). For example, multiple sample groups of data can be associated with the same protocol data, or they can be associated with different protocol data.

[0180] For example, each protocol data includes data on the analytical protocol for identifying biological samples, and may include one or more of, for example, unmixing configuration data (unmixing configuration), fluorescent dye setting data (palette), instrument setting data (measurement setting), and shared worksheet setting data (shared worksheet).

[0181] The unmixing configuration data may include data that identifies the unmixing process (e.g., unmixing matrix configuration, computational techniques, etc.).

[0182] Fluorescent dye setting data may include data related to the allocation of fluorescent dyes to biomolecules, and may include, for example, the results of panel design.

[0183] Instrument setting data may include data related to the settings of the biological sample analysis device.

[0184] Worksheet setup data may include data related to analytical settings to be applied collectively to all biological samples included in a single sample group. For example, worksheet setup data may include gate settings to be applied collectively to all biological samples included in a single sample group.

[0185] A sample group of data can be combined with one, two, or more sample data ( Figure 7The data may be associated with the samples in the dataset. For example, each sample data entry may include one or more of the following: individual worksheet setup data to be applied to the analysis for each sample (separate worksheets), unmixed configuration data to be applied to the analysis for each sample (sample unmixed configuration), a data path of light intensity data obtained by measuring each sample (raw data), and fluorescent label intensity data calculated from the light intensity data obtained by measuring each sample (unmixed data).

[0186] Metadata can include comparison worksheet data. For example, one experimental data point can be compared with one, two, or more comparison worksheet data points. Figure 7 The comparison worksheet is linked to the data in the comparison worksheet. The comparison worksheet data can be used to compare the analysis results for each sample data point or to display the analysis results in an overlay.

[0187] The following is for reference Figure 8 Explain in more detail Figure 7 The worksheet settings data depicted in the text.

[0188] like Figure 8 The worksheet settings data described herein may include range settings data ( Figure 8 The data may include one, two, three, four, or five data points from the following: area settings, door setting data (door), axis parameter setting data (axis parameter), drawing setting data (drawing), axis scale line setting data (axis scale line setting), and statistical setting data (statistics). Alternatively, all six data points may be included.

[0189] Region setting data is data relating to the region defined by a gate in the drawing displayed on the worksheet, and includes, for example, data relating to the range of fluorescent marker intensity and / or wavelength range specified by the gate.

[0190] For example, door setting data includes data related to the type of door setting in the drawing displayed on the worksheet and / or data related to door parameters. For example, data related to the type of door setting includes data related to the shape of the door, which can be, for example, a rectangle, line, polygon, ellipse, four quadrant, etc. For example, data related to door parameters can include data related to the position and / or size of the door.

[0191] For example, axis parameter setting data may include data related to the type of field in the plot displayed on the worksheet and / or data related to the type of signal used in the plot. For example, the field type could be forward scattered light (FSC), side scattered light (SSC), fluorescence, wavelength, channels (type or number of optical sensing channels), number of events, light intensity, etc. Any of these types of fields can be used as axis parameters. Furthermore, data related to the type of signal could be data related to area size, height, or width.

[0192] The plotting settings can include data related to the type of plot displayed on the worksheet. For example, the plot type can be a spectral plot, histogram plot, point plot, density plot, etc.

[0193] The axis scale setting data may include data related to the scale lines of the axis being plotted on the worksheet. For example, this data may include one or more of the following: data related to the scale ratio, data related to the maximum and / or minimum values ​​of the scale lines, and data related to whether the setting is associated with a double exponent.

[0194] For example, statistical data settings can include data from statistical outputs on a specified worksheet. For instance, statistics can be one or more of the following: number of events, the ratio of the number of events in a gate to the number of events in its parent gate (parent%), the ratio of the number of events in a gate to the total number of events in the relevant sample (total%), mean, maximum, minimum, standard deviation, coefficient of variation (CV), and median.

[0195] Figure 9 A hardware configuration example of the server devices included in server system 10 is described. Server system 10 may have multiple server devices. Furthermore, the multiple server devices may reside in a single data center, or they may be distributed across multiple data centers located in different locations or in different countries.

[0196] exist Figure 9 The server device 1000 described herein includes a CPU (Central Processing Unit) 1001, RAM 1002, and ROM 1003. The CPU 1001, RAM 1002, and ROM 1003 are interconnected via a bus 1004. The bus 1004 is further connected to an input / output interface 1005.

[0197] The input / output interface 1005 is connected to the communication device 1006, the storage device 1007, the driver 1008, the output unit 1009, and the input unit 1010.

[0198] Communication device 1006 connects server device 1000 to network 1011 via cable or wirelessly. Communication device 1006 allows server device 1000 to acquire various types of data (e.g., image data, etc.) via network 1011. For example, the acquired data can be stored on storage device 1007. The type of communication device 1006 can be appropriately selected by those skilled in the art.

[0199] Storage device 1007 may store an operating system, a program for enabling a server system to implement the information processing method according to this disclosure, and other programs of various types, as well as image data, various types of data used in the information processing method according to this disclosure, and other data of various types. For example, the operating system may be a UNIX OS, specifically a LINUX OS or a WINDOWS OS.

[0200] The driver 1008 can read data (e.g., light intensity data, fluorescence marker intensity data, analysis result data, data to be output, etc.) or programs recorded on the recording medium and output them to the RAM 1003. For example, the recording medium is an HDD, SSD, micro SD memory card, SD memory card, or flash memory, but is not limited to these.

[0201] The output unit 1009 can be connected to an output device (e.g., a display device). The input unit 1010 can accept input for operating the server system itself.

[0202] (3-2) Data Analysis Client Terminal

[0203] Reference Figure 10 Explaining data analysis client terminal 20. Figure 10 This is a block diagram illustrating an example of the functional configuration of the terminal. The data analysis client terminal 20 includes a processing unit 21, an analysis instruction unit 22, a connection unit 23, a data storage unit 24 for output, and a communication unit 25.

[0204] The processing unit 21 performs the process of causing the output device attached to the data analysis client terminal 20 to output the data to be output sent from the server system 10. Specifically, the data to be output is displayed on a window on the screen of the output device (specifically, the display device). That is, the data analysis client terminal may include the output device thereon that outputs the data to be output.

[0205] The analysis instruction unit 22 sends a start request for the above-mentioned interactive analysis processing to the server system 10.

[0206] Additionally, the analysis instruction unit 22 accepts input of analysis commands to be used in the aforementioned interactive analysis processing, and then sends the analysis commands to the server system 10. The analysis commands may include the aforementioned worksheet setting data. The interactive analysis processing unit 12 can perform analysis processing with reference to the worksheet setting data, thereby generating output data corresponding to the worksheet setting data. In this way, the data analysis client terminal according to this disclosure can be configured to send analysis commands for output data on the output device to the server system. Furthermore, the data analysis client terminal according to this disclosure can be configured to have the output device output a window displaying the output data, and to accept input of analysis commands on the window.

[0207] The connection unit 23 is a functional element for executing RPC (Remote Procedure Call) in the interactive processing between the server system 10 (specifically the interactive analysis processing unit 12) and the data analysis client terminal 20 (specifically the analysis instruction unit 22). For example, the combination of the connection unit 23 of the data analysis client terminal 20 and the connection unit 14 of the server system 10 can allow analysis commands entered on the data analysis client terminal 20 to be sent to the server system 10 and then executed in the server system 10.

[0208] The output data storage unit 24 stores the output data sent from the server system 10.

[0209] The communication unit 25 receives output data created by the server system based on fluorescent marker intensity data from the server system 10.

[0210] Figure 11 A hardware configuration example of data analysis client terminal 20 is described. It should be noted that, for example, the terminal can be a general information processing device (specifically, a computer).

[0211] exist Figure 11 The information processing apparatus 1100 described herein includes a CPU (central processing unit) 1101, RAM 1102, and ROM 1103. The CPU 1101, RAM 1102, and ROM 1103 are interconnected via a bus 1004. The bus 1104 is further connected to an input / output interface 1105.

[0212] The input / output interface 1105 is connected to the communication device 1106, the storage device 1107, the driver 1108, the output unit 1109, and the input unit 1110.

[0213] Communication device 1106 connects information processing device 1100 to network 1111 via cable or wirelessly. Communication device 1106 allows information processing device 1100 to send or receive various types of data via network 1111. For example, communication device 1106 can be used to send or receive various types of data to or from server system 10. The type of communication device 1106 can be appropriately selected by those skilled in the art.

[0214] Storage device 1107 may store an operating system, a program for causing the output data output unit to output data to be output, a program for implementing interactive analysis processing and other various types of programs, as well as various types of data and other various types of data to be used in information processing according to this disclosure. For example, the operating system may be a UNIX (registered trademark) OS, specifically a LINUX (registered trademark) or WINDOWS (registered trademark) OS.

[0215] The driver 1108 can read data (e.g., data to be output) or programs recorded on a recording medium and output them to RAM 1103. For example, the recording medium is an HDD, SSD, micro SD memory card, SD memory card, or flash memory, but is not limited to these.

[0216] The output unit 1109 causes the output device to output the data to be output. For example, the output device may be a display device. For example, the input unit 1110 accepts the input of analysis commands in interactive analysis processing. For example, the input unit 1110 may be connected to an input device such as a keyboard or mouse, and the input of analysis commands can be executed by using these input devices.

[0217] (3-3) Data Acquisition Client Terminal

[0218] Reference Figure 12 Explain the data acquisition client terminal 30. Figure 12 This is a block diagram illustrating an example of the functional configuration of the terminal. The data acquisition client terminal 30 includes a data acquisition unit 31, a transmission unit 32, a data processing unit 33, and a data storage unit 34.

[0219] The data acquisition unit 31 acquires measurement data sent from the biological sample analysis device 40. The measurement data includes light intensity data, which is processed by the automatic analysis processing unit 11. The light intensity data may be light intensity data acquired by irradiating the biological sample with light.

[0220] The transmitting unit 32 transmits measurement data (including light intensity data) acquired by the data acquisition unit 31 or measurement data that has been processed by the data processing unit 33 (described later) to the server system 10 (specifically, the optical data storage unit 15 of the server system 10). In addition to measurement data, the transmitting unit 32 can also transmit analysis setting data to the server system 10. Preferably, in response to the acquisition of light intensity data, the transmitting unit 32 transmits light intensity data, or light intensity data and analysis setting data, to the server system. For example, in response to the acquisition of measurement data, the transmitting unit 32 can automatically begin transmitting measurement data or processed measurement data (and analysis setting data). In this way, the data client terminal according to this disclosure can transmit light intensity data to the server system 10. Furthermore, the data acquisition client terminal according to this disclosure can be configured to transmit light intensity data (and analysis setting data) to the server system 10 in response to the acquisition of light intensity data.

[0221] Furthermore, in embodiments of this disclosure, the data acquisition client terminal 30 can perform calculation processing to calculate fluorescent marker intensity data from light intensity data. This calculation processing can be performed as explained above (3-1), and for example, it can be fluorescence correction processing or demixing processing. In this embodiment, the transmitting unit 32 can transmit the fluorescent marker intensity data (or the fluorescent marker intensity data that has already been processed by the data processing unit 33, described later) to the server system 10 (specifically, the optical data storage unit 15 of the server system 10). In this way, the data client terminal according to this disclosure can transmit the fluorescent marker intensity data to the server system 10. Furthermore, the data acquisition client terminal according to this disclosure can be configured to transmit the fluorescent marker intensity data to the server system 10 in response to the acquisition of the fluorescent marker intensity data.

[0222] Furthermore, in this disclosure, the transmitting unit 32 can transmit both measurement data and fluorescence label intensity data to the server system 10.

[0223] The data acquisition client terminal 30 has a transmission unit 32, which is a functional unit independent of the data acquisition unit 31, so that data acquisition processing and upload processing can be performed independently. As a result, without affecting the processing of data acquired from the biological sample analysis device 40, measurement data (including light intensity data) or fluorescence label intensity data calculated from the measurement data, or both types of data, can be uploaded to the server system 10.

[0224] The data processing unit 33 can perform predetermined processing on the measurement data and / or fluorescent marker intensity data acquired by the data acquisition unit 31. This processing can be compression processing, data processing (e.g., format conversion processing), etc. The data processing can convert the measurement data and / or fluorescent marker intensity data into a format suitable for information processing in the server system 10, thereby improving the efficiency of processing in the server system 10. In this way, the data acquisition client terminal according to this disclosure can be configured to perform predetermined processing on the light intensity data or fluorescent marker intensity data in response to the acquisition of light intensity data or fluorescent marker intensity data, and then send the processed light intensity data or fluorescent marker intensity data to the server system.

[0225] The data storage unit 34 is capable of storing measurement data or processed measurement data. The data storage unit 34 can store fluorescent label intensity data or processed fluorescent label intensity data.

[0226] The description of the data analysis client terminal 20 in (3-2) above applies to the hardware configuration example of the data acquisition client terminal 30. It should be noted that, for example, the data acquisition client terminal 30 can also be a general information processing device (specifically, a computer).

[0227] (3-4) Biological sample analysis device

[0228] For example, a biosample analysis device can be a flow cytometer as described above, but is not limited to this. An example configuration of a biosample analysis device is depicted in... Figure 13 In the middle. For example Figure 13 As shown, the biological sample analysis apparatus 40 includes: a light application unit 101 that illuminates a biological sample S flowing through a flow channel C; a sensing unit 102 that senses the light generated by the illumination; and an information processing unit 103 that processes information about the light sensed by the sensing unit. Examples of the biological sample analysis apparatus 40 include flow cytometers and imaging cytometers. The biological sample analysis apparatus 40 may include a separation unit 104 that separates specific biological particles P from the biological sample. Examples of the biological sample analysis apparatus 40 including a separation unit include a cell sorter.

[0229] (Biological sample)

[0230] The biological sample S can be a liquid sample containing biological particles. For example, these biological particles can be cells or cell-free biological particles. Cells can be living cells, and more specific examples include blood cells, such as red blood cells or white blood cells, and germ cells, such as sperm cells or fertilized eggs. Alternatively, cells can be cells obtained directly from a specimen such as whole blood, or can be cultured cells obtained after culturing. Examples of cell-free biological particles include extracellular vesicles, specifically exosome vesicles, microvesicles, etc. Biological particles can be labeled with one or more labeling substances (e.g., dyes (specifically fluorescent dyes), antibodies labeled with fluorescent dyes, etc.). It should be noted that the biological sample analysis device of this disclosure can analyze particles other than biological particles, and can analyze beads, etc., for calibration, etc.

[0231] (Flow path)

[0232] Flow path C can be configured to allow a biological sample to flow through it, specifically, to form a flow of approximately one row of biological particles contained within the biological sample. The flow path structure including flow path C can be designed to form laminar flow, and specifically, to form a laminar flow in which the biological sample flow (sample flow) is surrounded by a sheath fluid flow. The design of the flow path structure can be suitably chosen by those skilled in the art, or a known design can be employed. Flow path C can be formed as a flow path structure (specifically, a flow path structure in which focusing is performed), such as a microchip (a chip with micron-scale flow paths) or a flow cell. The width of flow path C is equal to or less than 1 mm, and specifically, can be equal to or greater than 10 µm and equal to or less than 1 mm. Flow path C and the flow path structure including flow path C can comprise materials such as plastic or glass.

[0233] The apparatus according to this disclosure can be configured to irradiate a biological sample flowing in a flow channel C with light from a light application unit, specifically, biological particles in the biological sample. The apparatus according to this disclosure can be configured such that the light irradiation point (probing point) on the biological sample is located within a flow path structure in which the flow path C is formed, or it can be configured such that the light irradiation point is located outside the flow path structure. Examples of the former include configurations in which the flow channel C in a microchip or flow cell is irradiated with light. In the latter case, biological particles after leaving the flow path structure (specifically, its nozzle portion) can be irradiated with light, and examples include, for instance, flow cytometers employing an air jet method.

[0234] (Light application section)

[0235] The light application unit 101 has a light source unit for emitting light and a light-guiding optical system for guiding light to the flow path C. The light source unit includes one or more light sources. For example, the type of light source may be a laser beam source or an LED. The wavelength of the light emitted from each light source may be any of the wavelengths of ultraviolet light, visible light, and infrared light. For example, the light-guiding optical system includes optical components such as a beam splitter group, a lens group, or an optical fiber. Furthermore, for example, the light-guiding optical system may include a lens group for converging light and may include an objective lens. One or more light illumination points may be present on the biological sample. The light application unit 101 may be configured to converge light applied from one or more different light sources to one illumination point.

[0236] (Sensing unit)

[0237] Sensing unit 102 includes at least one light sensor that senses light generated by light applied to the particle by a light application unit. For example, the light to be sensed is fluorescence or scattered light (e.g., any one or more of forward-scattered, back-scattered, and side-scattered light). For example, each light sensor includes one or more light-receiving elements and has an array of light-receiving elements. Each light sensor may include one or more PMTs (photomultipliers) and / or photodiodes, such as APDs or MPPCs, as light-receiving elements or multiple light-receiving elements. For example, each of the light sensors includes a PMT array comprising a plurality of PMTs arranged in a one-dimensional array. Furthermore, the sensing unit may include an imaging element such as a CCD or CMOS. By using the imaging element, the sensing unit can acquire images of the biological particle (e.g., bright-field images, dark-field images, fluorescence images, etc.).

[0238] The sensing unit includes a sensing optical system that directs light with a predetermined sensing wavelength to a corresponding photosensor. The sensing optical system includes a dispersive element such as a prism or diffraction grating, or a wavelength separation element such as a dichroic mirror or optical filter. For example, the sensing optical system can be configured to disperse light from biological particles, causing multiple photosensors, in numbers greater than the number of fluorescent dyes, to sense light in different wavelength bands. Flow cytometers including such sensing optical systems are called spectral flow cytometers. Furthermore, for example, the sensing optical system can be configured to separate light corresponding to the fluorescence wavelength band of the fluorescent dye from the light from the biological particles, causing corresponding photosensors to sense the separated light.

[0239] Furthermore, the sensing unit may include a signal processing unit that converts the electrical signal obtained by the light sensor into a digital signal. The signal processing unit may include an A / D converter as a device for performing the conversion. The digital signal obtained by the conversion by the signal processing unit may be sent to the information processing unit. The digital signal processing unit may process the digital signal as light-related data (hereinafter also referred to as "optical data"). For example, optical data may be optical data including fluorescence data. More specifically, optical data may be light intensity data, and light intensity may be light intensity data including fluorescence (which may include characteristic quantities such as area, height, or width).

[0240] (Information Processing Department)

[0241] For example, the information processing unit 103 includes a processing unit that performs processing of various types of data (e.g., optical data) and a storage unit that stores various types of data.

[0242] In the case where the biological sample analysis device includes a separation unit described later, the information processing unit can perform a determination on whether to separate biological particles based on optical data and / or pattern information. Then, based on the result of this determination, the information processing unit can control the separation unit, and the separation of biological particles can be performed through the separation unit.

[0243] The information processing unit can be configured as a general-purpose computer, and for example, as an information processing device including a CPU, RAM, and ROM. The information processing unit can be housed within a housing including a light application unit and a sensing unit, or it can be located outside the housing. For example, the information processing unit can be implemented via a data acquisition client terminal 30.

[0244] (Separation section)

[0245] For example, based on the determination result of the information processing unit, the separation unit 104 can perform the separation of biological particles. The separation method can be one 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 droplet propagation is controlled by electrodes. The separation method can also be one in which biological particles are separated by controlling the direction of propagation of the biological particles in a flow path structure. For example, the flow path structure is provided with a control mechanism using pressure (injection or suction) or electric charge. Examples of flow path structures include chips (e.g., the chip described in 2020-76736), in which the flow path C has a flow path structure that branches into a recovery flow path and an emission flow path in its downstream portion, and in which specific biological particles flow to the recovery flow path and are recovered by the recovery flow path.

[0246] For example, the biological sample analysis device 40 can be a microscope device for performing multicolor fluorescence imaging, specifically a fluorescence microscope device. In recent years, there has also been a trend of increasing numbers of fluorophores used in fluorescence imaging, and the information processing system according to this disclosure can process the light intensity data acquired by the microscope device.

[0247] (4) Examples of the processing flow of information processing systems

[0248] The information processing method executed by the information processing system 1 may include an automatic analysis processing step. Furthermore, in addition to the automatic analysis processing step, the information processing method may include an interactive analysis processing step using the output data generated by the automatic analysis processing step.

[0249] In addition, the information processing method executed by the information processing system 1 may include: a step of acquiring output data, acquiring output data based on analysis result data existing in the server system; and an interactive analysis processing step, using the output data obtained by the acquisition processing.

[0250] The following describes automatic analysis and processing, data acquisition and processing, and interactive analysis and processing.

[0251] (4-1) Automatic analysis and processing

[0252] Reference Figure 14 This describes the process of automatic analysis and processing performed by information processing system 1.

[0253] In step S101, the data acquisition client terminal 30 acquires light intensity data of the biological sample from the biological sample analysis device 40. The light intensity data can be obtained by illuminating the biological sample with light.

[0254] In step S101, in addition to the light intensity data, the data acquisition client terminal 30 may also acquire supplementary data, which will be referenced or used in the processing of the light intensity data analysis. For example, the supplementary data may include data related to the biological sample itself and / or data related to the analytical settings regarding the biological sample. Examples of data related to the biological sample itself include the attributes of the biological sample (e.g., the type of organism from which the biological sample originates, the type of bodily fluid from which the biological sample originates, the type of organ from which the biological sample originates, the type of disease, etc.), the fluorescent dye used to label the biological sample, the producer and date of production of the biological sample, etc. Examples of data related to the analytical settings regarding the biological sample include, but are not limited to, the settings of the analytical device, unmixing configuration, measurement conditions, etc.

[0255] It should be noted that in step S101, the data acquisition client terminal 30 can perform the processing of calculating the fluorescence label intensity data from the light intensity data.

[0256] In step S102, the data acquisition client terminal 30 sends light intensity data to the server system 10. In addition to the light intensity data, the data acquisition client terminal 30 may also send additional data in step S102.

[0257] It should be noted that when the process of calculating the fluorescence label intensity data is performed in step S101, in step S102, the data acquisition client terminal 30 can send the fluorescence label intensity data to the server system 10.

[0258] Preferably, in order to automatically execute the series of steps in (4-1), the data acquisition client terminal 30 may perform transmission in response to acquiring light intensity data or fluorescence labeling intensity data from the biological sample analysis device 40 in step S101. More specifically, the data acquisition client terminal 30 may begin sending light intensity data to the server system 10, triggered by the start of receiving light intensity data. Transmission may be performed via the network 50. Furthermore, as described above, although the data acquisition client terminal 30 may be triggered to begin sending light intensity data to the server system 10 by the successful reception of light intensity data, the upload may also be completed earlier by triggering the start of sending light intensity data.

[0259] In step S103, the server system 10 receives light intensity data or fluorescent marker intensity data sent from the data acquisition client terminal 30 via the network 50. The server system 10 stores the light intensity data or fluorescent marker intensity data on the optical data storage unit 15.

[0260] In step S104, the server system 10 performs automatic analysis processing on the light intensity data or the fluorescent label intensity data. This automatic analysis processing is specifically performed by the automatic analysis processing unit 11. In this automatic analysis processing, the server system 10 can calculate the fluorescent label intensity data from the light intensity data, and then perform analysis processing on the fluorescent label intensity data to generate output data. Furthermore, if the server system 10 has already received the fluorescent label intensity data, in the automatic analysis processing, the server system 10 can perform analysis processing on the received fluorescent label intensity data without performing the calculation of the fluorescent label intensity data.

[0261] The following reference Figure 15 Explain the details of the processing in step S104.

[0262] exist Figure 15 In step S151, the automatic analysis processing unit 11 begins automatic analysis processing. Preferably, automatic analysis processing begins in response to storing light intensity data (or fluorescent marker intensity data) on the optical data storage unit 15 in step S103. Specifically, the automatic analysis processing can be event-driven analysis processing triggered by storage.

[0263] For example, the processing of the automatic analysis and processing unit 11 can also be performed in a serverless manner, and the automatic analysis and processing unit 11 can be configured as a serverless structure as commonly referred to.

[0264] Note that "processing performed in a serverless manner" in this specification does not mean processing without using a server, but rather means performing only predetermined information processing in an event-driven format on a pre-built server architecture; that is, performing information processing on a functional basis. The pre-built server architecture can be a server architecture pre-built by a commercial operator that provides analysis services from server system 10 to users of data analysis client terminal 20, or it can be a server architecture built by a so-called cloud commercial operator.

[0265] For example, if server system 10 is Amazon Web Services (trademark), the processing of automated analysis and processing unit 11 can be performed, for example, by AWS Lambda, specifically by a system that includes a combination of AWS Lambda and Amazon EC2.

[0266] In step S152, the automatic analysis and processing unit 11 reserves computing resources on the server system 10 for performing the processing of analytical light intensity data (or fluorescence marker intensity data). The computing resources act as a virtual server for performing the analysis and processing. In step S152, the virtual server is activated.

[0267] In step S153, the automatic analysis and processing unit 11 (specifically, the virtual server) downloads the light intensity data (or fluorescent marker intensity data) stored on the optical data storage unit 15.

[0268] In step S154, the automatic analysis processing unit 11 (specifically a virtual server) acquires the data required for analysis. For example, the data required for analysis may include data for calculating fluorescent label intensity data and data for analyzing and processing the fluorescent intensity label data. This data may be stored in any storage unit or database within the server system 10, and may be stored, for example, in database 17.

[0269] The data used to calculate the fluorescence label intensity may include, for example, spectral reference data. This data is used for demixing.

[0270] The data used in the analysis and processing of fluorescence intensity labeling may further include analysis commands. For example, analysis commands include gate setting commands, plotting setting commands, axis setting commands, axis display setting commands, statistical setting commands, and region setting commands. For instance, analysis commands are used to obtain analysis result data, such as two-dimensional plots desired by the user. Furthermore, analysis commands may include clustering commands.

[0271] The analysis command may be a command that has been pre-sent to the server system 10 from the data analysis client terminal 20 or the data acquisition client terminal 30.

[0272] In step S155, the automatic analysis processing unit 11 (specifically a virtual server) can perform the process of calculating fluorescent marker intensity data from the light intensity data. Then, the automatic analysis processing unit 11 performs the process of generating analysis result data using the calculated fluorescent marker intensity data. It should be noted that if the server system 10 has already received the fluorescent marker intensity data in step S103, this calculation process can be omitted.

[0273] The process of calculating the fluorescent label intensity data can be performed using the data mentioned in step S154 for calculating the fluorescent label intensity data. For example, the calculation process may include obtaining the fluorescent label intensity data by performing a demixing process on the light intensity data using the data.

[0274] In addition, the automatic analysis and processing unit 11 can perform advanced analysis and processing, such as clustering or dimensionality compression.

[0275] The process of generating analysis result data can be performed using the analysis command mentioned in step S154. For example, the generation process may include generating analysis result data based on fluorescence label intensity data using the analysis command, and the analysis result data may include plotted images, clustering result display views, and one or more statistics.

[0276] The automatic analysis processing unit 11 (specifically a virtual server) stores the analysis result data generated in step S155, for example, in the analysis result data storage unit 16. Furthermore, for example, the automatic analysis processing unit 11 stores the fluorescence marker intensity data generated in step S155 in the optical data storage unit 15. For example, these storage processes can be performed after the processing in step S155, or after the processing in step S156 or step S157.

[0277] In step S156, the automatic analysis processing unit 11 (specifically, the virtual server) generates output data to be output to the data analysis client terminal 20. For example, the automatic analysis processing unit 11 can identify or extract the output data from the analysis result data generated in step S155. The output data may be a portion of the analysis result data. For example, the output data may include one or more of two-dimensional plot images, spectral plot images, and clustering result display views. The output data may include the data used to generate these images, but may not include fluorescence marker intensity data used in the analysis process. The output data may be configured to allow the data analysis client terminal 20 to edit these images. As a result, the data analysis client terminal 20 can perform the process of editing the output data, and this data can be readily used in the interactive analysis process described later. In addition, the output data may include, for example, numerical data such as statistical analysis result data.

[0278] In this specification, "analysis result data" refers to data generated through analysis and processing in server system 10. "Data to be output" is a part of the analysis result data, and specifically, it is the data used to output the analysis results on the data analysis client terminal 20.

[0279] In step S157, the automatic analysis and processing unit 11 (specifically a virtual server) ends the analysis and processing and proceeds to the processing in step S105.

[0280] In step S105, the automatic analysis processing unit 11 (specifically, the virtual server) sends an automatic analysis completion notification to the data analysis client terminal 20. The automatic analysis completion notification can be an email or a notification using server-side push technology. Upon completion of sending the automatic analysis completion notification, the automatic analysis processing unit 11 stops the virtual server. This avoids additional delays in virtual server activation time and reduces server usage costs.

[0281] In step S106, the data analysis client terminal 20 receives an automatic analysis completion notification. The data analysis client terminal 20 then causes the output device to output the automatic analysis completion notification. Furthermore, for example, in response to receiving the automatic analysis completion notification, the data analysis client terminal 20 may display a screen asking the user whether to send the data to be output to the server system 10. For example, a button for instructing the data analysis client terminal 20 to send the data to be output to the server system 10 may be provided on the screen.

[0282] In step S107, the data analysis client terminal 20 sends a request for data to be output to the server system 10. For example, the data analysis client terminal 20 can perform this sending in response to a user clicking or selecting a button on the screen.

[0283] In step S108, the server system 10 receives a request for data to be output.

[0284] In step S109, the server system 10 sends the output data generated in step S104 to the data analysis client terminal 20.

[0285] In step S110, the data analysis client terminal 20 receives the data to be output sent from the server system 10.

[0286] In step S111, the data analysis client terminal 20 (specifically, the processing unit 21) causes the output device to output the data to be output. Figure 23 This describes an example of the data to be output on the output device. For example... Figure 23 As shown, the output device displays a window showing the data to be output. Seven plotted images are displayed in the lower left corner of the window. Further, a clustering results view is displayed on the right side of the window. Additionally, statistics (number of events, parent % and total %) corresponding to each gate are displayed in the upper left corner of the window. In this way, the window can display image data and / or statistical data based on the data to be output. As mentioned above, the image data may include one or more plotted images and / or one or more clustering results views. Furthermore, the statistical data may include one or more statistical data points.

[0287] In the automated analysis and processing described above, virtual servers can be activated only when needed. Therefore, compared to servers that are always active, operating costs can be significantly reduced. Furthermore, scalability is also achieved.

[0288] (4-2) Examples of automated analysis and processing

[0289] The following is for reference. Figure 14 and Figure 15 This illustrates an example of an automated analytics process in the case where server system 10 is an Amazon Web Services (trademark).

[0290] In step S101, the data acquisition client terminal 30 acquires the light intensity data of the biological sample from the biological sample analysis device 40.

[0291] In step S102, the data acquisition client terminal 30 sends light intensity data to the server system 10. Preferably, the data acquisition client terminal 30 may perform the sending in response to acquiring light intensity data from the biological sample analysis device 40 in step S101.

[0292] In step S103, server system 10 receives light intensity data sent from data acquisition client terminal 30 via network 50. The light intensity data is uploaded to a bucket in Amazon S3 used as an optical data storage unit 15.

[0293] In step S104, the server system 10 performs automatic analysis and processing on the light intensity data. (See below for further details.) Figure 15 Explain the details of the processing in step S104.

[0294] In step S151, for example, in response to uploading an object including light intensity data to a bucket used as an optical data storage unit 15 in Amazon S3, AWS Lambda used as an automatic analysis and processing unit 11 starts automatic analysis and processing in step S151.

[0295] In step S152, AWS Lambda reserves computing resources on server system 10 for performing the analytics processing described later. Specifically, an Amazon EC2 instance is created as a virtual server for performing the analytics processing described later in step S155. AWS Lambda can terminate in response to the creation of the Amazon EC2 instance.

[0296] In step S153, the instance created in step S152 downloads the object containing light intensity data stored on the bucket.

[0297] In step S154, the instance created in step S152 acquires the data required for analysis, which is pre-stored on Amazon Aurora. The data required for analysis can be sent simultaneously with the light intensity data from step S102 and then pre-stored on Amazon Aurora.

[0298] In step S155, for example, by using spectral reference data, the instance calculates fluorescent label intensity data from the light intensity data. The fluorescent label intensity data can be stored on a bucket.

[0299] Furthermore, this example generates analytical results data using the calculated fluorescence labeling intensity data.

[0300] The instance stores the analysis results data generated in step S155 in a bucket. Additionally, the instance can also store the fluorescence label intensity data generated in step S155 on the bucket.

[0301] In step S156, for example, the instance generates output data from the analysis result data. For example, the instance identifies or extracts the output data from or within the analysis result data.

[0302] In step S157, the instance analysis process ends. In response to the end of the analysis process, the instance proceeds to the processing in step S105.

[0303] Note that the number of light intensity data points acquired in step S101 can be one or more. In the case of multiple light intensity data points, for example, multiple light intensity data points can be obtained by performing measurements on each of multiple biological samples.

[0304] In this scenario, in step S102, the data acquisition client terminal 30 sends multiple light intensity data to the server system 10. Furthermore, the data acquisition client terminal 30 can also send data required for analysis to the server system 10.

[0305] Next, in step S103, the server system 10 may record multiple light intensity data and / or the data required for analysis on a database such as Amazon Dynamo DB.

[0306] Next, in step S104, the instance can perform automatic analysis processing on each of the multiple light intensity data. Upon completion of the automatic analysis processing, the instance can update the database. The instance can then proceed to step S105 in response to the completion of analysis processing on all the multiple light intensity data.

[0307] In step S105, the instance sends an automatic analysis completion notification to the data analysis client terminal 20. The instance can be stopped upon the completion of sending the automatic analysis completion notification.

[0308] In step S106, the data analysis client terminal 20 receives an automatic analysis completion notification. The data analysis client terminal 20 then causes the output device to output the automatic analysis completion notification. Furthermore, for example, in response to receiving the automatic analysis completion notification, the data analysis client terminal 20 may display a screen asking the user whether to send the data to be output to the server system 10. For example, a button for instructing the data analysis client terminal 20 to send the data to be output to the server system 10 may be provided on the screen.

[0309] In step S107, the data analysis client terminal 20 sends a request for data to be output to the server system 10. For example, the data analysis client terminal 20 can perform this sending in response to a user clicking or selecting a button on the screen.

[0310] In step S108, the server system 10 receives a request for data to be output.

[0311] In step S109, the server system 10 sends the output data generated in step S104 to the data analysis client terminal 20.

[0312] In step S110, the data analysis client terminal 20 receives the data to be output sent from the server system 10.

[0313] In step S111, the data analysis client terminal 20 causes the output device to output the data to be output.

[0314] (4-3) Data Acquisition and Processing

[0315] The following reference Figure 16 This describes the data acquisition and processing performed by information processing system 1.

[0316] In step S201, the data analysis client terminal 20 sends a request for output data to the server system 10. This request may include information used by the server system 10 to acquire or generate the output data. For example, the request may include information for identifying the analysis result data and / or information for generating the output data from the analysis result data. In addition to this request, the information sent in step S201 may include user authentication information about the data analysis client terminal 20. The sending in step S201 may be performed via the network 50.

[0317] In step S202, the server system 10 receives a request for output data sent from the data analysis client terminal 20 via the network 50.

[0318] In step S203, the server system 10 (specifically, the output data generation unit 13) can obtain the output data from the analysis result data storage unit 16, or generate the output data from the analysis result data stored on the analysis result data storage unit 16. The output data may be a portion of the analysis result data. For example, the output data may include one or more of two-dimensional plot images, spectral plot images, and clustering result display views. The output data may include data used to generate these images, but may not include fluorescence marker intensity data used in the analysis process. The output data may be configured to allow editing of these images. As a result, for example, the data analysis client terminal can perform processing to edit the output data. Additionally, the output data may include numerical data such as statistical analysis result data.

[0319] The processing of the output data generation unit 13 can be performed by a virtual server that is active at any time in the server system 10. Since the virtual server is active at all times, the acquisition and processing of the output data can be performed at high speed.

[0320] Furthermore, the output data generation unit 13 can be configured as a serverless architecture.

[0321] For example, if server system 10 is Amazon Web Services (trademark), the output data acquisition or generation processing of output data generation unit 13 can be performed in a container, for example, generated or managed by Amazon ECS. For example, AWS Fargate can perform processing in a container. For example, AWS Fargate can acquire output data stored in Amazon S3, or generate output data from analytics results data stored in Amazon S3.

[0322] In step S204, the server system 10 sends the data to be output to the data analysis client terminal 20 via the network 50.

[0323] In step S205, the data analysis client terminal 20 receives the data to be output through the network 50.

[0324] In step S206, the data analysis client terminal 20 causes the output device to output the data to be output.

[0325] (4-4) Interactive Analysis and Processing

[0326] The following is for reference. Figure 17 This describes the interactive analysis and processing flow performed by information processing system 1. The analysis and processing can be performed after the automatic analysis and processing described in (4-1) above, or after the data acquisition and processing to be output described in (4-3) above.

[0327] In step S301, the data analysis client terminal 20 sends an interactive analysis processing start request to the server system 10 via the network 50. Before sending, the data analysis client terminal 20 may enable the output device to output the data to be output. For example, step S301 may be executed following step S107 described in (4-1) above, or it may be executed following step S206 described in (4-3) above.

[0328] For example, when performing the output data acquisition processing explained in (4-3) above, the data analysis client terminal 20 causes the output device to output, as shown in the figure. Figure 20 The window shown. Then, in step S206, as... Figure 21 As shown, the acquired output data (plotting data) is displayed on the window.

[0329] For example, the request includes data for identification, specifically data for identifying the object of analysis, which is the object of interactive analytical processing. For instance, the data for identification includes information for identifying a biological sample and information for identifying the analysis performed on that biological sample.

[0330] In step S302, the server system 10 receives the request via network 50.

[0331] In step S303, in response to receiving the request, the server system 10 (specifically, the interactive analysis and processing unit 12) reserves computing resources on the server system 10 for performing interactive analysis and processing. The computing resources are used as a virtual server for performing interactive analysis and processing.

[0332] In step S304, the interactive analysis processing unit 12 (specifically a virtual server) acquires the analysis object data. Referring to the data used for identification, the interactive analysis processing unit 12 can identify the analysis object data to be acquired.

[0333] The data to be analyzed may include analytical result data from which the output data is derived. Additionally, the data to be analyzed may include light intensity data and / or fluorescence label intensity data from which the analytical result data is derived.

[0334] After acquiring the analysis object data in step S304, the interactive analysis processing unit 12 may include monitoring whether the analysis command described later has been received, that is, waiting until the analysis command is received.

[0335] After step S304 but before step S305, a session for executing an RPC (Remote Procedure Call) can be established between server system 10 and data analysis client terminal 20. For example, for this establishment, server system 10 may send a notification to data analysis client terminal 20 indicating that computing resources have been reserved. The establishment can be performed in response to receiving this notification. For example, the notification may be a notification method using server-side push technology. Alternatively, the establishment can be performed by polling the status of data analysis client terminal 20 to server system 10.

[0336] By establishing an RPC, after step S305, the server system 10 can perform analysis processing according to the analysis commands sent from the data analysis client terminal 20. Both the server system 10 and the data analysis client terminal 20 may include a connection unit, which is a functional element for executing the RPC. A combination of the connection unit 14 of the server system 10 and the connection unit 23 of the data analysis client terminal 20 can execute the RPC. Examples of such connection units include MagicOnion, but are not limited to this.

[0337] In step S304, the interactive analysis processing unit 12 (specifically a virtual server) can acquire the data required for analysis. For example, the data required for analysis may include data for calculating fluorescence label intensity data and data for fluorescence intensity label data analysis processing.

[0338] The data used to calculate the fluorescence label intensity may include, for example, spectral reference data. This data is used for demixing.

[0339] The data used in the analysis and processing of fluorescence intensity labeling data can further include analysis commands.

[0340] In step S305, the data analysis client terminal 20 accepts input of analysis commands for the data to be output. In step S305, the data analysis client terminal 20 displays a window on the output device for accepting input of analysis commands. The user inputs analysis commands through the window. For example, the input analysis command may be an analysis command that modifies one or more of various types of setting commands included in the analysis command for obtaining the data to be output in step S301. For example, the input analysis command includes at least one of a modified gate setting command, a modified plotting setting command, a modified axis setting command, a modified axis display setting command, a modified statistical setting command, and a modified region setting command. Furthermore, the analysis command may include a modified clustering command.

[0341] In step S306, the data analysis client terminal 20 sends the analysis command entered in step S305 to the server system 10.

[0342] In step S307, the server system 10 receives an analysis command sent from the data analysis client terminal 20. In response to receiving the analysis command, the processes in steps S308 to S310 are executed. These processes can be event-driven analysis processes triggered by receiving the analysis command. That is, the interactive analysis processes according to this technology can be event-driven analysis processes.

[0343] For example, the processing of the interactive analysis processing unit 12 can be performed in a serverless manner, and the interactive analysis processing unit 12 can be configured in what is commonly referred to as a serverless architecture. For example, if the server system 10 is Amazon Web Services (trademark), the processing of the interactive analysis processing unit 12 can be performed by AWS Lambda, specifically by a system that includes a combination of AWS Lambda and Amazon EC2.

[0344] In step S308, the interactive analysis processing unit 12 (specifically, the virtual server) performs a process to calculate fluorescent label intensity data from the light intensity data. Then, the interactive analysis processing unit 12 performs a process to generate analysis result data using the calculated fluorescent label intensity data.

[0345] It should be noted that if the fluorescence label intensity data remains unchanged, the interactive analysis processing unit 12 may omit the process of calculating the fluorescence label intensity data; that is, it may only perform the process of generating analysis result data by using existing fluorescence label intensity data.

[0346] For example, the interactive analysis processing unit 12 stores the analysis result data generated in step S308 in the analysis result data storage unit 16. Furthermore, for example, the interactive analysis processing unit 12 stores the fluorescence marker intensity data generated in step S308 in the optical data storage unit. These storage processes can be performed after the processing in step S308 or after the processing in step S310.

[0347] In step S309, the interactive analysis processing unit 12 generates output data to be output to the data analysis client terminal. For example, the interactive analysis processing unit 12 can identify or extract the output data from the analysis result data generated in step S309. The output data may be a part of the analysis result data. For example, the output data may include one or more of two-dimensional plot images, spectral plot images, and clustering result display views. The output data may include data used to generate these images, but may not include fluorescence marker intensity data used in the analysis process. The output data may be configured to allow editing of these images. As a result, for example, the data analysis client terminal can perform processing to edit the output data. In addition, the output data may include numerical data such as statistical analysis result data.

[0348] In step S309, the server system 10 sends the generated output data to the data analysis client terminal 20. The output data can be sent individually for each data unit included in the output data, or the entire output data can be sent at once.

[0349] For the former, for example, in one possible case, the data to be output includes two of the following: statistical data and image data (one or more of a two-dimensional plot image, a spectral plot image, and a clustering result display view). For example, in the case of generating the statistical data to be output, the server system 10 can send the statistical data, and then, in the case of generating the image data to be output, the server system 10 can send the image data. Alternatively, conversely, the image data can be sent first, followed by the statistical data. In this way, the server system 10 can send the data to be output in each data unit.

[0350] In step S310, the data analysis client terminal 20 receives the data to be output.

[0351] In step S311, the data analysis client terminal 20 causes the output device to output the data to be output.

[0352] For example, such as Figure 22 As shown, the window displays the changes made by partially altering... Figure 21 The drawing image obtained from the drawing image in the drawing image.

[0353] The processing in steps S305 to S312 can be repeated on the data to be output in step S312. Each time these processes are repeated, the data to be output on the output device can be updated and rendered again.

[0354] As described above, the analysis command input on the data analysis client terminal 20 and the interactive analysis processing of the server system 10 are repeated, and in this way, the interactive analysis processing between the server system 10 and the data analysis client terminal 20 is repeated.

[0355] After the interactive analysis and processing is completed, the interactive analysis and processing unit 12 stops the virtual server. This avoids additional extension of the virtual server's active time and reduces the cost of using the server.

[0356] For example, when the data analysis client terminal 20 receives a command to terminate the interactive analysis process, it can terminate the process. In response to receiving the termination command, the data analysis client terminal 20 sends the termination command to the server system 10. Upon receiving the termination command, the interactive analysis processing unit 12 stops the virtual server. This avoids additional delays in the virtual server's active time and reduces the cost of using the server.

[0357] Alternatively, if the server system 10 does not receive analysis commands from the data analysis client terminal 20, the interactive analysis processing unit 12 may stop the virtual server in response to the elapsed time.

[0358] In the interactive analysis and processing described above, the virtual server is only activated when needed. Therefore, compared to a server that is always active, operating costs can be significantly reduced. Furthermore, scalability is also achieved.

[0359] (4-5) Examples of interactive analysis and processing

[0360] The following is for reference. Figure 18 and Figure 19 Further explanation is given that server system 10 is an Amazon Web Services (trademark). Figure 17 An example of the interactive analysis and processing flow in [the context of the application].

[0361] like Figure 18 As shown, in step S301, the data analysis client terminal 20 sends an interactive analysis processing start request to the server system 10 via the network 50.

[0362] In step S302, the server system 10 receives the request via network 50.

[0363] In step S303, in response to receiving the request, AWS Lambda, which serves as the interactive analytics processing unit 12, reserves computing resources on server system 10 for performing interactive analytics processing. Specifically, AWS Lambda causes Amazon ECS to execute containers in Amazon EC2 (specifically, Docker containers). Within these containers, the processing of the virtual server performing analytics processing, described later, is executed. Alternatively, the processing of the virtual server can be executed without building a container.

[0364] In step S304, the instance retrieves analytics object data stored, for example, on Amazon S3.

[0365] Additionally, in step S304, the instance may acquire the data required for analysis. For example, the instance acquires the data required for analysis, which (e.g., data used to calculate fluorescent label intensity data, etc.) is pre-stored on Amazon Aurora.

[0366] After step S304 but before step S305, a session for executing RPC (Remote Procedure Call) can be established between server system 10 and data analytics client terminal 20. For example, server system 10 can send a notification to data analytics client terminal 20 indicating that computing resources have been reserved in Amazon EC2. The session can be established in response to the receipt of the notification. Both server system 10 and data analytics client terminal 20 can include MagicOnion as a connection unit, which is a functional element for executing RPC.

[0367] After acquiring the analysis object data in step S304, the instance monitors whether it has received the analysis command described later, that is, it waits until the analysis command is received.

[0368] like Figure 19 As shown, in step S305, the data analysis client terminal 20 receives input of analysis commands for the data to be output.

[0369] In step S306, the data analysis client terminal 20 sends the analysis command entered in step S305 to the server system 10.

[0370] In step S307, the server system 10 receives an analysis command sent from the data analysis client terminal 20. In response to receiving the analysis command, the processes in steps S308 and S309 are executed. These processes can be event-driven analysis processes triggered by receiving the analysis command. That is, the interactive analysis processes according to this technology can be event-driven analysis processes.

[0371] In step S308, the instance performs the process of calculating fluorescent label intensity data from the light intensity data. Then, the instance performs the process of generating analysis result data using the calculated fluorescent label intensity data.

[0372] It should be noted that if the fluorescence label intensity data remains unchanged, the fluorescence label intensity data can be omitted in this example; that is, only the process of generating the analysis results data by using the existing fluorescence label intensity data can be performed.

[0373] For example, the instance stores the analysis result data generated in step S308 in bucket S3. Furthermore, the interactive analysis processing unit 12 also stores the fluorescence label intensity data generated in step S308 in bucket.

[0374] In step S309, the instance generates output data based on the analysis results data, and then sends the generated output data to the data analysis client terminal 20.

[0375] In step S310, the data analysis client terminal 20 receives the data to be output.

[0376] In step S311, the data analysis client terminal 20 causes the output device to output the data to be output.

[0377] The processing of steps S305 to S311 can be repeated on the data to be output in step S311. That is, the analysis command input on the data analysis client terminal 20 and the interactive analysis processing of the server system 10 are repeated, and in this way, the interactive analysis processing between the server system 10 and the data analysis client terminal 20 is repeated.

[0378] (5) Setting up the area

[0379] Server system 10 may include a group of servers, each residing in one of multiple geographically dispersed data centers. For example, the multiple data centers may be distributed across multiple countries. Each data center may be referred to as a region.

[0380] In this disclosure, based on the location of the data analysis client terminal 20 and / or the data acquisition client terminal 30, the server system 10 can identify data centers included in multiple data centers that perform automatic analysis processing and / or interactive analysis processing according to this disclosure, and servers in the identified data centers can perform the processing. Preferably, the server system 10 can identify the data center closer to the location from among the multiple data centers as the data center to perform the processing.

[0381] In this disclosure, based on preset information such as information related to the location of the data analysis client terminal 20 and / or the data acquisition client terminal 30, or contract information, the server system 10 can identify data centers included in a plurality of data centers, and data processed according to this disclosure is uploaded or stored to or on that data center, and the data can be uploaded or stored on a server in the identified data center. Preferably, the server system 10 can identify the data center closer to the location from among the multiple data centers as the data center on which the data will be uploaded or stored.

[0382] Particularly preferably, the server used to perform the processing and the server on which the data is uploaded or stored can reside in the same data center.

[0383] By selecting a data center as described above, the processing speed according to this disclosure can be improved. For example, by adopting a configuration that allows setting up a data center on which data to be stored for each user, contract, etc., a data center geographically close to the user can be used, which can improve the speed of data upload, improve the response speed during interactive analysis, etc.

[0384] (6) Data migration

[0385] In this disclosure, the optical data storage unit may include two or more types of memory with different access speeds. Furthermore, under predetermined conditions, the server system can perform a migration process to transfer light intensity data and / or fluorescence marker intensity data stored on memory with a higher access speed (hereinafter also referred to as "hot memory") to memory with a lower access speed (hereinafter also referred to as "cold memory").

[0386] Typically, storage services provided by cloud platforms include several types of storage services, such as hot storage services, which allow fast data access but require a higher unit price, and cold storage services, which conversely have lower data access response speeds but require a lower unit price. In this disclosure, by performing the migration process described above, data can be migrated from hot storage to cold storage, thereby reducing costs. The predetermined conditions for performing the migration process can be preset by the user. For example, the predetermined conditions could be conditions such as performing the migration process if a predetermined number of days have elapsed since the last access to the data without any further data access, or other conditions.

[0387] Preferably, the server system compresses the object data to be migrated before performing the migration process. This further reduces storage costs.

[0388] To access data stored on cold storage, server system 10 can perform data copying from cold storage to hot storage. Furthermore, to access compressed data, server system 10 can also perform decompression processing during data copying.

[0389] Server system 10 may include a database containing information about the storage where data resides (which cold storage or which hot storage has the data stored thereon). Server system 10 or data analysis client terminal 20 can easily and conveniently access data in cold storage by referring to the database.

[0390] (7) Use external storage or computing resources

[0391] As described above (4-1), the automatic analysis processing unit included in the information processing system according to this disclosure can initiate automatic analysis processing in response to light intensity data being stored on the optical data storage unit 15. Although in the embodiment explained above (4-1) the optical data storage unit 15 is located within the server system 10, in this disclosure, the optical data storage unit 15 may be located outside the server system 10. For example, an external memory located outside the server system 10 can be used as the optical data storage unit 15 for storing light intensity data.

[0392] For example, the external storage located outside the server system 10 can be an online storage device owned by a user of the information processing system according to this disclosure. The server system 10 (specifically, the automatic analysis processing unit 11) can initiate the automatic analysis processing described in (4-1) above in response to the storage of light intensity data in the external storage. For example, the external storage can notify the server system 10 that light intensity data has been stored. Triggered by receiving the notification, the automatic analysis processing unit 11 can perform the event-driven analysis processing described in (4-1) above.

[0393] In this case, steps S152 and S154 to S157 can be performed as described above (4-1). In step S153, the automatic analysis processing unit 11 downloads the light intensity data stored in the external memory used as the optical data storage unit 15.

[0394] Furthermore, the automatic analysis and processing unit 11 reserves computing resources within the server system 10 for performing the analysis and processing described in (4-1) above. However, these computing resources can also be computing resources outside the server system 10. For example, computing resources for performing the analysis and processing can be reserved in an information processing device located outside the server system 10.

[0395] In recent years, research institutions such as universities and corporate laboratories have acquired accounts for online storage and cloud platforms, for example, storing data in the cloud and utilizing cloud computing resources for analysis. By using an external online storage device as a data storage destination or performing analytical processing using external computing resources as described above, users can further reduce the operating costs of server systems through the information processing system according to this disclosure.

[0396] (8) Analysis - Partitioning of object data

[0397] In steps S155 described in (4-2) and S309 described in (4-4) above, a process is performed to calculate the fluorescence label intensity data based on the light intensity data. This process may include fluorescence correction or demixing, and specifically, in the case of a biological sample analysis device that is a biological particle analysis device (such as a flow cytometer), these processes are performed. The data acquired by the biological particle analysis device can be divided into events (i.e., measurements per biological particle).

[0398] Therefore, in the computational processing of fluorescent marker intensity data from light intensity data, the information processing system (specifically, the server system) according to this disclosure can first divide the light intensity data into units of events, and then perform computational processing on each piece of light intensity data obtained through the division. For example, the computational processing of each piece of light intensity data obtained through the division can be performed in parallel and simultaneously. For example, the server system (specifically, the automated analysis and processing unit) creates multiple AWS Lambdas, and then assigns a piece of light intensity data obtained through the division to each virtual server. Then, each virtual server performs computational processing on the assigned light intensity data obtained through the division.

[0399] By performing the partitioning process described above, processing speed can be improved. Furthermore, it may be possible to make the process more efficient.

[0400] (9) Data sharing

[0401] The metadata (specifically, analysis setting data), light intensity data, and fluorescence marker intensity data described above (4-1) can be stored on a memory or storage unit included in the server system 10 of this disclosure. In this disclosure, these data entries can be stored in the server system 10 in a state where they are correlated with each other. Because these data are correlated, the reproduction of measurements and / or the reproduction of analysis of measurement results becomes easier.

[0402] Furthermore, additional data, data used to calculate fluorescent label intensity data, and data to be used for the fluorescent intensity label data analysis processing described in (4-1) above can also be stored in server system 10 in a state of mutual correlation. Because these data are interconnected, the reproduction of measurements and / or the analysis of measurement results becomes easier.

[0403] Furthermore, this data (specifically, the data that is interconnected) may be available only on one data analytics client terminal and / or data acquisition client terminal, or it may be available on two or more data analytics client terminals and / or data acquisition client terminals. That is, this data may be shared by two or more data analytics client terminals and / or data acquisition client terminals.

[0404] For example, in this disclosure, multiple data analysis client terminals can share one or more of the light intensity data, fluorescence marker intensity data, and analysis setting data in the server system. Particularly preferably, multiple data analysis client terminals can share the analysis setting data in the server system.

[0405] Furthermore, in this disclosure, multiple data acquisition client terminals can share one or more of the light intensity data, fluorescence marker intensity data, and analysis setting data in the server system. Particularly preferably, multiple data acquisition client terminals can share the analysis setting data in the server system.

[0406] Because the information processing system (or the server system, data analysis client terminal or data acquisition client terminal included in the system) is configured in the manner described above in this disclosure, information about measurements and / or analyses of a particular user can be reused by another user, and the reproduction of measurements and / or the reproduction of analyses of measurement results becomes easier.

[0407] (10) Standardization of the output data

[0408] The output data generated by the automatic analysis processing described in (4-1), the output data acquired or generated by the output data acquisition processing described in (4-3), and the output data generated by the interactive analysis processing described in (4-4) can each be configured to be output on a window (specifically, a worksheet) with the same user interface. For example, these three output data can have the same metadata for output by an output device. As a result, for example, in interactive analysis processing, it becomes easier to use the output data generated by the automatic analysis processing or the output data acquired or generated by the output data acquisition processing. Furthermore, the analysis command input in step S305 of the interactive analysis processing can also be used to perform automatic analysis processing on newly acquired light intensity data.

[0409] Because the output data is displayed in this way on a worksheet with a similar user interface, users can view and perform automated analysis, output data generation, and interactive analysis on a consistent interface. Furthermore, since various analytical settings used in the measurement are stored along with the analysis results, measurements and analyses can be easily reproduced using similar settings. By combining these mechanisms and standardized functions for data across devices, it becomes easy to reproduce the same experiment on multiple different devices.

[0410] (11) Example 1 of output control for clustering result display view

[0411] As described above (4-1), in this disclosure, the data analysis client terminal 20 causes the output device to output the data to be output, including a clustering result display view. For example, the clustering result display view may be similar to... Figure 23 The clustering results are displayed in the view depicted in the diagram. Figure 23 The clustering results display view described in the text is the output obtained when a clustering algorithm called FlowSOM is executed, and is also known as a star diagram.

[0412] When the number of markers used in the analysis increases, the number of markers included in the output data also increases. If data related to all markers is displayed in the clustering results view in this case, the content of the clustering results view becomes complex and it becomes difficult to grasp the expression level of each marker. For example, if the number of marker types displayed in each cluster increases in the star plot or group pie chart in Flow SOM, it becomes difficult to grasp the expression level of each cluster in some cases.

[0413] In a preferred embodiment according to this disclosure, the data analysis client terminal 20 may be configured to change the number of markers to be displayed on the clustering results display view, and more specifically, may be configured to select the markers included in each cluster in the clustering results display view.

[0414] Because the markers to be included in each cluster can be selected, the clustering results display view can be tailored to the user's purpose. Furthermore, the ability to adjust the number of markers to be included in the clustering results display view makes it easier to control the level of marker representation.

[0415] For example, in response to selecting one or more markers from all markers included in the output data, the data analysis client terminal 20 can generate a clustering results display view associated with the selected one or more markers. The generated clustering results display view may be a clustering results display view that does not include data associated with one or more unselected surface markers.

[0416] That is, in response to this selection, the data analysis client terminal 20 can generate a clustering results display view based on data about one or more selected markers. The clustering results display view can be a star chart or a pie chart, but is not limited to these.

[0417] The following diagram further illustrates an example where the clustering results are displayed as a star diagram.

[0418] Assume a scenario where, in step S111 described above (4-1), based on the data to be output, the data analysis client terminal 20 causes the screen of the output device to display something similar to... Figure 24 The star chart 500 described herein, for example, is displayed as a clustering result view. Furthermore, it is assumed that the user desires to reduce the number of markers to be displayed on the star chart.

[0419] In this scenario, for example, in response to a user's selection of a star chart (click or touch) or a user's selection of a predetermined button, the data analysis client terminal 20 causes the output device to display a result related to... Figure 25 The marker selection window described herein is similar to marker selection window 501. The window includes color-coded elements 502 representing the groups of markers displayed in the star diagram and the color corresponding to each marker, and a marker list 503 of the markers displayed in the star diagram.

[0420] Here, the list of markers is configured to allow selection of markers that will be displayed in the star diagram.

[0421] Furthermore, although color-coding elements are described as similar Figure 25 The pie chart is acceptable, but if the color-coded elements represent the association between the marker and the color, or if another display format is available, then this is sufficient.

[0422] Next, the user selects the icon they wish to display in the star chart from the list of icons. For example, Figure 26 The list of markers 512 depicted in the image indicates that the user has selected five markers (grayish shades).

[0423] In response to the selection of five markers, the data analysis client terminal 20 causes the color coding element 513 to display only the color of the selected marker, as shown by... Figure 26The color-coded elements in the graph represent the information. As a result, users can understand the content displayed in the star chart after selecting the marker.

[0424] In response to the selection of five markers, the data analytics client terminal 20 also changes. Figure 24 The star diagram shown. For example, in response to the selection of five markers, the data analysis client terminal 20 transforms the star diagram 500 into... Figure 27 The star diagram 510 shown is illustrated.

[0425] As from Figure 24 Left magnified view 504 and Figure 27 A comparison between the left magnified views 514 shows that the data analysis client terminal 20 changes the number of data elements (colors) displayed in each cluster.

[0426] As described above, the data analysis client terminal 20 can be configured to change the clustering results display view in response to the selection of a marker in the marker list. Then, as described above, the clustering results display view can be changed so that only the data of the marker selected in the marker list (data based on expression level, color corresponding to the marker, etc.) is displayed.

[0427] Note that, as described above, the data analysis client terminal 20 can change the displayed view of clustering results by combining the marker selection operation on the marker list 512.

[0428] Optionally, the data analysis client terminal 20 may respond to a reservation button (e.g., a reservation button on a marker-selection window; for example, Figure 26 (The close button shown, etc.) can change the displayed star chart or change the displayed clustering results view.

[0429] While the number of markers is reduced in the examples described above, the number of markers can be increased. Furthermore, in conjunction with marker selection, the data analysis client terminal 20 can change the color-coded elements on the marker selection window. Additionally, in conjunction with marker selection, the data analysis client terminal 20 can also change the clustering results display view.

[0430] For example, Figure 28 The diagram depicts the state of selecting ten markers in the marker list 522 on the marker selection window 521. In response to this selection, the data analysis client terminal 20 causes color-coded elements 523, which are color-coded in ten colors, to be displayed. Then, in response to this selection, the data analysis client terminal 20 can display [the desired information]. Figure 29 The clustering results described in the image are shown in view 520, which is similar to the clustering results described in the image.

[0431] also, Figure 30The diagram depicts the state of selecting three markers in the marker list 532 on the marker selection window 531. In response to this selection, the data analysis client terminal 20 causes the display of color-coded elements 533, which are color-coded in three colors. Then, in response to this selection, the data analysis client terminal 20 can display similar... Figure 31 The clustering results shown in the diagram are displayed in view 530.

[0432] Additionally, the display order of the markers in the star chart can be changed as needed. That is, the data analysis client terminal 20 can be configured to allow users to change the display order of the markers in the star chart. For example, the display order can be changed in response to user actions on color-coded elements in the marker list or marker-selection window, or it can be executed in response to user actions on the star chart itself.

[0433] For example, in response to a pre-defined operation (e.g., a drag operation) by a user to change the positional relationship of two or more selected icons in a list of icons, the data analytics client terminal 20 can change the display order.

[0434] Optionally, in response to a pre-defined operation (e.g., a drag operation) by a user to change the positional relationship of two or more pies in the color-coded elements, the data analysis client terminal 20 can change the display order. The data analysis client terminal 20 can change the display order through a similar operation on the nodes in the star diagram.

[0435] Furthermore, although the data analysis client terminal 20 changed the clustering result display view in the above description, the server system 10 can also change the clustering result display view.

[0436] For example, in response to a user's selection of a marker in the marker selection window 501, the data analysis client terminal 20 sends data related to the selected marker to the server system 10. Then, based on the data related to the selected marker, the server system 10 generates a modified clustering result display view and sends the modified clustering result display view to the data analysis client terminal 20. The data analysis client terminal 20 can then display the modified clustering result display view on the output device.

[0437] (12) Example 2 of output control for clustering result display view

[0438] The clustering results view explained above (11) often contains a large number of clusters (e.g., nodes in FlowSOM). For example, the number of clusters increases as the number of cell types being analyzed increases. There is a function to group clusters with similar marker expression trends, which is called meta-clustering in FlowSOM, but star plots or population pie charts display clusters as a unit. If all clusters are displayed in the clustering results view in this case, it becomes difficult to grasp the trend or profile of the marker expression levels of the sample as a whole that has been measured (e.g., the trend of expression levels as a unit of meta-clustering) from the clustering results view.

[0439] Figure 32 This describes an instance of a clustering result display view (star diagram) that includes a large number of nodes. Because Figure 32 The clustering results in view 600 show a large number of clusters. For example, the large number of clusters shown in region 601, surrounded by dashed lines, overlap with each other, and trends in expression levels within these clusters are difficult to examine. For example, even if the region is as shown... Figure 32 As depicted on the right, the expansion makes it difficult to examine trends in expression levels within that region. Furthermore, it is even more challenging to grasp trends or characteristics in expression levels at the meta-cluster level, formed by grouping multiple clusters. Consequently, it is difficult to obtain an overview of the sample's overall performance tendencies.

[0440] In a preferred embodiment according to this disclosure, the data analysis client terminal 20 can be configured to output star charts or group pie charts in units of meta-clusters, each meta-cluster being formed by grouping one or more clusters with similar expression trends.

[0441] Meta-cluster 120 can be formed by automatically grouping clusters with similar tendency to express markers through an algorithm, or alternatively, it can be formed in response to the user’s selection of one or more clusters.

[0442] This clustering result outputs a view in units of meta-clustering, i.e., a meta-clustering graph, which makes it easier to grasp the trend of the expression level.

[0443] Figure 33 The text depicts an instance of a meta-clustering graph formed by a view displaying clustering results. Figure 33 On the left, view 600 displays the clustering results. For example, in response to accepting a user's pre-ordered action, data analysis client terminal 20 generates an image from clustering results view 600. Figure 33 Meta-clustering diagram 602 is depicted on the right side of the diagram. Here, for example, a pre-defined action could be a click or touch action such as a pre-defined action button.

[0444] like Figure 33 As shown, multiple clusters in the clustering results display view 600 can be classified into seven cluster groups (referred to as meta-clusters 611 to 617). Each meta-cluster shares common characteristics (e.g., common characteristics related to the representation of the markers). Cluster groups belonging to the same meta-cluster are encoded using the same color. Furthermore, different meta-clusters are encoded using distinct colors.

[0445] For each of these seven meta-clusters, the data analysis client terminal 20 generates a meta-cluster node based on data about one or more clusters belonging to the meta-cluster. Figure 33 The right side depicts a meta-clustering graph 602, which includes seven generated meta-clustering nodes 621 to 627.

[0446] The color of meta-cluster node 621 is the same as the color of meta-cluster 611. Similarly, the colors of meta-cluster nodes 622 to 627 are the same as the colors of meta-cluster nodes 612 to 617, respectively. Since the meta-clusters and meta-cluster nodes are encoded with the same color before and after the formation of the meta-cluster graph as described above, the relationship between the meta-clusters and meta-cluster nodes becomes easier to understand.

[0447] For example, the data analysis client terminal 20 can set the size of the meta-cluster node based on quantified data such as the number of events or the number of cells belonging to the meta-cluster node. Figure 33 (The diameter of the circle in the diagram). Figure 33 In this context, the larger the number of events included in the meta-cluster nodes, the larger the diameter of the corresponding circle.

[0448] Furthermore, in the meta-clustering graph, the data analysis client terminal 20 can position each meta-clustering node at a location identified based on the positions of one or more clusters belonging to the meta-clustering node. For example, the data analysis client terminal 20 can identify the position of each meta-clustering node in the meta-clustering graph based on the number of events included in each of the one or more clusters belonging to the meta-clustering node and the positions of the one or more clusters. In one example, the position of each meta-clustering node could be the centroid location of the set of positions of one or more clusters belonging to the meta-clustering node.

[0449] Note that the identification of the positions of meta-cluster nodes, as described above, can be performed outside of the meta-cluster graph. For example, as... Figure 34 As depicted in the meta-clustering diagram 603, the data analysis client terminal 20 can arrange one or more generated meta-clustering nodes to form a predetermined number of rows and / or columns, or it can arrange one or more generated meta-clustering nodes in a grid.

[0450] Furthermore, although the data analysis client terminal 20 forms a meta-cluster graph from the clustering result display view as explained above, the formation of the meta-cluster graph can be performed by the server system 10.

[0451] For example, a user performs a predetermined operation to form a meta-cluster graph from a specific clustering results display view. In response to receiving the predetermined operation, the data analysis client terminal 20 sends instruction data to the server system 10 to generate the meta-cluster graph from the clustering results display view. In response to receiving the instruction data, the server system 10 generates the meta-cluster graph from the clustering results display view and then sends the meta-cluster graph to the data analysis client terminal 20. The data analysis client terminal 20 can then display the meta-cluster graph on an output device.

[0452] (13) The relationship between meta-clustering and two-dimensional plotting in the clustering results display view

[0453] As described in (12) above, multiple clusters in the clustering results display view can be classified into meta-clusters, each of which includes one or more clusters with common characteristics. Here, if the meta-clusters in the clustering results display view can be associated with events in the two-dimensional drawing, the events belonging to the meta-clusters can be intuitively understood.

[0454] In a preferred embodiment according to this disclosure, in response to selecting a meta-cluster in the clustering results display view, the data analysis client terminal 20 can display a two-dimensional plot, enabling the identification of events belonging to that meta-cluster from events included in the two-dimensional plot. For example, the data analysis client terminal 20 can assign a color in the two-dimensional plot to events belonging to the meta-cluster, a color shared by the meta-cluster.

[0455] This two-dimensional drawing display allows for an intuitive understanding of events belonging to meta-clustering, and also makes background gate setting possible.

[0456] The display control technology used for the above two-dimensional drawing is explained further below.

[0457] Suppose a situation in which, in step S111 explained above (4-1), as follows: Figure 35 As described above, the data analysis client terminal 20 enables the screen of the output device to display clustering result display view 600 and two-dimensional plots 630 and 631 based on the data to be output. Clustering result display view 600 is the clustering result display view explained in (12) above, and as described in the description... Figure 36 As described, multiple clusters in the clustering results display view can be classified into seven meta-clusters.

[0458] like Figure 37As shown, when the user drags the mouse cursor from the position of reference symbol 632 to the position of reference symbol 633, the region 634 enclosed by the dashed line is selected. When region 634 is selected, the meta-clusters 611 to 617 that overlap with this region are also selected. In response to the selection of meta-clusters 611 to 617, as... Figure 37 As shown, the data analysis client terminal 20 assigns colors to events belonging to the meta-clusters in the two-dimensional plots 630 and 631. This color-based display technique allows for a visual understanding of events belonging to the meta-clusters, and furthermore, background settings become possible.

[0459] Note that, although in Figure 37 In the example depicted, multiple meta-clusters are selected via dragging, but one or more meta-clusters can be selected, for example, by clicking. Similarly, in this case, events belonging to the selected meta-cluster can be color-coded for that meta-cluster.

[0460] (14) Control of the pie chart axis in the star diagram

[0461] As illustrated in the accompanying figures explained above (11) through (13), in some cases, data on the expression levels of marker groups are depicted through pie charts at each node (cluster) in the star diagram. For example, the radial length of the region (pie) displayed corresponding to a marker increases with increasing marker expression levels. For example, in Figure 38 The node 700 depicted in the diagram illustrates the expression levels of multiple markers using a pie chart. The expression level of each marker corresponds to a length in the radial direction, and for example, the expression level of the marker corresponding to the region indicated by reference symbol 701 corresponds to a length 702 in the radial direction. It should be noted that although length 702 is... Figure 38 The arrow in the center represents the star, but this arrow may not be displayed in the star chart.

[0462] In this disclosure, the data analysis client terminal 20 can be configured to change the axis settings of the pie chart in each node. For example, the data analysis client terminal 20 can change the axis settings of the pie chart so that the axis scale of the pie chart in a certain node corresponds to the axis scale of the two-dimensional plot corresponding to that node. Here, each node as an event group may not be a cluster formed by performing clustering. For example, in the case of creating a door with a two-dimensional plot, each node can be formed based on the event groups existing in the door.

[0463] As mentioned above, the ability to change axis settings makes it easy to control the level of expression. Furthermore, it allows for a more precise understanding of the relationship with two-dimensional drawing.

[0464] Examples of axis scales in two-dimensional plots include linear, logarithmic, and double exponential scales. Examples of axis scales shown also include linear, logarithmic, and double exponential scales.

[0465] In this disclosure, the data analysis client terminal 20 can use any of these as the axis scale for the pie display in each node. For example, when using a double exponential scale as the axis scale for a two-dimensional plot, the data analysis client terminal 20 can use a double exponential scale as the axis scale for the pie display corresponding to one or more nodes of the two-dimensional plot.

[0466] In this disclosure, in order to change the aforementioned axis settings, the data analysis client terminal 20 can be configured to output a two-dimensional drawing settings window. Consider a scenario where, for example, the data analysis client terminal 20 enables... Figure 39 The two-dimensional drawing data 710 described herein is displayed on the output device as output data.

[0467] In this scenario, for example, in response to a user performing a pre-defined operation, the data analysis client terminal 20 causes the output device to display a drawing settings window 711. Figure 39 As shown, the plotting settings window has areas 712 and 713 for adjusting the settings of the X and Y axes of the two-dimensional plotting data 710. (As...) Figure 39 As shown, the area 712 for setting the X-axis has a list box 714 for selecting the axis scale table of the X-axis. Although the list box is in... Figure 39 The text displays "Double Exponential," but in addition to "Double Exponential," the list box is configured to also allow selection of "Linear" and "Logarithmic." The area 713 for setting the Y-axis also has a list box 715 for similarly selecting the Y-axis axis tick table. The list box is also configured to allow similar selection of any of the three axis ticks.

[0468] When a user selects the X-axis and / or Y-axis scales in the plotting settings window, in response to the selection, the data analysis client terminal 20 can change the pie chart display of the axis scales corresponding to one or more nodes of the two-dimensional plot controlled according to the plotting settings window. This change makes the pie chart display of the axis scales the same as the axis scales selected in the plotting settings.

[0469] This disclosure provides the aforementioned information processing system, as well as a server system, a data acquisition client terminal, and a data analysis client terminal included in the information processing system. Details of these are as described above.

[0470] 2. Information Processing Methods

[0471] This disclosure also provides an information processing method. The information processing method may include one or more of the processes explained in (4) above. For example, the information processing method may include: an automatic analysis processing step, performing analysis processing on light intensity data obtained by irradiating a biological sample with light or on fluorescent label intensity data calculated from the light intensity data, and generating output data; an analysis result data storage step, storing the output data generated based on the light intensity data or the fluorescent label intensity data; and an interactive analysis processing step, analyzing the fluorescent label intensity data and outputting analysis result data based on an analysis command for the output data to be output to an output device. The description in (4) above applies to each of these steps.

[0472] It should be noted that the following configurations may also be used in this disclosure. [1]

[0474] A server system, comprising: The automatic analysis and processing unit generates output data by analyzing and processing light intensity data or fluorescent label intensity data obtained by illuminating biological samples with light; The analysis result data storage unit stores the output data generated based on the light intensity data or the fluorescence label intensity data; and The interactive analysis and processing unit analyzes the fluorescent label intensity data based on the analysis command of the data to be output to the output device and outputs the analysis result data. [2]

[0476] According to the server system described in [1], the automatic analysis and processing unit calculates the fluorescent label intensity data based on the light intensity data. [3]

[0478] According to the server system described in [1] or [2], the processing of the automatic analysis processing unit and the processing of the interactive analysis processing unit are executed on different computing resources. [4]

[0480] According to any one of [1] to [3], the server system reserves computing resources for the processing of the automatic analysis processing unit and / or the processing of the interactive analysis processing unit in response to receiving the analysis start command. [5]

[0482] The server system according to any one of [1] to [4] further includes: A database that stores analysis setting data used in the processing of the automatic analysis processing unit and / or the processing of the interactive analysis processing unit. [6]

[0484] The server system according to any one of [1] to [5] further includes: An optical data storage unit stores the light intensity data and / or the fluorescent marker intensity data. [7]

[0486] According to the server system described in [6], wherein, The optical data storage unit includes two or more types of memory with different access speeds, and Under predetermined conditions, the server system performs a process to migrate light intensity data and / or fluorescence label intensity data stored in a memory with a higher access speed to a memory with a lower access speed. [8]

[0488] The server system according to any one of [1] to [7], wherein the data to be output includes at least one of a two-dimensional plot image, a spectral plot image, a one-dimensional histogram image, a two-dimensional contour image, a dimensionally compressed image, and a clustering result display view. [9]

[0490] An information processing system, comprising: Data acquisition client terminals acquire light intensity data obtained by illuminating biological samples or by processing and calculating fluorescent labeling intensity data; and The server system includes: The optical data storage unit stores the light intensity data or the fluorescent marker intensity data sent from the client terminal. The automatic analysis and processing unit generates output data by analyzing and processing the light intensity data or the fluorescent label intensity data. The analysis result data storage unit stores the output data generated based on the light intensity data or the fluorescence label intensity data, and The interactive analysis and processing unit analyzes the fluorescent label intensity data based on the analysis command of the data to be output to the output device and outputs the analysis result data.

[10]

[0492] According to the information processing system described in [9], in response to the acquisition of light intensity data or fluorescent label intensity data, the data acquisition client terminal sends the light intensity data or fluorescent label intensity data to the server system.

[11]

[0494] According to the information processing system described in [9] or

[10] , in response to the acquisition of light intensity data or fluorescent label intensity data, the data acquisition client terminal performs predetermined processing on the light intensity data or fluorescent label intensity data, and then sends the processed light intensity data or processed fluorescent label intensity data to the server system.

[12]

[0496] The information processing system according to any one of [9] to

[11] , wherein

[0497] The server system pre-stores the analysis settings data used in the processing of the automatic analysis and processing unit, and

[0498] The automatic analysis and processing unit calculates the fluorescent marker intensity data from the light intensity data using the analysis setting data.

[13]

[0500] According to any one of [9] to

[12] , in the information processing system, in response to the light intensity data being stored on the optical data storage unit, the automatic analysis processing unit performs processing to calculate the fluorescent marker intensity data from the light intensity data.

[14]

[0502] The information processing system according to any one of [9] to

[13] further includes: A data analysis client terminal, the data analysis client terminal including the output device.

[15]

[0504] According to the information processing system of

[14] , the data analysis client terminal sends the analysis command for the data to be output to the output device to the server system.

[16]

[0506] According to the information processing system described in

[14] or

[15] , the data analysis client terminal causes the output device to output a window displaying the data to be output and accepts the input of analysis commands on the window.

[17]

[0508] The information processing system according to any one of [9] to

[16] , wherein multiple data analysis client terminals are able to share any one or more of light intensity data, fluorescence intensity data and analysis setting data in the server system.

[18]

[0510] The information processing system according to any one of [9] to

[17] , wherein multiple data acquisition client terminals are able to share analysis setting data in the server system.

[19]

[0512] A data acquisition client terminal, comprising: The data acquisition unit acquires light intensity data by illuminating biological samples; and The transmitting unit, in response to the acquisition of the light intensity data, sends the light intensity data to the server system, wherein... In the server system, fluorescent label intensity data is calculated from the light intensity data.

[20]

[0514] A data analysis client terminal, comprising: The communication unit receives output data created by the server system based on fluorescent marker intensity data from the server system; and The processing unit performs the processing to cause the output device to output the data to be output. [twenty one]

[0516] According to the data analysis client terminal described in

[20] , the data analysis client terminal causes the output device to output a window on which the data to be output is displayed, and accepts input of analysis commands for the data to be output on the window. [twenty two]

[0518] An information processing method, comprising: The automated analysis and processing step performs analysis and processing on light intensity data obtained by illuminating biological samples with light or on fluorescent label intensity data calculated from the light intensity data and generates data to be output; The analysis result data storage step stores the output data generated based on the light intensity data or the fluorescence label intensity data; and The interactive analysis and processing step analyzes the fluorescent label intensity data based on the analysis command of the data to be output from the output device and outputs the analysis result data.

[0519] [List of Reference Numbers]

[0520] 1. Information Processing System

[0521] 10 Server Systems

[0522] 20 Data Analysis Client Terminal

[0523] 30 Data Acquisition Client Terminal

[0524] 40. Biological sample analysis device.

Claims

1. A server system, comprising: The automatic analysis and processing unit generates output data by analyzing and processing the light intensity data or fluorescent labeling intensity data obtained from irradiating biological samples with light; The analysis result data storage unit stores the output data generated based on the light intensity data or the fluorescent label intensity data; as well as The interactive analysis and processing unit analyzes the fluorescent label intensity data based on the analysis command of the data to be output to the output device and outputs the analysis result data.

2. The server system according to claim 1, wherein, The automatic analysis and processing unit calculates the fluorescent label intensity data based on the light intensity data.

3. The server system according to claim 1, wherein, The processing of the automatic analysis and processing unit and the processing of the interactive analysis and processing unit are executed on different computing resources.

4. The server system according to claim 1, wherein, In response to receiving the analysis start command, the server system reserves computing resources for the processing of the automatic analysis processing unit and / or the processing of the interactive analysis processing unit.

5. The server system according to claim 1, further comprising: A database containing analysis setting data to be used in the processing of the automatic analysis processing unit and / or the processing of the interactive analysis processing unit.

6. The server system according to claim 1, further comprising: An optical data storage unit stores the light intensity data and / or the fluorescent marker intensity data.

7. The server system according to claim 6, further comprising: The optical data storage unit includes two or more types of memory with different access speeds, and Under predetermined conditions, the server system performs a process of migrating the light intensity data and / or the fluorescent marker intensity data stored in a memory with a higher access speed to a memory with a lower access speed.

8. The server system according to claim 1, wherein, The data to be output includes at least one of the following: a two-dimensional plot image, a spectral plot image, a one-dimensional histogram image, a two-dimensional contour plot image, a dimensionally compressed image, and a clustering result display view.

9. An information processing system, comprising: The data acquisition client terminal acquires light intensity data obtained by illuminating biological samples with light or obtains fluorescent label intensity data by calculating and processing the light intensity data; as well as The server system includes: The optical data storage unit stores the light intensity data or the fluorescent marker intensity data sent from the client terminal. The automatic analysis and processing unit generates output data by analyzing and processing the light intensity data or the fluorescent label intensity data. The analysis result data storage unit stores the output data generated based on the light intensity data or the fluorescence label intensity data, and The interactive analysis and processing unit analyzes the fluorescent label intensity data based on the analysis command of the data to be output to the output device and outputs the analysis result data.

10. The information processing system according to claim 9, wherein, In response to acquiring the light intensity data or the fluorescent label intensity data, the data acquisition client terminal sends the light intensity data or the fluorescent label intensity data to the server system.

11. The information processing system according to claim 9, wherein, In response to acquiring the light intensity data or the fluorescent label intensity data, the data acquisition client terminal performs a predetermined process on the light intensity data or the fluorescent label intensity data, and then sends the processed light intensity data or the processed fluorescent label intensity data to the server system.

12. The information processing system according to claim 9, wherein, The server system pre-stores the analysis settings data to be used in the processing of the automatic analysis and processing unit, and The automatic analysis and processing unit calculates the fluorescent label intensity data based on the light intensity data using the analysis setting data.

13. The information processing system according to claim 9, wherein, In response to the light intensity data being stored in the optical data storage unit, the automatic analysis and processing unit performs processing to calculate fluorescent label intensity data based on the light intensity data.

14. The information processing system according to claim 9, further comprising: A data analysis client terminal, including the output device.

15. The information processing system according to claim 14, wherein, The data analysis client terminal sends the analysis command of the data to be output to the output device to the server system.

16. The information processing system according to claim 14, wherein, The data analysis client terminal enables the output device to output a window displaying the data to be output and to receive the input of the analysis command through the window.

17. The information processing system according to claim 9, wherein, Multiple data analysis client terminals can share one or more of the light intensity data, fluorescence marker intensity data, and analysis setting data in the server system.

18. The information processing system according to claim 9, wherein, Multiple data acquisition client terminals can share the analysis settings data in the server system.

19. A data acquisition client terminal, comprising: The data acquisition unit acquires light intensity data by illuminating biological samples. as well as The transmitting unit, in response to acquiring the light intensity data, sends the light intensity data to the server system, wherein... In the server system, fluorescent label intensity data is calculated based on the light intensity data.

20. A data analysis client terminal, comprising: The communication unit receives output data created by the server system based on fluorescent marker intensity data from the server system. as well as The processing unit performs the processing to cause the output device to output the data to be output.

21. The data analysis client terminal according to claim 20, wherein, The data analysis client terminal enables the output device to output a window displaying the data to be output and to receive input of analysis commands for the data to be output through the window.

22. An information processing method, comprising: The automatic analysis and processing step analyzes and processes the light intensity data obtained by irradiating biological samples with light or the fluorescent label intensity data calculated based on the light intensity data and generates data to be output. The analysis result data storage step stores the output data generated based on the light intensity data or the fluorescent label intensity data; as well as The interactive analysis processing step analyzes the fluorescent label intensity data based on the analysis command of the data to be output to the output device and outputs the analysis result data.

23. The information processing system according to claim 14, wherein, The server system or the data analysis client terminal generates data about meta-clustering, each meta-cluster being formed by grouping one or more clusters with common characteristics, and the data analysis client terminal causes the output device to output a meta-clustering graph containing meta-clustering nodes generated based on the meta-clustering related data.

24. The information processing system according to claim 23, wherein, The meta-clustering is formed by automatically grouping one or more clusters with similar marker expression trends through an algorithm.

25. The information processing system according to claim 23, wherein, The size of each meta-cluster node in the meta-cluster graph is set according to the quantized data.

26. The information processing system according to claim 25, wherein, The quantified data refers to the number of events or cells belonging to the meta-clustering node.

27. The information processing system according to claim 23, wherein, The server system or the data analysis client terminal generates the meta-cluster in response to the user's selection operation of one or more clusters.

28. The information processing system according to claim 23, wherein, In the meta-clustering graph, each meta-clustering node is located at the centroid of the location of the one or more clusters that are aggregated into the meta-clustering node.

29. The information processing system according to claim 28, wherein, The location of each meta-cluster node is identified based on the number of events included in each of the one or more clusters belonging to the meta-cluster node and the location of each of the one or more clusters.

30. The information processing system according to claim 23, wherein, The generated meta-cluster nodes are arranged in the meta-cluster graph to form a predetermined number of rows or columns, or in a grid-like arrangement.

31. The information processing system according to claim 23, wherein, Before or after the meta-clustering graph is formed, the data analysis client terminal outputs the meta-clustering and the meta-clustering nodes in the same color.

32. The information processing system according to claim 23, wherein, The data analysis client terminal causes the output device to output the meta-clustering diagram and the two-dimensional plot, and in response to a specific meta-cluster being selected in the meta-clustering diagram, events belonging to the meta-cluster in the two-dimensional plot are displayed identifiably by assigning a color shared by the meta-cluster.

33. The information processing system according to claim 23, wherein, The server system or the data analysis client terminal causes the output device to display a clustering result display view containing multiple clusters, and outputs a marker selection window for selecting one or more markers from all markers included in the clustering result display view for display, and updates the clustering result display view or the meta-clustering graph based on the relevant data of one or more selected markers.

34. The information processing system according to claim 33, wherein, The data analysis client terminal, in conjunction with the marker selection operation on the marker selection window, changes the data elements displayed within each cluster in the displayed clustering results view.

35. The information processing system according to claim 33, wherein, The data analysis client terminal is configured to change the axis scale of the pie chart displayed in each node in the clustering result display view, such that the axis scale corresponds to the axis scale of the two-dimensional drawing corresponding to the node.

36. The information processing system according to claim 23, wherein, The processing of the automatic analysis and processing unit and the processing of the interactive analysis and processing unit are executed on different computing resources within the server system.

37. The information processing system according to claim 23, wherein, The server system further includes a database that stores analysis setting data to be used by the automatic analysis processing unit or the interactive analysis processing unit.

38. The information processing system according to claim 23, further comprising a data acquisition client terminal for receiving light intensity data, wherein, The data acquisition client terminal is triggered by the start of receiving the light intensity data, and begins to send the light intensity data to the server system.

39. The information processing method according to claim 22, wherein, The server system or the data analysis client terminal generates data about meta-clustering, each meta-cluster being formed by grouping one or more clusters with common characteristics, and the data analysis client terminal causes the output device to output a meta-clustering graph based on the meta-clustering related data.

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