Information processing apparatus, biological sample analysis method, biological sample testing apparatus, and biological sample testing system

Abstract

Provided is a method for assisting selection of a binding molecule and a labeling molecule used in detection and / or analysis of a target molecule. Provided is an information processing apparatus including a processing unit that, when a plurality of different binding molecules labeled by different labeling molecules is used, calculates reactivity between a target molecule and each of the plurality of different binding molecules based on signals obtained from a sample including a biological specimen. The signals include a first signal group acquired when each of a plurality of different binding molecules labeled by the same type of labeling molecule reacts with the target molecule and a second signal group acquired when each of the same type of binding molecule labeled by different labeling molecules reacts with the target molecule.

Description

Technical Field

[0001] This technology relates to information processing devices. More specifically, this technology relates to information processing devices, biological sample analysis methods, biological sample detection devices, and biological sample detection systems used in the detection of biological samples. Background Technology

[0002] Labels are used in various analyses to analyze a wide range of molecules. For example, molecules such as antigenic proteins are detected and / or analyzed using antibodies labeled with a variety of fluorescent dyes via flow cytometry or microscopy. Furthermore, in addition to antigen-antibody reactions, the detection and analysis of molecules via nucleic acid hybridization using fluorescently labeled nucleic acid probes and the detection and analysis of enzyme molecules using fluorescently labeled matrices are widely being conducted. Various fluorescent dyes are used in these detections and / or analyses. Each fluorescent dye has unique properties, such as a unique fluorescence spectrum and fluorescence intensity.

[0003] For example, Patent Document 1 describes an invention relating to a technique for analyzing the types of fluorescence emitted from particles, etc. (paragraph 0001). Patent Document 1 below describes "a data display method comprising: obtaining detection data by simultaneously detecting fluorescence generated from particles flowing through a channel in multiple wavelength regions; and displaying a fluorescence spectrum obtained by accumulating or averaging multiple detection data corresponding to multiple said particles" (claim 1).

[0004] Furthermore, Patent Document 2 describes a method for presenting a combination of fluorescent dyes and antibodies, nucleic acid probes, etc., which are bound to antigens, nucleic acids, etc.

[0005] [List of Citations]

[0006] [Patent Literature]

[0007] [Patent Document 1]

[0008] JP 2014-206551A

[0009] [Patent Document 2]

[0010] US Patent No. 8731844 Specification Summary of the Invention

[0011] [Technical Issues]

[0012] When binding molecules such as antibodies or nucleic acid probes react with target molecules such as antigens or nucleic acids, the reactivity varies depending on the type of target molecule or binding molecule. Furthermore, the reactivity of the binding molecule relative to the target molecule also depends on the type of labeling molecule (such as fluorescence) used to label the binding molecule. Therefore, the type of binding molecule or labeling molecule used to detect the target molecule also affects the accuracy of the detection.

[0013] The selection of binding and labeling molecules is essential for the detection and / or analysis of the target molecule. While the selection of binding and labeling molecules is typically performed by the user performing the detection and / or analysis, this is a time-consuming task, even for experienced users. Furthermore, there may be situations where the selected binding and labeling molecules are unsuitable for the detection and / or analysis of the target molecule.

[0014] Patent Document 2 proposes a method for evaluating and presenting a combination of fluorescent dyes based on measured autofluorescence and single fluorescent staining. However, in this case, the actual fluorescently labeled antibody must be used as the object to actually measure the signal of the fluorescently labeled antibody, and the signal data of the antibody labeled with another fluorescent dye cannot be accurately calculated based on the signal data of the antibody labeled with another fluorescent dye. In other words, Patent Document 2 does not describe a method for calculating the unmeasured fluorescently labeled antibody.

[0015] With these considerations in mind, the primary objective of this technology is to provide a technique for supporting the selection of binding and labeling molecules to be used in the detection and / or analysis of target molecules.

[0016] [Solution to the problem]

[0017] In this technology, firstly, an information processing apparatus is provided, comprising: a processing unit configured to: when using a plurality of different binding molecules labeled by different marker molecules, calculate the reactivity between a target molecule and each of the plurality of different binding molecules based on signals obtained from a sample including a biological sample, wherein the signals include a first signal set and a second signal set, the first signal set being obtained when each of the plurality of different binding molecules labeled by the same type of marker molecules reacts with the target molecule, and the second signal set being obtained when each of the same type of binding molecules labeled by different marker molecules reacts with the target molecule.

[0018] In the information processing apparatus according to this technology, the number of bound molecules and / or the fluorescence intensity labeled by that number of bound molecules can be calculated as an indicator of reactivity.

[0019] In this case, fluorescence intensity can be calculated from one or more values ​​selected from excitation efficiency, quantum yield, absorption efficiency, and fluorescence labeling rate (F / P value).

[0020] In this technology, a signal may include at least one of a signal, a specific signal / background, and a specific signal / non-specific signal.

[0021] The processing unit of the information processing device according to this technology can be configured to calculate the background in each detection channel based on a third signal obtained when a negative control is used on the target molecule.

[0022] The processing unit of the information processing device according to this technology can be configured to calculate the leakage of autofluorescence signals and / or the leakage of signals obtained from another labeled molecule.

[0023] According to this technology, the processing unit of the information processing device can be configured to calculate the reactivity of the target molecule with respect to the combination of the labeled molecule and the bound molecule that is not actually measured.

[0024] The processing unit of the information processing device according to this technology can also be configured to select a combination of labeled molecules and binding molecules whose specific signal / background is equal to or exceeds a threshold.

[0025] The processing unit of the information processing device according to this technology can also be configured as a combination of selected labeled molecules and binding molecules that maximize the sum of specific signal and background.

[0026] The processing unit of the information processing device according to this technology can also be configured to select a combination of labeled molecules and binding molecules that maximize the sum of the differences between the signal and the background fluorescence intensity.

[0027] The processing unit of the information processing device according to this technology can also be configured to select a combination of labeled molecules and binding molecules based on the magnitude of the fluorescence intensity signal.

[0028] The processing unit of the information processing device according to this technology can also be configured to select a combination of labeled molecules and binding molecules, such that the label is assigned to binding molecules in ascending order of the value of a first signal group in descending order of fluorescence intensity signal.

[0029] The processing unit of the information processing device according to this technology can also be configured to select a combination of labeled molecules and binding molecules, such that the labeled molecules are distributed in ascending order of the length of the detection wavelength to binding molecules in descending order of the values ​​of the first signal group.

[0030] The information processing apparatus according to this technology may further include a presentation unit configured to present to a user supporting information about the combination of binding molecules and labeled molecules based on the calculated reactivity.

[0031] Furthermore, the information processing apparatus according to this technology may further include an evaluation unit configured to estimate the importance of the binding molecule and / or the labeled molecule relative to the target molecule based on image information.

[0032] In this technology, the first signal group and the second signal group can be standardized detection quantities using at least one selected from excitation power density, exposure time and detection device sensitivity.

[0033] In this technology, a method for analyzing biological samples is provided, comprising the following steps: acquiring a signal from a sample including a biological sample; calculating the reactivity between a target molecule and each of the plurality of different binding molecules when using a plurality of different binding molecules labeled by different marker molecules; and outputting the reactivity, wherein the signal comprises: a first set of signals obtained when each of the plurality of different binding molecules labeled by the same type of marker molecules reacts with the target molecule and a second set of signals obtained when each of the same type of binding molecules labeled by different marker molecules reacts with the target molecule.

[0034] Furthermore, this technology provides a biological sample detection device, comprising: a signal acquisition unit configured to acquire a signal obtained from a sample including a biological sample; a processing unit configured to calculate the reactivity between a target molecule and each of the plurality of different binding molecules labeled by different marker molecules based on the signal; an output unit configured to output the reactivity; and a detection unit configured to detect a signal emitted from a target molecule labeled with a binding molecule, the binding molecule being selected based on the output reactivity and labeled by the marker molecule, wherein the signal includes: a first set of signals obtained when each of the plurality of different binding molecules labeled by the same type of marker molecules reacts with the target molecule and a second set of signals obtained when each of the same type of binding molecules labeled by different marker molecules reacts with the target molecule.

[0035] The biological sample detection device according to the present technology may further include a labeling unit configured to label the target molecule using a binding molecule selected based on output reactivity and labeled by a labeling molecule.

[0036] The biological sample detection device according to the present technology may further include an analysis unit configured to analyze the sample based on a signal detected by the detection unit.

[0037] Furthermore, this technology provides a biological sample detection system, comprising: an information processing device and a detection device, the information processing device comprising: a signal acquisition unit configured to acquire a signal obtained from a sample including a biological sample; a processing unit configured to calculate, based on the signal, the reactivity between a target molecule and each of the plurality of different binding molecules labeled by different marker molecules; an output unit configured to output the reactivity, the signal comprising: a first set of signals obtained when each of the plurality of different binding molecules labeled by the same type of marker molecules reacts with the target molecule and a second set of signals obtained when each of the same type of binding molecules labeled by different marker molecules reacts with the target molecule; and the detection device configured to detect a signal emitted from a target molecule labeled with a binding molecule, the binding molecule being selected based on the output reactivity and labeled by the marker molecule. Attached Figure Description

[0038] Figure 1 This is a block diagram illustrating an example configuration of a biological sample detection system 4 according to the present technology.

[0039] Figure 2 This is a flowchart illustrating an example of a method for calculating the number of bound molecules.

[0040] Figure 3 This is a conceptual diagram illustrating an example of an information processing system 2 according to the present technology.

[0041] Figure 4 This is a block diagram illustrating an example of a biological sample detection device 3 according to the present technology.

[0042] Figure 5 This is a conceptual diagram illustrating an example of a biological sample detection system 4 according to the present technology.

[0043] Figure 6 It shows something different Figure 5 The diagram shows a conceptual illustration of an example of a biological sample detection system 4 according to the present technology.

[0044] Figure 7 It shows something different Figure 5 and Figure 6 The diagram shows a conceptual illustration of an example of a biological sample detection system 4 according to the present technology.

[0045] Figure 8 This is a flowchart illustrating an example of an information processing method according to the present technology.

[0046] Figure 9 This is a schematic diagram showing the overall configuration of the microscope system 5000.

[0047] Figure 10 This is a diagram illustrating an example of an imaging method.

[0048] Figure 11 This is a diagram illustrating an example of an imaging method.

[0049] Figure 12 This is a schematic diagram showing the overall configuration of the biological sample analysis device 6100.

[0050] Figure 13 The photograph is an alternative to the image showing the results of fluorescent immunostaining performed using the following substances in the first experimental example: a paraffin-embedded tissue section of human breast cancer as an example of the target molecule; AF488, AF647, AF700 and PE (phycoerythrin) from the Alexa Fluor (registered trademark) series as examples of the labeling molecule; and an anti-pan-cytokeratin antibody (clone: ​​AE1 / AE3) as an example of the binding molecule.

[0051] Figure 14 It is a photograph that serves as an alternative to the image shown in the second experimental example of each fluorescently labeled antibody in a multiplex staining image and an image of each fluorescent label in an unstained sample serving as a negative control.

[0052] Figure 15 It is a photograph that serves as an alternative to the image showing each fluorescently labeled antibody in a multiplexed image and each fluorescent label in an unstained sample serving as a negative control, as displayed in the second experimental example.

[0053] Figure 16 It is a photograph that serves as an alternative to the image showing each fluorescently labeled antibody in a multiplexed image and each fluorescent label in an unstained sample serving as a negative control, as displayed in the second experimental example. Detailed Implementation

[0054] In the following description, preferred embodiments for implementing the present technology will be described with reference to the accompanying drawings. The embodiments described below illustrate examples of representative embodiments of the present technology; however, the scope of the present technology should not be narrowly understood based on these embodiments. The description will be given in the following order.

[0055] 1. Information processing device 1

[0056] (1) Target molecule

[0057] (2) Combining molecules

[0058] (3) Labeled molecules

[0059] (4) Signal acquisition unit 11

[0060] (5) Processing Unit 12

[0061] (a) First signal group

[0062] (b) Second signal group

[0063] (c) Calculation of reactivity: Processing unit 12

[0064] (d) Third signal

[0065] (e) Selection of combinations of labeled and binding molecules: Processing unit 12

[0066] (6) Evaluation Unit 13

[0067] (7) Output Unit 14

[0068] (8) Presentation Unit 15

[0069] (9) Storage unit 16

[0070] (10) Display unit 17

[0071] (11) User Interface 18

[0072] 2. Information Processing System

[0073] 3. Biological sample detection device; 3. Biological sample detection system; 4.

[0074] (1) Detection unit 31, detection device 41

[0075] (2) Marking unit 32, marking device 42

[0076] (3) Analysis unit 33, analysis device 43

[0077] 4. Computer program

[0078] 5. Methods for analyzing biological samples

[0079] 6. Application Examples

[0080] [Microscope System 5000]

[0081] [Biosample Analysis Apparatus 6100]

[0082] <1. Information Processing Device 1>

[0083] Figure 1This is a block diagram illustrating a configuration example of a biological sample detection system 4 according to the present invention. The information processing device 1 according to the present invention is an information processing device that can be used in the biological sample detection system 4 according to the present invention, which will be described later. The information processing device 1 according to the present invention includes at least a processing unit 12. Furthermore, if necessary, the information processing device 1 may also include a signal acquisition unit 11, an evaluation unit 13, an output unit 14, a presentation unit 15, a storage unit 16, a display unit 17, and a user interface 18. Each unit will be described in detail below.

[0084] (1) Target molecule

[0085] In this technique, a target molecule refers to a molecule that can be detected and / or analyzed by binding to a binding molecule, which is labeled by a marker molecule described later, and the target molecule can be selected by those skilled in the art. Examples of target molecules include molecules that can be detected and / or analyzed by binding to a binding molecule labeled by a marker molecule in analyses such as flow cytometry, microscopic observation, Western blotting, various arrays, and ELISA. In other words, this technique can be used to support the selection of marker molecules and binding molecules used in these analyses.

[0086] More specifically, for example, target molecules are molecules that can exist in living organisms, including bodily fluids (such as blood and urine) and tissues, and examples include biomolecules, drug molecules, and toxic molecules. Examples of biomolecules include nucleic acids, proteins, sugars, lipids, and vitamins. Examples of nucleic acids include DNA and RNA. Examples of proteins include antigenic proteins, enzyme proteins, structural proteins, and adhesion proteins. Furthermore, target molecules include those that serve as biomarkers and are associated with changes in disease or responses to treatment, acting as indicators.

[0087] (2) Combining molecules

[0088] In this technique, a binding molecule refers to a molecule capable of detecting and / or analyzing the previously described target molecule by binding to it, and which can be selected by a person skilled in the art. Examples of binding molecules include molecules capable of detecting and / or analyzing the target molecule by binding to it in the various analyses previously described.

[0089] More specifically, binding molecules are molecules that specifically bind to a target molecule, and examples include biomolecules, drug molecules, high-molecular-weight compounds, and low-molecular-weight compounds. Examples of binding molecules include nucleic acids, artificial nucleic acids, proteins, peptides, sugars, lipids, and vitamins. Other examples of binding molecules include DNA, RNA, PNA, and LNA. Furthermore, examples of antibody-like molecules include antigens, cell surface markers, enzyme proteins, structural proteins, and adhesion proteins.

[0090] The method of analyzing target molecules using antibodies that bind to fluorescent dyes, which will be described later as marker molecules, is called fluorescent immunostaining. Examples of fluorescent immunostaining include immunocytochemistry (ICC) and immunohistochemistry (IHC). ICC is a method of staining cells isolated from tissue or cultured cells. IHC is a method of staining target molecules in tissue sections.

[0091] Furthermore, fluorescent immunostaining includes direct and indirect fluorescent immunostaining. Direct fluorescent immunostaining analyzes target molecules by directly binding an antibody to a fluorescent dye to the target molecule and detecting the fluorescent dye. In indirect fluorescent immunostaining, an antibody bound to a fluorescent dye (also called a secondary antibody) binds to an antibody specifically bound to the target molecule (also called a primary antibody), and the secondary antibody further binds to the target molecule. In other words, indirect fluorescent immunostaining analyzes target molecules by binding an antibody bound to a fluorescent dye (also called a secondary antibody) to the target molecule via a primary antibody and detecting the fluorescent dye.

[0092] This technique can be appropriately used to select binding and labeling molecules for use in fluorescent immunostaining.

[0093] (3) Labeled molecules

[0094] In this technique, a labeled molecule refers to a molecule that labels the previously described binding molecule, and the target molecule can be detected and / or analyzed by binding the binding molecule to the target molecule. Examples of labeled molecules include molecules that can be used as labeled molecules in the various analyses previously described.

[0095] More specifically, examples of labeled molecules are dyes. Examples of dyes include, but are not limited to, various fluorescent dyes having fluorescence wavelengths in the visible light range, such as fluorescent dyes in the Alexa Fluor (registered trademark) series, fluorescent dyes in the DyLight (registered trademark) series, fluorescent dyes in the BD Horizon Brilliant (registered trademark) series, and fluorescent dyes such as Atto, FITC, Cy3, Cy5, Cy5.5, Cy7, Rhodamine, PE (phycoerythrin), APC (allophycocyanin), and PerCP.

[0096] Furthermore, in this technique, the labeling molecule can be a molecule expressed as part of the target molecule or a binding molecule (such as a fluorescent protein included in a fluorescent fusion protein). Examples of fluorescent proteins include GFP, BFP, CFP, EGFP, EYFP, and PA-GFP.

[0097] (4) Signal acquisition unit 11

[0098] The signal acquisition unit 11 acquires signals from a sample including a biological sample. For example, the signal acquisition unit 11 acquires signals detected by the detection device 41, which is a flow cytometer, microscope, or various photodetectors.

[0099] The signal acquisition unit 11 can acquire not only signals detected by various detection devices 41, but also signal data stored in the database in the storage unit 16, which will be described later. For example, the signal acquisition unit 11 can acquire past detection data, data detected by other detection devices and accumulated in the database, etc.

[0100] (5) Processing Unit 12

[0101] Based on the signal acquired by the signal acquisition unit 11, when multiple different binding molecules labeled by different marker molecules are used, the processing unit 12 calculates the reactivity between the target molecule and each of the multiple different binding molecules.

[0102] In this technique, the signal can be the fluorescence signal itself, a specific signal / background, a specific signal / non-specific signal, etc. Furthermore, the fluorescence signal can be expressed in pixel units (e.g., signal / pixel) or in cell units (e.g., average fluorescence signal / cell or sum of fluorescence signals / cell).

[0103] The signals that form the basis for the calculations performed by the processing unit 12 include a first signal group and a second signal group. The signals may also include a third signal. Each signal group and the method for calculating responsiveness performed by the processing unit 12 will be described in detail below.

[0104] (a) First signal group

[0105] The first signal set is the set of signals obtained when each of a plurality of different binding molecules labeled with the same type of marker molecule reacts with the target molecule. For example, each of a plurality of binding molecules C1 to C5 (F1-C1, F1-C2, F1-C3, F1-C4, and F1-C5) labeled with marker molecule F1 reacts with the target molecule and obtains its signal.

[0106] Acquiring the first set of signals allows for the evaluation of the reactivity of the bound molecules C1 through C5 relative to the target molecule. In other words, the reactivity of different bound molecules C1 through C5 relative to the target molecule can be compared and these reactivity can be calculated. While there are no particular limitations on the metrics representing reactivity, anything that can evaluate reactivity is acceptable, but examples include fluorescence intensity, the number of bound molecules, absorbance, light scattering, phosphorescence, fluorescence lifetime, and quality.

[0107] Although there are no particular restrictions on the method for calculating the number of bound molecules, and general calculation methods can be used, for example, the antibody counting conversion method described in WO2020 / 022038 can be used to calculate the number of bound molecules.

[0108] Reference Figure 2 An example describing a method for calculating the number of bound molecules is provided. In step S1000, the user determines the labeling molecule, binding molecule, and target molecule to be used in the analysis. In step S1004, the user generates a stained sample by staining the target molecule using the labeling molecule and the binding molecule.

[0109] In step S1008, the signal acquisition unit 11 of the information processing device 1 images the stained sample to acquire captured image information. In step S1012, the signal acquisition unit 11 acquires reagent information such as fading coefficient, absorption cross-sectional area, quantum yield, and fluorescence labeling rate from the database in the storage unit 16 based on reagent identification information attached to the labeling molecules and binding molecules used to generate the stained sample. Furthermore, the signal acquisition unit 11 acquires the excitation power density, which has been measured separately.

[0110] In step S1016, processing unit 12 corrects the brightness of each pixel in the captured image information using fading coefficient, absorption cross-sectional area, excitation power density, etc. (performing fading correction processing). In step S1020, processing unit 12 converts the corrected brightness of each pixel into the number of photons. In step S1024, processing unit 12 converts the number of photons into the number of labeled molecules or the number of bound molecules that bind to the labeled molecules.

[0111] In step S1028, the processing unit 12 generates image information reflecting the number of labeled molecules or the number of binding molecules that bind to the labeled molecules. In step S1032, the display unit 17 displays the image information on the display, and the series of processes ends.

[0112] Furthermore, there are no particular limitations on the method for calculating fluorescence intensity, and general calculation methods can be used, for example, fluorescence intensity can be calculated using one or more values ​​selected from excitation efficiency, quantum yield, absorption efficiency, and fluorescence labeling rate (F / P value).

[0113] (b) Second signal group

[0114] The second signal set consists of signals acquired when each of the same type of binding molecules labeled with different marker molecules reacts with the target molecule. For example, each of the same type of binding molecules C1 (F1-C1, F2-C1, F3-C1, F4-C1, and F5-C1) labeled with different marker molecules F1 to F5 reacts with the target molecule and acquires its signal.

[0115] Obtaining the second signal set allows for the evaluation of differences in the reactivity of the bound molecules relative to the target molecule due to differences in the types of labeling molecules used to label the bound molecules. In other words, the reactivity of bound molecules labeled with different labeling molecules F1 to F5 with the target molecule can be compared and evaluated. While there are no particular limitations on the indicators representing reactivity, as long as reactivity can be evaluated in a manner similar to the first signal set, examples of such indicators include the number of bound molecules and fluorescence intensity.

[0116] Since the methods for calculating the number of bound molecules and the methods for calculating fluorescence intensity are similar to those previously described for the first signal group, their descriptions will be omitted here.

[0117] (c) Calculation of reactivity: Processing unit 12

[0118] In this technique, when multiple different binding molecules C2 to C5 are used, labeled with different marker molecules F2 to F5, the reactivity between the target molecule and each of the multiple different binding molecules C2 to C5 is calculated based on the first and second signal sets described above.

[0119] Specifically, since the first signal set can be used to evaluate the reactivity of the binding molecules C1 to C5 labeled by the labeled molecule F1 relative to the target molecule, for example, indicators representing the reactivity of the binding molecules C1 to C5 relative to the target molecule (e.g., the number of binding molecules or fluorescence intensity) can be displayed in a column of labeled molecule F1 in Table 1 below (refer to the vertical shaded lines in Table 1).

[0120] Furthermore, since the difference in the types of labeling molecules F1 to F5 that label the binding molecule C1 leads to differences in the reactivity of the binding molecule C1 relative to the target molecule, a second signal set can be used to evaluate this difference. For example, indicators representing the reactivity of the binding molecule C1 labeled with labeling molecules F1 to F5 relative to the target molecule (e.g., the number of binding molecules or fluorescence intensity) can be displayed in the row of the binding molecule C1 in Table 1 below (refer to the horizontal shaded line in Table 1).

[0121] Furthermore, when using multiple different binding molecules C2 to C5 labeled with different marker molecules F2 to F5, the reactivity between the target molecule and each of the multiple different binding molecules C2 to C5 is calculated based on a first signal set and a second signal set. Specifically, for example, based on the reactivity of binding molecule C1 labeled with marker molecule F1 relative to the target molecule and the reactivity of binding molecule C2 labeled with marker molecule F1 relative to the target molecule, a coefficient is estimated to represent the difference in reactivity between binding molecules C1 and C2 relative to the target molecule. Based on the reactivity of binding molecule C1 labeled with marker molecule F1 relative to the target molecule and the reactivity of binding molecule C1 labeled with marker molecule F2 relative to the target molecule, a coefficient is estimated to represent the difference in reactivity between the target molecule and binding molecule C1 when binding molecule C1 is labeled with marker molecule F1 and when binding molecule C1 is labeled with marker molecule F2. By applying these coefficients, the reactivity of binding molecule C2 labeled with marker molecule F2 relative to the target molecule can be calculated.

[0122] Using a similar method, a matrix can be built for each reactivity by calculating the reactivity with respect to the spatial domains in Table 1 below, when using multiple different binding molecules C1 to C5 labeled with different marker molecules F1 to F5.

[0123] [Table 1]

[0124]

[0125] The reactivity indices for the first signal group and the second signal group can differ from each other. For example, the following describes a specific example of a method for calculating the reactivity indices between the target molecule and the binding molecule in a combination of unmeasured labeled and binding molecules when the number of binding molecules is used for the first signal group and the fluorescence intensity is used for the second signal group.

[0126] The number of binding molecules calculated based on the capture images obtained when multiple different binding molecules C1 to C3, labeled with the same molecule F1, react with the target molecule is shown in Table 2 below as the first signal group.

[0127] [Table 2]

[0128] [Number of bound molecules]

[0129]

[0130] The fluorescence intensities calculated based on capture images obtained when the same type of binding molecule C1, labeled with different marker molecules F1 to F4, reacts with the target molecule are shown in Table 3 below as a second signal set. In this case, the signal / background ratio of the fluorescence intensity can also be used, as will be described later.

[0131] [Table 3]

[0132] [Fluorescence Intensity]

[0133]

[0134] In this case, for example, when fluorescence intensity is used as an indicator of reactivity, although the fluorescence intensity of the labeled molecule F4 and the bound molecule C3 is not actually measured, the fluorescence intensity can be calculated according to the following expression.

[0135] The reactivity index (fluorescence intensity) of F4-C3 = (number of F1-C3 bound molecules / number of F1-C1 bound molecules) × (fluorescence intensity of F4-C1)

[0136] Using a similar method, by calculating indices of reactivity with respect to the spatial domains in Table 3 above, a matrix can be generated relative to each fluorescence intensity when multiple different binding molecules C1 to C3 are labeled with different labeled molecules F1 to F4.

[0137] By referring to the matrix generated in this way, the optimal combination of binding and labeling molecules can be selected when choosing the binding and labeling molecules to use for the detection and / or analysis of target molecules. Therefore, the accuracy of target molecule detection and / or analysis can be improved.

[0138] As described above, in this technology, since the reactivity of each of the unmeasured types of labeled molecules F2 to F5 and the types of bound molecules C2 to C5 can be calculated based on the first and second signal sets, it is not necessary to actually measure all combinations of bound and labeled molecules. In other words, in this technology, the reactivity of each of the types of labeled molecules and the types of bound molecules can be calculated from a small amount of actually measured data. In other words, the processing unit 12 can calculate the reactivity of combinations of unmeasured labeled and bound molecules with the target molecule.

[0139] Furthermore, this technology allows users to avoid the hassle of selecting binding and labeling molecules when detecting and / or analyzing target molecules, and prevents the accuracy of target molecule detection and / or analysis from varying based on user experience.

[0140] (d) Third signal

[0141] The third signal is the signal obtained when a negative control is used. Examples of signals obtained when a negative control is used include signals obtained when reacting with an unlabeled binding molecule, signals obtained from unstained samples that do not use a binding molecule, and signals obtained when using an isotype control antibody.

[0142] The background in each detection channel can be calculated based on the third signal. Specifically, the background in each detection channel can be calculated as leakage of autofluorescence signal, leakage of signals from other labeled molecules (such as signals from other fluorescent dyes), etc.

[0143] In calculating the background in each detection channel, leakage from labeled molecules in channels other than the given channel is preferably considered. As leakage from other channels, for example, the sum of leakage from corresponding labeled molecules can be used, calculated from a second signal set (the signal set obtained when each reaction occurs in the same type of binding molecule labeled by different types of labeled molecules). Furthermore, for example, leakage to the given channel can be calculated when binding molecules are labeled by all labeled molecules in channels other than the given channel.

[0144] The first signal group, the second signal group, and the third signal can utilize at least one standardized detection quantity selected from excitation power density, exposure time, and detection device sensitivity. For example, when using ordinary equipment or when inter-equipment calibration can be performed by management before shipment, errors caused by degradation over time or in the operating environment can be calibrated during routine maintenance, either between or within the equipment. However, even when using different equipment or when inter-equipment or intra-equipment calibration cannot be performed before shipment or during routine maintenance, measurement conditions can be met by performing standardization using at least one selected from excitation power density, exposure time, and detection device sensitivity.

[0145] Furthermore, as will be described later, by accumulating data from the first signal group, the second signal group, the third signal, the background, etc., and creating a database, the accuracy of inter-device calibration can be further improved based on the trend of inter-device errors (correction coefficients).

[0146] In this technique, as previously described, while fluorescence intensity can be used as an indicator of reactivity, the signal-to-background ratio of fluorescence intensity is preferred. Since noise can be removed by using the signal-to-background ratio of fluorescence intensity as an indicator of reactivity, more accurate detection and / or analysis can be performed.

[0147] (e) Selection of combinations of labeled and binding molecules: Processing unit 12

[0148] In this technique, a preferred combination of labeling and binding molecules for the target molecule can be selected based on the calculated reactivity between the target molecule and the binding molecule.

[0149] For example, when using the signal-to-background ratio of fluorescence intensity as an indicator of reactivity, combinations of labeled and binding molecules can be selected that result in a signal-to-background ratio equal to or exceeding a threshold. Therefore, all labeled and bound molecules can be detected. Furthermore, combinations of labeled and binding molecules that maximize the sum of the signal-to-background ratios of fluorescence intensity can be selected. Consequently, detection becomes easier due to the increased signal-to-background ratio. Moreover, combinations of labeled and binding molecules can be selected by combining the above methods.

[0150] Furthermore, for example, a combination of labeled and binding molecules can be selected that maximizes the sum of the differences between the fluorescence intensity signal and the background fluorescence intensity. Therefore, detection becomes easier because a signal above the background intensity is obtained.

[0151] Furthermore, the combination of labeled and binding molecules can be selected based on the magnitude of the fluorescence signal. Therefore, since the autofluorescence of samples that become background tends to exhibit high brightness on the shorter wavelength side, the overall signal / background ratio can be increased.

[0152] Furthermore, combinations of labeled and binding molecules can be selected such that the label is assigned to binding molecules in ascending order of their fluorescence intensity signals, following a descending order of the signal intensity. Therefore, conditions can be set to facilitate the sequential detection of difficult-to-detect binding molecules and combinations equal to or below the detection limit can be eliminated.

[0153] Furthermore, combinations of labeled and binding molecules can be selected, such that the label is assigned in ascending order of detection wavelength length to binding molecules in descending order of the values ​​of the first signal group. In this case, when multiple candidates exist, a combination that provides a larger calculated index value can be selected.

[0154] (6) Evaluation Unit 13

[0155] The information processing apparatus 1 according to the present technology may further include an evaluation unit 13, which estimates the importance of the binding molecules and / or labeled molecules relative to the target molecules based on image information. Specifically, in the evaluation unit 13, the importance of the binding molecules and / or labeled molecules relative to the target molecules is estimated, such as determining whether to increase brightness based on the type of binding molecules or labeled molecules, considering how to set the threshold, and reducing the background when the signal is weak to make it easier to pick up the target signal.

[0156] (7) Output Unit 14

[0157] The information processing apparatus 1 according to the present technology may include an output unit 14 that outputs various types of information. In addition to the information processed by the aforementioned processing unit 12 and various information related to the calculation of reactivity, including various signals and various thresholds, the output unit 14 may also output all data including various information related to the detection performed for the calculation of reactivity.

[0158] Specifically, output unit 14 can output a matrix of combinations of bound molecules and labeled molecules calculated as described above, or combinations of bound molecules and labeled molecules selected as described above. Furthermore, output unit 14 can also output the importance of the bound molecules and / or labeled molecules relative to the target molecule, as estimated by evaluation unit 13.

[0159] (8) Presentation Unit 15

[0160] The presentation unit 15 presents support information about the combination of binding molecules and labeled molecules to the user based on the output reactivity. Generally, since the user selects the combination of labeled and binding molecules relative to the target molecule, inappropriate combinations can occur, and this selection is an extremely time-consuming task, even for experienced users. However, according to this technology, because the optimal combination of labeled and binding molecules is presented based on the reactivity calculated by the processing unit 12 and output by the output unit 14, according to factors such as the type of target molecule being detected or analyzed, and the state of the sample, detection with high accuracy can be performed regardless of the user's experience.

[0161] In this technology, the presentation unit 15 is not necessary, and the labeling device or the like can automatically label the target molecule without involving the user. For example, the combination of binding molecules and labeled molecules output by the output unit 14 can be directly output to various labeling devices, and based on the combination of binding molecules and labeled molecules obtained by various labeling devices, various labeling devices can also automatically label the target molecule using the combination of binding molecules and labeled molecules that is optimal for the target molecule.

[0162] (9) Storage unit 16

[0163] The information processing apparatus 1 according to the present technology may include a storage unit 16 for storing various types of information. The storage unit 16 may accumulate and store all types of data, including various information related to the calculation of reactivity (including information processed by the processing unit 12), various signals and various thresholds, and various information related to the detection performed for the calculation of reactivity.

[0164] Storage unit 16 is not required for the information processing apparatus 1 according to the present invention, and each piece of information can be output from output unit 14 to the outside of the apparatus and stored in an external storage device 21, such as that described later. Storage device 21 can be located in a cloud environment and connected to the information processing apparatus 1 according to the present invention via a network. In this case, various information stored in storage device 21 in the cloud can also be shared among multiple users.

[0165] Based on the information output by output unit 14, as described later... Figure 7 The detection data from external detection devices 41A to 41D, as well as detection data collected from other samples, can be used to construct a database in storage unit 16 or external storage device 21. In this case, processing unit 12 can perform various processes by referring to the database. Specifically, processing unit 12 can refer to a database accumulating first signal groups, second signal groups, third signals, background, and various calculated data. For example, by referring to past performance data, detection data from external detection devices 41A to 41D, detection data collected from other samples, etc., even if the user does not actually perform the measurements, the reactivity between the target molecule and the binding molecule for each type of labeled molecule and each type of binding molecule can be calculated, and a matrix of combinations of binding molecules and labeled molecules can be created.

[0166] Furthermore, by calculating correction coefficients using various signals aggregated in the database, the accuracy of the correction coefficients improves as the number of data entries increases.

[0167] Furthermore, the accuracy of recommended combinations can be improved by referencing a database that accumulates matrices of combinations of binding molecules and labeled molecules, and by using the results of detection and / or analysis of target molecules to validate combinations of binding molecules and labeled molecules.

[0168] (10) Display unit 17

[0169] The information processing apparatus 1 according to this technology may include a display unit 17 that displays various types of information output by the output unit 14. For example, a common display device such as a monitor or printer may be used as the display unit 17.

[0170] (11) User Interface 18

[0171] The information processing apparatus 1 according to this technology may further include a user interface 18 operated by a user. Through the user interface 18, the user can access and control each unit.

[0172] In this technology, the user interface 18 is not required and can be connected to an external operating device. For example, a mouse or keyboard can be used as the user interface 18.

[0173] <2. Information Processing System 2>

[0174] Figure 3 This is a conceptual diagram illustrating an example of an information processing system 2 according to the present invention. The information processing system 2 according to the present invention includes the information processing apparatus 1 described previously and a storage device 21 storing information calculated by the information processing apparatus 1. Furthermore, as needed, the information processing system 2 according to the present invention may include an evaluation device 24, a display device 22, and a user interface 23. Since the details of the information processing apparatus 1 are the same as those of the information processing apparatus 1 described previously according to the present invention, their description will be omitted here. Similarly, since the details of the evaluation device 24, storage device 21, display device 22, and user interface 23 are also the same as those of the evaluation unit 13, storage unit 16, display unit 17, and user interface 18 of the information processing apparatus 1 according to the present invention described previously, their description will be omitted here.

[0175] <3. Biological Sample Detection Device 3, Biological Sample Detection System 4>

[0176] Figure 4 This is a block diagram illustrating an example of a biological sample detection device 3 according to the present invention. The biological sample detection device 3 according to the present invention includes a detection unit 31, a signal acquisition unit 11, a processing unit 12, and an output unit 14. Furthermore, the biological sample detection device 3 according to the present invention may, as needed, include a marking unit 32, an evaluation unit 13, a presentation unit 15, a storage unit 16, a display unit 17, a user interface 18, an analysis unit 33, etc. Since the details of the signal acquisition unit 11, processing unit 12, output unit 14, evaluation unit 13, presentation unit 15, storage unit 16, display unit 17, and user interface 18 are the same as those of the previously described information processing device 1 according to the present invention, their description will be omitted here.

[0177] Figure 5This is a conceptual diagram illustrating an example of a biological sample detection system 4 according to the present invention. The biological sample detection system 4 according to the present invention includes the detection device 41 and information processing device 1 described previously. Furthermore, as needed, the biological sample detection system 4 according to the present invention may also include a marking unit 32 or marking device 42, an evaluation unit 13 or evaluation device 24, a storage unit 16 or storage device 21, a display device 22, a user interface 23, an analysis unit 33 or analysis device 43, etc. Since the details of the information processing device 1 are the same as those of the previously described information processing device 1, its description will be omitted here. Furthermore, since the details of the evaluation unit 13 or evaluation device 24, the storage unit 16 or storage device 21, the display device 22, and the user interface 23 are the same as those of the evaluation unit 13, storage unit 16, display unit 17, and user interface 18 of the previously described information processing device 1 according to the present invention, their description will also be omitted here.

[0178] Figure 6 It shows something different Figure 5 A conceptual diagram of an example of a biological sample detection system 4 according to the present technology. The biological sample detection system 4 according to the present technology includes a detection device 41 and a computer program, which will be described later.

[0179] (1) Detection unit 31, detection device 41

[0180] The detection unit 31 and the detection device 41 detect signals emitted from target molecules labeled with binding molecules that are also labeled with the marker molecules. The combination of the marker molecules and the binding molecules used to label the target molecules can be selected based on the reactivity output from the output unit 14.

[0181] As the detection unit 31 and detection device 41 that can be used in this technology, general-purpose detection units and devices can be freely used, as long as the signal emitted from the bound molecule can be detected. For example, detection units and devices that can be used in analyses such as flow cytometry, microscopic observation, Western blotting, various arrays, and ELISA can be used.

[0182] In the biological sample detection system 4 according to the present technology, the information processing device 1 and / or storage device 21 can be located in a cloud environment connected to the detection device 41 via a network. In this case, various information stored in the storage device 21 in the cloud can also be shared among multiple users. Specifically, for example, such as Figure 7As shown, multiple detection devices 41A to 41D can be connected to information processing device 1 and / or storage device 21 via a network. Information processing device 1 can use the signals detected by multiple detection devices 41A to 41D to perform processing, and the reactivity matrix of the combination of labeled molecules and bound molecules output from information processing device 1 can be shared among multiple detection devices 41A to 41D.

[0183] (2) Marking unit 32, marking device 42

[0184] Labeling unit 32 and labeling device 42 use bound molecules already labeled with labeled molecules to label target molecules within a biological sample. Based on the reactivity matrix of the combination of labeled and bound molecules output from output unit 14, labeling unit 32 and labeling device 42 can use the optimal combination of labeled and bound molecules to label the target molecule. As a result, the accuracy of target molecule detection can be improved.

[0185] In the biological sample detection device 3 and biological sample detection system 4 according to the present technology, the labeling unit 32 and the labeling device 42 are not necessary, and the target molecule can be labeled using an external labeling device or the like based on the reactivity matrix of the combination of labeled molecules and bound molecules output from the output unit 14.

[0186] (3) Analysis unit 33, analysis device 43

[0187] The analysis unit 33 and the analysis device 43 analyze the sample based on the signal detected by the detection unit 31 or the detection device 41. More specifically, the analysis can be based on the signal detected by the detection unit 31 or the detection device 41, including the type, amount, characteristics, etc. of target molecules in the sample.

[0188] The analysis unit 33 and analysis device 43 are not essential in the biological sample detection device 3 and biological sample detection system 4 according to the present technology, and the characteristics of target molecules inside the sample can also be analyzed using external analysis devices based on the signals detected by the detection unit 31 or detection device 41. For example, the analysis unit 33 and analysis device 43 can be implemented in a personal computer or CPU, or can be stored as a program in hardware resources including recording media (e.g., non-volatile memory (USB memory), HDD or CD), and can be activated by a personal computer or CPU. Furthermore, the analysis unit 33 and analysis device 43 can be connected to each unit of the biological sample detection device 3 and biological sample detection system 4 via a network.

[0189] <4. Computer Programs>

[0190] The computer program according to the present technology is a computer program that enables a computer to: acquire a signal from a sample including a biological sample; process a signal to calculate the reactivity between a target molecule and each of the plurality of different binding molecules when using a plurality of different binding molecules labeled by different marker molecules; and output a reactivity, wherein the signal includes a first signal set and a second signal set.

[0191] The computer program according to this technology is recorded on a suitable recording medium. Furthermore, the computer program according to this technology can be stored in a cloud environment or the like and downloaded to a personal computer or the like via a network for user use. Since the signal acquisition function, processing function, and output function in the computer program according to this technology are the same as the functions performed by the signal acquisition unit 11, processing unit 12, and output unit 14 of the information processing apparatus 1 described above, their description will be omitted here.

[0192] <5. Methods for the Analysis of Biological Samples>

[0193] Figure 8 This is a flowchart illustrating an example of a biological sample analysis method according to the present technology. The biological sample analysis method according to the present technology involves at least performing a signal acquisition step S1, a processing step S2, and an output step S3. Furthermore, if necessary, a presentation step S4 and (although not shown) evaluation, storage, and display steps may also be performed. Since the signal acquisition step S1, processing step S2, output step S3, presentation step S4, evaluation step, storage step, and display step are the same as those performed by the signal acquisition unit 11, processing unit 12, output unit 14, presentation unit 15, evaluation unit 13, storage unit 16, and display unit 17 of the information processing apparatus 1 described previously, their description will be omitted here.

[0194] <6. Application Examples>

[0195] For example, this technology can be applied to microscope system 5000 and biological sample analysis device 6100.

[0196] [Microscope System 5000]

[0197] Figure 9 An example configuration of a microscope system 5000 according to this disclosure is shown. Figure 9The microscope system 5000 shown includes a microscope device 5100, a control unit 5110, and an information processing unit 5120. The microscope device 5100 includes a light illumination unit 5101, an optical unit 5102, and a signal acquisition unit 5103. The microscope device 5100 may further include a sample placement unit 5104 on which a sample S derived from a living organism is placed. It should be noted that the configuration of the microscope device 5100 is not limited to... Figure 9 In the illustrated configuration, for example, the light illumination unit 5101 may be located outside the microscope apparatus 5100, and a light source not included in the microscope apparatus 5100 may be used as the light illumination unit 5101. Furthermore, the light illumination unit 5101 may be arranged such that the sample placement unit 5104 is sandwiched between the light illumination unit 5101 and the optical unit 5102, and may be arranged, for example, on the side where the optical unit 5102 is located. The microscope apparatus 5100 may be configured to perform one or more of the following: bright-field observation, phase-difference observation, differential interference observation, polarization observation, fluorescence observation, and dark-field observation.

[0198] The microscope system 5000 can be configured as a so-called WSI (whole slide imaging) system or a digital pathology imaging system, and can be used for pathological diagnosis. Furthermore, the microscope system 5000 can be configured as a fluorescence imaging system, and specifically, as a multiplex fluorescence imaging system.

[0199] For example, the microscope system 5000 can be used to perform intraoperative or remote pathological diagnosis. In intraoperative pathological diagnosis, during surgery, the microscope device 5100 acquires data from a sample S derived from living tissue obtained from the surgical subject and transmits this data to the information processing unit 5120. In remote pathological diagnosis, the microscope device 5100 can transmit the acquired sample S derived from living tissue to the information processing unit 5120 located separately from the microscope device 5100 (in another room, another building, etc.). Furthermore, in these diagnoses, the information processing unit 5120 receives and outputs data. The user of the information processing unit 5120 can perform pathological diagnosis based on the output data.

[0200] (Sample S derived from a living organism)

[0201] The sample S derived from a living organism can be a sample including biological materials. Biological materials can be living tissues or cells, living liquid components (blood, urine, etc.), cultures, or living cells (cardiomyocytes, nerve cells, fertilized eggs, etc.).

[0202] The specimen S derived from a living organism can be a solid object, and can be a sample fixed by a immobilization agent such as paraffin or a solid object formed by freezing. The specimen S derived from a living organism can be a slice of a fixed object. A specific example of a specimen S derived from a living organism is a slice of a biopsy specimen.

[0203] The living sample S can be treated with methods such as staining or labeling. Treatment may include staining to indicate the form of the biological material or to indicate substances included in the biological material (e.g., surface antigens), and examples of staining may include HE (hematoxylin-eosin) staining and immunohistochemical staining. The living sample S can be treated with one or more reagents as described above, and the reagents may be fluorescent dyes, staining agents, fluorescent proteins, or fluorescently labeled antibodies.

[0204] Samples can be prepared from tissue samples for purposes such as pathological diagnosis and clinical examination. Besides being derived from the human body, samples can be derived from animals, plants, or other materials. The characteristics of a sample vary depending on the type of tissue to be used (such as organs or cells), the type of disease being treated, the attributes of the subject (e.g., age, sex, blood type, or ethnicity), or the subject's lifestyle (e.g., diet, exercise habits, or smoking habits). Samples can be managed by attaching identification information that allows each sample to be identified (barcodes, QR codes (registered trademarks), etc.).

[0205] (Light Illumination Unit 5101)

[0206] The light illumination unit 5101 comprises a light source for illuminating the sample S originating from a living organism and an optical unit for guiding the light from the light source to the sample. The light source can illuminate the sample S originating from a living organism using visible light, ultraviolet light, infrared light, or a combination thereof. The light source can be one or more of a halogen light source, a laser light source, an LED light source, a mercury light source, and a xenon light source. Multiple types and / or wavelengths of light sources can be set for fluorescence observation, and can be appropriately selected by those skilled in the art. The light illumination unit 5101 can have a transmission type configuration, a reflection type configuration, or a vertical illumination type configuration (coaxial vertical illumination type or side illumination type).

[0207] (Optical Unit 5102)

[0208] The optical unit 5102 is configured to guide light from the living sample S to the signal acquisition unit 5103. The optical unit 5102 may be configured to enable the microscope device 5100 to observe or image the living sample S.

[0209] Optical unit 5102 may include an objective lens. Those skilled in the art can appropriately select the type of objective lens according to the observation system. Furthermore, optical unit 5102 may include a relay lens for relaying the image magnified by the objective lens to signal acquisition unit 5103. Optical unit 5102 may also include optical components other than the objective lens and relay lens, such as an eyepiece, a phase plate, and a condenser lens.

[0210] Furthermore, the optical unit 5102 may also include a wavelength separation unit configured to separate light having a predetermined wavelength from light originating from the living sample S. The wavelength separation unit may be configured to selectively direct light having a predetermined wavelength or a predetermined wavelength range to the signal acquisition unit 5103. The wavelength separation unit may include one or more of a filter, polarizer, prism (Wollaston prism), and diffraction grating for selectively transmitting light. The optical components included in the wavelength separation unit may be arranged, for example, in the optical path from the objective lens to the signal acquisition unit 5103. When performing fluorescence observation, and specifically, when an excitation light irradiation unit is included, the wavelength separation unit is disposed within the microscope apparatus 5100. The wavelength separation unit may be configured to separate fluorescent beams from each other or to separate white light and fluorescence from each other.

[0211] (Signal acquisition unit 5103)

[0212] The signal acquisition unit 5103 can be configured to receive light from a living sample S and convert the light into an electrical signal, specifically, a digital electrical signal. The signal acquisition unit 5103 can be configured to acquire data related to the living sample S based on the electrical signal. The signal acquisition unit 5103 can be configured to acquire data of an image (specifically, a still image, a time-lapse image, or a motion image) of the living sample S, and particularly, to acquire data of an image magnified by the optical unit 5102. The signal acquisition unit 5103 includes one or more imaging elements such as a CMOS or CCD, which have multiple pixels arranged in a one-dimensional or two-dimensional manner. The signal acquisition unit 5103 can include imaging elements for acquiring low-resolution images and imaging elements for acquiring high-resolution images, or it can include sensing imaging elements for AF (auto-sensing) and image output imaging elements for observation, etc. In addition to multiple pixels, the imaging element may also include a signal processing unit (including one or more of a CPU, DSP, and memory) and an output control unit. The signal processing unit performs signal processing using pixel signals from each pixel, and the output control unit controls the output of image data generated from the pixel signals and the processed data generated by the signal processing unit. The imaging element, signal processing unit, and output control unit, which include multiple pixels, may preferably be configured as a one-chip semiconductor device.

[0213] The microscope system 5000 may further include an event detection sensor. The event detection sensor can be configured to include pixels that photoelectrically convert incident light and detect brightness changes in pixels exceeding a predetermined threshold as events. Specifically, the event detection sensor may be an asynchronous sensor.

[0214] (Control Unit 5110)

[0215] Control unit 5110 controls the imaging of microscope apparatus 5100. For imaging control purposes, control unit 5110 can adjust the positional relationship between optical unit 5102 and sample placement unit 5104 by driving the movement of optical unit 5102 and / or sample placement unit 5104. Control unit 5110 can move optical unit 5102 and / or sample placement unit 5104 in directions that bring the units closer to or further apart from each other (e.g., in the optical axis direction of the objective lens). Furthermore, control unit 5110 can move optical unit 5102 and / or sample placement unit 5104 in any direction on a plane perpendicular to the optical axis direction. For imaging control purposes, control unit 5110 can control light illumination unit 5101 and / or signal acquisition unit 5103.

[0216] (Sample placement unit 5104)

[0217] Additionally, the sample placement unit 5104 can be configured to fix the position of the sample S originating from a living organism on the sample placement unit 5104, and can also be a so-called stage. The sample placement unit 5104 can be configured to move the position of the sample S originating from a living organism in the direction of the optical axis of the objective lens and / or in a direction perpendicular to the optical axis.

[0218] (Information Processing Unit 5120)

[0219] The information processing unit 5120 can acquire data (such as imaging data) acquired by the microscope device 5100. The information processing unit 5120 can perform image processing on the imaging data. Image processing may include unmixing, and specifically, spectral unmixing. Unmixing may include processing to extract data of optical components having a predetermined wavelength or wavelength range from the imaging data to generate image data, processing to remove data of optical components having a predetermined wavelength or wavelength range from the imaging data, etc. Additionally, image processing may include autofluorescence unmixing processing to separate autofluorescent components and pigment components from tissue sections, or fluorescence unmixing processing to separate wavelengths between pigments having different fluorescence wavelengths. In autofluorescence unmixing processing, a process may be performed in which an autofluorescent signal extracted from one of a plurality of samples having the same or similar characteristics is used to remove the autofluorescent component from the image information of another sample.

[0220] The information processing unit 5120 can transmit data for imaging control to the control unit 5110, and the control unit 5110, having received the data, can control the imaging of the microscope device 5100 according to the data.

[0221] The information processing unit 5120 may be configured as an information processing device (such as a general-purpose computer) and may include a CPU, RAM, and ROM. The information processing unit 5120 may be included within the housing of the microscope apparatus 5100 or may be disposed outside the housing. Furthermore, various processing or functions of the information processing unit 5120 may be implemented via a server computer or the cloud connected to a network.

[0222] Those skilled in the art can appropriately select a system for imaging a living specimen S using a microscope device 5100, depending on the type of specimen S and the object being imaged. An example of an imaging system will be described below.

[0223] An example of the imaging system is as follows. First, the microscope device 5100 can specify the imaging target area. The imaging target area can be specified as the entire area covering the sample S originating from a living organism, or it can be specified as covering a target portion (target tissue section, target cell, or target lesion) within the sample S originating from a living organism. Next, the microscope device 5100 divides the imaging target area into multiple subdivisions of predetermined size, and the microscope device 5100 images each subdivision in turn. Thus, images of each subdivision are acquired.

[0224] like Figure 10 As shown, the microscope apparatus 5100 defines an imaging target region R covering the entire sample S derived from a living organism. Furthermore, the microscope apparatus 5100 divides the imaging target region R into 16 subdivision regions. The microscope apparatus 5100 can image subdivision regions R1, and then image any region included in the imaging target region R (such as regions adjacent to subdivision regions R1). Furthermore, imaging of the subdivision regions is performed until no more subdivision regions remain unimaged. It should be noted that regions outside the imaging target region R can also be imaged based on the captured image information from the subdivision regions.

[0225] To image the next segmented region after imaging a given segmented region, the positional relationship between the microscope device 5100 and the sample placement unit 5104 is adjusted. This adjustment can be performed by moving the microscope device 5100, moving the sample placement unit 5104, or both. In this example, the imaging device for imaging each segmented region can be a two-dimensional imaging element (region sensor) or a one-dimensional imaging element (line sensor). The signal acquisition unit 5103 can image each segmented region via the optical unit 5102. Furthermore, imaging of each segmented region can be performed continuously while moving the microscope device 5100 and / or the sample placement unit 5104, or the movement of the microscope device 5100 and / or the sample placement unit 5104 can be stopped while imaging each segmented region. The imaging target area can be divided such that portions of the segments overlap, or the imaging target area can be divided such that portions of the segments do not overlap. By changing the imaging conditions (e.g., focal length and / or exposure time), each segmented region can be imaged multiple times.

[0226] Furthermore, the information processing device can generate image data over a wider area by stitching together multiple adjacent segmented regions. By performing stitching processing over the entire imaging target area, a wider image of the imaging target area can be obtained. Additionally, lower-resolution image data can be generated from images of segmented regions or from stitched images.

[0227] Another example of an imaging system is as follows. First, the microscope apparatus 5100 can specify the imaging target area. The imaging target area can be specified as the entire area covering the specimen S originating from a living organism, or it can be specified as covering a target portion (target tissue section, target cell, or target lesion) within the specimen S originating from a living organism. Next, the microscope apparatus 5100 images the area (also called "divided scan area") that constitutes part of the imaging target area by scanning in a direction (also called "scanning direction") perpendicular to the optical axis. When the scan of the divided scan area is completed, the divided scan areas adjacent to the scan area are then scanned. These scanning operations are repeated until the entire imaging target area is imaged.

[0228] like Figure 11 As shown, the microscope apparatus 5100 designates the area (gray area) containing tissue sections in the specimen S derived from a living organism as the imaging target area Sa. Furthermore, the microscope apparatus 5100 scans the imaging target area Sa in the Y-axis direction, dividing it into scanning regions Rs. Once the scanning of the divided scanning regions Rs is complete, the microscope apparatus 5100 then scans adjacent divided scanning regions in the X-axis direction. These operations are repeated until the entire imaging target area Sa is scanned.

[0229] To scan each divided scanning region and to image the next divided scanning region after imaging a given divided scanning region, the positional relationship between the microscope device 5100 and the sample mounting unit 5104 is adjusted. This adjustment can be performed by moving the microscope device 5100, moving the sample mounting unit 5104, or both. In this example, the imaging device for imaging each divided scanning region can be a one-dimensional imaging element (line sensor) or a two-dimensional imaging element (area sensor). The signal acquisition unit 5103 can image each divided region via a magnifying optical system. Furthermore, imaging of each divided scanning region can be performed continuously while moving the microscope device 5100 and / or the sample mounting unit 5104. The imaging target area can be divided such that portions of the respective divided scanning regions overlap, or the imaging target area can be divided such that portions of the respective divided scanning regions do not overlap. Each divided scanning region can be imaged multiple times by changing imaging conditions such as focal length and / or exposure time.

[0230] Furthermore, the information processing device can generate image data over a wider area by stitching together multiple adjacent, segmented scanning regions. By performing stitching processing over the entire imaging target area, a wider image can be acquired for the imaging target area. Additionally, lower-resolution image data can be generated from images of segmented scanning regions or from stitched images.

[0231] [Biosample Analysis Apparatus 6100]

[0232] Figure 12 An example configuration of a biological sample analysis apparatus 6100 according to the present technology is shown. Figure 12 The biosample analysis apparatus 6100 shown includes: a light irradiation unit 6101 for irradiating a biosample S flowing through a flow path C; a detection unit 6102 for detecting the light generated by irradiating the biosample S; and an information processing unit 6103 for processing information related to the light detected by the detection unit 6102. Examples of the biosample analysis apparatus 6100 include flow cytometers and imaging cytometers. The biosample analysis apparatus 6100 may include a sorting unit 6104 for sorting specific biological particles P in the biosample S. Examples of the biosample analysis apparatus 6100 including a sorting unit include a cell sorter.

[0233] (Biological sample S)

[0234] The biological sample S can be a liquid sample comprising biological particles P. Biological particles P can be, for example, cellular or non-cellular biological particles. Cells can be living cells, and more specific examples include blood cells such as red blood cells and white blood cells, and germ cells such as sperm and fertilized eggs. Furthermore, cells can be collected directly from a sample (e.g., whole blood), or can be cultured cells obtained after culturing. Examples of non-cellular biological particles include extracellular vesicles, and particularly exosomes and microvesicles. Biological particles P can be labeled with one or more labeling substances (such as pigments (specifically, fluorescent dyes) and antibodies labeled with fluorescent dyes). The biological sample analysis apparatus according to this disclosure can analyze particles other than biological particles P, and can analyze beads, etc., for calibration, etc.

[0235] (Flow path C)

[0236] Flow path C is configured to allow the biological sample S to flow through it. Specifically, flow path C is configured to form a flow comprising biological particles P, which are arranged in a substantially straight line, within the biological sample S. The flow path structure including flow path C can be designed to form laminar flow. Specifically, the flow path structure is designed such that the flow forming the biological sample S (sample flow) is surrounded by a flow of sheath fluid, creating a laminar flow. The design of the flow path structure can be suitably chosen by those skilled in the art and can employ known designs. Flow path C can be formed as a flow path structure such as a microchip (a chip with micron-scale flow paths) or a flow cell. The width of flow path C is 1 mm or less and can be particularly 10 μm or greater and 1 mm or less. Flow path C and the flow path structure including flow path C can be formed from materials such as plastic or glass.

[0237] The biosample analysis apparatus 6100 according to the present invention is configured such that a biosample S flowing inside a flow path C is irradiated with light from an irradiation unit 6101, and specifically, bioparticles P inside the biosample S. The biosample analysis apparatus 6100 according to the present invention can be configured such that the light irradiation point relative to the biosample S is inside the flow path structure forming the flow path C, or configured such that the light irradiation point is outside the flow path structure. Examples of the former include a configuration in which the flow path C inside a microchip or flow cell is irradiated with light. In the latter case, the bioparticles P are irradiated with light after leaving the flow path structure (specifically, its nozzle portion), and examples include an air jet flow cytometer.

[0238] (Light Illumination Unit 6101)

[0239] The light illumination unit 6101 includes a light source unit that emits light and a light-guiding optical system that guides the light to the illumination point. The light source unit includes one or more light sources. Examples of light source types include laser light sources and LED light sources. The wavelength of the light emitted from each light source can be any of the wavelengths of ultraviolet, visible, and infrared light. The light-guiding optical system includes optical components such as a beam splitter group, a mirror group, or optical fibers. Furthermore, the light-guiding optical system may include a lens group for collecting light and includes, for example, an objective lens. There may be one or more illumination points where the biological sample S and the light intersect each other. The light illumination unit 6101 can be configured to collect light irradiated from one or more different light sources for a single illumination point.

[0240] (Detection unit 6102)

[0241] Detection unit 6102 includes at least one photodetector for detecting light generated by light irradiating the biological particle P. The light to be detected is, for example, fluorescence or scattered light (e.g., one or more of forward-scattered, back-scattered, and side-scattered light). Each photodetector includes one or more light-receiving elements and has, for example, an array of light-receiving elements. Each photodetector may include one or more PMTs (photomultiplier tubes) and / or photodiodes such as APDs and MPPCs as light-receiving elements. The photodetector includes, for example, a PMT array, wherein multiple PMTs are arranged in a one-dimensional direction. Furthermore, detection unit 6102 may include an imaging element such as a CCD or CMOS. Using the imaging element, detection unit 6102 can acquire images of the biological particle P (e.g., bright-field images, dark-field images, fluorescence images, etc.).

[0242] The detection unit 6102 includes a detection optical system that directs light with a predetermined detection wavelength to a corresponding photodetector. The detection optical system includes a beam-splitting unit such as a prism or diffraction grating, or a wavelength-separating unit such as a dichroic mirror or filter. The detection optical system is configured to spectrally disperse the light generated by light irradiation of the biological particle P, such that the spectrally dispersed light is detected by multiple photodetectors, the number of photodetectors being greater than the number of fluorescent dyes labeling the biological particle P. A flow cytometer including such a detection optical system is called a spectroscopic flow cytometer. Furthermore, for example, the detection optical system is configured to separate light corresponding to the fluorescence wavelength of a specific fluorescent dye from the light generated by light irradiation of the biological particle P and to detect the separated light by a corresponding photodetector.

[0243] Furthermore, the detection unit 6102 may include a signal processing unit that converts the electrical signal obtained by the photodetector into a digital signal. The signal processing unit may include an analog-to-digital converter (ADC) as a means of performing the conversion. The digital signal obtained by the conversion by the signal processing unit may be transmitted to the information processing unit 6103. The digital signal may be processed by the information processing unit 6103 as light-related data (hereinafter also referred to as "optical data"). The optical data may include, for example, fluorescence data. More specifically, the optical data may be light intensity data, and the light intensity may be light intensity data including fluorescence (which may include characteristic quantities such as area, height, and width).

[0244] (Information Processing Unit 6103)

[0245] The information processing unit 6103 includes a processing unit that performs processing on various types of data (e.g., optical data) and a storage unit that stores various types of data. When optical data corresponding to a fluorescent dye is acquired from the detection unit 6102, the processing unit can perform fluorescence leakage correction (compensation processing) on ​​the light intensity data. Furthermore, in the case of a flow cytometer, the information processing unit 6103 performs fluorescence separation processing on the optical data and acquires light intensity data corresponding to the fluorescent dye.

[0246] Fluorescence separation processing can be performed according to, for example, the separation (unmix) method described in JP 2011-232259A. When the detection unit 6102 includes an imaging element, the information processing unit 6103 can acquire morphological information of the biological particle P based on the image acquired by the imaging element. The storage unit can be configured to store the acquired optical data. The storage unit can be further configured to store spectral reference data to be used in the separation.

[0247] When the biological sample analysis apparatus 6100 includes the sorting unit 6104 described later, the information processing unit 6103 can determine whether the biological particles P should be sorted based on optical data and / or morphological information. Furthermore, the information processing unit 6103 can control the sorting unit 6104 based on the determination result, and the sorting unit 6104 can perform sorting on the biological particles P.

[0248] The information processing unit 6103 can be configured to output various types of information (e.g., optical data and images). For example, the information processing unit 6103 can output various types of data generated based on optical data (e.g., two-dimensional curves or spectral curves). Furthermore, the information processing unit 6103 can be configured to accept various types of data input, and for example, can accept user gating of curves. The information processing unit 6103 may include an output unit (e.g., a display) or an input unit (e.g., a keyboard) for performing output or input.

[0249] The information processing unit 6103 can be configured as a general-purpose computer and as an information processing device, for example, including a CPU, RAM, and ROM. The information processing unit 6103 can be included in a housing that houses the light irradiation unit 6101 and the detection unit 6102, or it can exist outside the housing. Furthermore, various processing or functions of the information processing unit 6103 can be implemented via a server computer or the cloud connected to a network.

[0250] (Sorting Unit 6104)

[0251] The sorting unit 6104 performs the sorting of biological particles P based on the determination result of the information processing unit 6103. The sorting method may be to generate droplets containing biological particles P through vibration, to apply charge to the droplets as the sorting target, or to control the direction of travel of the droplets using electrodes. The sorting method may also be to perform the sorting by controlling the direction of travel of the biological particles P in the flow path structure. The flow path structure may, for example, be provided with a control mechanism attributable to pressure (injection or absorption) or charge. Examples of flow path structures include chips (e.g., chips described in JP2020-76736A) in which flow path C has branches to a recovery flow path and a downstream waste liquid flow path, and specific biological particles P are recovered into the recovery flow path.

[0252] [Example]

[0253] The invention will be described in more detail below with reference to embodiments. The embodiments described below are representative examples of the invention, and the scope of the invention should not be interpreted narrowly based on the embodiments.

[0254] <Example of the First Experiment>

[0255] In the first experimental example, a second signal set was obtained when the same type of binding molecules, which had been labeled with different marker molecules, reacted with the target molecule.

[0256] Specifically, the following were used for fluorescent immunostaining: paraffin-embedded tissue sections of human breast cancer as examples of target molecules; AF488, AF647, AF700 and PE (phycoerythrin) from the Alexa Fluor (registered trademark) series as examples of labeling molecules; and anti-pan-cytokeratin antibodies (clones: AE1 / AE3) as examples of binding molecules.

[0257] The results of fluorescent immunostaining were shown in Figure 13 In the middle. For example Figure 13 As shown, this confirms that reactivity is affected when the same type of binding molecules, which have been labeled with different marker molecules, react with the target molecule.

[0258] <Example of the Second Experiment>

[0259] In the second experimental example, a first signal set and a second signal set were obtained, and the reactivity of the target molecule and each of the multiple different binding molecules was calculated when multiple different binding molecules that had been labeled with different marker molecules were used.

[0260] Specifically, the following were used for fluorescent immunostaining: formalin-fixed and paraffin-embedded tissue sections of human amygdala as examples of target molecules; AF488, AF555, AF594 and AF647 of the Alexa Fluor (registered trademark) series of fluorescent dyes as examples of labeling molecules; and CD3 antibodies, CD5 antibodies and CD7 antibodies as examples of binding molecules.

[0261] (1) Acquisition of the first signal group

[0262] Captured images were obtained when each of several different binding molecules—CD3, CD5, and CD7 antibodies—labeled with the same type of marker molecule, AF647, reacted with the target molecule. Additionally, captured images were obtained when the target molecule was reacted with a binding molecule CD3 antibody labeled with AF488, a binding molecule CD3 antibody labeled with AF555, and a binding molecule CD5 antibody labeled with AF594, respectively. The number of binding molecules was calculated from each of the captured images. The calculated number of binding molecules is shown in Table 4 below.

[0263] [Table 4]

[0264] [Number of bound molecules]

[0265]

[0266] (2) Acquisition of the second signal group

[0267] Captured images were obtained when CD3-binding molecules of the same type, labeled with different markers AF488, AF555, and AF647, reacted with the target molecule. Furthermore, captured images were obtained when the target molecule was reacted with CD3-binding molecules labeled with AF488, AF555, and AF594, respectively. The fluorescence intensity / autofluorescence ratio was calculated from each captured image. The calculated fluorescence intensity / autofluorescence ratios are shown in Table 5 below.

[0268] [Table 5]

[0269] [Fluorescence Intensity / Autofluorescence Ratio]

[0270]

[0271] (3) Calculation of reactivity index

[0272] The reactivity index was calculated using the values ​​in Tables 4 and 5. In this experimental example, the fluorescence intensity / autofluorescence ratio was used as an indicator of reactivity. Specifically, the reactivity index was calculated using the following expression.

[0273] The calculated value of the reactivity index is: (number of antibodies with the same type of label that binds to the target molecule / number of antibodies with the same type of label that binds to the reference molecule) × [fluorescence intensity / autofluorescence ratio] of the reference molecule that binds to the target fluorescent dye.

[0274] For example, the reactivity index (fluorescence intensity / autofluorescence ratio) of CD7 labeled with AF488 is calculated as follows.

[0275] The number of antibodies with the same type of label that binds to the target molecule: the number of antibodies against AF647CD7 = 29.5 (Table 4).

[0276] Number of antibodies with the same type of label as the reference binding molecule: Number of antibodies against AF647CD3 = 3.40 (Table 4).

[0277] The [fluorescence intensity / autofluorescence ratio] of the reference binding molecule of the target fluorescent dye is calculated as follows: [fluorescence intensity / autofluorescence ratio] of AF488CD3 = 0.739.

[0278] (29.5 / 3.40)×0.739≈6.42

[0279] (4) Calculation results of reactivity index

[0280] The calculation results of the reactivity index are shown in Table 6 below.

[0281] [Table 6]

[0282] [Indicator (Fluorescence Intensity / Autofluorescence Ratio)]

[0283]

[0284] Calculated value in ()

[0285] (5) Selection of the combination of marker molecules and binding molecules

[0286] Based on the calculated reactivity index, a combination of labeled molecules and binding molecules is selected. In the second experimental example, as an example of selection, the selection is performed such that the label is assigned to the binding molecules in descending order of the signal value, in ascending order of the first signal set value.

[0287] Specifically, firstly, in Table 4, since the minimum value of the first signal in AF647 is 1.92 of AF647CD5, and the maximum value of the calculated reactivity index of CD5 is 2.58 of AF647CD5, the label AF647 is assigned to the binding molecule CD5.

[0288] In Table 4, although the next minimum value of the first signal in AF647 is 3.40 of AF647CD3 and the maximum value of the calculated reactivity index of CD3 is 4.58 of AF647CD3, since AF647 has already been assigned to CD5, 2.41 of AF555CD3, which indicates the next maximum value, is selected and the label AF555 is assigned to the binding molecule CD3.

[0289] In Table 4, although the next minimum value of the first signal in AF647 is 29.5 of AF647CD7, the maximum value of the calculated reactivity index of CD7 is 39.8 of AF647CD7, and the next maximum value of the calculated reactivity index of CD7 is 20.9 of AF555CD7. Since both have been assigned, 6.42 of AF488CD7, which indicates the next maximum value, is selected, and the mark AF488 is assigned to CD7.

[0290] Based on the above, AF488CD7, AF555CD3, and AF647CD5 were selected as the combination of marker molecules and binding molecules.

[0291] (6) Staining confirmation

[0292] Fluorescent immunostaining was performed on formalin-fixed and paraffin-embedded tissue sections of human amygdala using the combination of marker molecules and binding molecules selected as described above. Images of the individual fluorescently labeled antibodies in the multistaining images are shown below. Figures 14 to 16Images of each fluorescent label in the unstained samples are also shown as negative controls. For the label AF488, stained images of AF488CD5, which were not selected due to low calculated reactivity values, are also shown for reference.

[0293] (7) Quantitative determination of antibody quantity

[0294] The number of antibodies was also calculated based on multiple staining images using the combination of the selected labeling and binding molecules and images of each fluorescent label in the unstained sample. The results are shown in Tables 7 and 8 below.

[0295] [Table 7]

[0296] [Results of quantitative determination of the number of antibodies in selected combinations of labeled and binding molecules]

[0297]

[0298] [Table 8]

[0299] [Results of quantitative determination of antibody levels in unstained samples (negative control)]

[0300]

[0301] (8) Consider

[0302] like Figures 14 to 16 As shown, it has been demonstrated that using a selective combination of labeled and binding molecules for fluorescent immunostaining results in higher and fully detectable values ​​than the negative control. Furthermore, as... Figure 14 As shown, for AF488CD5, which was not selected as a combination due to the low calculated value of the AF488CD5 reactivity index, the signal is even lower than that of the already selected AF647CD5 and is difficult to detect, thus proving that the combination of AF488CD5 is unsuitable.

[0303] <Example of the Third Experiment>

[0304] In the third experimental example, the combination of labeled molecules and binding molecules was selected using a different method than in the second experimental example.

[0305] The combination of labeled and binding molecules was selected based on the reactivity index calculated in the second experimental example. In the third experimental example, as an example of selection, the fluorescent label on the short wavelength side was sequentially assigned to the binding molecules in descending order of the values ​​of the first signal group. When multiple candidates existed, the combination with the higher calculated panel design index value was selected.

[0306] Specifically, firstly, in Table 4, since the descending order of the first signal values ​​in AF647 is CD7, CD3, and CD5, and the values ​​are 29.5, 3.40, and 1.92 respectively, the fluorescent dyes AF488 and AF555, as short-wavelength side fluorescent dyes, are assigned in descending order of their first signal values. In this case, CD7 is assigned to AF488 and CD3 is assigned to AF555. Although AF594 and AF647 could be assigned to CD5, AF647CD5, which has a higher calculated reactivity index value, is selected because the calculated reactivity index value of AF594CD5 is 0.320 and that of AF647CD5 is 2.58.

[0307] Based on the above, AF488CD7, AF555CD3, and AF647CD5 were selected as the combination of marker molecules and binding molecules.

[0308] The following configurations can also be used in this technology.

[0309] (1) An information processing device, comprising:

[0310] A processing unit configured to: when using multiple different binding molecules labeled by different marker molecules, calculate the reactivity between a target molecule and each of the multiple different binding molecules based on signals obtained from a sample including a biological sample, wherein...

[0311] The signals include: a first set of signals acquired when each of a plurality of different binding molecules labeled by the same type of marker molecule reacts with the target molecule, and a second set of signals acquired when each of a plurality of different binding molecules labeled by different marker molecule reacts with the target molecule.

[0312] (2) According to the information processing apparatus of (1), the number of bound molecules and / or the fluorescence intensity marked by these bound molecules are calculated as an indicator of reactivity.

[0313] (3) The information processing apparatus according to (2), wherein the fluorescence intensity is calculated from one or more values ​​selected from excitation efficiency, quantum yield, absorption efficiency and fluorescence labeling rate (F / P value).

[0314] (4) The information processing apparatus according to any one of (1) to (3), wherein the signal includes at least one of a signal, a specific signal / background, and a specific signal / non-specific signal.

[0315] (5) The information processing apparatus according to any one of (1) to (3), wherein the processing unit is configured to calculate the background in each detection channel based on a third signal acquired when a negative control is used.

[0316] (6) The information processing apparatus according to (4), wherein the processing unit is configured to calculate leakage of autofluorescence signal and / or leakage of signal originating from another labeled molecule.

[0317] (7) The information processing apparatus according to any one of (1) to (6), wherein the processing unit is configured to calculate the reactivity with respect to a combination of labeled molecules and bound molecules that are not actually measured.

[0318] (8) The information processing apparatus according to any one of (4) to (7), wherein the processing unit is configured to select a combination of a marker molecule and a binding molecule, the specific signal / background of the combination being equal to or exceeding a threshold.

[0319] (9) The information processing apparatus according to any one of (4) to (8), wherein the processing unit is configured to select a combination of labeled molecules and binding molecules that maximizes the sum of the specific signal / background.

[0320] (10) The information processing apparatus according to any one of (2) to (9), wherein the processing unit is configured to select a combination of a labeling molecule and a binding molecule that maximizes the sum of the differences between the signal of the fluorescence intensity and the background of the fluorescence intensity.

[0321] (11) The information processing apparatus according to any one of (2) to (10), wherein the processing unit is configured to select a combination of labeled molecules and binding molecules based on the magnitude of the signal of fluorescence intensity.

[0322] (12) The information processing apparatus according to any one of (2) to (10), wherein the processing unit is configured to select a combination of a labeling molecule and a binding molecule such that the label is assigned to the binding molecules in ascending order of the values ​​of the first signal group in descending order of the fluorescence intensity signal.

[0323] (13) The information processing apparatus according to any one of (2) to (10), wherein the processing unit is configured to select a combination of marker molecules and binding molecules such that the markers are distributed to binding molecules in descending order of the length of the detection wavelength.

[0324] (14) The information processing apparatus according to any one of (1) to (13) further includes a presentation unit configured to present to a user supporting information about the combination of binding molecules and labeled molecules based on the calculated reactivity.

[0325] (15) The information processing apparatus according to any one of (1) to (14) further includes an evaluation unit configured to estimate the importance of the binding molecule and / or the labeled molecule relative to the target molecule based on image information.

[0326] (16) The information processing apparatus according to any one of (1) to (15), wherein the first signal group and the second signal group are at least one standardized detection quantity selected from excitation power density, exposure time and detection device sensitivity.

[0327] (17) A method for analyzing biological samples, comprising the following steps:

[0328] Acquire signals from samples, including biological specimens;

[0329] When using multiple different binding molecules labeled with different marker molecules, the reactivity between the target molecule and each of these multiple different binding molecules is calculated based on this signal; and

[0330] The reactivity is output, wherein

[0331] The signals include: a first set of signals acquired when each of a plurality of different binding molecules that have been labeled by the same type of marker molecule reacts with the target molecule; and a second set of signals acquired when each of a plurality of different binding molecules of the same type that have been labeled by different marker molecule reacts with the target molecule.

[0332] (18) An information processing system, comprising:

[0333] Information processing apparatus, including:

[0334] The signal acquisition unit is configured to acquire signals from samples including biological specimens;

[0335] The processing unit is configured to calculate the reactivity between the target molecule and each of the plurality of different binding molecules based on the signal when using a plurality of different binding molecules labeled by different marker molecules;

[0336] The output unit is configured to output the reactivity.

[0337] The signals include: a first set of signals acquired when each of a plurality of different binding molecules, already labeled with the same type of marker molecule, reacts with the target molecule; and a second set of signals acquired when each of a plurality of binding molecules, already labeled with different marker molecule molecule, reacts with the target molecule.

[0338] The storage device is configured to store information calculated by the information processing device.

[0339] (19) The information processing system according to (18), wherein the information processing device considers information stored in the storage device.

[0340] (20) A biological sample detection device, comprising:

[0341] The signal acquisition unit is configured to acquire signals from samples including biological specimens;

[0342] A processing unit configured to calculate, based on the signal, the reactivity between the target molecule and each of the plurality of different binding molecules when using a plurality of different binding molecules labeled by different marker molecules;

[0343] The output unit is configured to output the reactivity; and

[0344] The detection unit is configured to detect signals emitted from target molecules using output reactivity-based selection and labeled with a marker molecule, wherein

[0345] The signals include: a first set of signals acquired when each of a plurality of different binding molecules that have been labeled by the same type of marker molecule reacts with the target molecule; and a second set of signals acquired when each of a plurality of different binding molecules of the same type that have been labeled by different marker molecule reacts with the target molecule.

[0346] (21) The biological sample detection device according to (20) further includes a labeling unit configured to label a target molecule using a binding molecule selected based on output reactivity and labeled by the labeling molecule.

[0347] (22) The biological sample detection device according to (20) or (21) further includes an analysis unit configured to analyze the sample based on the signal detected by the detection unit.

[0348] (23) A biological sample detection system, comprising:

[0349] Information processing apparatus, including:

[0350] The signal acquisition unit is configured to acquire signals from samples including biological specimens;

[0351] The processing unit is configured to calculate the reactivity between the target molecule and each of the plurality of different binding molecules based on the signal when using a plurality of different binding molecules labeled by different marker molecules;

[0352] The output unit is configured to output the reactivity.

[0353] The signals include: a first set of signals acquired when each of a plurality of different binding molecules, each having been labeled with the same type of marker molecule, reacts with the target molecule; and a second set of signals acquired when each of a plurality of binding molecules of the same type, each labeled with different marker molecule, reacts with the target molecule.

[0354] The detection device is configured to detect signals emitted from target molecules that are labeled with binding molecular tags and selected based on the output reactivity.

[0355] (24) A computer program that enables a computer to:

[0356] Signal acquisition function, acquiring signals from samples including biological samples;

[0357] The processing function, when using multiple different binding molecules labeled with different marker molecules, calculates the reactivity between the target molecule and each of the multiple different binding molecules based on the signal; and

[0358] Output function, outputting the reactivity, wherein

[0359] The signals include: a first set of signals acquired when each of a plurality of different binding molecules that have been labeled by the same type of marker molecule reacts with the target molecule; and a second set of signals acquired when each of a plurality of different binding molecules of the same type that have been labeled by different marker molecule reacts with the target molecule.

[0360] (25) A biological sample detection system, comprising:

[0361] Computer programs are used to enable computers to perform:

[0362] Signal acquisition function, acquiring signals from samples including biological samples;

[0363] The processing function, when using multiple different binding molecules labeled with different marker molecules, calculates the reactivity between the target molecule and each of the multiple different binding molecules based on the signal; and

[0364] Output function, outputs this reactivity.

[0365] The signals include: a first set of signals acquired when each of a plurality of different binding molecules, already labeled with the same type of marker molecule, reacts with the target molecule; and a second set of signals acquired when each of a plurality of different binding molecules, already labeled with different marker molecule, reacts with the target molecule.

[0366] The detection device is configured to detect signals emitted from target molecules that are labeled with binding molecular tags and selected based on the output reactivity.

[0367] 1. Information processing device

[0368] 11 Signal Acquisition Unit

[0369] 12 processing units

[0370] 13 Evaluation Units

[0371] 14 Output Unit

[0372] 15 Presentation Units

[0373] 16 storage units

[0374] 17 Display Units

[0375] 18, 23 User Interface

[0376] 2. Information Processing System

[0377] 3. Biological Sample Detection Device

[0378] 4. Biological Sample Detection System

[0379] 21 Storage devices

[0380] 22 Display devices

[0381] 24 Evaluation Device

[0382] 31, 6102 Detection Unit

[0383] 41 Detection device

[0384] 32 Marking Units

[0385] 42 Marking device

[0386] 33 Analysis Units

[0387] 43 Analytical apparatus

[0388] 5000 Microscope System

[0389] 5100 Microscope Apparatus

[0390] 5110 Control Unit

[0391] Information processing units 5120 and 6103

[0392] 5101 and 6101 light irradiation units

[0393] 5102 Optical Unit

[0394] 5103 Signal Acquisition Unit

[0395] 5104 Sample Placement Unit

[0396] S Samples derived from living organisms, biological samples

[0397] R, Sa imaging target area

[0398] R1 divides the region

[0399] Rs represents the scan region.

[0400] 6100 Biosample Analysis Device

[0401] C flow path

[0402] P biological particles

[0403] 6104 Sorting Unit

Claims

1. An information processing apparatus, comprising: The processing unit is configured to: receive signals from a sample including a biological sample, the signals including a first signal set and a second signal set; acquire the first signal set when each of a plurality of different binding molecules labeled by the same type of marker molecule reacts with a target molecule; acquire the second signal set when each of a plurality of binding molecules of the same type labeled by different marker molecule reacts with a target molecule; and calculate the reactivity between the target molecule and the combination of unmeasured marker molecules and binding molecules based on the signals from the sample including the biological sample when using a combination of unmeasured marker molecules and binding molecules.

2. The information processing apparatus according to claim 1, wherein, The processing unit is configured to calculate the number of bound molecules and / or the fluorescence intensity labeled by the bound molecules as an indicator of the reactivity.

3. The information processing apparatus according to claim 2, wherein, The fluorescence intensity is calculated from one or more values ​​selected from excitation efficiency, quantum yield, absorption efficiency, and fluorescence labeling rate.

4. The information processing apparatus according to claim 1, wherein, The signal includes at least one of fluorescence signal, specific signal / background, and specific signal / non-specific signal.

5. The information processing apparatus according to claim 1, wherein, The processing unit is configured to calculate the background in each detection channel based on a third signal obtained when using a negative control.

6. The information processing apparatus according to claim 2, wherein, The processing unit is configured to calculate the leakage of the autofluorescence signal and / or the leakage of the signal obtained from another labeled molecule.

7. The information processing apparatus according to claim 1, wherein, The processing unit is configured to select a combination of labeled molecules and binding molecules by one or more methods selected from (a) to (f): (a) Select a combination of labeled molecules and binding molecules whose specific signal / background ratio is equal to or exceeds a threshold; (b) Select the combination of labeled and binding molecules that maximizes the sum of specific signal / background; (c) Select a combination of labeled and binding molecules that maximizes the sum of the differences between the signal and the background fluorescence intensity; (d) Selecting the combination of labeled and binding molecules based on the magnitude of the fluorescence intensity signal; (e) Select a combination of labeled molecules and binding molecules such that the label is assigned to binding molecules in descending order of fluorescence intensity signal in ascending order of the value of the first signal set; as well as (f) Select a combination of labeled molecules and binding molecules such that the labeled molecules are distributed in ascending order of the length of the detection wavelength to binding molecules in descending order of the value of the first signal set.

8. The information processing apparatus of claim 1, further comprising a presentation unit configured to present to a user supporting information about the combination of binding molecules and labeled molecules based on the calculated reactivity.

9. The information processing apparatus according to claim 1, further comprising: An evaluation unit is configured to estimate the importance of the binding molecule and / or the labeled molecule relative to the target molecule based on image information.

10. The information processing apparatus according to claim 1, wherein, The first signal group and the second signal group are detection quantities that are standardized using at least one of excitation power density, exposure time and detection device sensitivity.

11. A method for analyzing biological samples, comprising the following steps: Signals obtained from a sample including a biological sample, the signals comprising a first signal set and a second signal set, wherein the first signal set is obtained when each of a plurality of different binding molecules labeled by the same type of marker molecule reacts with a target molecule, and the second signal set is obtained when each of a plurality of different binding molecules labeled by different marker molecule reacts with a target molecule; When using a combination of unmeasured labeled molecules and bound molecules, the reactivity between the target molecule and the unmeasured combination of labeled molecules and bound molecules is calculated based on the signal. as well as Output the reactivity.

12. A biological sample detection device, comprising: A signal acquisition unit is configured to acquire signals obtained from a sample including a biological sample, the signals including a first signal set and a second signal set, wherein the first signal set is acquired when each of a plurality of different binding molecules labeled by the same type of labeling molecule reacts with a target molecule, and the second signal set is acquired when each of a plurality of different binding molecules labeled by different labeling molecule reacts with a target molecule. The processing unit is configured to calculate the reactivity between the target molecule and the unmeasured combination of the labeled and bound molecules based on the signal when using a combination of unmeasured labeled and bound molecules. The output unit is configured to output the reactivity; as well as The detection unit is configured to detect signals emitted from target molecules that are labeled with binding molecular tags and use output-based reactivity selection.

13. The biological sample detection device of claim 12, further comprising a labeling unit configured to label the target molecule using a binding molecule labeled by a labeling molecule and selected based on the reactivity of the output.

14. The biological sample detection device according to claim 12, further comprising: An analysis unit is configured to analyze the sample based on signals detected by the detection unit.

15. A biological sample detection system, comprising: Information processing apparatus, including: A signal acquisition unit is configured to acquire signals obtained from a sample including a biological sample, the signals including a first signal set and a second signal set, wherein the first signal set is acquired when each of a plurality of different binding molecules labeled by the same type of marker molecule reacts with a target molecule, and the second signal set is acquired when each of a plurality of different binding molecules labeled by different marker molecule reacts with a target molecule. The processing unit is configured to calculate the reactivity between the target molecule and the unmeasured combination of the labeled and bound molecules based on the signal when using a combination of unmeasured labeled and bound molecules. The output unit is configured to output the reactivity, and The detection device is configured to detect signals emitted from target molecules that are labeled with binding molecular tags and use output-based reactivity selection.

Images