Biological sample analyzer

Abstract

The purpose of the present invention is to provide a method for enhancing detection precision in a biological sample analysis device.　The present disclosure relates to a biological sample analysis device including a light irradiation unit for irradiating particles with light, a detection unit for detecting light produced by the light irradiation, and an information processing unit for controlling the light irradiation unit and the detection unit. In one embodiment of the present invention, the information processing unit corrects a signal intensity measurement value of light detected by the detection unit, on the basis of a relationship between a light irradiation output value from the light irradiation unit and a signal intensity measurement value of light detected by the detection unit. In one embodiment of the present invention, the information processing unit is configured so as to perform removal processing for removing, from signal intensity data of light produced by light irradiation of a sample including a particle collection constituted from a plurality of types of particle groups having fluorescence intensity levels that vary in stages, particle collection signal intensity data that relate to a particle group that does not belong to the particle collection.

Description

Technical Field

[0001] The present disclosure relates to a biological sample analysis apparatus. More specifically, the present disclosure relates to a biological sample analysis apparatus having a light irradiation unit that irradiates biological particles contained in a biological sample with light, and a detection unit that detects light generated by the light irradiation.

Background Art

[0002] For example, a particle population such as cells, microorganisms, and liposomes is labeled with a fluorescent dye, and the characteristics of the particles are measured by irradiating each particle of the particle population with laser light and measuring the intensity and / or pattern of fluorescence generated from the excited fluorescent dye. As a typical example of a particle analyzer for performing such measurement, a flow cytometer can be mentioned.

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

[0004] Before performing analysis of a biological sample by a flow cytometer, calibration is performed for, for example, a laser light source and a photodetector. Several methods related to such calibration have been proposed so far. For example, in Patent Document 1 below, a plurality of fluorescence intensities are obtained based on fluorescence signals from a sample composed of a plurality of particles labeled with fluorescent dyes having different fluorescence intensities, and intensity ranges for each of the plurality of fluorescence intensities detected based on the fluorescence intensity ratio of the sample are recognized, and an information processing apparatus including an information processing unit that calculates information regarding the sensitivity of a fluorescence detection unit is disclosed.

Prior Art Documents

Patent Documents

[0005] [Patent Document 1] Japanese Patent Publication No. 2016-217789 [Overview of the Initiative] [Problems that the invention aims to solve]

[0006] In biological sample analysis devices such as flow cytometers, photodiodes such as MPPCs (Multi-Pixel Photon Counters) are sometimes used as photodetectors. Linearity is required between the amount of incident light entering the photodetector and the detector's output value. However, this linearity may degrade depending on the amount of incident light. To improve detection accuracy, it is desirable to eliminate this degradation.

[0007] Furthermore, in order to improve the detection accuracy of biological sample analyzers such as flow cytometers, verification or adjustment processes are performed using beads configured to form predetermined peaks. For example, verification or adjustment processes are performed on a detection system using 8-peak beads. However, if the verification or adjustment process of the detection system is performed while other types of beads remain in the instrument, those other types of beads may affect the process. To improve detection accuracy, it is desirable to eliminate such influences.

[0008] This disclosure aims to solve at least one of these problems. [Means for solving the problem]

[0009] This disclosure is, A light irradiation unit that irradiates particles with light, A detection unit for detecting light generated by the aforementioned light irradiation, It includes an information processing unit that controls the light irradiation unit and the detection unit, The information processing unit corrects the measured light signal intensity value detected by the detection unit based on the relationship between the light irradiation output value of the light irradiation unit and the measured light signal intensity value detected by the detection unit. We provide a biological sample analysis device. The information processing unit may be configured to perform the correction using an nth-order approximation formula that shows the relationship. In the aforementioned n-th degree approximation formula, n may be an odd number greater than or equal to 3. The aforementioned information processing unit, A linear approximation formula is created to show the relationship between the light irradiation output value below a predetermined value and the signal intensity measurement value obtained at that light irradiation output value, and then, The first-order approximation formula may be used to obtain a set of data for generating the nth-order approximation formula. The information processing unit can obtain a correlation index for the first-order approximation formula. The information processing unit can determine whether the correlation index satisfies predetermined conditions. The information processing unit can generate the nth-order approximation formula using a data set that includes pairs of signal intensity measurements obtained when the light irradiation output value is greater than or equal to the predetermined value, and signal intensity calculation values ​​calculated by substituting the said light irradiation output value into the first-order approximation formula. The information processing unit can determine whether to correct the signal intensity measurement value using the signal intensity calculation value calculated using the first-order approximation formula as a threshold value when the light irradiation output value is the predetermined value. The detection unit may include one or more MPPCs as detectors for detecting the light. Furthermore, this disclosure is, A detection unit that detects light generated by light irradiation on particles, The system includes an information processing unit that processes the light signal intensity data detected by the detection unit, The information processing unit is configured to perform a removal process to remove signal intensity data relating to particle groups that do not belong to the particle group from the signal intensity data of light generated by light irradiation of a sample containing a particle group consisting of multiple particle groups having stepwise different fluorescence intensity levels. Here, the information processing unit performs a setting process in which, in the removal process, it sets two or more of the multiple photodetectors included in the detection unit as fluorescence channels to be used to identify particle groups that do not belong to the particle group. We also provide biological sample analysis equipment. The information processing unit can execute the setting process of the fluorescence channel so that the fluorescence intensity level of a particle group not belonging to the particle population is greater than the fluorescence intensity level of any of the plurality of types of particle groups included in the particle population. In the removal process, the information processing unit can execute a specifying process of specifying signal intensity data regarding a particle group having the maximum fluorescence intensity level among the plurality of types of particle groups constituting the particle population from among the signal intensity data obtained by the fluorescence channel. In the specifying process, the information processing unit can specify a signal intensity output value having the maximum number of events among the signal intensity data regarding the particle group having the maximum fluorescence intensity level. In the specifying process, the information processing unit can specify signal intensity data regarding a particle group having the maximum fluorescence intensity level based on the signal intensity output value having the maximum number of events. In the removal process, the information processing unit can set a removal condition for removing signal intensity data regarding a particle group not belonging to the particle population based on the signal intensity data regarding the particle group having the maximum fluorescence intensity level. The information processing unit can set the removal condition based on the maximum value of the signal intensity output values among the signal intensity data regarding the particle group having the maximum fluorescence intensity level. The information processing unit can further execute a setting process of setting two or more of the plurality of photodetectors included in the detection unit as fluorescence channels used to evaluate the signal intensity data after execution of the removal process. The information processing unit can execute a separation and discrimination process for the signal intensity data after execution of the removal process by the k-means method.

Brief Description of the Drawings

[0010] [Figure 1] It is a diagram showing a configuration example of the biological sample analysis apparatus of the present disclosure. [Figure 2]A diagram showing a graph for explaining the deterioration of linearity. [Figure 3] A diagram showing an example of a block diagram of a biological sample analyzer according to the present disclosure. [Figure 4] An example of a flowchart of a process for obtaining the relationship between the light irradiation output value of the light irradiation unit and the measured signal intensity value of the light detected by the detection unit. [Figure 5] An example of a flowchart of a correction process using an nth-order approximation formula. [Figure 6] A table showing the data obtained in the nth-order approximation formula generation process of the example. [Figure 7] An example of a flowchart of a process executed by the information processing unit. [Figure 8] An example of two-dimensional plot data generated by performing flow cytometry is shown. [Figure 9] An example of a flowchart of a specific process. [Figure 10] A diagram schematically showing the generated histogram. [Figure 11] A diagram for explaining the region divided by the sum of squares adopted under the removal condition. [Figure 12A] A diagram showing the evaluation result obtained by the separation and identification process. [Figure 12B] A diagram showing the evaluation result obtained by the separation and identification process. [Figure 13] Two-dimensional plot data with VioGreen and PE as the X-axis and Y-axis, respectively.

Mode for Carrying Out the Invention

[0011] Hereinafter, preferred embodiments for implementing the present disclosure will be described. Note that the embodiments described below show typical embodiments of the present disclosure, and the scope of the present disclosure is not limited only to these embodiments. The description of the present disclosure will be made in the following order. 1. First Embodiment (Biological Sample Analyzer) (1) Configuration Example (2) Linearity correction (2-1) Basic concepts of linearity correction (2-2) Flowchart for generating nth-order approximation formulas (2-3) Flowchart of correction process using the nth-order approximation formula (2-4) Examples of generating an nth-order approximation formula and correcting the formula using the approximation formula. (3) Carryover removal (3-1) Basic Concepts of Carryover Removal (3-2) Flowchart of the detection unit evaluation process including carryover removal process (3-3) Specific examples of detection unit evaluation processing including carryover removal processing (4) The manner in which (2) and (3) above are carried out.

[0012] 1. First Embodiment (Biological Sample Analysis Apparatus)

[0013] (1) Example configuration

[0014] Figure 1 shows an example of the configuration of the biological sample analyzer of this disclosure. The biological sample analyzer 6100 shown in Figure 1 includes a light irradiation unit 6101 that irradiates light onto a biological sample S flowing through a channel C, a detection unit 6102 that detects the light generated by irradiating the biological sample S with light, and an information processing unit 6103 that processes information related to the light detected by the detection unit. Examples of the biological sample analyzer 6100 include a flow cytometer and an imaging cytometer. The biological sample analyzer 6100 may also include a sorting unit 6104 that sorts specific biological particles P within the biological sample. An example of the biological sample analyzer 6100 including the sorting unit is a cell sorter.

[0015] (Biological sample) The biological sample S may be a liquid sample containing biological particles. These biological particles may be, for example, cells or non-cellular biological particles. The cells may be living cells, and more specifically, blood cells such as red blood cells and white blood cells, and germ cells such as sperm and fertilized eggs. The cells may be directly collected from a sample such as whole blood, or they may be cultured cells obtained after culturing. Examples of non-cellular biological particles include extracellular vesicles, particularly exosomes and microvesicles. The biological particles may be labeled with one or more labeling substances (for example, dyes (particularly fluorescent dyes) and fluorescent dye-labeled antibodies). The biological sample analyzer of this disclosure may also analyze particles other than biological particles, and beads may be analyzed for calibration purposes.

[0016] (Flow channel) The channel C is configured to allow a biological sample S to flow through it. In particular, the channel C may be configured to form a flow in which biological particles contained in the biological sample are arranged in a substantially straight line. The channel structure including the channel C may be designed to form a laminar flow. In particular, the channel structure is designed to form a laminar flow in which the flow of the biological sample (sample flow) is surrounded by the flow of the sheath fluid. The design of the channel structure may be appropriately selected by those skilled in the art, and known designs may be adopted. The channel C may be formed in a flow channel structure such as a microchip (a chip with a channel on the order of micrometers) or a flow cell. The width of the channel C is 1 mm or less, and in particular may be 10 μm or more and 1 mm or less. The channel C and the channel structure including it may be formed from materials such as plastic or glass.

[0017] The biological sample analyzer of this disclosure is configured such that light from the light irradiation unit 6101 is irradiated onto a biological sample, particularly biological particles, flowing through the channel C. The biological sample analyzer of this disclosure may be configured such that the light irradiation point (interrogation point) for the biological sample is located within the channel structure in which the channel C is formed, or it may be configured such that the light irradiation point is located outside the channel structure. An example of the former is a configuration in which the light is irradiated onto the channel C in a microchip or flow cell. In the latter case, the light may be irradiated onto biological particles after they have exited the channel structure (particularly its nozzle), for example, a Jet-in-Air type flow cytometer.

[0018] (Light irradiation area) The light irradiation unit 6101 includes a light source unit that emits light and a light guide optical system that guides the light to the irradiation point. The light source unit includes one or more light sources. The type of light source is, for example, a laser light source or an LED. The wavelength of the light emitted from each light source may be any of the wavelengths of ultraviolet light, visible light, or infrared light. The light guide optical system includes optical components such as a beam splitter group, a mirror group, or an optical fiber. The light guide optical system may also include a lens group for focusing the light, for example, an objective lens. There may be one or more irradiation points where the light intersects with the biological sample. The light irradiation unit 6101 may be configured to focus light irradiated from one or more different light sources to a single irradiation point.

[0019] (Detection unit) The detection unit 6102 includes at least one photodetector that detects light generated by light irradiation of biological particles. The light to be detected is, for example, fluorescence or scattered light (e.g., one or more of forward scattered light, back scattered light, and side scattered light). Each photodetector includes one or more photoreceiving elements, for example, a photoreceiving element array. Each photodetector may include one or more PMTs (photomultiplier tubes) and / or photodiodes such as APDs and MPPCs as photoreceiving elements. The photodetector may include, for example, a PMT array in which multiple PMTs are arranged in a one-dimensional direction. The detection unit 6102 may also include an image sensor such as a CCD or CMOS. The detection unit 6102 can acquire images of biological particles (e.g., bright-field images, dark-field images, and fluorescence images) using the image sensor.

[0020] The detection unit 6102 includes a detection optical system that directs light of a predetermined detection wavelength to a corresponding photodetector. The detection optical system includes a spectroscopic unit such as a prism or diffraction grating, or a wavelength separation unit such as a dichroic mirror or optical filter. The detection optical system is configured to spectrally analyze light generated by, for example, irradiation of biological particles, and to detect the spectrally analyzed light using a plurality of photodetectors, more than the number of fluorescent dyes on which the biological particles are labeled. A flow cytometer including such a detection optical system is called a spectral flow cytometer. The detection optical system is also configured to separate light corresponding to the fluorescence wavelength range of a specific fluorescent dye from light generated by, for example, irradiation of biological particles, and to detect the separated light using a corresponding photodetector.

[0021] 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 A / D converter as the device that performs the conversion. The digital signal obtained by the conversion by the signal processing unit may be transmitted to the information processing unit 6103. The digital signal may be treated by the information processing unit 6103 as data related to light (hereinafter also referred to as "light data"). The light data may be, for example, light data including fluorescence data. More specifically, the light data may be light intensity data, and the light intensity may be light intensity data of light including fluorescence (which may include feature quantities such as Area, Height, and Width).

[0022] (Information Processing Department) The information processing unit 6103 includes, for example, a processing unit that performs processing of various data (e.g., optical data) and a storage unit that stores various data. When the processing unit obtains optical data corresponding to a fluorescent dye from the detection unit 6102, it may perform fluorescence leakage correction (compensation processing) on ​​the optical intensity data. In the case of a spectral flow cytometer, the processing unit also performs fluorescence separation processing on the optical data to obtain optical intensity data corresponding to the fluorescent dye. The fluorescence separation processing may be performed, for example, according to the unmixing method described in Japanese Patent Application Publication No. 2011-232259. If the detection unit 6102 includes an image sensor, the processing unit may obtain morphological information of biological particles based on the image obtained by the image sensor. The storage unit may be configured to store the acquired optical data. The storage unit may further be configured to store spectral reference data used in the unmixing processing.

[0023] If the biological sample analyzer 6100 includes a sorting unit 6104 as described below, the information processing unit 6103 may determine whether to sort biological particles based on optical data and / or morphological information. Based on the result of this determination, the information processing unit 6103 controls the sorting unit 6104, and the sorting of biological particles by the sorting unit 6104 may be performed.

[0024] The information processing unit 6103 may be configured to output various types of data (e.g., optical data and images). For example, the information processing unit 6103 may output various types of data (e.g., two-dimensional plots, spectral plots, etc.) generated based on the optical data. The information processing unit 6103 may also be configured to accept input of various types of data, for example, to accept gating processing on a plot by a user. The information processing unit 6103 may include an output unit (e.g., a display) or an input unit (e.g., a keyboard) for executing such output or input.

[0025] The information processing unit 6103 may be configured as a general-purpose computer, for example, as an information processing device equipped with a CPU, RAM, and ROM. The information processing unit 6103 may be contained within the housing that houses the light irradiation unit 6101 and the detection unit 6102, or it may be located outside of that housing. Furthermore, various processing or functions performed by the information processing unit 6103 may be implemented by a server computer or cloud connected via a network.

[0026] (Preparative separation section) The sorting unit 6104 performs sorting of biological particles according to the determination result by the information processing unit 6103. The sorting method may be one in which droplets containing biological particles are generated by vibration, an electric charge is applied to the droplets to be sorted, and the direction of movement of the droplets is controlled by electrodes. The sorting method may also be one in which the direction of movement of biological particles is controlled and sorted within a flow channel structure. The flow channel structure may be provided with a control mechanism, for example, by pressure (injection or suction) or electric charge. An example of such a flow channel structure is a chip (for example, the chip described in Japanese Patent Application Publication No. 2020-76736) in which a flow channel C has a flow channel structure in which a recovery flow channel and a waste liquid flow channel branch downstream thereof, and specific biological particles are recovered into the recovery flow channel.

[0027] (2) Linearity correction

[0028] (2-1) Basic concepts of linearity correction

[0029] An MPPC (Multi-Pixel Photon Counter) is a type of SiPM (Silicon Processing Machine) that contains multiple APDs (avalanche photodiodes) arranged in an array. Each APD is also called a pixel. An MPPC detects photons that enter all pixels within the detection time.

[0030] Each pixel produces only one output pulse, and this number does not change depending on the number of photons that enter each pixel. In other words, whether one photon enters a pixel or two photons enter simultaneously, there is only one output pulse. Therefore, if two or more photons enter a pixel within the detection time, a discrepancy will occur in the linearity relationship between the amount of incident light and the MPPC output, and this becomes more likely as the amount of incident light increases.

[0031] Figure 2 shows a graph illustrating the degradation of linearity. In this graph, the horizontal axis represents laser power, and the vertical axis represents the output value (Height data of the FITC channel). As shown in the graph, in the region of high laser power, i.e., the region of high incident light intensity, the output value is lower than the value based on the expected linear relationship. For example, when the laser power of the excitation light is 60 mW, as shown in the figure, the output value is 9.1% lower than the expected value. Thus, linearity degradation occurs as the amount of incident light increases.

[0032] Linearity degradation occurs with a fixed probability at a certain amount of incident light and follows the same trend. Therefore, linearity degradation can be addressed by performing corrections based on previously acquired data.

[0033] A biological sample analyzer according to this disclosure includes an information processing unit that corrects the measured signal intensity of light detected by the detection unit based on the relationship between the light irradiation output value of the light irradiation unit and the measured signal intensity of light detected by the detection unit. This makes it possible to address linearity degradation when the amount of incident light increases.

[0034] The aforementioned relationship may be obtained, for example, by actually flowing sample beads while changing the laser power and performing photodetection processing, and acquiring the output due to the incident light to the MPPC. The aforementioned relationship may be expressed, for example, by an approximation formula, particularly an nth-order approximation formula. The process for generating the nth-order approximation formula will be described below in (2-2) with reference to Figures 3 and 4. Figure 3 is an example of a block diagram of a biological sample analyzer according to this disclosure. Figure 4 is an example of a flowchart for the process of acquiring the aforementioned relationship. Furthermore, the correction process using the nth-order approximation formula will be described below in (2-3) with reference to Figures 3 and 5. In addition, examples of the process for generating the nth-order approximation formula and the correction process using the nth-order approximation formula will be described below in (2-4) with reference to Figure 5.

[0035] The degradation of linearity due to high input to the MPPC depends on the characteristics of the incident light to the MPPC (e.g., shape, size, and intensity distribution). For biological sample analyzers such as flow cytometers, these incident light characteristics remain largely unchanged over the short term (e.g., a single analysis), but may change over the long term. To address these changes in incident light characteristics, it is desirable to perform linearity calibration at predetermined timings. For example, linearity calibration can be performed during the Quality Control (QC) stage, which is carried out before the analysis of the biological sample. That is, a biological sample analyzer according to this disclosure may perform a process to acquire the aforementioned relationship during the QC stage, and may be configured to perform a process to generate the nth-order approximation formula.

[0036] (2-2) Flowchart for generating nth-order approximation formulas

[0037] The biological sample analyzer 100 shown in Figure 3 comprises a light irradiation unit 101 that irradiates biological particles contained in the biological sample with light, a detection unit 102 that detects the light generated by the light irradiation, and an information processing unit 103 that controls the light irradiation unit. The light irradiation unit 101, the detection unit 102, and the information processing unit 103 are the same as the light irradiation unit 6101, the detection unit 6102, and the information processing unit 6103 described in (2) above.

[0038] In this disclosure, the detection unit 102 preferably includes one or more photodiodes, preferably one or more Si photodiodes, as a detector for detecting the light. The one or more photodiodes may include, for example, one or more APDs, one or more MPPCs, or a combination thereof. In one embodiment, the detection unit includes one or more MPPCs as a detector for detecting the light. This disclosure is suitable for solving the above-mentioned problems that arise when the detection unit includes such a light-receiving element.

[0039] In step S101 of the flowchart shown in Figure 4, the information processing unit 103 starts a process to acquire the relationship between the light irradiation output value of the light irradiation unit and the measured light signal intensity value detected by the detection unit. This process may be performed in the device setup stage before the analysis of the biological sample by the biological sample analyzer begins, for example, in the QC stage. Alternatively, this process may be performed in the middle of the analysis of the biological sample by the biological sample analyzer.

[0040] In step S102, the biological sample analyzer 100 performs a process to acquire signal intensity measurements for each of the multiple light irradiation output values. For example, the biological sample analyzer 100 performs flow cytometry for this acquisition. Through this flow cytometry, the information processing unit 103 acquires signal intensity measurements for each of the multiple light irradiation output values.

[0041] For example, in the process, the detection result of fluorescence from predetermined fluorescent beads is obtained. That is, in step S102, flow cytometry is performed on the predetermined fluorescent beads. The predetermined fluorescent beads are preferably uniform in terms of, for example, size and fluorescence intensity. The predetermined fluorescent beads may be beads that produce fluorescence in the wavelength range of 400 nm to 800 nm, for example. Examples of such beads include, but are not limited to, AlignCheck Beads and SortCal Beads (both from Sony Group Corporation).

[0042] The aforementioned plurality of light irradiation output values ​​include a plurality of values ​​that are less than or equal to a predetermined value and a plurality of values ​​that are greater than or equal to a predetermined value. The predetermined value is set such that linearity degradation does not occur or can be ignored at light irradiation output values ​​less than or equal to the predetermined value, and linearity degradation occurs at light irradiation output values ​​greater than the predetermined value. As described above, linearity degradation occurs with a certain probability at a given amount of incident light and exhibits the same tendency. That is, linearity degradation does not occur when the amount of incident light is low, but it does occur when the amount of incident light is high. Therefore, this predetermined value can be set in advance.

[0043] The signal intensity measurements obtained at each of the multiple light irradiation output values ​​below the predetermined value are used to generate the first-order approximation formula described later. In other words, in step S102, the information processing unit 103 acquires the signal intensity measurement values ​​obtained for each of the two or more light irradiation output values ​​that are less than or equal to the predetermined value. Furthermore, the signal intensity measurements obtained at each of the multiple light irradiation output values ​​above the predetermined value are used to generate the nth-order approximation formula described later. In other words, in step S102, the information processing unit 103 acquires the signal intensity measurement values ​​obtained for each of the (n+1) or more light irradiation output values ​​that are equal to or greater than the predetermined value.

[0044] In step S102, the number of events of the fluorescence signal acquired for each of the plurality of light irradiation output values ​​may be, for example, 500 to 10,000 events, preferably 1,000 to 70,000 events, and more preferably 2,000 to 50,000 events.

[0045] The signal intensity measurement obtained in step S102 may be, for example, the median or mean of the output of the fluorescence channel. When the detection unit includes multiple photodetectors (e.g., multiple APDs or multiple MPPCs), each photodetector may be configured as a single fluorescence channel. In this case, the information processing unit 103 may, in step S102, acquire signal intensity measurements for each of the one or more fluorescence channels for which linearity calibration is required. In this disclosure, the processing in steps S103 to S108 may be performed for each fluorescence channel.

[0046] In step S103, the information processing unit 103 creates a first-order approximation formula using the detection unit's measured value at a light irradiation output value less than or equal to the predetermined value.

[0047] In step S104, the information processing unit 103 obtains a correlation index for the first-order approximation formula. The correlation index is, for example, the coefficient of determination R 2 That's fine.

[0048] In step S105, the information processing unit 103 determines whether the correlation index is equal to or greater than a predetermined threshold. The predetermined threshold may be appropriately set by a person skilled in the art so as to determine the validity of the first-order approximation formula. The correlation index is the coefficient of determination R 2 That is In this case, the predetermined threshold may be any of the following values, for example, 0.9900 or more, 0.9990 or more, or 0.9995 or more. In one embodiment, in the same step, the information processing unit 103 determines that the correlation index is greater than 0.9995 (R 2 Determine if it is >0.9995.

[0049] In step S105, if the correlation index is greater than or equal to a predetermined threshold, the information processing unit 103 proceeds to step S106. If the correlation index is less than or equal to a predetermined threshold, the information processing unit 103 proceeds to step S110.

[0050] In step S110, the information processing unit 103 may generate an alert to inform the user that, for example, a suitable first-order approximation formula was not generated. Along with generating the alert, the information processing unit 103 may output a screen to the display device asking the user whether to run the nth-order approximation formula generation process again.

[0051] In step S106, the information processing unit 103 substitutes each of the optical irradiation output values ​​from the plurality of optical irradiation output values ​​that are greater than or equal to the predetermined value into the linear approximation formula generated in step S103 to obtain a signal intensity calculation value corresponding to each of the optical irradiation output values ​​that are greater than or equal to the predetermined value. This provides data including pairs of signal intensity measurement values ​​and signal intensity calculation values ​​for each of the optical irradiation output values ​​that are greater than or equal to the predetermined value. This data may be associated with each optical irradiation output value. The signal intensity calculation value can also be said to be the true value calculated based on the assumed linearity.

[0052] In step S107, the information processing unit 103 generates an nth-order approximation formula using a data set that includes pairs of signal intensity measurements obtained when the light irradiation output value is greater than or equal to the predetermined value, and signal intensity calculation values ​​calculated by substituting the light irradiation output value into the first-order approximation formula. Here, n may be an odd number greater than or equal to 3. n may be, for example, 3, 5, 7, or 9, and in order to reduce the number of data points required, it may be particularly 3, 5, or 7, more particularly 3 or 5, and even more particularly 3.

[0053] The number of pairs used to generate the nth-order approximation formula may be adjusted as appropriate depending on the nth-order approximation formula to be generated, and may be, for example, n+1 or more. For example, in the case of a third-order approximation formula, at least four pairs are prepared. Thus, in this disclosure, the information processing device can create a first-order approximation formula that shows the relationship between a light irradiation output value less than or equal to a predetermined value and a signal intensity measurement value obtained in the case of said light irradiation output value, and can use said first-order approximation formula to obtain a set of data for generating the nth-order approximation formula.

[0054] In step S108, the information processing unit 103 saves the nth-order approximation formula generated in step S107. This nth-order approximation formula is used in subsequent biological sample analysis processing.

[0055] In step S108, the signal intensity calculation value obtained when the light irradiation output value is the predetermined value is stored as a threshold value to be used in the correction process described later.

[0056] In step S109, the information processing unit 103 completes the nth-order approximation formula generation process. After this process is completed, biological sample analysis may be performed using the biological sample analyzer 100. In this biological sample analysis, the correction process described in (2-3) below is performed.

[0057] (2-3) Flowchart of correction process using the nth-order approximation formula

[0058] The biological sample analyzer 100 shown in Figure 3 performs an analysis of a biological sample. This analysis may include flow cytometry. In this analysis, the light irradiation unit 101 irradiates the biological particles contained in the biological sample with light. The detection unit 102 then detects the light generated by the light irradiation. The information processing unit 103 performs a correction process using the nth-order approximation formula on the data related to the detected light (particularly the fluorescence signal intensity data). An example of a flowchart of this correction process is shown in Figure 5.

[0059] In step S201 of the flowchart shown in Figure 5, the information processing unit 103 starts the correction process.

[0060] In step S202, the information processing unit 103 acquires the measured value of the fluorescence signal intensity of the fluorescence detected by the detection unit (also referred to as the "detection unit measurement value").

[0061] In step S203, the information processing unit 103 determines whether the measurement value from the detection unit is greater than the threshold value described in step S108. If the measurement value from the detection unit is greater than the threshold, the information processing unit 103 proceeds to step S204. If the measurement value from the detection unit is less than or equal to the threshold, the information processing unit 103 proceeds to step S206. Thus, in this disclosure, the information processing unit can determine whether to correct the signal intensity measurement value using the signal intensity calculation value calculated using the first-order approximation formula as a threshold value when the light irradiation output value is the predetermined value.

[0062] In step S204, the information processing unit 103 corrects the measurement value of the detection unit using the nth-order approximation formula described in step S108. For example, the information processing unit 103 substitutes the measurement value of the detection unit into the nth-order approximation formula to obtain the corrected measurement value.

[0063] In step S205, the information processing unit 103 adopts the corrected measurement value as the output value.

[0064] In step S206, the information processing unit 103 adopts the measurement value from the detection unit as the output value.

[0065] In step S207, the information processing unit 103 terminates the correction process.

[0066] As described in (2-2) above, when the detection unit includes multiple photodetectors (for example, multiple APDs or multiple MPPCs), each photodetector may be set as one fluorescence channel. In this case, the information processing unit 103 executes the processing in steps S102 to S108 for each fluorescence channel and obtains an nth-order approximation formula and threshold for each fluorescence channel. The information processing unit 103 may perform the correction processing described above for each fluorescence channel using the nth-order approximation formula and threshold for each fluorescence channel. Furthermore, the information processing unit 103 may perform the above correction processing for each event. The fluorescence signal data obtained in this way is more appropriate because the problem of linearity degradation has been resolved.

[0067] (2-4) Examples of generating an nth-order approximation formula and correcting the formula using the approximation formula.

[0068] Below, an example of the process for generating an nth-order approximation formula will be described with reference to Figure 6. Figure 6 shows the data obtained in the said generation process. The said generation process was performed using a flow cytometer equipped with a detection unit including an MPPC as a photodetector. The flow cytometer performed the following steps. In this example, the generation of a third-order approximation formula for correcting the output value of a FITC fluorescence channel is described. A third-order approximation formula may be generated similarly for other fluorescence channels.

[0069] Step 1. The laser power (light irradiation output value) of the laser light source included in the light irradiation section of the flow cytometer was set to 60 mW. With this setting, the flow cytometer performed analysis on a predetermined fluorescent bead and acquired 3,000 event data points. For these event data, the median value of the output value of the FITC fluorescence channel was obtained, and this median value was used as the signal intensity measurement value.

[0070] Step 2. The light irradiation output value and threshold value of the flow cytometer were set to 1 / a of the values ​​in Step 1. Here, a was 3 as shown in Figure 6. After this setting, the flow cytometer performed analysis on the fluorescent beads and acquired 3,000 event data points. For these event data, the median value of the output value of the FITC fluorescence channel was obtained, and this median value was used as the signal intensity measurement value. This measurement value is also shown in the same figure.

[0071] Step 3. The light irradiation output value and threshold value of the flow cytometer were set to 1 / b of the values ​​in Step 1. Here, b was 10 as shown in Figure 6. After this setting, the flow cytometer performed analysis on the fluorescent beads to acquire 3,000 event data points. For these event data, the median value of the output value of the FITC fluorescence channel was obtained, and this median value was used as the signal intensity measurement value.

[0072] Step 4. The light irradiation output value and threshold value of the flow cytometer were set to 1 / c of ​​the values ​​in Step 1. Here, c was 30 as shown in Figure 6. After this setting, the flow cytometer performed analysis on the fluorescent beads to acquire 3,000 event data points. For these event data, the median value of the output value of the FITC fluorescence channel was obtained, and this median value was used as the signal intensity measurement value.

[0073] Step 5. The light irradiation output value and threshold value of the flow cytometer were set to 1 / d of the values ​​in Step 4. Here, d was 2 as shown in Figure 6. After this setting, the flow cytometer performed analysis on the fluorescent beads to acquire 3,000 event data points. For these event data, the median value of the output value of the FITC fluorescence channel was obtained, and this median value was used as the signal intensity measurement value.

[0074] Step 6. The light irradiation output value and threshold value of the flow cytometer were set to 1 / e of the values ​​in Step 4. Here, e was 4 as shown in Figure 6. After this setting, the flow cytometer performed analysis on the fluorescent beads to acquire 3,000 event data points. For these event data, the median value of the output value of the FITC fluorescence channel was obtained, and this median value was used as the signal intensity measurement value.

[0075] Step 7. From the measurement results obtained in Steps 4, 5, and 6 above, a linear approximation formula between the light irradiation output value (laser power) and the signal intensity measurement value (Median) was generated. Also, regarding the linear approximation formula, the coefficient of determination R 2 was 0.999965. The coefficient of determination satisfied the condition of R 2 > 0.9995.

[0076] Step 8. The laser powers set in Steps 1 to 4 were substituted into the linear approximation formula generated in Step 7 to obtain the calculated signal intensity values for each laser power. These calculated signal intensity values are shown as true values in the figure.

[0077] Step 9. A cubic approximation formula was generated from the signal intensity measurement values and the calculated signal intensity values in Steps 1 to 4. The cubic approximation formula was adopted as an approximation formula for correction processing. Also, the calculated signal intensity value (true value) in Step 4 was adopted as the threshold value used in the correction processing.

[0078] Step 10. A biological particle analysis process was performed. In the analysis process, when the signal intensity measurement value was greater than or equal to the threshold value (the calculated signal intensity value calculated for Step 4), the signal intensity measurement value was corrected by the cubic approximation formula generated in Step 9. On the other hand, when the signal intensity measurement value was less than the calculated signal intensity value calculated for Step 4, the correction was not performed.

[0079] Note that a, b, c, d, and e mentioned in Steps 2 to 6 above were set to satisfy the following relationships. Setting the light irradiation output value to satisfy such relationships is effective for generating a good nth - order approximation formula and a linear approximation formula. 1 < a < b < c, and 1 < d < e

[0080] Regarding Steps 1 to 10 described above, a more detailed explanation will be given below.

[0081] The laser power used in Step 1 should ideally be such that the fluorescence signal intensity does not saturate, and is close to the saturation point. The laser power used in Steps 1-3 is high, and such high laser power raises concerns about linearity degradation. On the other hand, the laser power used in Steps 4-6 is relatively low, and linearity degradation is not a concern. The signal intensity at saturation in the fluorescence channel is 1048575.

[0082] Therefore, in step 8, a first-order approximation formula was generated that shows the relationship between the signal intensity measurements and laser power measured in steps 4-6. Note that two data points are sufficient to generate a first-order approximation formula, but three points were used above to account for measurement variability, etc. Furthermore, the generated first-order approximation formula was R 2 The validity was determined by the following. The calculated R 2 As mentioned above, this was 0.999965. Then, using this first-order approximation formula, the signal intensity (true value) for the laser power adopted in steps 4 to 6 was obtained.

[0083] Then, in step 9, a cubic approximation formula was generated using the signal intensity measurements and calculated signal intensity values ​​from steps 1 to 4.

[0084] Using the aforementioned third-order approximation formula, the signal intensity measurements taken when the laser power was 50, 40, 16, 7, and 3 mW were corrected (the row labeled "Verification Measurement" in Figure 6). These signal intensity measurements are greater than or equal to the true values ​​calculated in step 8 for step 4. The corrected values ​​are shown in the same figure. The difference percentage between the true value and the corrected value, as well as the difference percentage between the true value and the measured signal strength, are also shown. For example, the difference percentage between the true value and the measured signal strength was a maximum of 9.1%. On the other hand, the difference percentage between the true value and the corrected value was a maximum of 0.8%. Thus, the difference percentage is smaller by using the cubic approximation formula, indicating that the signal strength was appropriately corrected.

[0085] Furthermore, if the signal strength measurement is greater than or equal to the true value in step 4, 32008, a correction is performed using the aforementioned third-order approximation formula. If it is less than 32008, no correction is performed because the output is low and there is no concern about linearity. This prevents unnecessary correction processing from being performed and increases processing speed.

[0086] (3) Carryover removal

[0087] (3-1) Basic Concepts of Carryover Removal

[0088] As mentioned above, verification or adjustment processes using several types of beads are performed to improve the detection accuracy of biological sample analyzers such as flow cytometers, or for quality control (QC).

[0089] For example, beads composed of multiple particle groups having progressively different fluorescence intensity levels may be used for verifying or adjusting the detection unit. Such beads are used, for example, to verify the fluorescence sensitivity of the instrument, and examples include beads configured to form multiple peaks, such as 4-peak beads, 6-peak beads, and 8-peak beads. An example of such beads is 8peakBeads (Sony Group Corporation), which is used for verifying fluorescence sensitivity. In addition to the beads used for fluorescence sensitivity verification, other beads such as alignment beads or focus adjustment beads for the instrument may also be used. An example of such beads is AlignCheck Beads (Sony Group Corporation), which are used for adjusting or checking the condition of the instrument.

[0090] If verification or adjustment processing using the fluorescence sensitivity verification beads is performed while the aforementioned other beads remain in the device, the aforementioned other beads may affect the processing. To improve detection accuracy, it is desirable to eliminate this effect as much as possible. Thorough cleaning of the flow path can be considered to completely eliminate this effect, but this can take a considerable amount of time.

[0091] Based on the above, this disclosure aims to provide a technique for eliminating the influence of other beads in verification or adjustment processes using the aforementioned fluorescence sensitivity verification beads. Within this specification, the process for removing the effects of other particles is also referred to as the "carryover removal process."

[0092] A biological sample analyzer according to this disclosure includes a detection unit that detects light generated by light irradiation of particles, and an information processing unit that processes signal intensity data of the light detected by the detection unit. The information processing unit is configured to perform a removal process to remove signal intensity data relating to particle groups that do not belong to the particle group from the signal intensity data of light generated by irradiating a sample containing a particle group composed of multiple particle groups having stepwise different fluorescence intensity levels. Furthermore, in the removal process, the information processing unit performs a setting process to set two or more of the multiple photodetectors included in the detection unit as fluorescence channels used to identify particle groups that do not belong to the particle group. By performing the removal process and the setting process, the influence of other beads in the verification or adjustment process using the beads for fluorescence sensitivity verification can be eliminated, which enables more appropriate fluorescence sensitivity verification and also contributes to improving detection accuracy. Furthermore, since the aforementioned effects are removed by this removal process, it becomes possible to shorten the time required for the cleaning process or to simplify the cleaning process, thereby shortening the execution time of QC.

[0093] Preferably, the information processing unit performs the fluorescence channel setting process such that the fluorescence intensity level of the particle group not belonging to the particle group is greater than the fluorescence intensity level of any of the multiple particle groups included in the particle group. This makes it easier to perform the identification and removal processes described later.

[0094] (3-2) Flowchart of the detection unit evaluation process including carryover removal process

[0095] Examples of processing by the information processing unit described above will be explained with reference to Figures 3 and 7. Figure 3 is as described in (2) above. Figure 7 is an example of a flowchart of the processing performed by the information processing unit.

[0096] In step S301 of Figure 7, the information processing unit 103 starts the carryover removal process.

[0097] Before step S301 is initiated, the biological sample analyzer 100 may undergo a verification or adjustment process using the other particles (e.g., beads) described above. That is, the information processing unit 103 may be configured to perform a detection unit evaluation process, including a carryover removal process, after the verification or adjustment process using the other particles has been performed. The other particles will also be referred to as "carryover" below. That is, the carryover may remain in the biological sample analyzer 100.

[0098] In step S302, the information processing unit 103 acquires signal intensity data of light generated by light irradiation of a sample containing a particle population composed of multiple types of particles having progressively different fluorescence intensity levels. To acquire this data, the biological sample analyzer 100 performs flow cytometry on the sample. This acquires event data for the sample. In this step, the carryover may remain in the biological sample analyzer 100 (particularly in the flow path) as described above. The carryover is a group of particles that do not belong to the particle population. Furthermore, as described above, because the carryover remains in the biological sample analyzer 100 (particularly in the flow path), when flow cytometry is performed on the sample, flow cytometry is also performed on the carryover. Furthermore, the particle group composed of multiple types of particles having progressively different fluorescence intensity levels may be, for example, a group of fluorescent beads, and more specifically, a group of fluorescent beads used to verify the fluorescence sensitivity of an instrument. Examples of such a group of fluorescent beads include groups of beads configured to form multiple peaks, such as 4-peak beads, 6-peak beads, and 8-peak beads.

[0099] In step S303, the information processing unit 103 performs an extraction process to extract singlet data from the event data. For this extraction process, the information processing unit may, for example, generate scattered light plot data from event data acquired by flow cytometry, and extract singlet data by setting a predetermined gate on the scattered light plot data.

[0100] In step S304, the information processing unit 103 performs a process to set two or more of the multiple photodetectors included in the detection unit as fluorescence channels to be used to identify particle groups (carryover) that do not belong to the particle group, in the removal process described later. For example, in the same step, the information processing unit performs the setting process such that the fluorescence intensity level of the carryover is greater than the fluorescence intensity level of any of the multiple particle groups included in the particle population.

[0101] In a preferred embodiment, in step S304, the information processing unit sets two photodetectors as fluorescence channels to be used to identify the carryover. Preferably, the information processing unit sets the two fluorescence channels such that when two-dimensional plot data is generated based on the signal intensity data acquired by the two photodetectors set as two fluorescence channels, the carryover is greater than the fluorescence intensity level of any of the multiple particle groups included in the particle population. Each axis of the two-dimensional plot data may be the fluorescence signal intensity derived from the fluorescent dyes assigned to the two fluorescence channels. Alternatively, the two-dimensional plot data may be two-dimensional plot data generated from the singlet data obtained by the extraction process in step S303.

[0102] The setting of the aforementioned fluorescence channels will be explained below with reference to Figure 8. Figure 8 shows an example of two-dimensional plot data generated by performing the flow cytometry described above. This two-dimensional plot data has the fluorescence intensities of the two fluorescence channels as the X axis and Y axis, respectively. For example, in the same step, two fluorescence channels are set up such that the carryover plot (the plot in the dashed circle indicated by reference numeral 351) in the two-dimensional plot data 350 shown in the figure is located further from the intersection point 353 of the two axes of the plot data than any of the plots of the multiple types of particle groups (multiple plots in the dashed circle indicated by reference numeral 352).

[0103] In a preferred embodiment of the present disclosure, in the same step, the information processing unit calculates the sum of squares of the fluorescence intensities of each axis of the two-dimensional plot data for the plurality of particle groups and the carryover, and sets the fluorescence channels to be used as each axis of the two-dimensional plot data such that the sum of squares for the carryover is greater than any of the sum of squares for the plurality of particle groups. The characteristics of the fluorescence of the multiple particle groups and the carryover used to improve detection accuracy are usually known. A person skilled in the art can identify the fluorescence channels that should be appropriately set based on these characteristics. Therefore, the fluorescence channels to be set may be predetermined. For example, when the verification process using 8peakBeads is performed after the adjustment process using AlignCheck Beads, the FITC channel and the PE channel may be set as the two fluorescence channels.

[0104] In step S305, the information processing unit performs a selection process to identify signal intensity data from the signal intensity data acquired by the fluorescence channel, relating to the group of particles having the highest fluorescence intensity level among the multiple groups of particles constituting the particle population. By performing the setting process in step S304 and the selection process, it is possible to appropriately distinguish between carryover data to be removed and data relating to the multiple groups of particles that should not be removed.

[0105] In one preferred embodiment, the information processing unit generates a signal intensity histogram for each of the two fluorescence channels set in step S304 during the specific processing, and identifies the group of particles having the highest fluorescence intensity level based on the histogram. The histogram may be a histogram in which the signal intensity is used as a class and the number of events for each signal intensity is used as a frequency. Preferably, in this embodiment, the information processing unit identifies the signal intensity recording the maximum number of events (hereinafter referred to as the "maximum event signal intensity") in each of the two generated histograms. To identify the maximum event signal intensity, the proportion of the particle group having the maximum fluorescence intensity level among the multiple particle groups constituting the particle population may be referenced. Next, for each histogram, the information processing unit identifies the maximum signal intensity in the particle group having the maximum fluorescence intensity level based on the maximum event signal intensity. In this way, the range of the particle group having the maximum fluorescence intensity level is identified in each histogram, and the maximum signal intensity within that range is identified for each histogram. The sum of the squares of the maximum signal intensities for each histogram may be used for the removal conditions described later. This embodiment will be described below with reference to Figures 9 and 10. Figure 9 is an example of a flowchart of the specific process. Figure 10 is a schematic diagram showing the generated histogram. The information processing unit can execute the specific process shown in the flowchart for each of the two generated histograms.

[0106] In step S401, the information processing unit 103 starts the specific processing.

[0107] In step S402, the information processing unit 103 generates a signal intensity histogram for each of the two configured fluorescence channels. This histogram is a histogram in which the signal intensity is used as a class and the number of events for each signal intensity is used as a frequency. In step S402, such a histogram is generated for each of the two fluorescence channels. A schematic diagram of this histogram is shown in Figure 10, and the following explanation will refer to this schematic diagram. Note that this schematic diagram is provided to help understand the process being performed and may differ from the actual histogram.

[0108] In step S403, the information processing unit performs a filtering process to filter a portion of the signal intensity data acquired by the fluorescence channel using a numerical value set based on the proportion of the particle group having the highest fluorescence intensity level among the multiple particle groups constituting the particle population. For example, suppose the particle population consists of eight different particle groups, and the proportions of these eight particle groups are the same. In this case, the proportion of the particle group with the highest fluorescence intensity level is 12.5% ​​(=1 / 8). Considering the presence of carryover, when the event data is sorted by signal intensity, the particle group with the highest fluorescence intensity level is within the top 13%. Next, considering the uniformity of the particles, for example, within the top 6.5% (=13% / 2), there is a signal intensity with the largest number of events among the particle group with the highest fluorescence intensity level. Therefore, a filtering process is performed to filter out the top 6.5%. This makes it possible to identify the signal intensity with the largest number of events. In step S403, for example, in the histogram 451 of Figure 10, the range indicated by arrow 452 is filtered. By performing this filtering process, the specific processing in the next step, S404, is executed correctly.

[0109] In step S404, the information processing unit identifies the signal intensity that records the maximum number of events (maximum event count signal intensity) from the data within the range filtered in step S403. For example, in histogram 451 of Figure 10, the position indicated by "max" on the vertical axis represents the maximum number of events, and the signal intensity that recorded this maximum number of events is the value on the horizontal axis at the position indicated by arrow 453. Thus, in the specified processing, the information processing unit can identify the signal intensity output value that has the maximum number of events among the signal intensity data relating to the particle group having the maximum fluorescence intensity level.

[0110] In step S405, the information processing unit identifies signal intensity data relating to the group of particles having the maximum fluorescence intensity level based on the maximum event count signal intensity. In the same step, for example, the information processing unit may filter the group of particles having the maximum fluorescence intensity level based on the maximum event count signal intensity for this purpose. Identifying signal intensity data originating from particles belonging to the group of particles having the maximum fluorescence intensity level contributes to appropriate distinction from carryover signal intensity data. For example, the signal intensity recorded by multiplying the maximum number of events in the histogram by a predetermined ratio (e.g., any value between 0.0001 and 0.1, particularly 0.01) is calculated and identified. This identified signal intensity exists in two locations at the base of the histogram's peak. The region between these two locations is identified as the region corresponding to the signal intensity data for the particle group having the maximum fluorescence intensity level. In the histogram shown in Figure 10, the position indicated by "max × 0.01" on the vertical axis corresponds to the position of the event number obtained by multiplying the maximum number of events by the predetermined ratio. At this position, the corresponding range on the horizontal axis is indicated by arrow 454. This range indicated by arrow 454 is filtered as the region corresponding to the signal intensity data for the group of particles having the maximum fluorescence intensity level.

[0111] In step S406, the information processing unit identifies the maximum signal intensity from the signal intensity data identified in step S405. In the histogram shown in Figure 10, the value on the horizontal axis at the position indicated by arrow 455 is the maximum signal intensity.

[0112] In step S407, the information processing unit terminates the specific processing and proceeds to step S306.

[0113] In step S306, the information processing unit sets a removal condition for removing signal intensity data relating to particle groups that do not belong to the particle group, based on the signal intensity data relating to the particle group having the maximum fluorescence intensity level.

[0114] Preferably, in step S306, the information processing unit sets the removal condition based on the maximum signal intensity value among the signal intensity data relating to the group of particles having the maximum fluorescence intensity level. As described above, in step S305, the maximum signal intensity is identified from each of the two histograms as described above. The information processing unit calculates the sum of the squares of these two maximum signal intensity values. This sum of squares is adopted as the threshold used in the removal condition. This sum of squares separates the two groups on the two-dimensional plot data, as shown by the dashed line 460 in Figure 11. Of the region separated by the dashed line 460, the upper right portion is the region where event data to be removed (i.e., carryover data) exists. The information processing unit then sets a removal condition to remove event data if the sum of squares of the event data is equal to or greater than the threshold.

[0115] In step S307, a process is executed to remove signal intensity data that satisfies the removal conditions set in step S306. For example, the information processing unit calculates the sum of squares for each event data. The information processing unit removes the event data if the sum of squares is equal to or greater than the threshold. The information processing unit does not remove the event data if the sum of squares is less than or equal to the threshold. In the latter case, the event data that was not removed is used in the evaluation process of steps S308 to S310.

[0116] In step S308, two or more evaluation fluorescence channels, particularly two evaluation fluorescence channels, are set up to evaluate the signal intensity data after the removal process. Preferably, these two evaluation fluorescence channels are different from the two fluorescence channels set up in step S304. These two evaluation fluorescence channels may be predetermined for a sample containing a particle population composed of multiple particle groups having stepwise different fluorescence intensity levels. Note that the two evaluation fluorescence channels may be the same as the two fluorescence channels set in step S304.

[0117] In step S309, the information processing unit performs separation and identification processing on the signal intensity data acquired in the two evaluation fluorescence channels set in step S308. Through this separation and identification processing, the information processing unit identifies groups of particles having progressively different fluorescence intensity levels, corresponding to the number of stages (or the number of species) from the signal intensity data after the removal processing in step S307. In this way, data from multiple groups (the same number of groups as the number of stages) are identified from the signal intensity data after the removal processing. This separation and identification processing is preferably performed using the k-means method. The separation and identification processing using the k-means method may be performed using methods known in the art, for example, as described in Patent Document 1. For example, if the particle population consisting of multiple groups of particles having progressively different fluorescence intensity levels is an 8-peak bead, the information processing unit, through the separation and identification process, identifies the 8 populations from the signal intensity data after the removal process in step S307, for example, using the k-means method.

[0118] In step S310, the information processing unit performs an evaluation process using the data of the multiple groups identified in step S309. To perform the evaluation process, the information processing unit calculates, for example, statistical values ​​for each of the data of the multiple groups. These statistical values ​​may be, for example, the median or mean fluorescence intensity (MFI) of each group, or other statistical values ​​calculated using the median and / or MFI. The information processing unit uses the statistical values ​​of each of the multiple data sets to perform the evaluation process, particularly the evaluation process of the detection unit. This evaluation may be, for example, an evaluation based on sensitivity information. This sensitivity information may be, for example, an evaluation based on linearity or fluorescence detection sensitivity (MESF). The method for calculating this sensitivity information may be carried out by a method known in the art, for example, as described in Patent Document 1. The information processing unit calculates sensitivity information (for example, the linearity and / or the fluorescence detection sensitivity) using the statistical values ​​and evaluates the detection unit based on the calculated sensitivity information. For example, the linearity is evaluated as better the closer it is to 100%. Also, the fluorescence detection sensitivity is evaluated as better the closer it is to a predetermined reference value (for example, 0).

[0119] Since the separation and identification process in step S309 and the evaluation process in S310 are performed after the carryover has been removed as described above, more appropriate evaluation results are obtained, thereby improving detection accuracy.

[0120] In step S311, the information processing unit terminates the process.

[0121] (3-3) Specific examples of detection unit evaluation processing including carryover removal processing The carryover removal process described in (3-2) above was implemented in the information processing unit of the flow cytometer. Then, the flow cytometer was used to perform the adjustment process using the AlignCheck Beads. Next, with the carryover of the AlignCheck Beads still present in the flow cytometer, the processes of steps S301 to S311 described in (3-2) above were performed using the 8peakBeads. In other words, the 8peakBeads correspond to a sample containing a group of particles composed of multiple types of particles having progressively different fluorescence intensity levels in these steps.

[0122] The specific conditions adopted in steps S301 to S311 were as follows: In step S304, the FITC channel and the PE channel were set as the fluorescent channels used for carryover removal. In step S305, steps S401 to S407 described above were performed. Of these steps, in step S403, since the 8peakBeads contain eight types of particle groups, each with a composition ratio of 12.5%, a filtering process was performed to filter out the above 6.5%. In step S405, the signal intensity recorded was determined by multiplying the maximum number of events by the predetermined ratio of 0.01. In step S306, the sum of squares of the maximum signal intensity values ​​identified from the histograms of the FITC channel and the PE channel was calculated, and this sum of squares was adopted as the threshold value to be used in the removal condition. The information processing unit set a removal condition in which the event data is removed if the sum of squares of the event data is greater than or equal to the threshold value. In step S307, a process was executed to remove signal intensity data that satisfies the removal conditions set in step S306. In step S308, the VioGreen channel and the PE channel were set as two evaluation fluorescence channels for evaluating the signal intensity data after the removal process. In step S309, a separation and identification process was performed. This separation and identification process was carried out using the k-means method. The evaluation results for the multiple groups identified by this separation and identification process are shown in Figures 12A and 12B. In these figures, the Y axis represents the number of events. The X axis represents the signal intensity in the FITC channel and the signal intensity in the VioGreen channel, respectively. Figure 13 shows two-dimensional plot data with VioGreen and PE as the X and Y axes, respectively. From these figures, it can be seen that the peaks of each of the eight particle groups of the 8peakBeads were appropriately identified. In addition, although carryovers are indicated by symbols 470, 471, and 472 in these figures, it was confirmed that these carryovers were removed and the separation and identification process was performed.

[0123] (4) The manner in which (2) and (3) above are carried out.

[0124] The biological sample analyzer of this disclosure may perform both the linearity correction process described in (2) above and the evaluation process including the carryover removal process described in (3) above. The biological sample analyzer may perform the evaluation process including the carryover removal process after performing the linearity correction process. This improves the detection accuracy through the linearity correction process and removes the carryover from the linearity correction process through the carryover removal process, thereby enabling the evaluation process to be performed appropriately.

[0125] In other words, this disclosure includes a light irradiation unit that irradiates particles with light, A detection unit for detecting light generated by the aforementioned light irradiation, It includes an information processing unit that controls the light irradiation unit and the detection unit, The information processing unit performs a correction process to correct the measured light signal intensity value detected by the detection unit, based on the relationship between the light irradiation output value of the light irradiation unit and the measured light signal intensity value detected by the detection unit, and then, The information processing unit is configured to perform a removal process to remove signal intensity data relating to particle groups that do not belong to the particle group from the signal intensity data of light generated by light irradiation of a sample containing a particle group consisting of multiple particle groups having stepwise different fluorescence intensity levels. We also provide biological sample analysis equipment. The information processing unit may, in the removal process, perform a setting process to set two or more of the multiple photodetectors included in the detection unit as fluorescence channels used to identify particle groups that do not belong to the particle group.

[0126] Furthermore, this disclosure may also take the following form. [1] A light irradiation unit that irradiates particles with light, A detection unit for detecting light generated by the aforementioned light irradiation, It includes an information processing unit that controls the light irradiation unit and the detection unit, The information processing unit corrects the measured light signal intensity value detected by the detection unit based on the relationship between the light irradiation output value of the light irradiation unit and the measured light signal intensity value detected by the detection unit. Biological sample analysis device. [2] The information processing unit is configured to perform the correction using an nth-order approximation formula that shows the relationship, In the above n-th degree approximation formula, n is an odd number greater than or equal to 3. The biological sample analyzer described in [1]. [3] The aforementioned information processing unit, A linear approximation formula is created to show the relationship between the light irradiation output value below a predetermined value and the signal intensity measurement value obtained at that light irradiation output value, and then, Using the first-order approximation formula, a set of data for generating the nth-order approximation formula is obtained. The biological sample analyzer described in [2]. [4] The information processing unit is a biological sample analyzer according to [3], which obtains a correlation index for the first-order approximation formula. [5] The information processing unit determines whether the correlation index satisfies predetermined conditions, as described in [4], for the biological sample analysis apparatus. [6] The biological sample analyzer according to any one of [3] to [5], wherein the information processing unit generates the nth-order approximation formula using a data set that includes pairs of signal intensity measurements obtained when the light irradiation output value is greater than or equal to a predetermined value and signal intensity calculation values ​​calculated by substituting the said light irradiation output value into the first-order approximation formula. [7] The information processing unit determines whether to correct the signal intensity measurement value using the signal intensity calculation value calculated using the first-order approximation formula as a threshold when the light irradiation output value is the predetermined value, according to any one of [3] to [6]. [8] The detection unit includes one or more MPPCs as detectors for detecting the light, according to any one of [1] to [7], for the biological sample analysis apparatus. [9] A detection unit that detects light generated by light irradiation on particles, The system includes an information processing unit that processes the light signal intensity data detected by the detection unit, The information processing unit is configured to perform a removal process to remove signal intensity data relating to particle groups that do not belong to the particle group from the signal intensity data of light generated by light irradiation of a sample containing a particle group consisting of multiple particle groups having stepwise different fluorescence intensity levels. Here, the information processing unit performs a setting process in which, in the removal process, it sets two or more of the multiple photodetectors included in the detection unit as fluorescence channels to be used to identify particle groups that do not belong to the particle group. Biological sample analysis device.

[10] The biological sample analyzer according to [9], wherein the information processing unit performs the fluorescence channel setting process such that the fluorescence intensity level of the particle group not belonging to the particle group is greater than the fluorescence intensity level of any of the multiple particle groups included in the particle group.

[11] The information processing unit, in the removal process, From the signal intensity data acquired by the fluorescence channel, a specific processing is performed to identify the signal intensity data relating to the group of particles having the highest fluorescence intensity level among the multiple groups of particles constituting the particle population. A biological sample analyzer as described in [9] or

[10] .

[12] The information processing unit, in the specific processing, Identify the signal intensity output value that maximizes the number of events among the signal intensity data for the particle group with the highest fluorescence intensity level. The biological sample analyzer described in

[11] .

[13] The information processing unit, in the specific processing, Based on the signal intensity output value that maximizes the number of events, signal intensity data relating to the group of particles with the highest fluorescence intensity level is identified. The biological sample analyzer described in

[12] .

[14] The information processing unit, in the removal process, Based on the signal intensity data relating to the group of particles having the maximum fluorescence intensity level, a removal condition is set to remove signal intensity data relating to a group of particles that do not belong to the aforementioned particle group. A biological sample analyzer described in any one of

[11] to

[13] .

[15] The biological sample analyzer according to

[14] , wherein the information processing unit sets the removal conditions based on the maximum value of the signal intensity output value among the signal intensity data relating to the group of particles having the maximum fluorescence intensity level.

[16] The biological sample analyzer according to any one of [9] to

[15] , wherein the information processing unit further performs a setting process to set two or more of the plurality of photodetectors included in the detection unit as fluorescence channels used to evaluate the signal intensity data after the removal process.

[17] The biological sample analyzer according to

[16] , wherein the information processing unit performs a separation and identification process on the signal intensity data after the removal process using the k-means method. [Explanation of symbols]

[0127] 100 Biological Sample Analysis Devices 101 Light-irradiating section 102 Detection unit 103 Information Processing Department

Claims

1. A light irradiation unit that irradiates particles with light, A detection unit for detecting light generated by the aforementioned light irradiation, It includes an information processing unit that controls the light irradiation unit and the detection unit, The information processing unit corrects the measured light signal intensity value detected by the detection unit based on the relationship between the light irradiation output value of the light irradiation unit and the measured light signal intensity value detected by the detection unit. The system is configured to perform the correction using an nth-order approximation formula that shows the aforementioned relationship. In the above nth-order approximation formula, n is an odd number of 3 or greater. A first-order approximation formula is created that shows the relationship between a light irradiation output value below a predetermined value and a signal intensity measurement value obtained at that light irradiation output value, and a data set for generating the nth-order approximation formula is obtained using this first-order approximation formula. Biological sample analysis device.

2. The biological sample analysis apparatus according to claim 1, wherein the information processing unit obtains a correlation index of the first-order approximation formula.

3. The biological sample analyzer according to claim 2, wherein the information processing unit determines whether the correlation index satisfies predetermined conditions.

4. The biological sample analyzer according to claim 1, wherein the information processing unit generates the nth-order approximation formula using a data set that includes pairs of signal intensity measurements obtained when the light irradiation output value is greater than or equal to a predetermined value and signal intensity calculation values ​​calculated by substituting the light irradiation output value into the first-order approximation formula.

5. The biological sample analyzer according to claim 1, wherein the information processing unit determines whether to correct the signal intensity measurement value using the signal intensity calculation value calculated using the first-order approximation formula as a threshold value when the light irradiation output value is the predetermined value.

6. The biological sample analysis apparatus according to claim 1, wherein the detection unit includes one or more MPPCs as detectors for detecting the light.

7. A detection unit that detects light generated by light irradiation on particles, The system includes an information processing unit that processes the light signal intensity data detected by the detection unit, The information processing unit is configured to perform a removal process to remove signal intensity data relating to particle groups that do not belong to the particle group from the signal intensity data of light generated by light irradiation of a sample containing a particle group consisting of multiple particle groups having stepwise different fluorescence intensity levels. Here, the information processing unit performs a setting process in which, in the removal process, it sets two or more of the multiple photodetectors included in the detection unit as fluorescence channels to be used to identify particle groups that do not belong to the particle group. From the signal intensity data acquired by each of the two fluorescence channels among the configured fluorescence channels, a specific process is performed to identify the maximum value of the signal intensity output value of the signal intensity data relating to the group of particles having the highest fluorescence intensity level among the multiple groups of particles constituting the particle population. The sum of the squares of the maximum values ​​of these two signal intensity output values ​​is calculated, and this sum of squares is adopted as the threshold value to be used in the rejection condition. For each event data, the sum of the squares of the signal intensities of the two fluorescence channels is calculated, and if the sum of the squares of the event data is greater than or equal to the threshold, the event data is removed. Biological sample analysis device.

8. The biological sample analyzer according to claim 7, wherein the information processing unit performs the fluorescence channel setting process such that the fluorescence intensity level of the particle group not belonging to the particle group is greater than the fluorescence intensity level of any of the multiple particle groups included in the particle group.

9. The biological sample analyzer according to claim 7, wherein the information processing unit further performs a setting process to set two or more of the plurality of photodetectors included in the detection unit as fluorescence channels to be used for evaluating the signal intensity data after the removal process.

10. The biological sample analyzer according to claim 9, wherein the information processing unit performs a separation and identification process on the signal intensity data after the removal process using the k-means method.

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