Signal processing method, signal processing device and signal processing system

Abstract

A data processing device (12) according to the present invention is a signal processing device that processes the outputs of photomultipliers (11a, 11b, 11c, 11d) constituting a flow cytometry system (1) and is provided with a processor configured so as to acquire intensity signals of the photomultipliers (11a, 11b, 11c, 11d) on the basis of signal light generated by flow cytometry using the flow cytometry system (1), and to process the intensity signals of the photomultipliers (11a, 11b, 11c, 11d) on the basis of the acquired intensity signals of the photomultipliers (11a, 11b, 11c, 11d) on the basis of the signal light generated by flow cytometry using the flow cytometry system (1). The digital value (DN) of the intensity signal is divided by the gain (G) of the photomultiplier tubes (11a, 11b, 11c, 11d) and the quantum efficiency (QS) of the signal light, thereby calculating the virtual number of photons (S) of the signal light incident on the photomultiplier tubes (11a, 11b, 11c, 11d), and performing data analysis on the basis of the virtual number of photons (S) of the signal light.

Description

Technical Field

[0001] One aspect of an embodiment relates to a signal processing method, a signal processing apparatus, and a signal processing system. Background Art

[0002] At present, flow cytometry is known as a technology for counting, screening, and analyzing characteristics of samples such as cells using lasers. For example, the following patent document 1 discloses a flow cytometry system, which includes: a first sensor, which is arranged along the axial direction relative to a light source and senses a forward scattered component; and a second sensor, which is arranged at a certain angle relative to the first sensor and senses a side scattered component and / or a fluorescent component.

[0003] Prior art literature

[0004] Patent Literature

[0005] Patent Document 1: Japanese Patent Application No. 2013-504051 Summary of the invention

[0006] Problems to be solved by the invention

[0007] In the conventional apparatus as described above, the output signal from the sensor may fluctuate due to the deviation of the detection characteristics of the sensor. In order to suppress the fluctuation of the output signal of the sensor, the setting parameters such as the supply voltage of the sensor may be adjusted, but in this case, light of the same incident intensity is detected as different output signal values ​​due to different degrees of adjustment. As a result, different analysis results may be produced due to different degrees of adjustment.

[0008] Therefore, one aspect of the embodiment has been made in view of the above-mentioned problem, and an object of the embodiment is to provide a signal processing method, a signal processing device, and a signal processing system capable of reducing the variation of analysis results based on flow cytometry.

[0009] Technical solutions to solve problems

[0010] A signal processing method according to a first aspect of an embodiment is a signal processing method for processing the output of a photomultiplier tube constituting a flow cytometer, wherein an output current signal of the photomultiplier tube based on signal light generated by flow cytometry using the flow cytometer is obtained as a first current signal, an analytical evaluation value is calculated by dividing the value of the first current signal by the gain of the photomultiplier tube, and data analysis is performed based on the analytical evaluation value, the analytical evaluation value being either the number of photons of the signal light incident on the photomultiplier tube or the number of photoelectrons released from the photoelectric conversion portion of the photomultiplier tube.

[0011] Alternatively, the signal processing device of the second aspect of the embodiment is a signal processing device that processes the output of a photomultiplier tube that constitutes a flow cytometer and includes a processor, the processor being configured to obtain an output current signal of the photomultiplier tube based on signal light generated by flow cytometry using the flow cytometer as a first current signal, calculate an analytical evaluation value by dividing the value of the first current signal by the gain of the photomultiplier tube, and perform data analysis based on the analytical evaluation value, the analytical evaluation value being either the number of photons of the signal light incident on the photomultiplier tube or the number of photoelectrons released from the photoelectric conversion portion of the photomultiplier tube.

[0012] Alternatively, a signal processing system according to a third aspect of the embodiment includes: the above-mentioned signal processing device, a photomultiplier tube, and an optical system that guides signal light to the photomultiplier tube.

[0013] According to the first aspect, the second aspect, or the third aspect, a first current signal is acquired by detecting a signal light generated by a flow cytometer using a photomultiplier tube, and an analytical evaluation value is calculated based on the value of the first current signal. Data analysis is performed based on the calculated analytical evaluation value, and the analytical evaluation value is the number of photons of the signal light incident on the photomultiplier tube or the number of photoelectrons released from the photoelectric conversion unit of the photomultiplier tube. As a result, the signal light can be quantitatively analyzed, and the deviation of the analysis result based on the flow cytometer can be reduced.

[0014] Effects of the Invention

[0015] According to any aspect of the present disclosure, it is possible to reduce the deviation of analysis results based on flow cytometry. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 1 is a schematic diagram of the structure of a flow cytometer system 1 serving as a flow cytometer according to the embodiment.

[0017] Figure 2 Yes means Figure 1 A block diagram of an example of the hardware structure of the data processing device 12.

[0018] Figure 3 It is a block diagram showing the functional structure of the data processing device 12.

[0019] Figure 4 It means in Figure 3 A graph showing an example of a scatter plot generated and outputted in the data analysis performed by the analysis unit 203.

[0020] Figure 5 It means in Figure 3 A graph showing an example of a scatter plot generated and outputted in the data analysis performed by the analysis unit 203.

[0021] Figure 6 This is a flowchart showing the procedure of the signal processing method according to the embodiment.

[0022] Figure 7 This is a flowchart showing the procedure of the signal processing method according to the embodiment.

[0023] Figure 8 This is a graph showing an example of a scattergram generated and output in data analysis based on conventional flow cytometry.

[0024] Fig. 9 This is a graph showing an example of a scattergram generated and output in data analysis based on conventional flow cytometry.

[0025] Fig.10 This is a graph showing an example of a scattergram generated and output by data analysis according to the second modification. DETAILED DESCRIPTION

[0026] Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings. In the description, the same reference numerals are used for the same elements or elements having the same functions, and repeated descriptions are omitted.

[0027] Figure 1 Flow cytometer system 1 is a schematic block diagram of a flow cytometer (signal processing system) according to an embodiment. Flow cytometer system 1 is a system for performing flow cytometry, and includes a fluid system 2, an optical system (optical system) 3, and an electronic system (signal processing device) 4.

[0028] The fluid system 2 is configured to include a flow cell 6 into which a sample fluid containing an analyte such as cells or particles can be injected, and the analyte contained in the sample fluid is arranged and passes through a narrow channel 5. The flow cell 6 is also provided with a function (not shown) of sorting (classifying and distributing) the gated analyte by electric field control or the like.

[0029] The optical system 3 is a system for optically analyzing the analysis object passing through the flow cell 6 by flow cytometry. The optical system 3 includes a laser light source 7, a lens 8, filters 9a, 9b, 9c, 9d, dichroic mirrors 10b, 10c, and photomultiplier tubes 11a, 11b, 11c, and 11d, and guides various lights generated from the analysis object by flow cytometry to the photomultiplier tubes 11a, 11b, 11c, and 11d. The laser light source 7 is a light source device that generates a single-band laser (excitation light) at a specific frequency, and the lens 8 focuses the laser light emitted from the laser light source 7 to the channel 5 in the flow cell 6. The filter 9a allows the forward scattered light generated from the sample fluid by the irradiation of the laser to pass through. The dichroic mirror 10b reflects the side scattered light generated from the sample fluid by the irradiation of the laser, and allows the fluorescence generated from the sample fluid to pass through. The dichroic mirror 10c reflects the fluorescence of the first wavelength band among the fluorescence that has passed through the dichroic mirror 10b, and transmits the fluorescence of the remaining wavelength bands among the transmitted fluorescence. The optical filter 9b transmits the side scattered light reflected by the dichroic mirror 10b, and the optical filter 9c transmits the first fluorescence of the first wavelength band reflected by the dichroic mirror 10c. The optical filter 9d transmits the second fluorescence of the second wavelength band among the fluorescence that has passed through the dichroic mirror 10c. The photomultiplier tubes 11a, 11b, 11c, and 11d are respectively arranged on the optical axes of the forward scattered light, the side scattered light, the first fluorescence, and the second fluorescence, and measure the intensity of each of the forward scattered light, the side scattered light, the first fluorescence, and the second fluorescence.

[0030] The electronic system 4 is a device configured to include a data processing device 12 for analyzing the intensity of light measured by the optical system 3. Specifically, the data processing device 12 is electrically connected to the plurality of photomultiplier tubes 11a, 11b, 11c, and 11d, and performs data analysis such as making a histogram or a scattergram (also called a cell map) based on the intensity signal representing the intensity detected in each channel of the plurality of photomultiplier tubes 11a, 11b, 11c, and 11d, and performs gating processing. In addition, the data processing device 12 performs sorting (classification and allocation) based on the gating processing for the analysis object contained in the sample fluid.

[0031] Next, refer to Figure 2 and Figure 3 , describing the structure of the data processing device 12. Figure 2 is a block diagram showing an example of the hardware configuration of the data processing device 12. Figure 3 It is a block diagram showing the functional structure of the data processing device 12.

[0032] like Figure 2As shown, the data processing device 12 is a computer that physically includes a CPU (Central Processing Unit) 101 as a processor, a RAM (Random Access Memory) 102 or a ROM (Read Only Memory) 103 as a recording medium, a communication module 104, and an input / output module 106, etc., which are electrically connected to each other. In addition, the data processing device 12 may also include a display, a keyboard, a mouse, a touch panel display, etc. as input / output devices, and may also include a data recording device such as a hard disk drive and a semiconductor memory. In addition, the data processing device 12 may also be composed of multiple computers.

[0033] like Figure 3 As shown, the data processing device 12 includes a signal acquisition unit 201 , a calculation unit 202 , and an analysis unit 203 as functional components. Figure 3 Each functional unit of the data processing device 12 shown in FIG. 1 is implemented by reading a program into the hardware such as the CPU 101 and the RAM 102, and operating the communication module 104 and the input / output module 106 under the control of the CPU 101, and reading and writing data from the RAM 102. The CPU 101 of the data processing device 12 executes the program to Figure 3 Each functional unit plays a role and sequentially performs the processing corresponding to the signal processing method described later. In addition, CPU101 can also be a single hardware, or it can be installed in a programmable logic such as FPGA like a soft processor. RAM and ROM can also be single hardware, or they can be built into a programmable logic such as FPGA. All kinds of data required for executing the program and all kinds of data generated by executing the program are stored in built-in memories such as ROM103, RAM102, or recording media such as hard disk drives. The functions of the functional components of the data processing device 12 are described in detail below.

[0034] The signal acquisition unit 201 acquires the intensity signal (output current signal) output from each channel of the plurality of photomultiplier tubes 11a, 11b, 11c, and 11d. The acquired intensity signal is an analog signal obtained by detecting the current of the multiplied electrons corresponding to the intensity of the signal light such as the forward scattered light, the side scattered light, or the fluorescence generated by the flow cytometry in each photomultiplier tube. The signal acquisition unit 201 converts the acquired intensity signal of each channel into a digital value DN and outputs it to the calculation unit 202. In addition, the A / D conversion function of the signal acquisition unit 201 can also be realized by an external circuit unit of the data processing device 12.

[0035] The calculation unit 202 performs the following calculation to convert the digital value DN of each channel output from the signal acquisition unit 201 into an index (analysis evaluation value) based on the number of actual photons (photons) incident on each channel, that is, a virtual photon number (photon number). Here, before explaining the calculation function of the calculation unit 202, a theoretical model of the intensity and noise of the signal light studied by the inventors of the present application is explained.

[0036] According to the theoretical model studied, the average value A of the intensity signal S [DN: digital value] and the noise σ of the intensity signal this (Standard deviation) [DN rms] is represented by the following formula (1) and the following formula (2).

[0037] [Mathematical formula 1]

[0038] A s =A o +C*G*(D+Q B *B+Q S *S) (1)

[0039] [Mathematical formula 2]

[0040]

[0041] Here, among the parameters in the above formula, the parameters mainly related to the expected value are represented by capital letters, and the parameters mainly related to the noise are represented by lowercase Greek letters. C It represents the noise [DN rms] generated by the circuit (hereinafter referred to as "A / D conversion circuit") that transmits the intensity signal and converts it into a digital value between the output of the photomultiplier tube of each channel in the flow cytometer system 1 and the signal acquisition unit 201 of the data processing device 12. C represents the conversion coefficient [DN / e] of the digital value DN to the number of electrons output by the photomultiplier tube in the A / D conversion circuit. F is an index representing the degree of multiplication fluctuation of the photomultiplier tube with a multiplication mechanism (excess noise coefficient). G is the multiplication rate (gain) of the photomultiplier tube, that is, the ratio of the number of output electrons to the number of input electrons [e / e]. D is the expected value of the effective number of electrons of the dark current of the photomultiplier tube [e]. The effective value refers to the value calculated by removing the components that can be ignored in statistical calculations from the intensity signal output by the photomultiplier tube (the same below). In addition, Q Brepresents the conversion efficiency of photons from the photomultiplier tube to electrons (the quantum efficiency of the background light) [e / photon] corresponding to the wavelength spectrum of the background light incident on the photomultiplier tube, and B represents the expected value of the index based on the hypothetical photon number in the incident light amount of the background light incident on the photomultiplier tube (the expected value of the hypothetical photon number of the background light) [photon]. The "hypothetical photon number" refers to a value that maintains quantitativeness and relatively represents the actual photon number (the same applies below). In addition, Q S represents the conversion efficiency from photons of the photomultiplier tube to electrons (quantum efficiency of the signal light) [e / photon] corresponding to the wavelength spectrum of the signal light incident on the photomultiplier tube, S represents the expected value of the index based on the number of photons of the incident light amount of the signal light incident on the photomultiplier tube (the expected value of the hypothetical number of photons of the signal light) [photon], ρ represents the value of the degree of deviation of the signal light generated by the laser light source 7 or the sample fluid before it enters the photomultiplier tube by the ratio relative to the expected value of the signal light amount (the deviation rate on the light source and sample side), and A 0 This is a value (offset) [DN] indicating an offset inherent to the A / D conversion circuit.

[0042] In the above formulas (1) and (2), Q B *B represents the effective photoelectron number [e] of the background light incident on the photomultiplier tube, Q S *S represents the effective photoelectron number [e] of the signal light incident on the photomultiplier tube. These values ​​are listed in parallel with the expected value D of the effective electron number of the dark current in the above formulas (1) and (2), and are treated as the basic axis physical quantity in the theoretical model for studying these. The effective photoelectron number mentioned here refers to the value calculated by removing the component that can be ignored in statistical calculation from the number of photoelectrons released from the photoelectric conversion part according to the incident light in the photomultiplier tube (the same applies below).

[0043] The main causes of the variation in the value of the deviation rate ρ on the light source and sample side are mainly the deviation of the laser irradiation method to the analysis object such as cells flowing in the channel 5 in the flow cell 6 of the flow cytometer system 1, and the deviation generated for each analysis object. The deviation based on the laser irradiation method includes the deviation of the laser irradiation method to the channel 5 and the spatial deviation of the analysis object flowing in the channel 5. In addition, the deviation for each analysis object includes the deviation of the size of the analysis object and the deviation of the content rate of the molecule or molecular structure to be measured existing in the analysis object. In fact, the main reasons affecting the deviation rate ρ include: the deviation of the light amount of the laser light source 7 itself over time, the deviation of the laser irradiation method to the channel 5, the deviation of the flow rate in the channel 5 over time, the spatial deviation of the analysis object in the channel 5, the deviation of the size of the analysis object, the deviation of the content of the molecules or molecular structures present in the analysis object, the spatial deviation of the molecules or molecular structures present in the analysis object, the deviation of the binding rate between molecules or molecular structures and dye fluorescence, the deviation of the luminous efficiency of dye fluorescence, the deviation caused by foreign matter other than the analysis object flowing in the channel 5, etc.

[0044] In the above formula (2), the first and second terms in the square root are terms that are independent of the expected value of the hypothetical number of photons of the signal light, i.e., parameter S, and that vary mainly according to circuit noise including noise of the A / D conversion circuit, background light, and dark current, and the third term in the square root is a term that depends on the square root of parameter S. 1 / 2 , and it is a term that mainly changes according to the noise (shot noise) generated in the process of converting photons to electrons. The fourth term within the square root is a term that depends on the parameter S and mainly changes according to the deviation of the light source and the sample side. According to this formula, when the intensity of the signal light is low, if the influence of the first to third terms is considered, the tendency of the noise of the intensity signal can be evaluated. When the intensity of the signal light is high, the tendency of the noise of the intensity signal cannot be evaluated without also referring to the influence of the fourth term.

[0045] The parameters of each channel mentioned above include the known conversion coefficient C, gain G, and quantum efficiency Q of the signal light. S The data is inputted into the data processing device 12 by the user and stored in advance in an internal recording medium such as the RAM 102. The calculation unit 202 calculates the number of virtual photons corresponding to the signal light detected in each channel as follows, using the parameters stored in association with each channel.

[0046] That is, the calculation unit 202 stops the detection based on flow cytometry and does not input the signal light to the photomultiplier tube, obtains the digital value DN of the intensity signal (output current signal) output from the photomultiplier tube for a plurality of events, and calculates and obtains the average value (first average value) A of the digital value DN. S＝0 and standard deviation (second noise value) σ S＝0 . Calculate the average value A S＝0 and standard deviation σ S＝0 If the theoretical formulas of formula (1) and formula (2) are applied, the values ​​approximated are those shown in the following formula (3) and formula (4).

[0047] [Mathematical formula 3]

[0048]

[0049] [Formula 4]

[0050] A S =0=A 0 +C*G*(D+Q B *B) (4)

[0051] Furthermore, the calculation unit 202 calculates the parameter A S＝0 , σ S＝0 Stored in internal recording media.

[0052] In addition, the calculation unit 202 changes the intensity of the laser to a predetermined intensity, uses beads or dyed fluorescent substances that emit light of an appropriate amount, or installs a correction light source on the flow cytometer side, and starts flow cytometry detection with the sample fluid to be measured as the object under a state where the intensity of the signal light is sufficiently large, obtains the digital value DN of the intensity signal (output current signal) output from the photomultiplier tube for a plurality of events, and calculates and obtains the average value (second average value) A of the digital value DN. S＞＞H and standard deviation (third noise value) σ S＞＞H The intensity of the laser, beads, dye fluorescent substance, or calibration light source is determined according to the upper limit of the amount of light emitted by the object to be analyzed, or the upper limit of the input voltage of the signal processing circuit. The calculated standard deviation σ S＞＞H and average value A S＞＞H , if the theoretical formulas of formula (1) and formula (2) are applied, the values ​​approximated to those shown in the following formula (5) and formula (6) are obtained. Therefore, the calculation unit 202 can calculate the average value A based on the average value A. S＞＞H and standard deviation σ S＞＞H , use the following formula (7) to calculate the deviation rate ρ.

[0053] [Formula 5]

[0054]

[0055] [Mathematical formula 6]

[0056] A S＞＞H =C*g*Q S *S (6)

[0057] [Mathematical formula 7]

[0058]

[0059] Then, the calculation unit 202 stores the calculated parameter ρ in an internal recording medium.

[0060] In addition, the calculation unit 202 changes the intensity of the laser into two types, uses two types of beads or dyeing fluorescent substances that emit light of appropriate light intensity, installs a correction light source that emits two light intensities on the flow cytometer side, or uses an ND filter to dim the light in a state where the light intensity is sufficiently large, etc., and starts flow cytometry detection with the sample fluid to be measured as the object in each state where the intensity of the signal light is changed into two high and low states, and obtains the digital value DN of the intensity signal (output current signal) output from the photomultiplier tube for multiple event quantities in each state. Furthermore, the calculation unit 202 calculates and obtains: the average value A of the digital value DN when the incident light intensity to the photomultiplier tube is high H and standard deviation σ H , and the average value A of the digital value DN when the incident light amount to the photomultiplier tube is low L and standard deviation σ L In addition, the calculation unit 202 is based on the average value A H , A L , standard deviation σ H , σ L , and the average value A S＝0 , use the following formula (8) to calculate the excess noise factor F.

[0061] [Mathematical formula 8]

[0062]

[0063] Then, the calculation unit 202 stores the calculated parameter F in an internal recording medium.

[0064] Referring to the parameters of each channel stored as described above, the calculation unit 202 starts flow cytometry detection of the sample fluid to be measured, while setting the intensity of the laser to a predetermined value suitable for measurement, and obtains the digital value DN of the intensity signal (first current signal) output from the photomultiplier tube of each channel. Furthermore, the calculation unit 202 refers to the parameters of each channel stored, and calculates the following equation (9), thereby obtaining the average value A of the intensity signal represented by the digital value DN: S Derive the hypothetical photon number S.

[0065] [Mathematical formula 9]

[0066]

[0067] That is, the calculation unit 202 calculates the average value A of the intensity signal S Subtract the mean value A S＝0 , divide the value obtained by subtraction by the conversion coefficient C, gain G, and quantum efficiency Q of the signal light S , calculate the imaginary photon number S of the signal light incident on the photomultiplier tube.

[0068] In addition, the calculation unit 202 refers to the virtual photon number S calculated as described above and the stored parameters of each channel to derive the standard deviation (first noise value) σ of the intensity signal represented by the digital value DN. this Specifically, the calculation unit 202 uses the following formula (10) to calculate the standard deviation σ S＝0 The square value of the conversion coefficient C, gain G, and quantum efficiency Q of the signal light are added together. S , deviation rate ρ, excess noise coefficient F, and the value calculated by the number of virtual photons S, and the square root of the added value is obtained to calculate the standard deviation (first noise value) σ this Then, the calculation unit 202 calculates the standard deviation σ this Divided by the conversion coefficient C, gain G, and quantum efficiency Q of the signal light S , the standard deviation σ this [DN rms] is converted to the standard deviation σ of the hypothetical photon number this [photon rms].

[0069] [Formula 10]

[0070]

[0071] The analyzing unit 203 calculates the number of virtual photons S for each channel and the standard deviation σ for each channel based on the number of virtual photons S for each channel calculated by the calculating unit 202. this, and performs data analysis. Specifically, a histogram and a scatter plot are generated based on the virtual photon number S of multiple channels, and these are output to the input and output devices. In addition, the analysis unit 203 performs gating processing on the generated scatter plot to define the boundaries of different groups of analysis objects. Furthermore, the analysis unit 203 can also perform sorting processing to classify and allocate groups based on the boundaries of the groups of analysis objects by controlling the fluid system 2.

[0072] Figure 4 and Figure 5 2 is a graph showing an example of a scatter plot generated and outputted in the data analysis performed by the analysis unit 203. Figure 4 In the figure, a scatter plot is shown showing the relationship between the hypothetical photon number of the fluorescence channel corresponding to antibody A and the hypothetical photon number of the fluorescence channel corresponding to antibody B, so as to represent the boundary defined by the gating process. In this way, the proportion of the group in the overall analysis object of the detection object is calculated and output through the gating process, and the proportion of the group that is negative for both antibody A and antibody B is "36.6%", the proportion of the group that is only positive for antibody B is "34.7%", the proportion of the group that is only positive for antibody A is "27.7%", and the proportion of both antibodies A and B is "1.05%". Figure 5 , a scatter plot showing the relationship between the hypothetical photon number of the fluorescence channel using the fluorescent dye Cy5 and the hypothetical photon number of the fluorescence channel using the fluorescent dye TR is shown in FIG. In this way, when the boundary of a group is delimited by the gating process, a standard deviation σ corresponding to the average value S of the hypothetical photon number of the group can be automatically delimited. this The boundary of the range W of the gate interval is determined by the standard deviation σ this The determined range W is set to, for example, the number of imaginary photons S±3×σ this range.

[0073] Next, refer to Figure 6 and Figure 7 The following describes a procedure for processing the output signal of the photomultiplier tube of the flow cytometer system 1. Figure 6 FIG. 1 shows the process of preparing the parameters of each channel by the flow cytometer system 1. Figure 7 2 shows the analysis process based on flow cytometry on the sample fluid.

[0074] First, refer to Figure 6 The user can set the known conversion coefficient C, gain G, and quantum efficiency Q of the signal light in each channel parameter. SThen, in the state where the signal light is not incident on each channel in the flow cytometer system 1, the data processing device 12 acquires the data of the digital value DN of each channel of the plurality of event quantities (step S102). In this way, the data processing device 12 acquires and stores the parameter A of each channel. S＝0 , σ S＝0 (Step S103).

[0075] Next, by changing the laser to a predetermined intensity in the flow cytometer system 1, using beads or dyeing fluorescent substances that emit light of an appropriate amount, or installing a calibration light source on the flow cytometer side, the signal light is detected by the data processing device 12 with the sample fluid to be measured as the object under a sufficiently high intensity of the signal light, thereby acquiring data of digital values ​​DN of each channel of a plurality of event amounts (step S104). In this way, the parameter ρ of each channel is acquired and stored in the data processing device 12 (step S105).

[0076] Furthermore, the intensity of the laser is changed into two types in the flow cytometer system 1, two types of beads or fluorescent dyes are used to obtain appropriate luminous light amounts, two types of light sources for correction of light amounts are used, or two types of ND filters are used from a sufficiently bright state, etc., and the intensity of the signal light is changed into two high and low states, and the signal light is detected in the data processing device 12 with the sample fluid to be measured as the object, thereby obtaining the data of the digital value DN of each channel of the plurality of event amounts (step S106). In this way, the parameter F of each channel is obtained and saved in the data processing device 12 (step S107). Through the above, the preliminary preparation process is completed.

[0077] Reference Figure 7 , in the flow cytometer system 1, the intensity of the laser is set to a predetermined value suitable for the measurement, and the flow cytometry detection of the sample fluid to be measured is started, and accordingly, the data of the digital value DN of each channel is obtained by the data processing device 12 (step S201). Next, the data processing device 12 generates a histogram and a scatter plot of each channel using the digital value DN of each channel (step S202). At this time, the data processing device 12 refers to the stored parameter A of each channel. S＝0 , σ S＝0 , ρ, F, convert the digital value DN of each channel into the imaginary photon number S, and calculate the standard deviation σ corresponding to the imaginary photon number S this (Step S203).

[0078] Then, the data processing device 12 performs a calculation using the calculated virtual photon number S and standard deviation σ on the generated histogram and scattergram. thisThen, the data processing device 12 performs classification of the group of analysis objects (target group) in the data shown in the histogram and the scattergram based on the result of the gating process (step S205).

[0079] Next, when the data processing device 12 is set to perform sorting (step S206; yes), the data processing device 12 is controlled to sort the classified target groups (S207). On the other hand, when the data processing device 12 is set not to perform sorting (step S206; no), the data processing device 12 performs data analysis processing such as calculation of the proportion of the classified target groups to the whole (S208).

[0080] The effects of the flow cytometer system 1 according to the above-described embodiment will be described.

[0081] In the flow cytometer system 1 of the present embodiment, the signal light generated in each channel is detected by the photomultiplier tubes 11a, 11b, 11c, and 11d to obtain an intensity signal, and the virtual photon number S of the signal light incident on the photomultiplier tubes 11a, 11b, 11c, and 11d is calculated based on the digital value DN of the intensity signal, and data analysis is performed based on the calculated virtual photon number S of the signal light. As a result, the signal light can be quantitatively analyzed, and the deviation of the analysis result based on the flow cytometry can be reduced. In particular, in the present embodiment, the virtual photon number is used as the analysis evaluation value, so the deviation of the analysis result based on the flow cytometry between different devices can also be reduced.

[0082] In addition, the data processing device 12 of this embodiment performs a gating process for defining the boundaries of the group to be analyzed as data analysis. Thus, the gating process can be performed based on the quantitative signal light intensity information, and the accuracy of the identification of the group to be analyzed can be improved.

[0083] Furthermore, in the data processing device 12 of the present embodiment, based on the calculated virtual photon number S of the signal light, the standard deviation σ of the noise included in the virtual photon number S is calculated. this , using this standard deviation σ in the gating process this The gating interval is set. This can further improve the accuracy of group identification of the analysis target.

[0084] Furthermore, the data processing device 12 of the present embodiment acquires an average value A of the digital values ​​DN of the intensity signals of the photomultiplier tubes 11a, 11b, 11c, and 11d when no signal light is incident on the flow cytometer system 1. S＝0 , using the mean value A subtracted S＝0 The average value A S, calculate the virtual photon number S of the signal light. In this way, the virtual photon number S of the signal light can be calculated without the influence of the background light and the dark current. As a result, the reliability of the analysis result based on flow cytometry can be improved.

[0085] In addition, the data processing device 12 of the present embodiment obtains the standard deviation σ of the digital value DN of the intensity signal of the photomultiplier tubes 11a, 11b, 11c, and 11d when no signal light is incident on the flow cytometer system 1. S＝0 , further using the standard deviation σ S＝0 , calculate the standard deviation σ this In this way, the noise component of the virtual photon number of the signal light can be calculated taking into account the influence of the background light and the dark current. As a result, the accuracy of the group identification of the analysis object can be further improved.

[0086] In addition, the data processing device 12 of the present embodiment acquires an average value A of the digital values ​​DN of the intensity signals of the photomultiplier tubes 11a, 11b, 11c, and 11d based on the signal light corresponding to the laser light of a predetermined intensity in the flow cytometer system 1. S》H , and the standard deviation σ of the digital value DN S》H , further using the average value A S》H and standard deviation σ S》H , calculate the standard deviation σ this With the above configuration, it is possible to calculate the noise component of the virtual photon number of the signal light in consideration of the characteristics of the light source and the deviation of the analysis object. As a result, the accuracy of the group identification of the analysis object can be further improved.

[0087] Figure 8 This is a graph showing an example of a scatter plot generated and output by a system in data analysis based on conventional flow cytometry. Fig. 9 This is a chart showing an example of a scatter plot generated and output by another system in data analysis based on existing flow cytometry. In this way, even when a sample fluid containing the same analyte is set as the object, the ratio of the group based on data analysis in the two systems is calculated differently. For example, the ratio of both antibodies A and B being positive is calculated as "0.78%" in one system and "10.1%" in another system, and the ratios calculated by the two are calculated as values ​​with large differences. This is due to deviations in the characteristics of the devices contained in the system, deviations in the degree of adjustment of the devices caused by the operators of the system, and the like. According to this embodiment, the deviation of the analysis results caused by such deviations can be reliably reduced.

[0088] Although various embodiments of the present invention have been described above, the present invention is not limited to the above-described embodiments, and may be modified or applied to other forms within the scope of the spirit described in each claim.

[0089] For example, the gating process in the scattergram is not limited to the automatic setting based on the electronic system 4, but can also be set by the operator's input, and the automatically set process can also be adjusted by the operator's input. In addition, the photomultiplier tube of the embodiment not only has a dynode or an electron collecting electrode, that is, an anode, but can also be a HPD (Hybrid Photo Detector) that multiplies and detects photoelectrons emitted from the photoelectric conversion unit by a semiconductor element.

[0090] In addition, as a first variation, the data processing device 12 may use the effective photoelectron number (photoelectron number) instead of the virtual photon number as the analytical evaluation value. In this case, as the theoretical model of the intensity and noise of the signal light, the theoretical model represented by the following equation (11) and the following equation (12) may be applied.

[0091] [Mathematical formula 11]

[0092] A s =A o +C*G*(E D +E B +E S ) (11)

[0093] [Mathematical formula 12]

[0094]

[0095] In the first modification, the calculation unit 202 is the same as in the above embodiment, and uses the expected value E of the effective electron number of the dark current as D , the effective photoelectron number E of background light B , expected value of effective photoelectron number ES, calculation parameter A ES＝0 , σ ES＝0 , deviation rate ρ, and parameter F are stored in the internal storage medium. Moreover, when the calculation unit 202 starts to detect the sample fluid of the measurement object, it calculates the following formula (13) based on the acquired digital value DN, thereby obtaining the average value A of the intensity signal represented by the digital value DN: S Derive the effective photoelectron number E S .

[0096] [Mathematical formula 13]

[0097]

[0098] Furthermore, the calculation unit 202 calculates the effective photoelectron number E by applying the following equation (14): S , the stored parameter σ ES＝0 , F and deviation rate ρ, to calculate the standard deviation σ this .

[0099] [Mathematical formula 14]

[0100]

[0101] Then, the calculation unit 202 can calculate the standard deviation σ by this Divide by the conversion coefficient C and gain G to get the standard deviation σ this [DN rms] is converted to the standard deviation of the effective photoelectron number σ this [e rms].

[0102] According to the first modification, the effective photoelectron number E can also be calculated using S and standard deviation σ this Data analysis such as gating processing can quantitatively analyze signal light and reduce the deviation of analysis results based on flow cytometry.

[0103] In addition, as a second variation, the data processing device 12 may also use other theoretical models as theoretical models of the intensity and noise of the signal light to calculate the average value and noise of the intensity signal of each channel of the plurality of photomultiplier tubes 11a, 11b, 11c, and 11d. For example, in this second variation, the intensity of the fluorescence of multiple colors from the plurality of fluorescent bodies contained in the analysis object is measured by two photomultiplier tubes 11c and 11d, and data analysis processing is performed based on the digital values ​​of the two channels of the photomultiplier tubes 11c and 11d. In the case of measuring the fluorescence of multiple colors in two channels, leakage of fluorescence from one channel to another channel may occur. Therefore, in the second variation, the theoretical model that takes fluorescence leakage into consideration is used to perform data analysis. The details of the data analysis processing performed by the data processing device 12 in the second variation are described below together with the theoretical model.

[0104] In the data processing device 12, the theoretical model represented by the following equations (15) and (16) can be applied to the expected value X[DN] of the intensity of the first-band fluorescence of the stained fluorescent substance U in the analysis object corresponding to the channel (X-axis) of the photomultiplier tube 11c, and the expected value Y[DN] of the intensity of the second-band fluorescence of the stained fluorescent substance V corresponding to the channel (Y-axis) of the photomultiplier tube 11d.

[0105] [Mathematical formula 15]

[0106]

[0107] [Formula 16]

[0108]

[0109] In the above formulas (15) and (16), X 0 is the offset [DN] including the dark current component and background light component on the X axis, 0 is the offset [DN] including the dark current component and background light component on the Y axis, G X is the gain of the photomultiplier tube that outputs the intensity signal to the X-axis [e / e], G Y is the gain of the photomultiplier tube that outputs the intensity signal on the Y axis [e / e], Q X Q is the conversion efficiency of the photomultiplier tube from photons to electrons [e / photon] that outputs intensity signals on the X axis. Y is the conversion efficiency of the photomultiplier tube from photons to electrons [e / photon] that outputs the intensity signal on the Y axis, < S U > represents the expected value of the index corresponding to the wavelength spectrum of the signal light emitted from the dyed fluorescent body U and incident on the photomultiplier tube that outputs the intensity signal on the X-axis (the expected value of the number of virtual photons of the signal light) [photon], <S V > represents the expected value of the index corresponding to the wavelength spectrum of the signal light emitted from the dye phosphor V and incident on the photomultiplier tube that outputs the intensity signal on the Y axis (the expected value of the number of virtual photons of the signal light) [photon], R V→X R represents the ratio of signal light leakage from the photomultiplier tube that outputs the intensity signal to the X-axis, which is emitted from the dye fluorescent body V [photon / photon], U→Y Indicates the ratio of signal light leakage from the dyed fluorescent substance U to the photomultiplier tube that outputs the intensity signal to the Y axis [photon / photon].

[0110] In the above equations (15) and (16), σ cX The read noise (including noise generated by the circuit, dark current, and shot noise components of background light) in the intensity signal on the X-axis [DN rms], σ cY The read noise (including noise generated by the circuit, dark current, and shot noise components of background light) [DN rms] in the intensity signal on the Y axis is shown in F X is the excess noise coefficient of the photomultiplier tube that outputs the intensity signal to the X-axis, F Y is the excess noise coefficient of the photomultiplier tube that outputs the intensity signal to the Y axis, ρ Ui In the measurement of the dye fluorescent substance U, the output of the photomultiplier tube that outputs the intensity signal to the X axis and the output of the photomultiplier tube that outputs the intensity signal to the Y axis do not affect each other (are independent), ρ Ur In the measurement of the dye fluorescent substance U correlation, the output of the photomultiplier tube that outputs the intensity signal on the X axis and the output of the photomultiplier tube that outputs the intensity signal on the Y axis influence each other (correlation) to a certain extent, ρVi In the measurement of the dye fluorescent substance V, the output of the photomultiplier tube that outputs the intensity signal to the X axis and the output of the photomultiplier tube that outputs the intensity signal to the Y axis do not affect each other (are independent), ρ Vr In the measurement of the dye fluorescent substance V correlation, the output of the photomultiplier tube that outputs the intensity signal on the X axis and the output of the photomultiplier tube that outputs the intensity signal on the Y axis show the degree of deviation in mutual influence (correlation).

[0111] In the above equations (15) and (16), the first to third terms on the right side represent the average value [DN] of the intensity signal. Unlike equation (1), the influence of dark current and background light is included in the offset for evaluation. In addition, the fourth term on the right side of equation (15) represents the noise of the intensity signal. Unlike equation (2), the influence of dark current and background light is included in the circuit noise for evaluation. The noise of the intensity signal is as follows:

[0112] [Mathematical formula 17]

[0113] (σ cX ) 2

[0114] The term represents the read noise in its own channel. The noise of the intensity signal is expressed as follows;

[0115] [Mathematical formula 18]

[0116]

[0117] The term represents the shot noise in its own channel, as shown in the following formula;

[0118] [Mathematical formula 19]

[0119]

[0120] The term represents the shot noise from the other channel.

[0121] The noise of the above intensity signal is expressed as follows:

[0122] [Mathematical formula 20]

[0123] (CG X Q X ρ Ui U >) 2

[0124] ​The term is the independent noise (depending on the deviation of the light source and the sample side) caused by the signal light of its own channel, which reflects the expected value of the hypothetical number of photons of the signal light of the detection object in its own channel and the gain of the photomultiplier tube of its own channel. Independent noise refers to the noise of the components such that the deviation directions (degree and direction) of the intensity signals of the two channels are independent of each other. In addition, the noise of the intensity signal is as follows;

[0125] [Mathematical formula 21]

[0126] (CG X Q X R V→X ρ Vi V >) 2

[0127] The term reflects the expected value of the number of hypothetical photons of the signal light of the detection object in the other channel that leaks to the photomultiplier tube of its own channel, and the gain of the photomultiplier tube of its own channel, and other independent noise caused by the signal light of the other channel (depending on the deviation of the light source and the sample side). The noise of the intensity signal is as follows;

[0128] [Mathematical formula 22]

[0129]

[0130] The term reflects the expected value of the hypothetical photon number of the signal light of the detection object in its own channel and the gain of the photomultiplier tube of its own channel, and is caused by the correlation between the signal light of its own channel and the signal light of another channel (depending on the deviation of the light source and the sample side). The phase correlation noise refers to the noise of the component in which the deviation directions (degree and direction) of the intensity signals of the two channels are correlated with each other. The "^" mark in the formula indicates the phase correlation noise caused by the correlation between channels. The noise of the intensity signal is expressed as follows;

[0131] [Mathematical formula 23]

[0132]

[0133] The term reflects the expected value of the number of hypothetical photons of the signal light of the detection object in the other channel and the gain of the photomultiplier tube of the own channel, and other related noise caused by the correlation between the signal light of the own channel and the signal light of the other channel (depending on the deviation of the light source and the sample side). The "^" mark in this formula also represents the related noise caused by the correlation between channels. The fourth term on the right side of formula (16) also contains the same noise term.

[0134] ​As in the above-mentioned embodiment, the calculation unit 202 of the data processing device 12 acquires: the intensity signal when the signal light is not incident on the photomultiplier tube of each channel, the intensity signal when the intensity of the signal light of each channel is sufficiently large, the intensity signal when the intensity of the signal light of each channel is changed up or down, etc. Moreover, the calculation unit 202 refers to the acquired intensity signal to calculate and store the parameters in the above-mentioned formula (15) and the above-mentioned formula (16). At this time, the parameters of the terms other than the independent system noise and the related noise can be calculated in the same way as in the above-mentioned embodiment, and the parameters of the terms of the independent system noise and the related noise can be calculated by measuring in two channels in a state where each fluorescent body emits light, and the intensity signal obtained as a result is combined in the above-mentioned formula (15) and (16) to be calculated. Furthermore, the calculation unit 202 can derive the virtual photon number (or effective photoelectron number) and its standard deviation (first noise value) of each channel based on the intensity signal output from each channel when the detection based on flow cytometry is started, using the stored parameters. The analyzing unit 203 of the data processing device 12 can perform the same data analysis as in the above-described embodiment, using the virtual photon number (or effective photoelectron number) and the standard deviation calculated by the calculating unit 202 .

[0135] According to the second modification described above, the noise value of the analytical evaluation value caused by the deviation of the signal light of the detection object of the photomultiplier tube in its own channel can be predicted with high accuracy. As a result, the signal light can be quantitatively analyzed by data analysis. In particular, when multiple channels are used to measure the intensity of multiple colors of fluorescence, the prediction accuracy of the noise value is improved. In addition, even when the monochromatic signal light is set as the measurement object, the intensity signal also contains deviations caused by various main reasons such as the above (deviations caused by the irradiation method of the laser, deviations in the size of the analysis object, deviations in the structure of the analysis object, etc.). As in this modification, by dividing into independent system noise and related noise and evaluating them, these main reasons can be divided and the noise can be predicted. Furthermore, the main causes of the noise can also be divided and evaluated, and when improvements are made to reduce the noise value (improvements of the device, improvements of the phosphor, etc.), improvements can be effectively made. For example, when the component of the related noise is evaluated to be 0.25, if it is known that the component of the related system noise of the deviation caused by the irradiation method of the laser is about 0.05, it can be inferred at a considerable proportion that the component of the deviation caused by the analysis object as the main cause is the problem. In this case, it can be inferred that the noise value can be effectively reduced by improving the binding efficiency of the phosphor.

[0136] In addition, the data processing device 12 of the second modified example can also perform a signal correction process called compensation on the signal intensity of one channel and the signal intensity of another channel. Compensation is a process of subtracting the leakage amount from another channel from the signal intensity of one channel and correcting it in a manner that only the signal light from the target phosphor is reflected.

[0137] The calculation unit 202 of the data processing device 12 performs compensation as follows. That is, the calculation unit 202 uses the following formula:

[0138] [Mathematical formula 24]

[0139] Comp[X]=XR V→X 'Y

[0140] , converts the intensity signal X obtained in a channel into a correction value Comp[X]. Here, the coefficient R V→X ' is the following formula;

[0141] [Mathematical formula 25]

[0142]

[0143] The coefficient R V→X 'It can be obtained by performing flow cytometry detection only by the light emission of the dye fluorescent body V, and dividing the expected value of the intensity signal of the X axis by the expected value of the intensity signal of the Y axis. Then, the calculation unit 202 can derive the correction value Comp[X] based on the intensity signal output from the channel of the X axis when the detection based on the flow cytometry is started.

[0144] Similarly, the calculation unit 202 uses the following formula;

[0145] [Mathematical formula 26]

[0146] Comp[Y]=YR U→Y 'X

[0147] , converts the intensity signal Y obtained in another channel into a correction value Comp[Y]. Here, the coefficient R U→Y ' is the following formula;

[0148] [Mathematical formula 27]

[0149]

[0150] The coefficient R U→Y'It can be obtained by performing flow cytometry detection only by the light emission of the dye fluorescent body U, and dividing the expected value of the intensity signal of the Y axis by the expected value of the intensity signal of the X axis. Then, the calculation unit 202 can derive the correction value Comp[Y] based on the intensity signal output from the channel of the Y axis when the detection based on the flow cytometry is started.

[0151] When the intensity signal is corrected by compensation, in the data processing device 12, the theoretical model represented by the following equation (17) and the following equation (18) can be applied to the intensity signal Comp[X] and the intensity signal Comp[Y].

[0152] [Mathematical formula 28]

[0153]

[0154] [Mathematical formula 29]

[0155]

[0156] In the above equations (17) and (18), the first to third terms on the right represent the average value [DN] of the intensity signal, and the fourth and subsequent terms on the right represent the noise of the intensity signal.

[0157] The following formula;

[0158] [Math 30]

[0159] (σ cX ) 2

[0160] The term represents the readout noise of its own channel, as shown below:

[0161] [Mathematical formula 31]

[0162]

[0163] The term represents the readout noise of another channel. In addition, the noise in the above equation (17) is as follows;

[0164] [Math 32]

[0165]

[0166] The term represents the shot noise of its own channel, as shown in the following formula:

[0167] [Mathematical formula 33]

[0168]

[0169] The term represents the shot noise from the other channel.

[0170] The noise of the above intensity signal is expressed as follows:

[0171] [Math 34]

[0172] {(G X Q X ) 2 +(G X Q X R V→C R U→Y ) 2}(Cρ Ui U >) 2

[0173] The term is its own independent noise, as shown in the following formula:

[0174] [Mathematical formula 35]

[0175] {2(G X Q X R V→X ) 2}(Cρ Vi V >) 2

[0176] The term is another independent noise, as shown below:

[0177] [Mathematical formula 36]

[0178]

[0179] The term is its own relative noise. In the theoretical model of equation (17), unlike the theoretical model of equation (15), the readout noise of another channel is included, but other relative noises are not included. This reflects that the readout noise of another channel is mixed into the correction value by compensation, and on the other hand, other relative noises are offset by compensation. The noise of the above equation (18) also includes the same independent noise and relative noise.

[0180] The calculation unit 202 of the data processing device 12 can apply the theoretical model represented by the above equations (17) and (18) to derive the number of virtual photons (or the number of effective photoelectrons) and its standard deviation for each channel.

[0181] Fig.10 ​​The graphs are graphs showing examples of scatter plots generated and outputted by the data analysis performed by the analysis unit 203. (a) shows a graph when compensation is not performed, and (b) shows a graph when compensation is performed. In each graph, the relationship between the intensity signal of the channel (X axis) corresponding to the fluorescence from the dye fluorescent body U and the intensity signal of the channel (Y axis) corresponding to the fluorescence from the dye fluorescent body V is plotted. As can be seen, the data distribution that is concentrated in one part when compensation is not performed is corrected to a wider distribution area by compensation, and the evaluation of the distribution that eliminates the influence of light leakage between channels can be performed.

[0182] In addition, an evaluation example of the accuracy of the standard deviation of the intensity signal derived by the data processing device 12 of the second variant is described. In two measurements of the sample fluid of the analysis object, the standard deviation was derived from the intensity signal by the data processing device 12 using a theoretical model that removed the term of the independent noise from the above-mentioned equations (17) and (18). The standard deviations of each measurement were 30.1 and 338. This is larger than the actual standard deviations of 90.8 and 962 calculated from the scatter plot based on each measurement. In contrast, it can be seen that the standard deviations derived using the theoretical model of the above-mentioned equations (17) and (18) are 93.4 and 937, respectively, and the errors are suppressed to a small level.

[0183] In the data processing device 12 of the above-mentioned embodiment, the first variant, and the second variant, the intensity signal obtained is converted into the number of photons. The gain of the intensity signal of each channel changes for the individual difference of the photomultiplier tube of each channel or the detection condition of each channel. As a result, even if the characteristics of the optical filter contained in the optical system 3 are consistent, when the values ​​of the intensity signals of multiple channels of the multi-band light are kept measured and evaluated, the spectral shape is also obtained randomly due to the individual difference of the photomultiplier tube or the detection condition. In contrast, in the data processing device 12 of the above-mentioned embodiment, the first variant, and the second variant, the intensity signal of each channel can be converted into the number of photons and evaluated in a standardized manner, and the spectral shape can be accurately evaluated regardless of the individual difference of the photomultiplier tube of each channel or the detection condition of each channel.

[0184] In the data processing device 12 of the third variant, it is also possible to configure the following data analysis to be performed. That is, the calculation unit 202 calculates the analytical evaluation value such as the number of hypothetical photons and its standard deviation (first noise value) based on the data of the intensity signal obtained by measuring the new unknown analysis object. Moreover, the analysis unit 203 can process the group of data of the analytical evaluation value and the standard deviation of each data object using machine learning, or use an optimization algorithm to process, or use it for the evaluation of the reliability of the measurement result. In detail, the analysis unit 203 uses the weighted least squares method as a machine learning or optimization algorithm. Thus, the data points can be weighted, and the inverse of the standard deviation can be used as the weight of the analytical evaluation value, so that the inference processing based on the appropriateness calculated according to the theoretical model can be performed. Here, as a machine learning or optimization algorithm, in addition to the weighted least squares method, the analysis unit 203 can perform regularization, use Bayesian statistics, use the kernel method, and can also use vector machines, decision trees, neural networks, cluster links, etc.

[0185] In the first and second aspects of the embodiment, it is preferable that the analysis evaluation value is the number of photons. In this case, it is also possible to further reduce the variation between devices in the analysis results based on flow cytometry.

[0186] In the first and second aspects, preferably, data analysis includes gating processing to define boundaries of groups to be analyzed. This allows gating processing to be performed based on quantitative signal light intensity information, thereby improving accuracy in identifying groups to be analyzed.

[0187] In addition, in the first aspect, it is also preferred that a first noise value equivalent to the noise contained in the analytical evaluation value is further calculated based on the analytical evaluation value and the gain of the photomultiplier tube, and the gate interval is set using the analytical evaluation value and the first noise value in the gating process. In addition, in the second aspect, it is also preferred that the processor further calculates a first noise value equivalent to the noise contained in the analytical evaluation value based on the analytical evaluation value and the gain of the photomultiplier tube, and the gate interval is set using the analytical evaluation value and the first noise value in the gating process. In this case, a value equivalent to the noise contained in the calculated number of photons of the signal light is calculated, and the gate interval is set using the value in the gating process. Thus, the accuracy of group identification of the analysis object can be further improved.

[0188] In addition, further in the above-mentioned first aspect, it is also preferred that the average value of the output current signal of the photomultiplier tube, i.e., the second current signal, is obtained as the first average value, and the analytical evaluation value is calculated using the value of the first current signal from which the first average value is subtracted. In addition, further in the above-mentioned second aspect, it is also preferred that the processor obtains the average value of the output current signal of the photomultiplier tube, i.e., the second current signal, when the signal light is not incident in the flow cytometer, as the first average value, and the analytical evaluation value is calculated using the value of the first current signal from which the first average value is subtracted. In this way, the number of photons of the signal light can be calculated while eliminating the influence of background light and dark current. As a result, the reliability of the analysis results based on flow cytometry can be improved.

[0189] In addition, further in the above-mentioned first aspect, it is also preferred that the noise value of the output current signal of the photomultiplier tube, i.e., the second current signal, is obtained as the second noise value, and the first noise value is further calculated using the second noise value. In addition, further in the above-mentioned second aspect, the processor obtains the noise value of the output current signal of the photomultiplier tube, i.e., the second current signal, when the signal light is not incident in the flow cytometer as the second noise value, and the first noise value is further calculated using the second noise value. In this way, the noise component of the number of photons of the signal light can be calculated taking into account the influence of background light and dark current. As a result, the accuracy of group identification of the analysis object can be further improved.

[0190] In addition, in the above-mentioned first aspect, it is also preferred that the average value of the output current signal of the photomultiplier tube based on the signal light corresponding to the excitation light of the specified intensity in the flow cytometer, that is, the third current signal, that is, the second average value, and the third noise value as the noise value of the third current signal, are obtained, and the first noise value is further calculated using the second average value and the third noise value. In addition, in the above-mentioned second aspect, it is also preferred that the processor obtains the average value of the output current signal of the photomultiplier tube based on the signal light corresponding to the excitation light of the specified intensity in the flow cytometer, that is, the third current signal, that is, the second average value, and the third noise value as the noise value of the third current signal, and the first noise value is further calculated using the second average value and the third noise value. If the above-mentioned structure is adopted, the noise component of the number of photons of the signal light can be calculated taking into account the characteristics of the light source and the deviation of the analysis object. As a result, the accuracy of group identification of the analysis object can be further improved.

[0191] In addition, in the above-mentioned first aspect, it is also preferred to further calculate a first noise value, which includes: its own independent noise caused by its own signal light, which reflects the expected value of the signal light of the detection object of the photomultiplier tube, and the gain of the photomultiplier tube; other independent noise caused by other signal light, which reflects the expected value of the signal light leaked into the photomultiplier tube from the signal light of the detection object of other photomultiplier tubes different from the photomultiplier tube, and the gain of the photomultiplier tube; and its own correlation noise caused by the correlation between its own signal light and other signal light, which reflects the expected value of the signal light of the detection object of the photomultiplier tube and the gain of the photomultiplier tube, and data analysis is performed using the analytical evaluation value and the first noise value. In the second aspect, it is also preferred that the processor further calculates a first noise value, which includes: an expected value of the signal light of the detection object of the photomultiplier tube and its own independent noise caused by its own signal light, which reflects the gain of the photomultiplier tube; an expected value of the signal light leaked to the photomultiplier tube from the signal light of the detection object of other photomultiplier tubes different from the photomultiplier tube, and other independent noise caused by other signal light, which reflects the gain of the photomultiplier tube; and a first noise value of its own correlation noise caused by the correlation between its own signal light and other signal light, which reflects the expected value of the signal light of the detection object of the photomultiplier tube and the gain of the photomultiplier tube, and performs data analysis using the analysis evaluation value and the first noise value. If the above structure is adopted, the noise value of the analysis evaluation value caused by the deviation of the signal light of the detection object of the photomultiplier tube can be predicted with high accuracy. As a result, the signal light can be quantitatively analyzed by data analysis.

[0192] Furthermore, in the first aspect, it is also preferred that the first noise value is calculated, and the first noise value also includes: the expected value of the signal light of the detection object of other photomultiplier tubes is reflected, and the gain of the photomultiplier tube is caused by the correlation between its own signal light and other signal light. Furthermore, in the second aspect, the processor calculates the first noise value, and the first noise value also includes: the expected value of the signal light of the detection object of other photomultiplier tubes is reflected, and the gain of the photomultiplier tube is caused by the correlation between its own signal light and other signal light. In this case, the noise value of the analytical evaluation value caused by the deviation of the signal light of the detection object of the photomultiplier tube can be predicted with high accuracy.

[0193] In addition, further in the first aspect, it is also preferred that a first noise value equivalent to the noise contained in the analytical evaluation value is further calculated based on the analytical evaluation value and the gain of the photomultiplier tube, and data analysis is performed by an optimization algorithm using the analytical evaluation value and the first noise value. In addition, further in the second aspect, it is also preferred that the processor further calculates a first noise value equivalent to the noise contained in the analytical evaluation value based on the analytical evaluation value and the gain of the photomultiplier tube, and data analysis is performed by an optimization algorithm using the analytical evaluation value and the first noise value. In this case, the analytical evaluation value can be used as an object, and processing such as weighting that reflects the noise value can be applied, and data analysis can be performed. As a result, the analysis result based on flow cytometry can be used on the basis of reflecting the reliability.

[0194] Description of Reference Numerals

[0195] 1…flow cytometer system, 2…fluid system, 3…optical system, 4…electronic system (signal processing device), 5…channel, 6…flow cell, 7…laser light source, 8…lens, 9a, 9b, 9c, 9d…filter, 10b, 10c…color dichroic mirror, 11a, 11b, 11c, 11d…photomultiplier tube, 12…data processing device, 201…signal acquisition unit, 202…calculation unit, 203…analysis unit.

Claims

1. A signal processing method, wherein: It is a signal processing method for processing the output of the photomultiplier tube constituting the flow cytometer. acquiring, as a first current signal, an output current signal of the photomultiplier tube based on signal light generated by flow cytometry using the flow cytometer, By dividing the value of the first current signal by the gain of the photomultiplier tube, an analytical evaluation value is calculated, the analytical evaluation value being either the number of photons of the signal light incident on the photomultiplier tube or the number of photoelectrons released from the photoelectric conversion unit of the photomultiplier tube, Data analysis is performed based on the analysis evaluation value.

2. The signal processing method according to claim 1, wherein: The analytical evaluation value is the number of photons.

3. The signal processing method according to claim 1 or 2, wherein: The data analysis includes a gating process for defining the boundaries of a group to be analyzed.

4. The signal processing method according to claim 3, wherein: Based on the analytical evaluation value and the gain of the photomultiplier tube, a first noise value corresponding to the noise included in the analytical evaluation value is further calculated; In the gating process, a gating interval is set using the analytical evaluation value and the first noise value.

5. The signal processing method according to any one of claims 1 to 4, wherein: acquiring an average value of a second current signal as a first average value, wherein the second current signal is an output current signal of the photomultiplier tube when the signal light is not incident on the flow cytometer, The analytical evaluation value is calculated using the value of the first current signal from which the first average value has been subtracted.

6. The signal processing method according to claim 4, wherein: acquiring a noise value of a second current signal as a second noise value, wherein the second current signal is an output current signal of the photomultiplier tube when the signal light is not incident on the flow cytometer, The first noise value is further calculated using the second noise value.

7. The signal processing method according to claim 4, wherein: acquiring a second average value as an average value of a third current signal and a third noise value as a noise value of the third current signal, wherein the third current signal is an output current signal of the photomultiplier tube based on the signal light corresponding to the excitation light of a predetermined intensity in the flow cytometer, The first noise value is further calculated using the second average value and the third noise value.

8. The signal processing method according to claim 1, wherein: A first noise value is further calculated, the first noise value including: independent noise of the photomultiplier tube caused by its own signal light, which reflects the expected value of the signal light of the detection object of the photomultiplier tube and the gain of the photomultiplier tube; other independent noise caused by other signal light, which reflects the expected value of the signal light leaked to the photomultiplier tube from the signal light of other photomultiplier tubes different from the photomultiplier tube and the gain of the photomultiplier tube; and correlation noise of the photomultiplier tube caused by the correlation between its own signal light and other signal light, which reflects the expected value of the signal light of the detection object of the photomultiplier tube and the gain of the photomultiplier tube. Data analysis is performed using the analysis evaluation value and the first noise value.

9. The signal processing method according to claim 8, wherein: The first noise value is calculated, and the first noise value further includes other correlation noise caused by correlation between the own signal light and other signal light reflecting the expected value of the signal light to be detected by the other photomultiplier tube and the gain of the photomultiplier tube.

10. The signal processing method according to any one of claims 1 to 9, wherein: Based on the analytical evaluation value and the gain of the photomultiplier tube, a first noise value corresponding to the noise included in the analytical evaluation value is further calculated; Data analysis is performed by an optimization algorithm using the analysis evaluation value and the first noise value.

11. A signal processing device, wherein: A signal processing device equipped with a processor that processes the output of the photomultiplier tube that constitutes the flow cytometer. The processor is configured as follows: acquiring, as a first current signal, an output current signal of the photomultiplier tube based on signal light generated by flow cytometry using the flow cytometer, By dividing the value of the first current signal by the gain of the photomultiplier tube, an analytical evaluation value is calculated, wherein the analytical evaluation value is either the number of photons of the signal light incident on the photomultiplier tube or the number of photoelectrons released from the photoelectric conversion unit of the photomultiplier tube. Data analysis is performed based on the analysis evaluation value.

12. The signal processing device according to claim 11, wherein: The analytical evaluation value is the number of photons.

13. The signal processing device according to claim 11 or 12, wherein: The data analysis includes a gating process for defining the boundaries of a group to be analyzed.

14. The signal processing device according to claim 13, wherein: the processor, Based on the analytical evaluation value and the gain of the photomultiplier tube, a first noise value corresponding to the noise included in the analytical evaluation value is further calculated; In the gating process, a gating interval is set using the analytical evaluation value and the first noise value.

15. The signal processing device according to any one of claims 11 to 14, wherein: the processor, acquiring an average value of a second current signal as a first average value, wherein the second current signal is an output current signal of the photomultiplier tube when the signal light is not incident on the flow cytometer, The analytical evaluation value is calculated using the value of the first current signal from which the first average value has been subtracted.

16. The signal processing device according to claim 14, wherein: the processor, acquiring a noise value of a second current signal as a second noise value, wherein the second current signal is an output current signal of the photomultiplier tube when the signal light is not incident on the flow cytometer, The first noise value is further calculated using the second noise value.

17. The signal processing device according to claim 14, wherein: the processor, acquiring a second average value as an average value of a third current signal and a third noise value as a noise value of the third current signal, wherein the third current signal is an output current signal of the photomultiplier tube based on the signal light corresponding to the excitation light of a predetermined intensity in the flow cytometer, The first noise value is further calculated using the second average value and the third noise value.

18. The signal processing device according to claim 11, wherein: the processor, A first noise value is further calculated, the first noise value including: independent noise of the photomultiplier tube caused by its own signal light, which reflects the expected value of the signal light of the detection object of the photomultiplier tube and the gain of the photomultiplier tube; other independent noise caused by other signal light, which reflects the expected value of the signal light leaked to the photomultiplier tube from the signal light of other photomultiplier tubes different from the photomultiplier tube and the gain of the photomultiplier tube; and correlation noise of the photomultiplier tube caused by the correlation between its own signal light and other signal light, which reflects the expected value of the signal light of the detection object of the photomultiplier tube and the gain of the photomultiplier tube. Data analysis is performed using the analysis evaluation value and the first noise value.

19. The signal processing device according to claim 18, wherein: the processor, The first noise value is calculated, and the first noise value further includes other correlation noise caused by correlation between the own signal light and other signal light reflecting the expected value of the signal light to be detected by the other photomultiplier tube and the gain of the photomultiplier tube.

20. The signal processing device according to any one of claims 11 to 19, wherein: the processor, Based on the analytical evaluation value and the gain of the photomultiplier tube, a first noise value corresponding to the noise included in the analytical evaluation value is further calculated; Data analysis is performed by an optimization algorithm using the analysis evaluation value and the first noise value.

21. A signal processing system, wherein: have: The signal processing device according to any one of claims 11 to 20; The photomultiplier tube; and An optical system guides the signal light to the photomultiplier tube.

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