Cell classification method, classification device, and program

Abstract

The present invention relates to a cell classification method, a classification device, and a program. An object of the present invention is to suppress an analysis result with insufficient output accuracy due to the properties of a measurement object. A cell classification method for classifying cells contained in a measurement object includes: treating the measurement object using a first preparation condition to prepare a first measurement sample; obtaining a first signal from the prepared first measurement sample; classifying the cells contained in the first measurement sample using the first signal; treating the measurement object using a second preparation condition different from the first preparation condition to prepare a second measurement sample; obtaining a second signal from the prepared second measurement sample; classifying the cells contained in the second measurement sample using the second signal; comparing the classification result of the cells using the first signal and the classification result of the cells using the second signal, and outputting an analysis result including the number of cells based on the comparison result.

Description

Technical Field

[0001] The present invention relates to a cell classification method, a classification device, and a program. Background Art

[0002] There is known a technique for classifying white blood cells in blood into multiple species using information obtained by irradiating a sample obtained by hemolyzing blood with light. For example, Patent Document 1 discloses a system for analyzing a whole blood sample in such a way as to identify, classify, and / or quantify white blood cells and subclasses of white blood cells in a measurement sample prepared under specified preparation conditions.

[0003] Prior Art Documents

[0004] Patent Documents

[0005] Patent Document 1: Japanese Patent Application Laid-Open No. 2016-514267 Summary of the Invention

[0006] Problems to be Solved by the Invention

[0007] According to the system described in Patent Document 1, when classifying white blood cells in blood into multiple species, a measurement sample prepared under specified preparation conditions is analyzed, and depending on the properties of the analyte, it is sometimes impossible to classify white blood cells with high precision. For example, depending on the analyte, neutrophils and monocytes sometimes have the same degree of size and the same degree of permeability of the staining liquid membrane, and in such a case, it is impossible to classify neutrophils and monocytes with high precision. However, since the system described in Patent Document 1 analyzes a measurement sample prepared under specified preparation conditions, an analysis result with insufficient accuracy will be output depending on the properties of the analyte.

[0008] Therefore, an object of the present invention is to suppress the output of an analysis result with insufficient accuracy depending on the properties of the analyte.

[0009] Means for Solving the Problems

[0010] One aspect of the present invention relates to a cell classification method performed by an analysis device (1) for classifying cells contained in a measurement object, including: treating the measurement object under a first preparation condition to prepare a first measurement sample; obtaining a first signal from the prepared first measurement sample; classifying the cells contained in the first measurement sample using the first signal; treating the measurement object under a second preparation condition different from the first preparation condition to prepare a second measurement sample; obtaining a second signal from the prepared second measurement sample; classifying the cells contained in the second measurement sample using the second signal; and comparing the classification result of the cells using the first signal with the classification result of the cells using the second signal, and outputting an analysis result including the number of cells based on the comparison result. Thereby, it is possible to suppress the output of an analysis result with insufficient accuracy due to the properties of the measurement object.

[0011] Another aspect of the present invention relates to a cell classification method performed by an analysis device (1) for classifying cells contained in a measurement object, including: treating the measurement object under a first preparation condition to prepare a first measurement sample; obtaining a first signal from the prepared first measurement sample; classifying the cells contained in the first measurement sample using the first signal; evaluating the classification ability of the cell classification using the first signal; when the evaluation result of the classification ability satisfies a specified condition, treating the measurement object under a second preparation condition different from the first preparation condition to prepare a second measurement sample, obtaining a second signal from the prepared second measurement sample, classifying the cells contained in the second measurement sample using the second signal, and outputting the number of cells based on the second signal. Thereby, it is possible to suppress the output of an analysis result with insufficient accuracy due to the properties of the measurement object.

[0012] Another aspect of the present invention relates to a cell classification method performed by an analysis device (1) for classifying cells contained in a measurement object, including: treating the measurement object under a first preparation condition to prepare a first measurement sample; obtaining a first signal from the prepared first measurement sample; classifying the cells contained in the first measurement sample using the first signal; treating the measurement object under a second preparation condition different from the first preparation condition to prepare a second measurement sample; obtaining a second signal from the prepared second measurement sample; classifying the cells contained in the second measurement sample using the second signal; evaluating the classification ability of the cell classification using the first signal; and based on the evaluation result of the classification ability, outputting an analysis result including the number of cells based on the first signal or an analysis result including the number of cells based on the second signal. Thereby, it is possible to suppress the output of an analysis result with insufficient accuracy due to the properties of the measurement object.

[0013] Another aspect of the present invention relates to an analysis device (1) for classifying cells contained in a test substance. The analysis device (1) includes a sample preparation unit (25) that prepares a measurement sample by treating the test substance with a reagent, a detection unit (26) that obtains a signal from the measurement sample, and a control unit (500). For the control unit (500): in the sample preparation unit (25), the test substance is treated using a first preparation condition to prepare a first measurement sample, and the test substance is treated using a second preparation condition different from the first preparation condition to prepare a second measurement sample; in the detection unit (26), a first signal is obtained from the prepared first measurement sample, and a second signal is obtained from the prepared second measurement sample; the cells contained in the test substance are classified using the first signal; the cells contained in the test substance are classified using the second signal; and the classification result of the cells using the first signal is compared with the classification result of the cells using the second signal, and an analysis result is output based on the comparison result. Thereby, it is possible to suppress an analysis result with insufficient output accuracy due to the properties of the test substance.

[0014] Another aspect of the present invention relates to an analysis device (1) for classifying cells contained in a test substance. The analysis device (1) includes a sample preparation unit (25) that prepares a measurement sample by treating the test substance with a reagent, a detection unit (26) that obtains a signal from the measurement sample, and a control unit (500). For the control unit (500): in the sample preparation unit (25), the test substance is treated using a first preparation condition to prepare a first measurement sample; in the detection unit (26), a first signal is obtained from the prepared first measurement sample; the cells contained in the test substance are classified using the first signal; the classification ability of the cell classification using the first signal is evaluated; when the evaluation result of the classification ability satisfies a specified condition, in the sample preparation unit (25), the test substance is treated using a second preparation condition different from the first preparation condition to prepare a second measurement sample, in the detection unit (26), a second signal is obtained from the prepared second measurement sample, the cells contained in the second measurement sample are classified using the second signal, and the number of cells based on the second signal is output. Thereby, it is possible to suppress an analysis result with insufficient output accuracy due to the properties of the test substance.

[0015] Another aspect of the present invention relates to an analysis device (1) for classifying cells contained in a test substance. The analysis device (1) includes a sample preparation unit (25) that prepares a measurement sample by treating the test substance with a reagent, a detection unit (26) that obtains signals from the measurement sample, and a control unit (500). Regarding the control unit (500): the sample preparation unit (25) prepares a first measurement sample by treating the test substance using a first preparation condition, and prepares a second measurement sample by treating the test substance using a second preparation condition different from the first preparation condition; the detection unit (26) obtains a first signal from the prepared first measurement sample and a second signal from the prepared second measurement sample; the cells contained in the test substance are classified using the first signal; the cells contained in the test substance are classified using the second signal; the classification ability of the cell classification using the first signal is evaluated; and based on the evaluation result of the classification ability, an analysis result including the number of cells based on the first signal or an analysis result including the number of cells based on the second signal is output. Thereby, it is possible to suppress the output of an analysis result with insufficient accuracy due to the properties of the test substance.

[0016] Another aspect of the present invention relates to a program that is executed in a computer: in a sample preparation unit (25) that prepares a measurement sample by treating a test substance with a reagent, a first measurement sample is prepared by treating the test substance using a first preparation condition, and a second measurement sample is prepared by treating the test substance using a second preparation condition different from the first preparation condition; in a detection unit (26) that obtains signals from the measurement sample, a first signal is obtained from the prepared first measurement sample and a second signal is obtained from the prepared second measurement sample; the cells contained in the test substance are classified using the first signal; the cells contained in the test substance are classified using the second signal; and the classification result of the cells using the first signal and the classification result of the cells using the second signal are compared, and an analysis result is output based on the comparison result. Thereby, it is possible to suppress the output of an analysis result with insufficient accuracy due to the properties of the test substance.

[0017] Another aspect of the present invention relates to a program that is executed on a computer: in a sample preparation unit (25) that prepares a measurement sample by treating a test substance with a reagent, the test substance is treated using a first preparation condition to prepare a first measurement sample, and the test substance is treated using a second preparation condition different from the first preparation condition to prepare a second measurement sample; in a detection unit (26) that obtains a signal from the measurement sample, a first signal is obtained from the prepared first measurement sample; the classification ability of the cell classification using the first signal is evaluated; when the evaluation result of the classification ability satisfies a specified condition, in the sample preparation unit (25), the test substance is treated using a second preparation condition different from the first preparation condition to prepare a second measurement sample, in the detection unit (26), a second signal is obtained from the prepared second measurement sample, the cells contained in the second measurement sample are classified using the second signal, and the cell count based on the second signal is output. Thereby, it is possible to suppress an analysis result with insufficient output accuracy due to the nature of the test substance.

[0018] Another aspect of the present invention relates to a program that is executed on a computer: in a sample preparation unit (25) that prepares a measurement sample by treating a test substance with a reagent, the test substance is treated using a first preparation condition to prepare a first measurement sample, and the test substance is treated using a second preparation condition different from the first preparation condition to prepare a second measurement sample; in a detection unit (26) that obtains a signal from the measurement sample, a first signal is obtained from the prepared first measurement sample and a second signal is obtained from the prepared second measurement sample; the cells contained in the test substance are classified using the first signal; the cells contained in the test substance are classified using the second signal; the classification ability of the cell classification using the first signal is evaluated; and an analysis result including the cell count based on the first signal or an analysis result including the cell count based on the second signal is output based on the evaluation result of the classification ability. Thereby, it is possible to suppress an analysis result with insufficient output accuracy due to the nature of the test substance.

[0019] Advantages of the Invention

[0020] According to the present invention, it is possible to suppress an analysis result with insufficient output accuracy due to the nature of the test substance. Brief Description of the Drawings

[0021] Figure 1 A perspective view showing the appearance of the analysis device according to the present embodiment.

[0022] Figure 2 A diagram schematically showing a configuration example of the measurement unit.

[0023] Figure 3 A diagram schematically showing a configuration example of the optical detector.

[0024] Figure 4 A diagram showing a configuration example of an information processing unit for display.

[0025] Figure 5 A diagram showing a functional configuration example of an information processing unit for display.

[0026] Figure 6 A diagram showing an example of a scatter plot.

[0027] Figure 7 A diagram showing an example of the distribution of blood cells in a scatter plot.

[0028] Figure 8 A flowchart showing an example of the process of assigning each blood cell to a certain cluster.

[0029] Figure 9 A flowchart showing an example of the processing steps performed by an analysis device.

[0030] Figure 10 A diagram showing an example of the classification results obtained using preparation condition A and preparation condition B.

[0031] Figure 11 A diagram showing an example of the classification results obtained using preparation condition A and preparation condition B.

[0032] Figure 12 A diagram showing an example of the classification results obtained using preparation condition A and preparation condition B.

[0033] Figure 13 A diagram for explaining an example of the calculation of the degree of boundary contact between two clusters.

[0034] Figure 14 A diagram showing an example of the classification results obtained using preparation condition A and preparation condition B.

[0035] Figure 15 A diagram showing an example of the classification results obtained using preparation condition A and preparation condition B.

[0036] Figure 16 A diagram showing an example of the classification results obtained using preparation condition A and preparation condition B. Detailed implementation mode

[0037] The embodiments of the present invention will be described with reference to the accompanying drawings. It should be noted that in each figure, components marked with the same reference numerals have the same or similar configurations.

[0038] <Configuration of the analyte measurement system>

[0039] Figure 1This is a perspective view showing the appearance of the analysis device 1 according to this embodiment. The analysis device 1 is a multi-item blood cell analysis device for counting blood cells of white blood cells contained in a blood analyte. The analysis device 1 includes a measurement unit 2, a transport unit 3 disposed on the front side of the measurement unit 2, and an information processing unit 4. The blood analyte, which is peripheral blood collected from a patient, is accommodated in a analyte container (blood collection tube) T. A plurality of analyte containers T are supported by a sample rack L, and the sample rack L is transported by the transport unit 3, and the blood analyte is supplied to the measurement unit 2.

[0040] The information processing unit 4 includes a display unit 41 and an input unit 42, and the measurement unit 2 and the transport unit 3 are communicably connected to a host computer 5 (see Figure 2 ). The information processing unit 4 controls the operations of the measurement unit 2 and the transport unit 3, analyzes based on the measurement results obtained in the measurement unit 2, and sends the analysis results to the host computer 5 (see Figure 2 ).

[0041] Figure 2 This is a diagram schematically showing a configuration example of the measurement unit 2.

[0042] The measurement unit 2 includes a hand 21, an analyte container placement unit 22, a barcode unit 23, an analyte suction unit 24, a sample preparation unit 25, and a detection unit 26. The analyte suction unit 24 includes a piercer 24a and sucks the analyte from the analyte container T. The sample preparation unit 25 includes a mixing chamber MC and a heater H, and prepares a measurement sample used in the measurement by mixing a reagent into the analyte. The detection unit 26 includes an optical detector D and detects blood cells from the measurement sample. Each unit of the measurement unit 2 is controlled by the information processing unit 4.

[0043] The analyte container T positioned at position P1 by the transport unit 3 is grasped by the hand 21 and pulled upward from the sample rack L. Then, the analyte in the analyte container T is stirred by the shaking of the hand 21. The analyte container T after the stirring is finished is placed by the hand 21 on the analyte container placement unit 22 positioned at position P1. After that, the analyte container T is transported by the analyte container placement unit 22 to position P2.

[0044] If the container T of the analyte to be measured is positioned at position P2, the barcode unit 23 disposed near position P2 reads the analyte number from the barcode label pasted on the container T of the analyte to be measured. Thereafter, the container T of the analyte to be measured is conveyed by the analyte container placement unit 22 to position P3. If the container T of the analyte to be measured is positioned at position P3, a predetermined amount of the analyte to be measured is aspirated from the container T of the analyte to be measured by the analyte aspiration unit 24 using the drill 24a. If the aspiration of the analyte is completed, the container T of the analyte to be measured is conveyed forward by the analyte container placement unit 22 and returned to the support position of the original sample rack L by the hand 21. After the analyte aspirated by the drill 24a is transferred to the position of the mixing chamber MC, only a predetermined amount is discharged into the mixing chamber MC by the analyte aspiration unit 24.

[0045] The sample preparation unit 25 prepares a measurement sample by mixing the blood analyte and the reagent under predetermined preparation conditions. The reagent contains a hemolytic agent (first reagent) and a fluorescent substance (second reagent). The red blood cells and platelets in the blood are hemolyzed with the hemolytic agent, and the white blood cells are stained with the fluorescent substance.

[0046] The measurement sample prepared by the sample preparation unit 25 is supplied to the optical detector D of the detection unit 26.

[0047] Figure 3 A diagram schematically showing a configuration example of the optical detector D. The optical detector D includes a flow cell D1, a sheath flow system D2, a beam spot forming system D3, a forward scattered light receiving system D4, a side scattered light receiving system D5, and a fluorescence receiving system D6.

[0048] The sheath flow system D2 is configured to send the measurement sample into the flow cell D1 and generate a liquid flow in the flow cell D1. The beam spot forming system D3 is configured such that the light irradiated by the semiconductor laser D31 passes through the collimating lens D32 and the condenser lens D33 and irradiates the flow cell D1. Thereby, the blood cells contained in the liquid flow passing through the flow cell D1 are irradiated with the laser. In addition, the beam spot forming system D3 also includes a ray blocking device D34.

[0049] The forward scattered light receiving system D4 is configured to condense the forward scattered light (forward scattered light) using the forward condenser lens D41 and receive the light passing through the pinhole D42 with the photodiode D43. The photodiode D43 outputs a forward scattered light signal (FSC) based on the peak of the received forward scattered light. The side scattered light receiving system D5 is configured to condense the side scattered light (side scattered light) using the side condenser lens D51, reflect a part of the light by the dichroic mirror D52, and receive it with the photodiode D53. The photodiode D53 outputs a side scattered light signal (SSC) based on the peak of the received side scattered light.

[0050] Light scattering is a phenomenon caused by the following: If particles such as blood cells exist as obstacles in the traveling direction of light, the light will change its traveling direction due to the particles. By detecting this scattered light, information related to the size and material of the particles can be obtained. In particular, information related to the size of the particles (blood cells) can be obtained from the forward scattered light. In addition, information inside the particles can be obtained from the side scattered light. When a laser irradiates blood cell particles, the intensity of the side scattered light depends on the complexity inside the cell (the shape, size, density of the nucleus, and the amount of granules).

[0051] The fluorescence reception system D6 is configured such that the light (fluorescence) passing through the dichroic mirror D52 in the side scattered light further passes through the spectral filter D61 and is received by the avalanche photodiode D62. The avalanche photodiode D62 outputs a side fluorescence signal (SFL) based on the peak of the received fluorescence.

[0052] If light is irradiated onto blood cells stained with a fluorescent substance, fluorescence with a wavelength longer than that of the irradiated light is emitted. If the staining is good, the intensity of the fluorescence becomes stronger, and by measuring this fluorescence intensity, information related to the staining degree of the blood cells can be obtained.

[0053] The forward scattered light signal (FSC), side scattered light signal (SSC), and side fluorescence signal (SFL) obtained for each blood cell by the optical detector D are sent to the information processing unit 4.

[0054] Figure 4 This is a diagram showing a configuration example of the information processing unit 4.

[0055] The information processing unit 4 includes a personal computer and is composed of a main body 40, a display unit 41, and an input unit 42. The main body 40 has a CPU 401, a ROM 402, a RAM 403, a hard disk 404, a reading device 405, an image output interface 406, an input / output interface 407, and a communication interface 408.

[0056] The CPU 401 runs the computer programs stored in the ROM 402 and the computer programs loaded in the RAM 403. The RAM 403 is used for reading the computer programs recorded in the ROM 402 and the hard disk 404. In addition, the RAM 403 is also used as the working area of the CPU 401 when running these computer programs.

[0057] The hard disk 404 stores an operating system, a computer program to be run in the CPU 401, and data used during the running of the computer program. In addition, the hard disk 404 stores a program 404a for processing the information processing unit 4. The reading device 405 is composed of a CD drive, a DVD drive, etc., and can read a computer program and data recorded in a non-transitory computer readable medium 405a readable by a computer. It should be noted that when the above program 404a is recorded in the recording medium 405a, the program 404a read from the recording medium 405a by the reading device 405 is stored in the hard disk 404.

[0058] The image output interface 406 outputs a display signal corresponding to the image data to the display unit 41, and the display unit 41 displays an image based on the display signal output from the image output interface 406. The user inputs an instruction via the input unit 42, and the input / output interface 407 receives the signal input via the input unit 42. The communication interface 408 is connected to the measurement unit 2, the conveyance unit 3, and the host computer 5, and the CPU 401 receives and transmits instruction signals and data to and from these devices via the communication interface 408.

[0059] Figure 5 This is a diagram showing a functional configuration example of the information processing unit 4. The control unit 500 is implemented by the CPU 401 reading and running a computer program. The storage unit 505 is implemented using the RAM 403 or the hard disk 404.

[0060] The control unit 500 includes: a measurement control unit 501 that measures a test object by controlling the operations of the measurement unit 2 and the conveyance unit 3, an analysis processing unit 502 that classifies white blood cells and counts the number of blood cells based on the forward scatter light signal (FSC), side scatter light signal (SSC), and side fluorescence signal (SFL) measured by the detection unit 26 of the measurement unit 2, a display control unit 503 that performs processing to display the measured number of blood cells of various types of white blood cells, scatter plots (distribution maps), etc. on the display unit 41, and an input processing unit 504 that receives the input from the user via the input unit 42.

[0061] The storage unit 505 stores measurement information indicating the levels of the forward scatter light signal (FSC), side scatter light signal (SSC), and side fluorescence signal (SFL) received from the measurement unit 2 for each blood cell. This measurement information is the levels of the forward scatter light signal (FSC), side scatter light signal (SSC), and side fluorescence signal (SFL), and is information represented by quantized values called "channels". Channels will be described below.

[0062] The analysis and processing unit 502 classifies white blood cells present in the blood analyte into lymphocytes (LYMPH), monocytes (MONO), neutrophils (NEUT), basophils (BASO), and eosinophils (EO) based on the measurement information stored in the storage unit 505, and counts the numbers of various blood cells.

[0063] The analysis and processing unit 502 classifies the white blood cells present in the blood analyte by creating a scatter plot (distribution map) based on the measurement information.

[0064] Here, the scatter plot is a two-dimensional distribution map with two of the signals among the forward scatter light signal (FSC) output from the photodiode D43, the side scatter light signal (SSC) output from the photodiode D53, and the side fluorescence signal (SFL) output from the avalanche photodiode D62 as the vertical axis and the horizontal axis.

[0065] Depending on which two of the forward scatter light signal (FSC), side scatter light signal (SSC), and side fluorescence signal (SFL) are used, the types of white blood cells that can be classified are different. In the present embodiment, the side fluorescence signal (SFL) and the side scatter light signal (SSC) are used to classify four types of white blood cells: neutrophils, lymphocytes, monocytes, and eosinophils. In addition, the forward scatter light signal (FSC) and the side fluorescence signal (SFL) can be used to classify basophils and white blood cells other than basophils (neutrophils, lymphocytes, monocytes, and eosinophils).

[0066] Figure 6 The shown scatter plot is an example of the case where points corresponding to each blood cell are plotted on a two-dimensional distribution map with the horizontal axis set as the side scatter light signal (SSC) and the vertical axis set as the side fluorescence signal (SFL). When classifying basophils and white blood cells other than basophils with the forward scatter light signal (FSC) and the side fluorescence signal (SFL) as two axes, the blood cells can also be classified by the same method as follows.

[0067] The analysis and processing unit 502 classifies the points on the scatter plot into any one of four clusters (lymphocyte cluster A11, monocyte cluster A12, neutrophil cluster A13, and eosinophil cluster A14) corresponding to four subclasses of white blood cells respectively. Specifically, for example, the degree of belonging of each blood cell to each cluster is obtained from the distance between the point corresponding to each blood cell plotted on the scatter plot and the centroid position of each preset cluster. Then, based on these degrees of belonging, each blood cell is assigned to each cluster.

[0068] Here, an example of the step of allocating each blood cell to each cluster will be described in more detail. After being amplified by an amplifier, the two signals used in the production of the scatter plot are converted into digital signals by an A / D converter. That is, quantization is performed. Through this quantization, each signal is classified into one of, for example, a total of 256 channels from 0 to 255.

[0069] The information processing unit 4 obtains Figure 7 the scatter plot shown with the i-channel as the X-axis and the j-channel as the Y-axis. Since both the X-axis and the Y-axis in this scatter plot have 256 channels, it includes a total of 256×256 basic elements representing the states of blood cells, and the number of blood cells corresponding to each basic element is stored in each basic element. For example, if the value of the basic element with the X-axis being channel 1 and the Y-axis being channel 2 is 6, it means that there are 6 blood cells with a cytoplasmic size signal of 1 and a signal size obtained by synthesizing the density and size of the nucleus and granules of 2.

[0070] Figure 8 The flowchart showing an example of the process of allocating each blood cell to a certain cluster. First, the information processing unit 4 performs initial segmentation (S2). In this initial segmentation, ignoring Figure 7 the number of blood cells in each basic element in the shown scatter plot, the distribution of the basic elements is segmented by the following regions: a fixed region where lymphocytes are apparently distributed if assumed; a fixed region where monocytes are apparently distributed if assumed; a fixed region where neutrophils are apparently distributed if assumed; a fixed region where eosinophils are apparently distributed if assumed; a fixed region where a phantom (ghost) composed of platelets and red blood cells is apparently distributed if assumed. Each fixed region is preset. The degree of belonging of the blood cells existing in each fixed region is set to 1.

[0071] Next, the information processing unit 4 calculates the initial centroid of the lymphocyte cluster using the following number 1 and number 2 (S4).

[0072] [Number 1]

[0073]

[0074] [Number 2]

[0075]

[0076] Here, N ij is the number of blood cells in the basic elements i and j. The same operation is performed for the clusters of monocytes, neutrophils, eosinophils, and phantoms to obtain the centroid positions.

[0077] Next, the information processing unit 4 calculates the degree of belonging to each cluster for each blood cell that does not belong to any fixed area (S6). More specifically, the information processing unit 4 calculates the degree of belonging to each cluster based on the distance from the position of each blood cell to the centroid position of each cluster. Here, the distance from each blood cell to the centroid position of each cluster is not the distance (Euclidean distance) between the position of each blood cell and each cluster, but the length of the minor axis of a specified ellipse is set as the distance between the position of each blood cell and each cluster. Note that the specified ellipse is an ellipse centered on the centroid position of the cluster, having a specific slope determined in advance for the cluster, the blood cell as the object is located on its arc, and if the minor axis is set as a, the major axis is represented by ak (k is a proportionality constant).

[0078] If the distance L from each blood cell to each cluster is obtained x (x = 1 to N (N is the number of clusters)), then the degree of belonging of the blood cell to each cluster is obtained by Equation 3. Of course, these degrees of belonging are all values less than 1.

[0079] [Equation 3]

[0080]

[0081] By operating in this way, the information processing unit 4 calculates the degree of belonging to each cluster for each blood cell that does not belong to any fixed area.

[0082] Next, for each cluster, the weighted centroid is calculated with the degree of belonging of each blood cell to each cluster as the weight (S8). The weighted centroid is obtained by Equation 4 and Equation 5, for example.

[0083] [Equation 4]

[0084]

[0085] [Equation 5]

[0086]

[0087] Here, U ij represents the degree of belonging of the blood cell at the basic elements i, j to a certain cluster G1.

[0088] After performing such operations to obtain the weighted centroids of each cluster, compare them with the positions corresponding to the initial centroid positions of each cluster obtained through step S4, and determine whether the amount of change is a specified amount, such as 0 (S10). When it is not the specified amount, replace the positions of the weighted centroids of each cluster obtained this time with the initial centroids of each cluster, and repeat the processing steps of steps S6, S8, and S10. That is, calculate the distances of each blood cell from the positions of the weighted centroids of each cluster, calculate the degree of belonging of each blood cell to each cluster based on these distances, based on these degrees of belonging, recalculate the positions of the weighted centroids of each cluster, and determine whether the amount of change between the positions of the new weighted centroids of each cluster and the previous positions of the weighted centroids of each cluster is the specified amount. If it is still not the specified amount, use the positions of the weighted centroids of each cluster obtained this time as the initial centroid positions, and repeat the processing steps of steps S6, S8, and S10.

[0089] Among them, it is assumed that the situation where it will not become the specified amount even if steps S6, S8, and S10 are only repeated a predetermined number of times. Therefore, when the determination in step S10 is no, the information processing unit 4 determines whether the number of executions of steps S6, S8, and S10 is the specified number (S12). When the specified number is reached, "unanalyzable" is displayed on the display unit 41 (S14), and the process ends.

[0090] In addition, if the changes in the centroids of each cluster are all the specified amount, the information processing unit 4 determines the final belonging of each blood cell to each cluster, thereby determining the number of blood cells in each cluster (step S16).

[0091] The final belonging of each blood cell can also be set to belong to the cluster that shows the maximum value in the finally obtained degrees of belonging. For example, in a certain basic element, there are 10 blood cells. The degrees of belonging of these blood cells to the lymphocyte cluster are 0.95, the degrees of belonging to the monocyte cluster are 0.03, the degrees of belonging to the neutrophil cluster are 0.03, the degrees of belonging to the eosinophil cluster are 0.02, and the degrees of belonging to the phantom cluster are 0. Then, all these 10 blood cells belong to the lymphocyte cluster that shows the maximum degree of belonging.

[0092] By performing the above processing, each blood cell existing in the scatter plot is assigned to a certain cluster.

[0093] <Operation of the analysis device>

[0094] The analysis device 1 initially uses a measurement sample prepared according to predetermined preparation conditions to perform the classification of white blood cells and the counting of the number of blood cells, and performs the process of outputting the analysis results. In this embodiment, this process is called the "normal mode". In addition, when the analysis device 1 determines that it is possible that the white blood cells cannot be fully classified in the normal mode, it changes the preparation conditions to prepare a measurement sample, and performs the classification of white blood cells and the counting of the number of blood cells again, and performs the process of outputting the analysis results. In this embodiment, this process is called the "extended mode".

[0095] Figure 9 A flowchart showing an example of the processing steps performed by the display analysis device 1.

[0096] In step S100, the sample preparation unit 25 of the measurement unit 2 prepares a measurement sample by mixing a blood analyte and a reagent according to predetermined preparation conditions. Hereinafter, the predetermined preparation conditions are referred to as preparation condition A.

[0097] Preparation condition A (the same applies to preparation condition B described below) is a condition related to the length of the reaction time of the reagent with the blood analyte. Preparation condition A (the same applies to preparation condition B described below) may also be at least one of a condition related to the temperature when the reagent reacts with the blood analyte and a condition related to the mixing ratio of the blood analyte and the reagent. It should be noted that the condition related to the length of the reaction time of the reagent with the blood analyte may also be a condition related to the length of the heating time when the blood analyte and the reagent are mixed and heated with a heater.

[0098] Regarding the reaction time, temperature, and mixing ratio in preparation condition A, if it is a healthy blood analyte, they are predetermined to be values that can classify white blood cells into lymphocytes, monocytes, neutrophils, basophils, and eosinophils.

[0099] Next, the detection unit 26 of the measurement unit 2 irradiates the prepared measurement sample with light (laser) to obtain an optical signal. Next, the control unit 500 of the information processing unit 4 classifies the white blood cells contained in the blood analyte based on the optical signal and counts the number of each blood cell according to the type of white blood cell.

[0100] In step S101, the control unit 500 of the information processing unit 4 outputs a scatter plot and / or the number of blood cells of each white blood cell as the result of classifying the white blood cells.

[0101] In step S102, the control unit 500 evaluates the classification ability related to the classification of white blood cells (cell classification) for the measurement sample prepared using preparation condition A, and thus determines whether each white blood cell can be sufficiently separated. The classification ability is an index indicating the degree to which cells of multiple types can be correctly classified. More specifically, it is an index indicating the ability to correctly classify the blood analyte into types of white blood cells (lymphocytes, monocytes, neutrophils, basophils, and eosinophils) by analyzing the optical signal. For example, when at least any two of lymphocytes, monocytes, neutrophils, basophils, and eosinophils are not sufficiently separated in the result of classifying the white blood cells, the control unit 500 determines that each white blood cell cannot be sufficiently classified.

[0102] When the control unit 500 determines that each white blood cell can be sufficiently classified (which may also be referred to as "when the classification ability does not meet the specified conditions"), the process ends without transferring to the extended mode. On the other hand, when it is determined that each white blood cell may not be sufficiently classified (which may also be referred to as "when the classification ability meets the specified conditions"), the control unit 500 transfers to the extended mode, and thus enters the processing step of step S103. The situation where each white blood cell may not be sufficiently classified is considered to occur, for example, when analyzing an unhealthy blood sample such as blood collected from a subject with a certain disease.

[0103] In step S103, the sample preparation unit 25 of the measurement unit 2 prepares a measurement sample by mixing the blood sample to be tested with the reagent again according to the preparation condition A. Next, the detection unit 26 of the measurement unit 2 obtains an optical signal by irradiating the prepared measurement sample with light. Next, the control unit 500 of the information processing unit 4 classifies the white blood cells contained in the blood sample to be tested using the optical signal, and at the same time counts the number of each blood cell according to the type of white blood cell.

[0104] In step S104, the sample preparation unit 25 of the measurement unit 2 prepares a measurement sample by mixing the blood sample to be tested with the reagent according to the preparation condition B different from the preparation condition A. Next, the detection unit 26 of the measurement unit 2 obtains an optical signal by irradiating the prepared measurement sample with light. Next, the control unit 500 of the information processing unit 4 classifies the white blood cells contained in the blood sample to be tested using the optical signal, and at the same time counts the number of each blood cell according to the type of white blood cell.

[0105] In step S105, the control unit 500 compares the classification result of the white blood cells using the optical signal obtained under the preparation condition A with the classification result of the white blood cells using the optical signal obtained under the preparation condition B. When the comparison result between the classification result obtained under the preparation condition A and the classification result obtained under the preparation condition B is within the specified range, it is determined that each white blood cell in the preparation condition A can be sufficiently classified, and the process enters the processing step of step S106. On the other hand, when the comparison result exceeds the specified range, it is determined that the classification result under the preparation condition B can more appropriately classify each white blood cell than the preparation condition A, and the process enters the processing step of step S107.

[0106] In step S106, the control unit 500 outputs an analysis result including the number of blood cells of each type of white blood cell based on the comparison result in step S105. More specifically, the control unit 500 outputs the count and / or scatter plot of each white blood cell based on the optical signal obtained under the preparation condition A.

[0107] In step S107, the control unit 500 outputs an analysis result including the white blood cell count based on the comparison result in step S105. For example, the control unit 500 outputs information related to the reliability of the count and / or scatter plot of each white blood cell and the count of each white blood cell based on the optical signals obtained using preparation condition A on the display unit 41. This information related to reliability may also be, for example, a flag or a string indicating low reliability for the count and / or scatter plot of each white blood cell.

[0108] In the processing steps of step S100, step S103, and step S104 described above, the control unit 500 classifies white blood cells using two optical signals, namely, the side fluorescence (SFL) and the side scatter light signal (SSC) emitted from the measurement sample A (or measurement sample B). It should be noted that at least two of the two scatter light signals with different angles (i.e., the forward scatter light signal (FSC), the side scatter light signal (SSC), and the side fluorescence signal (SFL)) can also be used for the classification of white blood cells.

[0109] In addition, the control unit 500 obtains the classification result of white blood cells based on the scatter plot obtained by plotting the two optical signals, the side fluorescence (SFL) and the side scatter light signal (SSC), on different axes.

[0110] In addition, the control unit 500 obtains a classification result that classifies the blood analyte into four types: lymphocytes, monocytes, neutrophils, and eosinophils by using the side fluorescence signal (SFL) and the side scatter light signal (SSC).

[0111] It should be noted that the control unit 500 can also obtain a classification result that classifies the blood analyte into five types: lymphocytes, monocytes, neutrophils, basophils, and eosinophils by using all of the forward scatter light signal (FSC), the side scatter light signal (SSC), and the side fluorescence signal (SFL).

[0112] The preparation condition A, the measurement sample, and the optical signal in the processing step of step S103 described above are respectively referred to as the first preparation condition, the first measurement sample, and the first optical signal. In addition, the preparation condition B, the measurement sample, and the optical signal in the processing step of step S104 are respectively referred to as the second preparation condition, the second measurement sample, and the second optical signal. In addition, the preparation condition A, the measurement sample, and the optical signal in the processing step of step S100 are respectively referred to as the third preparation condition, the third measurement sample, and the third optical signal.

[0113] In addition, as another example, the preparation condition A, the measurement sample, and the optical signal in the processing steps of step S100 are respectively referred to as the first preparation condition, the first measurement sample, and the first optical signal. In addition, the preparation condition B, the measurement sample, and the optical signal in the processing steps of step S104 are respectively referred to as the second preparation condition, the second measurement sample, and the second optical signal.

[0114] In the above-described embodiment, in steps S100 and S103, the preparation, optical signal measurement, and classification of the sample using the preparation condition A are performed. Thus, even when a long time has elapsed from the preparation of the sample in step S100 to the preparation of the sample in step S103, a sample having a state close to that of the sample prepared by the preparation condition B in step S104 can be used to complete the preparation of the sample using the preparation condition A. Therefore, the accuracy of the analysis result output in step S106 or step S107 can be improved.

[0115] (Modification Example 1 Regarding the Operation of the Analysis Device)

[0116] The control unit 500 outputs the count and / or scatter plot of each white blood cell based on the optical signal obtained using the preparation condition B in the processing steps of step S107.

[0117] (Modification Example 2 Regarding the Operation of the Analysis Device)

[0118] The control unit 500 may also omit the processing steps of step S103 and step S105 in the Figure 9 shown processing steps. That is, when the evaluation result of evaluating the classification ability of white blood cells using the optical signal obtained using the preparation condition A satisfies a specified condition, the control unit 500 does not perform the processing step of step S103 but performs the processing step of step S104. In addition, the control unit 500 classifies the white blood cells contained in the blood sample to be measured using the optical signal obtained using the preparation condition B obtained in the processing steps of step S104, and at the same time, counts the number of each blood cell, and outputs the count and / or scatter plot of each white blood cell on the display unit 41.

[0119] In Modification Example 2, the preparation condition A, the measurement sample, and the optical signal in the processing steps of step S100 are respectively referred to as the first preparation condition, the first measurement sample, and the first optical signal. In addition, the preparation condition A, the measurement sample, and the optical signal in the processing steps of step S104 are respectively referred to as the second preparation condition, the second measurement sample, and the second optical signal.

[0120] (Modification Example 3 Regarding the Operation of the Analysis Device)

[0121] The control unit 500 receives from the user, via the input unit 42, a measurement instruction to measure the blood analyte in the extended mode instead of the standard mode. In this case, when the control unit 500 receives this measurement instruction, in Figure 9 the processing steps shown, the processing steps of steps S100 to S102 are omitted (that is, the standard mode is omitted).

[0122] (Modification example 4 of the operation of the analysis device)

[0123] The control unit 500 performs the process of combining Modification example 1 and Modification example 3 described above. That is, when the control unit 500 receives from the user a measurement instruction to measure the blood analyte in the extended mode instead of the standard mode, in Figure 9 the processing steps shown, the processing steps of steps S100 to S102 are omitted (that is, the standard mode is omitted). In addition, in the processing step of step S107, the count and / or scatter plot of each white blood cell based on the optical signal obtained using Preparation condition B are output.

[0124] <Examples of Preparation condition A and Preparation condition B>

[0125] Preparation condition B is a condition in which the reaction time of the reagent with the blood analyte is longer than that of Preparation condition A. It should be noted that Preparation condition B may also be a condition in which the temperature when the reagent reacts with the blood analyte is higher than that of Preparation condition A. In addition, Preparation condition B may also be a condition in which the proportion of the reagent is higher than that of Preparation condition A for the mixing ratio of the blood analyte and the reagent.

[0126] <Specific example of the process of determining or analyzing the classification result of white blood cells>

[0127] Specifically, the following processes in the process of determining whether to shift from the normal mode to the extended mode are described: the process of determining whether each white blood cell can be sufficiently classified (S102); and in the extended mode, the process of comparing the classification results of white blood cells in Preparation condition A and the classification results of white blood cells in Preparation condition B and outputting the analysis result (S105 to S107).

[0128] (Model A)

[0129] Model A is a method of determining or comparing the classification results of each white blood cell using the number of cells belonging to one of the multiple clusters present in the scatter plot.

[0130] [Process of determining whether to transfer from the normal mode to the extended mode]

[0131] When the count of white blood cells belonging to a certain one cluster as a result of classifying white blood cells is a specified value (e.g., 0) or less than or equal to the specified value (e.g., 5, 10, etc.), the control unit 500 determines that each white blood cell may not be fully classified (i.e., determines that the classification ability meets the specified conditions). On the other hand, when the count of all white blood cells is not the specified value (e.g., 0) or is greater than or equal to the specified value, it is determined that each white blood cell can be fully classified (i.e., determines that the classification ability does not meet the specified conditions).

[0132] As an example, using the side fluorescence signal (SFL) and the side scatter signal (SSC) as optical signals, white blood cells are classified into four types of white blood cells: neutrophils, lymphocytes, monocytes, and eosinophils. As a result, when the count of eosinophils is a specified value (e.g., 0), it is determined that each white blood cell cannot be fully classified.

[0133] [Process of comparing the classification results of preparation condition A and preparation condition B]

[0134] Based on the comparison result of the number of cells belonging to a certain one cluster among the multiple clusters included in the scatter plot obtained using preparation condition A and the number of cells belonging to a certain one cluster among the multiple clusters included in the scatter plot obtained using preparation condition B, the control unit 500 outputs an analysis result including the number of white blood cells.

[0135] More specifically, when the difference between the number of white blood cells belonging to a certain one cluster among the multiple clusters included in the scatter plot obtained using preparation condition A and the number of white blood cells belonging to a certain one cluster among the multiple clusters included in the scatter plot obtained using preparation condition B is within a specified range, the control unit 500 determines that each white blood cell in preparation condition A can be fully classified and proceeds to the processing step of step S106. On the other hand, when this difference exceeds the specified range, it is determined that the classification result in preparation condition B can more appropriately classify each white blood cell than preparation condition A, and the processing step of step S107 is entered.

[0136] Figure 10 A figure showing an example of the classification result obtained using preparation condition A and the classification result obtained using preparation condition B. Figure 10 In the example, in the classification result of preparation condition A, the eosinophil cluster does not exist (the number of eosinophils is 0), but in the classification result of preparation condition B, the eosinophil cluster (the number of eosinophils is 100 or more) exists. Therefore, the control unit 500 determines that the classification result in preparation condition B can more appropriately classify white blood cells than preparation condition A.

[0137] (Model B)

[0138] Model B is a method of determining or comparing the classification results of each white blood cell using the number of clusters existing in the scatter plot.

[0139] [Process for determining whether to transfer from normal mode to extended mode]

[0140] When the result of classifying white blood cells by the control unit 500 is that the number of clusters present in the scatter plot is below a specified value, it is determined that each white blood cell may not be fully classified (i.e., it is determined that the classification ability satisfies the specified conditions). On the other hand, when the number of clusters is the specified value, it is determined that each white blood cell can be fully classified (i.e., it is determined that the classification ability does not satisfy the specified conditions). The specified value is determined in advance according to the type of scatter plot. For example, in a scatter plot with the side fluorescence signal (SFL) as the Y-axis and the side scatter light signal (SSC) as the X-axis, the specified value is four types (neutrophils, lymphocytes, monocytes, and eosinophils).

[0141] [Process for comparing the classification results of white blood cells under preparation condition A and preparation condition B]

[0142] Based on the comparison result of the number of clusters present in the scatter plot obtained using preparation condition A and the number of clusters present in the scatter plot obtained using preparation condition B, the control unit 500 outputs an analysis result including the number of white blood cells.

[0143] More specifically, when the difference between the number of clusters present in the scatter plot obtained using preparation condition A and the number of clusters present in the scatter plot obtained using preparation condition B is within a specified range (for example, the difference is 0), the control unit 500 determines that each white blood cell can be fully classified under preparation condition A and proceeds to the processing step of step S106. On the other hand, when this difference exceeds the specified range (for example, the difference is 1 or more), the control unit 500 determines that the classification result under preparation condition B can more appropriately classify each white blood cell and proceeds to the processing step of step S107.

[0144] Figure 10 In the example, there are only 3 clusters in the classification result of preparation condition A, while there are 4 clusters in the classification result of preparation condition B. Therefore, the control unit 500 determines that the classification result under preparation condition B can more appropriately classify white blood cells than preparation condition A.

[0145] (Model C)

[0146] Model C is a method for determining or comparing the classification results of each white blood cell using the distance between two clusters contained in the scatter plot.

[0147] [Process for determining whether to transfer from normal mode to extended mode]

[0148] When the result of classifying white blood cells is such that the distance between two clusters present in the scatter plot is equal to or less than a specified value, the control unit 500 determines that each white blood cell may not be sufficiently classified (i.e., determines that the classification ability satisfies the specified conditions). On the other hand, when the distance between the two clusters exceeds the specified value, the control unit 500 determines that each white blood cell can be sufficiently classified (i.e., determines that the classification ability does not satisfy the specified conditions).

[0149] As an example, when the distance between the neutrophil cluster and the monocyte cluster is equal to or less than a specified value, the control unit 500 determines that each white blood cell cannot be sufficiently classified.

[0150] [Process of comparing the classification results of white blood cells under preparation condition A and preparation condition B]

[0151] Based on the comparison result of the distance between two clusters included in the scatter plot obtained under preparation condition A and the distance between two clusters included in the scatter plot obtained under preparation condition B, the control unit 500 outputs an analysis result including the number of white blood cells.

[0152] More specifically, when the difference between the distance between two specified clusters included in the scatter plot obtained under preparation condition A and the distance between the two specified clusters included in the scatter plot obtained under preparation condition B is within a specified range (for example, within 10 channels, etc.), the control unit 500 determines that each white blood cell under preparation condition A can be sufficiently classified, and proceeds to the processing step of step S106. On the other hand, when the difference exceeds the specified range (for example, when it exceeds 10 channels, etc.), the control unit 500 determines that the classification result under preparation condition B can more appropriately classify each white blood cell than preparation condition A, and proceeds to the processing step of step S107.

[0153] Figure 11 In the example, in the classification result of preparation condition A, the distance between the neutrophil cluster and the eosinophil cluster is 25 channels, while in the classification result of preparation condition B, the distance between the neutrophil cluster and the eosinophil cluster is 50 channels. In this case, the difference in the inter-cluster distance is 25 channels, which exceeds the specified range (10 channels). Therefore, the control unit 500 determines that the classification result under preparation condition B can more appropriately classify white blood cells than preparation condition A.

[0154] It should be noted that the distance between clusters can also be the distance obtained by comparing the average value of the X-axis of the positions of each blood cell belonging to the cluster. Here, the average value of the X-axis of the positions of each blood cell belonging to the cluster can be calculated by dividing the sum of the X-axis values (channel values) of each blood cell by the number of blood cells. For example, as Figure 7 shown, assuming a case where there is a cluster surrounded by the region of X = 0 to 2 and Y = 0 to 3, the average value of the X-axis is However, it is not limited thereto, and the distance between clusters may also be a distance obtained by comparing the average values of the Y - axes of the positions of each blood cell belonging to the cluster.

[0155] In model C, the two clusters for which the distance is compared can be determined in advance according to the type of scatter plot. For example, in a scatter plot with the side fluorescence signal (SFL) as the Y - axis and the side scatter light signal (SSC) as the X - axis, it can be determined in a way that compares the distance between the lymphocyte cluster and the monocyte cluster, or in a way that compares the distance between the monocyte cluster and the neutrophil cluster, or in a way that compares the distance between the neutrophil cluster and the eosinophil cluster.

[0156] (Model D)

[0157] Model D is a method for determining or comparing the classification results of each white blood cell using the degree of contact between the boundaries of two clusters existing in the scatter plot.

[0158] [Process for determining whether to transfer from the normal mode to the extended mode]

[0159] When the result of classifying white blood cells is such that the degree of contact between the boundaries of two clusters existing in the scatter plot is below a specified value, the control unit 500 determines that each white blood cell may not be fully classified (that is, determines that the classification ability satisfies the specified conditions). On the other hand, when the degree of contact between the boundaries of the two clusters exceeds the specified value, the control unit 500 determines that each white blood cell can be fully classified (that is, determines that the classification ability does not satisfy the specified conditions).

[0160] [Process for comparing the classification results of preparation condition A and preparation condition B]

[0161] Based on the comparison result of the degree of contact between the boundaries of the two clusters included in the scatter plot obtained using preparation condition A and the degree of contact between the boundaries of the two clusters included in the scatter plot obtained using preparation condition B, the control unit 500 outputs an analysis result including the number of white blood cells.

[0162] More specifically, when the difference between the degree of contact between the boundaries of the two clusters included in the scatter plot obtained using preparation condition A and the degree of contact between the boundaries of the two clusters included in the scatter plot obtained using preparation condition B is within a specified range, the control unit 500 determines that each white blood cell in preparation condition A can be fully classified and proceeds to the processing step of step S106. On the other hand, when this difference exceeds the specified range, the control unit 500 determines that the classification result in preparation condition B can more appropriately classify each white blood cell than preparation condition A and proceeds to the processing step of step S107.

[0163] Figure 12 A figure showing an example of the classification result obtained using preparation condition A and the classification result obtained using preparation condition B.Figure 12 In the example, in the classification result under Preparation Condition A, the neutrophil cluster and the eosinophil cluster are in contact, while in the classification result under Preparation Condition B, the neutrophil cluster and the eosinophil cluster are not in contact. Therefore, the control unit 500 determines that the classification result under Preparation Condition B can classify white blood cells more appropriately than that under Preparation Condition A.

[0164] Figure 13 It is a diagram for explaining a calculation example of the degree of boundary contact between two clusters. Figure 13 It is a diagram obtained by magnifying the area where the neutrophil cluster and the eosinophil cluster are adjacent in the scatter plot. The vertical axis (Y-axis) is the side fluorescence signal (SFL), and the horizontal axis (X-axis) is the side scatter light signal (SSC).

[0165] The degree of boundary contact between two clusters is a value obtained by counting the number of coordinates belonging to another cluster of blood cells among the coordinates existing around each coordinate on the boundary of any one of the two clusters. For example Figure 13 In (a), among the 8 coordinates around the A4 coordinate, the coordinate where a neutrophil exists is 1 coordinate at the upper left of the A4 coordinate. Similarly, among the 8 coordinates around the A8 coordinate, the coordinates where neutrophils exist are 3 coordinates to the left, upper left, and above the A8 coordinate. If counted in this way, Figure 13 in (a), the degree of boundary contact between the neutrophil cluster and the eosinophil cluster is "9". Figure 13 In (b), the degree of boundary contact between the neutrophil cluster and the eosinophil cluster is "1".

[0166] It should be noted that Figure 13 in the example, the coordinates where neutrophils exist among the coordinates around the eosinophil cluster are counted, and the number of coordinates is counted based on the neutrophil cluster. For example, the number of coordinates where eosinophils exist among the coordinates existing around the coordinates on the boundary of the neutrophil cluster is counted.

[0167] In Model D, the two clusters for comparing the degree of contact can be determined in advance according to the type of scatter plot. For example, in a scatter plot with the side fluorescence signal (SFL) as the Y-axis and the side scatter light signal (SSC) as the X-axis, the degree of contact between the lymphocyte cluster and the monocyte cluster can be compared, the degree of contact between the monocyte cluster and the neutrophil cluster can be compared, and the degree of contact between the neutrophil cluster and the eosinophil cluster can also be compared.

[0168] (Model E)

[0169] Model E is a method for determining or comparing the classification results of each white blood cell using the distribution shape of blood cells in the clusters existing in the scatter plot.

[0170] [Process for determining whether to transfer from normal mode to extended mode]

[0171] When the shape of the distribution of blood cells in the X-axis or Y-axis of at least one cluster present in the scatter plot in the result of classifying white blood cells is consistent with the specified conditions, the control unit 500 determines that each white blood cell may not be fully classified (that is, determines that the classification ability satisfies the specified conditions). On the other hand, when the shape of the distribution of blood cells in the X-axis or Y-axis of at least one cluster present in the scatter plot is inconsistent with the specified conditions, the control unit 500 determines that each white blood cell can be fully classified (that is, determines that the classification ability does not satisfy the specified conditions).

[0172] The specified conditions can be that there are two or more peaks when the number of blood cells present in each channel on the X-axis is represented by a histogram. Here, use Figure 7 The calculation method of the number of blood cells present in each channel on the X-axis will be described. For example, assume that there is a cluster surrounded by the region of X = 0 to 2 and Y = 0 to 3. In this case, the number of blood cells in each channel of X = 0, 1, 2 is 0, 9, 19 respectively.

[0173] [Process for comparing the classification results of preparation condition A and preparation condition B]

[0174] Based on the comparison result of the shape of the distribution of blood cells in the X-axis or Y-axis of the specified cluster present in the scatter plot obtained using preparation condition A and the shape of the distribution of blood cells in the X-axis or Y-axis of the specified cluster present in the scatter plot obtained using preparation condition B, the control unit 500 outputs an analysis result including the number of white blood cells.

[0175] More specifically, when the number of peaks included in the shape of the distribution of blood cells in the X-axis or Y-axis of the specified cluster present in the scatter plot obtained using preparation condition A is the same as the number of peaks included in the shape of the distribution of blood cells in the X-axis or Y-axis of the specified cluster present in the scatter plot obtained using preparation condition B, or when the number of peaks in preparation condition B is more than that in preparation condition A, the control unit 500 determines that each white blood cell in preparation condition A can be fully classified, and enters the processing step of step S106. On the other hand, when the number of peaks decreases and the number of clusters increases, it is determined that the classification result in preparation condition B can more appropriately classify each white blood cell than preparation condition A, and enters the processing step of step S107.

[0176] The specified cluster used in the determination of the number of peaks can be any of the lymphocyte cluster, monocyte cluster, neutrophil cluster, eosinophil cluster, basophil cluster, or any one cluster determined in advance (for example, neutrophil cluster).

[0177] Figure 14A diagram showing an example of the classification results obtained using preparation condition A and the classification results obtained using preparation condition B. Figure 14 In the example of (a), in preparation condition A, there are two peaks in the neutrophil cluster, while in preparation condition B, there is only 1 peak in the neutrophil cluster. In addition, the number of clusters is 3 in preparation condition A and 4 in preparation condition B. Therefore, the control unit 500 determines that the classification result in preparation condition B can classify white blood cells more appropriately than preparation condition A.

[0178] (Model F)

[0179] Model F is a method for determining or comparing the classification results of each white blood cell using the number of clusters and the number of boundary particles present in the scatter plot.

[0180] [Process for comparing the classification results of preparation condition A and preparation condition B]

[0181] Based on the comparison results of the number of clusters present in the scatter plot obtained using preparation condition A, the number of boundary particles in a specified cluster, the number of clusters present in the scatter plot obtained using preparation condition B, and the number of boundary particles in the specified cluster, the control unit 500 outputs an analysis result including the number of white blood cells.

[0182] More specifically, when the number of clusters present in the scatter plot obtained using preparation condition A is different from the number of clusters present in the scatter plot obtained using preparation condition B, or when the number of boundary particles in the specified cluster in preparation condition B is greater than or equal to the number of boundary particles in the specified cluster in preparation condition A, the control unit 500 determines that each white blood cell in preparation condition A can be sufficiently classified, and proceeds to the processing step of step S106.

[0183] On the other hand, when the number of clusters present in the scatter plot obtained using preparation condition A is the same as the number of clusters present in the scatter plot obtained using preparation condition B, and the number of boundary particles in the specified cluster in preparation condition B is less than the number of boundary particles in the specified cluster in preparation condition A, the control unit 500 determines that the classification result in preparation condition B can classify each white blood cell more appropriately than preparation condition A, and proceeds to the processing step of step S107.

[0184] It should be noted that in Model F, the "number of boundary particles in a specified cluster" means the number of blood cells of a different type from the specified cluster in the boundary part within the specified cluster. Specifically, in a scatter plot using side fluorescence signal (SFL) and side scatter light signal (SSC), for example, among the blood cells in the right boundary part of the neutrophil cluster, there may sometimes be blood cells that can be classified as eosinophils by considering the forward scatter light signal (FSC). In such a case, the number obtained by counting the number of blood cells that can be classified as eosinophils corresponds to the "number of boundary particles in the specified cluster".

[0185] In Model F, even if the number of boundary particles decreases, it is determined that the classification result under Preparation Condition B is more appropriate because it is considered that there is an error in the count obtained using Preparation Condition A (for example, even neutrophils may be counted as eosinophils).

[0186] (Model G)

[0187] Model G is a method for determining or comparing the classification results of each white blood cell using the width and number of specified clusters present in the scatter plot.

[0188] [Process of comparing the classification results of Preparation Condition A and Preparation Condition B]

[0189] Based on the comparison result of the width and number of specified clusters present in the scatter plot obtained using Preparation Condition A and the width and number of specified clusters present in the scatter plot obtained using Preparation Condition B, the control unit 500 outputs an analysis result including the number of white blood cells.

[0190] More specifically, when the width of the specified cluster present in the scatter plot obtained using Preparation Condition B is longer than or the same as the width of the specified cluster present in the scatter plot obtained using Preparation Condition A, or when the number of clusters in Preparation Condition B is less than or the same as the number of clusters in Preparation Condition A, the control unit 500 determines that each white blood cell in Preparation Condition A can be sufficiently classified and proceeds to the processing step of Step S106.

[0191] When the width of the specified cluster present in the scatter plot obtained using Preparation Condition B is shorter than the width of the specified cluster present in the scatter plot obtained using Preparation Condition A, and the number of clusters in Preparation Condition B is more than the number of clusters in Preparation Condition A, the control unit 500 determines that the classification result under Preparation Condition B can more appropriately classify each white blood cell than Preparation Condition A and proceeds to the processing step of Step S107.

[0192] Figure 16 A figure showing an example of the classification result obtained using Preparation Condition A and the classification result obtained using Preparation Condition B. Figure 16In the example, the width of the neutrophil cluster in preparation condition A is 50 channels, while the width of the neutrophil cluster in preparation condition B is 40 channels. In addition, the number of clusters in preparation condition A is 3, while the number of clusters in preparation condition B is 4. Therefore, the control unit 500 determines that the classification result in preparation condition B can classify white blood cells more appropriately than that in preparation condition A.

[0193] It should be noted that the width of the cluster can be the width on the X-axis or the width on the Y-axis. In addition, the width of the cluster can also be the width of the channels in the X-axis (or Y-axis) where the number of blood cells present in each channel is equal to or greater than a specified number.

[0194] The scatter plot of preparation condition A and the scatter plot of preparation condition B described above are respectively referred to as the first distribution diagram and the second distribution diagram.

[0195] In addition, in the processing step of step S105, the classification result of white blood cells using the optical signal obtained under preparation condition A can be expressed as at least any one of "the number of white blood cells belonging to any one of the multiple clusters contained in the scatter plot of preparation condition A", "the number of multiple clusters contained in the scatter plot of preparation condition A", "the distance between two clusters contained in the scatter plot of preparation condition A", "the degree of contact between the boundaries of two clusters contained in the scatter plot of preparation condition A", and "the width of a specified cluster among the multiple clusters contained in the scatter plot of preparation condition A".

[0196] In addition, in the processing step of step S105, the classification result of white blood cells using the optical signal obtained under preparation condition B can be expressed as at least any one of "the number of white blood cells belonging to any one of the multiple clusters contained in the scatter plot of preparation condition B", "the number of multiple clusters contained in the scatter plot of preparation condition B", "the distance between two clusters contained in the scatter plot of preparation condition B", "the degree of contact between the boundaries of two clusters contained in the scatter plot of preparation condition B", and "the width of a specified cluster among the multiple clusters contained in the scatter plot of preparation condition B".

[0197] In addition, in the processing steps of step S105, the comparison result between the classification result obtained using preparation condition A and the classification result obtained using preparation condition B can be expressed as "the comparison result of the number of white blood cells in any one of the multiple clusters contained in the scatter plot belonging to preparation condition A and the number of white blood cells in any one of the multiple clusters contained in the scatter plot belonging to preparation condition B", "the comparison result of the number of multiple clusters contained in the scatter plot of preparation condition A and the number of multiple clusters contained in the scatter plot of preparation condition B", "the comparison result of the distance between two clusters contained in the scatter plot of preparation condition A and the distance between two clusters contained in the scatter plot of preparation condition B", "the comparison result of the degree of boundary contact between two clusters contained in the scatter plot of preparation condition A and the degree of boundary contact between two clusters contained in the scatter plot of preparation condition B", and "the comparison result of the width of a specified cluster among the multiple clusters contained in the scatter plot of preparation condition A and the width of the specified cluster among the multiple clusters contained in the scatter plot of preparation condition B", and at least one of them.

[0198] <Summary>

[0199] According to the embodiments described above, in the standard mode, when it is determined that white blood cells may not be fully classified in the normal mode during the measurement of the blood analyte prepared based on preparation condition A, it is transferred to the extended mode, and white blood cells are classified for both the blood analyte prepared based on preparation condition A and the blood analyte prepared based on preparation condition B. In addition, the classification results of both are compared, and an analysis result including the comparison result is output. Thus, it is possible to suppress the analysis result with insufficient output accuracy due to the nature of the analyte.

[0200] The embodiments described above show an example of classifying neutrophils and eosinophils using two preparation conditions, but the present invention is not limited thereto. For example, it can also be applied to an example of classifying neutrophils and monocytes using side fluorescence signal (SFL) and side scatter light signal (SSC). In this case, it is possible to make Figure 13 the reaction time of the analyte and the reagent in the preparation condition shown in step S104 shorter than the reaction time of the analyte and the reagent in the preparation condition of step S103. Similarly, it is also possible to make the reaction temperature in the preparation condition of step S104 lower than the reaction temperature in the preparation condition of step S103, and it is also possible to make the mixing ratio of the reagent in the preparation condition of step S104 lower than the mixing ratio of the reagent in the preparation condition of step S103.

[0201] In addition, the types of optical signals used in the classification of blood cells are not limited. Two types other than the combinations described above can be combined, three or more optical signals can be combined, or one optical signal can be used.

[0202] In addition, the types of cells classified using the two preparation conditions are not limited to white blood cells, and may also be red blood cells, platelets, or other blood cells.

[0203] The embodiments described above are not limited to the analysis of the blood sample to be measured, and can also be applied to the analysis of various body fluids other than blood and urine. In this case, the expression "white blood cells" in the above description can be replaced with the expression "cells". In addition, for example, the analysis device 1 for analyzing urine can classify red blood cells, white blood cells, epithelial cells, squamous epithelial cells, cylinders, and bacteria contained in urine and count their respective numbers.

[0204] The embodiments described above are not limited to the form of obtaining optical signals by irradiating the first measurement sample and the second measurement sample with light. For example, the present invention can also be applied to the following cell analysis device: obtaining an electrical signal generated when a voltage is additionally applied to a pore of the first measurement sample and the second measurement sample, and classifying cells using the obtained electrical signal. In this case, in the above description, the "first optical signal" can also be referred to as the "first signal", the "second optical signal" as the "second signal", and the "third optical signal" as the "third signal".

[0205] The embodiments described above are for easy understanding of the present invention and are not restrictive interpretations of the present invention. The flowcharts, sequences, elements of the embodiments, their configurations, materials, conditions, shapes, sizes, etc. described in the embodiments are not limited by the exemplified data and can be appropriately changed. In addition, parts of the configurations given in different embodiments can be replaced or combined with each other.

[0206] Symbol Explanation

[0207] 1: Analysis device; 2: Measurement unit; 3: Conveying unit; 4: Information processing unit; 5: Host computer; 21: Hand; 22: Placement part for sample container; 23: Barcode unit; 24: Sample aspiration part; 24a: Drilling tool; 25: Sample preparation part; 26: Detection part; 40: Main body; 41: Display part; 42: Input part; 401: CPU; 404: Hard disk; 404a: Program; 405: Reading device; 405a: Recording medium; 406: Image output interface; 407: Input / output interface; 408: Communication interface; 500: Control part; 501: Measurement control part; 502: Analysis processing part; 503: Display control part; 504: Input processing part; 505: Storage part.

Claims

1. A cell classification method, which is a cell classification method for classifying cells contained in a test substance by an analysis device, comprising: processing the test substance under the first preparation condition to prepare a first measurement sample, obtaining a first signal from the prepared first measurement sample, classifying the cells contained in the first measurement sample using the first signal, processing the test substance under a second preparation condition different from the first preparation condition to prepare a second measurement sample, obtaining a second signal from the prepared second measurement sample, classifying the cells contained in the second measurement sample using the second signal, and comparing the classification result of the cells using the first signal with the classification result of the cells using the second signal, and outputting an analysis result including the number of cells based on the comparison result, wherein the first signal and the second signal are obtained using the same detector, wherein the first signal is a first optical signal, obtaining the first signal includes irradiating the first measurement sample with light to obtain the first optical signal, the first optical signal includes at least two optical signals among fluorescence emitted from the first measurement sample and two scattered lights with different angles, the classification result of the cells using the first optical signal is obtained based on a first distribution map obtained by plotting the at least two optical signals contained in the first optical signal on different axes, the classification result of the cells using the first optical signal is at least one of the following: the number of cells belonging to any one of the multiple clusters contained in the first distribution map, the number of the multiple clusters contained in the first distribution map, the distance between two clusters contained in the first distribution map, the degree of contact of the boundaries of two clusters contained in the first distribution map, and the width of a specified cluster among the multiple clusters contained in the first distribution map, wherein the second signal is a second optical signal, obtaining the second signal includes irradiating the second measurement sample with light to obtain the second optical signal, the second optical signal includes at least two optical signals among fluorescence emitted from the second measurement sample and two scattered lights with different angles, the classification result of the cells using the second optical signal is obtained based on a second distribution map obtained by plotting the at least two optical signals contained in the second optical signal on different axes, the classification result of the cells using the second optical signal is at least one of the following: the number of cells belonging to any one of the multiple clusters contained in the second distribution map, the number of the multiple clusters contained in the second distribution map, the distance between two clusters contained in the second distribution map, the degree of contact of the boundaries of two clusters contained in the second distribution map, and the width of a specified cluster among the multiple clusters contained in the second distribution map, wherein the comparison result is at least one of the following: the comparison result of the number of cells belonging to any one of the multiple clusters contained in the first distribution map and the number of cells belonging to any one of the multiple clusters contained in the second distribution map, the comparison result of the number of the multiple clusters contained in the first distribution map and the number of the multiple clusters contained in the second distribution map, The comparison result of the distance between two clusters included in the first distribution map and the distance between two clusters included in the second distribution map, the comparison result of the degree of boundary contact between two clusters included in the first distribution map and the degree of boundary contact between two clusters included in the second distribution map, and the comparison result of the width of a specified cluster among a plurality of clusters included in the first distribution map and the width of a specified cluster among a plurality of clusters included in the second distribution map.

2. The cell classification method according to claim 1, wherein, outputting the analysis result includes: when the comparison result is within a specified range, outputting the number of cells based on the first signal, and when the comparison result exceeds the specified range, outputting the number of cells based on the first signal and information related to the reliability of the number of cells.

3. The cell classification method according to claim 1, wherein, outputting the analysis result includes: when the comparison result is within a specified range, outputting the number of cells based on the first signal, and when the comparison result exceeds the specified range, outputting the number of cells based on the second signal.

4. The cell classification method according to claim 1, wherein, further comprising receiving an indication for measuring a test substance, when receiving the measurement indication, performing: preparing the first measurement sample, obtaining the first signal, classifying cells using the first signal, preparing the second measurement sample, obtaining the second signal, classifying cells using the second signal, and outputting the analysis result.

5. The cell classification method according to claim 1, wherein, performing: processing the test substance using the first preparation condition to prepare a third measurement sample, obtaining a third signal from the prepared third measurement sample, classifying the cells included in the third measurement sample using the third signal, when the classification result of the cells using the third signal satisfies a specified condition, preparing the first measurement sample, obtaining the first signal, classifying the cells included in the first measurement sample, preparing the second measurement sample, obtaining the second signal, classifying the cells included in the second measurement sample, and outputting the analysis result.

6. The cell classification method according to claim 1, wherein, the first and second preparation conditions are at least any one of the following conditions: conditions related to the length of time for a reagent to react with the test substance, conditions related to the temperature when a reagent reacts with the test substance, and conditions related to the mixing ratio of the test substance and the reagent.

7. The classification method according to claim 1, wherein, the test substance is blood, at least lymphocytes, monocytes, neutrophils, and eosinophils are included in the cells to be classified.

8. The classification method according to any one of claims 1 to 7, wherein, the first signal is a signal caused by the first measurement sample flowing in a flow cell, the second signal is a signal caused by the second measurement sample flowing in the flow cell.

9. An analysis device, which is an analysis device for classifying cells included in a test substance, and includes: A sample preparation unit that prepares a measurement sample by treating a test substance with a reagent, a detection unit that obtains a signal from the measurement sample, and a control unit, with respect to the control unit, causing the sample preparation unit to perform: treating the test substance using a first preparation condition to prepare a first measurement sample, and treating the test substance using a second preparation condition different from the first preparation condition to prepare a second measurement sample, causing the detection unit to perform: obtaining a first signal from the prepared first measurement sample, and obtaining a second signal from the prepared second measurement sample, classifying the cells contained in the test substance using the first signal, classifying the cells contained in the test substance using the second signal, and comparing the classification result of the cells using the first signal with the classification result of the cells using the second signal, and outputting an analysis result based on the comparison result, wherein the first signal and the second signal are obtained using the same detector, wherein the first signal is a first optical signal, obtaining the first signal includes irradiating the first measurement sample with light to obtain the first optical signal, the first optical signal includes at least two optical signals among fluorescence emitted from the first measurement sample and two scattered lights with different angles, the classification result of the cells using the first optical signal is obtained based on a first distribution map obtained by plotting the at least two optical signals contained in the first optical signal on different axes, the classification result of the cells using the first optical signal is at least one of the following: the number of cells belonging to any one of the multiple clusters contained in the first distribution map, the number of the multiple clusters contained in the first distribution map, the distance between two clusters contained in the first distribution map, the degree of contact of the boundaries of two clusters contained in the first distribution map, and the width of a specified cluster among the multiple clusters contained in the first distribution map, wherein the second signal is a second optical signal, obtaining the second signal includes irradiating the second measurement sample with light to obtain the second optical signal, the second optical signal includes at least two optical signals among fluorescence emitted from the second measurement sample and two scattered lights with different angles, the classification result of the cells using the second optical signal is obtained based on a second distribution map obtained by plotting the at least two optical signals contained in the second optical signal on different axes, the classification result of the cells using the second optical signal is at least one of the following: the number of cells belonging to any one of the multiple clusters contained in the second distribution map, the number of the multiple clusters contained in the second distribution map, the distance between two clusters contained in the second distribution map, the degree of contact of the boundaries of two clusters contained in the second distribution map, and the width of a specified cluster among the multiple clusters contained in the second distribution map, wherein the comparison result is at least one of the following: the comparison result of the number of cells belonging to any one of the multiple clusters contained in the first distribution map and the number of cells belonging to any one of the multiple clusters contained in the second distribution map, The comparison result of the number of multiple clusters included in the first distribution map and the number of multiple clusters included in the second distribution map, The comparison result of the distance between two clusters included in the first distribution map and the distance between two clusters included in the second distribution map, The comparison result of the degree of boundary contact between two clusters included in the first distribution map and the degree of boundary contact between two clusters included in the second distribution map, and The comparison result of the width of a specified cluster among the multiple clusters included in the first distribution map and the width of a specified cluster among the multiple clusters included in the second distribution map.

10. A computer-readable non-transitory recording medium having a program recorded thereon, wherein the program causes a computer to perform: In a sample preparation unit that prepares a measurement sample by treating a test substance with a reagent: Treat the test substance using a first preparation condition to prepare a first measurement sample, and Treat the test substance using a second preparation condition different from the first preparation condition to prepare a second measurement sample, In a detection unit that obtains a signal from the measurement sample: Obtain a first signal from the prepared first measurement sample, and Obtain a second signal from the prepared second measurement sample, Classify the cells contained in the test substance using the first signal, Classify the cells contained in the test substance using the second signal, and Compare the classification result of the cells using the first signal with the classification result of the cells using the second signal, and output an analysis result based on the comparison result, Wherein the first signal and the second signal are obtained using the same detector, Wherein the first signal is a first optical signal, Obtaining the first signal includes irradiating the first measurement sample with light to obtain the first optical signal, The first optical signal includes at least two optical signals among fluorescence emitted from the first measurement sample and two scattered lights with different angles, The classification result of the cells using the first optical signal is obtained based on a first distribution map obtained by plotting the at least two optical signals included in the first optical signal on different axes, The classification result of the cells using the first optical signal is at least one of the following: The number of cells belonging to any one of the multiple clusters included in the first distribution map, The number of multiple clusters included in the first distribution map, The distance between two clusters included in the first distribution map, The degree of boundary contact between two clusters included in the first distribution map, and The width of a specified cluster among the multiple clusters included in the first distribution map, Wherein the second signal is a second optical signal, Obtaining the second signal includes irradiating the second measurement sample with light to obtain the second optical signal, The second optical signal includes at least two optical signals among fluorescence emitted from the second measurement sample and two scattered lights with different angles, The classification result of the cells using the second optical signal is obtained based on a second distribution map obtained by plotting the at least two optical signals included in the second optical signal on different axes, The classification result of the cells using the second optical signal is at least one of the following: The number of cells belonging to any one of the multiple clusters included in the second distribution map, The number of a plurality of clusters included in the second distribution map, The distance between two clusters included in the second distribution map, The degree of boundary contact between two clusters included in the second distribution map, and The width of a specified cluster among a plurality of clusters included in the second distribution map, wherein the comparison result is at least one of the following: The comparison result of the number of cells belonging to any one cluster among the plurality of clusters included in the first distribution map and the number of cells belonging to any one cluster among the plurality of clusters included in the second distribution map, The comparison result of the number of a plurality of clusters included in the first distribution map and the number of a plurality of clusters included in the second distribution map, The comparison result of the distance between two clusters included in the first distribution map and the distance between two clusters included in the second distribution map, The comparison result of the degree of boundary contact between two clusters included in the first distribution map and the degree of boundary contact between two clusters included in the second distribution map, and The comparison result of the width of a specified cluster among the plurality of clusters included in the first distribution map and the width of a specified cluster among the plurality of clusters included in the second distribution map.

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