Blood cell analyzer and blood cell analysis method

Description

Blood cell analyzer and blood cell analysis method Technical Field

[0001] The present application relates to the field of blood analysis, and in particular to a blood cell analyzer and a blood cell analysis method. Background Art

[0002] A routine blood test is a basic clinical examination item. The test results generally include white blood cell count, red blood cell count, platelet count, hemoglobin concentration, reticulocyte count, etc. It also includes scatter plots or histograms of white blood cells, red blood cells, platelets and reticulocytes obtained during the test to assist doctors in making clinical diagnoses for patients.

[0003] Existing blood cell analyzers perform optical measurement of white blood cells based on fluorescent staining technology or chemical staining technology, and perform optical measurement of reticulocytes (or optical measurement of platelets) based on fluorescent staining technology. In the optical measurement of white blood cells, the count value and classification of white blood cell groups are obtained, and in the optical measurement of reticulocytes, parameters such as the red blood cell count value, platelet count value, and reticulocyte count value are obtained.

[0004] However, for blood cell analyzers that perform optical white blood cell measurement based on fluorescent staining technology, when performing optical white blood cell measurement on certain abnormal samples, accurate white blood cell classification results may not be obtained due to overlap between some white blood cell clusters in the scatter plot or between white blood cell clusters and other particle clusters, such as blood ghost particle clusters.

[0005] Summary of the Invention

[0006] In order to at least partially solve the above technical problems, the task of the present application is to provide a blood cell analyzer and a blood cell analysis method, which can improve the accuracy of white blood cell classification.

[0007] In order to achieve the above-mentioned tasks of the present application, the first aspect of the present application provides a blood cell analyzer, comprising:

[0008] A sample suction device, used for sucking a blood sample to be tested;

[0009] a sample preparation device for mixing a portion of the blood sample to be tested, a hemolytic agent, and a first fluorescent dye to prepare a first measurement sample for leukocyte differentiation, and for mixing another portion of the blood sample to be tested, a diluent, and a second fluorescent dye to prepare a second measurement sample for identifying platelets and / or reticulocytes;

[0010] an optical detection device comprising a flow cell, a light source, and a light detector, wherein the flow cell is configured to allow the first and second measurement samples to pass through the flow cell, respectively; the light source is configured to illuminate the first and second measurement samples, respectively passing through the flow cell, with light; and the light detector is configured to detect first and second optical information generated by the first and second measurement samples being illuminated by the light while respectively passing through the flow cell; and

[0011] A data processing device configured to:

[0012] generating a first leukocyte classification scattergram based on the first optical information and acquiring first leukocyte classification information of the blood sample to be tested based on the first leukocyte classification scattergram, wherein the first leukocyte classification information includes at least a lymphocyte percentage, a monocyte percentage, a neutrophil percentage, and an eosinophil percentage;

[0013] When it is determined that there is a classification abnormality in the first white blood cell classification scatter plot in which a lymphocyte population and / or a monocyte population overlaps with at least one other particle population, a second white blood cell classification scatter plot is generated based on the second optical information and second white blood cell classification information of the blood sample to be tested is obtained based on the second white blood cell classification scatter plot, the second white blood cell classification information including the percentage of lymphocytes, the percentage of monocytes and the percentage of granulocytes, the granulocytes including neutrophils and eosinophils, and the first white blood cell classification information is corrected using the second white blood cell classification information to obtain and output the corrected first white blood cell classification information.

[0014] A second aspect of the present application provides a blood cell analysis method, comprising:

[0015] Draw a blood sample to be tested;

[0016] Mixing a portion of the blood sample to be tested, a hemolytic agent, and a first fluorescent dye to prepare a first measurement sample for white blood cell differentiation, and allowing particles in the first measurement sample to pass through an optical detection area irradiated with light one by one to obtain first optical information generated by the particles in the first measurement sample after being irradiated with light;

[0017] Mixing another portion of the blood sample to be tested, a diluent, and a second fluorescent dye to prepare a second measurement sample for identifying platelets and / or reticulocytes, and allowing particles in the second measurement sample to pass through an optical detection area irradiated with light one by one to obtain second optical information generated by the particles in the second measurement sample after being irradiated with light;

[0018] generating a first leukocyte classification scattergram based on the first optical information and acquiring first leukocyte classification information of the blood sample to be tested based on the first leukocyte classification scattergram, wherein the first leukocyte classification information at least includes a lymphocyte percentage, a monocyte percentage, a neutrophil percentage, and an eosinophil percentage; and

[0019] When it is determined that there is a classification abnormality in the first white blood cell classification scatter plot in which a lymphocyte population and / or a monocyte population overlaps with at least one other particle population, a second white blood cell classification scatter plot is generated based on the second optical information and second white blood cell classification information of the blood sample to be tested is obtained based on the second white blood cell classification scatter plot, the second white blood cell classification information including the percentage of lymphocytes, the percentage of monocytes and the percentage of granulocytes, the granulocytes including neutrophils and eosinophils, and the first white blood cell classification information is corrected using the second white blood cell classification information to obtain and output the corrected first white blood cell classification information.

[0020] In the technical solutions proposed in various aspects of the present application, when it is determined that there is a classification abnormality in which a lymphocyte population and / or a monocyte population overlaps with at least one other particle population in the blood sample to be tested, the first white blood cell classification information obtained based on the first optical information is corrected using the second white blood cell classification information obtained based on the second optical information, thereby obtaining accurate white blood cell classification results without increasing the detection cost and reducing the detection efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] FIG1 is a schematic external view of a blood cell analyzer according to some embodiments of the present application.

[0022] FIG2 is a schematic block diagram of an optical detection device according to some embodiments of the present application.

[0023] FIG3 is a scatter plot of the FL-SS first leukocyte differential of a normal dog blood sample according to some embodiments of the present application.

[0024] FIG4 is a scatter plot of the FL-SS first leukocyte differential of a normal cat blood sample according to some embodiments of the present application.

[0025] FIG5 is a scatter plot of the FL-SS first leukocyte differential of an abnormal dog blood sample according to some embodiments of the present application.

[0026] FIG6 is a scatter plot of the FL-SS first leukocyte differential of an abnormal cat blood sample according to some embodiments of the present application.

[0027] FIG. 7 is a scatter plot of the FS-SS second leukocyte classification of blood samples according to some embodiments of the present application.

[0028] FIG. 8 is a third scattergram of FS-FL of the blood sample used in FIG. 7 .

[0029] FIG9 is a schematic flow chart of a blood cell analysis method according to some embodiments of the present application. DETAILED DESCRIPTION

[0030] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0031] It should be noted that the terms "first\second\third" involved in the embodiments of the present application are only used to distinguish similar objects and do not represent a specific order for the objects. It can be understood that "first\second\third" can be interchanged with a specific order or sequence when permitted.

[0032] Those skilled in the art will understand that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs.

[0033] To facilitate the subsequent explanation, here is a brief explanation of some of the terms involved below:

[0034] 1) Scatterplot: A 2D or 3D graph generated by a hematology analyzer that displays the 2D or 3D characteristic information of multiple particles. The X, Y, and Z axes of the scatterplot each represent a characteristic of each particle. For example, in a scatterplot, the X axis represents forward scattered light intensity, the Y axis represents fluorescence intensity, and the Z axis represents side scattered light intensity.

[0035] 2) Cell swarm: A particle cluster formed by multiple particles with the same characteristics distributed in a certain area of ​​the scatter plot, such as a leukocyte swarm, and a neutrophil swarm, lymphocyte swarm, monocyte swarm, eosinophil swarm, or basophil swarm among the leukocytes.

[0036] 3) Blood ghosts: fragments obtained by dissolving red blood cells and platelets in the blood with hemolytic reagents.

[0037] Currently, hematology analyzers can test samples of human blood, blood from other mammals (such as dogs, cats, and horses), poultry, and fish. Typically, hematology analyzers use the DIFF channel (white blood cell classification channel) to count and classify white blood cells, for example, classifying white blood cells into five types: lymphocytes (Lym), monocytes (Mon), neutrophils (Neu), eosinophils (Eos), and eosinophils (Eos). In addition, hematology analyzers use the RET channel (reticulocyte detection channel or platelet optical detection channel) to obtain reticulocyte counts, red blood cell counts, platelet counts, and other values.

[0038] The blood cell analyzer used in the embodiments of the present application classifies and counts particles in a sample by combining laser scattering and fluorescent staining flow cytometry. For example, the principle of white blood cell classification detection in a blood cell analyzer is as follows: first, a blood sample is drawn and treated with a hemolytic agent and a fluorescent dye for white blood cell classification. The red blood cells are destroyed and lysed by the hemolytic agent, while the white blood cells are not lysed. However, the fluorescent dye can enter the nucleus of the white blood cells with the help of the hemolytic agent and bind to the nucleic acid substances in the nucleus; then, the particles in the blood sample pass through the detection hole illuminated by the laser beam one by one. When the laser beam illuminates the particles, the characteristics of the particles themselves (such as volume, staining degree, size and content of cell contents, cell nuclear density, etc.) can block or change the direction of the laser beam, thereby generating scattered light at various angles corresponding to their characteristics. After receiving these scattered lights, the signal detector can obtain relevant information about the particle structure and composition. Forward scattered light (FS) reflects the number and volume of particles, side scattered light (SS) reflects the complexity of the cell's internal structure (such as intracellular granules or the nucleus), and fluorescence (FL) reflects the content of nucleic acid in the cell. This optical information can be used to classify and count particles in the sample.

[0039] FIG1 is a schematic diagram of the structure of a hematology analyzer according to some embodiments of the present application. The hematology analyzer 100 includes a sample aspirator 110, a sample preparation device 120, an optical detection device 130, and a data processing device 140. The hematology analyzer 100 also includes a fluidic system (not shown) for connecting the sample aspirator 110, the sample preparation device 120, and the optical detection device 130 to facilitate fluid transfer between these devices.

[0040] The sample aspirating device 110 is used to aspirate a blood sample to be tested.

[0041] In some embodiments, the sample aspirating device 110 includes a sampling needle (not shown) for aspirating a blood sample to be tested. Furthermore, the sample aspirating device 110 may further include a drive mechanism for driving the sampling needle to quantitatively aspirate the blood sample to be tested through the needle tip of the sampling needle. The sample aspirating device 110 may transport the collected blood sample to the sample preparation device 120.

[0042] The sample preparation device 120 is used to mix a portion of the blood sample to be tested, a hemolytic agent, and a first fluorescent dye to prepare a first measurement sample for white blood cell classification, and to mix another portion of the blood sample to be tested, a diluent, and a second fluorescent dye to prepare a second measurement sample for identifying platelets and / or reticulocytes. The second measurement sample can be used to distinguish platelets, mature red blood cells, reticulocytes, and white blood cells from one another.

[0043] In the embodiment of the present application, the hemolytic agent is used to dissolve the red blood cells in the blood, breaking the red blood cells into fragments, but being able to keep the morphology of the white blood cells basically unchanged.

[0044] In an embodiment of the present application, the first fluorescent dye is a fluorescent dye that stains DNA in white blood cells for white blood cell classification. For example, it can be a fluorescent dye that can classify white blood cells in a blood sample into five white blood cell subsets (neutrophils, lymphocytes, monocytes, eosinophils, and basophils). The second fluorescent dye is different from the first fluorescent dye and is a fluorescent dye that can be used to identify platelets in a blood sample (can be used to distinguish reticulocytes, red blood cells, platelets, and white blood cells).

[0045] In some embodiments, the sample preparation device 120 may include at least one reaction pool and a reagent supply device (not shown). The at least one reaction pool is used to receive the blood sample to be tested drawn by the sample aspirating device 110, and the reagent supply device provides processing reagents (including a hemolytic agent, a first fluorescent dye, a second fluorescent dye, etc.) to the at least one reaction pool, so that the blood sample to be tested drawn by the sample aspirating device 110 and the processing reagents provided by the reagent supply device are mixed in the reaction pool to prepare measurement samples (including a first measurement sample and a second measurement sample).

[0046] For example, the at least one reaction pool may include a first reaction pool and a second reaction pool, and the reagent supply device may include a first reagent supply unit and a second reagent supply unit. The sample suction device 110 is used to partially distribute the collected blood sample to be tested to the first reaction pool and the second reaction pool respectively. The first reagent supply unit is used to provide a hemolytic agent and a first fluorescent dye to the first reaction pool, so that the part of the blood sample to be tested distributed to the first reaction pool is mixed and reacted with the hemolytic agent and the first fluorescent dye to prepare a first measurement sample. The second reagent supply unit is used to provide a second fluorescent dye and a diluent, such as a low osmotic pressure diluent (for example, for spherizing red blood cells) to the second reaction pool, so that the part of the blood sample to be tested distributed to the second reaction pool is mixed and reacted with the second fluorescent dye and an optional diluent to prepare a second measurement sample.

[0047] The optical detection device 130 includes a flow chamber, a light source, and a light detector. The flow chamber is used for allowing the first measurement sample and the second measurement sample to pass through respectively, the light source is used to irradiate the first measurement sample and the second measurement sample passing through the flow chamber respectively with light, and the light detector is used to detect the first optical information and the second optical information generated after the first measurement sample and the second measurement sample are irradiated with light when passing through the flow chamber respectively.

[0048] It can be understood here that the first detection channel for white blood cell classification (also called DIFF channel) refers to the detection of the first measurement sample prepared by the sample preparation device 120 by the optical detection device 130, and the second detection channel for identifying platelets (also called RET channel) refers to the detection of the second measurement sample prepared by the sample preparation device 120 by the optical detection device 130.

[0049] As used herein, a flow chamber refers to a chamber containing a focused fluid stream suitable for detecting light scattering and fluorescence signals. When a particle, such as a blood cell, passes through the detection aperture of the flow chamber, the particle scatters an incident light beam directed from a light source into all directions. A light detector can be positioned at one or more different angles relative to the incident light beam to detect the light scattered by the particle, thereby obtaining a light scattering signal. Because different particles have different light scattering properties, light scattering signals can be used to distinguish between different particle populations. Specifically, the light scattering signal detected near the incident light beam is typically referred to as a forward light scattering signal or a low-angle light scattering signal. In some embodiments, the forward light scattering signal can be detected at an angle of approximately 1° to approximately 10° relative to the incident light beam. In other embodiments, the forward light scattering signal can be detected at an angle of approximately 2° to approximately 6° relative to the incident light beam. The light scattering signal detected at a direction approximately 90° relative to the incident light beam is typically referred to as a side light scattering signal. In some embodiments, the side light scattering signal can be detected at an angle of approximately 65° to approximately 115° relative to the incident light beam. Typically, fluorescent signals from blood cells stained with fluorescent dyes are also detected at a direction of approximately 90° to the incident light beam.

[0050] In some embodiments, the light detector may include a forward scattered light detector for detecting forward scattered light signals, a side scattered light detector for detecting side scattered light signals, and a fluorescence detector for detecting fluorescence signals. Accordingly, the first optical information may include the forward scattered light signals, side scattered light signals, and fluorescence signals of particles in the first measurement sample, and the second optical information may include the forward scattered light signals, side scattered light signals, and fluorescence signals of particles in the second measurement sample.

[0051] FIG2 illustrates a specific example of an optical detection device 130. This optical detection device 130 comprises a light source 101, a beam shaping assembly 102, a flow cell 103, and a forward scattered light detector 104, arranged sequentially in a straight line. A dichroic mirror 106 is positioned on one side of the flow cell 103 at a 45° angle to the straight line. Sidelight emitted by particles in the flow cell 103 partially passes through the dichroic mirror 106 and is captured by a fluorescence detector 105 positioned behind the dichroic mirror 106 at a 45° angle. Another portion of the sidelight is reflected by the dichroic mirror 106 and captured by a side scattered light detector 107 positioned in front of the dichroic mirror 106 at a 45° angle.

[0052] The data processing device 140 is used to process and calculate data to obtain the required results. For example, it can generate a two-dimensional or three-dimensional scatter plot based on the various collected optical signals and perform particle analysis on the scatter plot using gating methods. The data processing device 140 can also visualize intermediate or final calculation results and then display them on the display device 150. In the embodiment of the present application, the data processing device 140 is configured to implement the method steps described in detail below.

[0053] In the embodiments of the application, the data processing device includes, but is not limited to, a central processing unit (CPU), a microcontroller unit (MCU), a field-programmable gate array (FPGA), a digital signal processing device (DSP), and other devices used to interpret computer instructions and process data in computer software. For example, the data processing device is used to execute various computer applications stored in a computer-readable storage medium, thereby causing the hematology analyzer 100 to perform the corresponding detection process and analyze the optical information or optical signals detected by the optical detection device 130 in real time.

[0054] The hematology analyzer 100 may further include a first housing 160 and a second housing 170. The display device 150 may be, for example, a user interface. The optical detection device 130 and the data processing device 140 are disposed within the second housing 170. The sample preparation device 120 is, for example, disposed within the first housing 160. The display device 150 is, for example, disposed on the exterior of the first housing 160 and is used to display the test results of the hematology analyzer.

[0055] As mentioned in the background art, when measuring certain abnormal samples in the DIFF channel, there may be abnormalities in the leukocyte scatter plot obtained from the DIFF channel where there is no clear boundary between different leukocyte populations, resulting in an inability to obtain accurate leukocyte classification results from the DIFF channel.

[0056] For example, for a normal sample, such as a normal dog sample, the FL-SS leukocyte scatter plot obtained through the DIFF channel is shown in FIG3 (FL-SS leukocyte scatter plot of a normal dog sample) and FIG4 (FL-SS leukocyte scatter plot of a normal cat sample). There are clear boundaries between the lymphocyte (DIFF_Lym) group, the monocyte (DIFF_Mon) group, the neutrophil (DIFF_Neu) group, the eosinophil (DIFF_Eos) group, the basophil (DIFF_Bas) group, and the ghost particle group. At this time, the FL-SS leukocyte scatter plot of the DIFF channel can be used to accurately classify the leukocytes in the sample into five categories, that is, the leukocytes are classified into neutrophils (DIFF_Neu), lymphocytes (DIFF_Lym), monocytes (DIFF_Mon), neutrophils (DIFF_Neu), eosinophils (DIFF_Eos), basophils (DIFF_Bas), and ghost particles. ), monocytes (DIFF_Mon), eosinophils (DIFF_Eos), and basophils (DIFF_Baso), and the white blood cell count value is equal to the value after subtracting the blood ghost particles from all particles; for human samples with tumors or samples of animals such as dogs and cats with increased rod cells, as shown in Figure 5 (FL-SS white blood cell scatter plot of dog sample with increased rod cells) and Figure 6 (FL-SS white blood cell scatter plot of cat sample with increased rod cells), in the FL-SS white blood cell scatter plot obtained by the DIFF channel, the boundaries between neutrophil, lymphocyte, and monocyte populations are not clear, making it difficult to accurately give the classification results of lymphocytes (DIFF_Lym), monocytes (DIFF_Mon), and neutrophils (DIFF_Neu).

[0057] Furthermore, the applicant has found through research that, regardless of whether it is an abnormal sample or a normal sample, the second optical information obtained from the RET channel can at least accurately identify lymphocytes, monocytes, and granulocytes, where the granulocytes include neutrophils and eosinophils.

[0058] Based on this, an embodiment of the present application proposes that when there is a classification anomaly in which a lymphocyte population and / or a monocyte population overlaps with at least one other particle population in the white blood cell scatter plot obtained through the DIFF channel, the first white blood cell classification information obtained in the DIFF channel and the second white blood cell classification information obtained in the RET channel are combined to obtain a more accurate white blood cell classification result.

[0059] It is proposed herein to configure the data processing device 140 to obtain a leukocyte classification result of the blood sample to be tested by combining a first leukocyte scattergram obtained based on the first optical information and a second leukocyte scattergram obtained based on the second optical information.

[0060] In some embodiments, the data processing device 140 may be configured to:

[0061] A first white blood cell classification scatter plot is generated based on the first optical information, and first white blood cell classification information of the blood sample to be tested is obtained based on the first white blood cell classification scatter plot, wherein the first white blood cell classification information at least includes a lymphocyte percentage (DIFF_Lym%), a monocyte percentage (DIFF_Mon%), a neutrophil percentage (DIFF_Neu%), and an eosinophil percentage (DIFF_Eos%). Optionally, the first white blood cell classification information also includes a basophil percentage (DIFF_Baso%).

[0062] When it is determined that there is a classification abnormality in the first white blood cell classification scatter plot in which the lymphocyte group and / or the monocyte group overlaps with at least one other particle group, a second white blood cell classification scatter plot is generated based on the second optical information, as shown in Figure 7, and second white blood cell classification information of the blood sample to be tested is obtained based on the second white blood cell classification scatter plot, the second white blood cell classification information includes the lymphocyte percentage (RET_Lym%), the monocyte percentage (RET_Mon%) and the granulocyte percentage (RET_Gran%), the granulocytes include neutrophils and eosinophils, and the first white blood cell classification information is corrected using the second white blood cell classification information to obtain and output the corrected first white blood cell classification information.

[0063] Thus, accurate leukocyte classification results can be obtained for abnormal samples with classification abnormalities in which a lymphocyte population and / or a monocyte population overlaps with at least one other particle population.

[0064] Preferably, the data processing device 140 can be further configured to determine whether the classification abnormality exists for the blood sample to be tested based on the first optical information, in particular based on the first white blood cell classification scatter plot. For example, as shown in Figures 5 and 6, the classification abnormality exists when the data processing device 140 cannot clearly separate the lymphocyte population (DIFF_Lym), the monocyte population (DIFF_Mon), and the neutrophil population (DIFF_Neu) from each other based on the first white blood cell classification scatter plot.

[0065] In other embodiments, the data processing device 140 may be further configured to determine whether the classification abnormality exists based on disease information of the subject from whom the blood sample to be tested is collected. For example, when the subject is determined to have a tumor based on the subject's disease information, the classification abnormality may be determined to exist for the blood sample to be tested from the subject.

[0066] In some embodiments, the data processing device 140 uses the second white blood cell classification information to modify the first white blood cell classification information to obtain and output modified first white blood cell classification information, including: the data processing device 140

[0067] When it is determined that there is a classification abnormality in which the lymphocyte population, the monocyte population, and the neutrophil population all overlap with each other in the first white blood cell classification scattergram, the first white blood cell classification information is corrected using the lymphocyte percentage, the monocyte percentage, and the granulocyte percentage in the second white blood cell classification information.

[0068] For example, the data processing device 140 is configured to output the lymphocyte percentage in the second white blood cell classification information as the lymphocyte percentage in the corrected first white blood cell classification information, output the monocyte percentage in the second white blood cell classification information as the monocyte percentage in the corrected first white blood cell classification information, subtract the eosinophil percentage in the first white blood cell classification information before correction from the granulocyte percentage in the second white blood cell classification information as the neutrophil percentage in the corrected first white blood cell classification information, and output the eosinophil percentage in the first white blood cell classification information before correction as the eosinophil percentage in the corrected first white blood cell classification information, that is:

[0069] Corrected lymphocyte percentage = lymphocyte percentage in the second leukocyte classification information (RET_Lym%),

[0070] Corrected monocyte percentage = monocyte percentage in the second leukocyte classification information (RET_Mon%),

[0071] Corrected neutrophil percentage = granulocyte percentage in the second leukocyte classification information (RET_Gran%) - eosinophil percentage in the first leukocyte classification information before correction (DIFF_Eos%),

[0072] Corrected eosinophil percentage = eosinophil percentage in the first leukocyte classification information before correction (DIFF_Eos%),

[0073] Optionally, the corrected basophil percentage = the basophil percentage in the first leukocyte differential information before correction (DIFF_Baso%).

[0074] In this way, at least accurate results of four-category classification of white blood cells can be obtained, and optionally accurate results of five-category classification of white blood cells can be obtained.

[0075] As can be seen from Tables 1 and 2, for abnormal samples with abnormal classification, accurate five-category classification of white blood cells can be obtained through the embodiments of the present application, wherein the microscopic examination results are the five-category classification of white blood cells obtained by microscopy, which is the gold standard.

[0076] Table 1 Five types of white blood cell classification of abnormal dog samples

[0077] Table 2 Five-category classification of white blood cells in abnormal cat samples

[0078] In some embodiments, the data processing device 140 generating a first white blood cell classification scatterplot based on the first optical information may include: the data processing device 140 generating the first white blood cell classification scatterplot based on at least the side scattered light signal SS and the side fluorescence signal FL in the first optical information, as shown in Figures 3 and 4 , wherein the first white blood cell classification information also includes the basophil percentage. Accordingly, the data processing device 140 generating a second white blood cell classification scatterplot based on the second optical information may include: the data processing device 140 generating the second white blood cell classification scatterplot based on at least the forward scattered light signal FS and the side scattered light signal SS in the second optical information, as shown in Figure 7 .

[0079] In some embodiments, the data processing device 140 may be further configured to obtain a white blood cell count based on the first optical information, in particular based on a white blood cell differential scattergram.

[0080] In some embodiments, the data processing device 140 may be further configured to obtain at least one, and preferably all, of a red blood cell count, a platelet count, a reticulocyte count, and a reticulocyte classification based on the second optical information.

[0081] For example, as shown in FIG8 , the data processing device 140 can be further configured to generate a RET scatter plot based on the side fluorescence signal FL and the forward scattered light signal FS in the second optical information, and obtain the red blood cell count, platelet count, reticulocyte count, and reticulocyte classification based on the RET scatter plot. In the RET scatter plot shown in FIG8 , from left to right in the FL direction are mature red blood cells, low-fluorescent reticulocytes, medium-fluorescent reticulocytes, high-fluorescent reticulocytes, and white blood cells. By adjusting the staining time of blood samples from different species to be tested, a clear demarcation between white blood cells, reticulocytes, and platelets can be achieved while ensuring the accuracy of the test results for various reticulocyte parameters. The FL-FS scatter plot can accurately distinguish mature red blood cells, reticulocytes, platelets, and white blood cells.

[0082] In some embodiments, a chemical dye is additionally added when preparing the second measurement sample, so that the data processing device 140 can distinguish eosinophils from the granulocytes based on the second optical information, so that the second white blood cell detection result includes at least the percentage of lymphocytes, the percentage of monocytes, the percentage of eosinophils and the percentage of neutrophils.

[0083] For example, the chemical dye is added in a manner of being mixed in a diluent and / or a fluorescent dye or is added separately.

[0084] In some embodiments, the chemical dye is selected from acidic organic pigments. Alternatively, the chemical dye is selected from the acid blue series; alternatively, the chemical dye is selected from the group consisting of direct blue, acid green, acid yellow, acid orange, methyl red, methyl orange, aniline blue, alizarin yellow, reactive black, Sudan black B, and azo black E.

[0085] As shown in FIG9 , the embodiment of the present application further provides a blood cell analysis method 200 , comprising:

[0086] S210, drawing a blood sample to be tested;

[0087] S220, mixing a portion of the blood sample to be tested, a hemolytic agent, and a first fluorescent dye to prepare a first measurement sample for white blood cell differentiation, and allowing particles in the first measurement sample to pass through an optical detection area irradiated with light one by one to obtain first optical information generated by the particles in the first measurement sample after being irradiated with light;

[0088] S230, mixing another portion of the blood sample to be tested, a diluent, and a second fluorescent dye to prepare a second measurement sample for identifying platelets and / or reticulocytes, and allowing particles in the second measurement sample to pass through an optical detection area irradiated with light one by one to obtain second optical information generated by the particles in the second measurement sample after being irradiated with light;

[0089] S240, generating a first white blood cell classification scattergram based on the first optical information and obtaining first white blood cell classification information of the blood sample to be tested based on the first white blood cell classification scattergram, wherein the first white blood cell classification information at least includes a lymphocyte percentage, a monocyte percentage, a neutrophil percentage, and an eosinophil percentage; and

[0090] S250, determining whether there is a classification anomaly in the first white blood cell classification scattergram in which a lymphocyte population and / or a monocyte population overlaps with at least one other particle population;

[0091] S260, when it is determined that a classification anomaly exists in the first white blood cell classification scattergram in which a lymphocyte population and / or a monocyte population overlaps with at least one other particle population, generating a second white blood cell classification scattergram based on the second optical information, and obtaining second white blood cell classification information of the blood sample to be tested based on the second white blood cell classification scattergram, wherein the second white blood cell classification information includes a lymphocyte percentage, a monocyte percentage, and a granulocyte percentage, wherein the granulocytes include neutrophils and eosinophils, and the first white blood cell classification information is corrected using the second white blood cell classification information to obtain and output corrected first white blood cell classification information;

[0092] S270: If the classification abnormality does not exist, directly output the first white blood cell classification information.

[0093] In some embodiments, the blood cell analysis method 200 may further include: determining whether the blood sample to be tested has the classification abnormality according to the first optical information, preferably based on the first white blood cell classification scattergram.

[0094] In some embodiments, correcting the first white blood cell classification information using the second white blood cell classification information to obtain and output corrected first white blood cell classification information may include:

[0095] When it is determined that a classification abnormality exists in the first white blood cell classification scatter plot in which the lymphocyte population, the monocyte population, and the neutrophil population all overlap with each other, the first white blood cell classification information is corrected using the lymphocyte percentage, the monocyte percentage, and the granulocyte percentage in the second white blood cell classification information. For example, the lymphocyte percentage in the second white blood cell classification information is output as the corrected lymphocyte percentage in the first white blood cell classification information, the monocyte percentage in the second white blood cell classification information is output as the corrected monocyte percentage in the first white blood cell classification information, the granulocyte percentage in the second white blood cell classification information minus the eosinophil percentage in the first white blood cell classification information before correction is output as the corrected neutrophil percentage in the first white blood cell classification information, and the eosinophil percentage in the first white blood cell classification information before correction is output as the corrected eosinophil percentage in the first white blood cell classification information.

[0096] In some embodiments, generating a first white blood cell classification scatterplot based on the first optical information may include generating the first white blood cell classification scatterplot based on at least a side scattered light signal and a side fluorescence signal in the first optical information, wherein the first white blood cell classification information also includes a basophil percentage. Accordingly, generating a second white blood cell classification scatterplot based on the second optical information may include generating the second white blood cell classification scatterplot based on at least a forward scattered light signal and a side scattered light signal in the second optical information.

[0097] In some embodiments, the blood cell analysis method 200 may further include: obtaining at least one, and preferably all, of red blood cell count, platelet count, reticulocyte count, and reticulocyte classification based on the second optical information.

[0098] More embodiments and advantages of the blood cell analysis method 200 proposed in the embodiment of the present application can be found in the above description of the blood cell analyzer 100 and will not be repeated here.

[0099] The features or feature combinations mentioned above in the specification, drawings, and claims may be used in any combination or individually, as long as they are meaningful and not mutually inconsistent within the scope of this application. The advantages and features described with reference to the blood cell analyzer provided in the embodiments of this application apply in a corresponding manner to the blood cell analysis method provided in the embodiments of this application, and vice versa.

[0100] The above description is only a preferred embodiment of the present application and does not limit the patent scope of the present application. All equivalent transformation schemes made by using the contents of the present application description and drawings under the inventive concept of the present application, or directly / indirectly applied in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A blood cell analyzer, comprising: A sampling device for aspirating a blood sample to be tested; A sample preparation device for mixing a part of the blood sample to be tested, a hemolytic agent and a first fluorescent stain to prepare a first measurement sample for white blood cell classification, and for mixing another part of the blood sample to be tested, a diluent and a second fluorescent stain to prepare a second measurement sample for identifying platelets and / or reticulocytes; An optical detection device, including a flow cell, a light source and a light detector, the flow cell for allowing the first measurement sample and the second measurement sample to pass through respectively, the light source for irradiating the first measurement sample and the second measurement sample passing through the flow cell respectively with light, and the light detector for detecting first optical information and second optical information generated after the first measurement sample and the second measurement sample are irradiated by light when passing through the flow cell respectively; And A data processing device configured to: Generate a first white blood cell classification scatter plot based on the first optical information and obtain first white blood cell classification information of the blood sample to be tested based on the first white blood cell classification scatter plot, the first white blood cell classification information at least including lymphocyte percentage, monocyte percentage, neutrophil percentage and eosinophil percentage; When it is determined that there is a classification anomaly in which a lymphocyte population and / or a monocyte population coincides with at least one other particle population in the first white blood cell classification scatter plot, generate a second white blood cell classification scatter plot based on the second optical information and obtain second white blood cell classification information of the blood sample to be tested based on the second white blood cell classification scatter plot, the second white blood cell classification information including lymphocyte percentage, monocyte percentage and granulocyte percentage, the granulocytes including neutrophils and eosinophils, and use the second white blood cell classification information to correct the first white blood cell classification information to obtain and output the corrected first white blood cell classification information.

2. The blood cell analyzer according to claim 1, characterized in that, The data processing device is further configured to: judge whether there is such a classification anomaly for the blood sample to be tested according to the first optical information, preferably based on the first white blood cell classification scatter plot.

3. The blood cell analyzer according to any one of claims 1 to 2, characterized in that The data processing device uses the second white blood cell classification information to correct the first white blood cell classification information to obtain and output the corrected first white blood cell classification information, including: the data processing device When it is determined that there is an abnormal classification in the first white blood cell classification scatter plot where the lymphocyte population, monocyte population, and neutrophil population overlap with each other, the first white blood cell classification information is corrected using the lymphocyte percentage, monocyte percentage, and granulocyte percentage in the second white blood cell classification information. For example, the lymphocyte percentage in the second white blood cell classification information is output as the lymphocyte percentage in the corrected first white blood cell classification information, the monocyte percentage in the second white blood cell classification information is output as the monocyte percentage in the corrected first white blood cell classification information, the granulocyte percentage in the second white blood cell classification information minus the eosinophil percentage in the first white blood cell classification information before correction is output as the neutrophil percentage in the corrected first white blood cell classification information, and the eosinophil percentage in the first white blood cell classification information before correction is output as the eosinophil percentage in the corrected first white blood cell classification information.

4. The blood cell analyzer according to claim 3, wherein the data processing device generates a first white blood cell classification scatter plot based on the first optical information, including: the data processing device generates the first white blood cell classification scatter plot based on at least the side scatter light signal and side fluorescence signal in the first optical information, wherein the first white blood cell classification information further includes the basophil percentage; the data processing device generates a second white blood cell classification scatter plot based on the second optical information, including: the data processing device generates the second white blood cell classification scatter plot based on at least the forward scatter light signal and side scatter light signal in the second optical information.

5. The blood cell analyzer according to any one of claims 1 to 4, characterized in that, The data processing device is further configured to obtain at least one, preferably all, of the red blood cell count, platelet count, reticulocyte count, and reticulocyte classification based on the second optical information.

6. The blood cell analyzer according to any one of claims 1 to 4, characterized in that, The aspirated blood sample to be tested is from a mammal, especially from a dog or a cat.

7. A blood cell analysis method, comprising: aspirating a blood sample to be tested; mixing a part of the blood sample to be tested, a hemolytic agent, and a first fluorescent stain to prepare a first measurement sample for white blood cell classification, and passing the particles in the first measurement sample one by one through an optically detected area irradiated with light to obtain first optical information generated by the particles in the first measurement sample after being irradiated with light; mixing another part of the blood sample to be tested, a diluent, and a second fluorescent stain to prepare a second measurement sample for identifying platelets and / or reticulocytes, and passing the particles in the second measurement sample one by one through an optically detected area irradiated with light to obtain second optical information generated by the particles in the second measurement sample after being irradiated with light; Generate a first white blood cell classification scatter plot based on the first optical information and obtain first white blood cell classification information of the blood sample to be tested based on the first white blood cell classification scatter plot, where the first white blood cell classification information at least includes lymphocyte percentage, monocyte percentage, neutrophil percentage, and eosinophil percentage; and When it is determined that there is a classification anomaly in the first white blood cell classification scatter plot where the lymphocyte population and / or monocyte population overlap with at least one other particle population, generate a second white blood cell classification scatter plot based on the second optical information and obtain second white blood cell classification information of the blood sample to be tested based on the second white blood cell classification scatter plot. The second white blood cell classification information includes lymphocyte percentage, monocyte percentage, and granulocyte percentage, where the granulocytes include neutrophils and eosinophils, and use the second white blood cell classification information to correct the first white blood cell classification information to obtain and output the corrected first white blood cell classification information.

8. The blood cell analysis method according to claim 7, wherein The blood cell analysis method further includes: judging whether there is such a classification anomaly for the blood sample to be tested according to the first optical information, preferably based on the first white blood cell classification scatter plot.

9. The blood cell analysis method according to claim 7 or 8, characterized in that, Using the second white blood cell classification information to correct the first white blood cell classification information to obtain and output the corrected first white blood cell classification information includes: When it is determined that there is a classification anomaly in the first white blood cell classification scatter plot where the lymphocyte population, monocyte population, and neutrophil population all overlap with each other, use the lymphocyte percentage, monocyte percentage, and granulocyte percentage in the second white blood cell classification information to correct the first white blood cell classification information. For example, output the lymphocyte percentage in the second white blood cell classification information as the lymphocyte percentage in the corrected first white blood cell classification information, output the monocyte percentage in the second white blood cell classification information as the monocyte percentage in the corrected first white blood cell classification information, output the granulocyte percentage in the second white blood cell classification information minus the eosinophil percentage in the first white blood cell classification information before correction as the neutrophil percentage in the corrected first white blood cell classification information, and output the eosinophil percentage in the first white blood cell classification information before correction as the eosinophil percentage in the corrected first white blood cell classification information.

10. The blood cell analysis method according to claim 9, wherein Generating a first white blood cell classification scatter plot based on the first optical information includes: generating the first white blood cell classification scatter plot based on at least the side scatter light signal and side fluorescence signal in the first optical information, where the first white blood cell classification information further includes basophil percentage; Generating a second white blood cell classification scatter plot based on the second optical information includes: generating the second white blood cell classification scatter plot based on at least the forward scatter light signal and side scatter light signal in the second optical information.

11. The blood cell analysis method according to any one of claims 7 to 10, characterized in that, The blood cell analysis method further includes: obtaining at least one, preferably all, of the red blood cell count, platelet count, reticulocyte count, and reticulocyte classification based on the second optical information.

12. The blood cell analysis method according to any one of claims 7 to 11, characterized in that, The aspirated blood sample to be tested is from a mammal, particularly from a dog or a cat.

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