Sample analyzers, sample analysis methods, and computer-readable storage media

Abstract

A sample analyzer, a sample analysis method, and a computer-readable storage medium are disclosed. The sample analysis method includes: firstly, acquiring the scattered light signal and fluorescence signal generated by an optical detection device from particles in a hemolyzed and fluorescently stained body fluid sample; then generating a scatter plot of the body fluid sample based on the scattered light signal and fluorescence signal; next, identifying a first non-leukocyte region and a second non-leukocyte region in the scatter plot based on the scattered light signal and fluorescence signal; finally, determining the presence of tumor cells and / or mesothelial cells in the body fluid sample based on the scatter plot characteristics of these two non-leukocyte regions, thereby obtaining tumor cell information and / or mesothelial cell information of the body fluid. This sample analysis method can accurately identify tumor cells and / or mesothelial cells in the body fluid sample.

Description

Technical Field

[0001] This invention relates to the field of bodily fluid detection, and in particular to sample analyzers, sample analysis methods, and computer-readable storage media. Background Technology

[0002] Body fluid analysis plays a crucial role in the screening, diagnosis, treatment, and recurrence monitoring of cancer patients. Currently, clinically used body fluid analysis methods mainly include routine body fluid analysis, body fluid biochemical analysis, body fluid tumor marker analysis, and body fluid exfoliative cytology analysis.

[0003] Routine body fluid testing typically includes physical, chemical, and microscopic examination of body fluid samples. Although routine body fluid testing is a mandatory procedure, this method utilizes indirect changes in the body caused by tumors; therefore, its sensitivity and specificity for detecting tumors are very low.

[0004] Body fluid biochemical testing refers to the detection of proteins, glucose, lipids, enzymes, etc., in body fluid samples. For example, a total protein content of less than 25 g / L indicates that the tested body fluid is mostly benign, while a total protein content of more than 25 g / L indicates that the tested body fluid is mostly malignant or infectious. Similar to routine body fluid testing, this body fluid biochemical testing has relatively low sensitivity and specificity.

[0005] Body fluid tumor marker detection refers to the use of immunological methods to detect tumor-specific proteins in body fluids. Taking CEA as an example, the normal range is 0–5 μg / L. When CEA > 20 μg / L, the tested body fluid may be malignant. This method has high sensitivity; even without tumor cell shedding in the body fluid, tumor marker detection has good sensitivity when malignant tumors are present. However, this method must be performed under the guidance of a clinician, heavily relying on the clinician's experience. Furthermore, the test is expensive and not ideal for tumor screening.

[0006] Exfoliative cytology of body fluids involves staining cells in body fluid samples using histochemical or immunohistochemical methods and then examining the cells morphologically under a microscope. Exfoliative cytology can identify leukocytes, mesothelial cells, tumor cells, and other abnormal cells in body fluid samples, and can also determine the type of tumor cells, such as adenocarcinoma, squamous cell carcinoma, or leukemia cells. As the gold standard for detecting tumor cells in body fluids, exfoliative cytology has near 100% specificity. However, this method has relatively low sensitivity; some literature reports that the sensitivity for detecting tumor cells in exfoliative cytology is only 30%. Furthermore, as a manual microscopic examination method, it is highly dependent on the experience of the examiner and requires a high level of expertise. Therefore, exfoliative cytology may not meet the needs of tumor screening in medical institutions at all levels.

[0007] Blood cell analyzers are commonly used for routine body fluid testing. They utilize the fact that tumor cells have a higher nucleic acid content than normal cells to detect tumor cells in body fluids. In blood cell analyzers, the fluorescence signal of tumor cells is higher than that of normal cells. Due to their convenience, speed, and low cost, blood cell analyzers are very suitable for tumor cell screening. However, tumor cells in body fluids are easily interfered with by other cellular components. That is, cells with high fluorescence signals include not only tumor cells but also normal cells such as mesothelial cells and macrophages, with mesothelial cells having the greatest impact on tumor cell detection. Mesothelial cells are the cells that make up the serous membranes of human body cavities. A small number of mesothelial cells are present in the serous cavities of normal individuals, but a large number shed into the serous cavities after stimulation by inflammation or a tumor environment. Mesothelial cells are larger than leukocytes, with a diameter of approximately 15-30 μm. They are round, oval, or irregular in shape, with nuclei located in the center or off-center. Most have one nucleus, but two or more can be observed. They also contain a higher amount of nucleic acid. Therefore, on a scatter plot of a blood cell analyzer, mesothelial cells are also classified as highly fluorescent cells. The interference of mesothelial cells reduces the specificity of blood cell analyzers in detecting tumor cells in body fluids. Summary of the Invention

[0008] Therefore, the objective of this invention is to provide a detection scheme for tumor cells and / or mesothelial cells in body fluids with high specificity. In this detection scheme, a blood analyzer can be used to accurately distinguish tumor cells in body fluids from high-fluorescence cells, especially to accurately distinguish tumor cells and mesothelial cells in high-fluorescence cells, thereby reducing the influence of mesothelial cells on tumor cell detection.

[0009] To achieve the objectives of this invention, a first aspect of this invention provides a sample analyzer, the sample analyzer comprising:

[0010] A sampling device having a pipette with a pipette nozzle and a driving device for driving the pipette to quantitatively aspirate body fluid samples through the pipette nozzle;

[0011] A sample preparation apparatus has at least one reaction chamber and a reagent supply unit, wherein the at least one reaction chamber is used to receive a body fluid sample aspirated by a sampling device, and the reagent supply unit provides a hemolytic reagent and a fluorescent reagent to the at least one reaction chamber, thereby mixing the body fluid sample aspirated by the sampling device with the hemolytic reagent and the fluorescent reagent provided by the reagent supply unit in the reaction chamber to prepare a body fluid sample to be tested;

[0012] An optical detection device includes a light source, a flow chamber, a scattered light detector, and a fluorescence detector. The light source emits a light beam to illuminate the flow chamber. The flow chamber is connected to a reaction cell, and particles in the test fluid sample can pass through the flow chamber one by one. The scattered light detector detects the scattered light signal generated by the particles passing through the flow chamber after being illuminated. The fluorescence detector detects the fluorescence signal generated by the particles passing through the flow chamber after being illuminated.

[0013] The processor is configured to perform the following steps: acquiring scattered light signals and fluorescence signals of the body fluid sample to be tested from the optical detection device; generating a scatter plot of the body fluid sample to be tested based on the scattered light signals and the fluorescence signals; identifying a first non-leukocyte region and a second non-leukocyte region in the scatter plot based on the scattered light signals and the fluorescence signals; and obtaining tumor cell information and / or mesothelial cell information of the body fluid to be tested based on the scatter features of the first non-leukocyte region and the scatter features of the second non-leukocyte region.

[0014] A second aspect of the present invention provides another sample analyzer, the sample analyzer comprising:

[0015] A sampling device having a pipette with a pipette nozzle and a driving device for driving the pipette to quantitatively aspirate body fluid samples through the pipette nozzle;

[0016] A sample preparation apparatus has at least one reaction chamber and a reagent supply unit, wherein the at least one reaction chamber is used to receive a body fluid sample aspirated by a sampling device, and the reagent supply unit provides a hemolytic reagent and a fluorescent reagent to the at least one reaction chamber, thereby mixing the body fluid sample aspirated by the sampling device with the hemolytic reagent and the fluorescent reagent provided by the reagent supply unit in the reaction chamber to prepare a body fluid sample to be tested;

[0017] An optical detection device includes a light source, a flow chamber, a scattered light detector, and a fluorescence detector. The light source emits a light beam to illuminate the flow chamber. The flow chamber is connected to a reaction cell, and particles in the test fluid sample can pass through the flow chamber one by one. The scattered light detector detects the scattered light signal generated by the particles passing through the flow chamber after being illuminated. The fluorescence detector detects the fluorescence signal generated by the particles passing through the flow chamber after being illuminated.

[0018] The processor is configured to perform the following steps: acquiring scattered light signals and fluorescence signals of the body fluid sample to be tested from the optical detection device; generating a scatter plot of the body fluid sample to be tested based on the scattered light signals and the fluorescence signals; identifying a preset non-leukocyte region in the scatter plot based on the scattered light signals and the fluorescence signals; and obtaining tumor cell information and / or mesothelial cell information of the body fluid to be tested based on the positional characteristics and distribution morphology characteristics of the scatter clusters in the preset non-leukocyte region.

[0019] A third aspect of the present invention provides a sample analysis method for obtaining tumor cell information and / or mesothelial cell information of a body fluid sample to be tested, the sample analysis method comprising:

[0020] The scattered light signal and fluorescence signal generated by the optical detection device are obtained from the particles in the test body fluid sample after hemolysis and fluorescence staining.

[0021] A scatter plot of the body fluid sample to be tested is generated based on the scattered light signal and the fluorescence signal;

[0022] Based on the scattered light signal and the fluorescence signal, the first non-leukocyte region and the second non-leukocyte region are identified in the scatter plot;

[0023] The tumor cell information and / or mesothelial cell information of the body fluid to be tested are obtained based on the scatter plot characteristics of the first non-leukocyte region and the second non-leukocyte region.

[0024] A fourth aspect of the present invention provides another sample analysis method for obtaining tumor cell information and / or mesothelial cell information in a body fluid sample to be tested, the sample analysis method comprising:

[0025] The scattered light signal and fluorescence signal generated by the optical detection device are obtained from the particles in the test body fluid sample after hemolysis and fluorescence staining.

[0026] A scatter plot of the body fluid sample to be tested is generated based on the scattered light signal and the fluorescence signal;

[0027] Based on the scattered light signal and the fluorescence signal, a predetermined non-leukocyte region is identified in the scatter plot;

[0028] The tumor cell information and / or mesothelial cell information of the test fluid are obtained based on the location and distribution characteristics of the scatter plots in the preset non-leukocyte region.

[0029] The fifth aspect of the present invention provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a computer, cause the computer to implement the sample analysis method according to the third or fourth aspect of the present invention.

[0030] In the technical solutions provided by various aspects of the present invention, a blood cell analyzer is used to hemolyze and stain the body fluid to be tested, and then detect the scattered light signal and fluorescence signal. Tumor cells and mesothelial cells are accurately distinguished on the scatter plot composed of the scattered light signal and fluorescence signal. At the same time, white blood cells can be classified and counted, thereby achieving rapid and low-cost tumor screening. Attached Figure Description

[0031] Figure 1 This is a schematic diagram of one embodiment of the sample analyzer according to the present invention;

[0032] Figure 2 A schematic diagram of an embodiment of the optical detection device of the sample analyzer according to the present invention;

[0033] Figure 3A Forward scattered light-fluorescence scatter plot for distinguishing tumor cells and mesothelial cells according to the present invention;

[0034] Figure 3B Lateral scattering-fluorescence scatter plot for distinguishing tumor cells and mesothelial cells according to the present invention;

[0035] Figure 4A The image shows a lateral scattered light-fluorescence scatter plot of a body fluid sample containing mesothelial cells according to the present invention.

[0036] Figure 4B The image shows a lateral scattered light-fluorescence scatter plot of a body fluid sample containing tumor cells according to the present invention.

[0037] Figure 5 This is a schematic flowchart of the sample analysis method according to the first embodiment of the present invention;

[0038] Figure 6 This is another schematic flowchart of the sample analysis method according to the first embodiment of the present invention;

[0039] Figure 7 This is a schematic flowchart of the sample analysis method according to the second embodiment of the present invention;

[0040] Figure 8 This is a schematic flowchart of the sample analysis method according to the third embodiment of the present invention;

[0041] Figure 9 This is another schematic flowchart of the sample analysis method according to the third embodiment of the present invention. Detailed Implementation

[0042] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0043] The serial numbers assigned to components in this document, such as "first" and "second," are used only to distinguish the described objects and have no sequential or technical meaning. The terms "connection" and "linkage" used in this application, unless otherwise specified, include both direct and indirect connections (linkages).

[0044] As will be understood by those skilled in the art, the term "high-fluorescence cells" as used herein refers to cells exhibiting stronger fluorescence than leukocytes. A high-fluorescence region is defined in a scatter plot as the area where the fluorescence centroid (average fluorescence signal intensity) of the scatter clusters is higher than that of the scatter clusters in the leukocyte region. High-fluorescence regions can be determined by those skilled in the art through experience or experimentation.

[0045] Body fluids can be classified according to their location, such as cerebrospinal fluid, serous cavity fluid, and synovial fluid. Small amounts of each of these fluids are present in a normal human body. Serous cavity effusions include pleural effusion, ascites, and pericardial effusion, while joint effusions are synovial fluid.

[0046] Malignant tumors are immature in differentiation, grow rapidly, infiltrate and destroy the structure and function of organs, and metastasize to distant sites. They primarily cause increased body fluids through inflammatory responses, immune activation, and damage to organ physiological structures resulting from invasion and metastasis. The increased body fluids in patients with malignant tumors often contain tumor cells homologous to the primary tumor. Increased body fluids caused by malignant tumors are called malignant body fluids. While malignant body fluids do not necessarily contain tumor cells, body fluids containing tumor cells are definitely malignant body fluids. Therefore, using blood cell analyzers to test body fluids is of great significance for screening cancer patients.

[0047] The blood cell analyzer used in this invention classifies and counts particles in a sample by combining laser scattering and flow cytometry with fluorescence staining. The detection principle of the blood cell analyzer is as follows: First, a body fluid or blood sample is drawn and treated with a hemolysing agent and a fluorescent dye. Red blood cells are destroyed and dissolved by the hemolysing agent, while white blood cells are not dissolved. However, the fluorescent dye can enter the nucleus of white blood cells with the help of the hemolysing agent and bind to nucleic acid substances in the nucleus. Next, the particles in the sample pass one by one through a detection aperture illuminated by a laser beam. When the laser beam illuminates the particles, the characteristics of the particles themselves (such as volume, staining degree, size and content of cell contents, nucleus density, etc.) can block or change the direction of the laser beam, thereby generating scattered light at various angles corresponding to their characteristics. This scattered light is received by a signal detector to obtain relevant information about the particle structure and composition. Specifically, forward scatter (FS) reflects the number and volume of particles, side scatter (SS) reflects the complexity of the internal structure of the cell (such as intracellular particles or the cell nucleus), and fluorescence (FL) reflects the content of nucleic acid substances in the cell. This optical information can be used to classify and count particles in a sample.

[0048] Figure 1 This is a schematic diagram of an embodiment of the blood cell analyzer used in this invention. The blood cell analyzer 100 includes a sampling device 110, a sample preparation device 120, an optical detection device 130, and a processor 140. The blood cell analyzer 100 has a liquid path system (not shown) for connecting the sampling device 110, the sample preparation device 120, and the optical detection device 130 to facilitate liquid transfer between these devices.

[0049] The sampling device 110 has a pipette with a suction nozzle and a driving device for driving the pipette to quantitatively aspirate the sample to be tested, such as a body fluid sample or a blood sample, through the suction nozzle. The sampling device can transport the collected sample to the sample preparation device 120.

[0050] The sample preparation apparatus 120 has at least one reaction chamber and a reagent supply unit. The at least one reaction chamber receives the sample to be tested aspirated by the sampling device 110. The reagent supply unit provides a dissolving reagent to the at least one reaction chamber, thereby mixing the sample to be tested aspirated by the sampling device with the dissolving reagent provided by the reagent supply unit in the reaction chamber to prepare a test sample, such as a body fluid sample. The dissolving reagent includes a hemolytic reagent and a fluorescent reagent. The hemolytic agent can be any existing hemolytic reagent used for leukocyte classification in automated blood analyzers, and can be any one or a combination of several of cationic surfactants, nonionic surfactants, anionic surfactants, and amphiphilic surfactants. The fluorescent dye is used to stain the cells. The dissolving reagent can adopt the dissolving reagent formulation disclosed in US Patent 8,367,358, the entire disclosure of which is incorporated herein by reference. The dissolving reagent disclosed in US Patent 8,367,358 includes a cationic cyanine compound (a fluorescent dye), a cationic surfactant, a nonionic surfactant, and an anionic compound. Alternatively, fluorescent dyes as described in U.S. Patent 8,273,329 may be used, the entire disclosure of which is incorporated herein by reference.

[0051] The optical detection device 130 includes a light source, a flow chamber, a scattered light detector, and a fluorescence detector. The light source is used to emit a light beam to illuminate the flow chamber. The flow chamber is connected to the reaction cell, and particles in the body fluid sample to be tested can pass through the flow chamber one by one. The scattered light detector is used to detect the scattered light signal generated by the particles passing through the flow chamber after being illuminated by light. The fluorescence detector is used to detect the fluorescence signal generated by the particles passing through the flow chamber after being illuminated by light.

[0052] In some embodiments, the scattered light detector is a forward-scattering light detector for detecting forward-scattered light or a side-scattering light detector for detecting side-scattered light. The optical detection device 130 preferably includes both a forward-scattering light detector and a side-scattering light detector.

[0053] In this document, a flow chamber refers to a chamber in which a focused liquid stream is used to detect 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 from a light source, directed through the aperture, in various directions. A light scattering signal is obtained by placing a photodetector at one or more different angles relative to the incident light beam and detecting the light scattered by the particle. Since different particles have different light scattering characteristics, the light scattering signal can be used to distinguish different groups of particles. Specifically, the light scattering signal detected near the incident light beam is generally referred to as a forward light scattering signal or a small-angle light scattering signal. In some embodiments, the forward light scattering signal can be detected from an angle of about 1° to about 10° relative to the incident light beam. In other embodiments, the forward light scattering signal can be detected from an angle of about 2° to about 6° relative to the incident light beam. The light scattering signal detected at about 90° relative to the incident light beam is generally referred to as a side light scattering signal. In some embodiments, the side light scattering signal can be detected from an angle of about 65° to about 115° relative to the incident light beam. Typically, the fluorescent signal emitted by blood cells stained with fluorescent dye is also detected in a direction at approximately 90° to the incident light beam.

[0054] Figure 2 A specific example of an optical detection device 130 is shown. This optical detection device 130 has a light source 101, a beam shaping assembly 102, a flow chamber 103, and a forward scattering detector 104 arranged sequentially in a straight line. A dichroic mirror 106 is arranged at a 45° angle to the line on one side of the flow chamber 103. A portion of the side light emitted by particles in the flow chamber 103 passes through the dichroic mirror 106 and is captured by a fluorescence detector 105 arranged at a 45° angle behind the dichroic mirror 106; the other portion of the side light is reflected by the dichroic mirror 106 and captured by a side scattering detector 107 arranged at a 45° angle in front of the dichroic mirror 106.

[0055] The processor 140 is used to perform calculations on the data to obtain the required results. For example, it can generate two-dimensional or three-dimensional scatter plots based on various collected optical signals, and perform particle analysis on the scatter plots using a gated method. The processor 140 can also visualize intermediate or final calculation results and then display them through the display device 150. In this embodiment of the invention, the processor 140 is configured to implement the methods described in further detail below. The processor 140 includes, but is not limited to, devices such as a central processing unit (CPU), a microcontroller unit (MCU), a field-programmable gate array (FPGA), and a digital signal processor (DSP) used to interpret computer instructions and process data in computer software. For example, the processor 140 is used to execute various computer applications in a computer-readable storage medium, thereby enabling the blood cell analyzer 100 to execute corresponding detection procedures and analyze and process the optical signals detected by the optical detection device 130 in real time.

[0056] In addition, the blood cell analyzer 100 also includes a first housing 160 and a second housing 170. The display device 150 may be, for example, a user interface. An optical detection device 130 and a processor 140 are disposed inside the second housing 170. A sample preparation device 120 is disposed, for example, inside the first housing 160, and the display device 150 is disposed, for example, on the outer surface of the first housing 160 and is used to display the detection results of the blood cell analyzer.

[0057] Currently, when using flow cytometry to analyze body fluids, if hyperfluorescent cells are detected, the analyzer may output parameters for these cells, such as the hyperfluorescent cell count or the hyperfluorescent cell percentage. However, current flow cytometry analyzers cannot accurately distinguish between tumor cells and mesothelial cells from the detected hyperfluorescent cells. Accurately identifying tumor cells from hyperfluorescent cells is of great significance for rapid and low-cost tumor screening.

[0058] Mesothelial cells have round or oval cell bodies, abundant cytoplasm, and regularly centered nuclei with fine and homogeneous chromatin. Tumor cells, on the other hand, are larger, with irregular cell bodies, abundant cytoplasm, irregular nuclei, and coarse chromatin. Depending on the type of tumor cell, they can form different specific structures, such as glandular structures and cancer nests. The inventors unexpectedly observed that tumor cells and mesothelial cells are larger than normal leukocytes. Although tumor cells exhibit atypia, with variations in cell and nucleus morphology and size, compared to mesothelial cells, tumor cells almost always have an increased nucleocytic ratio, increased intranuclear nucleic acid content, and coarser nuclear chromatin. These characteristics manifest as stronger fluorescence signals or fluorescence intensity and side-scattered light signals or intensity on a blood cell analyzer. Based on these characteristics, tumor cells and mesothelial cells can be distinguished on a scatter plot of a blood cell analyzer according to the location of the scatter points.

[0059] Based on the above understanding, as shown in Figure 3, the inventors, through extensive research and experimentation, have concluded that in a scatter plot composed of fluorescence signals from body fluids and scattered light signals, a suitable non-leukocyte region T, or tumor cell region T, can be defined within the high-fluorescence region using a gating method. This ensures that the scatter points in non-leukocyte region T represent tumor cells, and the fluorescence signal / intensity of particles in non-leukocyte region T is greater than or substantially greater than the fluorescence signal / intensity of other particles in the high-fluorescence region. Similarly, another suitable non-leukocyte region M, or mesothelial cell region M, can be defined within the high-fluorescence region using a gating method. This ensures that the scatter points in non-leukocyte region M represent mesothelial cells, and the fluorescence signal / intensity of particles in non-leukocyte region M is less than or substantially less than the fluorescence signal / intensity of non-leukocyte region T. In other words, the fluorescence centroid of the particle group represented by the scatter points in non-leukocyte region M is smaller than the fluorescence centroid of the particle group represented by the scatter points in non-leukocyte region T. This allows for more accurate identification of tumor cells and mesothelial cells.

[0060] Furthermore, when the serous cavity of the human body is exposed to stimuli such as inflammation or peritoneal dialysis for a long period of time, mesothelial cells can undergo epithelial-mesenchymal transformation (EMT). The cells transform from an epithelial-like morphology to a fibroblast-like morphology, characterized by an increase in pseudopodes, enlarged nuclei, and increased invasiveness. These transformed cells are also known as atypical cells, atypia cells, anaplastic cells, or atypical cells. Such mesothelial cells appear as increased fluorescence signal and lateral scattering on a scatter plot, making them difficult to distinguish from tumor cells. The inventors realized that since epithelial-mesenchymal transformation is a continuous process, with newly shed mesothelial cells and those undergoing transformation coexisting in the serous cavity, the scatter plots representing these mesothelial cells exist in a continuous form from the mesothelial cell region M to the tumor cell region T, exhibiting a continuous extension trend. This extension trend is a specific manifestation of mesothelial cells. Figure 4A As shown in Figure 3, tumor cells are monoclonal, and newly generated tumor cells do not undergo a continuous process of change. Therefore, if tumor cells are present, they will appear as scattered clusters in the tumor cell region T, as shown in Figure 3, or as scattered areas in the highly fluorescent region H, as shown in Figure 3. Figure 4B As shown. Based on this understanding, the scattering pattern of high-fluorescence regions H can also be used to detect tumor cells and identify mesothelial cells. Here, the high-fluorescence region H includes the tumor cell region T and the mesothelial cell region M.

[0061] The method for detecting tumor cells and / or mesothelial cells proposed in this invention will then be described in detail. The various methods proposed in the embodiments of this invention are particularly applied to the aforementioned blood cell analyzer 100, and are implemented specifically by the processor 140 of the aforementioned blood cell analyzer 100.

[0062] like Figure 5 As shown, the first embodiment of the present invention provides a sample analysis method 200 for obtaining tumor cell information from a body fluid sample to be tested. This sample analysis method includes the following steps.

[0063] Step S201: Obtain the scattered light signal and fluorescence signal generated by the optical detection device when the particles in the test body fluid sample that has been hemolyzed and fluorescently stained pass through the sample.

[0064] Specifically, a sample of the body fluid to be tested is first provided. The sampling device 110 draws the sample through a pipette and transports it to the sample preparation device 120. The sample of the body fluid to be tested is mixed with a hemolysin and a fluorescent reagent in the reaction chamber of the sample preparation device 120 and incubated for a period of time to form the sample solution to be tested. The sample solution to be tested is transported to the flow chamber of the optical detection device 130 through a liquid circuit system, so that each particle in the sample solution to be tested passes through the detection hole of the flow chamber one by one. The scattered light detector and the fluorescence detector respectively detect the scattered light signal and the fluorescence signal generated by the particles passing through the flow chamber after being irradiated by light.

[0065] Step S202: Generate a scatter plot of the body fluid sample to be tested based on the scattered light signal and the fluorescence signal.

[0066] The scatter plot can be a two-dimensional scatter plot with the forward scattered light signal as the abscissa and the fluorescence signal as the ordinate (e.g., Figure 3A (As shown), it can also be a two-dimensional scatter plot with the side-scattered light signal as the abscissa and the fluorescence signal as the ordinate (as shown). Figure 3B (As shown), or it can be a three-dimensional scatter plot composed of forward-scattered light signals, side-scattered light signals, and fluorescence signals. Particularly preferred is that the scatter plot includes at least the side-scattered light signal. It should be noted that the scatter plots described herein are not limited to graphical forms and can also be in data form, such as a table or list with the same or similar resolution as the scatter plot, or presented in any other suitable manner known in the art.

[0067] Step S203: Identify the first non-leukocyte region and the second non-leukocyte region in the scatter plot based on the scattered light signal and the fluorescence signal.

[0068] The first non-leukocyte region can be the tumor cell region T, and the second non-leukocyte region can be the hyperfluorescent region H; or conversely, the first non-leukocyte region can be the hyperfluorescent region H, and the second non-leukocyte region can be the tumor cell region T. The tumor cell region T is a part of the hyperfluorescent region H. For example, when the first non-leukocyte region is the tumor cell region T and the second non-leukocyte region is the hyperfluorescent region H, the second non-leukocyte region H includes the first non-leukocyte region T.

[0069] Step S204: Obtain tumor cell information of the body fluid to be tested based on the scatter plot characteristics of the first non-leukocyte region and the scatter plot characteristics of the second non-leukocyte region.

[0070] In step S204, the presence of tumor cells in the body fluid sample to be tested can be determined based on the scatter plot characteristics of the first non-leukocyte region to obtain a first determination result; the presence of tumor cells in the body fluid sample to be tested can be determined based on the scatter plot characteristics of the second non-leukocyte region to obtain a second determination result; then, tumor cell information of the body fluid to be tested can be obtained based on the first determination result and the second determination result. Alternatively, the presence of tumor cells in the body fluid sample to be tested can be determined by comprehensively considering the scatter plot characteristics of the first non-leukocyte region and the scatter plot characteristics of the second non-leukocyte region to obtain tumor cell information.

[0071] By combining the two tumor cell identification methods described in Figures 3 and 4, the specificity and sensitivity of identifying tumor cells in body fluids using a blood cell analyzer can be greatly improved, thereby enabling reliable tumor screening at low cost using a blood cell analyzer.

[0072] For example, using the body fluid mode of the Mindray BC-6800Plus fully automated blood analyzer, the method provided in this embodiment of the invention was used to detect pleural and peritoneal effusions in 220 cases. Cell morphology was examined after slide staining of all body fluid samples. The detection efficacy of traditional high-fluorescence cell parameters and the improved algorithm for body fluid tumor cell parameters was compared. The results are shown in Table 1. Of the 220 body fluid samples, 61 were pathologically confirmed as tumor cell positive, and 159 were tumor cell negative.

[0073] Table 1. Results of traditional high-fluorescence cell parameter detection and results of tumor cell parameter detection according to the present invention.

[0074]

[0075] The new tumor cell parameters obtained using the improved algorithm eliminate interference from mesothelial cells and monocytes / macrophages. Analysis of the results shows that the sensitivity and specificity of traditional high-fluorescence cell parameter detection are 0.836 and 0.535, respectively, while the sensitivity and specificity of the method of this invention are 0.869 and 0.792, respectively. This indicates that the new tumor cell parameter detection using the improved algorithm has significantly higher specificity than traditional high-fluorescence cell parameter detection. In other words, this new tumor cell parameter has better diagnostic efficacy, enabling blood cell analyzers to be better used in clinical laboratories for screening and alarming of tumor cells in body fluids.

[0076] Here, tumor cell information may include tumor cell count. For example, when it is determined that tumor cells are present in the body fluid to be tested based on a first or second judgment result, the scattered points in the first or second non-leukocyte region are counted to obtain the tumor cell count.

[0077] Furthermore, the tumor cell information may also include a tumor cell ratio, which is the ratio of the tumor cell count to the nucleated cell count or hyperfluorescent cell count. Here, the sample analysis method 200 further includes obtaining the nucleated cell count or hyperfluorescent cell count of the body fluid sample to be tested based on the scattered light signal and the fluorescence signal, and then obtaining the tumor cell ratio based on the tumor cell count and the nucleated cell count or hyperfluorescent cell count.

[0078] Alternatively or additionally, tumor cell information may include tumor alarm information to alert the user that the body fluid sample to be tested may be a tumor sample. For example, based on characteristics such as the number, location, and degree of aggregation of scatter points in a first or second non-leukocyte region, a value indicating the probability of the presence of tumor cells is output, with a higher value indicating a greater probability of tumor cells being present.

[0079] In a specific embodiment, Figure 6 As shown, sample analysis method 200 includes:

[0080] Step S201: Obtain the scattered light signal and fluorescence signal generated by the optical detection device when the particles in the test body fluid sample after hemolysis and fluorescence staining pass through the sample.

[0081] Step S202: Generate a scatter plot of the body fluid sample to be tested based on the scattered light signal and the fluorescence signal;

[0082] Step S213: Identify the first non-leukocyte region in the scatter plot based on the scattered light signal and the fluorescence signal;

[0083] Step S214: When the scatter features in the first non-leukocyte region meet the first preset condition, obtain the tumor cell information of the body fluid sample to be tested based on the scattered light signal and fluorescence signal of the scatter points in the first non-leukocyte region.

[0084] Step S215: When the scatter features in the first non-leukocyte region do not meet the first preset condition, the second non-leukocyte region is identified in the scatter plot based on the scattered light signal and the fluorescence signal.

[0085] Step S216: When the scatter features of the second non-leukocyte region meet the second preset condition, the tumor cell information of the body fluid sample to be tested is obtained based on the scattered light signal and fluorescence signal of the scatter points in the second non-leukocyte region.

[0086] In this embodiment, it is preferable to first implement the method described in conjunction with Figure 3 (the first non-leukocyte region is the tumor cell region T), for example, first determining whether there are scattered clusters in the tumor cell region T, and then implement the method described in conjunction with Figure 4 (the second non-leukocyte region is the high-fluorescence region H), for example, determining whether there are scattered clusters in the high-fluorescence region H and analyzing the distribution morphology of the scattered points. Of course, in other embodiments, it is also possible to first implement the method described in conjunction with Figure 4 (the first non-leukocyte region is the high-fluorescence region H), and then implement the method described in conjunction with Figure 3 (the second non-leukocyte region is the tumor cell region T).

[0087] In some embodiments, when the first non-leukocyte region is a tumor cell region T and the second non-leukocyte region is a high-fluorescence region H, the first preset condition is that the scattered points in the first non-leukocyte region aggregate into clusters, that is, the first preset condition is that there are scattered point clusters (particle clusters) in the first non-leukocyte region. Those skilled in the art will understand that "aggregation" as used herein means that the number of scattered points in a certain region is greater than a preset number and the degree of aggregation of the scattered points is greater than a preset degree. In other words, when the number of scattered points in the tumor cell region T reaches a predetermined number and the degree of aggregation reaches a preset degree, and the scattered points aggregate into clusters, it indicates that tumor cells are present in the body fluid sample to be tested. The second preset condition is that the distribution pattern of the scattered points in the second non-leukocyte region meets a preset distribution pattern. That is, when the scattered points in the tumor cell region T do not aggregate into clusters, but the distribution pattern of the scattered points in the high-fluorescence region H meets the distribution of tumor cells, that is, when the scattered points in the high-fluorescence region H are scattered rather than extended, it indicates that tumor cells are present in the body fluid sample to be tested.

[0088] Furthermore, such as Figure 6 As shown, the sample analysis method 200 also includes the following steps:

[0089] Step S207: Identify the third non-leukocyte region, i.e., mesothelial cell region M, in the scatter plot based on the scattered light signal and the fluorescence signal;

[0090] Step S208: When the scatter characteristics of the third non-leukocyte region meet the third preset condition, the mesothelial cell information of the body fluid sample to be tested is obtained based on the scattered light signal and fluorescence signal of the scatter points in the third non-leukocyte region. The third preset condition, for example, is that the scatter points in the third non-leukocyte region aggregate into clusters; that is, when the number of scatter points in the mesothelial cell region M reaches a predetermined number and the degree of aggregation reaches a predetermined degree, and the scatter points aggregate into clusters, it indicates the presence of mesothelial cells in the body fluid sample to be tested.

[0091] Furthermore, such as Figure 6As shown, the sample analysis method 200 further includes step S217: when the scatter features of the second non-leukocyte region do not meet the second preset condition but meet the fourth preset condition, for example, when the scatter points of the second non-leukocyte region are clustered together and show a continuous extension, the mesothelial cell information of the body fluid sample to be tested is obtained based on the scattered light signal and fluorescence signal of the scatter points in the second non-leukocyte region.

[0092] This allows for the further identification of mesothelial cells in the body fluid sample being tested.

[0093] Here, mesothelial cell information may include mesothelial cell count and / or mesothelial cell proportion. When the scatter plot characteristics of the third non-leukocyte region meet a third preset condition, the scatter plots in the third non-leukocyte region are counted to obtain the mesothelial cell count. Similar to the tumor cell proportion, this mesothelial cell proportion is the ratio of the mesothelial cell count to the nucleated cell count or the hyperfluorescent cell count.

[0094] Furthermore, other particles, such as white blood cells, can also be identified simultaneously in sample analysis method 200. Sample analysis method 200 can be implemented in the white blood cell detection channel of a blood analyzer. For example, sample analysis method 200 includes the following steps:

[0095] White blood cell regions were identified in the scatter plot based on the scattered light signal and the fluorescence signal;

[0096] The white blood cell information of the body fluid sample to be tested is obtained based on the white blood cell region, and the white blood cell information includes white blood cell classification information and / or white blood cell count information.

[0097] In some specific embodiments, as shown in Figures 3 and 4, the steps of obtaining leukocyte information of the body fluid sample to be tested based on the leukocyte region include:

[0098] Based on the scattered light and fluorescence signals from the scattering points in the white blood cell region, the white blood cells in the tested body fluid sample are classified into lymphocyte populations, neutrophil populations, and monocyte / macrophage populations; or

[0099] Based on the scattered light and fluorescence signals of the scattered points in the white blood cell region, the white blood cells in the body fluid sample to be tested are classified into lymphocyte population, neutrophil population, monocyte / macrophage population and eosinophil population.

[0100] This allows the sample analysis method provided in this invention to be implemented in existing white blood cell detection channels (white blood cell classification channel or white blood cell counting channel), such as the DIFF channel of Mindray's blood analyzer, without the need to set up a separate channel, and white blood cell information, tumor cell information and mesothelial cell information can be obtained in a single test.

[0101] Furthermore, single-nuclear cell particle groups and / or multiple-nuclear cell particle groups can be distinguished based on the scattered light signal and the fluorescence signal. Even further, the single-nuclear cell particle groups and / or multiple-nuclear cell particle groups can be counted to obtain a single-nuclear cell count and single-nuclear cell proportion, or a multiple-nuclear cell count and multiple-nuclear cell proportion.

[0102] Particularly preferred is that the fluorescent dye used in the embodiments of the present invention is an asymmetric anthocyanin dye. Asymmetric anthocyanin dyes have the characteristics of good cell membrane permeability and strong nucleic acid binding specificity, thus enabling them to better distinguish leukocytes, mesothelial cells and tumor cells in terms of fluorescence signal.

[0103] Furthermore, embodiments of the present invention are particularly suitable for detecting tumor cells and / or mesothelial cells in cerebrospinal fluid, pleural effusion, and ascites.

[0104] In addition, such as Figure 7 As shown, a second embodiment of the present invention provides a sample analysis method 300 for obtaining mesothelial cell information of a body fluid sample to be tested. The sample analysis method 300 includes:

[0105] S301, acquire the scattered light signal and fluorescence signal generated by the optical detection device when particles in the test body fluid sample that has undergone hemolysis and fluorescence staining pass through it. Preferably, the scattered light signal includes a side-scattered light signal.

[0106] S302, Generate a scatter plot of the body fluid sample to be tested based on the scattered light signal and the fluorescence signal.

[0107] Steps S301 and S302 can be referred to steps S201 and S202 above.

[0108] S303, based on the scattered light signal and the fluorescence signal, identify the first non-leukocyte region M and the second non-leukocyte region H in the scatter plot.

[0109] The first non-leukocyte region can be the mesothelial cell region M, and the second non-leukocyte region can be the hyperfluorescent region H; or conversely, the first non-leukocyte region can be the hyperfluorescent region H, and the second non-leukocyte region can be the mesothelial cell region M. The mesothelial cell region M is a part of the hyperfluorescent region H.

[0110] S304, obtain the mesothelial cell information of the body fluid to be tested based on the scatter characteristics of the first non-leukocyte region and the scatter characteristics of the second non-leukocyte region.

[0111] In step S304, the presence of mesothelial cells in the body fluid sample to be tested can be determined based on the scatter plot characteristics of the first non-leukocyte region to obtain a first determination result; the presence of mesothelial cells in the body fluid sample to be tested can be determined based on the scatter plot characteristics of the second non-leukocyte region to obtain a second determination result; then, the mesothelial cell information of the body fluid to be tested can be obtained based on the first determination result and the second determination result. Alternatively, the presence of mesothelial cells in the body fluid sample to be tested can be determined by comprehensively considering the scatter plot characteristics of the first non-leukocyte region and the scatter plot characteristics of the second non-leukocyte region to obtain mesothelial cell information.

[0112] Other embodiments, features and advantages of the sample analysis method 300 provided by the second embodiment of the present invention can be found in the description of the sample analysis method 200 of the first embodiment of the present invention, and will not be repeated here.

[0113] like Figure 8 As shown, the third embodiment of the present invention also provides a sample analysis method 400 for obtaining tumor cell information and / or mesothelial cell information of a body fluid sample to be tested. The sample analysis method 400 includes:

[0114] S401, acquire the scattered light signal and fluorescence signal generated by the optical detection device when particles in the test body fluid sample after hemolysis and fluorescence staining pass through the sample;

[0115] S402, Generate a scatter plot of the body fluid sample to be tested based on the scattered light signal and the fluorescence signal;

[0116] S403, based on the scattered light signal and the fluorescence signal, identify a preset non-leukocyte region in the scatter plot;

[0117] S404, Obtain tumor cell information and / or mesothelial cell information of the body fluid to be tested based on the location characteristics and distribution morphology characteristics of the scatter clusters in the preset non-leukocyte region.

[0118] Similarly, steps S401 and S402 can refer to steps S201 and S202 described above. Preferably, the scattered light signal includes a side-scattered light signal.

[0119] Preferably, the preset non-leukocyte region is a high-fluorescence region H. By analyzing the location and distribution pattern of the scatter plots appearing in the high-fluorescence region H, tumor cells and / or mesothelial cells can be identified relatively accurately.

[0120] Furthermore, such as Figure 9 As shown, step S404 includes:

[0121] S404a, when the scattered points in the preset non-leukocyte region aggregate into a cluster and the fluorescence centroid of the scattered points is within the range of the first preset centroid, the tumor cell information of the body fluid sample to be tested is obtained based on the scattered light signal and fluorescence signal of the scattered points in the preset non-leukocyte region.

[0122] The first preset centroid range is set based on the tumor cell region T. For example, when there are clusters of scattered points in the preset non-leukocyte region, such as the high-fluorescence region H, if the fluorescence centroid of the scattered point cluster is high, it indicates that the scattered point cluster appears in the tumor cell region T of the high-fluorescence region H, and the scattered point cluster is the tumor cell cluster.

[0123] Furthermore, step S404 also includes:

[0124] S404b, when the scattered points in the preset non-leukocyte region do not cluster together and the distribution pattern of the scattered points conforms to a first predetermined distribution pattern, the tumor cell information of the body fluid sample to be tested is obtained based on the scattered light signal and fluorescence signal of the scattered points in the preset non-leukocyte region. In this case, the tumor cell information is specifically tumor alarm information, indicating the presence of tumor cells in the body fluid sample to be tested.

[0125] For example, if the scatter cluster in the preset non-leukocyte region does not cluster together, but the scatter cluster is scattered or the preset non-leukocyte region is a scattered region, then the scatter cluster contains tumor cells.

[0126] Furthermore, step S404 also includes:

[0127] When the scattered points in the preset non-leukocyte region cluster together and the fluorescence centroid of the scattered points is within the range of the second preset centroid, the mesothelial cell information of the body fluid sample to be tested is obtained based on the scattered light signal and fluorescence signal of the scattered points in the preset non-leukocyte region.

[0128] The second preset centroid range is set based on the mesothelial cell region M. For example, when there are clusters of scattered points in the preset non-leukocyte region, such as the high-fluorescence region H, if the fluorescence centroid of the scattered point cluster is low, it indicates that the scattered point cluster appears in the mesothelial cell region M of the high-fluorescence region H, and the scattered point cluster is a mesothelial cell cluster.

[0129] Furthermore, step S404 also includes:

[0130] When the scattered points in the preset non-leukocyte region cluster together and the distribution pattern of the scattered points conforms to the second predetermined distribution pattern, the mesothelial cell information of the body fluid sample to be tested is obtained based on the scattered light signal and fluorescence signal of the scattered points in the preset non-leukocyte region.

[0131] For example, when the scattered clusters in the preset non-leukocyte region aggregate into a group and the scattered clusters show a continuous extending trend, then the scattered clusters are mesothelial cell clusters.

[0132] In a specific example, the process first determines whether a cluster of scattered points exists within the predefined non-leukocyte region. If it exists, the location of the cluster within the predefined non-leukocyte region is determined, i.e., whether the cluster exists in the tumor cell region T or the mesothelial cell region M. If the cluster exists in the tumor cell region T, it is a tumor cell cluster; if it exists in the mesothelial cell region M, it is a mesothelial cell cluster. If no cluster of scattered points exists within the predefined non-leukocyte region, but the cluster is scattered, it suggests the presence of tumor cells. Furthermore, if a cluster of scattered points exists within the predefined non-leukocyte region, and this cluster spans both the tumor cell region T and the mesothelial cell region M, and exhibits a continuous extending trend, then this cluster is a mesothelial cell cluster.

[0133] Furthermore, the sample analysis method 400 also includes:

[0134] White blood cell regions were identified in the scatter plot based on the scattered light signal and the fluorescence signal;

[0135] The white blood cell information of the body fluid sample to be tested is obtained based on the white blood cell region, and the white blood cell information includes white blood cell classification information and / or white blood cell count information.

[0136] Other embodiments, features and advantages of the sample analysis method 400 provided in the third embodiment of the present invention can be found in the description of the sample analysis method 200 of the first embodiment of the present invention, and will not be repeated here.

[0137] In another aspect, the present invention provides a computer-readable storage medium having executable instructions stored thereon, which, when executed by a computer, cause the computer to implement one of the above-described sample analysis methods.

[0138] The aforementioned computer-readable storage medium can be volatile memory or non-volatile memory, or both. Non-volatile memory can be read-only memory, programmable read-only memory, erasable programmable read-only memory, electrically erasable programmable read-only memory, magnetic random access memory, flash memory, magnetic surface memory, optical disk, or read-only optical disk; magnetic surface memory can be disk storage or magnetic tape storage. Volatile memory can be random access memory used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory, synchronous static random access memory, dynamic random access memory, synchronous dynamic random access memory, double data rate synchronous dynamic random access memory, enhanced synchronous dynamic random access memory, synchronous linked dynamic random access memory, and direct memory bus random access memory. The memory described in the embodiments of the present invention is intended to include these and any other suitable types of memory.

[0139] All features or combinations of features mentioned above in the specification, drawings, and claims, as long as they are meaningful within the scope of this invention and do not contradict each other, can be used in any combination or individually. The advantages and features described with reference to the sample analysis methods provided in the various embodiments of this invention are accordingly applied to the sample analyzers and computer-readable storage media provided in the various embodiments of this invention, and vice versa.

[0140] The above description is merely a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural transformations made using the contents of the present invention's specification and drawings under the inventive concept of the present invention, or direct / indirect applications in other related technical fields, are included within the patent protection scope of the present invention.

Claims

1. A sample analyzer, characterized in that, The sample analyzer includes: A sampling device having a pipette with a pipette nozzle and a driving device for driving the pipette to quantitatively aspirate body fluid samples through the pipette nozzle; A sample preparation apparatus has at least one reaction chamber and a reagent supply unit, wherein the at least one reaction chamber is used to receive a body fluid sample aspirated by a sampling device, and the reagent supply unit provides a hemolytic reagent and a fluorescent reagent to the at least one reaction chamber, thereby mixing the body fluid sample aspirated by the sampling device with the hemolytic reagent and the fluorescent reagent provided by the reagent supply unit in the reaction chamber to prepare a body fluid sample to be tested; An optical detection device includes a light source, a flow chamber, a scattered light detector, and a fluorescence detector. The light source emits a light beam to illuminate the flow chamber, which is connected to a reaction cell, allowing particles in the test fluid sample to pass through the flow chamber one by one. The scattered light detector detects the scattered light signal generated by the particles passing through the flow chamber after being illuminated by light. The scattered light detector includes a side-scattering light detector. The fluorescence detector detects the fluorescence signal generated by the particles passing through the flow chamber after being illuminated by light. The processor is configured to perform the following steps: The scattered light signal and fluorescence signal of the body fluid sample to be tested are acquired from the optical detection device, wherein the scattered light signal includes a side-scattered light signal. A scatter plot of the body fluid sample to be tested is generated based on the side-scattered light signal and the fluorescence signal. The first non-leukocyte region and the second non-leukocyte region are identified in the scatter plot based on the side-scattered light signal and the fluorescence signal. The second non-leukocyte region is a high-fluorescence region, and the first non-leukocyte region is a part of the high-fluorescence region. When the scattered features in the first non-leukocyte region meet the first preset condition, the tumor cell information of the body fluid sample to be tested is obtained based on the lateral scattered light signal and fluorescence signal of the scattered points in the first non-leukocyte region. The first preset condition is that the scattered points in the first non-leukocyte region are clustered together. When the scatter features in the first non-leukocyte region do not meet the first preset condition and the scatter features in the second non-leukocyte region meet the second preset condition, the tumor cell information of the body fluid sample to be tested is obtained based on the lateral scattered light signal and fluorescence signal of the scatter points in the second non-leukocyte region. The second preset condition is that the scatter points in the second non-leukocyte region are scattered rather than extended; or Based on the side-scattered light signal and the fluorescence signal, a first non-leukocyte region and a second non-leukocyte region are identified in the scatter plot. The first non-leukocyte region is a high-fluorescence region, and the second non-leukocyte region is a part of the high-fluorescence region. When the scattered features in the first non-leukocyte region meet the first preset condition, the tumor cell information of the body fluid sample to be tested is obtained based on the lateral scattered light signal and fluorescence signal of the scattered points in the first non-leukocyte region. The first preset condition is that the scattered points in the first non-leukocyte region are scattered rather than extended. When the scatter features in the first non-leukocyte region do not meet the first preset condition and the scatter features in the second non-leukocyte region meet the second preset condition, the tumor cell information of the body fluid sample to be tested is obtained based on the lateral scattered light signal and fluorescence signal of the scatter points in the second non-leukocyte region. The second preset condition is that the scatter points in the second non-leukocyte region are clustered together.

2. The sample analyzer according to claim 1, characterized in that, The processor is also configured to perform the following steps: A third non-leukocyte region is identified in the scatter plot based on the side-scattered light signal and the fluorescence signal, and the third non-leukocyte region is part of the high-fluorescence region; When the scatter features of the third non-leukocyte region meet the third preset condition, the mesothelial cell information of the body fluid sample to be tested is obtained based on the lateral scattered light signal and fluorescence signal of the scatter points in the third non-leukocyte region. The third preset condition is that the scatter points in the third non-leukocyte region are clustered together.

3. The sample analyzer according to claim 1 or 2, characterized in that, The processor is also configured to: When the scatter features of the second non-leukocyte region do not meet the second preset condition but meet the fourth preset condition, the mesothelial cell information of the body fluid sample to be tested is obtained based on the lateral scattered light signal and fluorescence signal of the scatter points in the second non-leukocyte region. The second non-leukocyte region is a high-fluorescence region, and the first non-leukocyte region is a part of the high-fluorescence region. The second preset condition is that the scatter points of the second non-leukocyte region are scattered rather than extended. The fourth preset condition is that the scatter points of the second non-leukocyte region are clustered together and show a continuous extended shape.

4. The sample analyzer according to claim 1 or 2, characterized in that, The processor is also configured to perform the following steps: Leukocyte regions were identified in the scatter plot based on the side-scattered light signal and the fluorescence signal; The white blood cell information of the body fluid sample to be tested is obtained based on the white blood cell region, and the white blood cell information includes white blood cell classification information and / or white blood cell count information.

5. The sample analyzer according to claim 4, characterized in that, The processor is configured to, when obtaining leukocyte information of the body fluid sample to be tested based on the leukocyte region: Based on the lateral scattered light signal and fluorescence signal of the scattered points in the white blood cell region, the white blood cells in the body fluid sample to be tested are classified into lymphocyte population, neutrophil population, and monocyte / macrophage population. or Based on the lateral scattered light signal and fluorescence signal of the scattered points in the white blood cell region, the white blood cells in the body fluid sample to be tested are classified into lymphocyte population, neutrophil population, monocyte / macrophage population and eosinophil population.

6. The sample analyzer according to claim 1 or 2, characterized in that, The tumor cell information includes at least one of the following: tumor cell count, tumor cell percentage, and tumor cell alarm information.

7. The sample analyzer according to claim 1 or 2, characterized in that, The body fluid sample is cerebrospinal fluid, pleural effusion, or ascites.

8. The sample analyzer according to claim 1 or 2, characterized in that, The fluorescent reagent is an asymmetric anthocyanin dye.

9. A sample analyzer, characterized in that, The sample analyzer includes: A sampling device having a pipette with a pipette nozzle and a driving device for driving the pipette to quantitatively aspirate body fluid samples through the pipette nozzle; A sample preparation apparatus has at least one reaction chamber and a reagent supply unit, wherein the at least one reaction chamber is used to receive a body fluid sample aspirated by a sampling device, and the reagent supply unit provides a hemolytic reagent and a fluorescent reagent to the at least one reaction chamber, thereby mixing the body fluid sample aspirated by the sampling device with the hemolytic reagent and the fluorescent reagent provided by the reagent supply unit in the reaction chamber to prepare a body fluid sample to be tested; An optical detection device includes a light source, a flow chamber, a scattered light detector, and a fluorescence detector. The light source emits a light beam to illuminate the flow chamber. The flow chamber is connected to a reaction cell, and particles in the test fluid sample can pass through the flow chamber one by one. The scattered light detector detects the scattered light signal generated by the particles passing through the flow chamber after being illuminated. The fluorescence detector detects the fluorescence signal generated by the particles passing through the flow chamber after being illuminated. The processor is configured to perform the following steps: The scattered light signal and fluorescence signal of the body fluid sample to be tested are acquired from the optical detection device. A scatter plot of the body fluid sample to be tested is generated based on the scattered light signal and the fluorescence signal. Based on the scattered light signal and the fluorescence signal, a first non-leukocyte region and a second non-leukocyte region are identified in the scatter plot, wherein the first non-leukocyte region is a high-fluorescence region and the second non-leukocyte region is a part of the high-fluorescence region; When the scattered features in the first non-leukocyte region meet the first preset condition, the mesothelial cell information of the body fluid sample to be tested is obtained based on the lateral scattered light signal and fluorescence signal of the scattered points in the first non-leukocyte region. The first preset condition is that the scattered points in the first non-leukocyte region are clustered together and exhibit a continuous extension. When the scatter features in the first non-leukocyte region do not meet the first preset condition and the scatter features in the second non-leukocyte region meet the second preset condition, the mesothelial cell information of the body fluid sample to be tested is obtained based on the lateral scattered light signal and fluorescence signal of the scatter points in the second non-leukocyte region. The second preset condition is that the scatter points in the second non-leukocyte region aggregate into clusters; or Based on the scattered light signal and the fluorescence signal, a first non-leukocyte region and a second non-leukocyte region are identified in the scatter plot, wherein the second non-leukocyte region is a high-fluorescence region and the first non-leukocyte region is a part of the high-fluorescence region; When the scattered features in the first non-leukocyte region meet the first preset condition, the mesothelial cell information of the body fluid sample to be tested is obtained based on the lateral scattered light signal and fluorescence signal of the scattered points in the first non-leukocyte region. The first preset condition is that the scattered points in the first non-leukocyte region are clustered together. When the scatter features in the first non-leukocyte region do not meet the first preset condition and the scatter features in the second non-leukocyte region meet the second preset condition, the mesothelial cell information of the body fluid sample to be tested is obtained based on the lateral scattered light signal and fluorescence signal of the scatter points in the second non-leukocyte region. The second preset condition is that the scatter points in the second non-leukocyte region are clustered together and exhibit a continuous extension.

10. The sample analyzer according to claim 9, characterized in that, The processor is also configured to perform the following steps: White blood cell regions were identified in the scatter plot based on the scattered light signal and the fluorescence signal; The white blood cell information of the body fluid sample to be tested is obtained based on the white blood cell region, and the white blood cell information includes white blood cell classification information and / or white blood cell count information.

11. The sample analyzer according to claim 9 or 10, characterized in that, The mesothelial cell information includes at least one of mesothelial cell count and mesothelial cell ratio.

12. A sample analyzer, characterized in that, The sample analyzer includes: A sampling device having a pipette with a pipette nozzle and a driving device for driving the pipette to quantitatively aspirate body fluid samples through the pipette nozzle; A sample preparation apparatus has at least one reaction chamber and a reagent supply unit, wherein the at least one reaction chamber is used to receive a body fluid sample aspirated by a sampling device, and the reagent supply unit provides a hemolytic reagent and a fluorescent reagent to the at least one reaction chamber, thereby mixing the body fluid sample aspirated by the sampling device with the hemolytic reagent and the fluorescent reagent provided by the reagent supply unit in the reaction chamber to prepare a body fluid sample to be tested; An optical detection device includes a light source, a flow chamber, a scattered light detector, and a fluorescence detector. The light source emits a light beam to illuminate the flow chamber. The flow chamber is connected to a reaction cell, and particles in the test fluid sample can pass through the flow chamber one by one. The scattered light detector detects the scattered light signal generated by the particles passing through the flow chamber after being illuminated. The fluorescence detector detects the fluorescence signal generated by the particles passing through the flow chamber after being illuminated. The processor is configured to perform the following steps: The scattered light signal and fluorescence signal of the body fluid sample to be tested are acquired from the optical detection device. A scatter plot of the body fluid sample to be tested is generated based on the scattered light signal and the fluorescence signal. Based on the scattered light signal and the fluorescence signal, a predetermined non-leukocyte region is identified in the scatter plot. This predetermined non-leukocyte region is a high-fluorescence region. The tumor cell information and / or mesothelial cell information of the body fluid to be tested are obtained based on the positional characteristics and distribution morphology characteristics of the scatter clusters in the preset non-leukocyte region. Specifically, when the scatter clusters in the preset non-leukocyte region aggregate into a group and the fluorescence centroid of the scatter cluster is within the range of the first preset centroid, the tumor cell information of the body fluid sample to be tested is obtained based on the scattered light signal and fluorescence signal of the scatter points in the preset non-leukocyte region.

13. The sample analyzer according to claim 12, characterized in that, The processor is also configured to perform the following steps: When the scattered points in the preset non-leukocyte region do not cluster together and the distribution pattern of the scattered points is scattered, the tumor cell information of the body fluid sample to be tested is obtained based on the scattered light signal and fluorescence signal of the scattered points in the preset non-leukocyte region.

14. The sample analyzer according to claim 12 or 13, characterized in that, The processor is also configured to perform the following steps: When the scattered points in the preset non-leukocyte region cluster together and the fluorescence centroid of the scattered points is within the range of the second preset centroid, the mesothelial cell information of the body fluid sample to be tested is obtained based on the scattered light signal and fluorescence signal of the scattered points in the preset non-leukocyte region.

15. The sample analyzer according to claim 12 or 13, characterized in that, The processor is also configured to perform the following steps: When the scattered points in the preset non-leukocyte region cluster together and the distribution pattern of the scattered points shows a continuous extending trend, the mesothelial cell information of the body fluid sample to be tested is obtained based on the scattered light signal and fluorescence signal of the scattered points in the preset non-leukocyte region.

16. The sample analyzer according to claim 12 or 13, characterized in that, The processor is also configured to perform the following steps: White blood cell regions were identified in the scatter plot based on the scattered light signal and the fluorescence signal; The white blood cell information of the body fluid sample to be tested is obtained based on the white blood cell region, and the white blood cell information includes white blood cell classification information and / or white blood cell count information.

17. A sample analysis method for obtaining tumor cell information from a body fluid sample to be tested, characterized in that, The sample analysis method includes: The scattered light signal and fluorescence signal generated by the optical detection device after the particles in the test body fluid sample that has undergone hemolysis and fluorescence staining are obtained, and the scattered light signal includes the side-scattered light signal; A scatter plot of the body fluid sample to be tested is generated based on the side-scattered light signal and the fluorescence signal; The first non-leukocyte region and the second non-leukocyte region are identified in the scatter plot based on the side-scattered light signal and the fluorescence signal. The second non-leukocyte region is a high-fluorescence region, and the first non-leukocyte region is a part of the high-fluorescence region. When the scattered features in the first non-leukocyte region meet the first preset condition, the tumor cell information of the body fluid sample to be tested is obtained based on the lateral scattered light signal and fluorescence signal of the scattered points in the first non-leukocyte region. The first preset condition is that the scattered points in the first non-leukocyte region are clustered together. When the scatter features in the first non-leukocyte region do not meet the first preset condition and the scatter features in the second non-leukocyte region meet the second preset condition, the tumor cell information of the body fluid sample to be tested is obtained based on the lateral scattered light signal and fluorescence signal of the scatter points in the second non-leukocyte region. The second preset condition is that the scatter points in the second non-leukocyte region are scattered rather than extended; or Based on the side-scattered light signal and the fluorescence signal, a first non-leukocyte region and a second non-leukocyte region are identified in the scatter plot. The first non-leukocyte region is a high-fluorescence region, and the second non-leukocyte region is a part of the high-fluorescence region. When the scattered features in the first non-leukocyte region meet the first preset condition, the tumor cell information of the body fluid sample to be tested is obtained based on the lateral scattered light signal and fluorescence signal of the scattered points in the first non-leukocyte region. The first preset condition is that the scattered points in the first non-leukocyte region are scattered rather than extended. When the scatter features in the first non-leukocyte region do not meet the first preset condition and the scatter features in the second non-leukocyte region meet the second preset condition, the tumor cell information of the body fluid sample to be tested is obtained based on the lateral scattered light signal and fluorescence signal of the scatter points in the second non-leukocyte region. The second preset condition is that the scatter points in the second non-leukocyte region are clustered together.

18. The sample analysis method according to claim 17, characterized in that, The sample analysis method further includes: A third non-leukocyte region is identified in the scatter plot based on the side-scattered light signal and the fluorescence signal, and the third non-leukocyte region is part of the high-fluorescence region; When the scatter features of the third non-leukocyte region meet the third preset condition, the mesothelial cell information of the body fluid sample to be tested is obtained based on the lateral scattered light signal and fluorescence signal of the scatter points in the third non-leukocyte region. The third preset condition is that the scatter points in the third non-leukocyte region are clustered together.

19. The sample analysis method according to any one of claims 17-18, characterized in that the sample analysis method further comprises: When the scatter features of the second non-leukocyte region do not meet the second preset condition but meet the fourth preset condition, the mesothelial cell information of the body fluid sample to be tested is obtained based on the lateral scattered light signal and fluorescence signal of the scatter points in the second non-leukocyte region. The second non-leukocyte region is a high-fluorescence region, and the first non-leukocyte region is a part of the high-fluorescence region. The second preset condition is that the scatter points of the second non-leukocyte region are scattered rather than extended. The fourth preset condition is that the scatter points of the second non-leukocyte region are clustered together and show a continuous extended shape.

20. The sample analysis method according to any one of claims 17-18, characterized in that, The sample analysis method further includes: Leukocyte regions were identified in the scatter plot based on the side-scattered light signal and the fluorescence signal; The white blood cell information of the body fluid sample to be tested is obtained based on the white blood cell region, and the white blood cell information includes white blood cell classification information and / or white blood cell count information.

21. The sample analysis method according to any one of claims 17-18, characterized in that, The tumor cell information includes at least one of the following: tumor cell count, tumor cell percentage, and tumor cell alarm information.

22. The sample analysis method according to any one of claims 17-18, characterized in that, The fluorescent staining is achieved using asymmetric anthocyanin dyes.

23. A sample analysis method for obtaining mesothelial cell information of a body fluid sample to be tested, characterized in that, The sample analysis method includes: The scattered light signal and fluorescence signal generated by the optical detection device are obtained from the particles in the test body fluid sample after hemolysis and fluorescence staining. A scatter plot of the body fluid sample to be tested is generated based on the scattered light signal and the fluorescence signal; Based on the scattered light signal and the fluorescence signal, a first non-leukocyte region and a second non-leukocyte region are identified in the scatter plot, wherein the first non-leukocyte region is a high-fluorescence region and the second non-leukocyte region is a part of the high-fluorescence region; When the scattered features in the first non-leukocyte region meet the first preset condition, the mesothelial cell information of the body fluid sample to be tested is obtained based on the lateral scattered light signal and fluorescence signal of the scattered points in the first non-leukocyte region. The first preset condition is that the scattered points in the first non-leukocyte region are clustered together and exhibit a continuous extension. When the scatter features in the first non-leukocyte region do not meet the first preset condition and the scatter features in the second non-leukocyte region meet the second preset condition, the mesothelial cell information of the body fluid sample to be tested is obtained based on the lateral scattered light signal and fluorescence signal of the scatter points in the second non-leukocyte region. The second preset condition is that the scatter points in the second non-leukocyte region aggregate into clusters; or Based on the scattered light signal and the fluorescence signal, a first non-leukocyte region and a second non-leukocyte region are identified in the scatter plot, wherein the second non-leukocyte region is a high-fluorescence region and the first non-leukocyte region is a part of the high-fluorescence region; When the scattered features in the first non-leukocyte region meet the first preset condition, the mesothelial cell information of the body fluid sample to be tested is obtained based on the lateral scattered light signal and fluorescence signal of the scattered points in the first non-leukocyte region. The first preset condition is that the scattered points in the first non-leukocyte region are clustered together. When the scatter features in the first non-leukocyte region do not meet the first preset condition and the scatter features in the second non-leukocyte region meet the second preset condition, the mesothelial cell information of the body fluid sample to be tested is obtained based on the lateral scattered light signal and fluorescence signal of the scatter points in the second non-leukocyte region. The second preset condition is that the scatter points in the second non-leukocyte region are clustered together and exhibit a continuous extension.

24. The sample analysis method according to claim 23, characterized in that, The sample analysis method further includes: White blood cell regions were identified in the scatter plot based on the scattered light signal and the fluorescence signal; The white blood cell information of the body fluid sample to be tested is obtained based on the white blood cell region, and the white blood cell information includes white blood cell classification information and / or white blood cell count information.

25. The sample analysis method according to claim 23 or 24, characterized in that, The mesothelial cell information includes at least one of mesothelial cell count and mesothelial cell ratio.

26. A sample analysis method for obtaining tumor cell information and / or mesothelial cell information of a body fluid sample to be tested, characterized in that, The sample analysis method includes: The scattered light signal and fluorescence signal generated by the optical detection device are obtained from the particles in the test body fluid sample after hemolysis and fluorescence staining. A scatter plot of the body fluid sample to be tested is generated based on the scattered light signal and the fluorescence signal; Based on the scattered light signal and the fluorescence signal, a predetermined non-leukocyte region is identified in the scatter plot, and the predetermined non-leukocyte region is a high-fluorescence region. The tumor cell information and / or mesothelial cell information of the body fluid to be tested are obtained based on the positional characteristics and distribution morphology characteristics of the scatter clusters in the preset non-leukocyte region. Specifically, when the scatter clusters in the preset non-leukocyte region aggregate into a group and the fluorescence centroid of the scatter cluster is within the range of the first preset centroid, the tumor cell information of the body fluid sample to be tested is obtained based on the scattered light signal and fluorescence signal of the scatter points in the preset non-leukocyte region.

27. The sample analysis method according to claim 26, characterized in that, Obtaining tumor cell information and / or mesothelial cell information of the test fluid based on the location and distribution characteristics of the scatter plots in the preset non-leukocyte region also includes: When the scattered points in the preset non-leukocyte region do not cluster together and the distribution pattern of the scattered points is scattered, the tumor cell information of the body fluid sample to be tested is obtained based on the scattered light signal and fluorescence signal of the scattered points in the preset non-leukocyte region.

28. The sample analysis method according to claim 26 or 27, characterized in that, Obtaining tumor cell information and / or mesothelial cell information of the test fluid based on the location and distribution characteristics of the scatter plots in the preset non-leukocyte region also includes: When the scattered points in the preset non-leukocyte region cluster together and the fluorescence centroid of the scattered points is within the range of the second preset centroid, the mesothelial cell information of the body fluid sample to be tested is obtained based on the scattered light signal and fluorescence signal of the scattered points in the preset non-leukocyte region.

29. The sample analysis method according to claim 26 or 27, characterized in that, Obtaining tumor cell information and / or mesothelial cell information of the test fluid based on the location and distribution characteristics of the scatter plots in the preset non-leukocyte region also includes: When the scattered points in the preset non-leukocyte region cluster together and the distribution pattern of the scattered points shows a continuous extending trend, the mesothelial cell information of the body fluid sample to be tested is obtained based on the scattered light signal and fluorescence signal of the scattered points in the preset non-leukocyte region.

30. The sample analysis method according to claim 26 or 27, characterized in that, The sample analysis method further includes: White blood cell regions were identified in the scatter plot based on the scattered light signal and the fluorescence signal; The white blood cell information of the body fluid sample to be tested is obtained based on the white blood cell region, and the white blood cell information includes white blood cell classification information and / or white blood cell count information.

31. A computer-readable storage medium having stored thereon executable instructions, which, when executed by a computer, cause the computer to implement the sample analysis method according to any one of claims 17 to 30.

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