White blood cell classified counting device and method for acute respiratory infection patients
By combining fluorescent staining and laser processing, white blood cells are screened and classified, solving the problem of accuracy in white blood cell classification and counting under different growth conditions, and enabling more precise white blood cell detection in patients with acute respiratory infections.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- 北京怀柔医院
- Filing Date
- 2026-03-02
- Publication Date
- 2026-05-12
AI Technical Summary
The existing technology does not take into account the influence of cells in different growth states, resulting in insufficient accuracy of white blood cell differential counting, especially in the detection of patients with acute respiratory infections, which may lead to errors.
The intensity of different components of single cells was obtained by fluorescence staining. Combined with the intensity of scattered light from laser treatment, white blood cells were screened out, and cycle groups were divided and clustered. Red blood cell fragments were screened out based on the fluorescence intensity of the cell membrane and internal structure. Cell subpopulations were merged, and the growth status was determined by the fluorescence intensity of nuclear DNA. The final counting result was determined by combining the cell number distribution of multiple single-cell suspension samples.
It improves the accuracy of white blood cell differential counting, reduces interference from red blood cell debris, and achieves more accurate white blood cell classification and counting, especially for the detection of patients with acute respiratory infections.
Smart Images
Figure CN122016612A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of white blood cell counting technology, and more specifically to a white blood cell differential counting device and method for patients with acute respiratory infections. Background Technology
[0002] White blood cell differential count is a routine blood test performed on patients with acute respiratory infections. It typically helps doctors determine if an infection or inflammation is present in the patient's body. This test assesses the patient's immune system status by analyzing the number of different types of white blood cells in the blood, including neutrophils, lymphocytes, and monocytes. Flow cytometry is commonly used for white blood cell differential counts in patients with acute respiratory infections. This technology performs detection with single-cell precision. Through built-in laser and fluorescence signal modules, it acquires information such as the volume and internal particle complexity of each single cell in the blood sample, and then clusters this multidimensional parameter information to achieve a white blood cell differential count.
[0003] In actual blood scenarios, white blood cells may be in different cell cycles. Even for the same type of white blood cells, their size and internal structural components may differ depending on their growth state. When cell classification is obtained directly based on information such as cell volume and particle complexity without considering the influence of different growth states, the accuracy of cell classification results may be insufficient, which may further lead to insufficient accuracy in the classification and counting of white blood cells in patients. Summary of the Invention
[0004] To address the technical problem that existing technologies, which fail to consider the influence of different cell growth states, may result in insufficient accuracy in cell classification, leading to inaccurate white blood cell differential counting in patients, the present invention aims to provide a white blood cell differential counting device and method for patients with acute respiratory infections. The specific technical solution adopted is as follows: This invention provides a method for white blood cell differential counting in patients with acute respiratory infections, the method comprising: Two or more single-cell suspension samples were collected from the antecubital vein blood of patients with acute respiratory tract infection; the fluorescence intensity of different components of the single cells was obtained by fluorescence staining, including: cell membrane, nuclear DNA, endoplasmic reticulum, mitochondria and Golgi apparatus; the forward and side-scattered light intensity of the single cells was obtained by laser treatment. Based on the deviation of fluorescence intensity of individual cells on the cell membrane and the number of types of fluorescent components, white blood cells in each single-cell suspension sample were screened from all cells. In each single-cell suspension sample, leukocytes are divided into cycle groups based on the similarity of fluorescence intensity of nuclear DNA among leukocytes. Within each cycle group, leukocytes are clustered according to the distribution and aggregation of all leukocytes between forward and side-scattered light intensities to obtain a predetermined number of cell subpopulations. For cell subpopulations between any two cycle groups, they are merged one-to-one based on the similarity of fluorescence intensity among endoplasmic reticulum, mitochondria, and Golgi apparatus to obtain the cell category for each single-cell suspension sample. The count results for each cell category were determined by the distribution of cell number among all single-cell suspension samples.
[0005] Furthermore, the method for obtaining leukocytes from each single-cell suspension sample includes: For any given cell, the probability of cell membrane damage is obtained based on the degree of deviation of the fluorescence intensity corresponding to the cell membrane in the single-cell suspension sample. By negatively mapping the number of components exhibiting fluorescence intensity in the cell, the probability of structural loss in the cell can be obtained. The cell activity response rate is obtained by combining the cell membrane damage probability and structural loss probability of the cell; cells with an activity response rate greater than a preset activity threshold are classified as white blood cells.
[0006] Furthermore, the method for obtaining the probability of cell membrane damage includes: The average fluorescence intensity of the cell membrane of all cells in the single-cell suspension sample containing the cell is taken as the overall cell membrane intensity of the sample. The difference between the fluorescence intensity of the cell membrane and the overall cell membrane intensity of the sample is used as the probability of cell membrane damage.
[0007] Furthermore, the method for obtaining the periodic group includes: The fluorescence intensity corresponding to the nuclear DNA of each leukocyte was normalized to obtain the growth trend degree of each leukocyte; the difference in growth trend degree between two leukocytes was negatively correlated to obtain the growth approximation degree between the two leukocytes. Two white blood cells with a growth similarity greater than a preset periodic threshold are considered as a periodic group. Each white blood cell in the periodic group satisfies that there is at least one group of white blood cells with a growth similarity greater than the preset periodic threshold. When there is a group of white blood cells with a growth similarity greater than the preset periodic threshold among two or more periodic groups, the periodic group corresponding to the largest growth similarity is taken as the periodic group of the corresponding white blood cell.
[0008] Furthermore, the method for obtaining the cell subpopulation includes: A two-dimensional space will be established with forward scattered light intensity as the horizontal axis and side scattered light intensity as the vertical axis. For any periodicity group, all white blood cells in the periodicity group are mapped to a two-dimensional space to obtain a scatter plot of the periodicity group; the white blood cells in the scatter plot of the periodicity are clustered into a predetermined number of clusters using a clustering algorithm, and each cluster is a cell subpopulation.
[0009] Furthermore, the method for obtaining the cell type includes: For any periodic group, other periodic groups are successively treated as groups to be merged. For any cell subpopulation in the cycle group, calculate the approximate fluorescence intensity between the cell subpopulation and each cell subpopulation in the group to be merged in the endoplasmic reticulum, mitochondria and Golgi apparatus, and obtain the homogeneity of the cell subpopulation and each cell subpopulation in the group to be merged. The cell subpopulation is merged with the cell subpopulation with the highest homogeneity in the group to be merged into a new cell subpopulation; each cell subpopulation after merging the cycle group with all other cycle groups is taken as each cell category.
[0010] Furthermore, the method for obtaining the similarity tendency degree includes: The mean fluorescence intensity of all leukocytes in each common component within a cell subpopulation is taken as the component intensity of that cell subpopulation in each common component; common components include: endoplasmic reticulum, mitochondria, and Golgi apparatus; Each unmerged cell subpopulation in the target population is sequentially selected as the analysis subpopulation. The differences between the cell subpopulation and the analysis subpopulation in terms of the component intensity of each common component are calculated. The sum of all differences is then calculated and a negative correlation mapping is performed to obtain the homogeneity of the cell subpopulation and the analysis subpopulation.
[0011] Furthermore, the method for determining the counting results for each cell category includes: The cell categories in each single-cell suspension sample are sorted in ascending order based on the average forward scatter intensity of all white blood cells in the cell category, resulting in a category sequence for each single-cell suspension sample; each number under all category sequences is considered a final cell category. For any given sequence number, the classification confidence of each single-cell suspension sample under that sequence number is obtained based on the deviation between the number of cells of each cell category under that sequence number and the number of cells of the overall cell category under the same sequence number. The single-cell suspension sample with the highest classification confidence under that serial number is taken as the reliable sample; the number of cells of the cell class in the reliable sample under that serial number is taken as the final number of cells of the corresponding cell class. The final number of each cell type is the percentage of all white blood cells in the total number of cells, which is used as the count result for each cell type.
[0012] Furthermore, the method for obtaining the classification confidence score includes: The mean number of cells corresponding to all categories of sequences under that sequence number is taken as the mean number of cells for that sequence number. The difference between the number of cells and the mean number of cells in each cell category under that sequence number in each single-cell suspension sample is negatively correlated and normalized to obtain the classification confidence of each single-cell suspension sample under that sequence number.
[0013] The present invention also provides a white blood cell differential counting device for patients with acute respiratory infections, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of a white blood cell differential counting method for patients with acute respiratory infections as described in any of the above.
[0014] The present invention has the following beneficial effects: This invention first considers the presence of disruptive contaminants. Leukocytes are screened based on cell membrane fluorescence and the types of components detected. Cellular activity is assessed through cell membrane permeability analysis and intracellular structural integrity analysis. This cell activity analysis then filters out interfering factors such as red blood cell fragments, resulting in pure leukocytes and improving the accuracy of leukocyte analysis. Next, the fluorescence intensity of nuclear DNA within the cells determines the cell cycle state. Multiple cell populations at the same cycle state are grouped based on similarity, providing an initial classification of leukocyte growth states. Further analysis of the scattered light characteristics within individual cell populations at the same cycle state identifies cell subpopulations. This initial classification across different growth states improves accuracy. Then, cell subpopulations from each pair of cycle groups are merged based on differences in components to obtain a single-cell suspension sample's leukocyte classification, yielding more precise results. Finally, the cell number distribution from multiple single-cell suspension samples is used to obtain the final leukocyte count, improving accuracy through convergence among multiple samples. This invention combines the classification and merging of white blood cells under different growth states to improve the accuracy of white blood cell classification, thereby obtaining more accurate white blood cell differential count results for patients with acute respiratory infections. Attached Figure Description
[0015] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0016] Figure 1 This is a flowchart of a white blood cell differential counting method for patients with acute respiratory tract infections, provided as an embodiment of the present invention. Figure 2 This is a schematic diagram illustrating the principle of flow cytometry according to an embodiment of the present invention; Figure 3 This is a schematic diagram of a scatter plot of a periodic group provided in one embodiment of the present invention. Detailed Implementation
[0017] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a white blood cell differential counting device and method for patients with acute respiratory infections proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.
[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.
[0019] The following detailed description, in conjunction with the accompanying drawings, illustrates a specific scheme for a white blood cell differential counting device and method for patients with acute respiratory infections provided by the present invention.
[0020] Flow cytometry is commonly used to perform white blood cell differential counting in venous blood from the patient's elbow. Please refer to [link to relevant documentation]. Figure 2 The illustration shows a schematic diagram of the principle of flow cytometry provided by an embodiment of the present invention. First, a single-cell suspension is prepared from the antecubital vein blood of a patient with respiratory tract infection. Then, the single-cell suspension is encapsulated by a sheath flow to form single cells and transmitted to an optical system. The optical system collects the forward and side scattered light and various fluorescence signals of each single cell. The forward and side scattered light represent the volume and internal particle complexity information of the single cell, respectively. The fluorescence signal is used to detect the expression of specific components in the cell. For example, the mitochondrial membrane potential can be labeled by JC-1. Then, the fluorescence intensity of the single cell reflects the expression intensity of the mitochondria in the cell.
[0021] Since flow cytometry typically does not consider the influence of cell growth, it can introduce some error into the counting analysis. Therefore, this embodiment of the invention incorporates cell growth status into the counting analysis. Please refer to [link to relevant documentation]. Figure 1 The diagram illustrates a flowchart of a white blood cell differential counting method for patients with acute respiratory infections, provided by an embodiment of the present invention. The method includes the following steps: S1: Collect two or more single-cell suspension samples from the antecubital vein of patients with acute respiratory tract infection; obtain the fluorescence intensity of different components of the single cell by fluorescence staining, including: cell membrane, nuclear DNA, endoplasmic reticulum, mitochondria and Golgi apparatus; obtain the forward and side-scatter light intensity of the single cell by laser treatment.
[0022] First, venous blood is collected from the elbow vein of the target acute respiratory infection patient, and a single-cell suspension is prepared from the venous blood. In this embodiment of the invention, the number of single-cell suspension samples prepared from a single venous blood collection is selected as 5. The specific number can be adjusted by the implementer according to the specific implementation situation.
[0023] The single-cell suspension is then introduced into a flow cytometer, which sorts the cells through a sheath fluid system, allowing each cell to be transported to an optical system. The laser system in the optical system processes the data and collects the forward and side-scattered light intensity of each cell in the single-cell suspension.
[0024] Furthermore, a fluorescence signaling system was used to perform fluorescence staining on each single cell in the single-cell suspension to obtain fluorescence intensity data for different components. In this embodiment of the invention, for the cell membrane of a single cell, fluorescence intensity was obtained by staining the cell membrane of the cell in the suspension with BBcellProbe™ E42 fluorescent dye, which can reflect the integrity of the cell membrane; fluorescence intensity was obtained by staining the nuclear DNA of the cell in the suspension with DAPI fluorescent dye; fluorescence intensity was obtained by staining the mitochondrial membrane potential of the cell in the suspension with JC-10 fluorescent dye; fluorescence intensity was obtained by staining the endoplasmic reticulum of the cell in the suspension with ER-Tracker Green fluorescent dye; and fluorescence intensity was obtained by staining the Golgi apparatus of the cell in the suspension with Golgi-Tracker Green II fluorescent dye. It should be noted that laser and fluorescence treatment are well-known techniques to those skilled in the art and will not be described in detail here.
[0025] Finally, the forward and lateral light intensity data of all single cells in each single-cell suspension sample of the patient, as well as the fluorescence intensity data of various common components of white blood cells, were transferred to the data acquisition system for subsequent analysis.
[0026] This completes the preliminary preparations and data collection.
[0027] S2: Based on the deviation of fluorescence intensity of individual cells on the cell membrane and the number of types of fluorescent components, white blood cells in each single-cell suspension sample are screened from all cells.
[0028] If the centrifugation rate is not properly selected when preparing the single-cell suspension, large red blood cell fragments in the single-cell suspension may be misidentified as white blood cells in the patient during classification and counting. Considering that the patient's single-cell suspension contains a large number of white blood cells and a small number of red blood cell fragments, red blood cell fragments are often easily misidentified as white blood cells during white blood cell classification, causing white blood cell classification errors. Therefore, it is necessary to distinguish between the two by their distinguishing characteristics.
[0029] Preferably, in this embodiment of the invention, the method for obtaining white blood cells from each single-cell suspension sample includes: Considering that red blood cell fragments have lost their cellular activity compared to white blood cells, and their membrane structure has been damaged during centrifugation, their membrane permeability is relatively high. Fluorescent dyes used to detect cell membrane integrity can easily enter the cell and bind to intracellular proteins, emitting strong fluorescence. However, for severely damaged red blood cell fragments, only incomplete membrane structures remain. As a result, cytoprotein fluorescent dyes will only bind to a small amount of membrane proteins on the cell membrane, resulting in extremely low cytoprotein fluorescence intensity.
[0030] Therefore, compared to leukocytes, the fluorescence intensity of cytoproteins in erythrocyte fragments exhibits a bipolar pattern. For leukocytes, the permeability of the intact leukocyte cell membrane is relatively poor, so the fluorescent dye is difficult to enter the cell and only fluoresces by binding to proteins on the cell membrane. In order to distinguish between erythrocyte fragments and leukocytes, the permeability of the cell membrane of a single cell can be assessed.
[0031] First, for any given cell, the probability of cell membrane damage is determined by the degree of deviation of the cell's fluorescence intensity corresponding to its cell membrane from that of the single-cell suspension sample. A higher degree of deviation indicates a higher probability of cell membrane damage. In this embodiment of the invention, the average fluorescence intensity corresponding to the cell membrane of all cells in the single-cell suspension sample is taken as the overall cell membrane intensity of that cell in the sample. The difference between the fluorescence intensity of the cell's cell membrane and the overall cell membrane intensity of the sample is taken as the probability of cell membrane damage. The higher the deviation between the fluorescence intensity of a single cell's cell membrane and the average intensity, the more significant the polarization of fluorescence intensity, and the higher the probability that the cell is a red blood cell with a damaged cell membrane.
[0032] Furthermore, considering that the internal components of inactivated red blood cell fragments have been destroyed, their intracellular structural integrity is inferior to that of white blood cells. Therefore, based on cell membrane permeability analysis, combined with intracellular structural integrity analysis, the cell activity of individual cells is obtained, and then the white blood cell population to be analyzed is screened out.
[0033] Furthermore, by negatively mapping the number of fluorescent components in the cell, the probability of structural loss of the cell can be obtained. Since inactivated red blood cell fragments have a more damaged membrane structure than white blood cells, there may be loss of internal components. Therefore, the number of fluorescent components will be incomplete. Thus, the fewer the number of components, the worse the structural integrity.
[0034] Finally, by combining the probability of cell membrane damage and the probability of structural loss, the cell's activity response is obtained. In this embodiment of the invention, the product of the probability of cell membrane damage and the probability of structural loss is negatively correlated and normalized to obtain the cell's activity response. The smaller the probability of cell membrane damage and the probability of structural loss, that is, the larger the activity response, the more likely the cell is to be an intact leukocyte, and the more significant the activity characterization.
[0035] It should be noted that negative correlation mapping and normalization are both techniques well known to those skilled in the art. Negative correlation mapping can take the form of inverse proportional or negative exponential, and the choice of normalization can be linear normalization or standard normalization, etc. The specific method is not limited here.
[0036] Cells with an activity response greater than a preset activity threshold are ultimately classified as white blood cells. In this embodiment of the invention, the preset activity threshold is set to 0.36. Cells with an activity response lower than the preset activity threshold are identified as red blood cell fragments. The specific value can be adjusted by the implementer.
[0037] S3: In each single-cell suspension sample, leukocytes are divided into cycle groups based on the similarity of fluorescence intensity of nuclear DNA among leukocytes; within each cycle group, leukocytes are clustered according to the distribution and aggregation of all leukocytes between forward and side-scattered light intensities to obtain a preset number of cell subpopulations; for cell subpopulations between any two cycle groups, they are merged one-to-one according to the similarity of fluorescence intensity among endoplasmic reticulum, mitochondria, and Golgi apparatus to obtain the cell category of each single-cell suspension sample.
[0038] After screening leukocytes from single-cell suspension samples, different leukocytes may be in different stages of cell growth. Even leukocytes of the same type may have certain differences in properties at different growth stages. Therefore, this step divides all leukocyte populations into multiple cell populations in the same cycle state based on cell gene expression. Then, for a single cell population in the same cycle state, further differentiation is made based on the differences in volume and internal particle complexity of different types of leukocytes to obtain multiple cell subpopulations of a single cell population in the same cycle state. Finally, cell subpopulations in different cell populations in different cycle states are merged based on the expression of various common intracellular components to obtain the leukocyte classification results of a single suspension sample.
[0039] Based on the fact that white blood cells exhibit DNA replication during growth and division, the higher the nuclear gene expression intensity of white blood cells, i.e. the stronger the nuclear gene fluorescence intensity, the closer the white blood cell is to the mature stage. When the fluorescence intensity of expression between cells is closer, it reflects that the growth state of the two cells is closer to the same stage. Therefore, the growth cycle is initially divided and clustered based on the similarity of nuclear DNA fluorescence intensity.
[0040] Preferably, in this embodiment of the invention, the method for obtaining the periodic group includes: First, the fluorescence intensity corresponding to the nuclear DNA of each leukocyte is normalized to obtain the growth trend degree of each leukocyte, reflecting the growth status of each leukocyte. The difference in growth trend degree between two leukocytes is negatively correlated to obtain the growth approximation degree between the two leukocytes. The greater the growth approximation degree, the closer the cells are in their growth cycle.
[0041] By grouping two white blood cells with a growth similarity greater than a preset cycle threshold into one cycle group, multiple cell groups in the same cycle state can be obtained. In this embodiment of the invention, the preset cycle threshold is set to 0.88; if the value is greater than the threshold, the two white blood cells are considered to be in the same growth cycle.
[0042] Simultaneously, each leukocyte in the cycle group satisfies the condition that there is at least one group of leukocytes with a growth similarity greater than a preset cycle threshold. In other words, a single leukocyte only needs to satisfy a growth similarity greater than the cycle threshold with any leukocyte in its cycle group. Furthermore, when a leukocyte has a growth similarity greater than the preset cycle threshold with two or more cycle groups, the cycle group corresponding to the largest growth similarity is selected as the cycle group for that leukocyte. That is, if a leukocyte has a growth similarity greater than the cycle threshold with some leukocytes in multiple cycle groups, then when selecting a cycle group for that leukocyte, the cycle group corresponding to the largest growth similarity with that leukocyte is selected.
[0043] The main partitioning process may include: initializing all white blood cells to an unassigned state; selecting the first unassigned white blood cell and creating a new cycle group; for the next unassigned white blood cell, calculating its highest growth similarity to any white blood cell in the existing cycle groups; if the highest growth similarity exceeds a threshold, adding the white blood cell to that cycle group; otherwise, creating a new cycle group; if the highest growth similarity of the white blood cell with multiple cycle groups exceeds the threshold, selecting the cycle group with the highest growth similarity; repeating the analysis of unassigned white blood cells until all white blood cells are assigned.
[0044] Further analysis of the scattered light characteristics of individual cycle groups was conducted to classify cell subpopulations. The forward scattered light intensity of each leukocyte was proportional to the cell volume, and the lateral scattered light intensity was proportional to the complexity of the internal particles of the cell. Clustering was performed on the two-dimensional spatial distribution of the forward and lateral scattered light intensities of leukocytes.
[0045] Preferably, in this embodiment of the invention, the method for obtaining cell subpopulations includes: First, a two-dimensional space is established with forward scattered light intensity on the horizontal axis and side scattered light intensity on the vertical axis. For any periodicity group, all white blood cells in that periodicity group are mapped onto the two-dimensional space to obtain a scatter plot of that periodicity group. Please refer to [link to relevant documentation]. Figure 3 The diagram shows a schematic of a scatter plot of a periodic group provided in an embodiment of the present invention.
[0046] When identifying white blood cells in patients with acute respiratory infections, the white blood cell categories to be identified are divided into five categories: neutrophils, eosinophils, basophils, lymphocytes, and monocytes. There are significant differences in the volume and intracellular granule complexity among different types of white blood cells at the same growth stage, while the characteristic differences among white blood cells of the same type are relatively small.
[0047] Therefore, in this embodiment of the invention, the preset number of categories is set to 5. The white blood cells in the scatter plot of this period are further clustered into the preset number of clusters using a clustering algorithm. Each cluster is a cell subpopulation, and a single cell subpopulation represents a single type of white blood cell. It should be noted that the clustering algorithm is a technique well-known to those skilled in the art, such as K-means clustering, and is not limited here.
[0048] Each cycle group is further divided into cell subpopulations, specifically five subpopulations representing different leukocyte species within each cycle group. These subpopulations can then be merged over two weeks to obtain the final classification. For two cell subpopulations within different cycle groups, since the fluorescence intensity differences of common components of the same leukocyte species are relatively small across different growth stages, while the fluorescence intensity differences of common components of different leukocyte species are significant across different growth stages, subpopulations of the same category can be merged based on the similar fluorescence intensity of common components of each leukocyte, such as the endoplasmic reticulum, mitochondria, and Golgi apparatus.
[0049] Preferably, in this embodiment of the invention, the method for obtaining cell types includes: For any given periodic group, other periodic groups are successively designated as groups to be merged, and a merging analysis is performed on each of these other periodic groups sequentially. For any cell subpopulation within this periodic group, the approximate fluorescence intensity between the cell subpopulation and each cell subpopulation in the group to be merged is calculated sequentially to obtain the similarity tendency between the cell subpopulation and each cell subpopulation in the group to be merged. By sequentially calculating the approximate similarity of common components between two cell subpopulations, a possible merging analysis is performed.
[0050] In this embodiment of the invention, the average fluorescence intensity of all white blood cells in each common component is taken as the component intensity of the cell subpopulation in each common component. Common components include: endoplasmic reticulum, mitochondria and Golgi apparatus. The components are judged to be of the same category based on the approximation between the average fluorescence intensity of each component.
[0051] Each unmerged cell subpopulation in the target subpopulation is sequentially selected as the analysis subpopulation, ensuring that only subsequent unmerged subpopulations are analyzed after merging. The differences in component intensity between the cell subpopulation and the analysis subpopulation for each common component are calculated. The sum of all differences is then used for negative correlation mapping to obtain the homotropy between the cell subpopulation and the analysis subpopulation. The smaller the overall difference between each component, the more significant the characteristic similarity between the two subpopulations. As an example, the expression for homotropy is: In the formula, Represented as the th in this periodic group The cell subpopulation and the first in the group to be merged Homotropy among individual cell subpopulations This represents the total number of common ingredients, which is 3 in this embodiment of the invention. Represented as the th in this periodic group The first cell subpopulation The component strength of a common ingredient This represents the first element in the group to be merged. The first cell subpopulation The component strength of a common ingredient This is represented as an adjustment coefficient, which is set to 0.001 in this embodiment of the invention. The purpose of this is to prevent the formula from being meaningless when the denominator is zero.
[0052] Furthermore, this cell subpopulation is merged with the cell subpopulation with the highest homotropy in the target group to form a new cell subpopulation. The merging under the highest homotropy is characterized by merging white blood cell subpopulations identified as belonging to the same category at different growth cycle stages. The homotropy between the merged cell subpopulations is no longer analyzed between the subpopulations during the two cycles, meaning that each cell subpopulation participates in the merging only once during the two cycles, and the total number of all new cell subpopulations obtained during the two cycles is also 5.
[0053] All cycles are merged to obtain a cell category classification that combines different growth cycle states. Each cell subgroup after merging this cycle group with all other cycle groups is taken as each cell category, which is to obtain all cell categories in a single cell suspension sample.
[0054] S4: Determine the count results for each cell category by analyzing the cell number distribution across all single-cell suspension samples.
[0055] The above steps obtain the five-part differential white blood cell results for all single-cell suspension samples of the patient. At this point, the results of all samples can be integrated for counting. However, when the flow cytometer performs single-cell classification of the patient's white blood cell combination, there may be multiple cell adhesions, which may lead to some errors in some classification data. Therefore, the cell number distribution of cell categories is used to determine a more reliable cell detection quantity analysis for the final counting result.
[0056] If the number of white blood cells in a suspension sample under each category is less different from the number of white blood cells in multiple suspension samples under the same category, it indicates a higher confidence level for that type of white blood cell. Therefore, the number of cells in the final category is determined and counted based on the confidence level of the cell number distribution.
[0057] Preferably, in this embodiment of the invention, the method for determining the counting results for each cell type includes: First, the cell categories in each single-cell suspension sample are sorted in ascending order based on the average forward scattering intensity of all white blood cells in that cell category, resulting in a category sequence for each single-cell suspension sample. After classifying all samples, the sorting order is determined by the forward scattering intensity, using the same index. In this embodiment of the invention, the cells are sorted according to the forward scattering intensity, and cell categories in the same order are considered to be the same category. Therefore, each number in all category sequences is used as a final cell category, and the number of the five corresponding categories is finally determined.
[0058] For any given sequence number, the classification confidence of each single-cell suspension sample is obtained based on the deviation between the number of cells of each cell category under that sequence number and the number of cells of the overall cell category under the same sequence number. Under the same sequence number, the closer the number of cells in the sample is to the overall mean, the higher the confidence of the classification and the lower the chance of adhesion error.
[0059] In this embodiment of the invention, the mean number of cells corresponding to all categories of sequences under that sequence number is taken as the mean number of cells for that sequence number. The difference between the number of cells of each single-cell suspension sample and the mean number of cells for that cell category under that sequence number is negatively correlated and normalized to obtain the classification confidence of each single-cell suspension sample under that sequence number. The smaller the difference, the greater the confidence level.
[0060] Furthermore, the single-cell suspension sample with the highest classification confidence under that serial number is taken as the reliable sample. The number of cells of the cell category of the reliable sample under that serial number is taken as the final number of the final cell category corresponding to that serial number. The number of cells detected by the reliable sample has the highest optimization degree, and the number under the corresponding serial number can represent the final number of cells of the category corresponding to the serial number.
[0061] After obtaining the optimal detection quantity for each category, a counting analysis is performed, and the final quantity of each cell category is taken as the proportion of the total number of white blood cells, which is used as the counting result for each final cell category.
[0062] In this embodiment of the invention, the final counting results can be transmitted and displayed. Based on the characteristic trends of neutrophils, eosinophils, basophils, lymphocytes and monocytes in terms of volume and particle complexity, the white blood cell types in the final classification and counting results of patients with acute respiratory tract infection are matched and corresponding. The matched and corresponding results can be transmitted and displayed.
[0063] In summary, this invention first considers the presence of disruptive contaminants. It first screens leukocytes based on cell membrane fluorescence and the types of components detected. Cellular activity is assessed through cell membrane permeability analysis and intracellular structural integrity analysis. This cell activity analysis then filters out interfering factors such as red blood cell fragments, resulting in leukocytes and improving the accuracy of leukocyte analysis. Next, the fluorescence intensity of nuclear DNA within the cell determines the cell cycle state. Multiple cell populations at the same cycle state are grouped based on similarity, providing an initial classification of leukocyte growth states. Further analysis of the scattered light characteristics within individual cell populations at the same cycle state identifies cell subpopulations. This initial classification at different growth states improves the accuracy of classification. Then, cell subpopulations from each pair of cycle groups are merged based on differences in components to obtain the leukocyte classification for a single-cell suspension sample, yielding more precise results. Finally, based on the cell number distribution of multiple single-cell suspension samples, the final leukocyte count is obtained, improving the accuracy of the count by considering the convergence of multiple samples. This invention combines the classification and merging of white blood cells under different growth states to improve the accuracy of white blood cell classification, thereby obtaining more accurate white blood cell differential count results for patients with acute respiratory infections.
[0064] The present invention also provides a white blood cell differential counting device for patients with acute respiratory infections, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of a white blood cell differential counting method for patients with acute respiratory infections as described in any of the above.
[0065] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
[0066] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.
Claims
1. A method for differential counting of white blood cells in patients with acute respiratory infections, characterized in that, The method includes: Two or more single-cell suspension samples were collected from the antecubital vein blood of patients with acute respiratory tract infection; the fluorescence intensity of different components of the single cells was obtained by fluorescence staining, including: cell membrane, nuclear DNA, endoplasmic reticulum, mitochondria and Golgi apparatus; the forward and side-scattered light intensity of the single cells was obtained by laser treatment. Based on the deviation of fluorescence intensity of individual cells on the cell membrane and the number of types of fluorescent components, white blood cells in each single-cell suspension sample were screened from all cells. In each single-cell suspension sample, leukocytes are divided into cycle groups based on the similarity of fluorescence intensity of nuclear DNA among leukocytes. Within each cycle group, leukocytes are clustered according to the distribution and aggregation of all leukocytes between forward and side-scattered light intensities to obtain a predetermined number of cell subpopulations. For cell subpopulations between any two cycle groups, they are merged one-to-one based on the similarity of fluorescence intensity among endoplasmic reticulum, mitochondria, and Golgi apparatus to obtain the cell category for each single-cell suspension sample. The count results for each cell category were determined by the distribution of cell number among all single-cell suspension samples.
2. The white blood cell differential counting method for patients with acute respiratory infections according to claim 1, characterized in that, The method for obtaining white blood cells from each single-cell suspension sample includes: For any given cell, the probability of cell membrane damage is obtained based on the degree of deviation of the fluorescence intensity corresponding to the cell membrane in the single-cell suspension sample. By negatively mapping the number of components exhibiting fluorescence intensity in the cell, the probability of structural loss in the cell can be obtained. The cell activity response rate is obtained by combining the cell membrane damage probability and structural loss probability of the cell; cells with an activity response rate greater than a preset activity threshold are classified as white blood cells.
3. The white blood cell differential counting method for patients with acute respiratory infections according to claim 2, characterized in that, The method for obtaining the probability of cell membrane damage includes: The average fluorescence intensity of the cell membrane of all cells in the single-cell suspension sample containing the cell is taken as the overall cell membrane intensity of the sample. The difference between the fluorescence intensity of the cell membrane and the overall cell membrane intensity of the sample is used as the probability of cell membrane damage.
4. The white blood cell differential counting method for patients with acute respiratory infections according to claim 1, characterized in that, The method for obtaining the periodic group includes: The fluorescence intensity corresponding to the nuclear DNA of each leukocyte was normalized to obtain the growth trend degree of each leukocyte; the difference in growth trend degree between two leukocytes was negatively correlated to obtain the growth approximation degree between the two leukocytes. Two white blood cells with a growth similarity greater than a preset periodic threshold are considered as a periodic group. Each white blood cell in the periodic group satisfies that there is at least one group of white blood cells with a growth similarity greater than the preset periodic threshold. When there is a group of white blood cells with a growth similarity greater than the preset periodic threshold among two or more periodic groups, the periodic group corresponding to the largest growth similarity is taken as the periodic group of the corresponding white blood cell.
5. The white blood cell differential counting method for patients with acute respiratory tract infections according to claim 1, characterized in that, The method for obtaining the cell subpopulation includes: A two-dimensional space will be established with forward scattered light intensity as the horizontal axis and side scattered light intensity as the vertical axis. For any periodicity group, all white blood cells in the periodicity group are mapped to a two-dimensional space to obtain a scatter plot of the periodicity group; the white blood cells in the scatter plot of the periodicity are clustered into a predetermined number of clusters using a clustering algorithm, and each cluster is a cell subpopulation.
6. The white blood cell differential counting method for patients with acute respiratory infections according to claim 1, characterized in that, The methods for obtaining the cell types include: For any periodic group, other periodic groups are successively treated as groups to be merged. For any cell subpopulation in the cycle group, calculate the approximate fluorescence intensity between the cell subpopulation and each cell subpopulation in the group to be merged in the endoplasmic reticulum, mitochondria and Golgi apparatus, and obtain the homogeneity of the cell subpopulation and each cell subpopulation in the group to be merged. The cell subpopulation is merged with the cell subpopulation with the highest homogeneity in the group to be merged into a new cell subpopulation; each cell subpopulation after merging the cycle group with all other cycle groups is taken as each cell category.
7. The white blood cell differential counting method for patients with acute respiratory tract infections according to claim 6, characterized in that, The methods for obtaining the similarity tendency include: The mean fluorescence intensity of all leukocytes in each common component within a cell subpopulation is taken as the component intensity of that cell subpopulation in each common component; common components include: endoplasmic reticulum, mitochondria, and Golgi apparatus; Each unmerged cell subpopulation in the target population is sequentially selected as the analysis subpopulation. The differences between the cell subpopulation and the analysis subpopulation in terms of the component intensity of each common component are calculated. The sum of all differences is then calculated and a negative correlation mapping is performed to obtain the homogeneity of the cell subpopulation and the analysis subpopulation.
8. The white blood cell differential counting method for patients with acute respiratory tract infections according to claim 1, characterized in that, The method for determining the counting results for each cell category includes: The cell categories in each single-cell suspension sample are sorted in ascending order based on the average forward scatter intensity of all white blood cells in the cell category, resulting in a category sequence for each single-cell suspension sample; each number under all category sequences is considered a final cell category. For any given sequence number, the classification confidence of each single-cell suspension sample under that sequence number is obtained based on the deviation between the number of cells of each cell category under that sequence number and the number of cells of the overall cell category under the same sequence number. The single-cell suspension sample with the highest classification confidence under that serial number is taken as the reliable sample; the number of cells of the cell class in the reliable sample under that serial number is taken as the final number of cells of the corresponding cell class. The final number of each cell type is the percentage of all white blood cells in the total number of cells, which is used as the count result for each cell type.
9. A method for white blood cell differential counting in patients with acute respiratory infections according to claim 8, characterized in that, The method for obtaining the classification confidence score includes: The mean number of cells corresponding to all categories of sequences under that sequence number is taken as the mean number of cells for that sequence number. The difference between the number of cells and the mean number of cells in each cell category under that sequence number in each single-cell suspension sample is negatively correlated and normalized to obtain the classification confidence of each single-cell suspension sample under that sequence number.
10. A white blood cell differential counting device for patients with acute respiratory infections, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the white blood cell differential counting method for patients with acute respiratory tract infections as described in any one of claims 1 to 9.