Method for typing immune cells based on single cell metabolite atlas
By obtaining and screening single-cell metabolites profile data, the difficulties of flow cytometry in detecting single-cell metabolites were solved, and the fine segmentation and counting of immune cells was achieved, which reduced costs and expanded its clinical testing field. application.
Patent Information
- Application Number
- CN202410800649.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-06-20
- Publication Date
- 2024-12-17
AI Technical Summary
Existing flow cytometry has difficulties in detecting single-cell metabolites, and it is impossible to directly analyze metabolites in cells, resulting in the inability to effectively perform cell functional typing, and relying on expensive labeled antibodies, limiting its application in the field of clinical testing. .
By obtaining metabolic substance profile data of individual immune cells, high abundance and differential metabolites are screened out, and this information is used to typing and counting immune cells, avoiding the labeling process of cells.
The direct analysis of single-cell metabolites is realized, which can accurately distinguish different types of immune cells from their subtypes, reduces detection costs, improves detection efficiency, and has important clinical application prospects.
Smart Images
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Abstract
Description
Technical Field
[0001] The invention relates to a method for typing immune cells based on a single-cell metabolite profile, and belongs to the technical field of biological analysis. Background Art
[0002] Flow cytometry (FCM) is a high-throughput cell analysis technology developed in the 1970s. It has played an important role in basic disciplines and clinical research such as immunology, hematology, oncology, cell biology, and cytogenetics. In addition, flow cytometry has become a method for clinical laboratory testing to type and count immune cells. Fluorescence flow cytometry recognizes proteins on the cell surface through fluorophore-labeled antibodies and uses different fluorescence detection channels to achieve multi-component analysis. However, the overlap of fluorophore emission spectra limits its ability to analyze multiple components and requires complex compensation calculations, resulting in the current fluorescence flow cytometry being able to simultaneously detect parameters of more than a dozen channels. In order to achieve simultaneous analysis of more components, mass spectrometry flow cytometry came into being. Mass spectrometry flow cytometry uses stable isotopes as markers instead of fluorescent markers, and uses inductively coupled plasma mass spectrometry (ICP-MS) analyzers instead of optical detectors, which can completely avoid the problem of spectral peak overlap between fluorescence spectra, greatly improve the detection throughput, and can simultaneously analyze more than 50 marker substances in a single cell, so that cells can be more finely typed. However, both the classic fluorescence flow cytometer and the latest mass spectrometry flow cytometer use labeled antibodies to identify proteins on the cell surface. The need to use a large number of expensive labeled antibodies not only increases the cost of testing, but also makes the entire testing process complicated and lengthy, which seriously restricts the application and promotion of flow cytometry in the field of clinical testing.
[0003] Metabolites in cells are the final product of the life process and are the downstream information collection of the central dogma. Small changes at the gene level may bring about huge changes in metabolites, so metabolites can most intuitively reflect cell phenotypes. In particular, in recent years, people have discovered that the metabolic process of immune cells is closely related to specific immune functions and plays an important role in the occurrence, development and treatment of tumors. Therefore, if the metabolites in cells are used as the basis for cell typing, cells can be typed more precisely and accurately, thereby promoting the development of life sciences and clinical medicine.
[0004] However, there are many types of metabolites in cells, and they are all small molecules that are difficult to label. Traditional flow cytometers are not suitable for the analysis of metabolites in single cells. At present, metabolite information can only be obtained indirectly by detecting metabolite-related proteins in single cells with traditional flow cytometers. There is an urgent need for technology that can directly analyze single-cell metabolites. How to use the detected metabolite information to functionally type cells is a problem currently facing the development of this technology. Traditional cell typing is based on proteins or other biomolecules that can be labeled on the cell surface or inside. These biomolecules have clear biological functions, and after a long period of development, a set of cell typing marker systems have been formed, such as the CD series. However, there is still no clear marker system for cell typing based on metabolites, which restricts the development of flow cytometry based on metabolite detection. Summary of the invention
[0005] The purpose of the present invention is to provide a method for typing immune cells in blood based on single-cell metabolite mass spectrometry, which can detect a large number of metabolites from a single cell, and use the detected metabolite information to type different types of cells; the information of single-cell metabolites can be used to distinguish different types of immune cells, and even the cell subtypes of the same immune cell; and based on the information of single-cell metabolites, the immune cells can be typed and counted.
[0006] The method provided by the present invention for screening differential metabolites for mammalian immune cell typing based on single-cell metabolite mass spectrometry includes the following steps:
[0007] S1. Obtaining mass spectrometry data of metabolite ions in individual immune cells belonging to different classes;
[0008] S2, comparing the mass spectrometry data of the metabolite ions obtained in S1 with the mammalian metabolite database to determine the metabolite ions belonging to cellular metabolites as candidate metabolite molecules;
[0009] S3. Screening out metabolite molecules with high abundance and differences between different types of immune cells from the candidate metabolite molecules by statistical methods as differential metabolites for distinguishing the different types of immune cells.
[0010] The S1 step also includes retaining ion peaks with an appearance frequency greater than a certain proportion among the metabolite ions of the different types of immune cells for subsequent steps.
[0011] In step S2, the accurate mass of the metabolite ions is compared with the mammalian metabolite database.
[0012] In step S3 of the present invention, high abundance refers to sorting the relative intensities of the substances and selecting the substances at the front of the sequence, and difference refers to using methods such as ANOVA test to select the substances with the strongest difference.
[0013] Preferably, the candidate metabolite molecules include glycerophospholipids and sphingolipids.
[0014] The present invention also provides a method for typing mammalian immune cells, comprising: typing mammalian immune cells using differential metabolites screened by the method;
[0015] Preferably, the mammalian immune cells are derived from a body fluid sample, preferably a blood sample, of said mammal.
[0016] The present invention further provides a method for calculating the ratio of various types of immune cells in a mammalian sample to be tested, comprising the following steps:
[0017] 1) Screening out differential metabolites for typing various types of immune cells in the mammalian sample to be tested by the method;
[0018] 2) obtaining the differential metabolite data of single cells of various immune cells in the mammalian sample to be tested;
[0019] 3) According to the degree of clustering of different types of immune cells, the regions of various types of immune cells are divided on a two-dimensional plane;
[0020] 4) reducing the dimension of the differential metabolite data obtained in step 2) and projecting them onto the same two-dimensional plane, determining each cell in the mammalian sample to be tested as an immune cell of the corresponding type in the region according to the region in which it is located in the two-dimensional plane, and counting each type of immune cell;
[0021] 5) according to the counting results in step 4), the number of each type of immune cells and the total number of all immune cells are obtained, and the proportion of each type of immune cells to the total number of all immune cells is calculated;
[0022] The sample is a body fluid sample, preferably a blood sample;
[0023] The mammal can be an animal selected from bovines, equines, felines, canines, lagomorphs, swine, camelids, rodents and primates, including but not limited to cattle, horses, goats, sheep, cats, rabbits, pigs, camels, alpacas, rats, mice, guinea pigs, non-human primates (such as apes, monkeys, baboons, orangutans) and humans, preferably cattle, horses, dogs, goats, sheep, pigs, camels, rats, mice, monkeys and humans. Preferably, the mammal is an experimental animal, including but not limited to mice, rats, rabbits, guinea pigs, hamsters, monkeys, dogs, cats, pigs, sheep, horses, etc.
[0024] Experiments have shown that the method of the present invention has good stability.
[0025] Based on a large number of previous studies on single-cell metabolite mass spectrometry, this invention provides a flow cytometry with a new detection principle. This technology uses metabolites detected in single cells as biomarkers for cell differentiation. It does not require any antibodies to label or stain cells, thus avoiding the use of expensive antibodies and cumbersome labeling processes. This technology has important application prospects in the fields of clinical medical testing, immunology, cell biology, etc., and is an important supplement and development to existing flow cytometry. BRIEF DESCRIPTION OF THE DRAWINGS
[0026] Figure 1 The types of all metabolites detected in PBMCs, among which the major types are glycerophospholipids, phospholipids and fatty acyl, and the minor types are amino acids, peptides and analogs, organic nitrogen-containing compounds, glycerides, steroids and steroid derivatives and nucleotides.
[0027] Figure 2 Violin plots of the relative intensities of phosphorylcholine, ADP, PE (P-34:2) and PE (P-34:1) in T, B and NK cells, reflecting the differences in the contents of the four substances in the three types of cells.
[0028] Figure 3 This is a dimensionality reduction analysis diagram of T, B, NK and monocytes.
[0029] Figure 4 This is a dimensionality reduction analysis diagram of T, B, NK and mixed cells. The composition of the mixed cells is predicted by the proportion of the mixed cells falling into the three clusters of T, B and NK. DETAILED DESCRIPTION
[0030] Unless otherwise specified, the experimental methods used in the following examples are conventional methods.
[0031] Unless otherwise specified, the materials and reagents used in the following examples can be obtained from commercial sources.
[0032] Example 1: Immune cell typing in human peripheral blood
[0033] Human peripheral blood mononuclear cells (PBMCs) are mainly composed of three types of lymphoid cells (T cells, B cells, NK cells) and one type of myeloid cells (monocytes).
[0034] Mass spectrometry data of individual immune cells (T cells, B cells, NK cells, and monocytes) were obtained using organic mass spectrometry. After screening the raw data and subtracting the signal-to-noise ratio, ion peaks with a frequency greater than 20% in at least one group of cells were retained. By comparing the accurate mass numbers with the human metabolite database, 191 metabolites in PBMCs were identified. Figure 1 The metabolites identified were mainly glycerophospholipids and sphingolipids, and a variety of amino acids and nitrogen-containing organic compounds were also detected in the small molecule segment.
[0035] Through fold change analysis (FC) and T test (p), log2(FC) greater than 1, log 10 Substances with (p) greater than 1.30103 are considered to have significant differences, and the differential metabolites among immune cells are screened out. This example conducted a comparative study on three types of lymphocytes. The four most significant differences in lymphocyte metabolites were phosphorylcholine ([M+H] + 184.0739)、ADP([M+H] + 428.0367)、PE(P-34:2)([M+H] + 700.5276) and PE(P-34:1)([M+H] + 702.5432). The distribution of these four metabolites in lymphocytes is as follows Figure 2 shown.
[0036] In this example, the screened differential metabolites were used to perform unified manifold approximation and projection (UMAP) dimensionality reduction analysis on T cells, B cells, NK cells and monocytes. The four types of cells can be well separated, such as Figure 3 As shown, it is proved that the method of the present invention can be applied to both myeloid cells and lymphoid cells.
[0037] In addition, by Figure 3 It can be seen that the differences between the three lymphocytes are small, while the differences between lymphocytes and monocytes are large, and there are also differences within lymphocytes, which is consistent with biological facts. Therefore, differential metabolites can serve as the basis for immune cell classification, which lays a theoretical foundation for the method of the present invention to achieve immune cell typing and counting based on single cell metabolite profile analysis.
[0038] Example 2: Calculation of the proportion of immune cells in human peripheral blood using metabolite typing
[0039] The present invention provides a method for calculating the proportion of various immune cells in human peripheral blood using metabolite typing:
[0040] (1) Based on the typing method of Example 1, single-cell metabolite data of various immune cells in human peripheral blood were collected, differential metabolites were screened, and the metabolites were projected into a two-dimensional plane after dimensionality reduction;
[0041] (2) According to the degree of clustering of each type of cells, the area belonging to the cell cluster is divided on the plane;
[0042] (3) The same metabolite data is collected for the sample to be tested and projected on the same plane with reduced dimension. Each cell in the sample to be tested is determined to be a cell of the corresponding class according to its position in the plane, thereby completing the detection of the cell composition of the sample to be tested;
[0043] (4) Divide the predicted number by the total number of cells to calculate the cell ratio, as follows:
[0044] T cells, B cells and NK cells obtained by flow cytometry sorting were mixed in proportion to simulate the mixed state of immune cells in the blood. In this embodiment, mass spectrometry was performed on the three types of cells and the mixed sample mixed in the ratio of T:B:NK=30:25:45. After data processing, 751 T cells, 499 B cells, 606 NK cells and 303 mixed cells were obtained respectively.
[0045] This example uses the screened metabolites to perform data dimensionality reduction on these four groups of cells, and predicts the composition of each cell in the mixed sample based on the region where it falls. The results are as follows: Figure 4 As shown in Table 1. The calculated results of T cells, B cells and NK cells were 27.06% to 31.35%, 22.77% to 27.06% and 41.25% to 45.54%, respectively, and the error ranges were -2.94% to 1.35%, -2.23% to 2.06% and -3.75% to 0.54%, respectively. The above results preliminarily show the ability of the method of the present invention to perform label-free ratio counting of lymphocytes. The experiment was repeated in this embodiment, and the errors were within ± 5%.
[0046] In order to apply the method of the present invention to clinical practice, its feasibility and stability need to be verified. The method of the present invention extends the research object from lymphocytes to immune cells, and directly calculates the percentage of cells in PBMCs instead of mixed samples, which proves the feasibility of the method of the present invention.
[0047] This example quickly separates PBMCs from whole blood of volunteers, which is closer to clinical conditions than mixed samples. This example uses single cell data of 628 T cells, 484 B cells and 758 NK cells to analyze the composition of 479 PBMCs. The error range of T cells is -5.40% to -0.09%, the error range of B cells is 0.00% to 3.54%, and the error range of NK cells is 0.97% to 3.04%, as shown in Table 1. This example repeated the experiment, and the errors were all within ±6%.
[0048] In addition, the method of the present invention has good stability. The single-cell data obtained in the previous experiment was used as a data set to study the mixed samples obtained on different dates. 546 T cells, 484 B cells, and 500 NK cells were used to predict 195 mixed cells. The error of T cells was -3.85% to 1.79%, the error of B cells was -9.10% to -4.49%, and the error of NK cells was 1.15% to 6.28%, which proves that the method of the present invention has high stability, as shown in Table 1. The experiment was repeated in this example, and the error was within ±10%.
[0049] This example further changes the research object to PBMCs, thereby completely simulating the situation of clinical research. 583 T cells, 491 B cells, and 492 NK cells were used to predict 332 PBMCs on different days. The error of T cells was -8.89% to -4.54%, the error of B cells was -2.23% to 1.71%, and the error of NK cells was 5.87% to 9.79%, as shown in Table 1. All results are highly close to the FCM results, which proves the stability of the method of the present invention in clinical applications.
[0050] Table 1 Calculation results of immune cell ratio
[0051]
[0052]
Claims
1. A method for screening differential metabolites for mammalian immune cell typing based on single-cell metabolite mass spectrometry, comprising the following steps: S1. Obtaining mass spectrometry data of metabolite ions in individual immune cells belonging to different classes; S2, comparing the mass spectrometry data of the metabolite ions obtained in S1 with the mammalian metabolite database to determine the metabolite ions belonging to cellular metabolites as candidate metabolite molecules; S3. Screening out metabolite molecules with high abundance and differences between different types of immune cells from the candidate metabolite molecules by statistical methods as differential metabolites for distinguishing the different types of immune cells.
2. The method according to claim 1, It is characterized in that The S1 step also includes retaining ion peaks with an appearance frequency greater than a specific ratio among the metabolite ions of the different types of immune cells for subsequent steps.
3. The method according to claim 1, It is characterized in that In step S2, the accurate mass of the metabolite ions is compared with the mammalian metabolite database.
4. The method according to claim 1, It is characterized in that The candidate metabolite molecules include glycerophospholipids and sphingolipids.
5. A method for typing mammalian immune cells, include: The differential metabolites screened by the method of claim 1 are used to type mammalian immune cells.
6. The method according to any one of claims 1 to 5, It is characterized in that The mammalian immune cells are obtained from a body fluid sample, preferably a blood sample, of said mammal.
7. A method for calculating the proportion of various types of immune cells in a mammalian sample to be tested, The following steps are involved: 1) Screening out differential metabolites for typing various types of immune cells in the mammalian sample to be tested by the method according to any one of claims 1 to 4; 2) obtaining the differential metabolite data of single cells of various immune cells in the mammalian sample to be tested; 3) According to the degree of clustering of different types of immune cells, the regions of various types of immune cells are divided on a two-dimensional plane; 4) reducing the dimension of the differential metabolite data obtained in step 2) and projecting them onto the same two-dimensional plane, determining each cell in the mammalian sample to be tested as an immune cell of the corresponding type in the region according to the region in which it is located in the two-dimensional plane, and counting each type of immune cell; 5) According to the counting results in step 4), the number of each type of immune cells and the total number of all immune cells are obtained, and the proportion of each type of immune cells to the total number of all immune cells is calculated.
8. The method according to claim 7, It is characterized in that The sample is a body fluid sample, preferably a blood sample.
9. The method according to any one of claims 1 to 8, It is characterized in that The immune cells include myeloid cells and lymphoid cells.
10. The method according to claim 9, It is characterized in that The immune cells include any combination of two or more types of T cells, B cells, NK cells and monocytes.