High-throughput antigen-specific T cell detection method based on mass spectrum flow cytometry

Abstract

The invention discloses a high-throughput antigen-specific T cell detection method based on mass spectrum flow cytometry. The invention belongs to the technical field of biology, and particularly relates to a high-throughput antigen-specific T cell detection method based on mass spectrum flow cytometry. According to the present invention, the activation induction marker (AIM) technology and the intracellular cytokine staining (ICS) are integrated, and the CyTOF platform is combined to achieve gt; 20 parameters are synchronously detected, and multiple activation-induction marker double positive markers, multifunctional cytokine spectrums (IFN-gamma, TNF-alpha, GZMB, IL-2 and the like) and immune memory phenotypes (CD45RA / CCR7) are covered. The standardized detection scheme provides a high-dimensional and quantifiable biomarker combination for vaccine protection efficacy evaluation, and has important clinical application value.

Description

Technical Field

[0001] This invention belongs to the field of biotechnology, specifically relating to a high-throughput antigen-specific T-cell detection method based on mass flow cytometry. Background Technology

[0002] Antigen-specific T cells, as core effector cells of adaptive immunity, are of significant value for quantitative detection in vaccine efficacy assessment and immune monitoring. Current detection systems have significant technical limitations: while the traditional Elispot method can analyze T cell functional characteristics, its single-parameter detection mode cannot meet the needs of multidimensional immune analysis; although flow cytometry (CyTOF) has overcome the technical bottleneck of simultaneous detection of 40+ parameters, it still lacks standardized protocols for antigen-specific T cell identification. It is worth noting that clinical decision-making relies excessively on antibody titer as a substitute indicator, and the time-limited nature of antibody responses may lead to biases in immunization strategy formulation. Therefore, establishing a quantifiable and standardized T cell detection system has become an urgent need for vaccine development and immune assessment.

[0003] The World Health Organization (WHO) guidelines point out that quantitative detection of antigen-specific T cells faces multiple standardization challenges, including: methodological differences in PBMC isolation, types of stimulants (whole protein / peptide libraries), combinations of co-stimulatory factors (anti-CD28 / CD40, etc.), culture system parameters (cell density, culture medium composition), and the selection of detection biomarkers. The interaction of these variables leads to a significant reduction in data comparability across different research platforms (CV > 35%). Therefore, a standardized and quantifiable detection method is urgently needed to measure T cell responses to different pathogens, which can be used to determine the protective efficacy of vaccines and past infections.

[0004] ELISpot, as the gold standard for antigen-specific T cell detection, boasts high sensitivity but cannot acquire information on cell subsets. In contrast to ELISpot, intracellular cytokine staining (ICS) can achieve CD4+ detection via multicolor flow cytometry. + / CD8 + T cell subset differentiation and co-expression analysis of multifunctional cytokines. Activation-induced markers (AIM) detection is a method that recognizes and detects antigen-specific T cells by upregulating surface molecules after TCR stimulation.

[0005] In COVID-19 vaccine-related research, CD4 + T cell responses are typically assessed by detecting the co-expression of CD134 and CD137 in whole blood or peripheral blood mononuclear cells; while CD8... +T cell antigen-specific recognition largely depends on the co-expression of CD69 and CD137. In addition, activation markers such as CD25, PD-L1, CD200, CD40L, CD107a, and IFN-γ have been widely used in numerous studies. Selecting appropriate activation-inducing markers is crucial for accurate detection of antigen-specific T cells and assessment of vaccine immunogenicity. In the detection of activation-inducing markers and intracellular cytokines, the duration of antigen stimulation is a key variable, significantly affecting not only marker expression levels but also the specific timing of stimulation, which depends on the antigen type and the specific marker. However, the precise expression dynamics of different activation-inducing markers on SARS-CoV-2-specific T cells remain a research gap. Furthermore, there is currently a lack of systematic comparisons of the performance of different AIM combinations in T cell response detection, which to some extent limits the comparability and data integration between different research results. Summary of the Invention

[0006] The main problem that this invention aims to solve is that current methods for detecting antigen-specific T cells cannot be standardized and quantified due to the heterogeneity of experimental methods.

[0007] To address the aforementioned problems, this invention provides a high-throughput mass spectrometry flow cytometry method for simultaneously detecting antigen-specific T cell activation-inducing markers and intracellular cytokines.

[0008] This invention relates to a high-throughput antigen-specific T-cell detection method based on mass flow cytometry, specifically comprising the following steps:

[0009] 1. A high-throughput antigen-specific T-cell detection method based on mass flow cytometry, characterized by comprising the following steps:

[0010] 1) Isolation of peripheral blood mononuclear cells (PBMCs) from the test sample;

[0011] 2) Peripheral blood mononuclear cells were stimulated with a SARS-CoV-2 spike protein-specific peptide library and cultured for 24 hours with CD28 and CD49d added as co-stimulators.

[0012] The concentration of the SARS-CoV-2 spike protein-specific peptide library used was 2 μg / mL; the concentration of CD28 used was 1 μg / mL; and the concentration of CD49d used was 1 μg / mL.

[0013] 3) Barcode encoding is used for cell labeling, surface staining, fixation, cell perforation, and intracellular staining;

[0014] The barcode encoding consists of seven cadmium-labeled β-2-microglobulins (β2M) and seven cadmium-labeled Na+. + / K +-A live-cell barcode composed of ATPase (CD298);

[0015] 4) Then perform live cell staining;

[0016] 5) Detection on the instrument: Detect the signal intensity of each cell in the flow cytometer tube of the mass spectrometer:

[0017] a. The markers CD3, CD4, CD8, CD45RA, CD197, CD185, CD279, CD278, CD183, and CD196 were selected to classify memory T cell subsets;

[0018] b. Detect activation markers CD25, CD38, CD69, and HLA-DR on T cells, as well as exhaustion marker PD-1;

[0019] c. Select activation-inducing biomarkers of the response antigen, namely AIM activation markers CD134, CD25, CD137, CD200, CD40L, PD-L1, CD69, and CD107a, to detect AIM. + T cells;

[0020] d. Add functional cytokines IFN-γ, IL-2, IL-4, TNF-α, granzyme B (GZMB), IL-17 and IL-21 to recognize T cells that have a functional response to antigens;

[0021] 6) Data processing: Analyze the obtained mass spectrometry data.

[0022] Furthermore, in the above method, the barcode encoding is β2M-106Cd, β2M-110Cd, β

[0023] 2M-111Cd, β2M-112Cd, β2M-113Cd, β2M-114Cd, β2M-116C and CD298-106Cd, CD298-110Cd, CD298-111Cd, CD298-112Cd, CD298-113Cd, CD298-114Cd, CD 298-116C.

[0024] Step 3) The specific steps for labeling cells using barcode encoding are as follows: A "7-out-of-2" barcode encoding strategy is adopted, and seven Cd-conjugated β2M and CD298 antibodies are used to mix 12 samples. The decoding efficiency of the 12 samples and the influence of the barcode on cell antigens are analyzed.

[0025] Among them, β2M-106Cd, β2M-110Cd, β2M-111Cd, β2M-112Cd, β2M-113Cd, β2M-114Cd, β

[0026] The concentration of 2M-116C was 1:32; the concentration of CD298 antibody (CD298-106Cd, CD298-110Cd, CD298-111Cd, CD298-112Cd, CD298-113Cd, CD298-114Cd, CD298-116C) was 1:8.

[0027] Seven cadmium (Cd)-labeled β-2-microglobulin (β2M) and Na + / K +- Cells are labeled with live cell barcodes using the ATPase (CD298) enzyme. The labeled samples are then mixed and subjected to subsequent antibody staining. In subsequent data processing, different samples are distinguished based on the obtained barcode-positive and barcode-negative cell populations.

[0028] In the above method, step 3) involves stimulating PBMCs with phytohemagglutinin (PHA) before staining.

[0029] In one specific embodiment, PBMCs were stimulated with phytohemagglutinin (PHA) to ensure activation of cytokine expression. For each dilution, the staining index (SI, D / W) and signal-to-noise ratio (S / N) were calculated, and the concentration with the highest SI or S / N and the lowest background was selected as the final staining concentration.

[0030] In the above method, step 3) before staining also includes the preparation and titration optimization of metal antibodies: surface and intracellular antibodies are coupled to metal isotopes using Maxpar X8 or MCP9 antibody labeling kits (Standard Biotools); the concentration with the highest SI or S / N and the lowest background is selected as the final staining concentration.

[0031] In one specific embodiment, the specific steps of the metal antibody conjugation are as follows: partial reduction of antibody (TCEP treatment), incubation with metal-loaded polymer, centrifugation and washing, quantification with NanoDrop, adjustment to 0.5 mg / mL with antibody stabilizer, and storage at 4°C;

[0032] In one specific embodiment, the metal antibody further includes a titration step.

[0033] To ensure accurate and reproducible detection of surface and intracellular proteins, antibody concentrations were optimized through a series of titrations. Surface antibodies were started at twice the manufacturer's recommended concentration and subjected to six consecutive 1:2 serial dilutions.

[0034] In the above method, the screening step for the activation-inducing biomarker AIM is specifically as follows:

[0035] Step a: Identify AIMs combinations with high sensitivity and specificity; the AIMs combination is CD4. + CD69, CD25, PD-L1, CD134, CD137, CD40L, and CD200 on T cells, as well as CD8 + CD69, CD25, PD-L1, CD134, CD137, CD40L, and CD107a on T cells;

[0036] Selecting appropriate activation-inducing biomarkers (AIMs) is crucial for accurate detection of antigen-specific T cells and assessment of vaccine immunogenicity. One of the key variables in AIM / ICS assays is stimulation time, which significantly affects biomarker expression and varies with antigen type and specific biomarker.

[0037] In one specific embodiment, to detect the peak expression of AIM, CD4 levels can be measured at five time points (6, 12, 18, 24, and 30 hours) after antigen peptide stimulation. + The expression of CD69, CD25, PD-L1, CD134, CD137, CD40L, and CD200 on T cells, as well as CD8... + The expression levels of CD69, CD25, PD-L1, CD134, CD137, CD40L, and CD107a on T cells were determined to identify the peak expression of each individual AIM and different combinations of AIMs.

[0038] Furthermore, this invention also investigates the substitutability analysis between different AIM combinations, with the specific steps as follows:

[0039] CD4 + and CD8 + The seven antigen-specific antigens on T cells were combined in pairs to form 21 combinations of antigen-specific antigens. Since the levels of these 21 combinations of antigens on T cells varied at different time points, we further explored whether these combinations could be interchanged.

[0040] Focusing on the peak time after antigen peptide stimulation, Spearman rank correlation analysis was performed on all 21 combinations to assess consistency and interchangeability. To evaluate the overlap of antigen-specific T cells recognized by different AIM combinations, the widely used CD134 assay was employed. + CD137 + Combination (for CD4) + T cells and CD69 + CD137+ Combination (for CD8) + T cells were used as a reference. The overlap ratio of double-positive cells between the remaining 20 AIM combinations and the reference combination was calculated. A higher overlap ratio indicates that these AIM combinations are more substitutable.

[0041] Step b: Identify combinations of AIMs unaffected by protein transport inhibitors; the individual AIMs unaffected by protein transport inhibitors are CD25, CD69, CD134, and CD137; the combination of AIMs unaffected by protein transport inhibitors is CD69. + CD134 + CD25 + CD137 + CD25 + CD69 + CD25 + CD134 + CD69 + CD137 + and CD134 + CD137 + ;

[0042] In one specific embodiment, the steps for identifying AIMs combinations unaffected by protein transport inhibitors are as follows:

[0043] The surface and intracellular expression of single AIMs were measured under the condition of adding protein transport inhibitors Brefeldin A (BFA) and monensin (MN) during the last 6 hours of 24-hour antigen stimulation, with surface expression under the condition without protein transport inhibitors as a reference. The correlation between the surface and intracellular expression of AIMs with the addition of protein transport inhibitors Brefeldin A (BFA) and monensin (MN) and the extracellular expression without the two protein transport inhibitors was analyzed.

[0044] Step c: Correlation analysis of AIMs and ICS expression by joint AIM-ICS detection;

[0045] The correlation analysis between AIMs and ICS expression included the following steps: T cells from the same individual were separately analyzed for AIM(CD69) expression. + CD134 + CD25 + CD137 + CD25 + CD69 + CD25 + CD134 + CD69 + CD137 + and CD134+ CD137 + The expression of AIM and ICS (IFN-γ, GZMB, IL-2 and TNF-α) was detected, and Spearman correlation analysis was performed on AIM expression and ICS expression.

[0046] In a specific embodiment, the live cell staining step in step 4) is specifically as follows:

[0047] The cell pellet was resuspended in 990 μL of preheated PBS and then stained with 10 μL of cisplatin (Polaris Biology) for 5 min.

[0048] Subsequently, for surface molecules, extracellular antibody mixtures were prepared using LunaStain cell staining buffer, and each sample was resuspended in 100 μL of the mixture and incubated at room temperature for 30 min.

[0049] Then, LunaFix cell fixation buffer (Polaris Biology) was diluted with PBS at a ratio of 1:1. 100 μL of the fixative was added to the surface-stained cells, gently mixed, and incubated at room temperature for 5 min.

[0050] Then add 2 ml of LunaStain cell staining buffer (Polaris Biology), centrifuge, and discard the supernatant;

[0051] Cell permeabilization was performed by suspending cells in 2 ml of LunaPerm cell permeabilization buffer (Polaris Biology) and incubating at room temperature for 30 minutes.

[0052] In this study, for the intracellular staining, an intracellular antibody mixture was prepared using LunaPerm cell permeation buffer (Polaris Biology). Each sample was incubated with 100 μL of this antibody mixture, gently shaken, and then incubated at room temperature for 45 minutes. After staining, 5 mL of LunaPerm cell permeation buffer (Polaris Biology) was added, followed by washing the cells and discarding the supernatant.

[0053] Cell nuclei were stained using Ir-DNA (Polaris Biology). A staining solution was prepared by mixing 100 μL of LunaFix cell fixation buffer (Polaris Biology) with 2 μL of Ir-DNA insertion reagent (Polaris Biology). Each sample was treated with 100 μL of this solution, gently mixed, and incubated at room temperature for 10 minutes. Samples were then immediately processed or stored at 4°C until the Ir-DNA staining solution was removed prior to data acquisition.

[0054] In this study, a Polaris Biology (StarionX1) mass cytometer was used to collect cells. The cell concentration was adjusted to approximately 600,000-700,000 cells per milliliter of suspension using Chen'an loading buffer, and the acquisition rate was controlled at 400-500 events per second. The cell suspension was filtered through a 40-micron filter before loading. Data acquisition was performed after the Chen'an loading buffer signal and DNA signal appeared and stabilized, with the pressure maintained at a stable level below 8 kPa during acquisition. 300,000-500,000 events were collected for each individually stained sample, while for pooled samples, the number of data collected per sample after splitting was controlled to be approximately 300,000 events.

[0055] The data processing methods in this paper are as follows: The acquired mass spectrometry flow cytometry data were standardized and exported as standard FCS 3.0 files. Manual gating was performed using FlowJo (BD Biosciences), and the corresponding FCS files were encoded according to the above method. Uniform manifold approximation and projection (UMAP) was used to further illustrate the decoding of 12 barcode samples. To visualize the overall structure of the immune components, t-distributed random neighbor embedding (t-SNE) was applied, with opt-SNE parameters set to: 1,000 iterations, a perplexity of 30, and a learning rate of 0.5.

[0056] In one specific embodiment, the preferred antigen-specific surface markers are CD45RA, CD197, CD185, CD279, CD278, CD183, CD196, CD25, and CD127; the preferred AIMs are CD134, CD25, CD137, and CD69; and the preferred functional cytokines are IFN-γ, IL-2, IL-4, TNF-α, and GZMB.

[0057] This invention also provides a kit for high-throughput antigen-specific T-cell detection by mass cytometry, the kit containing reagent M and reagent N, wherein reagent M is an antigen, and reagent N contains seven cadmium (Cd)-labeled β-2-microglobulins (β2M) and Na+. + / K +- The live-cell barcode encoding of ATPase (CD298);

[0058] The barcode encoding is β2M-106Cd, β2M-110Cd, β2M-111Cd, β2M-112Cd, β

[0059] 2M-113Cd, β2M-114Cd, β2M-116C and CD298-106Cd, CD298-110Cd, CD298-111Cd, CD298-112Cd, CD298-113Cd, CD298-114Cd, CD 298-116C.

[0060] Furthermore, the kit may also contain conventional antibody reagents for specific antigen binding, cisplatin reagent (Polaris Biology, PB002), cell staining buffer LunaStain (Polaris Biology, PB005), cell fixation buffer (Polaris Biology, PB003), cell permeation buffer (Polaris Biology, PB004), and Ir-DNA (Polaris Biology, PB012).

[0061] This invention also provides the application of the detection method described above in high-throughput antigen-specific T-cell detection.

[0062] This study innovatively constructed a dual-modal detection system: by integrating activation-inducible marker (AIM) technology with intracellular cytokine staining (ICS), and combining it with a mass spectrometry flow cytometry platform, it can simultaneously detect more than 30 parameters, covering multiple activation-inducible marker double positive labels, multifunctional cytokine profiles (IFN-γ, TNF-α, GZMB and IL-2, etc.) and immune memory phenotypes (CD45RA / CCR7).

[0063] This method represents a breakthrough in increasing detection throughput, enabling simultaneous flow cytometry staining of 12 samples in a single experiment, and establishing a standardized operating procedure (SOP) for the entire process, keeping batch-to-batch variability within a small range. Validated in 46 clinical samples from COVID-19 vaccine trials, the CD4+ level after stimulation by the SARS-CoV-2 spike protein-specific peptide library... + and CD8 + The expression levels of T cell activation-inducing markers and functional cytokines were significantly increased. The standardized detection protocol of this invention provides a high-dimensional, quantifiable combination of biomarkers for evaluating vaccine efficacy, and has significant clinical application value. Attached Figure Description

[0064] Figure 1This is a titration diagram of surface antibodies bound to metal isotopes on peripheral blood mononuclear cells. The antibodies were titrated at a 1:2 dilution, from 1:50 (1 μg) to 1:3200 (0.156 μg / mL). The dilutions corresponding to the red boxes represent the optimal titers for antibody staining. The optimal dilutions for anti-human CD3_148Nd, anti-human CD4_142Nd, anti-human CD8_144Nd, anti-human CD45RA_150Nd, anti-human CD197_166Er ...42Nd, anti-human CD45RA_142Nd, anti-human CD45RA_142Nd, anti-human CD45RA_142Nd, anti-human CD45RA_142Nd, anti-human CD45RA_142Nd, anti-human CD45RA_142Nd, anti-human CD45RA_142Nd, anti-human CD45RA_142Nd, anti-human CD45 The optimal dilution for CD28_158Gd is 1:200; the optimal dilution for l.anti-human CD95_164Dy is 1:400; the optimal dilution for m.anti-human CD127_165Ho is 1:200; the optimal dilution for n.anti-human HLA-DR_141Pr is 1:200; and the optimal dilution for o.anti-human CD38_174Yb is 1:50.

[0065] Figure 2This is a titration diagram of intracellular antibodies bound to metal isotopes on peripheral blood mononuclear cells. The antibodies were titrated at a 1:2 dilution ratio, from 1:50 (1 μg) to 1:800 (0.625 μg / mL). The dilutions corresponding to the red boxes represent the optimal titers for antibody staining. The optimal dilutions for anti-human IFN-γ 170Er, GZMB, and IL-2 are 1:400; for anti-human IL-2 145Nd, 1:200; for anti-human TNF-α 143Nd, 1:200; for anti-human GZMB 149Sm, 1:200; for anti-human IL-4 172Yb, 1:400; for anti-human IL-17 147Sm, 1:100; for anti-human IL-21 173Yb, 1:100; for anti-human CD107a 161Dy, 1:200; for anti-human CD40L 175Lu, 1:100; and for anti-human TNF-α 143Nd, 1:200; for anti-human TNF-α 143Nd, 1:200; for anti-human TNF-α 143Nd, 1:200; for anti-human TNF-α 143Nd, 1:200; for anti-human TNF-α 149Sm, 1:200; for anti-human IL-2 172Yb, 1:400; for anti-human IL-17 147Sm, 1:100; for anti-human IL-2 173Yb ... The optimal dilution for CD200_156Gd is 1:100; the optimal dilution for k.anti-human CD69_160Gd is 1:100; the optimal dilution for l.anti-human CD134_162Dy is 1:100; the optimal dilution for m.anti-human CD137_167Er is 1:50; the optimal dilution for n.anti-human PD-L1_168Er is 1:200; and the optimal dilution for o.anti-human CD25_169Tm is 1:100.

[0066] Figure 3 This shows the expression of different activation-inducing markers and their combinations in antigen-specific T cells. Where a and c are CD4+ and CD4+, respectively. + The detection of SARS-CoV-2 spike protein-specific CD4+ using seven commonly used activation-inducing markers in T cells (CD134, CD25, CD137, CD200, CD40L, PD-L1, CD69) and 21 dual-marker combinations under different antigen stimulation durations, after subtracting the background for each sample. + T cell response; b and d are CD8+ responses, respectively. + The detection of SARS-CoV-2 spike protein-specific CD8+ using seven commonly used activation-inducing markers in T cells (CD134, CD25, CD137, CD107a, CD40L, PD-L1, CD69) and 21 dual-marker combinations under different antigen stimulation durations, after subtracting the background for each sample.+ T cells.

[0067] Figure 4 This study analyzed the correlation and overlapping cell proportions of 21 AIM dual-label combinations in antigen-specific T cells. a and c represent CD4+ from the same subject based on Spearman's rank correlation coefficient. + T cells and CD8 + Correlation analysis among 21 activation-inducing marker combinations for T cells; b and d show the correlation results between each activation-inducing marker combination and the standard AIM dual-marker combination (CD4+). + T cells are CD134 + CD137 + CD8 + T cells are CD69 + CD137 + Percentage of overlapping double-positive cells.

[0068] Figure 5 The correlation between surface and intracellular staining of activation-induced markers in the presence of protein secretion inhibitors and surface staining in the absence of protein secretion inhibitors is shown. Here, ag represents the CD4+ staining of surface and intracellular markers in the presence of protein secretion inhibitors. + The expression levels of activation-inducible markers in T cells and the surface staining of CD4 in the absence of protein secretion inhibitors. + Correlation between activation-induced marker expression levels in T cells; hn is CD8 surface and intracellular staining in the presence of protein secretion inhibitors. + The expression levels of activation-inducible markers in T cells and the surface staining of CD8 in the absence of protein secretion inhibitors. + Correlation between the expression levels of activation-inducible markers in T cells; o represents the surface staining of CD4 in the presence and absence of protein secretion inhibitors after background subtraction. + The frequency of activation-inducible marker combination expression in T cells; p represents CD8 in the presence or absence of protein secretion inhibitors after background subtraction. + The frequency of activation-inducible marker combination expression in T cells.

[0069] Figure 6 The correlation between activation-inducing marker analysis and intracellular cytokine analysis is shown. Where a and b are CD4+ and CD4+, respectively. + and CD8 + Spearman correlation analysis of the expression levels of IFN-γ, GZMB, IL-2, and TNF-α with the expression levels of six combinations of activation-inducing markers in T cell subsets; c and d show the expression levels of CD4+ with different combinations of activation-inducing markers in antigen-specific T cell populations secreting IFN-γ. + T cells and CD8+ Distribution frequency of T cells; e and f represent CD4 cells expressing different combinations of activation-inducing markers. + T cells and CD8 + Distribution frequency of cells producing different cytokines within the T cell population.

[0070] Figure 7 To use β-2-microglobulin (β2M) and Na + / K +- Live-cell barcoding technology for ATPase (CD298). Here, 'a' represents the barcoding strategy. Individual samples were barcoded using two different combinations of cadmium-labeled β2M and CD298 antibodies; 'b' shows the signal intensities of seven cadmium isotopes in 12 pooled samples labeled with β2M and CD298 antibodies; 'c' shows the dimensionality reduction and clustering of the 12 pooled samples using the UMAP algorithm. Each color code corresponds to one sample; 'd' demonstrates the decoding efficiency of the β2M and CD298-based barcoding strategy. Example: T cell populations and CD4+ secreting cytokines were compared between barcoded and individually treated samples. + and CD8 + Consistency analysis of T cell subsets.

[0071] Figure 8 To quantitatively analyze the expression levels of activation-inducible markers and the proportion of cytokine-secreting cells in samples from the SARS-CoV-2 spike protein-specific peptide library stimulation and non-stimulation groups. Where af represents the CD4+ expression level between the stimulation and non-stimulation groups. + Optimized expression levels of activation-inducing markers and frequency of cytokine-secreting T cells in T cells; gl shows the difference in CD8 expression between the stimulated and unstimulated groups. + Optimized expression levels of activation-inducing markers in T cells and the frequency of T cells secreting cytokines. Detailed Implementation

[0072] The present invention will now be described in further detail with reference to specific embodiments. The given embodiments are merely illustrative of the invention and not intended to limit its scope. The embodiments provided below can serve as a guide for further improvements by those skilled in the art and do not constitute a limitation on the invention in any way.

[0073] Unless otherwise specified, the experimental methods used in the following examples are conventional methods, performed according to the techniques or conditions described in the literature in this field or according to the product instructions. Unless otherwise specified, the materials and reagents used in the following examples are commercially available.

[0074] Unless otherwise specified, the quantitative experiments in the following examples are all repeated three times, and the results are averaged.

[0075] The 46 peripheral blood mononuclear cell samples in the following examples were obtained from the Jiangsu Provincial Center for Disease Control and Prevention (ethics approval number: JSJK2023-B004-02), and are described in: Jia S, Liu Y, He Q, et al. Effectiveness of abooster dose of aerosolized or intramuscular adenovirus type 5 vectored COVID-19 vaccine in adults: a multicenter, partially randomized, platform trial in China[J]. Nature Communications, 2025, 16(1):2969. This biological material is available to the public from the applicant and is intended solely for repeating experiments of this invention and may not be used for any other purpose.

[0076] The experimental material was peripheral blood mononuclear cells (PBMCs) from volunteers, plated at 2 million cells per well, and cultured for 24 hours with a SARS-CoV-2 spike protein-specific peptide library (2 mg / mL). DSMO stimulation was used as a negative control, and PHA (Roche, 1 mg / mL) stimulation was used as a positive control.

[0077] This study was conducted at the Affiliated Hospital of Yunnan University. All volunteers signed informed consent forms. This study was approved by the Ethics Committee of the Affiliated Hospital of Yunnan University (Ethics Approval No.: 2022026).

[0078] Example 1: Selection of membrane surface protein antibodies and determination of optimal staining concentration for intracellular antibodies

[0079] T cells are highly heterogeneous, composed of diverse phenotypes and functional subsets. A comprehensive understanding of an individual's immune response to a specific antigen and their overall immune status requires detailed knowledge of the frequency, differentiation stage (e.g., naive or memory), phenotype, and functional characteristics of antigen-specific T cells. To meet this need, an antibody combination regimen specifically designed for preclinical COVID-19 vaccine research aims to simultaneously evaluate CD4+. + and CD8 + T cell activation-inducible markers (AIMs) and intracellular cytokines stimulated by a SARS-CoV-2 spike protein-specific peptide library.

[0080] First, three T cell line markers—CD3, CD4, and CD8—were selected to differentiate helper T cells (CD4+). + ) and cytotoxic T cells (CD8) + ).

[0081] Subsequently, markers were selected to identify T cell subsets, including CD45RA, CCR7, CXCR5, PD-1, ICOS, CXCR3, CCR6, CD25, and CD127. These markers allow for the classification of memory T cell subsets (naive T cells, central memory [TCM], effector memory [TEM], terminally differentiated effector memory [TEMRA]) and follicular helper T cells (Tfh).

[0082] To further determine the functional status of antigen-specific T cells, functional cytokines such as IFN-γ, IL-2, IL-4, TNF-α, granzyme B (GZMB), IL-17, and IL-21 were added. These cytokines help identify T cells that are functionally responsive to antigens.

[0083] Simultaneously, activation-inducing markers, including CD25, CD69, CD134 (OX40), CD137 (4-1BB), CD200, CD274 (PD-L1), CD154 (CD40L), and CD107a, were selected to detect AIM. + T cells.

[0084] In addition, activation markers CD38 and HLA-DR, as well as exhaustion marker CD279 (PD-1) on T cells were detected.

[0085] Table 1 provides a complete list of all metal isotope-labeled antibodies used in this study. All naked antibodies were purchased from Biolegend and then metal-labeled using Fluidigm's metal antibody tagging kit.

[0086] Table 1. Antibody List

[0087]

[0088]

[0089] To ensure accurate and reproducible detection of T cell surface and intracellular markers, the antibody concentrations used in the experiment were optimized. Surface antibodies and intracellular antibodies were titrated separately.

[0090] For surface antibody titration, first use twice the manufacturer's recommended concentration of antibody (1 μL of antibody per 100 μL), then dilute at a ratio of 1:2 and perform a total of 6 gradient titrations.

[0091] For AIM and intracellular labeling titration, cells were stimulated with phytohemagglutinin (PHA) prior to detection.

[0092] Titration results as follows Figure 1 Zhongao and Figure 2 As shown in the middle of ao. Figure 1 For the surface antibody titration results, Figure 2 Based on the intracellular antibody titration results, the dilution that produces the brightest signal and minimizes background staining is ultimately selected as the optimal staining concentration for each antibody (i.e., the dilution corresponding to the red box is the optimal antibody staining concentration).

[0093] The surface antibody titration results showed that the optimal dilution for anti-human CD3_148Nd was 1:100; the optimal dilution for anti-human CD4_142Nd was 1:100; the optimal dilution for anti-human CD8_144Nd was 1:50; the optimal dilution for anti-human CD45RA_150Nd was 1:400; the optimal dilution for anti-human CD197_166Er was 1:50; the optimal dilution for anti-human CCR6_171Yb was 1:50; the optimal dilution for anti-human CXCR5_153Eu was 1:50; the optimal dilution for anti-human CXCR3_163Dy was 1:50; the optimal dilution for anti-human CD279_155Gd was 1:400; the optimal dilution for anti-human CD278_159Tb was 1:50; and the optimal dilution for anti-human... The optimal dilution for CD28_158Gd is 1:200; the optimal dilution for anti-human CD95_164Dy is 1:400; the optimal dilution for anti-human CD127_165Ho is 1:200; the optimal dilution for anti-human HLA-DR_141Pr is 1:200; and the optimal dilution for anti-human CD38_174Yb is 1:50.Intracellular antibody titration results showed that the optimal dilutions for anti-human IFN-γ 170Er, anti-human IL-2 145Nd, anti-human TNF-α 143Nd, anti-human GZMB 149Sm, anti-human IL-4 172Yb, anti-human IL-17 147Sm, anti-human IL-21 173Yb, anti-human CD107a 161Dy, anti-human CD40L 175Lu, and anti-human CD200 156Gd were 1:100. The optimal dilution for CD69_160Gd is 1:100; the optimal dilution for anti-human CD134_162Dy is 1:100; the optimal dilution for anti-human CD137_167Er is 1:50; the optimal dilution for anti-human PD-L1_168Er is 1:200; and the optimal dilution for anti-human CD25_169Tm is 1:100.

[0094] Example 2: Comparison of different activation-inducing markers in antigen-specific T cell detection

[0095] When using T-cell activation-inducing markers to detect antigen-specific T cells, selecting appropriate marker molecules is crucial. Activation-inducing marker technology can assess the overall response of antigen-specific T cells from a more comprehensive perspective, thus more accurately reflecting the immunogenicity of vaccines.

[0096] Eight activation-inducing biomarkers, CD25, CD69, CD134, CD137, CD200, PD-L1, CD40L, and CD107a, were selected for this study. These biomarkers (purchased from Biolegend) were metal-labeled using a Fluidigm metal antibody tagging kit according to Table 1, thereby systematically evaluating the differences in sensitivity and response intensity of different AIM combinations in antigen-specific T cell detection.

[0097] 1. Evaluate the effect of a single activation-inducing biomarker on antigen-specific CD4 after stimulation with a SARS-CoV-2 spike protein-specific peptide library for 6, 12, 18, 24, and 30 hours, respectively. + With CD8 +The detection efficacy of T cells.

[0098] The results are as follows Figure 3 As shown in a and b: in CD4 + Among the peak expression levels of T cells, CD69 showed the strongest activation response, followed by PD-L1, CD25, and CD134. However, markers such as CD40L and CD200 showed extremely low activation levels; CD8... + In T cells, CD69 exhibits the highest activation intensity, while CD25, PD-L1, and CD137 show moderate activation responses, and the expression levels of other markers are low. Activation-inducing markers such as CD69 and CD137 can reliably reflect antigen-specific activation status within 18-24 hours after stimulation, while activation-inducing markers such as CD40L or CD200 may show delayed or low-level expression in short-term stimulation protocols.

[0099] 2. Evaluation of the effects of dual-labeling combinations of 21 activation-inducing biomarkers on antigen-specific CD4 after stimulation with a SARS-CoV-2 spike protein-specific peptide library for 6, 12, 18, 24, and 30 hours. + With CD8 + The detection efficacy of T cells.

[0100] The results are as follows Figure 3 As shown in c and d: in CD4 + In T cells, CD25 + PD-L1 + The combination showed the highest peak activation level, while CD134 + PD-L1 + CD25 + CD69 + and CD25 + CD134 + The combination also showed high activation levels. Most involved CD40L. + Or CD200 + The combined activation level was significantly lower; in CD8 + T cells also show a similar trend, CD25 + PD-L1 + The combination showed the highest peak activation level; other effective combinations include CD25. + CD69 + CD25 + CD134 + CD69 + PD-L1 + CD25 + CD40L + and CD25 + CD137 +The remainder contains CD107a. + Or CD40L + The combined activation levels were extremely low. These results highlight the differences in the sensitivity of different combinations of activation-inducing markers to detecting SARS-CoV-2 spike protein-specific T cells.

[0101] 3. Given the differences in the number of SARS-CoV-2 spike protein-specific T cells detected by the 21 combinations of activation-inducing markers, in order to further explore whether these combinations of activation-inducing markers are interchangeable, a correlation analysis was performed on all combinations at 24 hours after antigen stimulation to assess their consistency and potential interchangeability.

[0102] The results are as follows Figure 4 As shown in Figures a and c: A significant positive correlation exists among most combinations of activation-inducing markers, indicating that they primarily recognize overlapping antigen-specific T cell populations. However, those involving CD4... + T cell PD-L1 markers and CD8 + T cells CD40L + CD69 + and CD40L + CD134 + The combination of markers showed weak correlation with other combinations of activation-inducing markers. This suggests that these specific marker combinations may define unique T cell subsets that most activation-inducing marker combinations fail to capture.

[0103] 4. To assess the overlap of different antigen-specific T cell recognition combinations, the widely used CD134 was employed. + CD137 + Combination as CD4 + The standard reference for T cells is CD69. + CD137 + Combination as CD8 + A standard reference for T cells was established. For each combination of activation-inducing markers, the percentage of double-positive cells overlapping with the corresponding reference population was calculated.

[0104] The results are as follows Figure 4 As shown in b and d: in CD4 + In T cells, CD25 + CD134 + CD69 + CD134 + CD134 + PDL1 + CD69 + CD137 + and CD25 + CD137 +The combination shows the same as the standard CD134 + CD137 + The highest co-recognition rate among the populations indicates that these combinations can capture most overlapping antigen-responsive T cell subsets; in contrast, CD69 + CD200 + or PDL1 + CD200 + The significantly lower overlap rate of these combinations suggests that they label distinct or partially overlapping subgroups; in CD8 + In T cells, most combinations showed similarities to standard CD69. + CD137 + The group has a high co-identification rate, while CD137 + PDL1 + CD40L + CD134 + and CD107a + CD137 + The overlap rate of these combinations was significantly low. The results indicate that although many dual-labeled antigen-specific T cell recognition combinations have not yet been reported, they can still reliably recognize antigen-specific T cell populations.

[0105] Example 3: Effect of protein secretion inhibitors on the detection of antigen-specific T cell responses

[0106] A key factor in optimizing the combination of reliable activation-inducing markers with cytokine detection is determining whether the staining of activation-inducing markers is affected by protein secretion inhibitors (brevidin A and monensin). These inhibitors are known for blocking endosome transport and can simultaneously inhibit the secretion of cytokines and surface markers. Comparing the consistency of intracellular and surface staining of these markers in the presence of protein secretion inhibitors with those in the absence of inhibitors helps determine the most reliable combination of activation-inducing markers for assessing antigen-specific T cell activation in conjunction with intracellular cytokine assays.

[0107] The results are as follows Figure 5 As shown: 1) In CD4 + In T cells, the expression of CD25, CD69, CD134, and CD137 was largely unaffected by protein secretion inhibitors. The intracellular expression of these markers showed a slightly lower correlation with their surface expression under inhibitor-free conditions, indicating less susceptibility to protein transport interference. In contrast, markers such as CD40L, PD-L1, and CD200—detectable both extracellularly and intracellularly—were significantly affected by protein transport inhibitors, suggesting greater sensitivity to protein transport inhibition. Figure 5 (ag).

[0108] Compared with intracellular staining, surface staining of most induced activation markers showed a stronger correlation between the presence of protein secretion inhibitors and surface staining in the absence of protein secretion inhibitors.

[0109] 2) In CD8 + Among T-cell immune markers, intracellular expression of CD25, CD69, and CD137 showed the strongest correlation, and the extracellular staining results for these markers remained stable. In contrast, CD134 showed a significantly higher correlation in extracellular staining than in intracellular staining. Meanwhile, markers such as CD40L, PD-L1, and CD107a consistently showed sensitivity to protein transport inhibitors. Figure 5 (zhonghn).

[0110] In summary, given the superior performance of surface staining techniques in inhibiting protein transport, and excluding the four activation-inducing markers CD40L, PD-L1, CD200, and CD107a that are sensitive to protein transport inhibitors, subsequent analysis was conducted using surface-based activation-inducing marker detection methods (CD25, CD69, CD134, and CD137).

[0111] We evaluated a combination of six activation-inducing biomarkers (CD25, CD69, CD134, and CD137) and assessed their consistency in detecting antigen-specific T cells by comparing experimental conditions with and without inhibitors.

[0112] The results are as follows Figure 5 As shown in o and p: in CD4 + In T cells, except for CD69 + CD137 + Except for one combination which showed a significant reduction after inhibitor treatment, the other combinations remained stable under both conditions; while on CD8 + T cells contain CD134 + CD137 + CD25 + CD134 + and CD69 + CD134 + The combination was significantly affected by inhibitors, while CD25 + CD137 + CD25 + CD69 + and CD69 + CD137 + The combination can be stably detected regardless of the presence of inhibitors.

[0113] Example 4: Detection method for activation-inducing markers for antigen-specific T cell capture

[0114] To further explore the correlation between T cell detection using activation-inducing marker assays and intracellular cytokine staining, a dual analysis of T cells from the same subject was performed using both methods. Subsequently, CD4... + and CD8 + Spearman correlation analysis was performed on the expression levels of T cell subsets, IFN-γ, GZMB, IL-2, and TNF-α, and the expression levels of six activation-inducing marker combinations.

[0115] The results are as follows Figure 6 As shown in a and b: CD25 + CD137 + CD4, which combines and secretes cytokines + T cells (especially IFN-γ) showed the strongest and most stable positive correlation. The other two groups of CD25... + CD134 + and CD25 + CD69 + It also showed a significant correlation, while CD134 + CD137 + and CD69 + CD137 + It is negatively correlated with cytokine response. In CD8 + In T cells, CD25 + CD137 + CD25 + CD134 + and CD25 + CD69 + The combination remained associated with cytokine responses, particularly IFN-γ. These findings suggest that the combination of CD25-based activation-inducing markers is significantly correlated with functional T cell responses, such as IFN-γ release.

[0116] The distribution frequency of cells expressing different combinations of activation-inducing markers in a population of IFN-γ-secreting T cells was quantified. Results are as follows: Figure 6 As shown in c and d: in CD4 + Among T cells, a significant proportion of cells that secrete IFN-γ also express CD25. + CD69 + Secondly, CD25 + CD134 + and CD134 + CD137 + In CD8 + In T cells, CD25 + CD69 +The combination again showed the highest overlap with IFN-γ-secreting cells, indicating that cytokine-secreting cells tend to preferentially upregulate activation-inducing markers, especially CD25. + CD69 + combination.

[0117] To further evaluate the function of T cells expressing different combinations of activation-inducing markers, the proportion of cells producing different cytokines in T cell populations expressing different combinations of activation-inducing markers was quantified.

[0118] The results are as follows Figure 6 As shown in e and f: CD4 expressing different combinations of activation-inducing markers + In T cells, less than 20% of cells can produce any single cytokine (GZMB, IFN-γ, IL-2, or TNF-α), with IFN-γ and TNF-α being the most frequent; similarly, in CD8... + Less than 20% of T cells produce any single cytokine (GZMB, IFN-γ, IL-2, or TNF-α). This finding is consistent with previous reports, indicating that activation-inducible marker assays identify antigen-specific T cells at a higher frequency than intracellular cytokine assays. Therefore, activation-inducible markers not only capture antigen-reactive T cells identified by conventional intracellular cytokine assays but also reveal other antigen-reactive cell subsets.

[0119] Example 5: The effect of pooling strategies on detecting antigen-specific T cell responses

[0120] Using seven cadmium (Cd)-labeled β2-microglobulin (β2M) and CD298 for live cell barcoding provides a more universal and robust new barcoding detection technology for subsequent immunophenotypic analysis.

[0121] This system employs a 7-out-of-2 encoding strategy, using β2M antibodies (β2M-106Cd, β2M-110Cd, β2M-116) coupled with metallic cadmium (Cd) and its isotopes (Cd-106, 110, 111, 112, 113, 114, 116).

[0122] Samples were barcoded using β2M-111Cd, β2M-112Cd, β2M-113Cd, β2M-114Cd, and β2M-116C at a concentration of 1:1600, and CD298 antibodies (CD298-106Cd, CD298-110Cd, CD298-111Cd, CD298-112Cd, CD298-113Cd, CD298-114Cd, and CD298-116C at a concentration of 1:100). Multiplex detection of up to 21 samples can be achieved. Based on the experimental design and data acquisition time, this experiment ultimately selected 12 samples mixed with barcodes for labeling. The labeling strategy is as follows: Figure 7 As shown in Figure a.

[0123] The specific testing steps are as follows:

[0124] I. Preparation of peripheral blood mononuclear cells (PBMCs) from 12 test samples:

[0125] 1) Isolation of peripheral blood mononuclear cells (PBMCs);

[0126] 2) Short-term in vitro stimulation: Peripheral blood mononuclear cells (PBMCs, number 2×10⁻⁶) were stimulated using a SARS-CoV-2 spike protein-specific peptide library (2 μg / mL). 6 (each), and CD28 and CD49d were added as costimulators and cultured for 24 hours;

[0127] II. Cell staining:

[0128] 3) Barcode the cells, stain their surface, fix them, perforate their membranes, and stain them intracellularly;

[0129] The steps for live cell staining are as follows:

[0130] After resuspending the cell pellet in 990 μL of preheated PBS, 10 μL of cisplatin (Polaris Biology) was added for staining for 5 min.

[0131] Subsequently, for surface molecules, extracellular antibody mixtures were prepared using LunaStain cell staining buffer [anti-human CD3_148Nd (1:100), anti-human CD4_142Nd (1:100), anti-human CD8_144Nd (1:50), anti-human CD45RA_150Nd (1:400), anti-human CCR7_166Er (1:50), anti-human CXCR5_153Eu (1:50), anti-human PD-1_155Gd (1:400), anti-human ICOS_159Tb (1:50), anti-human CXCR3_163Dy (1:50), anti-human CCR6_171Yb (1:50), anti-human CD127_165Ho (1:200), anti-human [CD28_158Gd (1:200), anti-human CD95_164Dy (1:400), anti-human HLA-DR_141Pr (1:200), anti-human CD38_174Yb (1:50), anti-human CD25_169Tm (1:100), anti-human CD69_160Gd (1:100), anti-human CD134_162Dy (1:100), anti-human CD137_167Er (1:50)], each sample was resuspended in 100 μL of the mixture and incubated at room temperature for 30 min;

[0132] Then, LunaFix cell fixation buffer (Polaris Biology) was diluted with PBS at a 1:1 ratio, and 100 μL of the fixative was added to the surface-stained cells. After gently mixing, the cells were incubated at room temperature for 5 min. Subsequently, 2 ml of LunaStain cell staining buffer (Polaris Biology) was added, and the supernatant was discarded after centrifugation.

[0133] Cell permeabilization was performed by suspending cells in 2 ml of LunaPerm cell permeabilization buffer (Polaris Biology) and incubating at room temperature for 30 minutes.

[0134] For intracellular staining, intracellular antibody mixtures [anti-human IFN-γ170Er (1:400), anti-human IL-2145Nd (1:200), anti-human TNF-α143Nd (1:200), anti-human GZMB149Sm (1:200)] were prepared using LunaPerm cell permeation buffer (Polaris Biology). Each sample was incubated with 100 μL of this antibody mixture, gently shaken, and then incubated at room temperature for 45 minutes.

[0135] After staining, 5 mL of LunaPerm cell permeation buffer (Polaris Biology) was added, followed by washing of the cells and discarding the supernatant. Finally, the cell nuclei were stained with Ir-DNA (Polaris Biology). A staining solution was prepared by mixing 100 μL of LunaFix cell fixation buffer (Polaris Biology) with 2 μL of Ir-DNA insertion reagent (Polaris Biology). Each sample was treated with 100 μL of this solution, gently mixed, and incubated at room temperature for 10 minutes. Samples were then immediately processed or stored at 4°C until the Ir-DNA staining solution was removed prior to data acquisition.

[0136] III. Detection on the flow cytometer: Detect the signal intensity of each cell in the flow cytometer tubes.

[0137] The surface antibodies CD3, CD4, CD8, CD45RA, CD197, CD185, CD279, CD278, CD183, CD28, CD95, CD127, and CD196 were selected to classify memory T cell subsets.

[0138] The addition of functional cytokines IFN-γ, IL-2, TNF-α, and granzyme B (GZMB) helps T cells that are functionally responsive to antigens to be recognized.

[0139] Activation induction markers of the response antigen, namely AIM activation markers CD134, CD25, CD137, and CD69, were selected for AIM detection. + T cells;

[0140] Detect activation markers CD25, CD38, CD69, and HLA-DR on T cells, as well as exhaustion marker CD279;

[0141] Mass Spectrometry Cell Analysis: Cells were collected using a Polaris Biology (StarionX1) mass cytometer. The cell concentration was adjusted to approximately 600,000-700,000 cells per milliliter of suspension using Chen'an loading buffer. The acquisition rate was controlled at 400-500 events per second. The cell suspension was filtered through a 40-micron filter before loading. Data acquisition began after the Chen'an loading buffer signal and DNA signal appeared and stabilized, with the pressure maintained stable and below 8 kPa during acquisition. 300,000-500,000 events were collected for each individually stained sample, while for pooled samples, the number of events collected per sample after splitting should be controlled to approximately 300,000.

[0142] IV. Data Processing: Analyze the obtained mass spectrometry data.

[0143] The acquired mass cytometry data were standardized and exported as standard FCS 3.0 files. Manual gating was performed using FlowJo (BD Biosciences), and the corresponding FCS files were encoded according to the above method. To visualize the overall structure of the immune components, t-distributed random neighbor embedding (t-SNE) was applied, with opt-SNE parameters set to 1,000 iterations, a perplexity of 30, and a learning rate of 0.5. Subsequently, the decoding of 12 barcode samples was further demonstrated using the uniform manifold approximation and projection (UMAP).

[0144] The staining results of β2M and CD298 are as follows Figure 7 As shown in Figure b: Comparative analysis revealed that both β2M antibody and CD298 antibody could be clearly distinguished between barcode positive and negative samples.

[0145] 2. Evaluate the separation of all barcode samples in the high-dimensional plane.

[0146] UMAP dimensionality reduction analysis was performed on the pooled cell data to divide the barcode dataset into subgroups. The results are as follows: Figure 7 As shown in Figure c, the 12 barcode samples are separated from each other, exhibiting obvious clustering characteristics and identifying 12 distinct subgroups. The coloring features of each sample match the specified barcode. Decoding efficiency was then evaluated, and the results are as follows: Figure 7 As shown in the diagram, the decoding efficiency of each sample is very close.

[0147] 3. Evaluate the potential impact of barcode technology on antigen detection and biological conclusions.

[0148] Twelve samples were divided into two groups: one group was not barcoded, and the other group was barcoded using the β2M / CD298 system before staining. CD4+ levels of secreted cytokines were analyzed in both groups. + and CD8 +T cell subsets that express CD4, an activation-inducing marker. + and CD8 + T cell subsets and other CD4+ + and CD8 + Correlation analysis was performed on T cell subsets.

[0149] The results are as follows Figure 7 As shown in Figure 6.0: The main CD4 values ​​in the barcode processed sample and the separately processed sample. + and CD8 + The relative distributions of T cell subsets showed a significant correlation. Notably, in CD4... + and CD8 + Within the T cell subsets, large T cell populations and rare subsets such as cells expressing activation-inducing markers and cells secreting cytokines also showed good concordance. These findings indicate that the cadmium-based β2M / CD298 barcoding strategy can maintain signal integrity, ensure reliable immunoassay results, and avoid introducing technical bias.

[0150] In summary, the optimal protocol for combined AIM-ICS detection of antigen-specific T cell responses is CD25. + CD134 + CD25 + CD69 + As a detection of antigen-specific CD4 + The optimal combination of activation-inducing markers for T cell responses, CD25 + CD137 + CD25 + CD69 + As a detection antigen-specific CD8 + The optimal combination of activation-inducing biomarkers for T cell responses: β2M-106Cd, β2M-110Cd, β2M-111Cd, β2M-112Cd, β2M-113Cd, β2M-114Cd, β

[0151] 2M-116C and CD298-106Cd, CD298-110Cd, CD298-111Cd, CD298-112Cd, CD298-113Cd, CD298-114Cd, CD298-116C are used as barcodes.

[0152] Example 6: Application of Antigen-Specific T Cell Detection Method

[0153] To verify the performance of this optimized protocol, T-cell analysis of activation-inducing markers and cytokine secretion was performed on 46 peripheral blood mononuclear cell samples. These subjects mainly had mixed immunity against SARS-CoV-2.

[0154] Peripheral blood mononuclear cell samples were cultured from volunteers and stimulated with a SARS-CoV-2 spike protein-specific peptide library for 24 hours. Following the detection method described in Example 5, the cells were barcoded, pooled, and subjected to 30-parameter immunophenotyping and mixed staining. After data acquisition and decoding, the expression levels of activation-inducing markers and the number of T cells secreting cytokines in different samples were analyzed.

[0155] The results are as follows Figure 8 As shown in Figure 1: When stimulated with a SARS-CoV-2 spike protein-specific peptide library, cells expressing activation-inducing markers (CD25) showed increased activity. + CD134 + and CD25 + CD69 + CD4+, which secretes cytokines (IFN-γ, IL-2, TNF-α, and GZMB), + The proportion of T cells was significantly higher than that in the unstimulated control group; in contrast, GZMB + CD4 + No significant difference was observed in T cells between the two groups. However, CD8... + After stimulation with a SARS-CoV-2 spike protein-specific peptide library, all detected cells expressing activation-inducing markers (CD25) of T cells... + CD137 + and CD25 + CD69 + CD8+, which secretes cytokines (GZMB, IFN-γ, IL-2, and TNF-α), and other cytokines. + The proportion of T cells was significantly upregulated.

[0156] In summary, the cadmium-labeled mixed-sample mass cytometry workflow exhibits strong robustness and versatility. This technology can not only achieve high-dimensional multiplex immunomics analysis, but also reliably detect SARS-CoV-2-specific T cell responses and abundance changes.

[0157] The present invention has been described in detail above. Those skilled in the art will recognize that the invention can be practiced in a wide range of ways with equivalent parameters, concentrations, and conditions without departing from its spirit and scope, and without requiring unnecessary experiments. While specific embodiments have been provided, it should be understood that further modifications can be made to the invention. In summary, according to the principles of the invention, this application is intended to include any changes, uses, or improvements to the invention, including changes made using conventional techniques known in the art that depart from the scope disclosed herein.

Claims

1. A high-throughput antigen-specific T-cell detection method based on mass flow cytometry, characterized in that, Includes the following steps: 1) Isolate peripheral blood mononuclear cells (PBMCs) from the sample to be tested; 2) The peripheral blood mononuclear cells described in step 1) were stimulated with a SARS-CoV-2 spike protein-specific peptide library and cultured for 24 hours with 1 μg / mL CD28 and 1 μg / mL CD49d as co-stimulators. 3) Use barcode encoding to label the cells cultured in step 2), followed by surface staining, fixation, membrane perforation, and intracellular staining; The barcode encoding consists of seven cadmium-labeled β-2-microglobulins and seven cadmium-labeled Na+. + / K +- A barcode of living cells composed of ATPases; 4) Subsequently, live cell surface molecules, AIM labeling, and cytokine staining steps were performed; 5) Detection on the instrument: Detect the signal intensity of each cell in the flow cytometer tubes of the mass spectrometer: a. The markers CD3, CD4, CD8, CD45RA, CD197, CD185, CD279, CD278, CD183, and CD196 were selected to classify memory T cell subsets; b. Detect activation markers CD25, CD38, CD69, and HLA-DR on T cells, as well as exhaustion marker PD-1; c. Select activation-inducing biomarkers of the response antigen, namely AIM activation markers CD134, CD25, CD137, CD200, CD40L, PD-L1, CD69, and CD107a, to detect AIM. + T cells; d. Add functional cytokines IFN-γ, IL-2, IL-4, TNF-α, granzyme B (GZMB), IL-17 and IL-21 to recognize T cells that have a functional response to antigens; 6) Data processing: Analyze the obtained mass spectrometry data.

2. The method according to claim 1, characterized in that: The barcodes are β2M-106Cd, β2M-110Cd, β2M-111Cd, β2M-112Cd, β2M-113Cd, β2M-114Cd, β2M-116C and CD298-106Cd, CD298-110Cd, CD298-111Cd, CD298-112Cd, CD298-113Cd, CD298-114Cd, and CD298-116C.

3. The method according to claim 1 or 2, characterized in that, Step 3) describes stimulating PBMCs with phytohemagglutinin (PHA) before staining.

4. The method according to claims 1-3, characterized in that, Step 3) The staining process also includes the preparation and titration optimization of metal antibodies: surface and intracellular antibodies are coupled to metal isotopes using Maxpar X8 or MCP9 antibody labeling kits (Standard Biotools); the concentration with the highest SI or S / N and the lowest background is selected as the final staining concentration.

5. The method according to any one of claims 1-4, characterized in that: The screening steps for the activation-inducing biomarker AIM are as follows: Step a: Identify AIMs combinations with high sensitivity and specificity; the AIMs combination is CD4. + CD69, CD25, PD-L1, CD134, CD137, CD40L, and CD200 on T cells, as well as CD8 + CD69, CD25, PD-L1, CD134, CD137, CD40L, and CD107a on T cells; Step b: Identify combinations of AIMs unaffected by protein transport inhibitors; the individual AIMs unaffected by protein transport inhibitors are CD25, CD69, CD134, and CD137; the combination of AIMs unaffected by protein transport inhibitors is CD69. + CD134 + CD25 + CD137 + CD25 + CD69 + CD25 + CD134 + CD69 + CD137 + and CD134 + CD137 + ; Step c: Correlation analysis of AIMs and ICS expression by joint AIM-ICS detection; The correlation analysis between AIMs and ICS expression involved detecting activation-inducing markers AIM and ICS in T cells from the same individual, and then performing Spearman correlation analysis on AIM expression and ICS expression. The activation-inducing marker AIM is CD69. + CD134 + CD25 + CD137 + CD25 + CD69 + CD25 + CD134 + CD69 + CD137 + and CD134 + CD137 + The intracellular cytokine detection included IFN-γ, GZMB, IL-2, and TNF-α.

6. The method according to claim 5, characterized in that, The identification of highly sensitive and specific AIMs combinations described in step a is achieved by stimulating CD4 at 6, 12, 18, 24, and 30 hours after stimulation of the antigen peptide library. + CD69, CD25, PD-L1, CD134, CD137, CD40L, and CD200 on T cells, as well as CD8 + The peak expression levels of individual AIMs (CD69, CD25, PD-L1, CD134, CD137, CD40L, and CD107a) on T cells, as well as the peak expression levels of 21 different AIM pairwise combinations, were analyzed to determine the peak time for AIM detection.

7. The method according to claim 5 or 6, characterized in that, In step b, the protein transport inhibitors are Brefeldin A and monensin.

8. The method according to any one of claims 5-7, characterized in that, When performing AIM-ICS joint detection as described in step c, CD25 is used. + CD134 + CD25 + CD69 + As a detection of antigen-specific CD4 + The optimal combination of activation-inducing markers for T cell responses, CD25 + CD137 + CD25 + CD69 + As a detection antigen-specific CD8 + The optimal combination of activation-inducing biomarkers for T cell responses.

9. A kit for high-throughput antigen-specific T-cell detection in mass cytometry, characterized in that, The kit contains reagent M and reagent N. Reagent M is an antigen, and reagent N contains seven cadmium (Cd)-labeled β-2-microglobulins (β2M) and Na+. + / K +- The live-cell barcode encoding of ATPase (CD298); The barcodes are β2M-106Cd, β2M-110Cd, β2M-111Cd, β2M-112Cd, β2M-113Cd, β2M-114Cd, β2M-116C and CD298-106Cd, CD298-110Cd, CD298-111Cd, CD298-112Cd, CD298-113Cd, CD298-114Cd, and CD298-116C.

10. The application of the method according to claims 1-7 in high-throughput antigen-specific T-cell detection.

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