| Part ?:Construction and Characterization of the Transcriptome Map of Bone Marrow T Cells in Immune Thrombocytopenia Background:Immune thrombocytopenia(ITP)is an autoimmune-mediated thrombocytopenia syndrome.The clinical manifestations are mucocutaneous bleeding without obvious predisposing causes,visceral bleeding or even intracranial bleeding,and severe cases are potentially life-threatening.About 30%-40% of patients fail to respond to treatment or relapse in a short term.Therefore,it is of great clinical significance to study the pathophysiological mechanism of ITP and develop new targeted therapy strategies.The mechanism of ITP mainly includes two aspects:increased platelet destruction and/or decreased platelet production,and more and more studies have focused on the role of T lymphocyte dysfunction in the development of ITP in recent years.Platelets are susceptible to immune surveillance of CD8+T cells because they express class I major histocompatibility complex(MHC I),which can induce degranulation of effector CD8+T cells and self-apoptosis when effector CD8+T cells recognize cognate peptides presented by MHC I.In addition,CD4+T cells are also involved in the immune intolerance of ITP,and among them,the disorder of CD4+CD25+regulatory T cells(Treg)in number and function is one of the important mechanisms causing autoimmune tolerance defects in ITP.Although the influence of some T cells subsets in the pathogenesis of ITP has been studied currently,the role of more subsets of T cells remains to be further explored.Bone marrow is an important immune response organ and one of the main sites of platelet production and destruction.In the occurrence of autoimmune diseases,immune cells accumulate in the bone marrow and participate in the body's pathogenic response.Among them,naive cells homing in the bone marrow and differentiate into effector T cells or memory T cells under antigen stimulation,and it is worth noting that there are bone marrow-specific resident memory T cells(TRM),which can reside in the bone marrow for a long time.In addition,there are also rare T cells subsets such as Treg and mucosal-associated invariable T cells(MAIT)existed in the bone marrow,which also have an irreplaceable role in the immune response.For example,bone marrow Treg cells can maintain self-tolerance and avoid excessive damage to the body by immune response.After bone marrow transplantation,Treg cells have a positive effect on the engraftment of hematopoietic stem cells and the control of graft-versus-host disease.Therefore,dissecting the characteristics of bone marrow T cells such as grouping and function is helpful to explore the pathogenic mechanism and new treatment of autoimmune diseases such as ITP.Single-cell RNA sequencing(scRNA-seq)means performing a series of operations such as RNA extraction,amplification,and sequencing at the single-cell level,ultimately obtaining transcript information from individual cells.Unlike population-level RNA sequencing,gene expression analysis at the single-cell level can better describe the biological reactions and cellular characteristics.For example,scRNA-seq analysis of PBMCs from patients with primary Sjogren's syndrome(pSS)showed that the proportions of two subsets of T cells were significantly higher in patients with pSS,and its specific amplification was involved in the pathogenesis of pSS.Moreover,sequencing analysis of mouse islet cells at various stages of non-obese autoimmune diabetes using scRNA-seq provides a single-cell map to define the stage of autoimmune diabetes.However,there is still a lack of single-cell genomics studies targeting the pathogenic mechanisms or therapeutic strategies of ITP.Objective:1.To collect bone marrow samples from ITP patients and healthy controls and construct a transcriptome map of bone marrow T cells at the single-cell level using scRNA sequencing technology.2.To explore the characteristics of some T cells clusters associated with autoimmune diseases in the bone marrow of ITP and their role in ITP,and to reveal the possible targets related to T cells to treat ITP in single cell level.Methods:1.ITP patients and healthy controls:Obtained bone marrow samples from ITP patients and healthy donors.2.Isolation of bone marrow mononuclear cells(BMMCs):BMMCs in bone marrow samples were isolated using Ficoll density gradient centrifugation.3.Sorting T cells:CD3+CD4+T cells and CD3+CD8+T cells were sorted using flow cytometry sorter,the sorted two populations were pooled for future use.4.Preparation of gel beads-in-emulsion(GEMs):Preparing the reaction mix with GEMs,and the Chromium Chip A program of the Chromium Controller was run.GEMs reverse transcription was performed using a PCR amplifier.5.Amplification and purification of cDNA:Configuring Dynabeads Cleanup mix,eluent I and amplification mix,and using a PCR instrument for cDNA amplification,the amplified cDNA was then purified with the SPRIselect kit.6.Construction of 5' sequencing libraries:cDNA fragmentation was performed and purified with the SPRIselect kit,then the connector ligation and post-ligation purification were completed.7.Single-cell sequencing:The 5' Gene Expression library and Chromium Single Cell V(D)J enriched library were sequenced using an Illumina NovaSeq sequencer.8.Flow cytometry analysis of TRM:Analyzing the proportion of CD4+TRM and CD8+TRM in CD4+T cells and CD8+T cells in the bone marrow,respectively,by detecting the cell surface markers CD3,CD8,CD69,CD45RA,and CD45RO.9.Data analysis9.1 Data processing:Cell Ranger software was used to align the fastq sequencing data generated by sequencing to the reference genome for cell and UMI counting to generate a cell-gene expression matrix.9.2 Data quality control and standardization:The data were quality controlled and filtered,and the cells were standardized.9.3 Data merging and batch effect correction:Seurat combined with Harmony algorithm was used to integrate the data of each sample and correct the batch effect of samples.9.4 Clustering and annotation of cells:Seurat was used to cluster the cells,and each cluster was annotated according to the expression of marker genes.9.5 Gene Ontology(GO)analysis:Using the GO database,the data were enriched and analyzed at three levels:cellular component,molecular function,and biological process.9.6 Kyoto Encyclopedia of Genes and Genomes(KEGG)analysis:Enrichment analysis of differentially expressed genes based on KEGG database..9.7 Gene Set Enrichment Analysis(GSEA):Gene sets of pathways with absolute NES>1,nominalp-value<0.05 and FDRq-value<0.25 were considered to be significant.9.8 Immune repertoire V(D)J analysis:The immune repertoire of T cells was visualized and analyzed.The contents include the nucleotide length of CDR3 region,proportion of inserted and deleted bases,abundance of different antigen receptor sequences and diversity and clonality of libraries.9.9 Flow Cytometry Data Analysis:The data were analyzed using Kaluza software,and were imported into SPSS 26.0 and GraphPad Prism 7 for statistical analysis.Results:1.Sample information:Four ITP patients and two healthy controls were included.All ITP patients were newly diagnosed with ITP and did not use drugs for autoimmune diseases.2.Quality evaluation and quality control of scRNA-seq data:The average number of single-cell genes in each sample was 1 190,the Fraction Reads of single cells were greater than 92%,and the valid Barcodes were greater than 82%for each sample.3.Unsupervised clustering of bone marrow T cells:The clustering of ITP group,healthy control group and merged group was more consistent.According to the specific marker gene,11 clusters of T cells including CD4+naive T cells(TN),CD4+effector memory T cells/resident memory T cells(TEM/TRM),CD4+central memory T cells(TCM),CD4+recently activated effector memory or effector T cells(TEMRA/TEFF),CD4+cytotoxic T cells(CTL),CD8+ TN,CD8+TEM/TRM,CD8+TEMRA/TEFF,CD8+CTL,Treg and MAIT were annotated.4.V(D)J analysis of bone marrow T cells:The results of clonotype diversity analysis using Chao and ACE indices showed that healthy controls had higher abundance,suggesting disease-associated clonal expansion in ITP patients.5.Bone marrow MAIT single cell transcriptome characteristics in ITP:Compared with normal controls,the enriched up-regulated genes of MAIT in the bone marrow of ITP patients mainly included interferon pathway-related genes IFI44L,IFI6,IFITM1 and genes regulating cytotoxicity and killing.The enriched down-regulated genes mainly included BTG2,a gene that maintains cell quiescence,and YPEL5,a cell cycle-related gene.Multiple differentially expressed genes were associated with immune checkpoints such as CTLA4 and PD1.In addition,KEGG analysis showed that genes upregulated by MAIT in ITP were associated with autoimmune diseases such as type I diabetes and autoimmune thyroid disease.6.Bone marrow Treg single cell transcriptome characteristics in ITP:Treg from ITP patients had up-regulation of interferon pathway-related genes similar to MAIT,and other up-regulated genes included DYNLT1,a gene regulating mitochondrial function.The down-regulated genes in the ITP group included the chemokines or cell adhesion-related genes,indicating that,abnormal chemotaxis,and adhesion function of Treg may also exist in ITP patients,in addition to abnormal interferon pathways.7.Bone marrow TN single cells transcriptome characteristics in ITP:Similar to MAIT and Treg,the up-regulated genes of CD4+TN in ITP patients also included interferon-related genes ILI6 and IFI44L,which could be enriched in type ?interferon pathway and antiviral/innate immune response process in GO analysis.The down-regulated genes mainly included LBH and BTG2,which regulated cell cycle and quiescence,and the down-regulated genes were also enriched in hormone response-related pathways.Similar to CD4+TN,up-regulation of IFI44L,CTSW gene expression and down-regulation of BTG2 expression were also present in CD8+TN from ITP patients.Differently,the genes down-regulated by ITP CD8+TN contained apoptosis-related gene SGK1 and autophagy regulatory gene TNFAIP3.In addition,CD8+TN also showed similar gene enrichment pathways to CD4+TN in gene enrichment analysis,but there were more down-regulation of genes related to oxidative stress.These results showed that CD4+and CD8+TN in ITP not only showed similar characteristics in interferon pathway and hormone response,but also had unique characteristics in autophagy and oxidative stress.8.Further clustering and annotation of some bone marrow T cells:We extracted memory T cells and effector T cells after the first grouping,divided them into two categories:CD4+T cells and CD8+T cells,and performed clustering at higher resolution on this basis.CD4+TCM-1,CD4+TCM-2,CD4+TEM,CD4+TRM,CD4+TEMRA,CD4+CTL and Th/Treg were obtained after CD4+T cell annotation,of which CD4+TCM-1 showed an association with TN compared to CD4+TCM-2.After re-clustering and annotation of CD8+T cells,CD8+TCM-1,CD8+Tcm-2,CD8+TEM,CD8+TRM,CD8+TEMRA,CD8+CTL-1,CD8+CTL-2,CD8+TN and NK-like CD8+T cells were obtained,of which the correlation between CD8+ TCM-1 and T N was greater than that between CD8+TCM-2.9.Proportion of bone marrow TRM and single cell transcriptome characteristics in ITP:The proportion of CD4+ TRM in CD4+T cells and the proportion of CD8+TRM in CD8+T cells in the bone marrow of ITP patients were increased compared with healthy controls.The genes with higher expression of CD4+TRM than other T cells were mainly enriched in gene pathways related to T cell activation and lymphocyte proliferation.Compared with normal controls,the enriched up-regulated genes mainly included genes which regulate cell activation or differentiation,in CD4+TRM from ITP patients.The down-regulated genes were enriched in hormone-responsive and apoptosis-related pathways.We also found that CD8+TRM in ITP patients had upregulation of activation/differentiation genes as well as chemokine-related genes.The up-regulated genes MAP3K8 were also associated with immune checkpoint signaling.These results suggest that TRM,especially CD4+TRM,show enrichment of T lymphocyte proliferation and activation pathways;TRM in ITP patients showed higher expression of cell activation-related genes and low expression of apoptosis-related genes compared with healthy controls.10.Single cell transcriptome characteristics of CD8+CTL and CD8+TCM in the bone marrow of ITP patients:CD8+CTL-1 and CD8+TCM-1 were more significantly enriched in T cell activation and differentiation pathways than other clusters.In CD8+CTL,ITP patients highly expressed the genes that promote activation or differentiation compared with normal controls;enrichment analysis results showed that their pathways associated with T cell toxicity were significantly enriched.The expression of ID2 and ZFP36 in CD8+TCM of ITP patients was up-regulated,while the down-regulated genes were enriched in apoptotic pathways and related to hormone therapy responsiveness.KEGG analysis suggested that the correlation with type I diabetes or autoimmune thyroid disease was enhanced compared with the healthy control group.Conclusion:A transcriptome map of bone marrow T cells at the single-cell level was constructed;the unsupervised clustering and annotation results of the ITP,healthy control,and combined groups were consistent.In ITP,the functional pathway of CD4+TN,CD8+TN,Treg and MAIT was related to interferon,and the up-regulated genes in ITP bone marrow showed enrichment in the interferon pathway for the four clusters.Some T cells in the bone marrow showed a higher activation level,comparing with other memory and effector T cells;TRM,CD8+CTL and CD8+TCM were mainly enriched in T lymphocyte activation-related pathways.Part ?: Immune Checkpoint-Related Single Nucleotide Polymorphisms Are Associated with Immune ThrombocytopeniaBackground:ITP is characterized by reduced platelet count and an increased risk of bleeding.ITP is an acquired autoimmune disease,in which platelets are opsonized by auto-antibodies and destroyed by phagocytic cells.ITP pathogenesis involves a hyper-activated T cell response,which is important for cell-mediated cytotoxicity and IgG production.Therefore,investigating T cell abnormalities in ITP patients may reveal the mechanism of pathogenesis and development of ITP.Immune checkpoints,including co-stimulation and co-inhibition signal pathways,are among the central mechanisms that regulate T-cell mediated immune responses,determine T cell function and fate.Co-stimulation and co-inhibition signals,also termed "second signals",modify the "first signal" provided by the T cell receptor and MHC recognition,and determine the outcome of adaptive T cell immunity synergistically.Aberrant expression of costimulatory molecules and co-inhibitory molecules may promote the generation of self-reactive T cells or cause evasion of self-reactive T cells from central and peripheral tolerance,contributing to autoimmunity.The costimulatory molecules of T cells consist of CD28,inducible costimulatory(ICOS),TNF superfamily member 4(TNFSF4),DNAM1(CD226)and the co-inhibitory molecules contain T cell immunoglobulin and mucin domain 3(TIM3),cytotoxic T-lymphocyte associated protein 4(CTLA4),programmed death-1(PD1)and lymphocyte activating 3(LAG3).Among these,CD28 and CTLA4 interact with two ligands(CD80 and CD86)on the surface of antigen-presenting cells,introducing a positive stimulatory and a negative inhibitory signal into T cells,respectively.PD1,a novel co-inhibitory member of the B7/CD28 family,is engaged by PD-L1 to inhibit T cell activation.Thus,co-stimulation and co-inhibition signals may contribute to the hyper-active state of T cells in autoimmune diseases.Single-nucleotide polymorphisms(SNPs)are the most common type of genetic variation among humans.Genetic studies have revealed that multiple polymorphisms in the genes encoding immune checkpoint molecules are associated with susceptibility to several autoimmune diseases.The CD28 rs1980422 CC genotype is associated with both rheumatoid factor(RF)and anti-citrullinated protein antibodies(ACPA)in rheumatoid arthritis(RA),while polymorphisms in ICOS rs6726035,PD1 rs36084323,DNAM1 rs763361,and TIM3 rs10515746 also act as related factors in RA development.The TT genotype of rs231779 in the CTLA4 gene increases one's risk of Graves' disease.In addition,there was a different distribution of the TT genotype in LAG3 rs870849 in multiple sclerosis(MS)patients compared to healthy controls.In systemic lupus erythematosus(SLE),the frequency of minor T alleles of TNFSF4 rs2205960 is associated with autoantibody production.As these SNPs are related to autoimmune diseases,few studies have focused on SNPs of immune checkpoint genes in ITP.Whether immune checkpoint gene polymorphisms are protective or risk factors in ITP and whether these SNPs are associated with susceptibility,severity,corticosteroid-sensitivity,or refractoriness of ITP remains to be explored.Objective:1.To investigate the distribution of the eight T lymphocyte immune checkpoint related SNPs in ITP patients and healthy controls.2.To investigate the association between single-nucleotide polymorphism of immune checkpoint genes and susceptibility,severity,corticosteroid-sensitivity or refractory in ITP,and to reveal the possible targets for the treatment of ITP associated with the immune checkpoint of T cells.Methods:1.Patients and controls:We recruited samples from primary ITP and healthy participants,ITP patients were further stratified by the following three indicators:severity,refractoriness,and corticosteroid sensitivity.2.DNA extraction:PBMCs were isolated by Ficoll density gradient centrifugation and the genomic DNA was extracted from PBMCs using a commercial DNA extraction kit.3.Selecting SNP sites and genotyping:TIM3 rs10515746,CD28 rs 1980422,TNFSF4 rs2205960,CTLA4 rs231779,PD1 rs36084323,ICOS rs6726035,DNAM1 rs763361,LAG3 rs870849 were selected,and MassArray system was used for genotyping to detect gene polymorphism.4.Flow cytometry:The phenotypes of PBMCs were analyzed for cell surface markers CD3,CD4,and CD28.5.Magnetic cell sorting(MACS):CD4+T cells were separated from PBMCs of patients,using a CD4+T cell isolation kit.The purity of CD4+cells was detected by flow cytometry.6.Real time RT-PCR:Total RNA was extracted from CD4+T cells using TRIzol reagent,and RNA was converted into cDNA.Quantitative PCR was performed on the LightCycler 480II Real-Time PCR system.7.Statistical analysis:We calculate the p value of the Hardy Weinberg equilibrium(HWE)and SPSS 26.0 and GraphPad Prism 7 were used for statistical analyses.In addition,generalized multifactor dimensionality reduction(GMDR)was performed to detect gene-gene interactions.Results:1.Study Population:There was no statistical difference in age structure or gender distribution between ITP patients(n=307)and healthy controls(n=295,p>0.05).All eight SNPs were in accordance with HWE in the control group.2.Polymorphisms associated with ITP susceptibility:We used four genetic models to analyze the relationship between eight immune checkpoint-related SNPs and ITP susceptibility.The different distributions of rs1980422 polymorphism in CD28 under codominant and dominant models showed a relationship with ITP susceptibility(p<0.05),which was also related to ITP susceptibility after FDR correction.Regarding rs 1980422 in CD28,the CT and CC/CT genotypes were associated with ITP susceptibility(p=0.006 and p=0.016,respectively).Thereafter,combined analysis revealed that the heterozygous genotypes of TIM3 rs10515746 and CD28 rs1980422 played a significant role in increased risk of ITP(OR>1,p<0.05),In addition,analysis using the dominant model showed that the CA/AA genotypes of TIM3 rs10515746 and CC/CT genotypes of CD28 rs1980422 significantly increased the ITP risk compared with homozygous major alleles.High-order interactions were further investigated for ITP susceptibility using the GMDR method.Indicating that TIM3 rs10515746,CD28 rs1980422,and ICOS rs6726035 exhibited interactive effects on ITP susceptibility.3.Polymorphisms associated with ITP severity:The TT genotype and T allele of PD1 rs36084323 were significantly associated with ITP severity in the four models(p<0.05).Under the allele model,the T allele of LAG3 rs870849 showed a statistical relationship with ITP severity(p<0.05),Combined analysis with the allele model revealed that ITP patients carrying the T allele of PD1 rs36084323 showed a 1.649-fold increased risk of developing severe ITP(OR=1.649,95%CI=1.186-2.291,p=0.003).In contrast,patients with the T allele of LAG3 rs870849 showed a decreased risk of severe ITP(OR=0.506,95%CI=0.312-0.818,p=0.005).4,Polymorphisms associated with corticosteroid sensitivity:For DNAM1 rs763361,after adjusting for sex and age,minor allele homozygotes rather than heterozygotes were significantly associated with corticosteroid-sensitivity in the codominant and recessive models(p=0.016 and p=0.030,respectively).Allelic frequencies of ICOS rs6726035 were significantly different under the allele model between corticosteroid-sensitive and-resistant groups(p=0.015).Results under the allele model revealed statistical associations between DNAM1 rs763361,ICOS rs6726035,and corticosteroid sensitivity analyzed using multivariate logistic regression analysis(p<0.05),The T allele of DNAM1 rs763361 was associated with a 1.939-fold increased risk of corticosteroid-resistance(OR=1.939,95%CI=1.278-2.942,p=0.002).Conversely,the T allele of ICOS rs6726035 had a protective effect(OR=0.538,95%CI=0.366-0.791,p=0.002).5.Polymorphisms associated with ITP refractoriness:The genotypic distribution of PD1 rs36084323 was significantly associated with ITP refractoriness(p<0.05).The genotypic distribution ofPD1 rs36084323 under the codominant and dominant models was statistically different between both groups(p=0.030 and p=0.034,respectively).In addition,the TT/CT genotypes of rs36084323 showed an 8.889-fold increased risk of developing refractory ITP compared to the CC genotype under the dominant model(OR=8.889,95%CI=1.183-66.771,p=0.034).6.CD28 rs1980422 polymorphism associated with CD28 expression:The proportion of CD28+cells and the mean fluorescence intensity(MFI)of CD28 in CD4+T cells derived from ITP patients with CT genotype or TT genotype of CD28 rs1980422 were analyzed.ITP patients with the CT genotype showed a higher level of CD28 protein expression than TT genotype according to the MFI of CD28(p=0.006).The CT genotype was also associated with increased CD28 mRNA levels compared to the TT genotype(p=0.028).Conclusion:The genotype and/or allele frequency distributions of the six SNPs related T cell immune checkpoints(TIM3 rs10515746,CD28 rs1980422,PD1 rs36084323,ICOS rs6726035,DNAM1 rs763361,LAG3 rs870849SNP)were different between ITP patients and healthy controls,and these SNPs were associated with susceptibility,severity,refractory or glucocorticoid sensitivity of ITP.ITP patients with CT genotype of CD28 rs1980422 had higher CD28 expression in both protein and mRNA levels than patients with TT genotype.The six SNPs may be used as predictors to-evaluate the occurrence,development and therapy response of ITP,and also provide a new target for ITP immunotherapy. |
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