新型抗原呈递细胞赋予肠道菌群的Treg依赖性耐受性

　　通过将靶向构建体插入RORC 3-UTR中，通过在C57BL/6背景上的胚胎茎（ES）细胞中的同源重组中插入RORCVENUS-T2A-CREERT2小鼠。IRES-VENUS-T2A-CREER-FRT-NEOR-FRT盒式靶向构建体是通过克隆创建的。从BAC克隆RP24-209K20取回同源臂。为了促进ES细胞靶向，使用了CRISPR -CAS9系统。使用重组技术在体外使用Mega Shortscript T7套件（Life Tech Corp，AM1354）在体外转录RNA。靶向载体Cas9蛋白（Fisher Scientific，A36498 Truecut Cas9蛋白V2）和引导RNA被共构型为G1 ES细胞中的G1 ES细胞，该细胞源自F1杂交胚泡的129S6×C57BL/6J。将最终的嵌合体与Flper小鼠一起饲养，以切除Neo Cassette。RAG1RFP-CREERT2（C57BL/6-TG（RAG1-RFP，-CRE/ERT2）33NARL）小鼠从啮齿动物模型资源中心获得的小鼠是通过将RAG1启动子和RFP-IRES-CREERT2组成的BAC转基因插入C57BL/6小鼠中的ES细胞中的。 　　先前已经描述了ADIG（AIREGFP），CLEC9ACRE，RORACRE，ITGB8FL/FL，CD4CRE，H2-DMA –/ - ，C7和ITGB8TDTOMATO小鼠，先前已被描述为26,46,46,46,47,48,49,49,49,50,51,52。RorgtCRE，H2-AB1FL/FL，R26LSL-TDTOMATO，R26LSL-YFP，ZBTB46GFP，IL22CRE，C57BL/6（CD45.2），CD45.1和BALB/C小鼠是从杰克逊实验室购买的。小鼠的产生和治疗是根据第21-05-007号议定书和08-10-023进行的，该协议得到了斯隆·凯特林研究所（SKI）机构动物护理和使用委员会批准的。根据机构指南和道德法规，所有小鼠菌株均在特定的无病原体条件下保持在滑雪动物设施中。雄性和雌性小鼠都被包括在研究中，我们没有观察到性别依赖性作用。除非另有说明，否则所有分析的小鼠均为年龄和垃圾匹配。这项研究中使用的所有动物都没有实验史，在分析时很幼稚。 　　Rorcvenus-Creert2H2-AB1FL/FL和同窝式Rorcvenus-Creert2H2-AB1FL/FWT小鼠在八周的八周大五周的含柠檬酸酸他莫昔芬柠檬酸含有柠檬酸的柠檬酸含柠檬酸酸他莫昔芬（TD.130860; Envigo）上。 　　通过二氧化碳吸入对小鼠进行安乐死。收集器官并处理如下。在补充的RPMI1640中，在胶原酶中消化淋巴器官，1％ - 谷氨酰胺，1％青霉素 - 链霉素，10 mM HEPES，1 mg MG ML -1胶原酶A10104159001）在37°C，250 rpm处持续45分钟。去除大肠，用PBS冲洗，并在补充有5％胎牛血清的PBS中孵育，1％ - 谷氨酰胺，1％青霉素 - 链霉素，10 mM HEPES，1 mM Dithiothreitol和1 mm Dithiothreitol和1 mm EDTA和1 mm EDTA 15分钟以除去上皮层。洗涤样品并在Digest溶液中孵育30分钟。将陶瓷珠（0.25英寸）（MP生物医学，116540034）添加到大肠样品（每个样品3）中，以帮助组织解离。通过100μm滤网过滤消化的样品，并离心以去除胶原酶溶液。Thymus samples were minced with scissors followed by enzymatic digestion in RPMI1640 supplemented with 10% fetal calf serum, 1% -glutamine, 10 mM HEPES, 62.5 μg ml−1 Liberase and 0.4 mg ml−1 DNase I. Density-gradient centrifugation using a 3-layer Percoll gradient with specific gravities of 1.115, 1.0651.0用于富集基质细胞进行流式细胞仪分析。为了分类MTEC，使用CD45 Microbeads（Miltenyi Biotec）将消化胸腺细胞的单细胞悬浮液耗尽了CD45+细胞。 　　为了进行流式细胞仪分析，在细胞表面染色之前，在4°C下用活/死固定的紫罗兰色或PBS中的僵尸NIR染色10分钟，将死细胞排除在外。然后将细胞与抗CD16/32在染色缓冲液（2％FBS，0.1％Na叠氮化物，PBS中）孵育10分钟，以阻止与FC受体的结合。将细胞外抗原在4°C或室温（CCR7染色）中染色20-30分钟。对于细胞内蛋白分析，根据制造商的说明，将细胞固定并用细胞膜（BD Biosciences）和/或Ebioscience Foxp3试剂盒透化。将细胞内抗原在相应的1×PERM/WASH缓冲液中在4°C下染色30分钟，或过夜，以进行细胞内AIRE染色。将活细胞用DNase（0.08 U ML -1）在室温下用DNase（0.08 U ML -1）处理10分钟，并在BD LSR或Cytek Aurora上获得染色缓冲液洗涤。添加了123个eBeads以量化绝对细胞数量。补充表3中列出了用于流式细胞仪和FAC的抗体。除非另有说明，否则我们使用以下毒剂：thetis细胞：lin（siglec-f，tcrβ，tcrβ，tcrγδ，cd19，b220，nk1.1）–cd64 – ly6c – ly6c –rorγT（insection c. rorce）（insection cons insece consect）cxcr6 – mhcii+;MHCII+ILC3S：LIN – CD64 – LY6C –RORγT（Rorcvenus-Creert2小鼠中金星的细胞内染色或表达）CXCR6+MHCII+和DC2S：LIN – CD64 – LY64 – LY6C – LY6C –RORγT–RorγT-CD11C+MHCI+MHCI+CD11b+。 　　通过CO2吸入将小鼠安乐死，并收集大肠并立即将其放入福尔马林10％。基于既定的评分系统，用于小鼠模型中的肠道炎症，对H＆E染色的部分进行了肠道炎症评分的组织病理学评估53。评估包括炎症细胞浸润，上皮变化和粘膜结构变化的严重程度和程度。简而言之，通过组织学评估了炎性细胞浸润的严重程度和程度。其他评估包括粘膜绒毛萎缩的上皮细胞的增殖，隐窝，杯状细胞的丧失，隐窝脓肿，侵蚀和溃疡。 　　对于RORγT+ MHCII+细胞的SCRNA-SEQ和SCATAC-SEQ，将来自2周龄（P14 – P17）Rorcvenus-Creert2小鼠的MLN从16个生物学重复中汇总，并如前所述进行处理。通过用生物素化抗体染色，然后是磁珠阴性选择，将细胞耗尽了LIN+（TCRB，TCRBγδ，CD19，B220，NK1.1）+细胞。将细胞与抗CD16/32在排序缓冲液（PBS中的2％FBS）中在4°C下孵育10分钟，以阻断与FC受体结合的结合。在排序缓冲液（2％FBS，2mm EDTA，在PBS中），将细胞外抗原染色30分钟。用Sytox Blue（Invitrogen）将细胞洗涤并重悬于排序缓冲液中，以排除死细胞。然后将纯化为cd45+ lin（Siglec-F，TCRβ，TCRγδ，CD19）–RORγT（Venus）+ MHCII+细胞。将细胞分类为CRPMI，然后重生并重悬于RPMI-2％FBS中。使用Chromium Next Gem单细胞多组试剂盒A（目录号1000282）和ATAC KIT A（目录号1000280编号），使用10倍基因组系统进行单细胞多物ATAC和基因表达分析。基因表达测序。简而言之，将> 50,000个细胞（可行性95％）裂解4分钟，并重悬于稀释的核缓冲液中（10x Genomics，2000207）。通过锥虫蓝色染色在伯爵夫人II自动细胞计数器上评估裂解效率和核浓度。将核（每个换位反应9,660个）加载，旨在封装后6,000个核的恢复。转座反应后，将核封装并进行了条形码。按照制造商的说明构建了下一代测序库，并在Illumina Novaseq 6000系统上进行了测序。 　　RORγT+ MHCII+细胞从三周大的（P21）Rorcvenus-Creert2小鼠的MLN池中富集。通过用生物素化抗体染色，然后是磁珠阴性选择，将细胞耗尽（TCRβ，TCRβ，CD19，B220，NK1.1）+细胞。LINE，LIN（CD3，TCRβ，TCRγδCD19，B220，NK1.1）–CD64 – LY6C – MHCII+RORγT（Venus）+细胞被分类为单个井。对CD90，CD11C和CD11B进行染色，以获取有关细胞表面表达和相同数量的CD90 – CD11C+，CD90 – CD11C –，CD90INT和CD90HI细胞的索引分类信息，以确保所有细胞类型的表示。通过用与CD45的生物素化抗体染色，从三周古老的小鼠中富含AIRE+ MTEC，从三周古老的小鼠中富集了AIRE+ MTEC，然后是磁珠阴性选择。CD45- EPCAM+MHCII+AIRE（GFP）+细胞分为单个井。Aire+树突状细胞从同一三周老鼠的MLN池中富集。如上所述，将细胞耗尽了LIN+细胞，LIN（CD3，TCRβ，TCRγδCD19，B220，NK1.1） - CD90 – CD64 – LY6C – CD11C+ MHCII+ MHCII+ AIRE（GFP）+细胞，然后将其分类为单个井。回顾性索引分析分析证实，AIRE（GFP）+细胞是CD11Clomhciihi，代表CCR7+树突状细胞。 　　将单细胞分类为缓冲液RLT（QIAGEN）。将细胞裂解物立即密封并旋转，然后转移到干冰并在-80°C下储存。使用Agencourt rnaclean XP珠（Beckman Coulter）以2.2倍的比例纯化RNA。使用Maxima H减去逆转录酶（Thermofisher）根据制造商的方案使用Oligo DT引物来实现第一链cDNA合成，并在1 mm的最终浓度中添加了自定义的模板转换寡核糖。使用KAPA HIFI HOTSTART READYMIX（KAPA Biosystems KK2601）扩增cDNA 24个周期。picogreen定量后，使用0.1-0.2 ng的cDNA用Nextera XT DNA文库制备试剂盒（Illumina）制备库，总体积为6.25 µl，并用12个循环进行PCR。用音量汇总索引的库，并以1×比例通过Ampure XP珠（Beckman Coulter）清洁。使用HISEQ 3000/4000 SBS KIT（Illumina）在PE50或PE100运行中在HISEQ 4000上测序池。每个样品平均产生了180万个成对读数，每个样品平均的mRNA碱基百分比为63％。 　　SCRNA-SEQ和SCATAC-SEQ FASTQ文件与MM10对齐（Cell Ranger Mouse参考基因组MM10-2020-A-2.0.0），并由Cell Ranger ARC v2.0.0计数，带有默认参数。根据RNA-Seqtranscript的数量过滤条形码（> 1,000和< 50,000), the number of detected genes (>500和 < 6,000), and the fraction of mitochondrial transcripts (<15%). Barcodes were further filtered based on the number of scATAC-seq fragments (3.5 < log10(number of fragments) < 4.5) and transcription start site enrichment score (>4). Arrow files were created from the scATAC-seq fragments using ArchR v1.0.154, and doublets were identified and removed with default parameters. Finally, any genes detected in <2 cells in the scRNA-seq data were discarded, leaving 20,779 genes. After clustering, the scRNA-seq data (described in ‘Dimensionality reduction, cell clustering, and visualization’), and based on the expression of marker genes, we identified 5 minor contaminant clusters (glial cells; cluster 17, plasmacytoid dendritic cell; cluster 18, Rorc–/lo CCR7+ dendritic or Thetis cell; cluster 19, mixed monocyte/cDC1; cluster 20, and macrophage; cluster 21) which were excluded from downstream analyses. In total, 10,145 cells remained, with a median scRNA-seq library size of 3,150 and a median of 13,885 scATAC-seq fragments. 　　Smart-seq2 sequencing data from demultiplexed samples was aligned to the mouse reference genome using STAR v2.7.7a55 with ‘--twopassMode Basic --outFilterMultimapNmax 1 --quantMode TranscriptomeSAM’. Sequence reads were aligned and annotated using a STAR index created from GENCODE GRCm38 (mm10) release M25 primary assembly genome and gene annotations56. Alignment files were individually name-sorted using Samtools v1.1157, and then used to create a cell-by-gene count matrix using featureCounts58 (subread v2.0.1). The count matrix was filtered based on the number of transcripts (>50,000), number of detected genes (>1,300），线粒体转录本的比例（<8%). Finally, genes detected in <2 cells were discarded. A total of 481 cells remained, with a median library size of 924,319 from 27,195 genes. 　　For each scRNA-seq dataset, the filtered count matrix was library-size-normalized, log-transformed (‘log-normalized’ expression values) and then centred and scaled (‘scaled’ expression values) using Seurat v4.0.4. Principal component analysis (PCA) was performed on the scaled data (total number of principal components = 50). PhenoGraph clustering59 was performed using the first N principal components with k-nearest neighbours (N = 30 and k = 30 for the multiome scRNA-seq data; N = 20 and k = 30 for the Smart-seq2 dataset; N = 30 and k = 20 for the human gut dendritic cells). Cell clustering was visualized using UMAP60, computed from the nearest neighbour graph built by PhenoGraph. 　　The multiome scATAC-seq data analysis was restricted to the cells in clusters 1–16 of the scRNA-seq results, as previously described for pre-processing. Latent Semantic Indexing (LSI) was performed on 100,000 top variable tiles (500 bp genomic bins) identified after ten iterations of ‘IterativeLSI’ by ArchR. Tiles from non-standard chromosomes, chrM, and chrY were not included in this analysis. Cells were clustered (method=Seurat, k.param = 30, resolution = 1.2) and visualized with UMAP (nNeighbors = 30) using 30 LSI components. In both the scRNA-seq and scATAC-seq data, we identified several clusters of LTi cells (scRNA clusters 9–16 and scATAC clusters 7–13). These clusters showed weak pairwise matchings between scRNA and scATAC; therefore, they were combined as one group of LTi cells for downstream analyses. 　　DEGs between groups of cells were identified with MAST61, performed using Seurat functions. MAST was run on the log-normalized expression values. In all tests, genes were only considered if they were detected in at least 10% of the cells in at least one of the two groups compared (min.pct = 0.1, logfc.threshold = 0). In one-vs-rest differential expression tests comparing multiple groups, each group was compared to all the cells from other groups. Specific differential expression comparisons are described in the results. DEGs were reported according to their fold change (>1.5) and adjusted P value (<0.01). Ribosomal and mitochondrial genes were removed from the final list of genes reported or visualized. Where stated, the top DEG markers were subsequently selected for each group, based on fold change. 　　MAGIC imputation62 was applied to the log-normalized expression values for the multiome scRNA-seq dataset to further de-noise and recover missing values. Imputed gene expression values were only used for data visualization on UMAP overlays and heatmaps, where stated. 　　Using standard Seurat functions, we computed cell cycle scores for known S-phase and G2/M-phase marker genes63 to identify proliferating cells. 　　Topics were identified by fitting a latent Dirichlet allocation model, also known as a Grade of Membership (GoM) model64, to the raw gene expression count matrix for Thetis cells (clusters 1–5 of the multiome scRNA-seq data) using CountClust v1.18.065. Genes that were detected in fewer than 10 Thetis cells were not included. The optimal number of topics (K = 8) was selected among values ranging from 3 to 15 with the maximum Bayes factor (BF). The role of a topic in each cell is measured by the degree to which it represents that topic, and the topic weights sum up to 1 in each cell. The importance of a gene for each topic is measured by how distinctively differentially expressed it is in that topic, by measuring the KL-divergence of its relative gene expression to other topics, assuming a Poisson distribution. One topic, defined by ribosomal and mitochondrial genes and shared across all clusters, was removed from the topic model visualizations. 　　The unspliced and spliced mRNAs for the scRNA-seq profiles of the multiome data were counted by Velocyto v0.17.1732 from the position-sorted BAM file containing GEX read alignments, outputted by Cell Ranger ARC in pre-processing. As annotation files for Velocyto, we used the same mm10 gene annotations used in pre-processing, in addition to the mm10 expressed repeat annotation from the RepeatMasker track of UCSC genome browser. Next, we used the Velocyto results to learn a generalized dynamical model of RNA velocities by scVelo v0.2.466. Count matrices were filtered, normalized, and log-transformed (min_shared_counts = 10, n_top_genes = 3000), cell cycle effect was corrected by regressing out S-phase and G2/M-phase scores, using Scanpy 1.6.067. After performing PCA on the corrected data (n_pcs = 30), first- and second-order moments were computed for each cell across its nearest neighbours in the PCA space (n_neighbors = 30). Finally, the full splicing kinetics were recovered and solved for each gene by scVelo’s dynamical model. 　　RORγt+MHCII+ transcriptomes (based on cell-type as sorted) from the SMART-seq2 dataset were integrated with transcriptomes from the 10X multiome scRNA-seq data, using Seurat68. Based on the variability of genes in both datasets, 5,000 top scoring genes were selected by Seurat functions to identify ‘integration anchors’ with canonical correlation analysis (CCA). Expression values for these genes were integrated, scaled, and used for PCA. A UMAP embedding was computed from the first N = 30 principal components (k = 30). Additionally, using Seurat functions, the RORγt+MHCII+ cells from the SMART-seq2 dataset (query) were mapped to multiome scRNA-seq clusters (reference) by projecting the PCA from the reference onto the query to identify ‘transfer anchors’, and then assigning a prediction score for each reference cluster to query cells. The cluster identity with the highest score was chosen as the predicted label for each cell. 　　Given a set of genes, we standardized the log-normalized expression values of each gene across cells and then averaged these values for all genes in the set, assigning an enrichment score to each cell. Where stated, these scoreswere standardized across cells and reported as z-scores. 　　Pseudo-bulk samples were created by averaging the unimputed log-normalized gene expression values for each cluster. In cases where scaled values were used for downstream analyses, these average expression values were standardized across the pseudo-bulk samples. 　　The RMA-normalized and log2-transformed gene expression data of 224 bulk microarray samples from a publicly availableImmGen dataset was downloaded from https://www.haemosphere.org69. For each gene, the probeset with the highest mean expression was retained. We included all cell types isolated from naive, untreated mice. Pseudo-bulk samples were generated from the multiome scRNA-seq data for each Thetis cell subset, and non-proliferating MHCII+ ILC3s (NCR+ ILC3 and LTi cells). The gene expression vectors were scaled across bulk and pseudo-bulk samples within each dataset, and their pairwise cosine similarities were used to compare the samples. These similarity scores were computed from the expression of 2,399 DEGs (FC > 1.3, adjusted P < 0.01) comparing the scRNA-seq clusters in a one-vs-rest test, that were also expressed in the microarray data. The proliferating and progenitor clusters were excluded from the differential expression test, and the LTi clusters were grouped together. For visualization of results, only cell lineages containing a cell type with > 0.25 cosine similarity with either Thetis cell or ILC3 clusters were plotted. 　　To determine similarity between Thetis cells and known cell types we used CellTypist (https://www.celltypist.org), with both low- and high-resolution models of immune cells to classify cells with coarse and fine granularities, respectively. Top predicted labels for each input cell were visualized. 　　scRNA-seq profiles of CD45– thymic epithelial cells were downloaded from a publicly available dataset (GSE103967)21. The raw counts were library-size-normalized, log-transformed, and used to create pseudo-bulk samples for each thymic epithelial cluster. Pseudo-bulk samples were also generated to represent the multiome scRNA-seq Thetis cell clusters (2–5). These pseudo-bulk gene expression vectors were scaled across samples within each dataset, and their pairwise cosine similarities were used to compare clusters from the two datasets. These similarity scores were computed from the expression of 1,740 DEGs (FC >1.3，调整后p< 0.01) identified in a one-vs-rest differential expression test for non-proliferating Thetis cell clusters (2–5), that were also expressed in the thymic epithelial cells. Among individual clusters of thymic epithelial cells defined in the original dataset, we identified 2 clusters of transit amplifying AIRE+ cells (clusters 25 and 26), distinguished by signature gene expression including cell cycle genes. 　　Dendritic cells (annotated as cDC1, cDC2 or lymphoid DC) within the myeloid dataset from the human gut atlas41 were re-clustered. From the gene markers for each Thetis cell subset (one-vs-rest differential expression test for non-proliferating Thetis cell scRNA-seq clusters, FC >1.5，调整后p< 0.01), we identified orthologous human genes that were uniquely mapped by gprofiler2 and computed enrichment scores for Thetis cell subset gene signatures for each human cell. 　　For peak calling of the scATAC-seq data, clusters for similar cell types were grouped: C1 (TC IV), C2–4 (TC I–TC III), C5–6 (NCR+ ILC3), and C7–13 (LTi). Filtered scATAC-seq fragments for each group were extracted from ArchR arrow files. We performed MACS2 v2.2.7.1 on fragments of each group with ‘--gsize mm --qval 0.01 --nomodel --ext 200 --shift −100 --call-summits’. The peak summits were extended by 100 bp in each direction. Regions extending outside of mm10 chromosomes, arising from chrY or chrM, overlapping with blacklist regions precompiled by ArchR (merged from the ENCODE mm10 v2 blacklist regions from https://github.com/Boyle-Lab/Blacklist/blob/master/lists/mm10-blacklist.v2.bed.gz and mitochondrial regions that are highly mappable to the mm10 nuclear genome from https://github.com/caleblareau/mitoblacklist/blob/master/peaks/mm10_peaks.narrowPeak), or containing ‘N’ nucleotides (>过滤序列的0.001。汇编了所有组的区域，并将重叠的区域合并到它们的联盟中，导致不重叠的176,942峰。Archr创建了一个逐个高峰计数矩阵，其“天花板”值为4，以避免强烈的偏见。 　　可访问的峰 <10 cells were filtered from the peak insertion counts, created as described in the previous section, and the resulting 176898 x 10145 peak-by-cell count matrix was used for motif enrichment with chromVAR v1.14.070. Mouse motif PWMs were downloaded from the CIS-BP database71 (‘Mus_musculus_2022_01_14_6-40_pm’), and the missing PWMs were extracted from ‘mouse_pwms_v1’ in chromVARmotifs v0.2.0. The GC content of the peaks was computed with chromVAR, and motifs were matched to them by motifmatchr v1.14.0. Then, chromVAR ‘deviations’ of the motifs were computed for the peak-by-cell count matrix. The ‘top motif’ for each transcription factor was selected by correlating its log-normalized gene expression values (from multiome scRNA-seq) with the deviation z-scores of its motifs, in the same cells, and picking the motif with the highest Pearson correlation coefficient. Finally, transcription factor–motif pairs with a correlation higher than 0.1 were selected. This resulted in 56 top transcription factors, out of 739 CIS-BP transcription factors that were expressed (that is, had any transcripts detected) in the multiome scRNA-seq profiles. The same process was repeated for the 139,528 × 1,552 peak-by-cell count matrix of Thetis cells (multiome scATAC-seq clusters 1–4) and the peaks accessible in at least 10 Thetis cells. Out of 652 CIS-BP transcription factors that were expressed in Thetis cells, 68 had a transcription factor–motif correlation higher than 0.1 and were selected as top transcription factors for Thetis cells. 　　For labelling of RORγt+ cells, RorcVenus-creERT2AireGFP mice were injected intraperitoneally on P1 with 25 μg 4-OH-tamoxifen (4-OHT) and analysed on P8, P15 and P21. For RAG1 fate mapping, Rag1RFP-creERT2R26lsl-YFP mice were injected with 25 μg 4-OHT intraperitoneally on P3, P5 and P7 and analysed on P15. 　　RORγt+MHCII+ cells were enriched from a pool of mLN from P18 RorcVenus-creERT2 mice (for TC IV or reference CCR7– and CCR7+ dendritic cells) and P18 AireGFP mice (for TC I). Cells were depleted of Lin+ (TCRb, TCRgd, CD19, B220, NK1.1)+ cells via staining with biotinylated antibodies followed by magnetic bead negative selection. mTECs were enriched from a pool of thymi from P18 AireGFP mice as described above. Live, Lin(TCRb, TCRgd, CD19, B220, NK1.1)–CD64–Ly6C–MHCII+RORγt(Venus)+CD11c+CD11b+cells (TC IV), Lin–RORγt(Venus)–CD11cloMHCIIhi (CCR7+ dendritic cell), Lin–RORγt(Venus)–CD11chiMHCII– (CCR7– dendritic cell), Lin–CXCR6–CD11c–/loAIRE(GFP)hi (TC I), Lin–RORγt(Venus)+CD90+MHCII+ (LTi/ILC3), or CD45–Epcam+MHCII+AIRE(GFP)+ cells were then sorted directly into 2% glutaraldehyde, 4% PFA, and 2 mM CaCl2 in 0.1 M sodium cacodylate buffer (pH 7.2), fixed for >在室温下1小时，在1％四氧化osmim骨中后缀，在丙酮中脱水，并加工以嵌入EPON。用乙酸铀酰和柠檬酸铅对超薄切片（60–65 nm）染色。使用配备了数码相机（AMT Biosprint29）的透射电子显微镜（Tecnai G2-12; FEI）拍摄图像。 　　从2至3周龄的Rorcvenus-Creert2或AireGFP小鼠中解剖肠系膜淋巴结（MLN），并使用解剖范围和镊子对脂肪进行修剪。MLN在2％多聚甲醛中固定4 h，在4°C中固定4 h，在PBS中洗涤3次，并在0.1 M磷酸盐缓冲液中脱水过夜（16-20 h）。将MLN嵌入最佳切割温度（OCT）化合物中，冷冻在干冰上，并储存在-80°C下。将15–20m的矢状切片放在超砂岩加上显微镜载玻片上，并以-20C储存直至染色。在室温下使用0.2％Triton X-100透化MLN切片，用PBS洗涤3次，在室温下以5％的兔子和驴血清阻塞1小时，并用PBS洗涤3次。接下来，将切片与PBS中以下主要抗体的组合在4°C：CD11C BV421（Biolegend，Clone n418，1：50），CD11B BV480（BD Biosciences（BD Biosciences）（克隆M1/70 1:50 1:50 1:50）CD4 AF647（FOX4 AF647）（Biole-fox clone，clone clone，clone clonu rmm44），孵化了PBS的以下主要抗体的组合。570（Thermofisher，克隆FJK-16，1：50），GFP AF488（Thermofisher A12311，多克隆，1：100），RORγTAPC（Thermofisher，Clone afkjs-9，1：50）和MHCII AF700（BioleGend）（Biolegend，Clone M5/114.15.2，1.2.2，1.2.2，biolegend af700。第二天将样品在PBS中洗涤3次，并安装在慢速钻石抗叶试剂（Thermofisher）中。第1.5号盖玻璃用于密封载玻片，然后在Leica SP8显微镜上进行所有后续成像。分析是使用Imaris软件通过组织 - 循环方法进行的。使用“表面对象创建”模块在Imaris中进行图像分割，该模块使用种子种植，K-均值和分水岭算法来定义单个单元格。 　　Naive CD4+Vβ10+CD25 – CD44LOCD62LHI C7 T细胞在用CD4+T细胞阴性选择试剂盒（Miltenyi Biotec）富集后纯化。使用上述门控策略将树突状细胞子集从Rorcvenus-Creert2小鼠中纯化，并将CCR6包括在内，以将TC I和TC II与TC III和TC IV区分开。通过CD11CHIMHCIIINT表达，将CDC2与CCR7+对应物区分开。在存在ESAT肽（1 µg ML-1; Invivogen）的情况下，以300个树突状细胞与1×103 T细胞的比例以300个树突状细胞与1×103 T细胞的比率共培养，并添加0.5 ng ml-1TGFβ1（peprotech），100 IU ML-ML-ML-1 of IL-1（NCI）。培养4天后评估FOXP3表达。 　　从指定的供体小鼠中分离出骨髓细胞，使用基于磁珠的耗竭将CD90.2+和TER-119+细胞耗尽。将骨髓细胞重悬于PBS中，并将2–3×106细胞注入6周大的CD45.1小鼠中，1天前用每只小鼠950 RAD辐照。6周后，收集了MLN和LI LP进行分析。 　　所有数据的分析均按照文本或图形传说中指定的未配对的两尾t检验，一或两向的ANOVA，具有95％置信区间的置信区间或Mann-Whitney U检验。p <0.05被认为是重要的：*p <0.05，** p <0.01，*** p <0.001，**** p <0.0001。每个实验的重复数量，样本量，显着性测试以及N的价值和含义的详细信息都包含在方法或图形传说中。使用Prism（GraphPad软件）进行统计测试。SCATAC和SCRNA-SEQ实验一次进行。将小鼠非随机分配给实验组，以确保治疗之间基因型的均等分布。在实验过程中，研究人员并未对基因型或治疗视而不见。尚未采取措施估算样本量，以确定数据是否符合所使用的统计方法的假设。校正多个比较后，在整个过程中将显着性（α）定义为<0.05。 　　有关研究设计的更多信息可在与本文有关的自然研究报告摘要中获得。