Signature of long-lived memory CD8+ T cells in acute SARS-CoV-2 infection

The datasets uploaded in this Zenodo entry were generated in the single cell RNA sequencing part of the project. For the scRNAseq analysis, cells from ten patients and the same time point were pooled together, generating four individual sample sets in total: (1) patients CoV2_T001- CoV2_T010, acute; (2) patients CoV2_T001- CoV2_T010, six months post-infection; (3) patients CoV2_T011- CoV2_T020, acute; (4) patients CoV2_T011- CoV2_T011-20, six months post-infection. We additionally generated two more sample sets: using 5000 unsorted PBMCs from each patient’s sample: (5) patients CoV2_T001- CoV2_T010, six months post-infection unsorted; (6) patients CoV2_T011- CoV2_T020, six months post-infection unsorted. The cells in these two sample sets were hashed. Finally, using PBMCs from four healthy donors, we generated sample set (7) by sorting and pooling 2000 CD8+ T cells from each healthy donor sample. We are here providing the pre-processed sets for each sample set (1-7), i.e. :<br> - "filtered feature bc matrix" files, as output from the ‘cellranger multi’ pipeline (Cell Ranger version 5.0.0), containing cell-RNA count matrices and cell-ADT matrices. ADTs comprise counts for TotalSeq antibodies and dCODE Dextramers.<br> - "filtered_contig_annotations.csv" files, as output from the ‘cellranger multi’ pipeline (Cell Ranger version 5.0.0), containing High-level annotations of each high-confidence, cellular contig for TCR clonal analysis. This file is not present for sets 5 and 6, because we did not perform TCR profiling for these samples.<br> - "clusters.tsv" files, as output from the souporcell SNP analysis (version 2). To cluster cells based on their patient specific genetic variants, we merged sample sets 1, 2 and 5 (comprising sorted cells from both time points of patients CoV2_T001- CoV2_T010 and unsorted cells of the same patients) and sets 3, 4 and 6 (comprising cells from both time points of patients CoV2_T011- CoV2_T020 and unsorted cells of the same patients). Then, we executed the souporcell pipeline with option <em>k=10 </em>(number of clusters to be determined) for each of the two merged sample sets. Together, these files allow to reproduce the analysis as reported in the paper. Additionally, we provide the Seurat Objects "Integrated.h5seurat" and "Integrated_NA_filtered.h5seurat" which can be used to skip the pre-processing steps of the data analysis. See the code provided on https://github.com/Moors-Code/SARS-CoV-2-Tcell-Boyman-collaboration for details.本Zenodo仓库上传的数据集源自本项目的单细胞RNA测序（single cell RNA sequencing, scRNA-seq）部分。在本次scRNA-seq分析中，我们将10名患者同一时间点的细胞混合，共生成4组独立样本集： 1. 急性感染期患者CoV2_T001至CoV2_T010； 2. 感染后6个月随访期患者CoV2_T001至CoV2_T010； 3. 急性感染期患者CoV2_T011至CoV2_T020； 4. 感染后6个月随访期患者CoV2_T011至CoV2_T020。 我们额外生成了2组样本集：分别从每名患者的样本中获取5000个未分选外周血单个核细胞（peripheral blood mononuclear cell, PBMC）： 5. 感染后6个月随访期患者CoV2_T001至CoV2_T010的未分选PBMC样本集； 6. 感染后6个月随访期患者CoV2_T011至CoV2_T020的未分选PBMC样本集。 该两组样本集的细胞均进行了哈希标记。 最后，我们利用4名健康供者的PBMC，通过分选并混合每名健康供者样本中的2000个CD8+ T细胞，生成了第7组样本集。 本次我们提供了1至7组样本集的预处理数据，具体包括： - "filtered feature bc matrix" 文件：由`cellranger multi`流程（Cell Ranger版本5.0.0）输出，包含细胞RNA计数矩阵与细胞抗体寡核苷酸偶联物（antibody-derived tag, ADT）计数矩阵。ADT计数涵盖TotalSeq抗体与dCODE Dextramers的检测信号。 - "filtered_contig_annotations.csv" 文件：同样由`cellranger multi`流程（Cell Ranger版本5.0.0）输出，包含每条高置信度细胞重叠群的高级注释信息，用于T细胞受体（T cell receptor, TCR）克隆型分析。第5、6组样本集无此文件，因我们未对这些样本开展TCR分型分析。 - "clusters.tsv" 文件：由souporcell SNP分析流程（版本2）输出。为基于患者特异性遗传变异对细胞进行聚类，我们将样本集1、2、5（涵盖患者CoV2_T001至CoV2_T010两个时间点的分选细胞以及该组患者的未分选细胞）与样本集3、4、6（涵盖患者CoV2_T011至CoV2_T020两个时间点的细胞以及该组患者的未分选细胞）分别合并，随后对两组合并后的样本集执行souporcell流程，并设置参数*k=10*（待确定的聚类数）。 上述所有文件可用于复现本文献中报道的分析流程。此外，我们还提供了Seurat对象文件"Integrated.h5seurat"与"Integrated_NA_filtered.h5seurat"，可用于跳过数据分析的预处理步骤。详细信息请参阅https://github.com/Moors-Code/SARS-CoV-2-Tcell-Boyman-collaboration 中提供的代码。