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. 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. :
- "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.
- "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.
- "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 k=10 (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.