Cardiac cells isolated from inflamed murine and human myocardial tissues

Droplet-based single-cell and single nucleus RNA sequencing analysis of murine heartsTo obtain sufficient numbers of cells from all cardiac cell types, a total of n=14 samples (WT: 4 samples; TCRM: 6 samples; TCRM isotype: 2 samples; TCRM 14-D10-2: 2 samples) from mouse hearts were processed for single cell RNA sequencing, and n=9 samples (WT: 2 samples; TCRM: 3 samples; TCRM isotype: 2 samples; TCRM 14-D10-2: 2 samples) were prepared for single nucleus RNA sequencing analysis. Samples were processed and sequenced in n=3 batches with all batches spanning multiple conditions. Single cell suspensions were run using the 10x Chromium (10x Genomics) system. The cDNA libraries were generated according to the established commercial protocols for Chromium Single Cell 3’ Reagent Kit (NextGem Chemistry) and Chromium Nuclei Isolation Kit. All libraries were sequenced by NovaSeq 6000 Illumina sequencing at the Functional Genomic Center Zurich. Gene expression was analyzed from sequencing data using CellRanger (v.5.0.1) <i>count</i>, with Ensembl GRCm38.9 as reference. Next, quality control was carried out in R (v.4.2.1) using the R/Bioconductor packages scater (v.1.24.0) and SingleCellExperiment (v.1.18.0) packages. This involved the identification and removal of damaged cells/nuclei or doublets, based on criteria including unusual UMI or gene counts (&gt;2.5 median absolute deviation from the median across all cells) and high mitochondrial gene content (&gt; 2.5 median absolute deviations above the median across all cells). After performing quality control, the final dataset included 31 078 cells and 24 995 nuclei.Downstream analysis was performed using the Seurat R package (v.4.1.1). First, all samples were merged and integrated across data type (single cell or single nucleus data) using the <i>IntegrateData </i>function from the Seurat R package to account for differences between single cell and single nucleus data. Downstream analysis further included normalization, scaling, dimensional reduction with PCA and UMAP, graph-based clustering and calculation of unbiased cluster markers. Clusters were characterized based on the expression of calculated cluster markers and canonical marker genes as reported in previous publications (refs). In order to examine expression signatures of Fibroblasts in more detail cells assigned as Fibroblasts were re-embedded and re-analysed individually.Droplet-based single nucleus RNA sequencing analysis of human heart biopsiesAs for murine samples, isolated nuclei from human heart biopsies were run using the 10x Chromium (10x Genomics) system and cDNA libraries were generated according to the established commercial protocols for Chromium Single Cell 3’ Reagent Kit (NextGem Chemistry) and Chromium Nuclei Isolation Kit. Libraries were sequenced by NovaSeq 6000 Illumina sequencing at the Functional Genomic Center Zurich and gene expression was estimated using CellRanger (v.5.0.1) <i>count</i>, with Ensembl GRCh38.103 as reference. Quality control included the removal of nuclei with unusual UMI or gene counts (&gt;2.5 median absolute deviation from the median across all cells) and was performed in R v.4.2.1 using the R/Bioconductor packages scater (v.1.24.0) and SingleCellExperiment (v.1.18.0).For downstream analysis with the Seurat R package (v.4.3.0) all samples were merged and integrated across patient ID using the <i>IntegrateData </i>function. Integrated data was further processed running normalization, scaling, dimensional reduction with PCA and UMAP, graph-based clustering and calculation of unbiased cluster markers. Clusters were characterized based on the expression of calculated cluster markers and canonical marker genes as reported in previous publications. Following cluster assignments samples were grouped based on their T cell proportions and groups were compared by calculating differentially expressed genes using the <i>FindAllMarkers</i> function from the Seurat R package.