Cardiac cells isolated from inflamed murine and human myocardial tissues

Droplet-based single-cell and single nucleus RNA sequencing analysis of murine hearts
To 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) count, 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 (>2.5 median absolute deviation from the median across all cells) and high mitochondrial gene content (> 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 IntegrateData 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 biopsies
As 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) count, with Ensembl GRCh38.103 as reference. Quality control included the removal of nuclei with unusual UMI or gene counts (>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 IntegrateData 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 FindAllMarkers function from the Seurat R package.

基于液滴的小鼠心脏单细胞及单细胞核RNA测序分析
为获取所有心脏细胞类型的足够细胞数量，本研究共对来自小鼠心脏的14份样本（野生型（WT）：4份；TCRM：6份；TCRM同型对照：2份；TCRM 14-D10-2：2份）进行单细胞RNA测序处理，并对9份样本（WT：2份；TCRM：3份；TCRM同型对照：2份；TCRM 14-D10-2：2份）开展单细胞核RNA测序分析。所有样本分3批进行处理与测序，每一批次覆盖多种实验条件。单细胞悬液使用10x Chromium（10x Genomics）系统进行上机操作。按照成熟的商业化标准流程，使用Chromium单细胞3’试剂试剂盒（NextGem Chemistry）及Chromium细胞核分离试剂盒构建cDNA文库。所有文库均在苏黎世功能基因组中心通过Illumina NovaSeq 6000完成测序。
测序数据的基因表达定量使用CellRanger（v.5.0.1）的count模块完成，参考基因组为Ensembl GRCm38.9。随后使用R（v.4.2.1）环境下的R/Bioconductor包scater（v.1.24.0）与SingleCellExperiment（v.1.18.0）开展质量控制：基于异常唯一分子标识符（Unique Molecular Identifier，简称UMI）计数或基因计数（与所有细胞的中位数偏差超过2.5倍中位绝对偏差（median absolute deviation））、高线粒体基因占比（与所有细胞的中位数偏差超过2.5倍中位绝对偏差）等标准，识别并移除受损细胞/细胞核或双细胞（doublets）。质量控制完成后，最终数据集包含31078个细胞及24995个细胞核。
后续分析使用Seurat R包（v.4.1.1）完成。首先，利用Seurat R包的IntegrateData函数对所有样本进行合并与整合，以校正单细胞与单细胞核数据间的系统差异。后续分析进一步包括标准化、缩放、主成分分析（PCA，Principal Component Analysis）与均匀流形近似与投影（UMAP，Uniform Manifold Approximation and Projection）降维、基于图的聚类以及无偏聚类标记基因计算。根据计算得到的聚类标记基因及既往文献报道的经典标记基因对聚类结果进行注释。为更细致地解析成纤维细胞的表达特征，我们将注释为成纤维细胞的细胞单独提取出来，进行重嵌入与二次分析。

基于液滴的人类心脏活检组织单细胞核RNA测序分析
与小鼠样本的处理流程一致，人类心脏活检组织分离得到的细胞核使用10x Chromium（10x Genomics）系统上机操作，并按照成熟的商业化标准流程，使用Chromium单细胞3’试剂试剂盒（NextGem Chemistry）及Chromium细胞核分离试剂盒构建cDNA文库。所有文库均在苏黎世功能基因组中心通过Illumina NovaSeq 6000完成测序，基因表达定量使用CellRanger（v.5.0.1）的count模块完成，参考基因组为Ensembl GRCh38.103。质量控制步骤包括移除异常UMI计数或基因计数的细胞核（与所有细胞的中位数偏差超过2.5倍中位绝对偏差），使用R（v.4.2.1）环境下的R/Bioconductor包scater（v.1.24.0）与SingleCellExperiment（v.1.18.0）完成该步骤。
使用Seurat R包（v.4.3.0）进行后续分析时，首先利用IntegrateData函数按照患者ID对所有样本进行合并与整合。整合后的数据进一步进行标准化、缩放、PCA与UMAP降维、基于图的聚类以及无偏聚类标记基因计算。根据计算得到的聚类标记基因及既往文献报道的经典标记基因对聚类结果进行注释。完成聚类注释后，按照样本的T细胞比例进行分组，并利用Seurat R包的FindAllMarkers函数计算差异表达基因以比较各组间的表达差异。