Human TBK1 Deficiency Leads to Autoinflammation Driven by TNF-Induced Cell Death - Single-Cell RNAseq
Description
Single-cell RNA sequencing of PBMCs from two TBK1-deficient patients and one healthy control.
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Steps to reproduce
Within 16 hours of isolation, fresh PBMCs were processed for 3’-end droplet-based scRNA-seq on the 10 Genomics Chromium (version 2) platform. For each sample, 12,000 cells were loaded onto a single lane of the 10X Genomics Chromium chip (version 2) and scRNA-seq libraries were prepared according to the manufacturer’s instructions. Libraries from the 3 donors were pooled and sequenced on the Illumina HiSeq 4000 in paired-end configuration (Read 1: 26nt; Read 2: 98nt). Base call sequencing image files were extracted off the sequencing, demultiplexed per sample, and converted into paired FASTQ reads using the mkfastq command in the 10X Genomics CellRanger suite. FASTQ reads were aligned to the human CGRh38 genome reference with the CellRanger count function using the default parameters. Gene x cell matrix outputs were for each donor were further processed using DoubletDetector to identify putative doublets. Both gene x cell matrices and DoubletDetector output were read into the R statistical framework and analyzed with the single-cell RNA-seq analysis package, Seurat (v4.0.1). Cells with < 500 genes, IDed as doublets, or with 10% UMIs from mitochondrial genes were removed. Gene expression data were independently normalized using SCTransform with the parameter ‘vars.to.regress’ set to regress out effects primarily due to mitochondrial gene expression. The top 3000 shared highly variable genes were then selected to proceed with integration analyses (immunoglobulin genes excluded). To minimize sample-specific effects, all datasets were integrated on the first 30 canonical correlation components using the SCTransform integration workflow described by the developer. Dimensional reduction was performed by principal component analysis (PCA) on the integrated dataset. Unsupervised graph-based clustering was performed on the first 30 principal components using the smart local moving algorithm at a resolution of 0.8 and clustering results were visualized by the Uniform manifold approximation and projection (UMAP) method. Cluster marker gene lists were generated using Seurat’s FindAllMarkers command. Transcriptional differences between TBK1-/- patients and the healthy control were conducted using Seurat’s FindMarkers command.