Impact of sequencing depth and read length on single cell RNA sequencing data of T cells

Single cell RNA sequencing (scRNA-seq) provides great potential in measuring the gene expression profiles of heterogeneous cell populations. In immunology, scRNA-seq allowed the characterisation of transcript sequence diversity of functionally relevant T cell subsets, and the identification of the full length T cell receptor (TCR alpha beta), which defines the specificity against cognate antigens. Several factors, e.g. RNA library capture, cell quality, and sequencing output affect the quality of scRNA-seq data. We studied the effects of read length and sequencing depth on the quality of gene expression profiles, cell type identification, and TCR alpha beta reconstruction, utilising 1,305 single cells from 8 publically available scRNA-seq datasets, and simulation-based analyses. Gene expression was characterised by an increased number of unique genes identified with short read lengths (< 50 bp), but these featured higher technical variability compared to profiles from longer reads. Successful TCR alpha beta reconstruction was achieved for 6 datasets (81% - 100%) with at least 0.25 millions (PE) reads of length > 50 bp, while it failed for datasets with < 30 bp reads. Sufficient read length and sequencing depth can control technical noise to enable accurate identification of TCR alpha beta and gene expression profiles from scRNA-seq data of T cells.