Abstract
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 (TCRαβ), 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 TCRαβ 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 TCRαβ 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 TCRαβ and gene expression profiles from scRNA-seq data of T cells.
Highlights
IntroductionSingle cell RNA sequencing (scRNA-seq) has vastly improved our ability to determine gene expression and transcript isoform diversity at a genome-wide scale in different populations of cells. scRNA-seq is becoming a powerful technology for the analysis of heterogeneous immune cells subsets[1,2] and studying how cell-to-cell variations affect biological processes[3,4]
Single cell RNA sequencing has vastly improved our ability to determine gene expression and transcript isoform diversity at a genome-wide scale in different populations of cells. scRNA-seq is becoming a powerful technology for the analysis of heterogeneous immune cells subsets[1,2] and studying how cell-to-cell variations affect biological processes[3,4]
The detection rate of full length TCRαβ is at least 80% for reads with a sequencing depth >0.25 million PE reads of length at least 50 bp
Summary
Single cell RNA sequencing (scRNA-seq) has vastly improved our ability to determine gene expression and transcript isoform diversity at a genome-wide scale in different populations of cells. scRNA-seq is becoming a powerful technology for the analysis of heterogeneous immune cells subsets[1,2] and studying how cell-to-cell variations affect biological processes[3,4]. One important consideration in designing scRNA-seq experiments is to decide on the desired sequencing depth (i.e., the expected number of reads per cell) and read length[3,6]. In designing an scRNA-seq experiment it is optimal to generate data by maximising sequencing depth and utilising the longest read length This approach would improve the quality of the reads alignment and maximise the chance of detecting low abundant transcripts. A more practical question is to ask what is the minimum sequencing depth and read length that allows users to obtain adequate information for their desired downstream analyses To answer these questions, we have focussed on assessing the quality of available scRNA-seq data from T cells, which form a highly heterogeneous population of lymphocytes that play a vital role in mounting successful adaptive immune responses against intracellular pathogens and tumours[16]. This has led to the capacity to simultaneously detect TCRαβ and full gene expression profiles in one experiment, thereby allowing direct study of TCR diversity and its interaction with the T cell functions reflected in gene expression profiles
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