Systematic comparison of high-throughput single-cell RNA-seq methods for immune cell profiling

Abstract

Background: Elucidation of immune populations with single-cell RNA-seq has greatly benefited the field of immunology by deepening the characterization of immune heterogeneity and leading to the discovery of new subtypes. However, single-cell methods inherently suffer from limitations in the recovery of complete transcriptomes due to the prevalence of cellular and transcriptional dropout events. This issue is often compounded by limited sample availability and limited prior knowledge of heterogeneity, which can confound data interpretation. Results: Here, we systematically benchmarked seven high-throughput single-cell RNA-seq methods. We prepared 21 libraries under identical conditions of a defined mixture of two human and two murine lymphocyte cell lines, simulating heterogeneity across immune-cell types and cell sizes. We evaluate methods by their cell recovery rate, library efficiency, sensitivity, and ability to recover expression signatures for each cell type. We observed higher mRNA detection sensitivity with the 10x Genomics 5’ v1 and 3’ v3 methods. We demonstrate that these methods have fewer drop-out events which facilitates the identification of differentially-expressed genes and improves the concordance of single-cell profiles to immune bulk RNA-seq signatures.Conclusion: Overall, our characterization of immune cell mixtures provides useful metrics, which can guide selection of a high-throughput single-cell RNA-seq method for profiling more complex immune-cell heterogeneity usually found in vivo.

Single-nuclei transcriptomics enable detection of somatic variants in patient brain tissue

“…Our results show comparable performance of 10 × Genomics 3' and 5' gene expression kits in quality control metrics, gene expression, and cell type recovery when directly comparing the same patient samples. Our observations are consistent with a previous study that demonstrated high transcript and gene detection sensitivity of 10 × Genomics Single-cell 3'-RNA-seq v.3.0 and 5'-RNA-seq v.1 kits compared to other scRNA-seq methods 14 . We expanded on these findings by investigating the most recent 5' v.2 and 3' v.3.1 kits and found similar numbers of transcripts and genes detected per cell, as well as similar gene expression and cell type recovery.…”

Epigenetic signals that direct cell type specific interferon beta response in mouse cells

“…The number of cells where transcripts of the two genes were detected, largely increased from the 1 h to the 6 h time point as more RNA is produced. We conclude that the apparent heterogeneity after 1 h IFNβ appears to arise to a significant extend from the reduced detection sensitivity of scRNA-seq for lowly expressed genes that show an increased drop-out frequency (Yamawaki et al, 2021). Furthermore, the expression patterns and IFNβ response dynamics of the two MEF clusters (cluster 0 vs 1; cluster 2 vs 3) were highly similar in terms of ISGs and their induced gene expression levels (Fig.…”

Rapid and sensitive single cell RNA sequencing with SHERRY2

“…For low RNA-content cells, such as immune cells 17 , we found that removal of intergenic DNA contaminations by DNase treatment was especially crucial for SHERRY2 scRNA-seq. In such cells, the open DNA regions of disassembled chromatin might be favored over RNA/DNA hybrids during Tn5 tagmentation.…”