From single-cell to cell-pool transcriptomes: Stochasticity in gene expression and RNA splicing

Abstract

Single-cell RNA-seq mammalian transcriptome studies are at an early stage in uncovering cell-to-cell variation in gene expression, transcript processing and editing, and regulatory module activity. Despite great progress recently, substantial challenges remain, including discriminating biological variation from technical noise. Here we apply the SMART-seq single-cell RNA-seq protocol to study the reference lymphoblastoid cell line GM12878. By using spike-in quantification standards, we estimate the absolute number of RNA molecules per cell for each gene and find significant variation in total mRNA content: between 50,000 and 300,000 transcripts per cell. We directly measure technical stochasticity by a pool/split design and find that there are significant differences in expression between individual cells, over and above technical variation. Specific gene coexpression modules were preferentially expressed in subsets of individual cells, including one enriched for mRNA processing and splicing factors. We assess cell-to-cell variation in alternative splicing and allelic bias and report evidence of significant differences in splice site usage that exceed splice variation in the pool/split comparison. Finally, we show that transcriptomes from small pools of 30–100 cells approach the information content and reproducibility of contemporary RNA-seq from large amounts of input material. Together, our results define an experimental and computational path forward for analyzing gene expression in rare cell types and cell states.

Detection of Imprinted Genes by Single-Cell Allele-Specific Gene Expression

“…Previously, our lab and others demonstrated that single-cell RNA-seq facilitates the identification of ASE patterns. [21][22][23][24] In this study, we sequenced the RNA of 1,084 single fibroblasts from 5 individuals. For 2 of these individuals, the parental DNA was available.…”

Single-cell transcriptomics of small microbial eukaryotes: limitations and potential

“…Gene transcription generally occurs in stochastic bursts (Golding et al, 2005;Suter et al, 2011), and single-cell transcriptomes of cells with relatively few mRNA molecules are much more susceptible to biological and technical stochasticity (Marinov et al, 2014). Because of the small mRNA copy numbers in the two species examined in this study, it was doubtful that the single-cell gene expression levels and transcript presence/absence in different cells were reliable.…”

“…Single-cell RNA-seq has been applied successfully in human cells, which are estimated to contain 50 000-300 000 mRNA molecules per cell (Marinov et al, 2014). On the other hand, it is considered not suitable for bacteria (Taniguchi et al, 2010), which have only 200-2000 mRNA molecules per cell (Moran et al, 2013).…”

DEsingle for detecting three types of differential expression in single-cell RNA-seq data

“…Most dropout zeros are produced because of the very low mRNA capture efficiency (~5-25%, up to ~40%) and the small mRNA copy number of each gene (thousands of genes only have 1-30 mRNA copies) in a cell (21)(22)(23)(24). As a result, mRNA molecules in a cell can be randomly missed during the reverse transcription step and the following cDNA amplification step, and the mRNA products of some genes may be totally missed in the capturing procedure, which then produces dropout zeros in the scRNA-seq data (3,25,26).…”

“…Due to the tiny amount of mRNAs in one cell (~0.01-2.5pg), the small mRNA copy number of each gene in a cell (thousands of genes have only 1-30 mRNA copies) and the very low mRNA capture efficiency (~5-25%, up to ~40%) (21)(22)(23)(24), some mRNAs are totally missed during the reverse transcription step and the following cDNA amplification step, and consequently undetectable in the later sequencing step (3). This phenomenon is called dropout events (25,26).…”