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

TLDR

Single‑cell RNA‑seq studies are still early in revealing cell‑to‑cell variation in gene expression, transcript processing, and regulatory activity, and distinguishing biological differences from technical noise remains a major challenge. The authors aim to apply the SMART‑seq protocol to the GM12878 lymphoblastoid cell line to characterize gene expression variability. They quantified absolute RNA abundance using spike‑in standards and employed a pool/split design to separate biological from technical stochasticity, revealing 50,000–300,000 transcripts per cell and significant inter‑cell expression differences. They identified cell‑specific coexpression modules enriched for splicing factors, observed substantial alternative‑splicing and allelic bias differences exceeding technical variation, and showed that pools of 30–100 cells yield transcriptomes comparable to bulk RNA‑seq, outlining a path for studying rare cell types.

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.