From single-cell to cell-pool transcriptomes: stochasticity in gene expression and RNA splicing
- Georgi K Marinov1,
- Brian A Williams1,
- Kenneth McCue1,
- Gary P Schroth2,
- Jason Gertz3,
- Richard M Myers3 and
- Barbara J Wold1,4
- ↵* Corresponding author; email: woldb{at}caltech.edu
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 to 300,000 transcripts per cell. We directly measure technical stochasticity by a pool/spilt 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.
- Received May 24, 2013.
- Accepted November 20, 2013.
- Published by Cold Spring Harbor Laboratory Press
This manuscript is Open Access.
This article, published in Genome Research, is available under a Creative Commons License (Attribution-NonCommercial 3.0 Unported), as described at http://creativecommons.org/licenses/by-nc/3.0/.
