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Published March 2014 | Supplemental Material + Published
Journal Article Open

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


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.

Additional Information

© 2014 Marinov et al. 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/. Received May 24, 2013; accepted in revised form November 20, 2013. We thank Henry Amrhein, Diane Trout, and Sean Upchurch for computational assistance, and members of the Wold laboratory for helpful discussions. This work has been supported by NIH grants U54 HG004576 and U54 HG006998, the Simons Foundation, and the McDonnell Foundation. In addition, G.K.M., B.A.W., and B.J.W. are supported by the Beckman Foundation and the Donald Bren Endowment.

Attached Files

Published - Genome_Res.-2014-Marinov-496-510.pdf

Supplemental Material - Supplemental_Material.pdf


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