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Genome Biol., Published: 9 June 2015, 16:122 | doi:10.1186/s13059-015-0683-4
Deep sequencing reveals cell-type-specific patterns of single-cell transcriptome variation
Abstract
Hannah Dueck1, Mugdha Khaladkar2, Tae Kyung Kim34, Jennifer M. Spaethling3, Chantal Francis2, Sangita Suresh56, Stephen Fisher2, Patrick Seale7, Sheryl G. Beck8, Tamas Bartfai9, Bernhard Kuhn1056, Jim Eberwine3† and Junhyong Kim2*†
† Equal contributors
1 Department of Genomics and Computational Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
2 Department of Biology, School of Arts and Sciences, University of Pennsylvania, 103I Lynch Laboratory, 433 S University Avenue, Philadelphia 19104, PA, USA
3 Department of Pharmacology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
4 Current address: Allen Institute for Brain Science, Seattle, WA, USA
5 Department of Pediatrics, Harvard Medical School, Boston, MA, USA
6 Department of Cardiology, Boston Children¿s Hospital, Boston, MA, USA
7 Department of Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
8 Department of Anesthesiology, The Children¿s Hospital of Philadelphia Research Institute, Philadelphia, PA, USA
9 The Department of Chemical Physiology, The Scripps Research Institute, La Jolla, CA, USA
10 Current address: Richard King Mellon Institute for Pediatric Research, Children¿s Hospital of Pittsburgh of UPMC, Pittsburgh, USA
* Corresponding author: Junhyong Kim
Abstract (provisional)
Background Differentiation of metazoan cells requires execution of different gene expression programs but recent single-cell transcriptome profiling has revealed considerable variation within cells of seeming identical phenotype. This brings into question the relationship between transcriptome states and cell phenotypes. Additionally, single-cell transcriptomics presents unique analysis challenges that need to be addressed to answer this question. Results We present high quality deep read-depth single-cell RNA sequencing for 91 cells from five mouse tissues and 18 cells from two rat tissues, along with 30 control samples of bulk RNA diluted to single-cell levels. We find that transcriptomes differ globally across tissues with regard to the number of genes expressed, the average expression patterns, and within-cell-type variation patterns. We develop methods to filter genes for reliable quantification and to calibrate biological variation. All cell types include genes with high variability in expression, in a tissue-specific manner. We also find evidence that single-cell variability of neuronal genes in mice is correlated with that in rats consistent with the hypothesis that levels of variation may be conserved. Conclusions Single-cell RNA-sequencing data provide a unique view of transcriptome function; however, careful analysis is required in order to use single-cell RNA-sequencing measurements for this purpose. Technical variation must be considered in single-cell RNA-sequencing studies of expression variation. For a subset of genes, biological variability within each cell type appears to be regulated in order to perform dynamic functions, rather than solely molecular noise.
논문정보
- Research article
- 2015년 06월 (BRIC 등록일 2015-06-17)
- 국외 연구진
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