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Genome Biol. 2015 Jun 9;16:122. doi: 10.1186/s13059-015-0683-4.

Deep sequencing reveals cell-type-specific patterns of single-cell transcriptome variation.

Author information

1
Department of Genomics and Computational Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. hdueck@mail.upenn.edu.
2
Department of Biology, School of Arts and Sciences, University of Pennsylvania, 301A/B Lynch Laboratory, 433 S University Avenue, Philadelphia, PA, 19104, USA. mugdhak@pcbi.upenn.edu.
3
Department of Pharmacology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. sean.taekyung@gmail.com.
4
Current address: Allen Institute for Brain Science, Seattle, WA, USA. sean.taekyung@gmail.com.
5
Department of Pharmacology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. jspaethl@mail.med.upenn.edu.
6
Department of Biology, School of Arts and Sciences, University of Pennsylvania, 301A/B Lynch Laboratory, 433 S University Avenue, Philadelphia, PA, 19104, USA. chantalfedde@gmail.com.
7
Department of Pediatrics, Harvard Medical School, Boston, MA, USA. sangita.suresh@gmail.com.
8
Department of Cardiology, Boston Children's Hospital, Boston, MA, USA. sangita.suresh@gmail.com.
9
Department of Biology, School of Arts and Sciences, University of Pennsylvania, 301A/B Lynch Laboratory, 433 S University Avenue, Philadelphia, PA, 19104, USA. safisher@sas.upenn.edu.
10
Department of Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. sealep@mail.med.upenn.edu.
11
Department of Anesthesiology, The Children's Hospital of Philadelphia Research Institute, Philadelphia, PA, USA. BECKS@email.chop.edu.
12
The Department of Chemical Physiology, The Scripps Research Institute, La Jolla, CA, USA. tbartfai@scripps.edu.
13
Department of Pediatrics, Harvard Medical School, Boston, MA, USA. Bernhard.Kuhn2@CHP.edu.
14
Department of Cardiology, Boston Children's Hospital, Boston, MA, USA. Bernhard.Kuhn2@CHP.edu.
15
Harvard Stem Cell Institute, Cambridge, MA, USA. Bernhard.Kuhn2@CHP.edu.
16
Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA. Bernhard.Kuhn2@CHP.edu.
17
Current address: Richard King Mellon Institute for Pediatric Research, Children's Hospital of Pittsburgh of UPMC, Pittsburgh, PA, USA. Bernhard.Kuhn2@CHP.edu.
18
Department of Pharmacology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. eberwine@mail.med.upenn.edu.
19
Department of Biology, School of Arts and Sciences, University of Pennsylvania, 301A/B Lynch Laboratory, 433 S University Avenue, Philadelphia, PA, 19104, USA. junhyong@sas.upenn.edu.

Abstract

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.

PMID:
26056000
PMCID:
PMC4480509
DOI:
10.1186/s13059-015-0683-4
[Indexed for MEDLINE]
Free PMC Article

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