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

Dueck, H and Khaladkar, M and Kim, TK and Spaethling, JM and Francis, C and Suresh, S and Fisher, SA and Seale, P and Beck, SG and Bartfai, T and Kuhn, B and Eberwine, J and Kim, J (2015) Deep sequencing reveals cell-type-specific patterns of single-cell transcriptome variation. Genome Biology, 16 (1). ISSN 1474-7596

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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.


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Item Type: Article
Status: Published
CreatorsEmailPitt UsernameORCID
Dueck, H
Khaladkar, M
Kim, TK
Spaethling, JM
Francis, C
Suresh, S
Fisher, SA
Seale, P
Beck, SG
Bartfai, T
Kuhn, B
Eberwine, J
Kim, J
Date: 9 June 2015
Date Type: Publication
Journal or Publication Title: Genome Biology
Volume: 16
Number: 1
DOI or Unique Handle: 10.1186/s13059-015-0683-4
Schools and Programs: School of Medicine > Pediatrics
Refereed: Yes
ISSN: 1474-7596
Date Deposited: 17 Aug 2016 13:23
Last Modified: 30 Mar 2021 13:56


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