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

Author:
Dueck, Hannah, Khaladkar, Mugdha, Kim, Tae Kyung, Spaethling, Jennifer M., Francis, Chantal, Suresh, Sangita, Fisher, Stephen A., Seale, Patrick, Beck, Sheryl G., Bartfai, Tamas, Kuhn, Bernhard, Eberwine, James, Kim, Junhyong
Source:
GenomeBiology.com 2015 v.16 no.1 pp. 122
ISSN:
1465-6906
Subject:
Animalia, RNA, gene expression, genes, high-throughput nucleotide sequencing, mice, neurons, phenotype, rats, transcriptome, transcriptomics
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.
Agid:
5559649