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

TitleDeep sequencing reveals cell-type-specific patterns of single-cell transcriptome variation.
Publication TypeJournal Article
Year of Publication2015
AuthorsDueck H, Khaladkar M, Kim TKyung, Spaethling JM, Francis C, Suresh S, Fisher SA, Seale P, Beck SG, Bartfai T, Kühn B, Eberwine J, Kim J
JournalGenome Biol
Volume16
Issue1
Pagination122
Date Published2015 Jun 09
ISSN1474-760X
KeywordsAnimals, Cells, Cultured, Gene Expression Profiling, Genetic Variation, High-Throughput Nucleotide Sequencing, Mice, Mice, Inbred C57BL, Rats, Rats, Sprague-Dawley, RNA Stability, Sequence Analysis, RNA, Single-Cell Analysis, Transcriptome
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.

DOI10.1186/s13059-015-0683-4
Alternate JournalGenome Biol
PubMed ID26056000
PubMed Central IDPMC4480509
Grant ListU01 MH098953 / MH / NIMH NIH HHS / United States
K08 HL085143 / HL / NHLBI NIH HHS / United States
DP1 OD004117 / OD / NIH HHS / United States
5U01MH098953 / MH / NIMH NIH HHS / United States
R01HL106302 / HL / NHLBI NIH HHS / United States
5R01MH088849 / MH / NIMH NIH HHS / United States
R01 MH088849 / MH / NIMH NIH HHS / United States
R01 HL106302 / HL / NHLBI NIH HHS / United States