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. Technical variation must be considered in single-cell RNA-sequencing studies of expression variation. 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.
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. All cell types include genes with high variability in expression, in a tissue-specific manner. We develop methods to filter genes for reliable quantification and to calibrate biological variation. 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 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. Additionally, single-cell transcriptomics presents unique analysis challenges that need to be addressed to answer this question. This brings into question the relationship between transcriptome states and cell phenotypes. 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.
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