Abstract

Alternative transcription start sites (TSSs) have been extensively studied genome‐wide for many cell types and have been shown to be important during development and to regulate transcript abundance between cell types. Likewise, single‐cell gene expression has been extensively studied for many cell types. However, how single cells use TSSs has not yet been examined. In particular, it is unknown whether alternative TSSs are independently expressed, or whether they are co‐activated or even mutually exclusive in single cells. Here, we use a previously published single‐cell RNA‐seq dataset, comprising thousands of cells, to study alternative TSS usage. We find that alternative TSS usage is a regulated process, and the correlation between two TSSs expressed in single cells of the same cell type is surprisingly high. Our findings indicate that TSSs generally are regulated by common factors rather than being independently regulated or stochastically expressed.

Highlights

  • Our understanding of transcription start sites (TSSs) usage has increased dramatically over the last decade since the introduction of deep sequencing technologies

  • We selected a set of 2,816 single-cell transcriptomes representing seven cell types: interneurons, somatosensory cortex pyramidal neurons, hippocampal pyramidal neurons, oligodendrocytes, astrocytes/ependymal cells, microglia, and vascular cells

  • Raw reads were mapped to the genome, assigned to FANTOM annotated TSSs, and converted to absolute number of molecules using unique molecular identifiers (UMI) (Islam et al, 2014)

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Summary

Introduction

Our understanding of TSS usage has increased dramatically over the last decade since the introduction of deep sequencing technologies. There may be hitherto unexplored rules that determine a strict preference for each regulatory sequence to a specific TSS, that is, TSSs are regulated by specific factors. In the former case, alternative TSSs would be expected to be always active in the same tissues and cell types, perhaps in some fixed proportion. Another study examined single-cell splice isoforms and found that genes having multiple splice isoforms at the population level tended to have only one expressed isoform at the singlecell level (Shalek et al, 2013). The inclusion of unique molecular identifiers (UMI) ensured an increased quantitative accuracy by eliminating most PCR bias and allowed the absolute counting of mRNA molecules (Kivioja et al, 2012)

Results
F Pooled 50 cells - Major promoter
Discussion
Materials and Methods
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