Epigenetics & Chromatin (Nov 2018)

Integrative analysis of single-cell expression data reveals distinct regulatory states in bidirectional promoters

  • Fatemeh Behjati Ardakani,
  • Kathrin Kattler,
  • Karl Nordström,
  • Nina Gasparoni,
  • Gilles Gasparoni,
  • Sarah Fuchs,
  • Anupam Sinha,
  • Matthias Barann,
  • Peter Ebert,
  • Jonas Fischer,
  • Barbara Hutter,
  • Gideon Zipprich,
  • Charles D. Imbusch,
  • Bärbel Felder,
  • Jürgen Eils,
  • Benedikt Brors,
  • Thomas Lengauer,
  • Thomas Manke,
  • Philip Rosenstiel,
  • Jörn Walter,
  • Marcel H. Schulz

DOI
https://doi.org/10.1186/s13072-018-0236-7
Journal volume & issue
Vol. 11, no. 1
pp. 1 – 14

Abstract

Read online

Abstract Background Bidirectional promoters (BPs) are prevalent in eukaryotic genomes. However, it is poorly understood how the cell integrates different epigenomic information, such as transcription factor (TF) binding and chromatin marks, to drive gene expression at BPs. Single-cell sequencing technologies are revolutionizing the field of genome biology. Therefore, this study focuses on the integration of single-cell RNA-seq data with bulk ChIP-seq and other epigenetics data, for which single-cell technologies are not yet established, in the context of BPs. Results We performed integrative analyses of novel human single-cell RNA-seq (scRNA-seq) data with bulk ChIP-seq and other epigenetics data. scRNA-seq data revealed distinct transcription states of BPs that were previously not recognized. We find associations between these transcription states to distinct patterns in structural gene features, DNA accessibility, histone modification, DNA methylation and TF binding profiles. Conclusions Our results suggest that a complex interplay of all of these elements is required to achieve BP-specific transcriptional output in this specialized promoter configuration. Further, our study implies that novel statistical methods can be developed to deconvolute masked subpopulations of cells measured with different bulk epigenomic assays using scRNA-seq data.

Keywords