End Sequence Analysis Toolkit (ESAT) expands the extractable information from single-cell RNA-seq data

Genome Res. 2016 Oct;26(10):1397-1410. doi: 10.1101/gr.207902.116. Epub 2016 Jul 28.


RNA-seq protocols that focus on transcript termini are well suited for applications in which template quantity is limiting. Here we show that, when applied to end-sequencing data, analytical methods designed for global RNA-seq produce computational artifacts. To remedy this, we created the End Sequence Analysis Toolkit (ESAT). As a test, we first compared end-sequencing and bulk RNA-seq using RNA from dendritic cells stimulated with lipopolysaccharide (LPS). As predicted by the telescripting model for transcriptional bursts, ESAT detected an LPS-stimulated shift to shorter 3'-isoforms that was not evident by conventional computational methods. Then, droplet-based microfluidics was used to generate 1000 cDNA libraries, each from an individual pancreatic islet cell. ESAT identified nine distinct cell types, three distinct β-cell types, and a complex interplay between hormone secretion and vascularization. ESAT, then, offers a much-needed and generally applicable computational pipeline for either bulk or single-cell RNA end-sequencing.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Animals
  • Cells, Cultured
  • Dendritic Cells / cytology
  • Dendritic Cells / metabolism
  • Gene Library
  • Islets of Langerhans / cytology*
  • Islets of Langerhans / metabolism
  • Microfluidics / methods
  • Rats
  • Sequence Analysis, RNA / methods*
  • Sequence Analysis, RNA / standards
  • Single-Cell Analysis / methods*
  • Single-Cell Analysis / standards
  • Transcriptome*