End Sequence Analysis ToolKit (ESAT) expands the extractable from single cell RNA-seq experiments
- Alan Derr1,
- Chaoxing Yang1,
- Rapolas Zilionis2,
- Alexey Sergushichev3,
- David M Blodgett1,
- Sambra Redick1,
- Rita Bortell1,
- Jeremy Luban1,
- David M Harlan1,
- Sebastian Kadener4,
- Dale L Greiner5,
- Allon Klein2,
- Maxim Artyomov5 and
- Manuel Garber1,6
- 1 UMass Medical School;
- 2 Harvard Medical School;
- 3 ITMO University;
- 4 Hebrew University of Jerusalem;
- 5 Washington University
- ↵* Corresponding author; email: manuel.garber{at}umassmed.edu
Abstract
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 1,000 cDNA libraries, each from an individual pancreatic islet cell. ESAT identified 9 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.
- Received April 4, 2016.
- Accepted July 27, 2016.
- Published by Cold Spring Harbor Laboratory Press
This manuscript is Open Access.
This article, published in Genome Research, is available under a Creative Commons License (Attribution-NonCommercial 4.0 International license), as described at http://creativecommons.org/licenses/by-nc/4.0/.
