RT Journal
A1 Derr, Alan
A1 Yang, Chaoxing
A1 Zilionis, Rapolas
A1 Sergushichev, Alexey
A1 Blodgett, David M.
A1 Redick, Sambra
A1 Bortell, Rita
A1 Luban, Jeremy
A1 Harlan, David M.
A1 Kadener, Sebastian
A1 Greiner, Dale L.
A1 Klein, Allon
A1 Artyomov, Maxim N.
A1 Garber, Manuel
T1 End Sequence Analysis Toolkit (ESAT) expands the extractable information from single-cell RNA-seq data
JF Genome Research 
JO Genome Research 
YR 2016 
FD October 01 
VO 26 
IS 10 
SP 1397 
OP 1410 
DO 10.1101/gr.207902.116 
UL http://genome.cshlp.org/content/26/10/1397.abstract 
AB 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.