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

Derr A, Yang C, Zilionis R, Sergushichev A, Blodgett DM, Redick S, Bortell R, Luban J, Harlan DM, Kadener S, Greiner DL, Klein A, Artyomov MN, Garber M
Genome Res. 2016 26 (10): 1397-1410

PMID: 27470110 · PMCID: PMC5052061 · DOI:10.1101/gr.207902.116

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.

© 2016 Derr et al.; Published by Cold Spring Harbor Laboratory Press.

MeSH Terms (10)

Animals Cells, Cultured Dendritic Cells Gene Library Islets of Langerhans Microfluidics Rats Sequence Analysis, RNA Single-Cell Analysis Transcriptome

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