Embracing systems toxicology at single-cell resolution

• Type of Material: Article
• Authors: Qiang Zhang, Emory University; William Caudle, Emory University; Jingbo Pi, China Medical University Shenyang; Sudin Bhattacharya, Michigan State University; Melvin E. Andersen, ScitoVation, LLC; Norbert E. Kaminski, Michigan State University; Rory B. Conolly, United States Environmental Protection Agency
• Language: English
• Date: 2019-08-01
• Publisher: Elsevier Science Inc.
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2019 Elsevier B.V. All rights reserved.
• License: Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1016/j.cotox.2019.04.003
• Title of Journal or Parent Work: Current Opinion in Toxicology
• Volume: 16
• Start Page: 49
• End Page: 57
• Grant/Funding Information: The work was supported by NIEHS Superfund Research grant P42ES04911 and Pilot Grant through NIEHS HERCULES grant P30ES019776.
• Keywords: computational toxicology; machine learning; systems toxicology; single-cell RNA sequencing; bulk assay; transcriptomics
• Research Categories: Health Sciences, Toxicology; Health Sciences, Public Health; Environmental Sciences; Health Sciences, Pharmacology; Health Sciences, Occupational Health and Safety

Abstract

As systems biology expands its multi-omic spectrum to increasing resolutions, distinguishing cells based on single-cell profiles becomes feasible. Unlike traditional bulk assays that average cellular responses and blur the distinct identities of responsive cells, single-cell technologies enable sensitive detection of small cellular changes and precise identification of those cells perturbed by toxicants. Among the suite of omic technologies that continue to expand and become affordable, single-cell RNA sequencing is at the cutting edge and leading the way to transform systems toxicology. Single-cell systems toxicology can provide a wealth of information to elucidate cell-specific alterations and response trajectories, detect points of departure, and map and develop dynamical models of toxicity pathways.