Answer ALS, a large-scale resource for sporadic and familial ALS combining clinical and multi-omics data from induced pluripotent cell lines

• Type of Material: Article
• Authors: Emily G. Baxi, Johns Hopkins University; Terri Thompson, On Point Scientific Inc.; Jonathan Li, Massachusetts Institute of Technology; Julia A. Kaye, University of California San Francisco; Ryan G. Lim, University of California Irvine; Jie Wu, University of California Irvine; Arish Jamil, Emory University; Jonathan Glass, Emory University; Jeffrey D. Rothstein, Johns Hopkins University
• Language: English
• Date: 2022-02-03
• Publisher: Nature Portfolio
• Publication Version: Final Published Version
• Copyright Statement: © The Author(s) 2022.
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1038/s41593-021-01006-0
• Title of Journal or Parent Work: Nature Neuroscience
• Volume: 25
• Issue: 2
• Start Page: 226
• End Page: +
• Grant/Funding Information: None declared
• Keywords: Neurons; ACMG recommendations; Life Sciences & Biomedicine; Model; Science & Technology; Genotype; Functional predictions; Neurosciences & Neurology; American college; Neurosciences; Standards; Disease; Data independent acquisition; Medical genetics
• Research Categories: Biology, Neuroscience; Chemistry, Biochemistry; Engineering, Biomedical; Health Sciences, Rehabilitation and Therapy

Abstract

Answer ALS is a biological and clinical resource of patient-derived, induced pluripotent stem (iPS) cell lines, multi-omic data derived from iPS neurons and longitudinal clinical and smartphone data from over 1,000 patients with ALS. This resource provides population-level biological and clinical data that may be employed to identify clinical-molecular-biochemical subtypes of amyotrophic lateral sclerosis (ALS). A unique smartphone-based system was employed to collect deep clinical data, including fine motor activity, speech, breathing and linguistics/cognition. The iPS spinal neurons were blood derived from each patient and these cells underwent multi-omic analytics including whole-genome sequencing, RNA transcriptomics, ATAC-sequencing and proteomics. The intent of these data is for the generation of integrated clinical and biological signatures using bioinformatics, statistics and computational biology to establish patterns that may lead to a better understanding of the underlying mechanisms of disease, including subgroup identification. A web portal for open-source sharing of all data was developed for widespread community-based data analytics.