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

Emily G. Baxi, Terri Thompson, Jonathan Li, Julia A. Kaye, Ryan G. Lim, Jie Wu, Divya Ramamoorthy, Leandro Lima, Vineet Vaibhav, Andrea Matlock, Aaron Frank, Alyssa N. Coyne, Barry Landin, Loren Ornelas, Elizabeth Mosmiller, Sara Thrower, S. Michelle Farr, Lindsey Panther, Emilda Gomez, Erick GalvezDaniel Perez, Imara Meepe, Susan Lei, Berhan Mandefro, Hannah Trost, Louis Pinedo, Maria G. Banuelos, Chunyan Liu, Ruby Moran, Veronica Garcia, Michael Workman, Richie Ho, Stacia Wyman, Jennifer Roggenbuck, Matthew B. Harms, Jennifer Stocksdale, Ricardo Miramontes, Keona Wang, Vidya Venkatraman, Ronald Holewenski, Niveda Sundararaman, Rakhi Pandey, Danica Mae Manalo, Aneesh Donde, Nhan Huynh, Miriam Adam, Brook T. Wassie, Edward Vertudes, Naufa Amirani, Krishna Raja, Reuben Thomas, Lindsey Hayes, Alex Lenail, Aianna Cerezo, Sarah Luppino, Alanna Farrar, Lindsay Pothier, Carolyn Prina, Todd Morgan, Arish Jamil, Sarah Heintzman, Jennifer Jockel-Balsarotti, Elizabeth Karanja, Jesse Markway, Molly McCallum, Ben Joslin, Deniz Alibazoglu, Stephen Kolb, Senda Ajroud-Driss, Robert Baloh, Daragh Heitzman, Tim Miller, Jonathan D. Glass, Natasha Leanna Patel-Murray, Hong Yu, Ervin Sinani, Prasha Vigneswaran, Alexander V. Sherman, Omar Ahmad, Promit Roy, Jay C. Beavers, Steven Zeiler, John W. Krakauer, Carla Agurto, Guillermo Cecchi, Mary Bellard, Yogindra Raghav, Karen Sachs, Tobias Ehrenberger, Elizabeth Bruce, Merit E. Cudkowicz, Nicholas Maragakis, Raquel Norel, Jennifer E. Van Eyk, Steven Finkbeiner, James Berry, Dhruv Sareen, Leslie M. Thompson, Ernest Fraenkel, Clive N. Svendsen, Jeffrey D. Rothstein*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)226-237
Number of pages12
JournalNature neuroscience
Volume25
Issue number2
DOIs
StatePublished - Feb 2022

ASJC Scopus subject areas

  • Neuroscience(all)

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