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 G. Thompson, Jonathan Li, Julia Kaye, Ryan G. Lim, Jie Wu, Divya Ramamoorthy, Leandro de Araújo Lima, Vineet Vaibhav, Andrea Matlock, Aaron P. Frank, Alyssa N. Coyne, Barry Landin, Loren Ornelas, Elizabeth Mosmiller, Sara Thrower, S. Michelle Farr, Lindsey Panther, Emilda Gomez, Erick Galvez, Daniel I. Pérez, Imara Meepe, Susan Lei, Berhan Mandefro, Hannah Trost, Louis Pinedo, Maria G. Bañuelos, Chunyan Liu, Ruby Moran, Veronica J. Garcia, Michael J. Workman, Ritchie Ho, Stacia K. Wyman, Jennifer Roggenbuck, Matthew B. Harms, Jennifer Stocksdale, Ricardo Miramontes, Keona Q. 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 R. Hayes, Alex Lenail, Aianna Cerezo, Sarah Luppino, Alanna Farrar, Lindsay Pothier, Carolyn Prina, Todd E. Morgan, Arish Jamil, Sarah Heintzman, Jennifer Jockel‐Balsarotti, Elizabeth Karanja, Jesse Markway, Molly McCallum, Ben Joslin, Deniz Alibazoglu, Stephen J. Kolb, Senda Ajroud‐Driss, Robert H. Baloh, Daragh Heitzman, T. W. Miller, Jonathan D. Glass, Natasha Leanna Patel-Murray, Hong Yu, Ervin Sinani, Prasha Vigneswaran, Alexander Sherman, Omar Ahmad, Promit Roy, Jay Beavers, Steven R. Zeiler, John W. Krakauer, Carla Agurto, Guillermo Cecchi, Mary Bellard, Yogindra Raghav, Karen Sachs, Tobias Ehrenberger, Elizabeth Bruce, Merit Cudkowicz, Nicholas J. Maragakis, Raquel Norel, Jennifer E. Van Eyk, Steven Finkbeiner, James D. Berry, Dhruv Sareen, Leslie M. Thompson, Ernest Fraenkel, Clive N. Svendsen
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.