학술논문
Answer ALS, a large-scale resource for sporadic and familial ALS combining clinical and multi-omics data from induced pluripotent cell lines
Document Type
article
Author
Baxi, Emily G; Thompson, Terri; Li, Jonathan; Kaye, Julia A; Lim, Ryan G; Wu, Jie; Ramamoorthy, Divya; Lima, Leandro; Vaibhav, Vineet; Matlock, Andrea; Frank, Aaron; Coyne, Alyssa N; Landin, Barry; Ornelas, Loren; Mosmiller, Elizabeth; Thrower, Sara; Farr, S Michelle; Panther, Lindsey; Gomez, Emilda; Galvez, Erick; Perez, Daniel; Meepe, Imara; Lei, Susan; Mandefro, Berhan; Trost, Hannah; Pinedo, Louis; Banuelos, Maria G; Liu, Chunyan; Moran, Ruby; Garcia, Veronica; Workman, Michael; Ho, Richie; Wyman, Stacia; Roggenbuck, Jennifer; Harms, Matthew B; Stocksdale, Jennifer; Miramontes, Ricardo; Wang, Keona; Venkatraman, Vidya; Holewenski, Ronald; Sundararaman, Niveda; Pandey, Rakhi; Manalo, Danica-Mae; Donde, Aneesh; Huynh, Nhan; Adam, Miriam; Wassie, Brook T; Vertudes, Edward; Amirani, Naufa; Raja, Krishna; Thomas, Reuben; Hayes, Lindsey; Lenail, Alex; Cerezo, Aianna; Luppino, Sarah; Farrar, Alanna; Pothier, Lindsay; Prina, Carolyn; Morgan, Todd; Jamil, Arish; Heintzman, Sarah; Jockel-Balsarotti, Jennifer; Karanja, Elizabeth; Markway, Jesse; McCallum, Molly; Joslin, Ben; Alibazoglu, Deniz; Kolb, Stephen; Ajroud-Driss, Senda; Baloh, Robert; Heitzman, Daragh; Miller, Tim; Glass, Jonathan D; Patel-Murray, Natasha Leanna; Yu, Hong; Sinani, Ervin; Vigneswaran, Prasha; Sherman, Alexander V; Ahmad, Omar; Roy, Promit; Beavers, Jay C; Zeiler, Steven; Krakauer, John W; Agurto, Carla; Cecchi, Guillermo; Bellard, Mary; Raghav, Yogindra; Sachs, Karen; Ehrenberger, Tobias; Bruce, Elizabeth; Cudkowicz, Merit E; Maragakis, Nicholas; Norel, Raquel; Van Eyk, Jennifer E; Finkbeiner, Steven; Berry, James; Sareen, Dhruv; Thompson, Leslie M; Fraenkel, Ernest; Svendsen, Clive N
Source
Nature Neuroscience. 25(2)
Subject
Language
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.