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

Author:

Baxi Emily G.,Thompson Terri,Li Jonathan,Kaye Julia A.,Lim Ryan G.ORCID,Wu Jie,Ramamoorthy DivyaORCID,Lima LeandroORCID,Vaibhav Vineet,Matlock Andrea,Frank Aaron,Coyne Alyssa N.ORCID,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 LouisORCID,Banuelos Maria G.,Liu Chunyan,Moran Ruby,Garcia Veronica,Workman Michael,Ho RichieORCID,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 LindsayORCID,Prina Carolyn,Morgan Todd,Jamil Arish,Heintzman Sarah,Jockel-Balsarotti Jennifer,Karanja Elizabeth,Markway Jesse,McCallum Molly,Joslin Ben,Alibazoglu Deniz,Kolb StephenORCID,Ajroud-Driss Senda,Baloh RobertORCID,Heitzman Daragh,Miller Tim,Glass Jonathan D.ORCID,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 CarlaORCID,Cecchi GuillermoORCID,Bellard Mary,Raghav YogindraORCID,Sachs Karen,Ehrenberger Tobias,Bruce Elizabeth,Cudkowicz Merit E.ORCID,Maragakis NicholasORCID,Norel RaquelORCID,Van Eyk Jennifer E.ORCID,Finkbeiner StevenORCID,Berry James,Sareen DhruvORCID,Thompson Leslie M.ORCID,Fraenkel ErnestORCID,Svendsen Clive N.ORCID,Rothstein Jeffrey D.ORCID

Abstract

AbstractAnswer 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.

Funder

U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke

Amyotrophic Lateral Sclerosis Association

NFL Foundation

Judith and Jean Pape Adams Charitable Foundation

Muscular Dystrophy Association

Les Turner ALS Foundation

Microsoft

Caterpillar

American Airlines

AbbVie Inc. | AbbVie Biotherapeutics

Publisher

Springer Science and Business Media LLC

Subject

General Neuroscience

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