期刊
NATURE NEUROSCIENCE
卷 25, 期 2, 页码 226-+出版社
NATURE PORTFOLIO
DOI: 10.1038/s41593-021-01006-0
关键词
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资金
- Robert Packard Center for ALS Research at Johns Hopkins
- Travelers Insurance
- ALS Finding a Cure Foundation
- Stay Strong Vs. ALS
- Answer ALS Foundation
- Microsoft
- Caterpillar Foundation
- American Airlines
- Team Gleason
- National Institutes of Health
- Fishman Family Foundation
- Aviators Against ALS
- AbbVie Foundation
- Chan Zuckerberg Initiative
- ALS Association
- National Football League
- F. Prime
- Bruce Edwards Foundation
- Judith and Jean Pape Adams Charitable Foundation
- Muscular Dystrophy Association
- Les Turner ALS Foundation
- PGA Tour
- Bari Lipp Foundation
Answer ALS is a resource that includes patient-derived iPS cell lines, multi-omic data from iPS neurons, and clinical and smartphone data from over 1,000 ALS patients. This data can be used to identify distinct disease subgroups in ALS.
Answer ALS is a resource of patient-derived iPS cell lines, multi-omic data derived from iPS neurons and longitudinal clinical and smartphone data from over 1,000 patients with ALS. This serves as a foundation to identify distinct disease subgroups. 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.
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