Predictive Cellular Signatures from Live Human Motor Neurons Distinguish TDP-43 ALS and Enable ALS Subtype Stratification
or derived from sALS revealed both overlapping and distinguishable signatures, suggesting shared yet distinct mechanistic pathways. Together, these findings establish ML-driven phenotypic profiling as a powerful approach to stratify people with ALS, help disentangle the molecular heterogeneity of ALS and produce a more holistic phenotypic definition in cell-based models, and ultimately find causes and treatments. This strategy offers a scalable and innovative paradigm for uncovering early disease mechanisms not only in ALS but potentially across a spectrum of neurodegenerative and sporadic disorders.
Authors
- Noura Al Bistami
- Reuben Thomas (ORCID: https://orcid.org/0000-0003-3176-7303)
- Zohreh Faghihmonzavi
- Edward Vertudes
- Krishna Raja
- Naufa Amirani
- Julia Kaye (ORCID: https://orcid.org/0000-0001-7442-0882)
- Úna Chan
- Ana Jovičić (ORCID: https://orcid.org/0000-0002-7210-1404)
- Mariya Barch
- Wesley Robinson
- Drew Linsley
- Steve Finkbeiner
Institutions
- Gladstone Institutes (US)
- University of California, San Francisco (US)
- Brown University (US)
- Taube Koret Center (US)
- Sunesis (United States) (US)
Publication Details
- Journal
- bioRxiv (Cold Spring Harbor Laboratory)
- Published
- 2026-04-24
- DOI
- https://doi.org/10.64898/2026.04.22.719920
- Primary Topic
- Amyotrophic Lateral Sclerosis Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- California Institute for Regenerative Medicine