Identifying FUS amyotrophic lateral sclerosis disease signatures in patient dermal fibroblasts
Karl Kumbier, Maike Roth, Zizheng Li, Julia R. Lazzari-Dean, C. Waters, Sabrina Hammerlindl, Capria Rinaldi, Ping Huang, Vladislav A. Korobeynikov, Hemali Phatnani, Neil A. Shneider, Matthew P. Jacobson, Lani F. Wu, Steven J. Altschuler
Abstract
Amyotrophic lateral sclerosis (ALS) is a rapidly progressing, highly heterogeneous neurodegenerative disease, underscoring the importance of obtaining information to personalize clinical decisions quickly after diagnosis. Here, we investigated whether ALS-relevant signatures can be detected directly from biopsied patient fibroblasts. We profiled familial ALS (fALS) fibroblasts, representing a range of mutations in the fused in sarcoma (FUS) gene and ages of onset. To differentiate FUS fALS and healthy control fibroblasts, machine-learning classifiers were trained separately on high-content imaging and transcriptional profiles. "Molecular ALS phenotype" scores, derived from these classifiers, captured a spectrum from disease to health. Interestingly, these scores negatively correlated with age of onset, identified several pre-symptomatic individuals and sporadic ALS (sALS) patients with FUS-like fibroblasts, and quantified "movement" of FUS fALS and "FUS-like" sALS toward health upon FUS ASO treatment. Taken together, these findings provide evidence that non-neuronal patient fibroblasts can be used for rapid, personalized assessment in ALS.