%0 Journal Article
%T Identifying FUS amyotrophic lateral sclerosis disease signatures in patient dermal fibroblasts.
%A Kumbier K
%A Roth M
%A Li Z
%A Lazzari-Dean J
%A Waters C
%A Hammerlindl S
%A Rinaldi C
%A Huang P
%A Korobeynikov VA
%A  
%A Phatnani H
%A Shneider N
%A Jacobson MP
%A Wu LF
%A Altschuler SJ
%J Dev Cell
%V 59
%N 16
%D 2024 Aug 19
%M 38878774
%F 13.417
%R 10.1016/j.devcel.2024.05.011
%X 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.