A deep-learning-based screening platform in aging-relevant human motor neurons to identify therapeutic compounds for amyotrophic lateral sclerosis
Age is the primary risk factor for most neurodegenerative diseases including amyo-trophic lateral sclerosis (ALS). Despite the clear importance of age on the development and progression of ALS, age is rarely considered a factor in cellular or animal models of ALS. The recent advent of direct reprogramming methodologies, whereby somatic cells such as fibroblasts can be converted into neurons while retaining the age signature of the host, has facilitated the study of age-relevant human cells in vitro for the first time. Despite the promise of this technology, age is a complex, multidimensional, and dy-namic phenotype that makes the interpretation of the interplay between age and dis-ease a great challenge. To circumvent these challenges, we developed a screening platform for directly reprogrammed neurons in an age-relevant human cellular model to better model and identify disease-modifying targets and pathways for ALS. This plat-form uses deep learning image analysis to screen compounds for efficacy against ALS-associated cellular phenotypes and efficiently classifies ALS-like phenotypic signatures as well as predicts the age of the original fibroblast donor. Notably, our work identified NCB-0846, a TNIK/MAP4K7 inhibitor, as a novel compound with the ability to revert ALS-like phenotypes. Moreover, we show that chronic dosing of NCB-0846 in the SOD1G93A mouse model of ALS significantly reduced serum neurofilament-L levels. Our findings establish the power of a platform that combines direct reprogramming of human cells and deep learning as a powerful tool for combatting aging and age-related diseases.