Grant Award Details
- Develop and validate a cellular model of sporadic ALS, using i-MNs differentiated from hiPSCs, including testing the efficacy of small molecules in reverting disease phenotypes and developing Machine Learning methods to detect early stages of motor neuron degeneration.
Grant Application Details
- Developing a Human Model of Sporadic ALS Using Machine Learning and Robotic Microscopy
We will develop the first human stem cell model of sporadic ALS (sALS) to identify disease mechanisms in the most common form of ALS and to discover drugs to treat the vast majority of ALS patients.
Failure of drugs to treat sALS may be due to the use of models of familial ALS to establish preclinical efficacy. Our model of sALS may increase discovery of drugs to treat a wider range of patients.
Major Proposed Activities
- Establish that the larger population of iPSC derived motor neurons (i-MN) from 20 sALS patients have significantly increased risk of death compared to controls.
- Establish that the larger population of i-MNs from 20 sALS patients have increased morphological changes indicative of early degenerative phenotypes compared to controls using machine learning.
- Establish that i-MNs from sALS patients express impaired protein clearance, TDP43 turnover and/or mitophagy compared to controls.
- Determine the utility of our sALS platform for drug discovery by testing if small molecule autophagy inducers and mitoxantrone slows degeneration of neurons from patients with sALS.
Neurodegenerative diseases are a major health problem in California, especially since there are no disease modifying therapeutics for any of these diseases. Our studies focus on ALS. While this is a rare neurodegenerative disease, our development of the first model of sporadic ALS could lead to the discovery and eventual development of the first therapeutic to slow progression of this disease and provide an example for developing similar models for more prevalent neurodegeneration diseases.