New study uses AI technology to understand cell dysfunction in ALS

By Jill Pease

head shot of Sai Zhang
Dr. Sai Zhang

In patients with amyotrophic lateral sclerosis, or ALS, nerve cells called motor neurons degenerate, causing muscle atrophy and eventually death. Other cells in the central nervous system are known to play a role in ALS, but scientists don’t yet have a clear picture of how they contribute to the disease.

A team of researchers from the University of Florida College of Public Health and Health Professions and the University of Sheffield in the U.K. has received a grant from the Motor Neurone Disease Association to combine single cell profiling with an artificial intelligence method known as deep learning to analyze the different types of central nervous system cells affected by ALS, also known as Lou Gehrig’s disease, in a detailed and systemic way.

“We hope to uncover important information about how these cells interact with each other and how their dysfunction relates to the degeneration of motor neurons,” said study co-principal investigator Sai Zhang, Ph.D., an assistant professor in the UF PHHP department of epidemiology. “This knowledge could be crucial in finding new ways to treat ALS and develop targeted therapies.”

For the study, Zhang and Johnathan Cooper-Knock, Ph.D., a clinical lecturer in the School of Medicine and Population Health at the University of Sheffield, will measure the gene regulatory process in the motor cortex tissues of people with ALS and people without the disease by examining every single cell in these tissues.

“This grant from the MND Association is a key step for us applying cutting edge single-cell profiling to brains donated by patients with motor neurone disease,” Cooper-Knock said. “By doing this, we can start to understand the roles of individual cell types within the brain in the development of disease. Ultimately, this understanding is crucial if we are going to send future interventions, including drugs, to the correct cell type.”

An illustration of brain neurons, astrocytes and glia cells, which play a role in ALS.

A study this comprehensive will generate billions of measurements — the largest dataset of its kind for an ALS study — that would be nearly impossible to analyze without the use of AI and machine learning algorithms, Zhang said.

“Imagine trying to find small needles within a large haystack,” Zhang said. “Machine learning algorithms will enable us to search through this massive dataset quickly and precisely, pinpointing patterns and relationships that are characteristic of ALS. This will help us uncover important insights and potential biomarkers associated with the disease.”

The researchers expect to gain a detailed understanding of how genes are controlled in different cell types affected by ALS, which will help them uncover the specific mechanisms driving ALS pathology.

“This project represents a significant advancement in ALS research by combining multiple cutting-edge technologies,” Zhang said. “By elucidating the cell-type-specific gene regulation and exploring the intricate molecular and cellular landscape of ALS, we aspire to open up new avenues for therapeutic approaches that could make a meaningful impact on the lives of individuals affected by this devastating disease.”