Artificial Intelligence (AI) has been making its way into more scientific advances. We’ve seen how AI has improved efforts in bioprocessing, cancer research, neurodegenerative disease, and even COVID-19. The latest news comes from researchers from North Carolina State University (NC State) who have used AI to identify and categorize neural degeneration in the model organism C. elegans.
“Researchers want to study the mechanisms that drive neural degeneration, with the long-term goal of finding ways to slow or prevent the degeneration associated with age or disease,” explained Adriana San Miguel, an assistant professor of chemical and biomolecular engineering at NC State. “Our work here shows that deep learning can accurately identify physical symptoms of neural degeneration, can do it more quickly than humans, and can distinguish between neural degeneration caused by different factors.”
Their findings, “Deep learning-enabled analysis reveals distinct neuronal phenotypes induced by aging and cold-shock,” were published in BMC Biology.
“Having tools that allow us to identify these patterns of neural degeneration will help us determine the role that different genes play in these processes,” San Miguel noted. “It will also help us evaluate the effect of various pharmaceutical interventions on neural degeneration in the model organism. This is one way we can identify promising candidates for therapeutic drugs to address neurological disorders.”
The researchers focused on the roundworm, C. elegans, that is widely used to study aging and the development of the nervous system. The researchers observed PVD neurons, which are nerve cells that can detect both touch and temperature. The researchers chose the PVD neuron because it is known to degenerate due to aging in C. elegans.
“In this work, we apply deep learning to perform quantitative image-based analysis of complex neurodegeneration patterns exhibited by the PVD neuron in C. elegans. We apply a convolutional neural network algorithm (Mask R-CNN) to identify neurodegenerative subcellular protrusions that appear after cold-shock or as a result of aging,” noted the researchers.
Kevin Flores, co-author of the study and an assistant professor of mathematics at NC State noted that they were able to collect all of the relevant data from an image in a matter of seconds, by combining the power of deep-learning with GPU computing.
Besides monitoring the effects of age on neural degeneration, the researchers also examined the effects of “cold shock,” or prolonged exposure to low temperatures since the PVD neurons can detect temperature and touch. To their surprise, the researchers learned that cold shock could also induce neural degeneration.
“We also found that neural degeneration caused by cold shock had a different pattern of bubbles than the degeneration caused by aging,” San Miguel added. “It is difficult or impossible to distinguish the difference with the naked eye, but the deep learning program found it consistently.”
“This pipeline is a promising approach to further explore the mechanisms underlying of beading in these and other contexts (such as oxidative stress, dietary restriction, and neurodegenerative disease models), to understand the differences that lead to distinct aging and cold-shock-induced morphological changes, and to identify whether beads are a result of loss of neuronal integrity or could act as a protective mechanism,” explained the researchers.
Their findings are another example of how deep learning tools are able to detect and uncover patterns that may be missed, which will not only benefit research in neurodegeneration but in other areas. “…we may be just scratching the surface of their utility in advancing our understanding of neural degeneration,” concluded San Miguel.
Artificial intelligence has the power to advance our research by revealing what was once unseen, and will hopefully open more doors of understanding when it comes to understanding the mechanisms of disease.