(Left to right) Prof. Asaf Madi, Prof. Roded Sharan & PhD student Ron Sheinin.
Researchers at Tel Aviv University have developed scNET, a groundbreaking method designed to enhance our understanding of cell behavior in dynamic biological environments, such as cancerous tumors.
The scNET system integrates single-cell gene expression data with gene interaction networks, allowing scientists to identify critical biological patterns, including responses to drug treatments. This innovative approach has the potential to revolutionize cancer research and accelerate the development of new treatments.
A detailed study on scNET was published in the prestigious journal Nature Methods. The research was led by PhD student Ron Sheinin under the guidance of Prof. Asaf Madi from the Faculty of Medicine and Prof. Roded Sharan, head of the School of Computer Science and AI at Tel Aviv University.
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This advancement in AI-driven medical research could pave the way for more effective personalized treatments for cancer and other diseases.
Today, advanced sequencing technologies allow the measurement of gene expression at the single-cell level and, for the first time, researchers can investigate the gene expression profiles of different cell populations within a biological sample and discover their effects on the functional behavior of each cell type. One fascinating example is understanding the impact of cancer treatments – not only on the cancer cells themselves but also on the pro-cancer supporting cells or, alternatively, anti-cancer cell populations, such as some cells of the immune system surrounding the tumor.
Despite the amazing resolution, these measurements are characterized by high levels of noise, which makes it difficult to identify precise changes in genetic programs that underlie vital cellular functions. This is where scNET comes into play.
“scNET integrates single-cell sequencing data with networks that describe possible gene interactions, much like a social network, providing a map of how different genes might influence and interact with each other,” said Ron Sheinin. “scNET enables more accurate identification of existing cell populations in the sample. Thus, it is possible to investigate the common behavior of genes under different conditions and to expose the complex mechanisms that characterize the healthy state or response to treatments.”
Ultimately, scNET exemplifies the transformative potential of integrating AI with biomedical research, paving the way for novel therapeutic strategies, the discovery of obscured disease mechanisms, and the proposal of innovative treatment avenues.
