Method Developed to Analyze Cellular Behavior in Complex Environments

Researchers at Tel Aviv University have introduced scNET, a computational method designed to enhance the understanding of cellular behavior in dynamic biological environments, such as cancerous tumors. The system integrates single-cell gene expression data with gene interaction networks, enabling researchers to uncover critical biological patterns, including responses to drug treatments.

The study, published in Nature Methods, was led by first author Ron Sheinin under the guidance of Asaf Madi and Roded Sharan.

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.

According to Sheinin, "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. 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."

Prof. Madi highlighted scNET’s application to T cells, “scNET revealed the effects of treatments on these T cells and how they became more active in their cytotoxic activity against the tumor, something that was not possible to discover before due to the high level of noise in the original data."

According to the team, scNET offers a promising approach for exploring cellular mechanisms and improving disease treatment options by combining artificial intelligence with biomedical research methodologies.