imAIgene-lab
Dynamic Cancer Insight with AI
Dynamic Cancer Insight with AI
Our research is based on the premise that cellular function cannot be fully understood without directly measuring cellular behavior. In cancer, critical phenomena such as immune engagement, invasion, and therapeutic resistance emerge from dynamic interactions between cells and their microenvironment. These processes are poorly captured by static molecular measurements alone.
We therefore position microscopy as a new functional omics modality, in which live-cell imaging is used to systematically quantify behavior at single-cell resolution. By treating dynamic features—such as morphology, motility, and interaction patterns—as high-dimensional variables, microscopy becomes a scalable source of functional information that complements molecular and transcriptomic data.
At imAIgene-lab, we develop computational frameworks that convert imaging data into quantitative phenotypic representations and integrate them with single-cell omic data. This enables the identification of functional cell states and response phenotypes that are not defined by molecular markers, but by how cells act in specific contexts.
Our computational developments are directed to solving two major challenges in oncology:
• Understanding the mode‑of‑action of T‑cell immunotherapy against solid tumors to overcome resistance.
• Unravelling how the tumor microenvironment shapes invasive behavior and drives therapeutic failure.
While our applications currently focus on cancer, the underlying concept—using microscopy as a functional omics layer and integrating it with molecular data—is broadly applicable to biological systems in which cellular behavior is a determinant of outcome.
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Bridging the gap between wet-lab and computational scientist.