Advances in microscopy now enable the generation of complex, high-content imaging data that can be interpreted as a form of functional and phenotypic omics. However, their systematic exploitation is often limited by the lack of appropriate analytical tools. At imAIgene-lab, we develop and apply computational science approaches, including computer vision, data analysis, and multi-omics integration with single-cell technologies, to transform dynamic imaging data into quantitative biological information. Our goal is to integrate functional phenotypes with molecular profiles to uncover biologically meaningful patterns relevant for drug response prediction, the design of combinatorial strategies in immuno-oncology, and the development of AI-driven personalized diagnostic and therapeutic approaches.
Visit our website: www.imAIgene-lab.com
We provide comprehensive protocols to give biologists access to AI tools, making it easier to apply the latest advancements in AI for image analysis: https://www.imaigene-lab.com/resources
Our computational developments are directed to solving two challenges of oncology:
- Understanding the mode-of-action of T cell immunotherapy against solid tumors to overcome resistance.
Despite the revolutionary success of cancer T-Cell Immunotherapies their efficacy in treating solid tumours, is still very limited. Cancer heterogeneity between patients, and even within the same tumour, challenges the achievement of consistent clinical outcomes and has triggered the development of a multitude of T cell-based therapies using sophisticated engineering approaches: CART, bi-specific antibodies, TCR based, metabolome sensing. However, these widely exploited ‘living’ drugs are inherently dynamic and utilize different cellular strategies to achieve tumour targeting that cannot be analysed through a single snapshot readout. Dynamic live imaging can capture complex cellular function that spans overtime, explore functional differences between T cells, and define cellular events leading to tumour killing. In this research line we exploit computer vision and analytical tools to understand how T cell achieve successful killing, or different modes of disfunction and their relation to tumor resistance.
- Unravelling the role of the tumor microenvironment in tumor invasive behavior and resistance to immunotherapy in Diffuse Midline Glioma.
Modern technologies allow to study the distinct aspects involved in tumor progression from different angles, such as in vivo and ex vivo time-lapse imaging (e.g. intravital microscopy) for tumor cell invasion studies; multiplexed imaging for TME characterization or sc transcriptomics to quantitively dissect tumor heterogeneity and complexity. Separately each of these single omic approaches can mainly reflect one aspect of tumor biology, thus computational integrative approaches are necessary to acquire systematic understanding of how these different aspects interact define predictive paths to invasion and provide workflows to bridge phenotypic, molecular and contextual networks driving this process. To identify tumor intrinsic and extrinsic drivers of tumor invasion in this research line we develop a computational framework for multi-omic integration of three complementary layers: tumor cell dynamics; TME and tumor transcriptome. With this framework we aim to get a better understanding on the biology of Diffuse Midline Glioma, an invasive pediatric brain tumor with a very dire prognosis.