Principal Investigator (PI): Davide Risso

Funder: European Research Council Executive Agency (ERCEA). Consolidator Grant funded under Grant agreement No. 101171662

General objectives:

The SPECOLA project will combine machine learning, artificial intelligence (AI), and statistical models to analyze gene expression in a spatial, sub-cellular context, helping to advance biomedical research, personalized medicine and computer-assisted pathology.

Although still a relatively new technique, spatial transcriptomics is quickly becoming a popular tool, working alongside single-cell RNA sequencing to study gene activity in complex tissues, with uses in areas like cancer research and brain studies. In addition to measuring gene activity and locating where genes are expressed in tissues, this method also provides images of the samples that help us understand the structure of cells and tissues. The main goal of this proposal is to bring together gene activity, tissue structure, and cell features to improve how we analyze spatial transcriptomics data.

Specifically, the project will:

• Improve understanding of spatial transcriptomics data by analyzing its statistical properties and creating models for gene expression in tissue.
• Combine imaging and tabular data to better define cell types and states using statistical models and AI.
• Develop an inferential framework to predict where genes are located within cells and across tissue samples.

Duration: 01/05/2025 – 30/04/2030