Single-cell omics data science for cancer and immunology
Single-cell and spatial omics technologies are opening new avenues for understanding disease mechanisms at cellular resolution, offering new insights into why diseases and their responses to therapy vary across patients. In cancer immunology, these approaches reveal tumours as complex ecosystems composed of diverse and interacting cancer, stromal, and immune cell populations, each contributing in distinct ways to disease progression and therapeutic response. However, the high-dimensional data generated by these rapidly evolving technologies pose major analytical challenges for biological interpretation and for the identification of actionable biomarkers and therapeutic targets.
Our lab develops computational and statistical methods to analyse high-throughput single-cell and spatial omics data. We aim to understand how cell states and cell–cell interactions shape disease trajectories and influence treatment outcomes. A central focus of our research is to characterise patterns of cell heterogeneity – both within individual tumours and across patients – and to understand their roles in cancer progression and therapy resistance.
EXPERTISE
Single-cell omics; Spatial omics; Data science; Computational methods development; Cell heterogeneity; Cancer; Tumor microenvironment; Immune response
SELECTED PUBLICATIONS
- Massimo Andreatta, Juan Garnica, Santiago J. Carmona. Identification of malignant cells in single-cell transcriptomics data. Communications Biology (2025) 8:1264 – https://doi.org/10.1038/s42003-025-08695-4
- Laura Yerly#, Massimo Andreatta#, Jeremy Di Domizio, Michel Gilliet, Santiago J. Carmona*, François Kuonen*. Wounding triggers invasive progression in human basal cell carcinoma (#co-first authors; *co-corresponding authors) – preprint, bioRxiv 2025 https://doi.org/10.1101/2024.05.31.596823
- Massimo Andreatta, Leonard Herault, Paul Gueguen, David Gfeller, Ariel J Berenstein, Santiago J Carmona. Semi-supervised integration of single-cell transcriptomics data. Nature Communications (2024) – https://www.nature.com/articles/s41467-024-45240-z
- Massimo Andreatta, Fabrice P.A. David, Christian Iseli, Nicolas Guex, Santiago J.Carmona SPICA: Swiss Portal for Immune Cell Analysis. Nucleic Acids Research (2022) – https://doi.org/10.1093/nar/gkab1055
- Massimo Andreatta, Ariel Tjitropranoto, Zachary Sherman, Michael C. Kelly, Thomas Ciucci, Santiago J. Carmona. A CD4+ T cell reference map delineates subtype-specific adaptation during acute and chronic viral infections. eLife (2022) 11:e76339 – https://doi.org/10.7554/eLife.76339
- M Andreatta, JC Osorio, S Muller, R Cubas, G Coukos, SJ Carmona. Interpretation of T cell states from single-cell transcriptomics data using reference atlases. Nature Communications (2021) – https://doi.org/10.1038/s41467-021-23324-4