Our laboratory develops novel computational methods and single-cell data-driven models to understand immunity in cancer and infection.
A meta-analysis of T-cell states in cancer
Multiple signals affect T cell differentiation and function in cancer, giving rise to T cell states that differ from those generated upon acute infections. In recent years, single-cell transcriptomics have revealed a large diversity of tumor-infiltrating T lymphocyte (TIL) states, some of which appear to be associated with improved prognosis or response to immune checkpoint blockade. Because of the variability between individuals and tissues, there are major computational challenges associated to data integration and the delineation of discrete cell states. Moreover, it remains unclear to what extent T cells states are conserved between humans and mice. As a consequence, a robust definition of T cell states across studies, tumor models, tissues and organisms is still lacking.
The goal of this project is to generate curated, multi-species cell atlases that accurately describe transcriptional, epigenetic and metabolic states of T cells in health and disease.
Interpretation of immune responses by computational modeling of single T-cell transcriptomes
While the pace of scRNA-seq data generation is rapidly increasing, the lack of reference frameworks to compare experiments and experimental conditions imposes important limitations on the biological interpretation of the data. The objective of this project is to develop computational methods for the analysis of single-cell transcriptomics data in the context of reference cell atlases, and in this way aid the interpretation of in vivo immunological states using robust and reproducible computational pipelines.
Determining T cell differentiation pathways in cancer and chronic infection by spatio-temporal analysis of transcriptional programs and clonotypes
Multi-modal single T-cell data including transcriptome, chromatin accessibility, T cell receptor and antigen specificity open unprecedented opportunities to explore the interplay between transcriptional and epigenetic programs, T cell affinity and clonotype.
The aim of this project is to generate multi-modal multi-tissue time-course single-T cell data and develop computational frameworks to exploit them to reconstruct T cell differentiation pathways, identify master gene regulators and learn the molecular rules that determine fate decisions in cancer and chronic infection. In collaboration with our experimental partners, we use CRISPR/Cas9 to target candidate gene regulators and reprogram T cell fate in vivo.
Prediction of tumor-reactive T cell receptors for personalized adoptive cell cancer therapy by computational modeling of T cell differentiation states
A promising approach to treat solid tumors consists of adoptive transfer of genetically modified T cells with enhanced functionality and bearing neoantigen-specific transgenic antigen receptors. However, identification of potent anti-tumor T cell receptors remains a major challenge.
This project aims to develop data-driven solutions for epitope-agnostic discovery of high-affinity anti-tumor TCRs using scRNA-seq of patients’ biopsies of tumors, lymph nodes or peripheral blood, for their use in next-generation adoptive T cell cancer therapies.
- Massimo Andreatta and Santiago J Carmona. UCell: Robust and scalable single-cell gene signature scoring Computational and Structural Biotechnology Journal (2021)
- Andreatta, M., Corria-Osorio, J., Müller, S., Cubas R., Coukos G., Carmona S.J. Interpretation of T cell states from single-cell transcriptomics data using reference atlases.Nat. Commun. 12, 2965 (2021).
- Massimo Andreatta and Santiago J Carmona*. STACAS: Sub-Type Anchor Correction for Alignment in Seurat to integrate single-cell RNA-seq data.Bioinformatics (2020)
Advanced search is available through Serval
Publications can be managed by accessing Serval via MyUnil