Marco Tognetti

This paper develops an automated sample preparation platform for bottom-up proteomics, demonstrating improved reproducibility, throughput, and suitability for drug development.

The paper organizes a DREAM challenge to benchmark predictive models of single-cell signaling using mass cytometry data from breast cancer cell lines.

The paper uses single-cell proteomics and perturbation experiments to improve drug sensitivity predictions in breast cancer and identify genomic features associated with treatment response.

The paper provides detailed experimental protocols and an open-source tool, MhcVizPipe, for analyzing MHC I and II ligand datasets in immunopeptidomics.