ESPRESSO-Omics · Beyond a snapshot: Spatiotemporal Multiomics for the study of Breast Cancer phenotypic transitions

This proposal aims to address a critical knowledge gap in understanding phenotypic transitions in the context of chemotherapy resistance. Phenotypic transitions encompass various mechanisms that modify cellular phenotypes over time, including differentiation and metabolic reprogramming, among others. As some cellular phenotypes are transient and chemoresistant cells constitute a rare subpopulation, studying these important phenomena is challenging even with modern, high dimensional phenotyping methods (e.g. proteomics and transcriptomics), as they are intrinsically destructive for the sample and can only provide a single snapshot of these fleeting cellular states.To overcome this limitation, I propose the development of a groundbreaking framework that combines spatial proteomics with ESPRESSO, a novel omics approach I developed for high-dimensional phenotyping applicable to living system. The resulting ESPRESSO-Omics framework will enable real-time monitoring of both organelle and protein expression profiles in living cells, that will allow us to go back in time and identify the subpopulation with the potential to become chemoresistant, as well as the transitions that lead it there, providing a revolutionary point of view in the identification of therapeutic targets.Multiple deliverables including an ESPRESSO-Omics atlas, a high-throughput hyperspectral light-sheet microscope and a machine learning framework will be provided during the course of this award, articulated over two work packages (WPs): WP1: Establishment of the ESPRESSO-omics frameworkWP2: Investigation of phenotypic transitions in breast cancerUnderstanding phenotypic transitions has broad implications, not only in breast cancer research but also in other cancers and biological systems. The open-source cell atlas, instrumentation specifications and data analysis tools will be valuable resources for the scientific community, democratizing advanced tools for the study of phenotypic transitions