The epithelium-mesenchyme transition (EMT) is an essential process broadly used in development, wound healing and disease (e.g. fibrosis, cancer). It is activated by a handful of transcription factors (EMT-TFs) collectively controlling cell-cell adhesion and cell motility. Although numerous studies have analyzed these factors individually, our current knowledge includes significant gaps, with key open questions about EMT either in development or in cancer [1]. This project addresses one of them: what is the spatial and temporal dynamics of EMT-TFs co-activation and what is its functional significance for the process of EMT at the scale of cells and tissue and for cell migration? We explore if and when EMT-TFs are activated simultaneously in a given cell, if there are specific characteristics for these expressions, a precise combinatorial logic and a temporal dynamics, that can be related to a specific cell state; or alternatively if there is a degree of stochastic activation of these genes in a given tissue. Secondly, when the EMT-TFs are co-activated, do they cooperate molecularly in the same cell and for which outcome on the modalities of EMT? By combining approaches of cell biology, molecular embryology and bioinformatics, this project explores EMT from single cell scale to tissue level, in the classical model of the embryonic neural crest. Neural crest EMT is a well-understood and extensively explored model of a stereotypical EMT, accessible for studies in vivo and ex-vivo, providing a framework for more general understanding of EMT in pathological contexts. We will describe the spatial and temporal dynamics of EMT-TFs co- expression and compare with expression of their transcriptional regulators during neural border development. Using single cell transcriptomes and single cell multiplexed in situ hybridization, we will provide the first in vivo map of EMT-TFs relative expression during development with individual cell resolution. We will push limits of the current analyses, (e.g. low depth of single cell transcriptomes, biostatistics issues for evaluation cell-to-cell heterogeneity of a given gene...): we will prepare novel, deeper, and neural crest-focused datasets and novel analysis tools with the two partners specialized in single cell transcriptomics, L. Peshkin (Partner 3, Harvard Medical School, Dept Systems Biology, [2]) et I. Adameyko (Partner 4, U. Vienna [3]). We will develop quantitative in situ hybridization (with single cell and single molecule resolution) with external collaborator T. Walter (Mines ParisTech, [4]) to produce a quantitative map during neural crest development. To understand the functional impact of EMT-TFs cooperation, we will experimentally manipulate the gene regulatory network of neural crest EMT using a suite of dedicated and custom made molecular tools, used to measure the fine- tuned parameters of EMT and cell migration in vivo and ex vivo. A.H. Monsoro-Burq (Coordinator, Partner 1, I. Curie, U. Paris Saclay, [5, 6]) et E. Théveneau (Partner 2, U. Toulouse, [7, 8]) will thus set-up an integrated series of assays testing the developmental and cellular consequences of EMT- TFs conjoined expressions. Our project will bring a new dimension in the landscape of EMT control and mechanism and will advance significantly our understanding of EMT both in normal and in pathological contexts. [1] Stemmler et al, Nat.Cell.Biol 2019; 10.1038/s41556-018-0196-y [2] Briggs et al., Science 2018, 10.1126/science.aar5780 [3] Soldatov et al., Science 2019; 10.1126/science.aas9536 [4] Tsanov et al., Nucl Ac.Res.2016;10.1093/nar/gkw784 [5] Plouhinec et al., PLOS Biology 2017; DOI:10.1371/journal.pbio.2004045. [6] Scerbo and Monsoro-Burq 2020; Science Advances, DOI: 10.1126/sciadv.aaz1469 [7] Bajanca et al., Nature Communications 2019; DOI:10.1038/s41467-019-09548-5 [8] Yang et al, Nat Rev Mol Cell Biol. 2020, DOI: 10.1038/s41580-020-0237-9.

Coma induced by Out of Hospital Cardiac Arrest (OHCA) is a major global health problem. An early and accurate prognostication is the cornerstone for the clinical management of these patients, mainly because the vast majority of the related mortality observed in this setting comes from withdraw life-sustaining treatment (WLST) decisions following prognostication of a poor neurological outcome. However, currently recommended predictors, based on a multimodal assessment encompassing the use of one fluid-derived biomarker in isolation (NSE, Neuron Specific Enolase) are only informative in a minority of patients, leaving up to 50-77% of anoxo-ischemic coma patients in a ‘gray zone’ of prognostication. Moreover, a further important gap in the existing anoxic coma literature concerns the limited treatment strategies to enhance neurological recovery after OHCA. The current proposal aims to develop and validate over the course of 4 years a new mechanistically coherent battery of fluid-derived biomarkers for the early neuroprognostication and individually-tailored treatment for anoxic coma patients. As a significant paradigm shift in the field, we seek to identify the best combination of fluid-derived biomarkers related to key neuroinflammatory, neurodegenerative and neuroprotective (3N) processes known to be triggered by cardiac arrest. To this end, we will use for the first time in this setting ground-breaking and highly synergic deep 3N multiomics profiling methods that will be empowered by innovative artificial intelligence methods. Following a step-wise approach, first we will capitalize on already existing data to identify among a panel of candidate biomarkers from peripheral blood samples. Second, to increase the robustness and explainability of the predictive models, we will cross-validate the fluid biomarkers and characterize them in relation to well-established 3N hallmarks using multi-dimensional data from an additional independent prospective patient cohort. Finally, a third cohort will be gathered to ensure the prospective and external validation of the identified fluid biomarkers at a European level, aiming to guarantee the predictive model’s generazability and inform the feasibility and design of future clinical trials. The final deliverable will consist of a potential game-changer AI-based predictive classifier for anoxic coma patients based on easily accessible and inexpensive peripheral blood-derived biomarkers. The whole dataset will be made FAIR to facilitate data sharing with the broader research community.