PhD candidate in genetics and bioinformatics at Ecole Normale Superieure (PSL) and Institut Pasteur, my goal is to understand the functional states of microglia at single-cell level using machine learning approaches. I also teach courses in programming and bioinformatics at the university.
Microglia are immune cells of the brain that contribute to its healthy development, function, and maintenance. They are highly heterogeneous at transcriptomic and morphological levels during early development, related to their brain localization and function. Such heterogeneity has also been observed in neurodegenerative diseases (e.g., Alzheimer’s disease) in which some subsets are proposed to be beneficial or detrimental. Yet, our understanding of the spatiotemporal diversity of microglial cell states and their link with functional properties remains limited.
We address this question using machine learning tools for single-cell data integration and gene regulatory network inference. Our computational integrative approach will deliver an extensive and consistent characterization of microglial heterogeneity during development and neurodegenerative diseases.
Noteworthy, the identification of intrinsic and extrinsic signals controlling microglial states and subsets in healthy and pathological development has potential therapeutic implications.
This project is financed by the Fondation de la Recherche Medicale.