Cell-ID International PhD Program

PhD


Deadline 14 March 2025


France






Cell fate transition and histone H3 variants

Geneviève Almouzni and Laura Cantini

The project aims at characterizing how mutations or alterations in histone H3 variants impact development and cell fate. We will use models established in our laboratories to generate data in cellular populations and at individual cell level. We will apply analysis that exploit multiscale integration and computational methods. 

Development of volumetric multi-omic imaging technologies to detect cellular alterations in pediatric cancers

Marcelo Nollmann and Cédric Maurange

This PhD project aims to develop a cutting-edge high-throughput microscope for imaging large tissue volumes, coupled with a microfluidics device for robust multiplexed imaging. This system will enable the study of complex biological processes, such as pediatric cancers and brain development in Drosophila, at unprecedented depth and resolution. The approach addresses current limitations in imaging technologies, providing a valuable tool for systems biology and cancer research.

Modelling OPCs diversity of the hindbrain and their lineage derail during DMG oncogenesis using brain organoids

Stéphane Nédélec and David Castel

The project will consist in specifying the diversity in progenitors and OPC subtypes in the hindbrain, focusing on regional differences. We will further investigate how the oncogenic H3-K27M driver mutation impacts genetic networks controlling OPC commitment and differentiation of these different populations. Combining cancer biology, developmental neuroscience, organoid modeling, and single-cell genomics, this study will uncover mechanisms driving diffuse midline glioma pathogenesis.

Decoding the transcriptional basis for spatial positioning in developing organisms

Sophie Jarriault and Thomas Walter

This project, with significant bio-computing and wet lab components, aims at understanding cellular trajectories during neural development in WT and mutant animals, and how their spatial information is encoded in the transcriptional space. For this, the project will take advantage of the model system C. elegans and its known cellular lineage (invariant in fate, time and space), and of existing scRNA seq data in the lab. The applicant will work at the interface between cell and developmental biology and computational sciences and should therefore have good programming skills, a good command of statistics, an interest in wet lab experiments, and ideally some experience in Machine Learning.