Mots-Clés

scRNA-Seq Transcriptomique spatiale communication inter-cellulaire Rayonnement ionisants

Description

Environnement, organisation

The Institute for Radiation Protection and Nuclear Safety (IRSN) is the French public expert, with industrial and commercial activities, in nuclear and radiation risks, and its activities cover all the related scientific and technical issues. The Institute is supervised jointly by the French Minister of the Ecological transition, the French Minister of Defense, and the French Ministers of Energy transition, Research and Health.

This project is funded by INCA as part of the AAP projet INCA seq2022 relies on a closed collaboration between the IRSN and Dr Michele MONDINI at Gustave Roussy.

Context:

Radiation-induced digestive toxicity is a clinical concern for cancer patients treated with radiotherapy for tumors of the abdomino-pelvic area. The digestive system is made up of a set of different cell populations, each of which are heterogeneous and presents highly variable degrees of plasticity and states of differentiation. Our aim is to understand the cellular events and communication networks that contribute to the pathogenesis of radiation-induced digestive lesions. We aim to characterize cell/cell interactions in preclinical models, as well as in humans, in order to identify promising targets for predicting, preventing or treating digestive toxicities following radiotherapy.

Work context and objectives:

The aim of the postdoctoral fellow will be to carry out bioinformatics analyses of “single cell” RNAseq and spatial transcriptomics data generated in this project, using state-of-the-art computational biology tools to map intercellular communications between endothelial and immune cells that could contribute to digestive toxicity after irradiation. scRNAseq analyses: use of Seurat, Monocle, Scanpy packages, and bioinformatics enrichment and intercellular communication network inference tools CellChat, ICellnet will be employed. Spatial transcriptomic analysis: the candidate will identify and implement the methods needed to address the challenges of image segmentation and colocalization analysis to determine the hubs and centroids of cells and/or cell clusters. The idea will be to train a new segmentation algorithm specifically adapted to the needs of the project (deep learning model cellPose). Finally, the COMMOT (COMMunication analysis by Optimal Transport) algorithm could also be used to deduce cell/cell communication data, considering the spatial distances between cells.

Required profile: The candidate will have PhD experience in genomic/transcriptomic data analysis and/or machine learning, with the desire and ability to acquire expertise in multi-omics analysis in the field of oncology. Skills appreciated for this position include: a good background in biology, scRNAseq analysis pipelines, spatial transcriptomic analysis experience, integrative multi-omics models, statistical classification, gene network and gene module analysis, image analysis.

Excellent communication, didactic and teamwork skills.

Starting date: January 2025

language: french / english

Contact for applications:  fabien.milliat@irsn.fr and mohamedamine.benadjaoud@irsn.fr