Spatial mapping of gene expression to decipher the thymic cellular crosstalk controlling central immune tolerance
Magali IRLA / CIML / firstname.lastname@example.org
Denis PUTHIER / TAGC / email@example.com
Arnauld SERGE / LAI / firstname.lastname@example.org
Recent transcriptomic analyses allow to spatially resolve genome-wide gene expression in individual tissue sections at a subcompartment or near single-cell levels. In contrast to scRNA-seq, these emerging cutting-edge technologies conserve the footprint of the tissue structure in which the occurring cellular crosstalk dictates cell fates. The thymus that ensures the generation of a self-tolerant T-cell repertoire is composed of a large diversity of cell types (i.e. distinct stromal cells and developing T cells) that reciprocally control their respective differentiation. The main objective of this project is to determine in situ, using transgenic mouse models, the impact of developing T cells on the transcriptional programs governing the stromal cell heterogeneity. To this end, the candidate will use both commercially available solutions (e.g. 10X Genomics) and setup highly resolutive solutions (e.g. Slide-seq) to spatially study the thymic tissue transcriptome. Gene expression and spatial measurements will be combined to feed a model of complex cellular interplay.
Immune tolerance; Cellular crosstalk; Thymus; Spatial transcriptomics; Computational analyses; RNA-sequencing; Image data processing
The objectives of this project are (i) to provide the first spatially resolved comprehensive picture of thymus transcriptome, (ii) to spatially map the transcriptional response to thymic crosstalk perturbations and (iii) to develop computational models of cellular crosstalk and the associated bioinformatics tools. By implementing dedicated spatiofunctional graphical rendering, this project is expected to reveal dynamically regulated transcriptional signatures by crosstalk in specific thymic niches.
The expected candidate will benefit of the expertise in immunology, bioinformatics, statistics and image analysis of the three partners and will work in close collaboration with the TGML genomic platform. We are seeking for a highly motivated candidate with an interdisciplinary profile and a strong background in molecular biology, bioinformatics and statistics. Knowledge in sequencing technologies, immunology and image analysis methods would be a real asset.
Articles related to the project
1. Rodriques SG et al. Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution. Science. 2019 Mar 29;363(6434):1463-1467.
2. Ståhl PL et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 2016 Jul 1;353(6294):78-82.
3. Kadouri N et al. Thymic epithelial cell heterogeneity: TEC by TEC. Nat Rev Immunol. 2019 Dec 5.
4. Lopes N et al. Thymic Crosstalk Coordinates Medulla Organization and T-Cell Tolerance Induction. Front Immunol. 2015 Jul 20;6:365.