Mots-Clés

spatial biology, systems biology, molecular networks, autoimmunity, machine learning

Description

Postdoctoral Position in Modeling the Spatial and Temporal Variation of the Microenvironment

The Colinge Laboratory Cancer Bioinformatics and Systems Biology at IRCM (Cancer Research Institute of Montpellier) is looking for a talented postdoctoral fellow.

Autoimmune diseases (AID) have a complex, multifactorial etiology that remains poorly understood in most cases. Their installation and persistence involve cellular networks and abnormal communication, e.g., triggered by autoreactive antibodies continuously activating immune system effectors. Through its work packages, the Immun2Cure (I4C) consortium will collect diverse molecular data sets aimed at discovering such pathological networks among other objectives.

A range of high-throughput molecular data sets including single-cell RNA-sequencing, single-cell proteomics in cytometry and mass-spectrometry, and spatial transcriptomics and proteomics. These data sets will be challenging to integrate and understand to achieve I4C objectives. A well-established and efficient approach to conduct such data integration and interpretation tasks relies on molecular networks, and here on cellular networks as well. The exploitation of known, intra-cellular biological pathways and ligand-receptor interactions mediating cellular exchanges provides a framework to combine new data and existing knowledge. When combined with the ability to infer networks de novo to add novel, data-evidenced interactions, networks become a powerful and rather general tool. Data integration may consist in simple projections on relevant networks, but we usually employ more advanced data modeling techniques such as random walks, machine learning, or convolutional neural networks on graphs.

The Colinge Lab has developed a number of algorithms and machine learning models to infer both intra-cellular and cellular networks [1–3], and to integrate data over such networks to extract actionable biological information such as candidate targets or biomarkers [4–6], including in single-cell and spatial transcriptomics [7,8]. A very challenging question remains, which is to understand how such networks and the data we integrate over them vary in space and time. The successful candidate will develop new methods to address this problem and apply them to I4C data.

Preferred qualifications are either a bioinformatics PhD and solid computational skills or a mathematics/physics/computer science PhD with strong interests in life science applications. Machine learning and deep neural network practical experience would be a plus. The position is initially funded for 2 years with 1,5 more years available to investigate similar questions in the context of solid tumors and their microenvironment. A first contract will be established for 1 year and extended upon performance to up to 3,5 years in total.

Interested applicants should e-mail their CV, a letter of motivation and the names and e-mails of 2 references to Prof Jacques Colinge (jacques.colinge@umontpellier.fr). Application deadline is December 31, 2024. Expected starting date February 1, 2025.

References

1.  Villemin J-P, Bassaganyas L, Pourquier D, Boissière F, Cabello-Aguilar S, Crapez E, et al. Inferring ligand-receptor cellular networks from bulk and spatial transcriptomic datasets with BulkSignalR. Nucleic Acids Res. 2023; gkad352. doi:10.1093/nar/gkad352

2.  Cabello-Aguilar S, Alame M, Kon-Sun-Tack F, Fau C, Lacroix M, Colinge J. SingleCellSignalR: inference of intercellular networks from single-cell transcriptomics. Nucleic Acids Res. 2020. doi:10.1093/nar/gkaa183

3.  Borg J-P, Colinge J, Ravel P. Modular response analysis reformulated as a multilinear regression problem. Bioinforma Oxf Engl. 2023; btad166. doi:10.1093/bioinformatics/btad166

4.  Alame M, Cornillot E, Cacheux V, Tosato G, Four M, Oliveira LD, et al. The molecular landscape and microenvironment of salivary duct carcinoma reveal new therapeutic opportunities. Theranostics. 2020;10: 4383–4394. doi:10.7150/thno.42986

5.  Blomen VA, Majek P, Jae LT, Bigenzahn JW, Nieuwenhuis J, Staring J, et al. Gene essentiality and synthetic lethality in haploid human cells. Science. 2015;350: 1092–1096. doi:10.1126/science.aac7557

6.  Muellner MK, Mair B, Ibrahim Y, Kerzendorfer C, Lechtermann H, Trefzer C, et al. Targeting a cell state common to triple-negative breast cancers. Mol Syst Biol. 2015;11: 789. doi:10.15252/msb.20145664

7.  Giguelay A, Turtoi E, Khelaf L, Tosato G, Dadi I, Chastel T, et al. The landscape of cancer-associated fibroblasts in colorectal cancer liver metastases. Theranostics. 2022;12: 7624–7639. doi:10.7150/thno.72853

8.  Honda CK, Kurozumi S, Fujii T, Pourquier D, Khellaf L, Boissiere F, et al. Cancer-associated fibroblast spatial heterogeneity and EMILIN1 expression in the tumor microenvironment modulate TGF-β activity and CD8+ T-cell infiltration in breast cancer. Theranostics. 2024;14: 1873–1885. doi:10.7150/thno.90627