Mots-Clés
Type 1 diabetes
regulatory T cells (Tregs)
systems immunology
T cell receptor (TCR)
Machine Learning
RNA-seq
single-cell
artificial intelligence
python
NGS
deep learning
feature extraction
Description
Identifying TCR Biomarkers in Type 1 Diabetes
Name: UMRS 959 “Immunoloregulation-Immunopathology-Immunotherapy”
Affiliation: Inserm /Sorbonne Université
Address: Hôpital Pitié-Salpêtrière, 83 boulevard de l’hôpital, Bâtiment CERVI, 4ème étage, 75013 Paris
Website: https://www.i3-immuno.fr/en/#Research
Supervisor:
Encarnita Mariotti-Ferrandiz (encarnita.mariotti@sorbonne-universite.fr)
David Klatzmann (David.Klatzmann@Sorbonne-universite.fr)
Subject keywords:
Type 1 diabetes; regulatory T cells (Tregs); systems immunology; T cell receptor (TCR); Machine Learning; RNA-seq; single-cell; artificial intelligence; systems biology; Immunology; python; NGS; deep learning; feature extraction
Tools and methodologies:
Bioinformatics; public clonotype analysis; TCRdist3; advanced feature extraction; binary classification; machine learning; deep learning
Summary of lab’s interests:
The i3 laboratory is interested in systems immunology approaches to identify novel biomarkers for diagnostic and therapy with a particular focus on Treg biology and autoimmune disorders (AD). One of the unique expertise of the laboratory consists in identifying antigen-specific T cells though the analysis of T cell receptor repertoire using deep sequencing.
Summary of the proposed project:
Regulatory T cells (Tregs) are crucial for maintaining immune homeostasis by suppressing self-reactive effector T cells. Dysfunction of Tregs contributes to autoimmune diseases, including type 1 diabetes (T1D). Like other T cells, Tregs are antigen-specific through their T cell receptor (TCR) and their diversity can be analysed by next-generation sequencing, which offers a promising source of biomarkers for immune diseases (Six et al, 2013).
The non-obese diabetic (NOD) mouse is one of the most studied experimental models of autoimmune disease, as it spontaneously develops insulin-dependent diabetes mellitus around 12 weeks of age. Studying the TCR repertoire in this model may offer deeper insights into T-cell responses during disease progression and allow for the identification of TCR-based biomarkers of murine diabetes. Previous analyses identified reduced TCR diversity in activated Tregs from the spleens of pre-diabetic NOD mice compared to healthy controls (Mhanna et al., 2021), highlighting their potential involvement in disease onset. However, further studies are required to elucidate the antigen specificity of the TCR repertoire of pancreatic Tregs in diabetic mice.
The aim of this project is to identify a TCR-based disease signature using published computational tools. The candidate will have access to a collection of sequenced TCR datasets from diabetic and non-diabetic NOD mice, covering different cell subsets collected from the lymph node draining the main site of inflammation, the pancreas.
Analyses will include public clonotype identification, TCR sequence similarity clustering (TCRdist3), and advanced feature extraction (e.g., positional amino acid frequencies, gene usage, length distribution). The candidate will implement and evaluate binary classifiers to predict diabetic versus non-diabetic states, assessing their robustness and performance rigorously. Furthermore, the specificity of the identified TCRs may be inferred using state-of-the-art tools such as ERGO-II (Springer et al., 2021), TULIP (Meynard-Piganeau et al., 2024), SABRE (Wang & Shen, 2023), and TITAN (Weber et al., 2021).
Candidate profile:
The expected candidate will have a training in bioinformatics, with a strong interest in systems biology and immunology. Proficiency in programming (Python or R) and a foundational understanding of machine learning are required. Experience or interest in advanced deep learning techniques (e.g., neural networks, Keras) is advantageous but not mandatory.
Lab description:
The laboratory is offering a unique interdisciplinary environment, with biologists, immunologists, clinicians, computer scientists and bioinformaticians. The candidates will be based in the i3 laboratory located on the Pitié‐Salpêtrière hospital campus in Paris (13ème).
Publication supervisors (related to the project):
- Vantomme et al, biorchiv, 2025
- Jouannet et al, bioRxiv, 2024
- Mhanna V et al., Nat Rev Methods Primers, 2024
- Mhanna V. et al., Cell Rep Methods, 2024
- Le Gouge et al, MedRXiv, 2023
- Quiniou V. et al, elife, 2023
- Mhanna V et al, Diabetes 2021
- Barennes P et al., Nature Biotechnologies 2020
- Ritvo PG et al, PNAS 2018