Stage Master 2_Bioinformatique_Analyses multi-omiques_Deep immunophenotyping of Still's disease

SFBI; Paris; Sur place; EUR 12 000 - 16 000; Temps partiel

Résumé du poste

SFBI invites a Master 2 student to join the Deep immunophenotyping of Still's disease project in Paris. The intern will perform unsupervised and supervised analyses on multi-omics data, focusing on clustering, PCA, and integrative approaches under supervision. Proficiency in R or Python and experience with data cleaning are required.

The position offers hands-on exposure to computational immunology, with collaboration across biology, immunology, and clinical teams at the i3 laboratory in Paris.

Qualifications

• Enrollment in a Master 2 program in bioinformatics.
• Proficiency in R or Python for data analysis.
• Experience with data cleaning and omics data preferred.

Responsabilités

• Conduct unsupervised and supervised analyses on multi-omics data.
• Perform data integration across flow cytometry, cytokines, and clinical data.
• Interpret computational immunology models and prepare results for publication.

Connaissances

Bioinformatics

R or Python

Data analysis

Unsupervised learning

Formation

Master's student in Bioinformatics

Outils

R

Python

Description du poste

Stage Master 2_Bioinformatique_Analyses multi-omiques_Deep immunophenotyping of Still's disease

bioinformatics deep immunophenotyping unsupervised and supervised analyses clustering k-means encodings and programming. multiomics and integrative analyses PCA hierarchical clustering

Description

Still’s disease (SD) is a rare and complex autoinflammatory disorder at the interface of innate and adaptive immunity. It is characterized by fever, a skin rash, joint pain, neutrophilic leukocytosis, significant systemic inflammation, and many other manifestations. The disease is heterogeneous, with some patients presenting with simple (i.e., uncomplicated) forms and others developing life‑threatening complications, such as macrophage activation syndrome (MAS), pulmonary involvement, or liver damage.
It has been suggested that these different clinical phenotypes may be related to different endotypes, that is, different immunological mechanisms. Thus, simple forms may result primarily from innate immunity, particularly that of monocytes and neutrophils, while complicated forms may also involve adaptive immunity: activated CD4+ and CD8+ T-cell populations, a deficiency in regulatory T cells, etc. Recent European recommendations (Fautrel et al., 2024) have highlighted the need to better characterize the pathophysiological pathways of this disease on the research agenda.
It was in this context that the DEEPSTILL study was launched by the i3 Laboratory, in collaboration with the Rheumatology Department at Pitié-Salpêtrière Hospital, which serves as the French (CEREMAIA) and European (ERN-RITA) reference centre for SD and which led the European Recommendations on the disease. The primary objective is to identify different SD endotypes through precision immunomonitoring by studying immune cell populations in peripheral blood in order to identify the various immunological characteristics (predominant cell types, immunological signatures, and “omics” signatures) specific to each group. Secondary objectives are to study the different immunological profiles and cell types based on SD activity (active vs. inactive), and to compare the immunological profiles of patients with SD and those with other inflammatory conditions.
A cohort of patients with SD has been established, and supervised analyses are currently underway. The aim of the internship will be to conduct complementary unsupervised and integrative analyses. The project will be carried out in several phases:

1. 1. Unsupervised analyses: The objective is to identify clusters, endotypes, or immunophenotypic groups of patients using unsupervised approaches. Approaches such as k-means, principal component analysis (PCA), or hierarchical cluster analysis will be used to identify cellular populations that are discriminatory or explanatory of patient groups. This initial step will allow to identify distinct cellular profiles within the patient cohort. Once these patient groups have been established, a retrospective analysis of clinical data will be conducted, examining patient outcomes as well as the occurrence of specific complications in each group. This analysis will help determine whether certain clusters of patients exhibit similar patterns of complications or clinical outcomes, thereby providing insights into subtypes of the disease that could reveal variations in pathogenesis or response to treatment.
2. 2. Combining unsupervised and supervised analyses to gain a deeper understanding of the mechanisms underlying the disease and identify potential targets for more personalised and effective treatments.
3. 3. The integrative analysis will focus primarily on integrating data from flow cytometry, cytokine assays, and clinical data.

The candidate will gain hands‑on experience with data cleaning, unsupervised and supervised analysis, multi‑omic data integration, and interpretation of computational immunology models.
Candidate profile: The expected candidate will have training in bioinformatics and a strong interest in computational immunology. Proficiency in programming (R or Python) is required. Experience in data cleaning and biological sequence handling is advantageous.
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 or to the tools and methodology):
Fautrel B et al. Ann Rheum Dis. 2024 Nov 14;83(12):1614-1627. doi: 10.1136/ard-2024-225851.
Vaineau R et al. JCI insight. 2025 May 20;10(12):e188724.doi: 10.1172/jci.insight.188724
Tchitchek N et al. Ann Rheum Dis. 2024 Apr 11;83(5):638-650. doi: 10.1136/ard-2023-225179.
Pitoiset F et al. Cytometry A. 2018 Aug;93(8):793-802.doi: 10.1002/cyto.a.23570.