Description du projet
La caractérisation cellulaire de la sclérose en plaques susceptible de fournir de nouveaux biomarqueurs de la maladie
Il n’existe actuellement aucun biomarqueur pour surveiller la progression de la sclérose en plaques (SP), une maladie inflammatoire chronique. Pour répondre à cette lacune, les scientifiques du projet IMPACT, financé par l’UE, étudieront le compartiment immunitaire de certains patients à l’échelle unicellulaire en employant des approches d’apprentissage automatique. Ils élimineront également l’impact de la composition génétique d’un personne sur la signature de la maladie en étudiant une cohorte unique de jumeaux monozygotes. Les résultats contribueront à faire la lumière sur les signaux spécifiques aux maladies et permettront d’entreprendre une caractérisation plus précise des populations cellulaires immunitaires impliquées dans la pathologie de la SP. En définitive, l’étude mènera à l’identification de biomarqueurs susceptibles d’être utilisés couramment dans le domaine de la clinique.
Objectif
Multiple Sclerosis (MS) is a chronic inflammatory disease, where immune cell invasion into the central nervous system causes immunopathology and neurological deficit. Although disease-modifying therapies dramatically reduce disease activity, they hold the potential for severe adverse effects while long-term disability prospects remain poor. Moreover, there is to date no biomarker for monitoring the disease activity and to guide therapy decisions. I propose that the key to identifying such biomarkers is to combine single-cell mapping of leukocytes across well-curated patient cohorts with unbiased machine-learning based data interrogation. Using such an approach, we have already delineated a disease signature in a helper T cell population specific for MS. However, the immune compartment of cross-sectional cohorts is influenced by the individual genetic make up, which masks disease-specific signals and hinders a more precise characterisation of involved immune cell populations. To eliminate genetic influences, I here propose in aim 1 to interrogate the immune compartment of a unique cohort of monozygotic twin pairs -discordant for MS- and deeply analyse peripheral blood lymphocytes by single-cell mass cytometry, combined TcR and single cell sequencing, and epigenetic profiling. aim 2 to develop representation-learning methods to account for the paired genetics of twins or longitudinal samples and to include clinical covariates into the high-dimensional data set. aim 3 to use well-defined patient samples of MS-like disorders (MS-Mimics) and longitudinal samples of patients undergoing disease-modifying therapy (e.g. B cell depletion, autologous stem cell transplant) using single-cell mass cytometry. Ultimately, the goal is to reduce the dimensionality of disease signature(s) towards a clinically translatable low-dimensional biomarker that could be identified and quantified by routine methods available in the clinics.
Champ scientifique
- natural sciencesbiological sciencesneurobiology
- medical and health scienceshealth sciencesinflammatory diseases
- medical and health sciencesbasic medicineneurologymultiple sclerosis
- medical and health sciencesbasic medicineimmunology
- medical and health sciencesmedical biotechnologycells technologiesstem cells
Mots‑clés
Programme(s)
Thème(s)
Régime de financement
ERC-ADG - Advanced GrantInstitution d’accueil
8006 Zurich
Suisse