Projektbeschreibung
Immunzellcharakterisierung bei Multipler Sklerose zur Ermittlung neuer Krankheitsbiomarker
Derzeit liegt kein Biomarker für die Überwachung des Fortschreitens der chronischen entzündlichen Krankheit Multiple Sklerose (MS) vor. Aus diesem Grund wollen Forschende des EU-finanzierten Projekts IMPACT mithilfe maschinellen Lernens das Immunkompartiment von Erkrankten auf Einzelzellniveau erforschen. Außerdem werden sie die Auswirkung des individuellen Erbguts auf die Krankheitssignatur ausschließen, indem sie eine einzigartige Kohorte eineiiger Zwillingspaare untersuchen. Mithilfe der Ergebnisse sollen für die Erkrankung spezifische Signale erkannt und eine genauere Charakterisierung der Immunzellpopulationen, die bei einer MS-Pathologie auftreten, vorgenommen werden. Schließlich wird die Studie zur Bestimmung von Biomarkern führen, die routinemäßig in Kliniken eingesetzt werden könnten.
Ziel
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.
Wissenschaftliches Gebiet
- 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
Schlüsselbegriffe
Programm/Programme
Thema/Themen
Finanzierungsplan
ERC-ADG - Advanced GrantGastgebende Einrichtung
8006 Zurich
Schweiz