Project description
Multiple sclerosis immune cell characterisation to provide new disease biomarker
Currently, there is no biomarker for monitoring the progression of the chronic inflammatory disease multiple sclerosis (MS). To address this, scientists of the EU-funded IMPACT project will investigate the immune compartment of patients at a single cell level using machine learning approaches. They will also eliminate the impact of an individual's genetic makeup on disease signature by studying a unique cohort of monozygotic twin pairs. Results will help unveil disease-specific signals and undertake a more precise characterisation of immune cell populations implicated in MS pathology. Ultimately, the study will lead to the identification of biomarkers that could be routinely used in the clinic.
Objective
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.
Fields of science
- 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
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Funding Scheme
ERC-ADG - Advanced GrantHost institution
8006 Zurich
Switzerland