Descrizione del progetto
Caratterizzazione delle cellule immunitarie della sclerosi multipla per fornire un nuovo biomarcatore della malattia
Attualmente non esiste un biomarcatore per il monitoraggio della progressione della malattia infiammatoria cronica della sclerosi multipla (SM). Per risolvere questo problema, gli scienziati del progetto IMPACT, finanziato dall’UE, studieranno il compartimento immunitario dei pazienti a livello di singola cellula utilizzando approcci di apprendimento automatico. Essi elimineranno anche l’impatto del patrimonio genetico di un individuo sulla firma della malattia studiando una coorte unica di coppie di gemelli monozigoti. I risultati aiuteranno a svelare segnali specifici della malattia e a intraprendere una caratterizzazione più precisa delle popolazioni di cellule immunitarie implicate nella patologia della SM. Lo studio porterà infine all’identificazione di biomarcatori che potrebbero essere utilizzati di routine in ambito clinico.
Obiettivo
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.
Campo scientifico
- 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
Parole chiave
Programma(i)
Argomento(i)
Meccanismo di finanziamento
ERC-ADG - Advanced GrantIstituzione ospitante
8006 Zurich
Svizzera