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High-Dimensional single cell mapping of inflammatory disease signatures in monozygotic twins

Descripción del proyecto

Caracterización de las células inmunitarias de la esclerosis múltiple para proporcionar un nuevo biomarcador de la enfermedad

En la actualidad no existe ningún biomarcador que permita supervisar la progresión de la esclerosis múltiple (EM), una enfermedad inflamatoria crónica. En este sentido, un grupo de científicos del proyecto financiado con fondos europeos IMPACT investigará el compartimento inmunitario de pacientes a nivel unicelular a través de enfoques de aprendizaje automático. También eliminarán el impacto de la dotación genética de un individuo sobre la firma patológica estudiando una cohorte única de parejas de gemelos monocigóticos. Los resultados ayudarán a desvelar señales específicas de la enfermedad y a caracterizar de manera más precisa las poblaciones celulares inmunitarias implicadas en la patología de la EM. El última instancia, el estudio permitirá identificar biomarcadores que podrían utilizarse de forma rutinaria en el ámbito clínico.

Objetivo

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.

Régimen de financiación

ERC-ADG - Advanced Grant

Institución de acogida

UNIVERSITAT ZURICH
Aportación neta de la UEn
€ 2 492 221,00
Dirección
RAMISTRASSE 71
8006 Zurich
Suiza

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Región
Schweiz/Suisse/Svizzera Zürich Zürich
Tipo de actividad
Higher or Secondary Education Establishments
Enlaces
Coste total
€ 2 492 221,00

Beneficiarios (1)