Cellular crosstalk driving Tertiary Lymphoid Structure formation across tissues
Formation of Tertiary Lymphoid Structures (TLS) is a recurring consequence of chronic tissue inflammation and autoimmunity, as well as several solid tumours. TLS originate in various tissues from infiltrating cells attracted by lo...
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Información proyecto TissueLymphOmics
Duración del proyecto: 24 meses
Fecha Inicio: 2024-03-04
Fecha Fin: 2026-03-31
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Descripción del proyecto
Formation of Tertiary Lymphoid Structures (TLS) is a recurring consequence of chronic tissue inflammation and autoimmunity, as well as several solid tumours. TLS originate in various tissues from infiltrating cells attracted by local signalling cascades. These structures can enhance local immune response by, similarly to Secondary Lymphoid Organs (SLO), functioning as spots for lymphocyte activation upon antigen recognition. However, the cellular interactions driving TLS emergence and function across tissues are not fully known. This proposal aims to unravel and characterise the cell-cell interactions involved in the formation and maintenance of TLS, in particular their tissue specificity and relationship to autoimmune diseases. A comprehensive understanding of intercellular interactions will be obtained from publicly available and newly generated single-cell RNA-sequencing (scRNA-seq) datasets from TLS-containing tissues of autoimmunity patients. Molecules involved in key signalling interactions will be spatially resolved by assessing their tissue expression using spatial transcriptomics. Then, using mouse models where TLS triggering can be controlled, scRNA-seq data will be obtained to outline a time course of TLS formation in different tissues, revealing the temporal role of the discovered interactions and their specific function in each tissue and when compared with SLOs. Finally, regulation and downstream effects of ligands and receptors will be unraveled with multiome (RNA+open chromatin) sequencing using human tonsil organoids. The observed effects will be generalised to other tissues using machine learning and deep learning models, resulting in a tissue-specific reference of signalling and regulatory genes controlling TLS biogenesis. Overall, this project aspires to provide a detailed look into TLS biology, while defining the bases for their therapy-oriented manipulation.