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Abstract
Molecular descriptions of human disease have relied on transcriptomics, the genome-wide measurement of gene expression. In the last years the emergence of capture-based technologies have enabled the transcriptomic profiling of single cells both from dissociated and intact tissues, providing a spatial and cell type specific context that complements the catalog of gene expression changes reported from bulk technologies. In the context of cardiovascular disease, these technologies open the opportunity to study the inter and intra-cellular mechanisms that regulate myocardial remodeling. In this thesis I present comprehensive descriptions of the transcriptional changes in acute and chronic human heart failure using bulk, single cell, and spatial technologies. First, I describe the creation of the Reference of the Heart Failure Transcriptome, a resource built from the meta-analysis of 16 independent studies of human heart failure transcriptomics. Then, I report the first spatial and single cell atlas of human myocardial infarction, and propose a computational strategy to identify compositional, organizational, and molecular tissue differences across distinct time points and physiological zones of damaged myocardium. Finally, I outline a methodology for the multicellular analysis of single cell data that allows for a better understanding of tissue responses and cell type coordination events in cardiovascular disease and that links the knowledge of independent studies at multiple scales. Overall my work demonstrates the importance of the generation of reliable molecular references of disease across scales.
Document type: | Dissertation |
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Supervisor: | Saez-Rodriguez, Prof. Dr. Julio |
Place of Publication: | Heidelberg |
Date of thesis defense: | 27 March 2023 |
Date Deposited: | 15 Jun 2023 10:45 |
Date: | 2023 |
Faculties / Institutes: | The Faculty of Bio Sciences > Dean's Office of the Faculty of Bio Sciences |
DDC-classification: | 570 Life sciences 610 Medical sciences Medicine |
Controlled Keywords: | Bioinformatik, Genexpression, Kardiologie |