Dissecting enhancer-genes regulatory network in single cells to characterize immune cells’ response to solid tumours
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Publication Date
2025-01-14
Type
doctoral thesis
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Abstract
The characterization of epigenetic heterogeneity at the population level is a critical problem in genomics. In particular, tumour infiltrating T cells are a heterogeneous set of cells for which a comprehensive description of epigenetics regulation is still missing. The advent of single cell ATAC-seq (scATAC-seq) opens the opportunity to identify putative enhancers at the granularity of single cells. However, the prediction of potential enhancer-promoter regulatory interactions over a wide breadth of tumour infiltrating T cell states is missing and remains a gap, because methods to reconstruct cis-regulatory interactions are still lacking. Relying on the understanding that distal non-coding regulatory elements (e.g., enhancers) play a pivotal role in orchestrating the molecular and genetic mechanisms underlying cell identity, we hypothesized that identifying distinct subpopulations within immune cells infiltrating solid tumours could significantly improve patient stratification strategies. Based on these hypotheses we integrated Single- cell RNA sequencing (scRNA-seq) and scATAC-seq data performed on lymph nodes to characterize tumour-infiltrated lymphocytes (TILs) subpopulations in breast cancer. We studied CD8+ T cell differentiation by trajectory analysis, and identified key transcriptional regulatory networks. We also developed a novel epigenomic-based machine learning method, the 3D-SC-EG Profiler, for reconstructing the enhancer-target gene (ETG) regulatory network by integrating multi-omics data and 3D chromatin architecture using Hi-C data. We identified distinct CD8+ T cell subpopulations, including TSTEM and TPEX, critical to T cell exhaustion and stemness. Trajectory analysis revealed key branching points in CD8+ T cell differentiation, with markers like TOX and PDCD1 associated with terminal exhaustion. The 3D-SC-EG Profiler method successfully predicted enhancer-promoter interactions, outperforming existing methods with an AUC of 0.85, providing novel insights into the epigenetic regulation of immune cells. Our study uncovers crucial regulatory networks governing CD8+ T cell differentiation, with implications for improving immunotherapy by targeting transcriptional and epigenetic pathways. Future research should focus on experimentally validating predicted enhancer-gene interactions and refining the 3D-SC-EG Profiler method. Applying this approach to broader datasets, particularly from metastatic breast cancer, could further elucidate disease progression and resistance mechanisms.Additionally, developing the 3D-SC-EG profiler method as an accessible R package would accelerate its adoption by the research community.
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University of Galway
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Attribution-NonCommercial-NoDerivatives 4.0 International