T cells develop in the human thymus, but the genetic switches that control this process remain largely unknown. Ninety percent of disease-associated genetic variation falls in non-coding DNA—the regions that act as regulatory control panels for genes—yet scientists lack a complete map of how transcription factors, enhancers, and promoters interact to guide a developing T cell toward its final identity. Without this map, efforts to grow T cells in the lab for therapies or to understand why certain people are prone to immune disorders are working partly in the dark. ThyGeneReg will build that map by analysing gene expression and chromatin accessibility in thousands of individual cells from human thymus tissue. The researcher will use machine learning to predict which transcription factors drive specific T cell fates, then test those predictions by engineering artificial thymic organoids and overexpressing the candidate factors. If successful, the project will produce a validated gene regulatory network for human T cell development. This could improve protocols for generating T cells in the lab—relevant to cell therapies for cancer and autoimmune disease—and help interpret the functional impact of non-coding genetic variants linked to immune conditions. The work is fundamental science: it asks how a core immune cell type is built, with no immediate clinical product, but the regulatory map it produces will be a resource for future translational efforts.
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T cells are central to adaptive immunity and thus crucial for understanding and treating human disease. While extensively studied in model organisms, translating this knowledge to humans can sometimes be limited by their rapid cross-species evolution and diverse subtype repertoire. In particular we lack a comprehensive understanding of the gene regulatory networks (GRNs), i.e., the interactions between transcription factors (TFs), cis-regulatory elements (CREs), such as enhancers and promoters, and their target genes, that control T cell development in the human thymus. Identifying these networks is important for understanding disease-associated polymorphism, 90% of which is found in noncoding, putative regulatory regions, and for mimicking human T cell development in vitro for research and therapeutic purposes. ThyGeneReg aims to address this gap by studying GRNs directly in the human thymus. By analysing multiomic profiles of gene expression and chromatin accessibility from single cells, I will identify CREs and characterise their cell type-specific activities. I will use state-of-the-art machine learning methods, including sequence-based models of CRE activity, to infer GRNs and prioritise TFs that drive distinct T cell fates. These inferences will be tested by overexpressing TFs (individually and in combination) in artificial thymic organoid systems, which will be subsequently profiled with single-cell multiomics. This will allow me to evaluate my GRN predictions, and to improve existing T cell engineering protocols. By combining my previous experience in studying gene regulation with the host laboratory's expertise in the human thymus, the proposed work will advance our understanding of human T cell development, provide insights into disease and aid efforts to engineer desired T cell fates. It will also widen my scientific horizons and train me in new skills, such as the use of organoid technologies and gene editing.
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