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Almeida, A. R. M.

Publications and source records attributed to Almeida, A. R. M..

2 recordsLinked to original sources

Decoding mutational hotspots in human disease through the gene modules governing thymic regulatory T cells

ABTRACTComputational strategies to extract meaningful biological information from multiomics data are in great demand for effective clinical use. This is most relevant in immune-mediated disorders, where the combined impact of multiple variants is difficult to determine. Regulatory T cells (Tregs), particularly those lineage-committed in the thymus, are essential for immune homeostasis and self-tolerance, controlling inflammatory and autoimmune processes in many diseases with a multigenic basis. Here, we quantify the Transcription Factor (TF) differential occupancy landscape to uncover the Gene Regulatory Modules governing human thymic Tregs, providing a tool to prioritise variants in complex diseases. Combined RNA-seq and ATAC-seq generated a matrix of differential TF binding to genes differentially expressed in Tregs, in contrast to their counterpart conventional CD4 single-positive thymocytes. The gene loci of both established and novel genetic interactions uncovered by the Gene Regulatory Modules were significantly enriched in rare variants carried by patients with common variable immunodeficiency, here used as a model of polygenic-based disease with severe inflammatory and autoimmune manifestations. The Gene Regulatory Modules controlling the Treg signature can, therefore, be a valuable resource for variant classification, and to uncover new therapeutic targets. Overall, we provide a tool to decipher mutational hotspots in individual genomes.

immunology↗

Deconstructing Complexity: A Computational Topology Approach to Trajectory Inference in the Human Thymus with tviblindi

Understanding complex, organ-level single-cell datasets represents a formidable interdisciplinary challenge. This study aims to describe developmental trajectories of thymocytes and mature T cells. We developed tviblindi, a trajectory inference algorithm that integrates several autonomous modules - pseudotime inference, random walk simulations, real-time topological classification using persistent homology, and autoencoder-based 2D visualization using the vaevictis algorithm. This integration facilitates interactive exploration of developmental trajectories, revealing not only the canonical CD4 and CD8 development but also offering insights into checkpoints such as TCR{beta} selection and positive/negative selection. Furthermore, tviblindi allowed us to thoroughly characterize thymic regulatory T cells, tracing their development passed the negative selection stage to mature thymic regulatory T cells. At the very end of the developmental trajectory we discovered a previously undescribed subpopulation of thymic regulatory T cells. Experimentally, we confirmed its extensive proliferation history and an immunophenotype characteristic of activated and recirculating cells. tviblindi represents a new class of methods that is complementary to fully automated trajectory inference tools. It offers a semi-automated tool that leverages features derived from data in an unbiased and mathematically rigorous manner. These features include pseudotime, homology classes, and appropriate low-dimensional representations. These features can be integrated with expert knowledge to formulate hypotheses regarding the underlying dynamics, tailored to the specific trajectory or biological process under investigation.

immunology↗