
The study developed a novel computational tool, tviblindi, that integrates several autonomous modules to enable interactive exploration of developmental trajectories in complex single-cell datasets, revealing insights into T-cell development in the human thymus.


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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 TCRβ selection and positive/negative selection. Furthermore, it allows us to thoroughly characterize thymic regulatory T cells, tracing their development from the negative selection stage to mature thymic regulatory T cells with an extensive proliferation history and an immunophenotype of activated and recirculating cells. tviblindi is a versatile and generic approach suitable for any mass cytometry or single-cell RNA-seq dataset, equipping biologists with an effective tool for interpreting complex data.

### Competing Interest Statement

The authors have declared no competing interest.

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

comprehensive-analysis-of-human-t-cell-development-using-a-novel-computational-topology-approach


Comprehensive Analysis of Human T-Cell Development Using a Novel Computational Topology Approach
