Kuzilkova, D. | Search bioRxiv

bioRxiv2024

Tviblindi algorithm identifies branching developmental trajectories of human B cell development

Detailed knowledge of the human B-cell development is crucial for proper interpretation of inborn errors of immunity and for malignant diseases. It is of interest to understand the kinetics of protein expression changes during the B cell development, but also to properly interpret the major and possibly alternative developmental trajectories. We have investigated human bone marrow and peripheral blood samples from healthy individuals with the aim to describe all B-cell developmental trajectories across the two tissues. We validated a 30-parameter mass cytometry panel and demonstrated the utility of "vaevictis" visualization of B-cell developmental stages. We used our recently developed trajectory inference tool "tviblindi" to exhaustively describe all trajectories leading to all developmental ends discovered in the data. Focusing on Natural Effector B cells, we demonstrated the dynamics of expression of nuclear factors (PAX-5, TdT, Ki-67, Bcl-2), cytokine and chemokine receptors (CD127, CXCR4, CXCR5) in relation to the canonical B-cell developmental stage markers (CD34, CD10, sIgM, IgD, CD20, CD27). Lastly, we performed analysis of the expression changes related to developmental branching points (Natural Effector versus Switched Memory B cells, marked by up-regulation of CD73). In conclusion, we developed, validated and presented a comprehensive set of tools for investigation of B-cell development.

immunology↗

bioRxiv2023

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↗