Demmer, J. | bioRxiv Science

bioRxiv2025

TCRanalyzer: A user-friendly tool for comprehensive analysis of T-cell diversity, dynamics and potential antigen targets

T cells are critical for immune responses, recognizing antigens via their unique T-cell receptors (TCRs). Analyzing the diverse TCR repertoires, especially the hypervariable CDR3 region, is essential for understanding immune function in health and disease. Current TCR analysis tools often require specialized expertise, computational resources, or sacrifice biological information for efficiency. To address these limitations, we developed TCRanalyzer, a fast and comprehensive TCR analysis pipeline within a user-friendly graphical interface. TCRanalyzer covers all steps from data loading, aggregation and optional sequence clustering, to the analysis of TCR diversity metrics, clonal expansion and antigen specificity. Applied to datasets from patients with either benign or malignant tumors, TCRanalyzer identified changes in TCR clonality, clonal expansion and shifts in antigen specificity across different cohorts or following immunotherapy, thereby demonstrating its potential to dissect critical immunological processes. TCRanalyzer provides a robust and user-friendly tool for TCR sequence analysis, enhancing research in immunology and related fields. AvailabilityTCRanalyzer is available at https://hub.docker.com/r/tcranalyzer/application. Contactnicole.seifert@bioinf.med.uni-goettingen.de

bioinformatics↗

bioRxiv2025

DynaMiCs - Dynamic cell-type deconvolution ensembles for Mapping in mixed Conditions

Single-cell techniques facilitate the molecular analysis of individual cells, providing insights into cellular diversity, function, and the complexity of biological systems. However, their application is typically limited to small-scale studies involving individual or a few dozen samples, as a consequence of costs and experimental requirement. This complicates the inference of robust conclusions about populations. Bulk transcriptomics offers cost-efficient measurements with low experimental requirements. However, the cellular resolution is lost and only a complex linear combination of signals from multiple cells is observed. Thus, gene expression changes cannot be attributed to individual cells or cell populations. Cell-type deconvolution methods infer cellular compositions from bulk transcriptomics data. State-of-the-art approaches use single-cell data to build molecular reference profiles and identify powerful cell-type markers for improved deconvolution. In this context, we propose Dynamic cell-type deconvolution ensembles for Mapping in mixed Conditions (DynaMiCs) for the integration of single-cell and bulk transcriptomics data. Specifically, DynaMiCs dynamically extracts information from single-cell experiments to (1) provide more accurate estimates of cellular compositions, and (2) establish a mapping between bulk and single-cell data. Consequently, DynaMiCs enables the investigation of how cell populations change in both quantity and molecular characteristics between different phenotypes, informed by single-cell experiments.

bioinformatics↗