Biology subjects

Cossarizza, A.

Publications and source records attributed to Cossarizza, A..

3 recordsLinked to original sources

bioRxiv2026

Optimizing High Parameter T Cell Immunophenotyping Through Direct Comparison of Conventional and Spectral Flow Cytometry

High-parameter flow cytometry is essential for dissecting the intricate landscape of T-cell diversity. In this study, we directly compare conventional flow cytometry (CFC) and spectral flow cytometry (SFC) for high-dimensional T-cell phenotyping, assessing how spectral detection and panel-design strategies influence analytical performance. Using peripheral blood mononuclear cells from healthy donors stained with both an established (v1) and an optimized (v2) fluorochrome-labelled antibody panel, and analyzed through manual gating and unsupervised approaches, we found that CFC reliably identified major T-cell subsets. However, spectral acquisition consistently delivered clear technical advantages, including improved signal-to-noise ratios, higher staining index values, and superior resolution of low-intensity and co-expressed markers. These improvements translated into more sharply delineated multidimensional clusters and a markedly enhanced resolution of T-cell differentiation states. Moreover, the optimized spectral panel enhanced the unsupervised detection of rare populations, such as cytotoxic CD4 T-cells (PD-1GZMB). However, despite the overall increase in data quality achieved with SFC, the selection of antibody clones may influence the measured frequencies of the identified populations. Finally, SFC - particularly when coupled with rational panel optimization and the use of advanced fluorophores - consistently delivers superior, higher-quality measurements and improved multidimensional resolution, thereby substantially enhancing the robustness and sensitivity of high-parameter T-cell phenotyping for comprehensive immunological studies.

immunology↗

bioRxiv2025

Citizen science gamers enable automated flow cytometry gating through machine learning

Manual flow cytometry gating requires up to one hour per sample with 32% inter-expert variability, creating critical bottlenecks in immunological research reproducibility. To address this, we developed flowMagic, a machine learning algorithm for automated gating that is trained on both expert-curated data (template data) and crowdsourced annotations from citizen science gaming. Through EVE Online, 839,199 players analyzed 52,178 bivariate plots from 37 studies, generating 31,703 quality-controlled training plots. Evaluated against 92,203 expert-validated files spanning 79 immune populations (i.e., a biologically defined cell subset within each bivariate plot), flowMagic achieved 90% accuracy for abundant populations and 65% for rare populations, outperforming existing methods. The algorithm reproduced biological patterns including neutrophil dynamics in COVID-19 patients and immune development in newborns. This gaming-based approach demonstrates that crowd-sourced pattern recognition generates robust training data for complex biomedical applications, offering transformative potential for standardizing flow cytometry analysis and accelerating immunological discovery.

bioinformatics↗

bioRxiv2018

DNA Topoisomerase I differentially modulates R-loops across the human genome

BackgroundCo-transcriptional R-loops are abundant non-B DNA structures in mammalian genomes. DNA Topoisomerase I (Top1) is often thought to regulate R-loop formation owing to its ability to resolve both positive and negative supercoils. How Top1 regulates R-loop structures at a global level is unknown.\n\nResultsHere, we performed high-resolution strand-specific R-loop mapping in human cells depleted for Top 1 and found that Top1 depletion resulted in both R-loop gains and losses at thousands of transcribed loci, delineating two distinct gene classes. R-loop gains were characteristic for long, highly transcribed, genes located in gene-poor regions anchored to Lamin B1 domains and in proximity to H3K9me3-marked heterochromatic patches. R-loop losses, by contrast, occurred in gene-rich regions overlapping H3K27me3-marked active replication initiation regions. Interestingly, Top1 depletion coincided with a block of the cell cycle in G0/G1 phase and a trend towards replication delay.\n\nConclusionsOur findings reveal new properties of Top1 in regulating R-loop homeostasis and suggest a potential role for Top1 in controlling replication initiation via R-loop formation.

genomics↗