Henes, J. | Search bioRxiv

bioRxiv2024

Spatially informed phenotyping by cyclic-in-situ-hybridization identifies novel fibroblast populations and their pathogenic niches in systemic sclerosis

Spatially non-resolved transcriptomic data identified functionally distinct populations of fibroblasts in health and disease. However, in-depth transcriptional profiling in situ at single-cell resolution has not been possible so far. Here, we studied fibroblast populations in the skin of SSc patients and healthy individuals using cyclic in situ hybridization (cISH) as a novel approach for spatially-resolved transcriptional phenotyping with subcellular resolution. cISH deconvoluted the heterogeneity of 20,979 cells including 3,764 fibroblasts (FB). BANKSY-based spatially-informed clustering identified nine FB subpopulations, with SFRP2+ RetD FB and CCL19+ nonPV FB as novel subpopulations that reside in specific cellular niches and display unique gene expression profiles. SFRP2+ RetD FB and CCL19+ nonPV FB as well as COL8A1+ FB, display altered frequencies in SSc skin and play specific, disease-promoting roles for extracellular matrix release and leukocyte recruitment as revealed by their transcriptional profile, their cellular interactions and ligand-receptor analyses. The frequencies of COL8A1+ FB and their interactions with monocytic cells and B cells are associated with progression of skin fibrosis in SSc. In summary, our spatially-resolved transcriptomic approach identified novel fibroblast subpopulations deregulated in SSc skin with specific pathogenic roles, some of which may potentially serve as biomarkers for progression of skin fibrosis.

immunology↗

bioRxiv2024

Accurate, fast and memory efficient quantification of immune cell phenotypes in cytometry using machine learning

To achieve accurate and reproducible cytometry data analysis, we benchmarked 19 machine learning algorithms for supervised and unsupervised cell classification. The underlying data encompassed 138 million cells from seven independent datasets including conventional flow cytometry, spectral flow cytometry and mass cytometry. We found that tree-based classifiers and in particular Decision Trees, outperformed other approaches in classification accuracy, speed and memory use. High accuracy was achieved even for cell populations rarer than 1% using decision trees. We validated our decision tree-based approach in a clinical setting using diagnostic blood T cell phenotyping of 107 patients. Automatic quantification of CD4 helper T cell phenotypes achieved 99 % accuracy compared to manual expert assessment. Finally, we combined automated data transformation, supervised and unsupervised gating, an application program interface and a user-friendly desktop-application into FACSPy and FACSPyUI, a fast and scalable open-source toolbox for the analysis and visualization of cytometry data.

bioinformatics↗