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Biomedical subjects

Annika Vannan

Publications and source records attributed to Annika Vannan.

2 recordsLinked to original sources

Multiomic analysis identifies T cell subsets and mechanisms of epithelial interaction in idiopathic pulmonary fibrosis.

Idiopathic pulmonary fibrosis (IPF) is a fatal interstitial lung disease characterized by progressive scarring and respiratory failure. While T cells are elevated in IPF lungs, their contributions to fibrosis beyond inflammation remain poorly understood. Here, we performed multiplex imaging and single-cell RNA and protein profiling on about 90,000 CD3+ T cells from control and fibrotic lungs, revealing 11 distinct subsets of CD4+ and CD8+ T cells, including a rare CD56+ regulatory T cell. In addition to increased T cell numbers in severely fibrotic lungs compared with non-diseased controls, we observed CD4+ and CD8+ T cells localized near epithelial cells and in niches of abnormal epithelium. CXCR4/MIF signaling emerged as a central axis mediating T cell-epithelial interactions, while epidermal growth factor receptor (EGFR) and TGF-β pathways dominated in multiple T cell subsets. Our findings support the concept that T cells in IPF adopt nonclassical activation patterns that are driven by epithelial interactions within the fibrotic microenvironment. These studies provide a foundation for exploring alternative therapeutic strategies in IPF lungs by modulating T cell behavior and communication networks.

Idiopathic Pulmonary Fibrosis

SpatialRNA: a Python package for easy application of Graph Neural Network models on single-molecule spatial transcriptomics dataset.

SUMMARY: Image-based spatial transcriptomics (iST) deliver gene expression measurements of RNA transcripts in tissue slices with single-molecule resolution and spatial context preserved. Modern Graph Neural Network (GNN) models are promising methods for capturing the complex molecular and cellular phenotypes in tissues at single-transcript and single-cell levels. A key application of GNNs is the detection of spatial domains or niches, that is, groups of molecules and/or cells that collaboratively work together to produce complex phenotypes. Due to the vast number of detected transcripts in (iST) dataset, applying GNNs on RNA molecule graphs is not trivial. We present a Python package, SpatialRNA, for easy (sub)graph generation from tissue samples and provide comprehensive tutorials for convenient and efficient application of Graph Neural Network models under the PyG framework. This highly scalable tool comprehensively segments tissue into spatial domains, aiding in biological interpretation of iST data and its underlying molecular microenvironments. AVAILABILITY AND IMPLEMENTATION: The SpatialRNA package is freely accessible from online repository https://github.com/ruqianl/spatialrna and can be installed via pip. Comprehensive tutorials, guidance on parameter selection, and complete workflows of case studies are available from the documentation website https://ruqianl.github.io/spatialrna_docs/, and uploaded on Zenodo with a DOI 10.5281/zenodo.17339575.

Neural Networks, Computer