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Identification of High-Risk Cells in Single-Cell Spatially Resolved Transcriptomics Data Using DEGAS Spatial

Debolina Chatterjee, Justin L Couetil, Ziyu Liu

    Biorxiv : the Preprint Server for Biology
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    PubMed
    Summary

    DEGAS, a deep learning tool, precisely identifies high-risk cells and tissue regions in spatial transcriptomics data. This advances disease diagnostics by pinpointing specific cellular contributions to disease states.

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    Area of Science:

    • Computational Biology
    • Genomics
    • Pathology

    Background:

    • Spatially resolved transcriptomics enables examination of high-risk cells and regions in tissue samples for disease insights.
    • Current methods struggle to associate individual cells with disease attributes, potentially missing disease-specific cell subsets.
    • This limitation is particularly problematic when disease-associated cells cluster with non-disease-associated cells.

    Purpose of the Study:

    • To present DEGAS (Diagnostic Evidence Gauge of Single-cells), a deep transfer learning algorithm for identifying high-risk components in single-cell RNA sequencing data.
    • To demonstrate DEGAS's adaptability to single-cell spatially resolved transcriptomics platforms like 10X Genomics Xenium and Nanostring CosMx.
    • To integrate spatial location information for pinpointing disease-associated locations within tissue slides.

    Main Methods:

    • DEGAS utilizes latent representations of gene expression data and domain adaptation to transfer disease attributes from patients to individual cells.
    • The algorithm was evaluated on multiple platforms, including 10X Genomics Xenium and Nanostring CosMx.
    • DEGAS was applied to new T2D Xenium and public melanoma Xenium datasets, as well as Nanostring CosMx FFPE samples.

    Main Results:

    • DEGAS successfully identified high-risk cells and regions, validated by known markers.
    • Application to T2D and melanoma datasets revealed high-risk cells and topologies associated with key pathways.
    • High-risk regions were predominantly enriched in tumor tissue, uncovering heterogeneity correlating with aggressive disease markers and cell type diversity.

    Conclusions:

    • DEGAS effectively identifies high-risk cells and spatial regions in complex tissue samples.
    • The tool's adaptability across platforms enhances its utility in diverse research settings.
    • DEGAS provides novel insights into disease heterogeneity and cellular contributions to disease states.