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FUSION: a web-based application for in-depth exploration of multi-omics data with brightfield histology

Author

Listed:
  • Samuel P. Border

    (University of Florida)

  • Ricardo Melo Ferreira

    (Indiana University School of Medicine)

  • Nicholas Lucarelli

    (University of Florida)

  • Suhas Katari Chaluva Kumar

    (University of Florida)

  • Anindya S. Paul

    (University of Florida)

  • David Manthey

    (Inc)

  • Laura Barisoni

    (Duke University
    Duke University)

  • Yulia A. Levites Strekalova

    (University of Florida)

  • Jessica Ray

    (University of Florida)

  • Ying-Hua Cheng

    (Indiana University School of Medicine)

  • Avi Z. Rosenberg

    (Johns Hopkins University School of Medicine)

  • John E. Tomaszewski

    (State University of New York)

  • Sayat Mimar

    (University of Florida)

  • Jeffrey B. Hodgin

    (University of Michigan)

  • John W. Hickey

    (Duke University)

  • Bei Wei

    (Stanford School of Medicine)

  • Fiona Ginty

    (GE HealthCare Technology and Innovation Center)

  • Arivarasan Karunamurthy

    (University of Pittsburgh)

  • Juexin Wang

    (Indiana University Indianapolis)

  • Mauminah Raina

    (Indiana University Indianapolis)

  • Gloria S. Pryhuber

    (University of Rochester)

  • Jeffrey Purkerson

    (University of Rochester)

  • Tarek M. El-Achkar

    (Indiana University School of Medicine
    Indianapolis VA Medical Center)

  • Sanjay Jain

    (Washington University School of Medicine)

  • Michael T. Eadon

    (Indiana University School of Medicine
    Indianapolis VA Medical Center)

  • Pinaki Sarder

    (University of Florida)

Abstract

Spatial technologies examining the cell and tissue microenvironment at near single-cell resolution are revealing important molecular insights. However, few tools enable integrated, interactive analysis of spatial-omics with tissue morphology in the same functional tissue unit. Here, we present FUSION (Functional Unit State Identification in Whole Slide Images), a web-based platform for visualizing and analyzing spatial-omics data with high-resolution histology. FUSION provides workflows for assessing cell compositions, quantitative morphometrics, and comparative tissue analyses. We demonstrate applicability across spatial assays, including 10x Visium, Visium HD, 10x Xenium, Cell DIVE, and PhenoCycler, applied to healthy and diseased tissues from kidney, small intestine, lung, and skin in the Human BioMolecular Atlas Program. FUSION is cloud-based, open-source, and accessible at https://fusion.hubmapconsortium.org/ , hosting over 50 paired datasets and tutorials. In a series of use cases, we show its capacity to distinguish renal glomeruli injury states, quantify morphometric changes, and characterize fibrosis with immune infiltration.

Suggested Citation

  • Samuel P. Border & Ricardo Melo Ferreira & Nicholas Lucarelli & Suhas Katari Chaluva Kumar & Anindya S. Paul & David Manthey & Laura Barisoni & Yulia A. Levites Strekalova & Jessica Ray & Ying-Hua Che, 2025. "FUSION: a web-based application for in-depth exploration of multi-omics data with brightfield histology," Nature Communications, Nature, vol. 16(1), pages 1-15, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-63050-9
    DOI: 10.1038/s41467-025-63050-9
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    References listed on IDEAS

    as
    1. Brendan F. Miller & Feiyang Huang & Lyla Atta & Arpan Sahoo & Jean Fan, 2022. "Reference-free cell type deconvolution of multi-cellular pixel-resolution spatially resolved transcriptomics data," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    2. Juexin Wang & Jinpu Li & Skyler T. Kramer & Li Su & Yuzhou Chang & Chunhui Xu & Michael T. Eadon & Krzysztof Kiryluk & Qin Ma & Dong Xu, 2023. "Dimension-agnostic and granularity-based spatially variable gene identification using BSP," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    Full references (including those not matched with items on IDEAS)

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