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LungVis 1.0: an automatic AI-powered 3D imaging ecosystem unveils spatial profiling of nanoparticle delivery and acinar migration of lung macrophages

Author

Listed:
  • Lin Yang

    (Member of the German Center for Lung Research (DZL))

  • Qiongliang Liu

    (Member of the German Center for Lung Research (DZL)
    Shanghai Jiao Tong University School of Medicine)

  • Pramod Kumar

    (Member of the German Center for Lung Research (DZL))

  • Arunima Sengupta

    (Member of the German Center for Lung Research (DZL))

  • Ali Farnoud

    (Member of the German Center for Lung Research (DZL))

  • Ruolin Shen

    (Helmholtz Munich)

  • Darya Trofimova

    (German Cancer Research Center (DKFZ)
    German Cancer Research Center (DKFZ))

  • Sebastian Ziegler

    (German Cancer Research Center (DKFZ)
    German Cancer Research Center (DKFZ))

  • Neda Davoudi

    (University of Zurich
    ETH Zurich)

  • Ali Doryab

    (Member of the German Center for Lung Research (DZL))

  • Ali Önder Yildirim

    (Member of the German Center for Lung Research (DZL))

  • Markus E. Diefenbacher

    (Member of the German Center for Lung Research (DZL)
    Ludwig Maximilian University Munich
    DKTK Munich)

  • Herbert B. Schiller

    (Member of the German Center for Lung Research (DZL)
    Research Unit for Precision Regenerative Medicine (PRM), Helmholtz Munich)

  • Daniel Razansky

    (University of Zurich
    ETH Zurich)

  • Marie Piraud

    (Helmholtz Munich)

  • Gerald Burgstaller

    (Member of the German Center for Lung Research (DZL))

  • Wolfgang G. Kreyling

    (Member of the German Center for Lung Research (DZL)
    Helmholtz Munich)

  • Fabian Isensee

    (German Cancer Research Center (DKFZ)
    German Cancer Research Center (DKFZ))

  • Markus Rehberg

    (Member of the German Center for Lung Research (DZL))

  • Tobias Stoeger

    (Member of the German Center for Lung Research (DZL))

  • Otmar Schmid

    (Member of the German Center for Lung Research (DZL))

Abstract

Targeted (nano-)drug delivery is essential for treating respiratory diseases, which are often confined to distinct lung regions. However, spatio-temporal profiling of drugs or nanoparticles (NPs) and their interactions with lung macrophages remains unresolved. Here, we present LungVis 1.0, an AI-powered imaging ecosystem that integrates light sheet fluorescence microscopy with deep learning-based image analysis pipelines to map NP deposition and dosage holistically and quantitatively across bronchial and alveolar (acinar) regions in murine lungs for widely-used bulk-liquid and aerosol-based delivery methods. We demonstrate that bulk-liquid delivery results in patchy NP distribution with elevated bronchial doses, whereas aerosols achieve uniform deposition reaching distal alveoli. Furthermore, we reveal that lung tissue-resident macrophages (TRMs) are dynamic, actively patrolling and redistributing NPs within alveoli, contesting the conventional paradigm of TRMs as static entities. LungVis 1.0 provides an advanced framework for exploring pulmonary delivery dynamics and deepening insights into TRM-mediated lung immunity.

Suggested Citation

  • Lin Yang & Qiongliang Liu & Pramod Kumar & Arunima Sengupta & Ali Farnoud & Ruolin Shen & Darya Trofimova & Sebastian Ziegler & Neda Davoudi & Ali Doryab & Ali Önder Yildirim & Markus E. Diefenbacher , 2024. "LungVis 1.0: an automatic AI-powered 3D imaging ecosystem unveils spatial profiling of nanoparticle delivery and acinar migration of lung macrophages," Nature Communications, Nature, vol. 15(1), pages 1-22, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-54267-1
    DOI: 10.1038/s41467-024-54267-1
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    References listed on IDEAS

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