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

LungVis 1.0:人工智能驱动的自动化 3D 成像生态系统揭示了纳米颗粒输送和肺巨噬细胞腺泡迁移的空间特征

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作者: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, Herbert B Schiller, Daniel Razansky, Marie Piraud, Gerald Burgstaller, Wolfgang G Kreyling, Fabian Isensee, Mar

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.

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