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May 20, 2026American Journal of Respiratory and Critical Care Medicine0 citations

C20-02 Multi-scale Functional Atlas of the Lungs

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HKH A KahveciogluGGG N GrifnoRLR LeBourdais

Key Points

  • The aim is to create an atlas linking lung mechanics to immune cell organization at micro and macro levels in healthy and diseased states.
  • Used Nia Lab’s crystal ribcage system to maintain intact mouse lungs under physiologic conditions.
  • Employed μCT imaging to generate organ-scale strain maps and followed with multiplex-staining for key cell types.
  • Utilized image registration to overlay dynamic mechanical data with static maps of cellular architecture.
  • Achieved 2 µm/pixel resolution in whole-lung µCT imaging, capturing near-alveolar structures under controlled ventilation.
  • Generated inflation-dependent strain maps demonstrating regional mechanical behavior of lungs during imaging.
  • Successfully combined dynamic functional data with static cellular organization through cross-modality alignment.

Abstract

Abstract Introduction Lung’s ability to coordinate gas exchange, immune defense, and mechanical ventilation depends on tightly coupled cellular and biomechanical processes at organ scale. However, current imaging methods cannot capture both dynamic ventilation mechanics and spatial immune architecture at organ scale: histology fixes tissue and eliminates motion; live imaging lacks depth; therefore, no existing technique resolves whole-lung mechanical behavior alongside cellular organization. As a result, there is lack of knowledge on how lung mechanics shape immune function, and how diseases and aging disrupt this coupling. Here, we introduce an integrated multiscale imaging pipeline that generates the first dynamic, alveolar-resolution atlas of entire mouse lung for health and disease conditions. Methods Using Nia Lab’s crystal ribcage system, intact mouse lungs are kept under physiologic-like conditions, and μCT imaging at varying pressure points captures an organ-scale strain map for lungs. Same lungs are then fixed, cleared, and multiplex-stained to map key cell types such as endothelial, epithelial, and immune cells. Image registration is used to overlay the dynamic features on the static atlas to provide the functional atlas of lungs, revealing how immune and stromal organization relate to regional mechanical strain. By extending this platform to disease models such as emphysema, fibrosis, and cancer, we provide a framework to explain how mechanical failure caused by pathology drives immune dysregulation and tissue remodeling at organ scale. Results Whole-lung µCT imaging achieved 2 µm/pixel resolution across intact mouse lungs, enabling visualization of near-alveolar structures during controlled mechanical ventilation. Using crystal ribcage system, lungs remained physiologically stable during µCT scanning at 7.5 and 10.5 cmH2O, allowing generation of inflation-dependent strain (deformation) maps across organ. Following fixation and clearing, multiplex immunolabeling preserved tissue architecture and enabled cellular-resolution imaging throughout entire lung lobes. Tissue-clearing paired with optical imaging enabled whole-organ visualization at cellular resolution without mechanical sectioning, allowing comprehensive recording of spatial staining patterns across the intact lung. Cross-modality alignment successfully overlaid µCT-derived mechanical maps with post-hoc fluorescence volumes, demonstrating feasibility of combining dynamic functional data with spatial cellular organization in the same lung. Conclusion This pipeline enables whole-lung imaging that links ventilation mechanics to cellular organization in the same intact organ, filling a critical gap between micro- and macro-imaging, enabling mechanobiology-guided insights that cannot be obtained with either histology or in-vivo imaging alone. Ultimately, this approach establishes a foundation for understanding, and eventually targeting, mechanical-immune interactions in lung health, disease, and aging. This abstract is funded by: DP2HL168562, Beckman Young Investigator Award, NSF CAREER Award

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Kahvecioglu et al. (2026) studied this question.

synapsesocial.com/papers/6a0d5100f03e14405aa9d423https://doi.org/10.1093/ajrccm/aamag162.5410
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