Lung diseases such as cancer substantially alter the mechanical properties of the organ with direct impact on the development, progression, diagnosis, and treatment response of diseases. Despite significant interest in the lung’s material properties, measuring the stiffness of intact lungs at sub-alveolar resolution has not been possible. Recently, we developed the crystal ribcage to image functioning lungs at optical resolution while controlling physiological parameters such as air pressure. Here, we introduce a data-driven, multiscale network model that takes images of the lung at different distending pressures, acquired via the crystal ribcage, and produces corresponding absolute stiffness maps. Following validation, we report absolute stiffness maps of the functioning lung at microscale resolution in health and disease. For representative images of a healthy lung and a lung with primary cancer, we find that while the lung exhibits significant stiffness heterogeneity at the microscale, primary tumors introduce even greater heterogeneity into the lung’s microenvironment. Additionally, we observe that while the healthy alveoli exhibit strain-stiffening of ∼1.75 times, the tumor’s stiffness increases by a factor of six across the range of measured transpulmonary pressures. While the tumor stiffness is 1.4 times the lung stiffness at a transpulmonary pressure of three cmH2O, the tumor’s mean stiffness is nearly five times greater than that of the surrounding tissue at a transpulmonary pressure of 18 cmH2O. Finally, we report that the variance in both strain and stiffness increases with transpulmonary pressure in both the healthy and cancerous lungs. Our new method allows quantitative assessment of disease-induced stiffness changes in the alveoli with implications for mechanotransduction.
Current literature reports a wide range of stiffness values and constitutive models for lung tissue across different spatial scales. Comparing the reported lung tissue stiffness values across different spatial scales may provide insights into how well those mechanical properties and the proposed constitutive models represent lung tissue’s mechanical behavior. Thus, this study applies in silico modeling to compare and potentially bridge the differences reported in lung tissue mechanical properties at different length scales. Specifically, we predicted the mesoscale mechanical behavior of rat lung tissue based on in situ and in vitro microscale test data using finite element (FE) analysis and compared those computational predictions to the reported data using mesoscale uniaxial experiments. Our simulations showed that microscale-based stiffness values differed from the mesoscale data in the simulated strain range of 0–60%, with the atomic force microscopy (AFM)-based data overestimating the mesoscale data above 15% strain. This research demonstrates that computational modeling can be used as an informative and guiding tool to investigate and potentially bridge the differences in reported lung tissue material properties across length scales.
more » « less- Award ID(s):
- 2034964
- PAR ID:
- 10469918
- Publisher / Repository:
- Journal of Mechanics in Medicine and Biology
- Date Published:
- Journal Name:
- Journal of Mechanics in Medicine and Biology
- Volume:
- 23
- Issue:
- 07
- ISSN:
- 0219-5194
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1142/S0219519423500756
