Understanding Lung Acinar Micromechanics in Health and Disease: Linking Quantitative Imaging and Organ Scale Mechanics by Computational Modeling

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Review
05 May 2020

Surfactant protein C (SP-C) is an important player in enhancing the interfacial adsorption of lung surfactant lipid films to the alveolar air-liquid interface. Doing so, surface tension drops down enough to stabilize alveoli and the lung, reducing the work of breathing. In addition, it has been shown that SP-C counteracts the deleterious effect of high amounts of cholesterol in the surfactant lipid films. On its side, cholesterol is a well-known modulator of the biophysical properties of biological membranes and it has been proven that it activates the inflammasome pathways in the lung. Even though the molecular mechanism is not known, there are evidences suggesting that these two molecules may interplay with each other in order to keep the proper function of the lung. This review focuses in the role of SP-C and cholesterol in the development of lung fibrosis and the potential pathways in which impairment of both molecules leads to aberrant lung repair, and therefore impaired alveolar dynamics. From molecular to cellular mechanisms to evidences in animal models and human diseases. The evidences revised here highlight a potential SP-C/cholesterol axis as target for the treatment of lung fibrosis.

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Review
03 April 2020

Intravital microscopy (IVM) offers unique possibilities for the observation of biological processes and disease related mechanisms in vivo. Especially for anatomically complex and dynamic organs such as the lung and its main functional unit, the alveolus, IVM provides exclusive advantages in terms of spatial and temporal resolution. By the use of lung windows, which have advanced and improved over time, direct access to the lung surface is provided. In this review we will discuss two main topics, namely alveolar dynamics and perfusion from the perspective of IVM-based studies. Of special interest are unanswered questions regarding alveolar dynamics such as: What are physiologic alveolar dynamics? How do these dynamics change under pathologic conditions and how do those changes contribute to ventilator-induced lung injury? How can alveolar dynamics be targeted in a beneficial way? With respect to alveolar perfusion IVM has propelled our understanding of the pulmonary microcirculation and its perfusion, as well as pulmonary vasoreactivity, permeability and immunological aspects. Whereas the general mechanism behind these processes are understood, we still lack a proper understanding of the complex, multidimensional interplay between alveolar ventilation and microvascular perfusion, capillary recruitment, or vascular immune responses under physiologic and pathologic conditions. These are only part of the unanswered questions and problems, which we still have to overcome. IVM as the tool of choice might allow us to answer part of these questions within the next years or decades. As every method, IVM has advantages as well as limitations, which have to be taken into account for data analysis and interpretation, which will be addressed in this review.

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Original Research
25 February 2020
An Analytical Model for Estimating Alveolar Wall Elastic Moduli From Lung Tissue Uniaxial Stress-Strain Curves
Samer Bou Jawde
2 more and 
Béla Suki
Uniaxial analytical and the network model-based alveolar angle and strain distributions. The graphs show alveolar angle probability distribution functions (A,C,E,G) and alveolar strain probability distribution functions (B,D,F,H) from the analytical model and the network at four levels of tissue strains (0.15, 0.3, 0.45, and 0.6). Initial alveolar strains are zero. Initial angle distribution is shown in Appendix B, Figure 9B.

The non-linear stress-strain behavior of uniaxially-stretched lung parenchyma is thought to be an emergent phenomenon arising from the ensemble behavior of its microscopic constituents. Such behavior includes the alignment and elongation of randomly oriented alveolar walls with initially flaccid fibers in the direction of strain. To account for the link between microscopic wall behavior and the macroscopic stress-strain curve, we developed an analytical model that represents both alignment and elongation of alveolar walls during uniaxial stretching. The model includes the kinetics and mechanical behavior of randomly oriented elastic alveolar walls that have a bending stiffness at their intersections. The alignment and stretch of the walls following incremental stretch of the tissue were determined based on energy minimization, and the total stress was obtained by differentiating the total energy density with respect to strain. The stress-strain curves predicted by the model were comparable to curves generated by a previously published numerical alveolar network model. The model was also fit to experimentally measured stress-strain curves in parenchymal strips obtained from mice with decreased lung collagen content, and from young and aged mice. This yielded estimates for the elastic modulus of an alveolar wall, which increased with age from 4.4 to 5.9 kPa (p = 0.043), and for the elastic modulus of fibers within the wall, which increased with age from 311 to 620 kPa (p = 0.001). This demonstrates the possibility of estimating alveolar wall mechanical properties in biological soft tissue from its macroscopic behavior given appropriate assumptions about tissue structure.

7,361 views
26 citations
Original Research
04 December 2019
Alveolar epithelial type II (ATII) cell characteristics. (A–E) Representative light micrographs of each age group show pulmonary histology. Tissue sections were stained with toluidine blue. Arrowheads indicate ATII cells, typically located in alveolar “corners”. Intracellular lamellar bodies appear violet due to a metachromatic staining behavior. The ATII cells look quite homogeneous at any age in light microscopic images. However, the cell number density seems to be highest in 3 mo mice (A). Scale bar = 20 μm. (F–J) Graphs show stereological results of ATII cell number density (F) and ATII cell number (G) as well as mean ATII cell number per alveolus (H), mean ATII cell number per alveolar septal surface area (I), and mean ATII cell volume (J). Each point represents one animal; bars show means and lines indicate statistically significant differences between age groups (one-way ANOVA with Bonferroni t-test, p < 0.05).
11,451 views
74 citations
Original Research
21 November 2019
(a) Slices of the entire tissue block (525 × 525 μm) in the z-direction: A, artery; ACN, alveolar-capillary network; B, bronchiolus; CT, connective tissue; RBC, red blood cells; L, lymphatic vessel; MA, microatelectasis; SA, saccular airspace; V, vein. (b) Segmentation of the structures of interest (same slice as in a): red = blood vessels, green = airspace, yellow = lymphatic vessels. (c) 3D Surface rendering (525 × 525 × 420 μm) of the segmented structures of interest, air: semi-transparent gray, vasculature: red to blue, lymphatic vessels: yellow. (d) Like (c) without the air surface but with the slice from (a).
7,351 views
22 citations
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