Various lung diseases, including pulmonary hypertension, chronic obstructive pulmonary disease or bronchopulmonary dysplasia, are associated with structural and architectural alterations of the pulmonary vasculature. The light microscopic (LM) analysis of the blood vessels is limited by the fact that it is impossible to identify which generation of the arterial tree an arterial profile within a LM microscopic section belongs to. Therefore, we established a workflow that allows for the generation-specific quantitative (stereological) analysis of pulmonary blood vessels. A whole left rabbit lung was fixed by vascular perfusion, embedded in glycol methacrylate and imaged by micro-computed tomography (µCT). The lung was then exhaustively sectioned and 20 consecutive sections were collected every 100 µm to obtain a systematic uniform random sample of the whole lung. The digital processing involved segmentation of the arterial tree, generation analysis, registration of LM sections with the µCT data as well as registration of the segmentation and the LM images. The present study demonstrates that it is feasible to identify arterial profiles according to their generation based on a generation-specific color code. Stereological analysis for the first three arterial generations of the monopodial branching of the vasculature included volume fraction, total volume, lumen-to-wall ratio and wall thickness for each arterial generation. In conclusion, the correlative image analysis of µCT and LM-based datasets is an innovative method to assess the pulmonary vasculature quantitatively.

Mammalian pulmonary arteries divide multiple times before reaching the vast capillary network of the alveoli. Morphological analyses of the arterial branches can be challenging because more proximal branches are likely biologically distinct from more peripheral parts. Thus, it is useful to group the arterial branches into groups of coherent biology. While the generational approach of dichotomous branching is straightforward, the grouping of arterial branches in the asymmetrically branching monopodial lung is less clear. Several established classification methods return highly dissimilar groupings when employed on the same organ. Here, we established a workflow allowing the quantification of grouping results for the monopodial lung and tested various methods to group the branches of the arterial tree into coherent groups. A mouse lung was imaged by synchrotron x-ray microcomputed tomography, and the arteries were digitally segmented. The arterial tree was divided into its individual segments, morphological properties were assessed from corresponding light microscopic scans, and different grouping methods were employed, such as (fractal) generation or (Strahler) order. The results were ranked by the morphological similarity within and dissimilarity between the resulting groups. Additionally, a method from the mathematical field of cluster analysis was employed for creating a reference classification. In conclusion, there were significant differences in method performance. The Strahler order was significantly superior to the generation system commonly used to classify human lung structure. Furthermore, a clustering approach indicated more precise ways to classify the monopodial lung vasculature exist.

Mechanical forces affect the alveolar shape, depend on location and tissue composition and vary during the respiratory cycle. This study performs alveolar morphomics in different lobes of human lungs using models generated from 3D microCT images. Cylindrical tissue samples (1.6x2 cm) were extracted from two non-transplantable donor lungs (one ex-smoker and one smoker, 3 samples per subject) that were air inflated and frozen solid in liquid nitrogen vapor. Samples were scanned with microCT (11 µm/voxel). Within representative cubic regions of interest (5.5 mm edge length), alveoli were segmented to produce corresponding 3D models from which quantitative data were obtained. The surface of segmented alveoli (n=23,587) was divided into individual planar surfaces (facets) and angles between facet normals were calculated. Moreover, the number of neighboring alveoli was estimated for every alveolus. The main results are: Higher mean alveolar volumes and surface sizes in both upper lung lobes compared with the lower lobes. An increasing number of facets from top to bottom, as well as a decreasing number of median alveolar neighbors from the upper lobes to the lower lobes, an increasing ratio of alveolar entrance size to the surface size of the alveoli from top to bottom and larger median angles between facet normals in the upper lobes of both lungs than in the lower lobes. By using this new approach of analyzing alveolar 3D data, which enables the estimation of facet, neighbor and shape characteristics, we aimed to establish the baseline measures for in-depth studies of mechanical conditions and morphology.

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.