Dataset Information

A semi-automated method for unbiased alveolar morphometry: Validation in a bronchopulmonary dysplasia model.

ABSTRACT: Reproducible and unbiased methods to quantify alveolar structure are important for research on many lung diseases. However, manually estimating alveolar structure through stereology is time consuming and inter-observer variability is high. The objective of this work was to develop and validate a fast, reproducible and accurate (semi-)automatic alternative. A FIJI-macro was designed that automatically segments lung images to binary masks, and counts the number of test points falling on tissue and the number of intersections of the air-tissue interface with a set of test lines. Manual selection remains necessary for the recognition of non-parenchymal tissue and alveolar exudates. Volume density of alveolar septa ([Formula: see text]) and mean linear intercept of the airspaces (Lm) as measured by the macro were compared to theoretical values for 11 artificial test images and to manually counted values for 17 lungs slides using linear regression and Bland-Altman plots. Inter-observer agreement between 3 observers, measuring 8 lungs both manually and automatically, was assessed using intraclass correlation coefficients (ICC). [Formula: see text] and Lm measured by the macro closely approached theoretical values for artificial test images (R2 of 0.9750 and 0.9573 and bias of 0.34% and 8.7%). The macro data in lungs were slightly higher for [Formula: see text] and slightly lower for Lm in comparison to manually counted values (R2 of 0.8262 and 0.8288 and bias of -6.0% and 12.1%). Visually, semi-automatic segmentation was accurate. Most importantly, manually counted [Formula: see text] and Lm had only moderate to good inter-observer agreement (ICC 0.859 and 0.643), but agreements were excellent for semi-automatically counted values (ICC 0.956 and 0.900). This semi-automatic method provides accurate and highly reproducible alveolar morphometry results. Future efforts should focus on refining methods for automatic detection of non-parenchymal tissue or exudates, and for assessment of lung structure on 3D reconstructions of lungs scanned with microCT.

SUBMITTER: Salaets T

PROVIDER: S-EPMC7511023 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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A semi-automated method for unbiased alveolar morphometry: Validation in a bronchopulmonary dysplasia model.

Salaets Thomas T Tack Bieke B Gie André A Pavie Benjamin B Sindhwani Nikhil N Jimenez Julio J Regin Yannick Y Allegaert Karel K Deprest Jan J Toelen Jaan J

PloS one 20200923 9

Reproducible and unbiased methods to quantify alveolar structure are important for research on many lung diseases. However, manually estimating alveolar structure through stereology is time consuming and inter-observer variability is high. The objective of this work was to develop and validate a fast, reproducible and accurate (semi-)automatic alternative. A FIJI-macro was designed that automatically segments lung images to binary masks, and counts the number of test points falling on tissue and ...[more]

PMID: 32966330

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Project description:Introduction: Vascular remodeling and compromised alveolar development are hallmarks of chronic pulmonary diseases such as bronchopulmonary dysplasia (BPD). Despite advances in neonatal healthcare the number of BPD cases worldwide continues to increase. One approach to overcoming the premature arrest in lung development seen in BPD is to stimulate neonatal angiogenesis via delivery and engraftment of endothelial progenitor cells (EPCs). One such population is resident to the pulmonary microvasculature and expresses both FOXF1 and c-KIT. Previous studies have shown that c-KIT+FOXF1+ EPCs are highly sensitive to elevated levels of oxygen (hyperoxia) and are decreased in premature infants with BPD and hyperoxia-induced BPD mouse models. We hypothesize that restoring EPCs through transplantation of c-KIT+FOXF1+ EPCs derived in vitro from pluripotent embryonic stem cells (ESCs), will stimulate neonatal angiogenesis and alveolarization in mice with hyperoxia-induced lung injury. Methods: Utilizing a novel ESC line with a FOXF1:GFP reporter, we generated ESC-derived c-KIT+FOXF1+ EPCs in vitro. Using a second ESC line which contains FOXF1:GFP and tdTomato transgenes, we differentiated ESCs towards c-KIT+FOXF1+ EPCs and tracked them in vivo after injection into the neonatal circulation of hyperoxia-injured mice. After a recovery period in room air conditions, we analyzed c-KIT+FOXF1+ EPC engraftment and quantified the number of resident and circulating endothelial cells, the size of alveolar spaces, and the capillary density after EPC transplantations. Results and conclusion: Herein, we demonstrate that addition of BMP9 to the directed endothelial differentiation protocol results in very efficient generation of c-KIT+FOXF1+ EPCs from pluripotent ESCs. ESC-derived c-KIT+FOXF1+ EPCs effectively engraft into the pulmonary microvasculature of hyperoxia-injured mice, promote vascular remodeling in alveoli, increase the number of resident and circulating endothelial cells, and improve alveolarization. Altogether, these results provide a proof-of-principle that cell therapy with ESC-derived c-KIT+FOXF1+ EPCs can prevent alveolar simplification in a hyperoxia-induced BPD mouse model.

Project description:In recent years, the role of sympathetic nervous fibers in chronic inflammation has become increasingly evident. At the onset of inflammation, sympathetic activity is increased in the affected tissue. However, sympathetic fibers are largely absent from chronically inflamed tissue. Apparently, there is a very dynamic relationship between sympathetic innervation and the immune system in areas of inflammation, and hence a rapid and easy method for quantification of nerve fiber density of target organs is of great value to answer potential research questions. Currently, nervous fiber densities are either determined by tedious manual counting, which is not suitable for high throughput approaches, or by expensive automated processes relying on specialized software and high-end microscopy equipment. Usually, tyrosine hydroxylase (TH) is used as the marker for sympathetic fibers. In order to overcome the current quantification bottleneck with a cost-efficient alternative, an automated process was established and compared to the classic manual approach of counting TH-positive sympathetic fibers. Since TH is not exclusively expressed on sympathetic fibers, but also in a number of catecholamine-producing cells, a prerequisite for automated determination of fiber densities is to reliably distinct between cells and fibers. Therefore, an additional staining using peripherin exclusively expressed in nervous fibers as a secondary marker was established. Using this novel approach, we studied the spleens from a syndecan-3 knockout (SDC3KO) mouse line, and demonstrated equal results on SNS fiber density for both manual and automated counts (Manual counts: wildtype: 22.57 +/- 11.72 fibers per mm2; ko: 31.95 +/- 18.85 fibers per mm2; p = 0.05; Automated counts: wildtype: 31.6 +/- 18.98 fibers per mm2; ko: 45.49 +/- 19.65 fibers per mm2; p = 0.02). In conclusion, this new and simple method can be used as a high-throughput approach to reliably and quickly estimate SNS nerve fiber density in target tissues.

Dataset Information

A semi-automated method for unbiased alveolar morphometry: Validation in a bronchopulmonary dysplasia model.

Publications

A semi-automated method for unbiased alveolar morphometry: Validation in a bronchopulmonary dysplasia model.

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