An ultrastructural 3D reconstruction method for observing the arrangement of collagen fibrils and proteoglycans in the human aortic wall under mechanical load.

Biaxial extension test Collagen fibrils and proteoglycans Electron tomography Human aorta Segmentation

Journal

Acta biomaterialia
ISSN: 1878-7568
Titre abrégé: Acta Biomater
Pays: England
ID NLM: 101233144

Informations de publication

Date de publication:
15 03 2022
Historique:
received: 15 09 2021
revised: 10 01 2022
accepted: 14 01 2022
pubmed: 23 1 2022
medline: 12 4 2022
entrez: 22 1 2022
Statut: ppublish

Résumé

An insight into changes of soft biological tissue ultrastructures under loading conditions is essential to understand their response to mechanical stimuli. Therefore, this study offers an approach to investigate the arrangement of collagen fibrils and proteoglycans (PGs), which are located within the mechanically loaded aortic wall. The human aortic samples were either fixed directly with glutaraldehyde in the load-free state or subjected to a planar biaxial extension test prior to fixation. The aortic ultrastructure was recorded using electron tomography. Collagen fibrils and PGs were segmented using convolutional neural networks, particularly the ESPNet model. The 3D ultrastructural reconstructions revealed a complex organization of collagen fibrils and PGs. In particular, we observed that not all PGs are attached to the collagen fibrils, but some fill the spaces between the fibrils with a clear distance to the collagen. The complex organization cannot be fully captured or can be severely misinterpreted in 2D. The approach developed opens up practical possibilities, including the quantification of the spatial relationship between collagen fibrils and PGs as a function of the mechanical load. Such quantification can also be used to compare tissues under different conditions, e.g., healthy and diseased, to improve or develop new material models. STATEMENT OF SIGNIFICANCE: The developed approach enables the 3D reconstruction of collagen fibrils and proteoglycans as they are embedded in the loaded human aortic wall. This methodological pipeline comprises the knowledge of arterial mechanics, imaging with transmission electron microscopy and electron tomography, segmentation of 3D image data sets with convolutional neural networks and finally offers a unique insight into the ultrastructural changes in the aortic tissue caused by mechanical stimuli.

Identifiants

pubmed: 35065266
pii: S1742-7061(22)00044-7
doi: 10.1016/j.actbio.2022.01.036
pii:
doi:

Substances chimiques

Proteoglycans 0
Collagen 9007-34-5

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

300-314

Subventions

Organisme : Austrian Science Fund FWF
ID : P 30260
Pays : Austria

Informations de copyright

Copyright © 2022 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Auteurs

Anna Pukaluk (A)

Institute of Biomechanics, Graz University of Technology, Graz, Austria; BioTechMed-Graz, Austria.

Anna-Sophie Wittgenstein (AS)

Institute of Biomechanics, Graz University of Technology, Graz, Austria.

Gerd Leitinger (G)

Division of Cell Biology, Histology and Embryology, Gottfried Schatz Research Center, Medical University of Graz, Graz, Austria; Core Facility Ultrastructure Analysis, Center for Medical Research, Medical University of Graz, Graz, Austria; BioTechMed-Graz, Austria.

Dagmar Kolb (D)

Division of Cell Biology, Histology and Embryology, Gottfried Schatz Research Center, Medical University of Graz, Graz, Austria; Core Facility Ultrastructure Analysis, Center for Medical Research, Medical University of Graz, Graz, Austria; BioTechMed-Graz, Austria.

Dominique Pernitsch (D)

Core Facility Ultrastructure Analysis, Center for Medical Research, Medical University of Graz, Graz, Austria.

Sarah A Schneider (SA)

Institute of Biomechanics, Graz University of Technology, Graz, Austria; High-Performance Vision Systems, Center for Vision, Automation and Control, Austrian Institute of Technology, Vienna, Austria, and Human-Robot Interaction Lab, Tufts University, Medford, USA.

Patrick Knöbelreiter (P)

Institute of Computer Graphics and Vision, Graz University of Technology, Graz, Austria.

Verena Horak (V)

Institute of Mathematics and Scientific Computing, University of Graz, Graz, Austria; BioTechMed-Graz, Austria.

Kristian Bredies (K)

Institute of Mathematics and Scientific Computing, University of Graz, Graz, Austria; BioTechMed-Graz, Austria.

Gerhard A Holzapfel (GA)

Institute of Biomechanics, Graz University of Technology, Graz, Austria; Department of Structural Engineering, NTNU, Trondheim, Norway; BioTechMed-Graz, Austria.

Thomas Pock (T)

Institute of Computer Graphics and Vision, Graz University of Technology, Graz, Austria; BioTechMed-Graz, Austria.

Gerhard Sommer (G)

Institute of Biomechanics, Graz University of Technology, Graz, Austria; BioTechMed-Graz, Austria. Electronic address: sommer@tugraz.at.

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