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Quantification of the temporal evolution of collagen orientation in mechanically conditioned engineered cardiovascular tissues.

Rubbens MP, Driessen-Mol A, Boerboom RA, Koppert MM, van Assen HC, TerHaar Romeny BM, Baaijens FP, Bouten CV - Ann Biomed Eng (2009)

Bottom Line: Engineered tissues often lack properly organized collagen and consequently do not meet in vivo mechanical demands.Most importantly, intermittent straining improved and accelerated the alignment of the collagen fibers, as compared to constraining the constructs.Both the method and the results are relevant to create and monitor load-bearing tissues with an organized anisotropic collagen network.

View Article: PubMed Central - PubMed

Affiliation: Soft Tissue Biomechanics & Engineering, Department of Biomedical Engineering, Eindhoven University of Technology, WH 4.107, Den Dolech 2, P.O. Box 513, 5600 MB, Eindhoven, The Netherlands. m.p.rubbens@tue.nl

ABSTRACT
Load-bearing soft tissues predominantly consist of collagen and exhibit anisotropic, non-linear visco-elastic behavior, coupled to the organization of the collagen fibers. Mimicking native mechanical behavior forms a major goal in cardiovascular tissue engineering. Engineered tissues often lack properly organized collagen and consequently do not meet in vivo mechanical demands. To improve collagen architecture and mechanical properties, mechanical stimulation of the tissue during in vitro tissue growth is crucial. This study describes the evolution of collagen fiber orientation with culture time in engineered tissue constructs in response to mechanical loading. To achieve this, a novel technique for the quantification of collagen fiber orientation is used, based on 3D vital imaging using multiphoton microscopy combined with image analysis. The engineered tissue constructs consisted of cell-seeded biodegradable rectangular scaffolds, which were either constrained or intermittently strained in longitudinal direction. Collagen fiber orientation analyses revealed that mechanical loading induced collagen alignment. The alignment shifted from oblique at the surface of the construct towards parallel to the straining direction in deeper tissue layers. Most importantly, intermittent straining improved and accelerated the alignment of the collagen fibers, as compared to constraining the constructs. Both the method and the results are relevant to create and monitor load-bearing tissues with an organized anisotropic collagen network.

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Example of circular statistics. A circular distribution is represented in a histogram (a) and depicted as unit vectors with corresponding angles of 30 and 60 degrees in a unit circle (b). The mean vector (dashed line) is calculated by averaging the decomposed sine and cosine vector components of the individual vectors. α represents the mean angle and r the length of the mean vector. A vector length of 1 indicates no variation in fiber orientations, while a vector length of 0 indicates a random orientation of fiber orientations
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Fig2: Example of circular statistics. A circular distribution is represented in a histogram (a) and depicted as unit vectors with corresponding angles of 30 and 60 degrees in a unit circle (b). The mean vector (dashed line) is calculated by averaging the decomposed sine and cosine vector components of the individual vectors. α represents the mean angle and r the length of the mean vector. A vector length of 1 indicates no variation in fiber orientations, while a vector length of 0 indicates a random orientation of fiber orientations

Mentions: The collagen fiber orientations were determined by analyzing the individual images of each image stack as described by Daniels et al.11 In brief, coherence-enhancing diffusion (CED) was applied for denoising to improve the quality of structures in the image without destroying the boundaries of the fibers.44 Using CED, smoothing occurs along, but not perpendicular, to the preferred orientation of the structures in the image. Subsequently, the local orientations of all collagen fibers were determined by calculating the principal curvature directions from the eigenvalues and the eigenvectors of the Hessian matrix (second order structure).40 As fibers appear at different widths, the second order derivatives were determined at a scale adaptive to the local width of the fiber. The optimal scale was determined with a contextual confidence measure.31 At each location a stack of (in-plane) orientation histograms was obtained, representing the statistical distribution of local orientations in each image. To calculate the mean fiber angle and the dispersity of the distributions, circular statistics were required due to the periodicity of the fiber distributions.23,41,48 Circular statistics handle periodic data by representing each angle as a unit vector oriented at that angle. To calculate the mean angle, the individual unit vectors are decomposed into vector components on which statistical operations, such as averaging, are permitted (Fig. 2). Histograms of collagen orientations were obtained from each multiphoton image, and of each the mean vector was calculated, representing a mean angle α and a mean vector length r. The mean vector length represents a measure for the dispersity of the fiber orientations. A vector length of 1 indicates no variation in fiber orientations (i.e., all fibers perfectly aligned) while a vector length of 0 indicates a random distribution of fiber orientations. Courses of mean angles and vector lengths as a function of tissue depth were obtained per image location. To generalize the results, the vector components at each depth were averaged over all samples per loading condition per time point. Subsequently, averaged courses of mean angles and vector lengths were obtained. It should be noted that when the dispersity of the collagen orientation distribution is large, the distribution becomes essentially random. This diminishes the meaning of the value of the mean angle. Hence, for clarity, orientation angels were not shown when mean vector lengths were consistently lower than 0.2.Figure 2


Quantification of the temporal evolution of collagen orientation in mechanically conditioned engineered cardiovascular tissues.

Rubbens MP, Driessen-Mol A, Boerboom RA, Koppert MM, van Assen HC, TerHaar Romeny BM, Baaijens FP, Bouten CV - Ann Biomed Eng (2009)

Example of circular statistics. A circular distribution is represented in a histogram (a) and depicted as unit vectors with corresponding angles of 30 and 60 degrees in a unit circle (b). The mean vector (dashed line) is calculated by averaging the decomposed sine and cosine vector components of the individual vectors. α represents the mean angle and r the length of the mean vector. A vector length of 1 indicates no variation in fiber orientations, while a vector length of 0 indicates a random orientation of fiber orientations
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2690830&req=5

Fig2: Example of circular statistics. A circular distribution is represented in a histogram (a) and depicted as unit vectors with corresponding angles of 30 and 60 degrees in a unit circle (b). The mean vector (dashed line) is calculated by averaging the decomposed sine and cosine vector components of the individual vectors. α represents the mean angle and r the length of the mean vector. A vector length of 1 indicates no variation in fiber orientations, while a vector length of 0 indicates a random orientation of fiber orientations
Mentions: The collagen fiber orientations were determined by analyzing the individual images of each image stack as described by Daniels et al.11 In brief, coherence-enhancing diffusion (CED) was applied for denoising to improve the quality of structures in the image without destroying the boundaries of the fibers.44 Using CED, smoothing occurs along, but not perpendicular, to the preferred orientation of the structures in the image. Subsequently, the local orientations of all collagen fibers were determined by calculating the principal curvature directions from the eigenvalues and the eigenvectors of the Hessian matrix (second order structure).40 As fibers appear at different widths, the second order derivatives were determined at a scale adaptive to the local width of the fiber. The optimal scale was determined with a contextual confidence measure.31 At each location a stack of (in-plane) orientation histograms was obtained, representing the statistical distribution of local orientations in each image. To calculate the mean fiber angle and the dispersity of the distributions, circular statistics were required due to the periodicity of the fiber distributions.23,41,48 Circular statistics handle periodic data by representing each angle as a unit vector oriented at that angle. To calculate the mean angle, the individual unit vectors are decomposed into vector components on which statistical operations, such as averaging, are permitted (Fig. 2). Histograms of collagen orientations were obtained from each multiphoton image, and of each the mean vector was calculated, representing a mean angle α and a mean vector length r. The mean vector length represents a measure for the dispersity of the fiber orientations. A vector length of 1 indicates no variation in fiber orientations (i.e., all fibers perfectly aligned) while a vector length of 0 indicates a random distribution of fiber orientations. Courses of mean angles and vector lengths as a function of tissue depth were obtained per image location. To generalize the results, the vector components at each depth were averaged over all samples per loading condition per time point. Subsequently, averaged courses of mean angles and vector lengths were obtained. It should be noted that when the dispersity of the collagen orientation distribution is large, the distribution becomes essentially random. This diminishes the meaning of the value of the mean angle. Hence, for clarity, orientation angels were not shown when mean vector lengths were consistently lower than 0.2.Figure 2

Bottom Line: Engineered tissues often lack properly organized collagen and consequently do not meet in vivo mechanical demands.Most importantly, intermittent straining improved and accelerated the alignment of the collagen fibers, as compared to constraining the constructs.Both the method and the results are relevant to create and monitor load-bearing tissues with an organized anisotropic collagen network.

View Article: PubMed Central - PubMed

Affiliation: Soft Tissue Biomechanics & Engineering, Department of Biomedical Engineering, Eindhoven University of Technology, WH 4.107, Den Dolech 2, P.O. Box 513, 5600 MB, Eindhoven, The Netherlands. m.p.rubbens@tue.nl

ABSTRACT
Load-bearing soft tissues predominantly consist of collagen and exhibit anisotropic, non-linear visco-elastic behavior, coupled to the organization of the collagen fibers. Mimicking native mechanical behavior forms a major goal in cardiovascular tissue engineering. Engineered tissues often lack properly organized collagen and consequently do not meet in vivo mechanical demands. To improve collagen architecture and mechanical properties, mechanical stimulation of the tissue during in vitro tissue growth is crucial. This study describes the evolution of collagen fiber orientation with culture time in engineered tissue constructs in response to mechanical loading. To achieve this, a novel technique for the quantification of collagen fiber orientation is used, based on 3D vital imaging using multiphoton microscopy combined with image analysis. The engineered tissue constructs consisted of cell-seeded biodegradable rectangular scaffolds, which were either constrained or intermittently strained in longitudinal direction. Collagen fiber orientation analyses revealed that mechanical loading induced collagen alignment. The alignment shifted from oblique at the surface of the construct towards parallel to the straining direction in deeper tissue layers. Most importantly, intermittent straining improved and accelerated the alignment of the collagen fibers, as compared to constraining the constructs. Both the method and the results are relevant to create and monitor load-bearing tissues with an organized anisotropic collagen network.

Show MeSH