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Biomechanics

Biomechanics - Poster Session E

Poster D6 - Quantitative analysis of the changing collagen fiber organization during tissue loading

Saturday, October 26, 2024
10:00 AM - 11:00 AM EST
Location: Exhibit Hall E, F & G

Introduction:  Collagen, the main structural protein in tissues, is crucial for maintaining the strength and integrity of the extracellular matrix, especially in skin, tendons, and arteries. Changes in collagen are linked to several diseases, including fibrosis, aneurysms, and urinary bladder disorders. Understanding the microstructure of collagen fibers is vital for understanding the impact of these diseases on tissue mechanics and for creating precise, structurally informed constitutive models. These models are essential for accurately predicting the behavior of collagenous tissues under both normal and extreme conditions.
Although commercial software and algorithms are available to quantify collagen fiber microstructure—such as tortuosity and orientation—these tools often face challenges in accurately capturing global orientation and tend to have high error rates for long, intricate fibers. Our research addresses these issues by developing a semi-automatic tracing method and custom postprocessing code to more precisely analyze collagen fiber orientation and tortuosity in human cerebral arteries under various stretch conditions.

Materials and

Methods:  Cerebral vessels from the Circle of Willis (CoW) were acquired post-mortem from the Brain Bank at the University of Pittsburgh. These samples were subjected to uniaxial testing under a multiphoton microscope (MPM), allowing simultaneous mechanical response assessment and adventitia collagen fiber imaging at various stretch levels (λ = 1, 1.3, 1.5, … , 2.1).
The raw MPM images were first enhanced for contrast using NIH ImageJ software (version 1.54f) to make the fibers more visible. Substacks of 10 sequential slides (5 µm thick) were merged into 2D images using the Z-project function in ImageJ, with a 2.5 µm gap between stacks to avoid duplicating fiber images. Fibers, ranging from 35 to 55 per scan, were manually traced using ImageJ’s multi-point tool (Fig. 1(a)), and their coordinates were exported to a text file. A Python script was then used to analyze these coordinates and reconstruct the fibers (Fig. 1(b)), calculating the length of each fiber based on the initial points.
The tortuosity of each fiber (T) was calculated using the:
T= s/l
where s is the fiber length and l is the direct distance. A collagen fiber was considered recruited if its tortuosity fell below a specified threshold (1.02).
Additionally, the orientation of each fiber was derived by computing the slope of the linear regression line fitted to the coordinates of the seed points for each fiber.

Results, Conclusions, and Discussions: Figure 2 presents the fiber angle probability distribution at each stretch level. The fibers are categorized into two groups based on their tortuosity. Fibers with a tortuosity less than the threshold (1.02) are considered recruited and are shown in green, while those with a tortuosity greater than the threshold are considered not recruited and are shown in red.
Fibers in the adventitia exhibit a tortuous, bimodal orientation distribution (bidirectional alignment) before stretching. As stretching increases, the number of recruited fibers rises, and the bimodal distribution of fiber orientations gradually transitions into a unimodal distribution. Eventually, this unimodal distribution centers around a zero-degree angle, reflecting the alignment of fibers in the direction of the applied stretch.
Using our semi-automatic tracing method and custom-developed postprocessing code, we have successfully enhanced the analysis of collagen fiber orientation and tortuosity in human cerebral arteries. This approach overcomes the limitations of existing tools by providing more accurate measurements of long, complex fibers. It offers a more precise assessment of collagen fiber behavior, enhancing the biomechanical modeling of cerebral arteries and other collagenous tissues.

Acknowledgements (Optional):  The authors would like to acknowledge support from the National Institute of Health through grant 2R01NS097457.