Characterization of Spatially Graded Biomechanical Scaffolds

Abstract

Advances in fabrication have allowed tissue engineers to better mimic complex structures and tissue interfaces by designing nanofibrous scaffolds with spatially graded material properties. However, the nonuniform properties that grant the desired biomechanical function also make these constructs difficult to characterize. In light of this, we developed a novel procedure to create graded nanofibrous scaffolds and determine the spatial distribution of their material properties. Multilayered nanofiber constructs were synthesized, controlling spatial gradation of the stiffness to mimic the soft tissue gradients found in tendon or ligament tissue. Constructs were characterized using uniaxial tension testing with digital image correlation (DIC) to measure the displacements throughout the sample, in a noncontacting fashion, as it deformed. Noise was removed from the displacement data using principal component analysis (PCA), and the final denoised field served as the input to an inverse elasticity problem whose solution determines the spatial distribution of the Young's modulus throughout the material, up to a multiplicative factor. Our approach was able to construct, characterize, and determine the spatially varying moduli, in four electrospun scaffolds, highlighting its great promise for analyzing tissues and engineered constructs with spatial gradations in modulus, such as those at the interfaces between two disparate tissues (e.g., myotendinous junction, tendon- and ligament-to-bone entheses).

Recent advances in 3D bioprinting of musculoskeletal tissues

“…This can largely be explained as there are numerous challenges mentioned earlier such as creating a tissue construct capable of successfully imitating microenvironment conditions for multiple cell types while also being able to withstand varying mechanical demands [154]. Moreover, there exist developed methods for characterizing varying mechanical properties over a spatial gradient such as an interface tissue [162][163][164]. These characterization methods are crucial for ensuring that 3D bioprinted tissues satisfy the variable mechanical properties required of musculoskeletal interface tissues.…”

On accounting for speckle extinction via DIC and PCA

“…Concerning DIC, if small displacements occur, it can be applied directly on images to extract kinematic bases for DIC analyses [8]. It can also be used for filtering out noise from measured displacement fields [9,10,11]. Other applications include estimating dynamic properties in viscoelastic materials [12], evaluating vibrations modes of cantilever plate [13], and clustering displacement vectors to analyze fracture mechanisms in rocks [14].…”

Exploring the potential of transfer learning for metamodels of heterogeneous material deformation

“…In addition, the material properties of biological tissue often vary on macroscopic length scales [6]. Computational modeling is the ideal tool for directly investigating the effects of this heterogeneity [7,8]. With computational modeling, it is possible to capture spatial heterogeneity and subsequently measure its effects [9].…”