Monitoring of extracellular matrix (ECM) microstructure is essential in studying structure-associated cellular processes, improving cellular function, and for ensuring sufficient mechanical integrity in engineered tissues. This paper describes a novel method to study the microscale alignment of the matrix in engineered tissue scaffolds (ETS) that are usually composed of a variety of biomacromolecules derived by cells. First, a trained loading function was derived from Raman spectra of highly aligned native tissue via principal component analysis (PCA), where prominent changes associated with specific Raman bands (e.g., 1444, 1465, 1605, 1627-1660, and 1665-1689 cm-1) were detected with respect to the polarization angle. These changes were mainly caused by the aligned matrix of many compounds within the tissue relative to the laser polarization, including proteins, lipids, and carbohydrates. Hence this trained function was applied to quantify the alignment within ETS of various matrix components derived by cells. Furthermore, a simple metric called Amplitude Alignment Metric (AAM) was derived to correlate the orientation dependence of polarized Raman spectra of ETS to the degree of matrix alignment. It was found that the AAM was significantly higher in anisotropic ETS than isotropic ones. The PRS method revealed a lower p-value for distinguishing the alignment between these two types of ETS as compared to the microscopic method for detecting fluorescent-labeled protein matrices at a similar microscopic scale. These results indicate that the anisotropy of a complex matrix in engineered tissue can be assessed at the microscopic scale using a PRS-based simple metric, which is superior to the traditional microscopic method. This PRS-based method can serve as a complementary tool for the design and assessment of engineered tissues that mimic the native matrix organizational microstructures.
Keywords: anisotropy; engineered tissue scaffolds; extracellular matrix; matrix alignment; polarized Raman spectroscopy; principal component analysis.