Aging is a major independent risk factor for aortic stiffness and cardiovascular diseases. The strength of the aorta is imparted by collagen fibers, which are the dominant fibrins within the aortic wall. Therefore, a three-dimensional (3D) quantitative assessment of age-related changes in the collagen fibers within the aortic wall is expected to provide important clues for research on cardiovascular diseases. Second harmonic generation (SHG) microscopy is an ideal tool for observing collagen fibers in biological tissues. Compared to the traditional histological analysis method, which requires tissue sectioning and staining, SHG microscopy has an intrinsic optical sectioning ability for the 3D imaging of intact tissues and allows label-free and high-specificity imaging of collagen fibers owing to its inversion-asymmetric and spatially ordered structure. Moreover, the high resolution, large depth penetration, low photobleaching and phototoxicity of SHG microscopy have significantly benefited the detailed imaging of thick tissues such as the aortic wall. However, the SHG-based 3D quantitative assessment of aortic collagen fibers has not yet been extensively demonstrated in aging-related research. In this study, we proposed combining SHG imaging with a representative spatial texture analysis algorithm, a 3D gray-level co-occurrence matrix (GLCM), to investigate age-related changes in the aorta from the perspective of collagen fiber microstructures. We hope that the proposed method and our findings can provide novel strategies and potential indicators for aortic aging assessment, and further benefit studies on age-related cardiovascular diseases.

Wistar-Kyoto (WKY) rats at 3 weeks (3 w), 12 weeks (12 w), and 44 weeks (44 w) were used in this study. First, the abdominal aortas were removed, cleaned, and cut open along the longitudinal axis. Subsequently, en-face 3D SHG imaging of the inner and outer surfaces of the aortic wall was performed using a commercial multiphoton microscope (A1R-MP; Nikon). Then, 11 texture feature parameters, including the correlation, contrast, entropy, energy, sum mean, variance, homogeneity, cluster shade, cluster prominence, max probability, and inverse variance, of the aortic collagen fibers were extracted from the 3D SHG image stacks using the 3D GLCM algorithm (Fig. 1). Finally, statistical analysis based on one-way ANOVA and Tukey's multiple comparison test was performed using GraphPad Prism software to sift out aging-associated features.

By comparing the SHG images of the WKY rats of different ages, we found that the aortic collagen fibers gradually became thicker, less dense, and more evenly distributed from 3 w to 12 w and 44 w (Figs. 2 and 3), regardless of the intima, media, or adventitia. However, the general morphology of the collagen fibers in the aortic intima and media was remarkably different from that in the aortic adventitia. The intima and media collagen fibers were relatively straight (Fig. 2), whereas the adventitial collagen fibers were arranged in curved bundles and had stronger SHG signals (Fig. 3). The 3D GLCM analysis and statistics of the aforementioned SHG images further showed that in the aortic intima and media, six texture features of the collagen fibers, including the correlation, contrast, entropy, sum mean, variance, and homogeneity, were significantly different among the three age groups. These features characterized the consistency, clarity, strength heterogeneity, overall strength, strength concentration, and structural isotropy of the fiber textures (Fig. 4). Similarly, for the adventitial layers, three aging-associated textural features-the sum mean, variance, and homogeneity-were sifted out (Fig. 5). The age-related changes revealed by these preferential texture features were generally consistent with those observed in the 3D SHG image stacks. These results demonstrated that combining SHG imaging with the 3D GLCM algorithm is a practical strategy for assessing aging-related changes in the collagen fibers in the aortic wall, and that 3D GLCM texture features such as the correlation, contrast, entropy, sum mean, variance, and homogeneity are promising quantitative indicators of aorta aging.

This study proposed a novel strategy that combined SHG imaging with 3D GLCM for aortic-aging assessment from the fresh perspective of the collagen fiber microstructure. The collagen fibers within the aortic intima-media and adventitia of WKY rats with different weeks of age were imaged using SHG microscopy. The 3D GLCM was then used to quantify the stere omicrostructural characteristics of the collagen fibers based on the 3D SHG image stacks, and a variety of aging-related texture features, including the correlation, contrast, entropy, sum mean, variance, and homogeneity, were sifted out. The proposed method and derived texture features are expected to provide a powerful tool and important reference indicators for assessing the degree of vascular aging. Moreover, this method may benefit the research on age-related cardiovascular diseases. Nevertheless, it should be noted that the SHG intensity was highly dependent on the overlap of the laser polarization with the fiber alignment. The excitation light used in this study was linearly polarized. The intensity of the SHG signal appeared to be at a maximum when the laser polarization direction was parallel to the orientation of the collagen fibers, whereas it appeared at a minimum when the two directions were perpendicular. We hope to consider laser polarization in our future studies, despite a variety of measures taken to minimize the effects of polarization on the quantitative analysis results of the SHG images in the present study. In addition, we found that the three age groups considered in this study could not be completely distinguished from each other by relying merely on a single 3D GLCM texture feature, although the 3D GLCM algorithm is considered highly sensitive to fiber microstructures. Therefore, more sensitive and valuable quantitative analytical methods merit further investigation.