Three-dimensional image quantification as a new morphometry method for tissue engineering

Morphological analysis is an essential step in verifying the success of a tissue engineering strategy where the presence of a desired cellular phenotype must be determined. While morphometry has transitioned from observational grading to computational quantification, established quantitative methods eliminate information by relying on two-dimensional (2D) analysis to describe three-dimensional (3D) niches. In this study, we demonstrate the validity and utility of 3D morphological quantification using two common angiogenesis assays in our fibrin-based in vitro model: (1) the microcarrier bead assay with human mesenchymal stem cells and (2) the rat aortic ring outgrowth assay. The quantification method is based on collecting and segmenting fluorescent confocal z-stacks into 3D models with 3D Slicer, an open-source magnetic resonance imaging/computed tomography analysis program. Data from 3D models are then processed into biologically relevant metrics in MATLAB for statistical analysis. Metrics include descriptive parameters such as vascular network length, volume, number of network segments, and degree of network branching. Our results indicate that 2D measures are significantly different than their 3D counterparts unless the vascular network exhibits anisotropic growth along the plane of imaging. Additionally, the statistical outcomes of 3D morphological quantification agreed with our initial qualitative observations among different test groups. This novel quantification approach generates more spatially accurate and objective measures, representing an important step toward improving the reliability of morphological comparisons.