Multiscale analysis of collagen microstructure with generalized image correlation spectroscopy and the detection of tissue prestress

Prestress in tissue is currently detected through destructive methods which obviate both invivo and longitudinal assessment. We hypothesized that prestress could be detected and quantified by analyzing the microstructure of the extracellular matrix at different spatial scales using non-invasive and non-destructive optical imaging. A simple model of tissue prestress was created using fibroblast-mediated contraction of collagen gels around a central mandrel. Using a quantitative, multiscale, image processing technique, termed generalized image correlation spectroscopy (GICS) of second harmonic images, collagen fiber number and alignment at three different length scales characteristic of the collagen fibril, collagen fiber, and cell were analyzed. GICS fiber alignment (σ_maj/min) was significantly different across load state, level of prestress, and length scale. The largest fiber ratio, and thus highest alignment, was seen in prestressed, externally loaded gels at a length scale equivalent to the size of the fibroblast cells. Alignment at both fiber and cell scale correlated with prestress in this model. We conclude that GICS of second harmonic images of collagen can predict prestress, and that microstructural organization at the collagen fiber and cell scale are the primary determinants of prestress in cellularized collagen gels.