Predicting bulk mechanical properties of cellularized collagen gels using multiphoton microscopy

使用多光子显微镜预测细胞胶原凝胶的体积机械性能

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作者:C B Raub, A J Putnam, B J Tromberg, S C George

Abstract

Cellularized collagen gels are a common model in tissue engineering, but the relationship between the microstructure and bulk mechanical properties is only partially understood. Multiphoton microscopy (MPM) is an ideal non-invasive tool for examining collagen microstructure, cellularity and crosslink content in these gels. In order to identify robust image parameters that characterize microstructural determinants of the bulk elastic modulus, we performed serial MPM and mechanical tests on acellular and cellularized (normal human lung fibroblasts) collagen hydrogels, before and after glutaraldehyde crosslinking. Following gel contraction over 16 days, cellularized collagen gel content approached that of native connective tissues (∼200 mg ml⁻¹). Young's modulus (E) measurements from acellular collagen gels (range 0.5-12 kPa) exhibited a power-law concentration dependence (range 3-9 mg ml⁻¹) with exponents from 2.1 to 2.2, similar to other semiflexible biopolymer networks such as fibrin and actin. In contrast, cellularized collagen gel stiffness (range 0.5-27 kPa) produced concentration-dependent exponents of 0.7 uncrosslinked and 1.1 crosslinked (range ∼5-200 mg ml⁻¹). The variation in E of cellularized collagen hydrogels can be explained by a power-law dependence on robust image parameters: either the second harmonic generation (SHG) and two-photon fluorescence (TPF) (matrix component) skewness (R²=0.75, exponents of -1.0 and -0.6, respectively); or alternatively the SHG and TPF (matrix component) speckle contrast (R²=0.83, exponents of -0.7 and -1.8, respectively). Image parameters based on the cellular component of TPF signal did not improve the fits. The concentration dependence of E suggests enhanced stress relaxation in cellularized vs. acellular gels. SHG and TPF image skewness and speckle contrast from cellularized collagen gels can predict E by capturing mechanically relevant information on collagen fiber, cell and crosslink density.

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