Department of Biomedical Engineering, University of California Davis, Davis, CA, USA.
Department of Biomedical Engineering, University of California Irvine, Irvine, CA, USA.
J Tissue Eng Regen Med. 2019 Apr;13(4):637-648. doi: 10.1002/term.2824. Epub 2019 Mar 20.
Tissue engineers rely on expensive, time-consuming, and destructive techniques to monitor the composition, microstructure, and function of engineered tissue equivalents. A non-destructive solution to monitor tissue quality and maturation would greatly reduce costs and accelerate the development of tissue-engineered products. The objectives of this study were to (a) determine whether matrix stabilization with exogenous lysyl oxidase-like protein-2 (LOXL2) with recombinant hyaluronan and proteoglycan link protein-1 (LINK) would result in increased compressive and tensile properties in self-assembled articular cartilage constructs, (b) evaluate whether label-free, non-destructive fluorescence lifetime imaging (FLIm) could be used to infer changes in both biochemical composition and biomechanical properties, (c) form quantitative relationships between destructive and non-destructive measurements to determine whether the strength of these correlations is sufficient to replace destructive testing methods, and (d) determine whether support vector machine (SVM) learning can predict LOXL2-induced collagen crosslinking. The combination of exogenous LOXL2 and LINK proteins created a synergistic 4.9-fold increase in collagen crosslinking density and an 8.3-fold increase in tensile strength as compared with control (CTL). Compressive relaxation modulus was increased 5.9-fold with addition of LOXL2 and 3.4-fold with combined treatments over CTL. FLIm parameters had strong and significant correlations with tensile properties (R = 0.82; p < 0.001) and compressive properties (R = 0.59; p < 0.001). SVM learning based on FLIm-derived parameters was capable of automating tissue maturation assessment with a discriminant ability of 98.4%. These results showed marked improvements in mechanical properties with matrix stabilization and suggest that FLIm-based tools have great potential for the non-destructive assessment of tissue-engineered cartilage.
组织工程师依赖昂贵、耗时且具有破坏性的技术来监测工程化组织等效物的组成、微观结构和功能。一种用于监测组织质量和成熟度的非破坏性解决方案将大大降低成本并加速组织工程产品的开发。本研究的目的是:(a)确定用外源性赖氨酰氧化酶样蛋白 2 (LOXL2)与重组透明质酸和蛋白聚糖连接蛋白 1 (LINK)稳定基质是否会导致自组装关节软骨构建体的压缩和拉伸性能增加;(b)评估无标记、无损荧光寿命成像 (FLIm) 是否可用于推断生化组成和生物力学性能的变化;(c)形成破坏性和非破坏性测量之间的定量关系,以确定这些相关性的强度是否足以替代破坏性测试方法;(d)确定支持向量机 (SVM) 学习是否可以预测 LOXL2 诱导的胶原交联。与对照 (CTL) 相比,外源性 LOXL2 和 LINK 蛋白的组合使胶原交联密度增加了 4.9 倍,拉伸强度增加了 8.3 倍。添加 LOXL2 后,压缩松弛模量增加了 5.9 倍,联合处理后增加了 3.4 倍。FLIm 参数与拉伸性能(R = 0.82;p < 0.001)和压缩性能(R = 0.59;p < 0.001)具有很强的显著相关性。基于 FLIm 衍生参数的 SVM 学习能够以 98.4%的判别能力自动进行组织成熟度评估。这些结果表明,基质稳定可显著改善机械性能,并表明基于 FLIm 的工具在无损评估组织工程软骨方面具有很大的潜力。