Decellularized scaffolds can serve as an excellent three-dimensional environment for cell repopulation. They maintain tissue-specific microarchitecture of extracellular matrix proteins with important spatial cues for cell adhesion, migration, growth, and differentiation. However, criteria for quality assessment of the three-dimensional structure of decellularized scaffolds are rather fragmented, usually study-specific, and mostly semi-quantitative. Thus, we aimed to develop a robust structural assessment system for decellularized porcine liver scaffolds. Five scaffolds of different quality were used to establish the new evaluation system. We combined conventional semi-quantitative scoring criteria with a quantitative scaffold evaluation based on automated image analysis. For the quantitation, we developed a specific open source software tool (ScaffAn) applying algorithms designed for texture analysis, segmentation, and skeletonization. ScaffAn calculates selected parameters characterizing structural features of porcine liver scaffolds such as the sinusoidal network. After evaluating individual scaffolds, the total scores predicted scaffold interaction with cells in terms of cell adhesion. Higher scores corresponded to higher numbers of cells attached to the scaffolds. Moreover, our analysis revealed that the conventional system could not identify fine differences between good quality scaffolds while the additional use of ScaffAn allowed discrimination. This led us to the conclusion that only using the combined score resulted in the best discrimination between different quality scaffolds. Overall, our newly defined evaluation system has the potential to select the liver scaffolds most suitable for recellularization, and can represent a step toward better success in liver tissue engineering.

脱细胞支架可以作为细胞再增殖的良好三维环境。它们维持细胞外基质蛋白的组织特异性微结构，并为细胞粘附、迁移、生长和分化提供重要的空间线索。然而，脱细胞支架三维结构的质量评估标准相当分散，通常是特定于研究的，并且大多是半定量的。因此，我们的目标是为脱细胞猪肝脏支架开发强大的结构评估系统。使用五个不同质量的支架来建立新的评估体系。我们将传统的半定量评分标准与基于自动图像分析的定量支架评估相结合。为了进行定量，我们开发了一个特定的开源软件工具 (ScaffAn)，应用为纹理分析、分割和骨架化设计的算法。 ScaffAn 计算表征猪肝脏支架结构特征的选定参数，例如正弦网络。在评估各个支架后，总分预测了支架与细胞在细胞粘附方面的相互作用。分数越高对应于支架上附着的细胞数量越多。此外，我们的分析表明，传统系统无法识别优质支架之间的细微差异，而额外使用 ScaffAn 则可以进行区分。这使我们得出这样的结论：仅使用综合评分才能最好地区分不同质量的支架。 总体而言，我们新定义的评估系统有可能选择最适合再细胞化的肝脏支架，并且可以代表肝脏组织工程取得更大成功的一步。