Multi-scale structural assessment of biological soft tissue is challenging but essential to gain insight into structure–function relationships of tissue/organ. Using the human placenta as an example, this study brings together sophisticated sample preparation protocols, advanced imaging and robust, validated machine-learning segmentation techniques to provide the first massively multi-scale and multi-domain information that enables detailed morphological and functional analyses of both maternal and fetal placental domains. Finally, we quantify the scale-dependent error in morphological metrics of heterogeneous placental tissue, estimating the minimal tissue scale needed in extracting meaningful biological data. The developed protocol is beneficial for high-throughput investigation of structure–function relationships in both normal and diseased placentas, allowing us to optimize therapeutic approaches for pathological pregnancies. In addition, the methodology presented is applicable in the characterization of tissue architecture and physiological behaviours of other complex organs with similarity to the placenta, where an exchange barrier possesses circulating vascular and avascular fluid spaces.

生物软组织的多尺度结构评估具有挑战性，但对于深入了解组织/器官的结构-功能关系至关重要。以人类胎盘为例，这项研究汇集了复杂的样品制备方案、先进的成像技术和稳健的、经过验证的机器学习分割技术，以提供第一个大规模多尺度和多领域信息，从而能够对两者进行详细的形态学和功能分析母体和胎儿胎盘区域。最后，我们量化异质胎盘组织形态学指标中的尺度相关误差，估计提取有意义的生物数据所需的最小组织尺度。所开发的协议有利于对正常和患病胎盘中结构-功能关系的高通量研究，使我们能够优化病理性妊娠的治疗方法。此外，所提出的方法适用于与胎盘相似的其他复杂器官的组织结构和生理行为的表征，其中交换屏障具有循环血管和无血管流体空间。