Fetal growth restriction (FGR) affects 5–10% of pregnancies, leading to clinically significant fetal morbidity and mortality. FGR placentae frequently exhibit poor vascular branching, but the mechanisms driving this are poorly understood. We hypothesize that vascular structural malformation at the organ level alters microvascular shear stress, impairing angiogenesis. A computational model of placental vasculature predicted elevated placental micro-vascular shear stress in FGR placentae (0.2 Pa in severe FGR vs 0.05 Pa in normal placentae). Endothelial cells cultured under predicted FGR shear stresses migrated significantly slower and with greater persistence than in shear stresses predicted in normal placentae. These cell behaviors suggest a dominance of vessel elongation over branching. Taken together, these results suggest (1) poor vascular development increases vessel shear stress, (2) increased shear stress induces cell behaviors that impair capillary branching angiogenesis, and (3) impaired branching angiogenesis continues to drive elevated shear stress, jeopardizing further vascular formation. Inadequate vascular branching early in gestation could kick off this cyclic loop and continue to negatively impact placental angiogenesis throughout gestation.

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.

When trophoblasts migrate and invade in vivo, they do so by interacting with a range of other cell types, extracellular matrix proteins, chemotactic factors and physical forces such as fluid shear stress. These factors combine to influence overall trophoblast migration and invasion into the decidua, which in turn determines the success of spiral artery remodeling, and pregnancy itself. Our understanding of these important but complex processes is limited by the simplified conditions in which we often study cell migration in vitro, and many discrepancies are observed between different in vitro models in the literature. On top of these experimental considerations, the migration of individual trophoblasts can vary widely. While time-lapse microscopy provides a wealth of information on trophoblast migration, manual tracking of individual cell migration is a time consuming task that ultimately restricts the numbers of cells quantified, and thus the ability to extract meaningful information from the data. However, the development of automated imaging algorithms is likely to aid our ability to accurately interpret trophoblast migration in vitro, and better allow us to relate these observations to in vivo scenarios. This commentary discusses the advantages and disadvantages of techniques commonly used to quantify trophoblast migration and invasion, both from a cell biology and a mathematical perspective, and examines how such techniques could be improved to help us relate trophoblast migration more accurately to in vivo function in the future.

As sample preparation and imaging techniques have expanded and improved to include a variety of options for larger sized and numbers of samples, the bottleneck in volumetric imaging is now data analysis. Annotation and segmentation are both common, yet difficult, data analysis tasks which are required to bring meaning to the volumetric data. The SuRVoS application has been updated and redesigned to provide access to both manual and machine learning-based segmentation and annotation techniques, including support for crowd sourced data. Combining adjacent, similar voxels (supervoxels) provides a mechanism for speeding up segmentation both in the painting of annotation and by training a segmentation model on a small amount of annotation. The support for layers allows multiple datasets to be viewed and annotated together which, for example, enables the use of correlative data (e.g. crowd-sourced annotations or secondary imaging techniques) to guide segmentation. The ability to work with larger data on high-performance servers with GPUs has been added through a client-server architecture and the Pytorch-based image processing and segmentation server is flexible and extensible, and allows the implementation of deep learning-based segmentation modules. The client side has been built around Napari allowing integration of SuRVoS into an ecosystem for open-source image analysis while the server side has been built with cloud computing and extensibility through plugins in mind. Together these improvements to SuRVoS provide a platform for accelerating the annotation and segmentation of volumetric and correlative imaging data across modalities and scales.

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 optimise therapeutic approaches for pathological pregnancies. In addition, the methodology presented is applicable in characterisation 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.SummaryUsing the human placenta as an example, this study brings together sophisticated sample preparation protocols, advanced 3D X-ray imaging, and robust, validated machine-learning segmentation techniques to provide massively multi-scale and multi-domain insights on vascular-rich organ morphology and function.