Tun, Win MinPoologasundarampillai, GowsihanBischof, H.Nye, GarethKing, Oliver N. F.Basham, MarkTokudome, Y.Lewis, Rohan M.Johnstone, E. D.Brownbill, PaulDarrow, MicheleChernyavsky, Igor2021-06-022021-06-022021-06-022021-02-16Tun W. M., Poologasundarampillai G., Bischof H., Nye G., King O. N. F., Basham M., Tokudome Y., Lewis R. M., Johnstone E. D., Brownbill P., Darrow M. and Chernyavsky I. L. (2021). A massively multi-scale approach to characterizing tissue architecture by synchrotron micro-CT applied to the human placentaJ. Journal of the Royal Society Interface, 18(179), 20210140 http://doi.org/10.1098/rsif.2021.014010.1098/rsif.2021.0140http://hdl.handle.net/10034/624809From The Royal Society via Jisc Publications RouterHistory: received 2021-02-16, accepted 2021-05-06, collection 2021-06, pub-electronic 2021-06-02Article version: VoRPublication status: PublishedFunder: Engineering and Physical Sciences Research Council; Id: http://dx.doi.org/10.13039/501100000266; Grant(s): EP/M023877/1, EP/T008725/1Funder: Medical Research Council; Id: http://dx.doi.org/10.13039/501100000265; Grant(s): MR/N011538/1Funder: Wellcome Trust; Id: http://dx.doi.org/10.13039/100004440; Grant(s): 212980/Z/18/ZFunder: Great Britain Sasakawa Foundation; Id: http://dx.doi.org/10.13039/501100000625Multi-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.Licence for VoR version of this article: http://creativecommons.org/licenses/by/4.0/Life Sciences–Engineering interfaceResearch articleshuman placentacomputed tomographycontrast agentmachine-learning segmentationflow networkspatial statisticsarticle2021-06-02https://royalsocietypublishing.org/doi/10.1098/rsif.2021.0140