A massively multi-scale approach to characterizing tissue architecture by synchrotron micro-CT applied to the human placenta.
A massively multi-scale approach to characterizing tissue architecture by synchrotron micro-CT applied to the human placenta.
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DOI：
10.1098/rsif.2021.0140
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发表时间：
2021-06
期刊：
影响因子：
--
通讯作者：
Chernyavsky IL
中科院分区：
文献类型：
--
作者：
Tun WM;Poologasundarampillai G;Bischof H;Nye G;King ONF;Basham M;Tokudome Y;Lewis RM;Johnstone ED;Brownbill P;Darrow M;Chernyavsky IL
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，先进的成像和强大的，经过验证的机器学习分割技术，以提供第一个大规模的多尺度和多领域信息，从而实现对母体和胎儿胎盘领域的详细形态和功能分析。最后，我们量化了异质胎盘组织形态学指标的尺度相关误差，估计了提取有意义的生物数据所需的最小组织尺度。开发的协议是有益的高通量调查的结构-功能的关系，在正常和患病的胎盘，使我们能够优化病理妊娠的治疗方法。此外，所提出的方法适用于表征与胎盘相似的其他复杂器官的组织结构和生理行为，其中交换屏障具有循环血管和无血管流体空间。
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.
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影响因子：
4.4
作者：
Erlich, Alexander;Nye, Gareth A.;Chernyavsky, Igor L.
通讯作者：
Chernyavsky, Igor L.
DOI：
10.1177/2472555216682725
发表时间：
2017-03
期刊：
SLAS discovery : advancing life sciences R & D
影响因子：
--
作者：
Gough A;Stern AM;Maier J;Lezon T;Shun TY;Chennubhotla C;Schurdak ME;Haney SA;Taylor DL
通讯作者：
Taylor DL
影响因子：
3.8
作者：
Aughwane, R.;Schaaf, C.;David, A. L.
通讯作者：
David, A. L.
影响因子：
8.6
作者：
Gash, AE;Tillotson, TM;Simpson, RL
通讯作者：
Simpson, RL
影响因子：
3.8
作者：
Burton, G. J.;Woods, A. W.;Jauniaux, E.;Kingdom, J. C. P.
通讯作者：
Kingdom, J. C. P.