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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
A massively multi-scale approach to characterizing tissue architecture by synchrotron micro-CT applied to the human placenta

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.

computed tomography, contrast agent, flow network, human placenta, machine-learning segmentation, spatial statistics
1742-5689
Tun, W. M.
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Poologasundarampillai, G.
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Bischof, H.
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Nye, G.
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King, O. N.F.
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Basham, M.
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Tokudome, Y.
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Lewis, R. M.
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Johnstone, E. D.
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Brownbill, P.
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Darrow, M.
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Chernyavsky, I. L.
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Tun, W. M.
2cdcf0e2-442a-4d3e-919a-36ce7f9d8572
Poologasundarampillai, G.
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Bischof, H.
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Nye, G.
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King, O. N.F.
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Basham, M.
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Tokudome, Y.
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Lewis, R. M.
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Johnstone, E. D.
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Brownbill, P.
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Darrow, M.
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Chernyavsky, I. L.
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Tun, 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 placenta. Journal of the Royal Society Interface, 18 (179), [20210140]. (doi:10.1098/rsif.2021.0140).

Record type: Article

Abstract

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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rsif.2021.0140 - Version of Record
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More information

Accepted/In Press date: 6 May 2021
Published date: 2 June 2021
Additional Information: Publisher Copyright: © 2021 The Authors.
Keywords: computed tomography, contrast agent, flow network, human placenta, machine-learning segmentation, spatial statistics

Identifiers

Local EPrints ID: 455133
URI: http://eprints.soton.ac.uk/id/eprint/455133
ISSN: 1742-5689
PURE UUID: cc48cfb2-2273-48cb-bc3e-4deff0d658fc
ORCID for R. M. Lewis: ORCID iD orcid.org/0000-0003-4044-9104

Catalogue record

Date deposited: 10 Mar 2022 17:51
Last modified: 17 Mar 2024 02:53

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Contributors

Author: W. M. Tun
Author: G. Poologasundarampillai
Author: H. Bischof
Author: G. Nye
Author: O. N.F. King
Author: M. Basham
Author: Y. Tokudome
Author: R. M. Lewis ORCID iD
Author: E. D. Johnstone
Author: P. Brownbill
Author: M. Darrow
Author: I. L. Chernyavsky

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