Bridging biological and preclinical imaging through 3D X-ray histology
Bridging biological and preclinical imaging through 3D X-ray histology
Living structures are an intricate three- dimensional (3D) arrangement of cells and tissue matrix across many length scales. However, structural analysis of tissues, whether for research or diagnostic purposes, remains overwhelmingly bounded and constrained by microscopic examination of relatively sparse 2D tissue sections, providing only a snapshot from which 3D spatial relationships can only be inferred. Therefore, whilst 3D medical imaging is commonplace, microscopic tissue structure analysis (i.e., histology) remains overwhelmingly wedded to 200-year-old practices of microscopic 2D examination of tissue sections. We have demonstrated previously that X-ray imaging by micro-computed tomography (µCT) allows noninvasive 3D imaging of the microstructure of standard tissue biosies (Scott et al. 2015, doi:10.1371/journal.pone.0126230). This yields details comparable to two-dimensional (2D) optical microscope sections but for the whole tissue volume, which can for example overturn misconceptions of disease development based on 2D assessment. One exemplar is the pathogenesis of idiopathic pulmonary fibrosis (Jones et al. 2016, doi:10.1172/jci.insight.86375), where 3D structural insight into colocalisation of tissue features suggested previously unrecognised fibroblast foci plasticity. Based on this encouraging µCT results for soft tissues, in collaboration with an industrial partner, we developed a custom-design and soft-tissue optimised µCT scanner that can bridge the gap between biological and preclinical imaging (Katsamenis et al., doi:10.1016/j.ajpath.2019.05.004). Currently, we are establishing the foundations for routine 3D X-ray histology (http://www.xrayhistology.org), including new X-ray equipment and standardised & automated workflows and augmented sample throughput. Applicable to vast existing sample archives and a wide range of soft tissue types, the technology will open new research areas, such as large-scale 3D histological phenotyping (i.e., histomics). Computing and data handling power is now more than capable of handling the image resolutions and processing required for 3D µCT data analysis and X-ray histology workflows. Furthermore, 3D X-ray histology can translate directly into next-generation clinical image-based diagnostics and patient stratification using artificial intelligence and deep learning, and time-critical intraoperative 3D examination of tissue biopsies will become a realistic future target in this research programme. Here, we will present first results of our 3D X-ray histology approach and portray a vision, how highthroughput and non-destructive 3D histological assessment can offer new opportunities in basic biology, biomedical and translational research.
Schneider, P.
ca82320e-1f19-4c91-ada2-1c5a3cb2bd47
1 November 2019
Schneider, P.
ca82320e-1f19-4c91-ada2-1c5a3cb2bd47
Schneider, P.
(2019)
Bridging biological and preclinical imaging through 3D X-ray histology.
In COMULIS BioImaging Austria/CMI Conference.
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Conference or Workshop Item
(Paper)
Abstract
Living structures are an intricate three- dimensional (3D) arrangement of cells and tissue matrix across many length scales. However, structural analysis of tissues, whether for research or diagnostic purposes, remains overwhelmingly bounded and constrained by microscopic examination of relatively sparse 2D tissue sections, providing only a snapshot from which 3D spatial relationships can only be inferred. Therefore, whilst 3D medical imaging is commonplace, microscopic tissue structure analysis (i.e., histology) remains overwhelmingly wedded to 200-year-old practices of microscopic 2D examination of tissue sections. We have demonstrated previously that X-ray imaging by micro-computed tomography (µCT) allows noninvasive 3D imaging of the microstructure of standard tissue biosies (Scott et al. 2015, doi:10.1371/journal.pone.0126230). This yields details comparable to two-dimensional (2D) optical microscope sections but for the whole tissue volume, which can for example overturn misconceptions of disease development based on 2D assessment. One exemplar is the pathogenesis of idiopathic pulmonary fibrosis (Jones et al. 2016, doi:10.1172/jci.insight.86375), where 3D structural insight into colocalisation of tissue features suggested previously unrecognised fibroblast foci plasticity. Based on this encouraging µCT results for soft tissues, in collaboration with an industrial partner, we developed a custom-design and soft-tissue optimised µCT scanner that can bridge the gap between biological and preclinical imaging (Katsamenis et al., doi:10.1016/j.ajpath.2019.05.004). Currently, we are establishing the foundations for routine 3D X-ray histology (http://www.xrayhistology.org), including new X-ray equipment and standardised & automated workflows and augmented sample throughput. Applicable to vast existing sample archives and a wide range of soft tissue types, the technology will open new research areas, such as large-scale 3D histological phenotyping (i.e., histomics). Computing and data handling power is now more than capable of handling the image resolutions and processing required for 3D µCT data analysis and X-ray histology workflows. Furthermore, 3D X-ray histology can translate directly into next-generation clinical image-based diagnostics and patient stratification using artificial intelligence and deep learning, and time-critical intraoperative 3D examination of tissue biopsies will become a realistic future target in this research programme. Here, we will present first results of our 3D X-ray histology approach and portray a vision, how highthroughput and non-destructive 3D histological assessment can offer new opportunities in basic biology, biomedical and translational research.
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Published date: 1 November 2019
Additional Information:
2019, “Bridging biological and preclinical imaging through 3D X-ray histology”, COMULIS BioImaging Austria/CMI Work Group Meetings Conference, Vienna, Austria, November 20-22 (2019) https://www.comulis.eu/comulis-conference-vienna https://www.comulis.eu/s/Abstractbook.pdf
Venue - Dates:
Conference on Multimodality Imaging in Life Sciences, Natural History Museum Vienna, Vienna, Austria, 2019-11-21 - 2019-11-22
Identifiers
Local EPrints ID: 448248
URI: http://eprints.soton.ac.uk/id/eprint/448248
PURE UUID: a771b171-244d-4e22-b557-7c24a48ccce5
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Date deposited: 16 Apr 2021 16:30
Last modified: 16 Mar 2024 11:50
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Author:
P. Schneider
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