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Dataset for X-ray micro-computed tomography for non-destructive 3D X-ray histology

Dataset for X-ray micro-computed tomography for non-destructive 3D X-ray histology
Dataset for X-ray micro-computed tomography for non-destructive 3D X-ray histology
Datasets used for the study entitled 'X-ray micro-computed tomography for non-destructive 3D X-ray histology' byOrestis L. Katsamenis, Michael Olding, Jane A. Warner, David S. Chatelet, Mark G. Jones, Giacomo Sgalla, Bennie Smit, Oliver J. Larkin, Ian Haig, Luca Richeldi, Ian Sinclair, Peter M. Lackie, Philipp Schneider. The American Journal of PathologyAbstract for the paper Historically, micro-computed tomography has been considered unsuitable for histological analysis of unstained formalin-fixed and paraffin-embedded (FFPE) soft tissue biopsies due to a lack of image contrast between the tissue and the paraffin. However, we recently demonstrated that μCT can successfully resolve microstructural detail in routinely prepared tissue specimens. Here, we illustrate how μCT imaging of standard FFPE biopsies can be seamlessly integrated into conventional histology workflows, enabling non-destructive three-dimensional (3D) X-ray histology, the use and benefits of which we showcase for the exemplar of human lung biopsy specimens. This technology advancement was achieved through manufacturing a first-of-kind μCT scanner for X-ray histology and developing optimised imaging protocols, which do not require any additional sample preparation. 3D X-ray histology allows for non-destructive 3D imaging of tissue microstructure, resolving structural connectivity and heterogeneity of complex tissue networks, such as the vascular or the respiratory tract. We also demonstrate that 3D X-ray histology can yield consistent and reproducible image quality, enabling quantitative assessment of tissue’s 3D microstructures, which is inaccessible to conventional two-dimensional histology. Being non-destructive the technique does not interfere with histology workflows, permitting subsequent tissue characterisation by means of conventional light microscopy-based histology, immunohistochemistry, and immunofluorescence. 3D X-ray histology can be readily applied to a plethora of archival materials, yielding unprecedented opportunities in diagnosis and research of disease.
University of Southampton
Katsamenis, Orestis
8553e7c3-d860-4b7a-a883-abf6c0c4b438
Olding, Michael
4194199f-96d5-4c34-90d5-bab40adf06a3
Warner, Jane
8571b049-31bb-4a2a-a3c7-4184be20fe25
Chatelet, David
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Jones, Mark G.
119d23fa-c777-482a-8eb4-69d3bd499791
Sgalla, Giacomo
f7c37658-a00c-4b08-8dea-fa90b279de79
Smit, Bennie
83a5f31d-f138-4c1c-b440-ce912a6a3eb6
Larkin, Oliver
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Haig, Ian
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Richeldi, Luca
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Sinclair, Ian
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Lackie, Peter
4afbbe1a-22a6-4ceb-8cad-f3696dc43a7a
Schneider, Philipp
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Katsamenis, Orestis
8553e7c3-d860-4b7a-a883-abf6c0c4b438
Olding, Michael
4194199f-96d5-4c34-90d5-bab40adf06a3
Warner, Jane
8571b049-31bb-4a2a-a3c7-4184be20fe25
Chatelet, David
6371fd7a-e274-4738-9ccb-3dd4dab32928
Jones, Mark G.
119d23fa-c777-482a-8eb4-69d3bd499791
Sgalla, Giacomo
f7c37658-a00c-4b08-8dea-fa90b279de79
Smit, Bennie
83a5f31d-f138-4c1c-b440-ce912a6a3eb6
Larkin, Oliver
5bde48ce-ef5d-43fa-998d-f38800240514
Haig, Ian
8c3dc208-c92b-4229-ab67-08dda8b45d64
Richeldi, Luca
47177d9c-731a-49a1-9cc6-4ac8f6bbbf26
Sinclair, Ian
6005f6c1-f478-434e-a52d-d310c18ade0d
Lackie, Peter
4afbbe1a-22a6-4ceb-8cad-f3696dc43a7a
Schneider, Philipp
a810f925-4808-44e4-8a4a-a51586f9d7ad

Katsamenis, Orestis, Warner, Jane, Sinclair, Ian, Lackie, Peter and Schneider, Philipp (2019) Dataset for X-ray micro-computed tomography for non-destructive 3D X-ray histology. University of Southampton doi:10.5258/SOTON/D0902 [Dataset]

Record type: Dataset

Abstract

Datasets used for the study entitled 'X-ray micro-computed tomography for non-destructive 3D X-ray histology' byOrestis L. Katsamenis, Michael Olding, Jane A. Warner, David S. Chatelet, Mark G. Jones, Giacomo Sgalla, Bennie Smit, Oliver J. Larkin, Ian Haig, Luca Richeldi, Ian Sinclair, Peter M. Lackie, Philipp Schneider. The American Journal of PathologyAbstract for the paper Historically, micro-computed tomography has been considered unsuitable for histological analysis of unstained formalin-fixed and paraffin-embedded (FFPE) soft tissue biopsies due to a lack of image contrast between the tissue and the paraffin. However, we recently demonstrated that μCT can successfully resolve microstructural detail in routinely prepared tissue specimens. Here, we illustrate how μCT imaging of standard FFPE biopsies can be seamlessly integrated into conventional histology workflows, enabling non-destructive three-dimensional (3D) X-ray histology, the use and benefits of which we showcase for the exemplar of human lung biopsy specimens. This technology advancement was achieved through manufacturing a first-of-kind μCT scanner for X-ray histology and developing optimised imaging protocols, which do not require any additional sample preparation. 3D X-ray histology allows for non-destructive 3D imaging of tissue microstructure, resolving structural connectivity and heterogeneity of complex tissue networks, such as the vascular or the respiratory tract. We also demonstrate that 3D X-ray histology can yield consistent and reproducible image quality, enabling quantitative assessment of tissue’s 3D microstructures, which is inaccessible to conventional two-dimensional histology. Being non-destructive the technique does not interfere with histology workflows, permitting subsequent tissue characterisation by means of conventional light microscopy-based histology, immunohistochemistry, and immunofluorescence. 3D X-ray histology can be readily applied to a plethora of archival materials, yielding unprecedented opportunities in diagnosis and research of disease.

Text
D0902_README.txt - Text
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40_landmarks_backup.csv - Dataset
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CAL_SPST10024_InPlane_1566x950x360_16bit_MO_slice60.tif - Dataset
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Screenshot_2019_03_07_16.52.41.png - Dataset
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CAL_SPST10024_InPlane_1566x950x360_16bit_MO_slice60_scaledtoHisto.tif - Dataset
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SPST10024_15_OriginalHistology.tif - Dataset
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Info_for_Med3D_HPass_CAL_20161209_MEDX_1381_SPST10081_960x1800x418x16bit.txt - Dataset
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Info_for_Med3D_HPass_CAL_20170120_MEDX_1381_SPST10024_1566x950x360x16bit.txt - Dataset
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SPST10024_15_OriginalHistology.tif_xfm_0.tif - Dataset
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Med3D_HPass_CAL_20161209_MEDX_1381_SPST10081_960x1800x418x16bit.raw - Dataset
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Med3D_HPass_CAL_20170120_MEDX_1381_SPST10024_1566x950x360x16bit.raw - Dataset
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10024_Thickness_tissue_Stack_Tb.tif - Dataset
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10081_Thickness_Tissue_Stack_Tb.tif - Dataset
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More information

Published date: 26 April 2019

Identifiers

Local EPrints ID: 430601
URI: https://eprints.soton.ac.uk/id/eprint/430601
PURE UUID: dd8bacaf-e5a8-4998-985c-a818503f4b12
ORCID for Orestis Katsamenis: ORCID iD orcid.org/0000-0003-4367-4147
ORCID for Peter Lackie: ORCID iD orcid.org/0000-0001-7138-3764
ORCID for Philipp Schneider: ORCID iD orcid.org/0000-0001-7499-3576

Catalogue record

Date deposited: 03 May 2019 16:30
Last modified: 15 May 2019 00:37

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Contributors

UNSPECIFIED: Michael Olding
Creator: Jane Warner
UNSPECIFIED: David Chatelet
UNSPECIFIED: Mark G. Jones
UNSPECIFIED: Giacomo Sgalla
UNSPECIFIED: Bennie Smit
Other: Oliver Larkin
Other: Ian Haig
UNSPECIFIED: Luca Richeldi
Creator: Ian Sinclair
Creator: Peter Lackie ORCID iD

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