The University of Southampton
University of Southampton Institutional Repository
Warning ePrints Soton is experiencing an issue with some file downloads not being available. We are working hard to fix this. Please bear with us.

Mapping 3D networks in human lung tissue using micro-computed tomography and immunofluorescence

Mapping 3D networks in human lung tissue using micro-computed tomography and immunofluorescence
Mapping 3D networks in human lung tissue using micro-computed tomography and immunofluorescence
Micro-computed tomography (µCT) is a non-destructive imaging technique that can reveal the 3D lung microstructure. 3D networks in µCT images are generally identified and segmented by manually tracing their outline, which is very time consuming and requires specialist knowledge of the tissue. We devised a new method to segment 3D networks and specific cell types semi-automatically by correlating µCT imaging with immunofluorescence microscopy. Using a prototype µCT system optimised for unstained soft tissues (Nikon Metrology, UK) we imaged unstained formalin-fixed paraffin-embedded human lung tissue at a voxel (3D pixel) size of 6-10 µm. The tissue was then sectioned and specific immunofluorescence staining performed at 20 µm intervals with primary antibodies for CK18 (airway epithelium) and CD68 (macrophages). Fluorescence microscopy images were digitised and registered to the µCT data. The blood vessel network was semi-automatically segmented using the µCT data and a region growing tool in the open source itk-SNAP software package (http://www.itksnap.org). Immunofluorescence staining was successfully distinguished from the background autofluorescence on paraffin-embedded lung tissue by using a far-red (>650 nm) emission secondary antibody. The autofluorescence was used to align the two-dimensional (2D) fluorescence microscopy to the three-dimensional (3D) µCT images using the BigWarp plugin in ImageJ (https://imagej.net/BigWarp). The aligned immunofluorescence images indicated the specific location of airway epithelium in the 3D lung volume and were used to semi-automatically segment the networks and cells in the µCT. Gaps in the 3D network between the immunofluorescently stained sections were filled by digital interpolation guided by the µCT data using itk-SNAP. The segmented 3D network of blood vessels and airways can then be further related to the location of immune cells (macrophages). In summary, correlation of 2D immunofluorescence and 3D µCT data permits localisation and segmentation of 3D lung networks and individual cell types in fixed human lung tissue. This novel correlative workflow allows for accurate, specific, and faster 3D network segmentation of human soft tissue.
Lawson, Matthew John
5c14101b-e305-463d-97fd-d38b0000c3d6
Katsamenis, Orestis L.
8553e7c3-d860-4b7a-a883-abf6c0c4b438
Olding, Micheal
1cf20488-68c0-4ef7-8cf1-943b697d6741
Larkin, Oliver
5bde48ce-ef5d-43fa-998d-f38800240514
Smit, Bennie
83a5f31d-f138-4c1c-b440-ce912a6a3eb6
Haig, Ian
8c3dc208-c92b-4229-ab67-08dda8b45d64
Schneider, Philipp
a810f925-4808-44e4-8a4a-a51586f9d7ad
Lackie, Peter
4afbbe1a-22a6-4ceb-8cad-f3696dc43a7a
Warner, Jane
8571b049-31bb-4a2a-a3c7-4184be20fe25
Lawson, Matthew John
5c14101b-e305-463d-97fd-d38b0000c3d6
Katsamenis, Orestis L.
8553e7c3-d860-4b7a-a883-abf6c0c4b438
Olding, Micheal
1cf20488-68c0-4ef7-8cf1-943b697d6741
Larkin, Oliver
5bde48ce-ef5d-43fa-998d-f38800240514
Smit, Bennie
83a5f31d-f138-4c1c-b440-ce912a6a3eb6
Haig, Ian
8c3dc208-c92b-4229-ab67-08dda8b45d64
Schneider, Philipp
a810f925-4808-44e4-8a4a-a51586f9d7ad
Lackie, Peter
4afbbe1a-22a6-4ceb-8cad-f3696dc43a7a
Warner, Jane
8571b049-31bb-4a2a-a3c7-4184be20fe25

Lawson, Matthew John, Katsamenis, Orestis L., Olding, Micheal, Larkin, Oliver, Smit, Bennie, Haig, Ian, Schneider, Philipp, Lackie, Peter and Warner, Jane (2018) Mapping 3D networks in human lung tissue using micro-computed tomography and immunofluorescence. 6th annual Tomography for Scientific Advancement (ToScA) symposium, University of Warwick, Warwick, United Kingdom. 10 - 12 Sep 2018.

Record type: Conference or Workshop Item (Other)

Abstract

Micro-computed tomography (µCT) is a non-destructive imaging technique that can reveal the 3D lung microstructure. 3D networks in µCT images are generally identified and segmented by manually tracing their outline, which is very time consuming and requires specialist knowledge of the tissue. We devised a new method to segment 3D networks and specific cell types semi-automatically by correlating µCT imaging with immunofluorescence microscopy. Using a prototype µCT system optimised for unstained soft tissues (Nikon Metrology, UK) we imaged unstained formalin-fixed paraffin-embedded human lung tissue at a voxel (3D pixel) size of 6-10 µm. The tissue was then sectioned and specific immunofluorescence staining performed at 20 µm intervals with primary antibodies for CK18 (airway epithelium) and CD68 (macrophages). Fluorescence microscopy images were digitised and registered to the µCT data. The blood vessel network was semi-automatically segmented using the µCT data and a region growing tool in the open source itk-SNAP software package (http://www.itksnap.org). Immunofluorescence staining was successfully distinguished from the background autofluorescence on paraffin-embedded lung tissue by using a far-red (>650 nm) emission secondary antibody. The autofluorescence was used to align the two-dimensional (2D) fluorescence microscopy to the three-dimensional (3D) µCT images using the BigWarp plugin in ImageJ (https://imagej.net/BigWarp). The aligned immunofluorescence images indicated the specific location of airway epithelium in the 3D lung volume and were used to semi-automatically segment the networks and cells in the µCT. Gaps in the 3D network between the immunofluorescently stained sections were filled by digital interpolation guided by the µCT data using itk-SNAP. The segmented 3D network of blood vessels and airways can then be further related to the location of immune cells (macrophages). In summary, correlation of 2D immunofluorescence and 3D µCT data permits localisation and segmentation of 3D lung networks and individual cell types in fixed human lung tissue. This novel correlative workflow allows for accurate, specific, and faster 3D network segmentation of human soft tissue.

This record has no associated files available for download.

More information

Published date: 10 September 2018
Venue - Dates: 6th annual Tomography for Scientific Advancement (ToScA) symposium, University of Warwick, Warwick, United Kingdom, 2018-09-10 - 2018-09-12

Identifiers

Local EPrints ID: 448225
URI: http://eprints.soton.ac.uk/id/eprint/448225
PURE UUID: 15d60a5b-8f3e-4dce-bf9e-1b69eaed3f07
ORCID for Matthew John Lawson: ORCID iD orcid.org/0000-0003-0115-1698
ORCID for Orestis L. Katsamenis: ORCID iD orcid.org/0000-0003-4367-4147
ORCID for Philipp Schneider: ORCID iD orcid.org/0000-0001-7499-3576
ORCID for Peter Lackie: ORCID iD orcid.org/0000-0001-7138-3764

Catalogue record

Date deposited: 15 Apr 2021 16:33
Last modified: 16 Apr 2021 01:53

Export record

Contributors

Author: Matthew John Lawson ORCID iD
Author: Micheal Olding
Author: Oliver Larkin
Author: Bennie Smit
Author: Ian Haig
Author: Peter Lackie ORCID iD
Author: Jane Warner

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×