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

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.

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.

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Contributors

Author: Matthew John Lawson

Author: Orestis L. Katsamenis

Author: Philipp Schneider

Author: Peter Lackie

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