Adding a new dimension to soft tissue imaging through correlative 2D thin-section & 3D X-ray histology

Abstract

Histology is the study of the microscopic structure of tissues such as lung, skin or brain. Classical histology techniques developed in the 18th century examine thin tissue sections under the microscope. These methods have underpinned many discoveries in modern biology and are still widely used today in biomedical research and the medical diagnosis of disease. However, classical histology only provides a 2 dimensional view of the 3 dimensional (3D) tissue features.
X-ray micro-computed tomography (μCT) is non-destructive and generates 3D images of biological structures in the millimetre down to micron (1/1000 mm) scale, approaching that of standard light microscopy. Examples of biological structures in this range include capillaries, lung alveoli and even whole organs of small animals. As μCT imaging is non-invasive and does not destroy the sample, the same specimens can subsequently be imaged in other ways, combining the benefits of several techniques to provide a comprehensive understanding of tissue microstructure in 3D. For example, having a 3D μCT image of a specimen before it is cut into thin sections allows specific regions of the
specimen to be targeted for further analysis. This ensures that relevant portions of the specimen are studied in greater detail and reduces the chances of missing the feature of interest in the specimen. This project aims to develop a method for correlative μCT and classical histology which is compatible with routine clinical practice for processing soft tissue biopsies. This method should be applicable to a range of soft tissues and provide good quality images of the entire specimen within a short timeframe of a few days. To this end, each stage of the workflow was analysed to identify potential issues and devise robust solutions.
Firstly, μCT scanning of routinely processed soft tissue specimens is performed to obtain a 3D image of the specimen. The optimum μCT imaging protocol is determined objectively using a semi-automated software tool for measuring image quality. Next, thin histology sections are cut and stained according to standard protocols. The sections are digitised and aligned to the 3D μCT image, which corrects for any distortions occurring during classical histology processing. The 2D histology sections are then viewed in the
context of the 3D μCT image with a custom-built intuitive interface.
This correlative imaging method extends classical histological techniques developed over hundreds of years with modern 3D imaging technology. It is anticipated that the method will facilitate imaging studies on a previously infeasible scale by collecting information about whole specimens in 3D, adding an entirely new dimension to our understanding of biological structure.

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Identifiers

ORCID for Peter Lackie: orcid.org/0000-0001-7138-3764

ORCID for Orestis L. Katsamenis: orcid.org/0000-0003-4367-4147

ORCID for Philipp Schneider: orcid.org/0000-0001-7499-3576

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