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Related publication: Strotton MC & Bodey AJ (joint first authors), Wanelik K, Darrow MC, Medina E, Hobbs C, Rau C, Bradbury EJ. Optimising complementary soft tissue synchrotron X-ray microtomography for reversibly-stained central nervous system samples. Scientific Reports 8, 12017 (2018). DOI: 10.1038/s41598-018-30520-8.
Complementary techniques that can maximise information derived from precious soft tissue samples are extremely valuable. A team from the Wolfson Centre for Age Related Diseases wanted to use 3D imaging to track how disease spreads through spinal tissue, followed by traditional 2D histology to probe the regions of interest identified in these scans at the molecular level. However, most 3D imaging methodologies require tissue preparation that prevents its subsequent use, or are too slow to practically image large sample sizes at a useful spatial resolution.
Histology is a widely-adopted technique in life science research that uses tissue staining and microscopy to address a multitude of questions in thin tissue slices. It can reveal the anatomical organisation of tissues, diagnose biopsy samples in the clinic, or probe molecular markers to reveal cell subtypes, subcellular features, and cell activation states. Studying different markers in adjacent tissue sections is a powerful approach to obtain multi-layered information from individual tissues, but these 2D snapshots of 3D tissues can risk overlooking key features. The desire to track 3D features such as a branching vasculature, or long-range neuronal projections and pathways across the nervous system, has spurred the development of novel 3D imaging technologies that are up to the task. In the last decade, the combination of fluorescent markers with tissue clearing and light-sheet microscopy has enabled imaging of tagged features of interest at cellular resolution across whole organs1, while more established technologies like magnetic resonance imaging show ever-improving spatial and temporal resolution. However, these approaches have notable disadvantages. The former is typically a ‘one-shot’ approach that limits subsequent use of tissue, while the latter suffers from extensive acquisition times (over 10 hours for high resolution of thin tissue slices) that prohibit imaging large sample sizes. It is desirable to extract maximal information from precious tissues by adopting methods that enable high-resolution imaging, whilst also preserving tissue integrity for subsequent use. Synchrotron Micro- Computed Tomography (SR-μCT) offers the potential to rapidly derive high-resolution 3D information from biological specimens, which could then be probed with standard 2D histology techniques.
Computed tomography reconstructs a 3D volume from a series of 2D images (projections) collected during sample (or detector) rotation across a range of equally spaced angles. The bright, coherent X-ray beam provided by a synchrotron enables short exposures (micro-millisecond) per 2D image acquisition, summating to rapid 3D imaging. Samples must not drift or deform during data acquisition (2D image collection), as this will compromise the final quality of tomography data. This is a problem for soft tissues, which lack the physical stability that is inherent to those sample types (e.g. bones, metals, and rocks) commonly probed by X-ray imaging. This study began by comparing various embedding agents used in standard histology procedures to confer tissue stability. Paraffin wax was identified as optimal for conferring sufficient SR-μCT stability to rodent spinal cord soft tissue, while retaining the viability of samples for processing and probing with subsequent histology.
Along with optimising sample preparation to make soft tissue SR-μCT compatible with histology, this study outlined a single scan, iterative downsampling methodology that informs efficient SR-μCT data collection more generally. This approach collects a single scan with an excessive number of 2D projections, then compares the image quality of reconstructions made from the full range, and an ever-smaller subset, of this 2D projection set. Image quality improvements delivered by increasing projection numbers can then be quantified, in this instance revealing how to deliver near-maximum signal-to-noise while keeping data acquisition times below just 12 minutes. Even with these rapid scan times, fine tissue features could still be identified across the tomographic volume, as shown by their volume extraction (Fig. 3) with the open source, semi-automated, shallow machine learning 3D segmentation software SuRVoS (Super-Region Volume Segmentation), developed at Diamond4.
References:
Funding acknowledgement:
King’s Bioscience Institute; Guy’s and St Thomas’ Charity Prize PhD Programme in Biomedical and Translational Science; Medical Research Council UK (SNCF Award G1002055).
Corresponding authors:
Dr Merrick Strotton, Wolfson Centre for Age Related Diseases, King’s College London, merrick.strotton@kcl.ac.uk and Dr Andrew Bodey, Diamond Light Source.
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