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Katsamenis OL, Basford PJ, Robinson SK, Boardman RP, Konstantinopoulou E, Lackie PM, Page A, Ratnayaka JA, Goggin PM, Thomas GJ, Cox SJ, Sinclair I, Schneider P. A high-throughput 3D X-ray histology facility for biomedical research and preclinical applications. Wellcome Open Res 2023; 8:366. [PMID: 37928208 PMCID: PMC10620852 DOI: 10.12688/wellcomeopenres.19666.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/28/2023] [Indexed: 11/07/2023] Open
Abstract
Background The University of Southampton, in collaboration with the University Hospital Southampton (UHS) NHS Foundation Trust and industrial partners, has been at the forefront of developing three-dimensional (3D) imaging workflows using X-ray microfocus computed tomography (μCT) -based technology. This article presents the outcomes of these endeavours and highlights the distinctive characteristics of a μCT facility tailored explicitly for 3D X-ray Histology, with a primary focus on applications in biomedical research and preclinical and clinical studies. Methods The UHS houses a unique 3D X-ray Histology (XRH) facility, offering a range of services to national and international clients. The facility employs specialised μCT equipment explicitly designed for histology applications, allowing whole-block XRH imaging of formalin-fixed and paraffin-embedded tissue specimens. It also enables correlative imaging by combining μCT imaging with other microscopy techniques, such as immunohistochemistry (IHC) and serial block-face scanning electron microscopy, as well as data visualisation, image quantification, and bespoke analysis. Results Over the past seven years, the XRH facility has successfully completed over 120 projects in collaboration with researchers from 60 affiliations, resulting in numerous published manuscripts and conference proceedings. The facility has streamlined the μCT imaging process, improving productivity and enabling efficient acquisition of 3D datasets. Discussion & Conclusions The 3D X-ray Histology (XRH) facility at UHS is a pioneering platform in the field of histology and biomedical imaging. To the best of our knowledge, it stands out as the world's first dedicated XRH facility, encompassing every aspect of the imaging process, from user support to data generation, analysis, training, archiving, and metadata generation. This article serves as a comprehensive guide for establishing similar XRH facilities, covering key aspects of facility setup and operation. Researchers and institutions interested in developing state-of-the-art histology and imaging facilities can utilise this resource to explore new frontiers in their research and discoveries.
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Affiliation(s)
- Orestis L. Katsamenis
- μ-VIS X-ray Imaging Centre, Faculty of Engineering and Physical Sciences, University of Southampton, Southampton, England, SO17 1BJ, UK
- Institute for Life Sciences, University of Southampton, Southampton, UK
| | - Philip J. Basford
- μ-VIS X-ray Imaging Centre, Faculty of Engineering and Physical Sciences, University of Southampton, Southampton, England, SO17 1BJ, UK
- Institute for Life Sciences, University of Southampton, Southampton, UK
- Computational Engineering and Design, Faculty of Engineering and Physical Sciences,, University of Southampton, Southampton, England, SO17 1BJ, UK
| | - Stephanie K. Robinson
- μ-VIS X-ray Imaging Centre, Faculty of Engineering and Physical Sciences, University of Southampton, Southampton, England, SO17 1BJ, UK
| | - Richard P. Boardman
- μ-VIS X-ray Imaging Centre, Faculty of Engineering and Physical Sciences, University of Southampton, Southampton, England, SO17 1BJ, UK
| | - Elena Konstantinopoulou
- Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, England, SO16 6YD, UK
| | - Peter M. Lackie
- Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, England, SO16 6YD, UK
- Biomedical Imaging Unit, Faculty of Medicine, University of Southampton, Southampton, England, SO16 6YD, UK
| | - Anton Page
- University Hospital Southampton NHS Foundation Trust, Southampton, SO16 6YD, UK
| | - J. Arjuna Ratnayaka
- Institute for Life Sciences, University of Southampton, Southampton, UK
- Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, England, SO16 6YD, UK
- Biomedical Imaging Unit, Faculty of Medicine, University of Southampton, Southampton, England, SO16 6YD, UK
- University Hospital Southampton NHS Foundation Trust, Southampton, SO16 6YD, UK
| | - Patricia M. Goggin
- Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton, England, SO16 6YD, UK
- Biomedical Imaging Unit, Faculty of Medicine, University of Southampton, Southampton, England, SO16 6YD, UK
- University Hospital Southampton NHS Foundation Trust, Southampton, SO16 6YD, UK
| | - Gareth J. Thomas
- Institute for Life Sciences, University of Southampton, Southampton, UK
- University Hospital Southampton NHS Foundation Trust, Southampton, SO16 6YD, UK
- School of Cancer Sciences, Faculty of Medicine, University of Southampton, Southampton, England, SO16 6YD, UK
| | - Simon J. Cox
- Institute for Life Sciences, University of Southampton, Southampton, UK
- Computational Engineering and Design, Faculty of Engineering and Physical Sciences,, University of Southampton, Southampton, England, SO17 1BJ, UK
| | - Ian Sinclair
- μ-VIS X-ray Imaging Centre, Faculty of Engineering and Physical Sciences, University of Southampton, Southampton, England, SO17 1BJ, UK
- Institute for Life Sciences, University of Southampton, Southampton, UK
| | - Philipp Schneider
- μ-VIS X-ray Imaging Centre, Faculty of Engineering and Physical Sciences, University of Southampton, Southampton, England, SO17 1BJ, UK
- High-Performance Vision Systems, Center for Vision, Automation & Control, AIT Austrian Institute of Technology, Vienna, Austria
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Robinson SK, Ramsden JJ, Warner J, Lackie PM, Roose T. Correlative 3D Imaging and Microfluidic Modelling of Human Pulmonary Lymphatics using Immunohistochemistry and High-resolution μCT. Sci Rep 2019; 9:6415. [PMID: 31015547 PMCID: PMC6478691 DOI: 10.1038/s41598-019-42794-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2018] [Accepted: 04/08/2019] [Indexed: 11/09/2022] Open
Abstract
Lung lymphatics maintain fluid homoeostasis by providing a drainage system that returns fluid, cells and metabolites to the circulatory system. The 3D structure of the human pulmonary lymphatic network is essential to lung function, but it is poorly characterised. Image-based 3D mathematical modelling of pulmonary lymphatic microfluidics has been limited by the lack of accurate and representative image geometries. This is due to the microstructural similarity of the lymphatics to the blood vessel network, the lack of lymphatic-specific biomarkers, the technical limitations associated with image resolution in 3D, and sectioning artefacts present in 2D techniques. We present a method that combines lymphatic specific (D240 antibody) immunohistochemistry (IHC), optimised high-resolution X-ray microfocus computed tomography (μCT) and finite-element mathematical modelling to assess the function of human peripheral lung tissue. The initial results identify lymphatic heterogeneity within and between lung tissue. Lymphatic vessel volume fraction and fractal dimension significantly decreases away from the lung pleural surface (p < 0.001, n = 25 and p < 0.01, n = 20, respectively). Microfluidic modelling successfully shows that in lung tissue the fluid derived from the blood vessels drains through the interstitium into the lymphatic vessel network and this drainage is different in the subpleural space compared to the intralobular space. When comparing lung tissue from health and disease, human pulmonary lymphatics were significantly different across five morphometric measures used in this study (p ≤ 0.0001). This proof of principle study establishes a new engineering technology and workflow for further studies of pulmonary lymphatics and demonstrates for the first time the combination of correlative μCT and IHC to enable 3D mathematical modelling of human lung microfluidics at micrometre resolution.
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Affiliation(s)
- Stephanie K Robinson
- Bioengineering Sciences Research Group, School of Engineering, Faculty of Engineering and Physical Science, University of Southampton, SO17 1BJ, Southampton, England. .,Clinical and Experimental Sciences, Faculty of Medicine, Southampton General Hospital, University of Southampton, SO16 6YD, Southampton, England.
| | - Jonathan J Ramsden
- Clinical and Experimental Sciences, Faculty of Medicine, Southampton General Hospital, University of Southampton, SO16 6YD, Southampton, England
| | - Jane Warner
- Clinical and Experimental Sciences, Faculty of Medicine, Southampton General Hospital, University of Southampton, SO16 6YD, Southampton, England
| | - Peter M Lackie
- Clinical and Experimental Sciences, Faculty of Medicine, Southampton General Hospital, University of Southampton, SO16 6YD, Southampton, England
| | - Tiina Roose
- Bioengineering Sciences Research Group, School of Engineering, Faculty of Engineering and Physical Science, University of Southampton, SO17 1BJ, Southampton, England
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Abstract
Lactoferrin and lysozyme are important antimicrobial compounds of airway surface liquid, derived predominantly from serous cells of submucosal glands but also from surface epithelium. Here we compared release of these compounds from the following human cell cultures: primary cultures of tracheal epithelium (HTE), Calu-3 cells (a lung adenocarcinoma cell line frequently used as a model of serous gland cells), 16HBE14o- cells (an SV40 transformed line from airway surface epithelium), T84 cells (a colon carcinoma cell line), and human foreskin fibroblasts (HFF). For lysozyme, baseline secretory rates were in the order Calu-3 > 16HBE14o- > HTE ≈ T84 > HFF = 0; for lactoferrin, the only cell type showing measurable release was HTE; for mucus, HTE > Calu-3 > 16HBE14o- ≈ T84 > HFF = 0. A wide variety of neurohumoral agents and inflammatory stimuli was without effect on lactoferrin and lysozyme release from HTE or Calu-3 cells, although forskolin did stimulate secretion of water and lysozyme from Calu-3 cells. However, the concentration of lysozyme in the forskolin-induced secretions was much less than in airway gland secretions. Thus our data cast doubt on the utility of Calu-3 cells as a model of airway serous gland cells but do suggest that HTE could prove highly suitable for studies of mucin synthesis and release.
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Affiliation(s)
- R F Dubin
- Dept. of Human Physiology, Univ. of California-Davis, Davis, CA 95616-8664, USA.
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