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Quantifying multiple stain distributions in bioimaging by hyperspectral X-ray tomography
Chemical staining of biological specimens is commonly utilised to boost contrast in soft tissue structures, but unambiguous identification of staining location and distribution is difficult without confirmation of the elemental signature, especially for chemicals of similar density contrast. Hypersp...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9763266/ https://www.ncbi.nlm.nih.gov/pubmed/36535963 http://dx.doi.org/10.1038/s41598-022-23592-0 |
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author | Warr, Ryan Handschuh, Stephan Glösmann, Martin Cernik, Robert J. Withers, Philip J. |
author_facet | Warr, Ryan Handschuh, Stephan Glösmann, Martin Cernik, Robert J. Withers, Philip J. |
author_sort | Warr, Ryan |
collection | PubMed |
description | Chemical staining of biological specimens is commonly utilised to boost contrast in soft tissue structures, but unambiguous identification of staining location and distribution is difficult without confirmation of the elemental signature, especially for chemicals of similar density contrast. Hyperspectral X-ray computed tomography (XCT) enables the non-destructive identification, segmentation and mapping of elemental composition within a sample. With the availability of hundreds of narrow, high resolution (~ 1 keV) energy channels, the technique allows the simultaneous detection of multiple contrast agents across different tissue structures. Here we describe a hyperspectral imaging routine for distinguishing multiple chemical agents, regardless of contrast similarity. Using a set of elemental calibration phantoms, we perform a first instance of direct stain concentration measurement using spectral absorption edge markers. Applied to a set of double- and triple-stained biological specimens, the study analyses the extent of stain overlap and uptake regions for commonly used contrast markers. An improved understanding of stain concentration as a function of position, and the interaction between multiple stains, would help inform future studies on multi-staining procedures, as well as enable future exploration of heavy metal uptake across medical, agricultural and ecological fields. |
format | Online Article Text |
id | pubmed-9763266 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-97632662022-12-21 Quantifying multiple stain distributions in bioimaging by hyperspectral X-ray tomography Warr, Ryan Handschuh, Stephan Glösmann, Martin Cernik, Robert J. Withers, Philip J. Sci Rep Article Chemical staining of biological specimens is commonly utilised to boost contrast in soft tissue structures, but unambiguous identification of staining location and distribution is difficult without confirmation of the elemental signature, especially for chemicals of similar density contrast. Hyperspectral X-ray computed tomography (XCT) enables the non-destructive identification, segmentation and mapping of elemental composition within a sample. With the availability of hundreds of narrow, high resolution (~ 1 keV) energy channels, the technique allows the simultaneous detection of multiple contrast agents across different tissue structures. Here we describe a hyperspectral imaging routine for distinguishing multiple chemical agents, regardless of contrast similarity. Using a set of elemental calibration phantoms, we perform a first instance of direct stain concentration measurement using spectral absorption edge markers. Applied to a set of double- and triple-stained biological specimens, the study analyses the extent of stain overlap and uptake regions for commonly used contrast markers. An improved understanding of stain concentration as a function of position, and the interaction between multiple stains, would help inform future studies on multi-staining procedures, as well as enable future exploration of heavy metal uptake across medical, agricultural and ecological fields. Nature Publishing Group UK 2022-12-19 /pmc/articles/PMC9763266/ /pubmed/36535963 http://dx.doi.org/10.1038/s41598-022-23592-0 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Warr, Ryan Handschuh, Stephan Glösmann, Martin Cernik, Robert J. Withers, Philip J. Quantifying multiple stain distributions in bioimaging by hyperspectral X-ray tomography |
title | Quantifying multiple stain distributions in bioimaging by hyperspectral X-ray tomography |
title_full | Quantifying multiple stain distributions in bioimaging by hyperspectral X-ray tomography |
title_fullStr | Quantifying multiple stain distributions in bioimaging by hyperspectral X-ray tomography |
title_full_unstemmed | Quantifying multiple stain distributions in bioimaging by hyperspectral X-ray tomography |
title_short | Quantifying multiple stain distributions in bioimaging by hyperspectral X-ray tomography |
title_sort | quantifying multiple stain distributions in bioimaging by hyperspectral x-ray tomography |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9763266/ https://www.ncbi.nlm.nih.gov/pubmed/36535963 http://dx.doi.org/10.1038/s41598-022-23592-0 |
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