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Multi-dimensional TOF-SIMS analysis for effective profiling of disease-related ions from the tissue surface
Time-of-flight secondary ion mass spectrometry (TOF-SIMS) emerges as a promising tool to identify the ions (small molecules) indicative of disease states from the surface of patient tissues. In TOF-SIMS analysis, an enhanced ionization of surface molecules is critical to increase the number of detec...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4457153/ https://www.ncbi.nlm.nih.gov/pubmed/26046669 http://dx.doi.org/10.1038/srep11077 |
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author | Park, Ji-Won Jeong, Hyobin Kang, Byeongsoo Kim, Su Jin Park, Sang Yoon Kang, Sokbom Kim, Hark Kyun Choi, Joon Sig Hwang, Daehee Lee, Tae Geol |
author_facet | Park, Ji-Won Jeong, Hyobin Kang, Byeongsoo Kim, Su Jin Park, Sang Yoon Kang, Sokbom Kim, Hark Kyun Choi, Joon Sig Hwang, Daehee Lee, Tae Geol |
author_sort | Park, Ji-Won |
collection | PubMed |
description | Time-of-flight secondary ion mass spectrometry (TOF-SIMS) emerges as a promising tool to identify the ions (small molecules) indicative of disease states from the surface of patient tissues. In TOF-SIMS analysis, an enhanced ionization of surface molecules is critical to increase the number of detected ions. Several methods have been developed to enhance ionization capability. However, how these methods improve identification of disease-related ions has not been systematically explored. Here, we present a multi-dimensional SIMS (MD-SIMS) that combines conventional TOF-SIMS and metal-assisted SIMS (MetA-SIMS). Using this approach, we analyzed cancer and adjacent normal tissues first by TOF-SIMS and subsequently by MetA-SIMS. In total, TOF- and MetA-SIMS detected 632 and 959 ions, respectively. Among them, 426 were commonly detected by both methods, while 206 and 533 were detected uniquely by TOF- and MetA-SIMS, respectively. Of the 426 commonly detected ions, 250 increased in their intensities by MetA-SIMS, whereas 176 decreased. The integrated analysis of the ions detected by the two methods resulted in an increased number of discriminatory ions leading to an enhanced separation between cancer and normal tissues. Therefore, the results show that MD-SIMS can be a useful approach to provide a comprehensive list of discriminatory ions indicative of disease states. |
format | Online Article Text |
id | pubmed-4457153 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-44571532015-06-12 Multi-dimensional TOF-SIMS analysis for effective profiling of disease-related ions from the tissue surface Park, Ji-Won Jeong, Hyobin Kang, Byeongsoo Kim, Su Jin Park, Sang Yoon Kang, Sokbom Kim, Hark Kyun Choi, Joon Sig Hwang, Daehee Lee, Tae Geol Sci Rep Article Time-of-flight secondary ion mass spectrometry (TOF-SIMS) emerges as a promising tool to identify the ions (small molecules) indicative of disease states from the surface of patient tissues. In TOF-SIMS analysis, an enhanced ionization of surface molecules is critical to increase the number of detected ions. Several methods have been developed to enhance ionization capability. However, how these methods improve identification of disease-related ions has not been systematically explored. Here, we present a multi-dimensional SIMS (MD-SIMS) that combines conventional TOF-SIMS and metal-assisted SIMS (MetA-SIMS). Using this approach, we analyzed cancer and adjacent normal tissues first by TOF-SIMS and subsequently by MetA-SIMS. In total, TOF- and MetA-SIMS detected 632 and 959 ions, respectively. Among them, 426 were commonly detected by both methods, while 206 and 533 were detected uniquely by TOF- and MetA-SIMS, respectively. Of the 426 commonly detected ions, 250 increased in their intensities by MetA-SIMS, whereas 176 decreased. The integrated analysis of the ions detected by the two methods resulted in an increased number of discriminatory ions leading to an enhanced separation between cancer and normal tissues. Therefore, the results show that MD-SIMS can be a useful approach to provide a comprehensive list of discriminatory ions indicative of disease states. Nature Publishing Group 2015-06-05 /pmc/articles/PMC4457153/ /pubmed/26046669 http://dx.doi.org/10.1038/srep11077 Text en Copyright © 2015, Macmillan Publishers Limited http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
spellingShingle | Article Park, Ji-Won Jeong, Hyobin Kang, Byeongsoo Kim, Su Jin Park, Sang Yoon Kang, Sokbom Kim, Hark Kyun Choi, Joon Sig Hwang, Daehee Lee, Tae Geol Multi-dimensional TOF-SIMS analysis for effective profiling of disease-related ions from the tissue surface |
title | Multi-dimensional TOF-SIMS analysis for effective profiling of disease-related ions from the tissue surface |
title_full | Multi-dimensional TOF-SIMS analysis for effective profiling of disease-related ions from the tissue surface |
title_fullStr | Multi-dimensional TOF-SIMS analysis for effective profiling of disease-related ions from the tissue surface |
title_full_unstemmed | Multi-dimensional TOF-SIMS analysis for effective profiling of disease-related ions from the tissue surface |
title_short | Multi-dimensional TOF-SIMS analysis for effective profiling of disease-related ions from the tissue surface |
title_sort | multi-dimensional tof-sims analysis for effective profiling of disease-related ions from the tissue surface |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4457153/ https://www.ncbi.nlm.nih.gov/pubmed/26046669 http://dx.doi.org/10.1038/srep11077 |
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