Application of the Dynamical Network Biomarker Theory to Raman Spectra
The dynamical network biomarker (DNB) theory detects the early warning signals of state transitions utilizing fluctuations in and correlations between variables in complex systems. Although the DNB theory has been applied to gene expression in several diseases, destructive testing by microarrays is...
| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Biomolecules |
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| Online Access: | https://www.mdpi.com/2218-273X/12/12/1730 |
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| author | Takayuki Haruki Shota Yonezawa Keiichi Koizumi Yasuhiko Yoshida Tomonobu M. Watanabe Hideaki Fujita Yusuke Oshima Makito Oku Akinori Taketani Moe Yamazaki Taro Ichimura Makoto Kadowaki Isao Kitajima Shigeru Saito |
| author_facet | Takayuki Haruki Shota Yonezawa Keiichi Koizumi Yasuhiko Yoshida Tomonobu M. Watanabe Hideaki Fujita Yusuke Oshima Makito Oku Akinori Taketani Moe Yamazaki Taro Ichimura Makoto Kadowaki Isao Kitajima Shigeru Saito |
| author_sort | Takayuki Haruki |
| collection | DOAJ |
| description | The dynamical network biomarker (DNB) theory detects the early warning signals of state transitions utilizing fluctuations in and correlations between variables in complex systems. Although the DNB theory has been applied to gene expression in several diseases, destructive testing by microarrays is a critical issue. Therefore, other biological information obtained by non-destructive testing is desirable; one such piece of information is Raman spectra measured by Raman spectroscopy. Raman spectroscopy is a powerful tool in life sciences and many other fields that enable the label-free non-invasive imaging of live cells and tissues along with detailed molecular fingerprints. Naïve and activated T cells have recently been successfully distinguished from each other using Raman spectroscopy without labeling. In the present study, we applied the DNB theory to Raman spectra of T cell activation as a model case. The dataset consisted of Raman spectra of the T cell activation process observed at 0 (naïve T cells), 2, 6, 12, 24 and 48 h (fully activated T cells). In the DNB analysis, the F-test and hierarchical clustering were used to detect the transition state and identify DNB Raman shifts. We successfully detected the transition state at 6 h and related DNB Raman shifts during the T cell activation process. The present results suggest novel applications of the DNB theory to Raman spectra ranging from fundamental research on cellular mechanisms to clinical examinations. |
| first_indexed | 2024-03-09T17:16:52Z |
| format | Article |
| id | doaj.art-036875494552472395a6935312e1b895 |
| institution | Directory Open Access Journal |
| issn | 2218-273X |
| language | English |
| last_indexed | 2024-03-09T17:16:52Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomolecules |
| spelling | doaj.art-036875494552472395a6935312e1b8952023-11-24T13:32:20ZengMDPI AGBiomolecules2218-273X2022-11-011212173010.3390/biom12121730Application of the Dynamical Network Biomarker Theory to Raman SpectraTakayuki Haruki0Shota Yonezawa1Keiichi Koizumi2Yasuhiko Yoshida3Tomonobu M. Watanabe4Hideaki Fujita5Yusuke Oshima6Makito Oku7Akinori Taketani8Moe Yamazaki9Taro Ichimura10Makoto Kadowaki11Isao Kitajima12Shigeru Saito13Faculty of Sustainable Design, University of Toyama, Toyama 930-8555, JapanResearch Center for Pre-Disease Science, University of Toyama, Toyama 930-8555, JapanResearch Center for Pre-Disease Science, University of Toyama, Toyama 930-8555, JapanGraduate School of Science and Engineering, University of Toyama, Toyama 930-8555, JapanRIKEN Center for Biosystems Dynamics Research, Kobe 650-0047, JapanResearch Institute for Radiation Biology and Medicine, Hiroshima University, Hiroshima 734-8553, JapanResearch Center for Pre-Disease Science, University of Toyama, Toyama 930-8555, JapanResearch Center for Pre-Disease Science, University of Toyama, Toyama 930-8555, JapanResearch Center for Pre-Disease Science, University of Toyama, Toyama 930-8555, JapanDivision of Presymptomatic Disease, Institute of Natural Medicine, University of Toyama, Toyama 930-0194, JapanInstitute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita 565-0871, JapanResearch Center for Pre-Disease Science, University of Toyama, Toyama 930-8555, JapanResearch Center for Pre-Disease Science, University of Toyama, Toyama 930-8555, JapanResearch Center for Pre-Disease Science, University of Toyama, Toyama 930-8555, JapanThe dynamical network biomarker (DNB) theory detects the early warning signals of state transitions utilizing fluctuations in and correlations between variables in complex systems. Although the DNB theory has been applied to gene expression in several diseases, destructive testing by microarrays is a critical issue. Therefore, other biological information obtained by non-destructive testing is desirable; one such piece of information is Raman spectra measured by Raman spectroscopy. Raman spectroscopy is a powerful tool in life sciences and many other fields that enable the label-free non-invasive imaging of live cells and tissues along with detailed molecular fingerprints. Naïve and activated T cells have recently been successfully distinguished from each other using Raman spectroscopy without labeling. In the present study, we applied the DNB theory to Raman spectra of T cell activation as a model case. The dataset consisted of Raman spectra of the T cell activation process observed at 0 (naïve T cells), 2, 6, 12, 24 and 48 h (fully activated T cells). In the DNB analysis, the F-test and hierarchical clustering were used to detect the transition state and identify DNB Raman shifts. We successfully detected the transition state at 6 h and related DNB Raman shifts during the T cell activation process. The present results suggest novel applications of the DNB theory to Raman spectra ranging from fundamental research on cellular mechanisms to clinical examinations.https://www.mdpi.com/2218-273X/12/12/1730dynamical network biomarker (DNB) theoryRaman spectraRaman spectroscopyT cell activationtransition state |
| spellingShingle | Takayuki Haruki Shota Yonezawa Keiichi Koizumi Yasuhiko Yoshida Tomonobu M. Watanabe Hideaki Fujita Yusuke Oshima Makito Oku Akinori Taketani Moe Yamazaki Taro Ichimura Makoto Kadowaki Isao Kitajima Shigeru Saito Application of the Dynamical Network Biomarker Theory to Raman Spectra Biomolecules dynamical network biomarker (DNB) theory Raman spectra Raman spectroscopy T cell activation transition state |
| title | Application of the Dynamical Network Biomarker Theory to Raman Spectra |
| title_full | Application of the Dynamical Network Biomarker Theory to Raman Spectra |
| title_fullStr | Application of the Dynamical Network Biomarker Theory to Raman Spectra |
| title_full_unstemmed | Application of the Dynamical Network Biomarker Theory to Raman Spectra |
| title_short | Application of the Dynamical Network Biomarker Theory to Raman Spectra |
| title_sort | application of the dynamical network biomarker theory to raman spectra |
| topic | dynamical network biomarker (DNB) theory Raman spectra Raman spectroscopy T cell activation transition state |
| url | https://www.mdpi.com/2218-273X/12/12/1730 |
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