Identification of the Cellular Tipping Point in the Inflammation Model of LPS-Induced RAW264.7 Macrophages Through Raman Spectroscopy and the Dynamical Network Biomarker Theory
Raman spectroscopy is a non-destructive spectroscopic technique that provides complex molecular information. It is used to examine the physiological and pathological responses of living cells, such as differentiation, malignancy, and inflammation. The responses of two cellular states, initial and fu...
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2025-02-01
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| author | Akinori Taketani Shota Koshiyama Takayuki Haruki Shota Yonezawa Jun Tahara Moe Yamazaki Yusuke Oshima Akinori Wada Tsutomu Sato Keiichi Koizumi Isao Kitajima Shigeru Saito |
| author_facet | Akinori Taketani Shota Koshiyama Takayuki Haruki Shota Yonezawa Jun Tahara Moe Yamazaki Yusuke Oshima Akinori Wada Tsutomu Sato Keiichi Koizumi Isao Kitajima Shigeru Saito |
| author_sort | Akinori Taketani |
| collection | DOAJ |
| description | Raman spectroscopy is a non-destructive spectroscopic technique that provides complex molecular information. It is used to examine the physiological and pathological responses of living cells, such as differentiation, malignancy, and inflammation. The responses of two cellular states, initial and full-blown inflammation, have mainly been investigated using a comparative analysis with Raman spectra. However, the tipping point of the inflammatory state transition remains unclear. Therefore, the present study attempted to identify the tipping point of inflammation using a cell model. We stimulated RAW264.7 mouse macrophages with lipopolysaccharide (LPS) and continuously collected Raman spectra every 2 h for 24 h from the initial and full-blown inflammation states. A Partial Least Squares analysis and Principal Component Analysis—Linear Discriminant Analysis predicted the tipping point as 14 h after the LPS stimulation. In addition, a Dynamical Network Biomarker (DNB) analysis, identifying the tipping point of a state transition in various phenomena, indicated that the tipping point was 14 h and identified tryptophan as a biomarker. The results of a multivariate analysis and DNB analysis show the cellular tipping point. |
| format | Article |
| id | doaj-art-66bc6201f1be44d6a9d23bc4f9a2b848 |
| institution | DOAJ |
| issn | 1420-3049 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-66bc6201f1be44d6a9d23bc4f9a2b8482025-08-20T02:44:53ZengMDPI AGMolecules1420-30492025-02-0130492010.3390/molecules30040920Identification of the Cellular Tipping Point in the Inflammation Model of LPS-Induced RAW264.7 Macrophages Through Raman Spectroscopy and the Dynamical Network Biomarker TheoryAkinori Taketani0Shota Koshiyama1Takayuki Haruki2Shota Yonezawa3Jun Tahara4Moe Yamazaki5Yusuke Oshima6Akinori Wada7Tsutomu Sato8Keiichi Koizumi9Isao Kitajima10Shigeru Saito11Research 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, 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, JapanDivision of Presymptomatic Disease, Institute of Natural Medicine, University of Toyama, Toyama 930-0194, JapanResearch Center for Pre-Disease Science, University of Toyama, Toyama 930-8555, JapanDepartment of Hematology, Faculty of Medicine, Academic Assembly, University of Toyama, Toyama 930-8555, JapanDepartment of Hematology, Faculty of Medicine, Academic Assembly, 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, JapanResearch Center for Pre-Disease Science, University of Toyama, Toyama 930-8555, JapanRaman spectroscopy is a non-destructive spectroscopic technique that provides complex molecular information. It is used to examine the physiological and pathological responses of living cells, such as differentiation, malignancy, and inflammation. The responses of two cellular states, initial and full-blown inflammation, have mainly been investigated using a comparative analysis with Raman spectra. However, the tipping point of the inflammatory state transition remains unclear. Therefore, the present study attempted to identify the tipping point of inflammation using a cell model. We stimulated RAW264.7 mouse macrophages with lipopolysaccharide (LPS) and continuously collected Raman spectra every 2 h for 24 h from the initial and full-blown inflammation states. A Partial Least Squares analysis and Principal Component Analysis—Linear Discriminant Analysis predicted the tipping point as 14 h after the LPS stimulation. In addition, a Dynamical Network Biomarker (DNB) analysis, identifying the tipping point of a state transition in various phenomena, indicated that the tipping point was 14 h and identified tryptophan as a biomarker. The results of a multivariate analysis and DNB analysis show the cellular tipping point.https://www.mdpi.com/1420-3049/30/4/920DNB theoryinflammation modelRaman spectroscopytipping pointtryptophan |
| spellingShingle | Akinori Taketani Shota Koshiyama Takayuki Haruki Shota Yonezawa Jun Tahara Moe Yamazaki Yusuke Oshima Akinori Wada Tsutomu Sato Keiichi Koizumi Isao Kitajima Shigeru Saito Identification of the Cellular Tipping Point in the Inflammation Model of LPS-Induced RAW264.7 Macrophages Through Raman Spectroscopy and the Dynamical Network Biomarker Theory Molecules DNB theory inflammation model Raman spectroscopy tipping point tryptophan |
| title | Identification of the Cellular Tipping Point in the Inflammation Model of LPS-Induced RAW264.7 Macrophages Through Raman Spectroscopy and the Dynamical Network Biomarker Theory |
| title_full | Identification of the Cellular Tipping Point in the Inflammation Model of LPS-Induced RAW264.7 Macrophages Through Raman Spectroscopy and the Dynamical Network Biomarker Theory |
| title_fullStr | Identification of the Cellular Tipping Point in the Inflammation Model of LPS-Induced RAW264.7 Macrophages Through Raman Spectroscopy and the Dynamical Network Biomarker Theory |
| title_full_unstemmed | Identification of the Cellular Tipping Point in the Inflammation Model of LPS-Induced RAW264.7 Macrophages Through Raman Spectroscopy and the Dynamical Network Biomarker Theory |
| title_short | Identification of the Cellular Tipping Point in the Inflammation Model of LPS-Induced RAW264.7 Macrophages Through Raman Spectroscopy and the Dynamical Network Biomarker Theory |
| title_sort | identification of the cellular tipping point in the inflammation model of lps induced raw264 7 macrophages through raman spectroscopy and the dynamical network biomarker theory |
| topic | DNB theory inflammation model Raman spectroscopy tipping point tryptophan |
| url | https://www.mdpi.com/1420-3049/30/4/920 |
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