Non-invasive real-time investigation of colorectal cells tight junctions by Raman microspectroscopy analysis combined with machine learning algorithms for organ-on-chip applications
IntroductionColorectal cancer is the third most common malignancy in developed countries. Diagnosis strongly depends on the pathologist’s expertise and laboratory equipment, and patient survival is influenced by the cancer’s stage at detection. Non-invasive spectroscopic techniques can aid early dia...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-11-01
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| Series: | Frontiers in Bioengineering and Biotechnology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fbioe.2024.1458404/full |
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| author | A. Calogiuri D. Bellisario E. Sciurti L. Blasi V. Esposito F. Casino P. Siciliano L. Francioso |
| author_facet | A. Calogiuri D. Bellisario E. Sciurti L. Blasi V. Esposito F. Casino P. Siciliano L. Francioso |
| author_sort | A. Calogiuri |
| collection | DOAJ |
| description | IntroductionColorectal cancer is the third most common malignancy in developed countries. Diagnosis strongly depends on the pathologist’s expertise and laboratory equipment, and patient survival is influenced by the cancer’s stage at detection. Non-invasive spectroscopic techniques can aid early diagnosis, monitor disease progression, and assess changes in physiological parameters in both heterogeneous samples and advanced platforms like Organ-on-Chip (OoC).MethodsIn this study, Raman microspectroscopy combined with Machine Learning was used to analyse structural and biochemical changes in a Caco-2 cell-based intestinal epithelial model before and after treatment with a calcium chelating agent.ResultsThe Machine Learning (ML) algorithm successfully classified different epithelium damage conditions, achieving an accuracy of 91.9% using only 7 features. Two data-splitting approaches, “sample-based” and “spectra-based,” were also compared. Further, Raman microspectroscopy results were confirmed by TEER measurements and immunofluorescence staining.DiscussionExperimental results demonstrate that this approach, combined with supervised Machine Learning, can investigate dynamic biomolecular changes in real-time with high spatial resolution. This represents a promising non-invasive alternative technique for characterizing cells and biological barriers in organoids and OoC platforms, with potential applications in cytology diagnostics, tumor monitoring, and drug efficacy analysis. |
| format | Article |
| id | doaj-art-87d2b2cd3087425ab903d226e1232f86 |
| institution | Kabale University |
| issn | 2296-4185 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Bioengineering and Biotechnology |
| spelling | doaj-art-87d2b2cd3087425ab903d226e1232f862024-11-11T04:37:28ZengFrontiers Media S.A.Frontiers in Bioengineering and Biotechnology2296-41852024-11-011210.3389/fbioe.2024.14584041458404Non-invasive real-time investigation of colorectal cells tight junctions by Raman microspectroscopy analysis combined with machine learning algorithms for organ-on-chip applicationsA. CalogiuriD. BellisarioE. SciurtiL. BlasiV. EspositoF. CasinoP. SicilianoL. FranciosoIntroductionColorectal cancer is the third most common malignancy in developed countries. Diagnosis strongly depends on the pathologist’s expertise and laboratory equipment, and patient survival is influenced by the cancer’s stage at detection. Non-invasive spectroscopic techniques can aid early diagnosis, monitor disease progression, and assess changes in physiological parameters in both heterogeneous samples and advanced platforms like Organ-on-Chip (OoC).MethodsIn this study, Raman microspectroscopy combined with Machine Learning was used to analyse structural and biochemical changes in a Caco-2 cell-based intestinal epithelial model before and after treatment with a calcium chelating agent.ResultsThe Machine Learning (ML) algorithm successfully classified different epithelium damage conditions, achieving an accuracy of 91.9% using only 7 features. Two data-splitting approaches, “sample-based” and “spectra-based,” were also compared. Further, Raman microspectroscopy results were confirmed by TEER measurements and immunofluorescence staining.DiscussionExperimental results demonstrate that this approach, combined with supervised Machine Learning, can investigate dynamic biomolecular changes in real-time with high spatial resolution. This represents a promising non-invasive alternative technique for characterizing cells and biological barriers in organoids and OoC platforms, with potential applications in cytology diagnostics, tumor monitoring, and drug efficacy analysis.https://www.frontiersin.org/articles/10.3389/fbioe.2024.1458404/fullmicro-Raman spectroscopymachine learningprincipal component analysis (PCA)Caco-2 cellsorgan-on-chip |
| spellingShingle | A. Calogiuri D. Bellisario E. Sciurti L. Blasi V. Esposito F. Casino P. Siciliano L. Francioso Non-invasive real-time investigation of colorectal cells tight junctions by Raman microspectroscopy analysis combined with machine learning algorithms for organ-on-chip applications Frontiers in Bioengineering and Biotechnology micro-Raman spectroscopy machine learning principal component analysis (PCA) Caco-2 cells organ-on-chip |
| title | Non-invasive real-time investigation of colorectal cells tight junctions by Raman microspectroscopy analysis combined with machine learning algorithms for organ-on-chip applications |
| title_full | Non-invasive real-time investigation of colorectal cells tight junctions by Raman microspectroscopy analysis combined with machine learning algorithms for organ-on-chip applications |
| title_fullStr | Non-invasive real-time investigation of colorectal cells tight junctions by Raman microspectroscopy analysis combined with machine learning algorithms for organ-on-chip applications |
| title_full_unstemmed | Non-invasive real-time investigation of colorectal cells tight junctions by Raman microspectroscopy analysis combined with machine learning algorithms for organ-on-chip applications |
| title_short | Non-invasive real-time investigation of colorectal cells tight junctions by Raman microspectroscopy analysis combined with machine learning algorithms for organ-on-chip applications |
| title_sort | non invasive real time investigation of colorectal cells tight junctions by raman microspectroscopy analysis combined with machine learning algorithms for organ on chip applications |
| topic | micro-Raman spectroscopy machine learning principal component analysis (PCA) Caco-2 cells organ-on-chip |
| url | https://www.frontiersin.org/articles/10.3389/fbioe.2024.1458404/full |
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