Coherent anti-Stokes Raman scattering cell imaging and segmentation with unsupervised data analysis
Coherent Raman imaging has been extensively applied to live-cell imaging in the last 2 decades, allowing to probe the intracellular lipid, protein, nucleic acid, and water content with a high-acquisition rate and sensitivity. In this context, multiplex coherent anti-Stokes Raman scattering (MCARS) m...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-08-01
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| Series: | Frontiers in Cell and Developmental Biology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fcell.2022.933897/full |
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| author | Damien Boildieu Damien Boildieu Tiffany Guerenne-Del Ben Ludovic Duponchel Vincent Sol Jean-Michel Petit Éric Champion Hideaki Kano David Helbert Amandine Magnaudeix Philippe Leproux Philippe Carré |
| author_facet | Damien Boildieu Damien Boildieu Tiffany Guerenne-Del Ben Ludovic Duponchel Vincent Sol Jean-Michel Petit Éric Champion Hideaki Kano David Helbert Amandine Magnaudeix Philippe Leproux Philippe Carré |
| author_sort | Damien Boildieu |
| collection | DOAJ |
| description | Coherent Raman imaging has been extensively applied to live-cell imaging in the last 2 decades, allowing to probe the intracellular lipid, protein, nucleic acid, and water content with a high-acquisition rate and sensitivity. In this context, multiplex coherent anti-Stokes Raman scattering (MCARS) microspectroscopy using sub-nanosecond laser pulses is now recognized as a mature and straightforward technology for label-free bioimaging, offering the high spectral resolution of conventional Raman spectroscopy with reduced acquisition time. Here, we introduce the combination of the MCARS imaging technique with unsupervised data analysis based on multivariate curve resolution (MCR). The MCR process is implemented under the classical signal non-negativity constraint and, even more originally, under a new spatial constraint based on cell segmentation. We thus introduce a new methodology for hyperspectral cell imaging and segmentation, based on a simple, unsupervised workflow without any spectrum-to-spectrum phase retrieval computation. We first assess the robustness of our approach by considering cells of different types, namely, from the human HEK293 and murine C2C12 lines. To evaluate its applicability over a broader range, we then study HEK293 cells in different physiological states and experimental situations. Specifically, we compare an interphasic cell with a mitotic (prophase) one. We also present a comparison between a fixed cell and a living cell, in order to visualize the potential changes induced by the fixation protocol in cellular architecture. Next, with the aim of assessing more precisely the sensitivity of our approach, we study HEK293 living cells overexpressing tropomyosin-related kinase B (TrkB), a cancer-related membrane receptor, depending on the presence of its ligand, brain-derived neurotrophic factor (BDNF). Finally, the segmentation capability of the approach is evaluated in the case of a single cell and also by considering cell clusters of various sizes. |
| first_indexed | 2024-04-13T13:16:17Z |
| format | Article |
| id | doaj.art-857d3add90c44a80904aad3de48c2c04 |
| institution | Directory Open Access Journal |
| issn | 2296-634X |
| language | English |
| last_indexed | 2024-04-13T13:16:17Z |
| publishDate | 2022-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Cell and Developmental Biology |
| spelling | doaj.art-857d3add90c44a80904aad3de48c2c042022-12-22T02:45:27ZengFrontiers Media S.A.Frontiers in Cell and Developmental Biology2296-634X2022-08-011010.3389/fcell.2022.933897933897Coherent anti-Stokes Raman scattering cell imaging and segmentation with unsupervised data analysisDamien Boildieu0Damien Boildieu1Tiffany Guerenne-Del Ben2Ludovic Duponchel3Vincent Sol4Jean-Michel Petit5Éric Champion6Hideaki Kano7David Helbert8Amandine Magnaudeix9Philippe Leproux10Philippe Carré11University of Limoges, CNRS, XLIM, UMR 7252, Limoges, FranceUniversity of Poitiers, CNRS, XLIM, UMR 7252, Poitiers, FranceUniversity of Limoges, PEIRENE, UR 22722, Limoges, FranceUniversity of Lille, CNRS, UMR 8516, LASIRE - Laboratoire de Spectroscopie Pour Les Interactions, La Réactivité et L’Environnement, Lille, FranceUniversity of Limoges, PEIRENE, UR 22722, Limoges, FranceUniversity of Limoges, PEIRENE, UR 22722, Limoges, FranceUniversity of Limoges, CNRS, Institut de Recherche sur Les Céramiques, UMR 7315, Limoges, FranceDepartment of Chemistry, Faculty of Science, Kyushu University, Fukuoka, JapanUniversity of Poitiers, CNRS, XLIM, UMR 7252, Poitiers, FranceUniversity of Limoges, CNRS, Institut de Recherche sur Les Céramiques, UMR 7315, Limoges, FranceUniversity of Limoges, CNRS, XLIM, UMR 7252, Limoges, FranceUniversity of Poitiers, CNRS, XLIM, UMR 7252, Poitiers, FranceCoherent Raman imaging has been extensively applied to live-cell imaging in the last 2 decades, allowing to probe the intracellular lipid, protein, nucleic acid, and water content with a high-acquisition rate and sensitivity. In this context, multiplex coherent anti-Stokes Raman scattering (MCARS) microspectroscopy using sub-nanosecond laser pulses is now recognized as a mature and straightforward technology for label-free bioimaging, offering the high spectral resolution of conventional Raman spectroscopy with reduced acquisition time. Here, we introduce the combination of the MCARS imaging technique with unsupervised data analysis based on multivariate curve resolution (MCR). The MCR process is implemented under the classical signal non-negativity constraint and, even more originally, under a new spatial constraint based on cell segmentation. We thus introduce a new methodology for hyperspectral cell imaging and segmentation, based on a simple, unsupervised workflow without any spectrum-to-spectrum phase retrieval computation. We first assess the robustness of our approach by considering cells of different types, namely, from the human HEK293 and murine C2C12 lines. To evaluate its applicability over a broader range, we then study HEK293 cells in different physiological states and experimental situations. Specifically, we compare an interphasic cell with a mitotic (prophase) one. We also present a comparison between a fixed cell and a living cell, in order to visualize the potential changes induced by the fixation protocol in cellular architecture. Next, with the aim of assessing more precisely the sensitivity of our approach, we study HEK293 living cells overexpressing tropomyosin-related kinase B (TrkB), a cancer-related membrane receptor, depending on the presence of its ligand, brain-derived neurotrophic factor (BDNF). Finally, the segmentation capability of the approach is evaluated in the case of a single cell and also by considering cell clusters of various sizes.https://www.frontiersin.org/articles/10.3389/fcell.2022.933897/fullcell imagingcell segmentationcoherent anti-Stokes Raman scatteringunsupervised data analysiscoherent Raman imaginglabel-free imaging |
| spellingShingle | Damien Boildieu Damien Boildieu Tiffany Guerenne-Del Ben Ludovic Duponchel Vincent Sol Jean-Michel Petit Éric Champion Hideaki Kano David Helbert Amandine Magnaudeix Philippe Leproux Philippe Carré Coherent anti-Stokes Raman scattering cell imaging and segmentation with unsupervised data analysis Frontiers in Cell and Developmental Biology cell imaging cell segmentation coherent anti-Stokes Raman scattering unsupervised data analysis coherent Raman imaging label-free imaging |
| title | Coherent anti-Stokes Raman scattering cell imaging and segmentation with unsupervised data analysis |
| title_full | Coherent anti-Stokes Raman scattering cell imaging and segmentation with unsupervised data analysis |
| title_fullStr | Coherent anti-Stokes Raman scattering cell imaging and segmentation with unsupervised data analysis |
| title_full_unstemmed | Coherent anti-Stokes Raman scattering cell imaging and segmentation with unsupervised data analysis |
| title_short | Coherent anti-Stokes Raman scattering cell imaging and segmentation with unsupervised data analysis |
| title_sort | coherent anti stokes raman scattering cell imaging and segmentation with unsupervised data analysis |
| topic | cell imaging cell segmentation coherent anti-Stokes Raman scattering unsupervised data analysis coherent Raman imaging label-free imaging |
| url | https://www.frontiersin.org/articles/10.3389/fcell.2022.933897/full |
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