Correlative Fluorescence and Raman Microscopy to Define Mitotic Stages at the Single-Cell Level: Opportunities and Limitations in the AI Era
Nowadays, morphology and molecular analyses at the single-cell level have a fundamental role in understanding biology better. These methods are utilized for cell phenotyping and in-depth studies of cellular processes, such as mitosis. Fluorescence microscopy and optical spectroscopy techniques, incl...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Biosensors |
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| Online Access: | https://www.mdpi.com/2079-6374/13/2/187 |
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| author | Csaba Voros David Bauer Ede Migh Istvan Grexa Attila Gergely Végh Balázs Szalontai Gastone Castellani Tivadar Danka Saso Dzeroski Krisztian Koos Filippo Piccinini Peter Horvath |
| author_facet | Csaba Voros David Bauer Ede Migh Istvan Grexa Attila Gergely Végh Balázs Szalontai Gastone Castellani Tivadar Danka Saso Dzeroski Krisztian Koos Filippo Piccinini Peter Horvath |
| author_sort | Csaba Voros |
| collection | DOAJ |
| description | Nowadays, morphology and molecular analyses at the single-cell level have a fundamental role in understanding biology better. These methods are utilized for cell phenotyping and in-depth studies of cellular processes, such as mitosis. Fluorescence microscopy and optical spectroscopy techniques, including Raman micro-spectroscopy, allow researchers to examine biological samples at the single-cell level in a non-destructive manner. Fluorescence microscopy can give detailed morphological information about the localization of stained molecules, while Raman microscopy can produce label-free images at the subcellular level; thus, it can reveal the spatial distribution of molecular fingerprints, even in live samples. Accordingly, the combination of correlative fluorescence and Raman microscopy (CFRM) offers a unique approach for studying cellular stages at the single-cell level. However, subcellular spectral maps are complex and challenging to interpret. Artificial intelligence (AI) may serve as a valuable solution to characterize the molecular backgrounds of phenotypes and biological processes by finding the characteristic patterns in spectral maps. The major contributions of the manuscript are: (<i>I</i>) it gives a comprehensive review of the literature focusing on AI techniques in Raman-based cellular phenotyping; (<i>II</i>) via the presentation of a case study, a new neural network-based approach is described, and the opportunities and limitations of AI, specifically deep learning, are discussed regarding the analysis of Raman spectroscopy data to classify mitotic cellular stages based on their spectral maps. |
| first_indexed | 2024-03-11T09:04:46Z |
| format | Article |
| id | doaj.art-2b016728f9074aebbf128a98788e6a5e |
| institution | Directory Open Access Journal |
| issn | 2079-6374 |
| language | English |
| last_indexed | 2024-03-11T09:04:46Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biosensors |
| spelling | doaj.art-2b016728f9074aebbf128a98788e6a5e2023-11-16T19:25:16ZengMDPI AGBiosensors2079-63742023-01-0113218710.3390/bios13020187Correlative Fluorescence and Raman Microscopy to Define Mitotic Stages at the Single-Cell Level: Opportunities and Limitations in the AI EraCsaba Voros0David Bauer1Ede Migh2Istvan Grexa3Attila Gergely Végh4Balázs Szalontai5Gastone Castellani6Tivadar Danka7Saso Dzeroski8Krisztian Koos9Filippo Piccinini10Peter Horvath11Synthetic and Systems Biology Unit, Biological Research Centre (BRC), Temesvári krt. 62, H-6726 Szeged, HungarySynthetic and Systems Biology Unit, Biological Research Centre (BRC), Temesvári krt. 62, H-6726 Szeged, HungarySynthetic and Systems Biology Unit, Biological Research Centre (BRC), Temesvári krt. 62, H-6726 Szeged, HungarySynthetic and Systems Biology Unit, Biological Research Centre (BRC), Temesvári krt. 62, H-6726 Szeged, HungaryInstitute of Biophysics, Biological Research Centre (BRC), Temesvári krt. 62, H-6726 Szeged, HungaryInstitute of Biophysics, Biological Research Centre (BRC), Temesvári krt. 62, H-6726 Szeged, HungaryDepartment of Medical and Surgical Sciences (DIMEC), University of Bologna, Via G. Massarenti 9, I-40126 Bologna, ItalySynthetic and Systems Biology Unit, Biological Research Centre (BRC), Temesvári krt. 62, H-6726 Szeged, HungaryDepartment of Knowledge Technologies, Jozef Stefan Institute, Jamova Cesta 39, SI-1000 Ljubljana, SloveniaSynthetic and Systems Biology Unit, Biological Research Centre (BRC), Temesvári krt. 62, H-6726 Szeged, HungaryDepartment of Medical and Surgical Sciences (DIMEC), University of Bologna, Via G. Massarenti 9, I-40126 Bologna, ItalySynthetic and Systems Biology Unit, Biological Research Centre (BRC), Temesvári krt. 62, H-6726 Szeged, HungaryNowadays, morphology and molecular analyses at the single-cell level have a fundamental role in understanding biology better. These methods are utilized for cell phenotyping and in-depth studies of cellular processes, such as mitosis. Fluorescence microscopy and optical spectroscopy techniques, including Raman micro-spectroscopy, allow researchers to examine biological samples at the single-cell level in a non-destructive manner. Fluorescence microscopy can give detailed morphological information about the localization of stained molecules, while Raman microscopy can produce label-free images at the subcellular level; thus, it can reveal the spatial distribution of molecular fingerprints, even in live samples. Accordingly, the combination of correlative fluorescence and Raman microscopy (CFRM) offers a unique approach for studying cellular stages at the single-cell level. However, subcellular spectral maps are complex and challenging to interpret. Artificial intelligence (AI) may serve as a valuable solution to characterize the molecular backgrounds of phenotypes and biological processes by finding the characteristic patterns in spectral maps. The major contributions of the manuscript are: (<i>I</i>) it gives a comprehensive review of the literature focusing on AI techniques in Raman-based cellular phenotyping; (<i>II</i>) via the presentation of a case study, a new neural network-based approach is described, and the opportunities and limitations of AI, specifically deep learning, are discussed regarding the analysis of Raman spectroscopy data to classify mitotic cellular stages based on their spectral maps.https://www.mdpi.com/2079-6374/13/2/187microscopyRaman spectroscopysingle-cell analysisphenotypic discoverymitosismachine learning |
| spellingShingle | Csaba Voros David Bauer Ede Migh Istvan Grexa Attila Gergely Végh Balázs Szalontai Gastone Castellani Tivadar Danka Saso Dzeroski Krisztian Koos Filippo Piccinini Peter Horvath Correlative Fluorescence and Raman Microscopy to Define Mitotic Stages at the Single-Cell Level: Opportunities and Limitations in the AI Era Biosensors microscopy Raman spectroscopy single-cell analysis phenotypic discovery mitosis machine learning |
| title | Correlative Fluorescence and Raman Microscopy to Define Mitotic Stages at the Single-Cell Level: Opportunities and Limitations in the AI Era |
| title_full | Correlative Fluorescence and Raman Microscopy to Define Mitotic Stages at the Single-Cell Level: Opportunities and Limitations in the AI Era |
| title_fullStr | Correlative Fluorescence and Raman Microscopy to Define Mitotic Stages at the Single-Cell Level: Opportunities and Limitations in the AI Era |
| title_full_unstemmed | Correlative Fluorescence and Raman Microscopy to Define Mitotic Stages at the Single-Cell Level: Opportunities and Limitations in the AI Era |
| title_short | Correlative Fluorescence and Raman Microscopy to Define Mitotic Stages at the Single-Cell Level: Opportunities and Limitations in the AI Era |
| title_sort | correlative fluorescence and raman microscopy to define mitotic stages at the single cell level opportunities and limitations in the ai era |
| topic | microscopy Raman spectroscopy single-cell analysis phenotypic discovery mitosis machine learning |
| url | https://www.mdpi.com/2079-6374/13/2/187 |
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