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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...

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Autores principales: Voros, Csaba, Bauer, David, Migh, Ede, Grexa, Istvan, Végh, Attila Gergely, Szalontai, Balázs, Castellani, Gastone, Danka, Tivadar, Dzeroski, Saso, Koos, Krisztian, Piccinini, Filippo, Horvath, Peter
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9953278/
https://www.ncbi.nlm.nih.gov/pubmed/36831953
http://dx.doi.org/10.3390/bios13020187
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author Voros, Csaba
Bauer, David
Migh, Ede
Grexa, Istvan
Végh, Attila Gergely
Szalontai, Balázs
Castellani, Gastone
Danka, Tivadar
Dzeroski, Saso
Koos, Krisztian
Piccinini, Filippo
Horvath, Peter
author_facet Voros, Csaba
Bauer, David
Migh, Ede
Grexa, Istvan
Végh, Attila Gergely
Szalontai, Balázs
Castellani, Gastone
Danka, Tivadar
Dzeroski, Saso
Koos, Krisztian
Piccinini, Filippo
Horvath, Peter
author_sort Voros, Csaba
collection PubMed
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) it gives a comprehensive review of the literature focusing on AI techniques in Raman-based cellular phenotyping; (II) 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.
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spelling pubmed-99532782023-02-25 Correlative Fluorescence and Raman Microscopy to Define Mitotic Stages at the Single-Cell Level: Opportunities and Limitations in the AI Era Voros, Csaba Bauer, David Migh, Ede Grexa, Istvan Végh, Attila Gergely Szalontai, Balázs Castellani, Gastone Danka, Tivadar Dzeroski, Saso Koos, Krisztian Piccinini, Filippo Horvath, Peter Biosensors (Basel) Perspective 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) it gives a comprehensive review of the literature focusing on AI techniques in Raman-based cellular phenotyping; (II) 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. MDPI 2023-01-26 /pmc/articles/PMC9953278/ /pubmed/36831953 http://dx.doi.org/10.3390/bios13020187 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Perspective
Voros, Csaba
Bauer, David
Migh, Ede
Grexa, Istvan
Végh, Attila Gergely
Szalontai, Balázs
Castellani, Gastone
Danka, Tivadar
Dzeroski, Saso
Koos, Krisztian
Piccinini, Filippo
Horvath, Peter
Correlative Fluorescence and Raman Microscopy to Define Mitotic Stages at the Single-Cell Level: Opportunities and Limitations in the AI Era
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 Perspective
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9953278/
https://www.ncbi.nlm.nih.gov/pubmed/36831953
http://dx.doi.org/10.3390/bios13020187
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