Hyperspectral Chemical Imaging of Single Bacterial Cell Structure by Raman Spectroscopy and Machine Learning
In this work, biomolecules, such as membrane proteins, lipids, and DNA, were identified and their spatial distribution was mapped within a single <i>Escherichia coli</i> cell by Raman hyperspectral imaging. Raman spectroscopy allows direct, nondestructive, rapid, and cost-effective analy...
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MDPI AG
2021-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/8/3409 |
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| author | Giulia Barzan Alessio Sacco Luisa Mandrile Andrea Mario Giovannozzi Chiara Portesi Andrea Mario Rossi |
| author_facet | Giulia Barzan Alessio Sacco Luisa Mandrile Andrea Mario Giovannozzi Chiara Portesi Andrea Mario Rossi |
| author_sort | Giulia Barzan |
| collection | DOAJ |
| description | In this work, biomolecules, such as membrane proteins, lipids, and DNA, were identified and their spatial distribution was mapped within a single <i>Escherichia coli</i> cell by Raman hyperspectral imaging. Raman spectroscopy allows direct, nondestructive, rapid, and cost-effective analysis of biological samples, minimizing the sample preparation and without the need of chemical label or immunological staining. Firstly, a comparison between an air-dried and a freeze-dried cell was made, and the principal vibrational modes associated to the membrane and nucleic acids were identified by the bacterium’s Raman chemical fingerprint. Then, analyzing the Raman hyperspectral images by multivariate statistical analysis, the bacterium biological status was investigated at a subcellular level. Principal components analysis (PCA) was applied for dimensionality reduction of the spectral data, then spectral unmixing was performed by multivariate curve resolution–alternating least squares (MCR-ALS). Thanks to multivariate data analysis, the DNA segregation and the Z-ring formation of a replicating bacterial cell were detected at a sub-micrometer level, opening the way to real-time molecular analysis that could be easily applied on in vivo or ex vivo biological samples, avoiding long preparation and analysis process. |
| first_indexed | 2024-03-10T12:26:19Z |
| format | Article |
| id | doaj.art-aae8b7c5506241e1b3fdb398200056eb |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T12:26:19Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-aae8b7c5506241e1b3fdb398200056eb2023-11-21T15:01:07ZengMDPI AGApplied Sciences2076-34172021-04-01118340910.3390/app11083409Hyperspectral Chemical Imaging of Single Bacterial Cell Structure by Raman Spectroscopy and Machine LearningGiulia Barzan0Alessio Sacco1Luisa Mandrile2Andrea Mario Giovannozzi3Chiara Portesi4Andrea Mario Rossi5Quantum Metrology and Nano Technologies Division, Istituto Nazionale di Ricerca Metrologica (INRiM), Strada delle Cacce, 91, 10135 Turin, ItalyQuantum Metrology and Nano Technologies Division, Istituto Nazionale di Ricerca Metrologica (INRiM), Strada delle Cacce, 91, 10135 Turin, ItalyQuantum Metrology and Nano Technologies Division, Istituto Nazionale di Ricerca Metrologica (INRiM), Strada delle Cacce, 91, 10135 Turin, ItalyQuantum Metrology and Nano Technologies Division, Istituto Nazionale di Ricerca Metrologica (INRiM), Strada delle Cacce, 91, 10135 Turin, ItalyQuantum Metrology and Nano Technologies Division, Istituto Nazionale di Ricerca Metrologica (INRiM), Strada delle Cacce, 91, 10135 Turin, ItalyQuantum Metrology and Nano Technologies Division, Istituto Nazionale di Ricerca Metrologica (INRiM), Strada delle Cacce, 91, 10135 Turin, ItalyIn this work, biomolecules, such as membrane proteins, lipids, and DNA, were identified and their spatial distribution was mapped within a single <i>Escherichia coli</i> cell by Raman hyperspectral imaging. Raman spectroscopy allows direct, nondestructive, rapid, and cost-effective analysis of biological samples, minimizing the sample preparation and without the need of chemical label or immunological staining. Firstly, a comparison between an air-dried and a freeze-dried cell was made, and the principal vibrational modes associated to the membrane and nucleic acids were identified by the bacterium’s Raman chemical fingerprint. Then, analyzing the Raman hyperspectral images by multivariate statistical analysis, the bacterium biological status was investigated at a subcellular level. Principal components analysis (PCA) was applied for dimensionality reduction of the spectral data, then spectral unmixing was performed by multivariate curve resolution–alternating least squares (MCR-ALS). Thanks to multivariate data analysis, the DNA segregation and the Z-ring formation of a replicating bacterial cell were detected at a sub-micrometer level, opening the way to real-time molecular analysis that could be easily applied on in vivo or ex vivo biological samples, avoiding long preparation and analysis process.https://www.mdpi.com/2076-3417/11/8/3409Raman spectroscopyRaman imaging<i>E. coli</i>multivariate curve resolutionhyperspectral imagingbacteria |
| spellingShingle | Giulia Barzan Alessio Sacco Luisa Mandrile Andrea Mario Giovannozzi Chiara Portesi Andrea Mario Rossi Hyperspectral Chemical Imaging of Single Bacterial Cell Structure by Raman Spectroscopy and Machine Learning Applied Sciences Raman spectroscopy Raman imaging <i>E. coli</i> multivariate curve resolution hyperspectral imaging bacteria |
| title | Hyperspectral Chemical Imaging of Single Bacterial Cell Structure by Raman Spectroscopy and Machine Learning |
| title_full | Hyperspectral Chemical Imaging of Single Bacterial Cell Structure by Raman Spectroscopy and Machine Learning |
| title_fullStr | Hyperspectral Chemical Imaging of Single Bacterial Cell Structure by Raman Spectroscopy and Machine Learning |
| title_full_unstemmed | Hyperspectral Chemical Imaging of Single Bacterial Cell Structure by Raman Spectroscopy and Machine Learning |
| title_short | Hyperspectral Chemical Imaging of Single Bacterial Cell Structure by Raman Spectroscopy and Machine Learning |
| title_sort | hyperspectral chemical imaging of single bacterial cell structure by raman spectroscopy and machine learning |
| topic | Raman spectroscopy Raman imaging <i>E. coli</i> multivariate curve resolution hyperspectral imaging bacteria |
| url | https://www.mdpi.com/2076-3417/11/8/3409 |
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