A Pilot Study on Single-Cell Raman Spectroscopy Combined with Machine Learning for Phenotypic Characterization of <i>Staphylococcus aureus</i>
Rapid and accurate identification of pathogenic bacteria phenotypic traits, including virulence, drug resistance, and metabolic activity, is essential for clinical diagnosis and infectious disease control. Traditional methods are time-consuming, highlighting the need for more efficient approaches. T...
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2025-06-01
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| author | Li Liu Junjing Xue Yang Song Taijie Zhan Yang Liu Xiaohui Song Li Mei Duochun Wang Yu Vincent Fu Qiang Wei |
| author_facet | Li Liu Junjing Xue Yang Song Taijie Zhan Yang Liu Xiaohui Song Li Mei Duochun Wang Yu Vincent Fu Qiang Wei |
| author_sort | Li Liu |
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| description | Rapid and accurate identification of pathogenic bacteria phenotypic traits, including virulence, drug resistance, and metabolic activity, is essential for clinical diagnosis and infectious disease control. Traditional methods are time-consuming, highlighting the need for more efficient approaches. This study develops a single-cell Raman spectroscopy approach to detect multiple phenotypic traits of <i>Staphylococcus aureus</i> (<i>S. aureus</i>) as a proof of concept. We constructed a single-cell Raman spectral database encompassing 6240 spectra from 10 strains of <i>S. aureus</i> with diverse phenotypic traits and developed a convolutional neural network (CNN) to predict these phenotypes from the Raman spectra. The CNN model achieved 93.90%, 98.73%, and 98.66% accuracy in identifying enterotoxin-producing strains, methicillin-resistant <i>S. aureus</i> (MRSA), and growth stages, respectively. Characteristic Raman peaks for enterotoxin producers mainly appeared at 781, 939, 1161, 1337, 1451, and 1524 cm<sup>−1</sup>, whereas MRSA primarily exhibited peaks at 723, 780, 939, 1095, 1162, 1340, 1451, 1523, and 1660 cm<sup>−1</sup>. During culture, nucleic acid-related peaks weakened, lipid peaks increased, and protein peaks initially increased and subsequently decreased. This integration of Raman spectroscopy and machine learning demonstrates considerable potential for rapid bacterial phenotyping. Future research should expand to a wider range of bacterial species and phenotypes to enhance the diagnosis, prevention, and management of infectious diseases. |
| format | Article |
| id | doaj-art-4d60c80cd35b48458fd392832cb220dd |
| institution | OA Journals |
| issn | 2076-2607 |
| language | English |
| publishDate | 2025-06-01 |
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| series | Microorganisms |
| spelling | doaj-art-4d60c80cd35b48458fd392832cb220dd2025-08-20T02:21:04ZengMDPI AGMicroorganisms2076-26072025-06-01136133310.3390/microorganisms13061333A Pilot Study on Single-Cell Raman Spectroscopy Combined with Machine Learning for Phenotypic Characterization of <i>Staphylococcus aureus</i>Li Liu0Junjing Xue1Yang Song2Taijie Zhan3Yang Liu4Xiaohui Song5Li Mei6Duochun Wang7Yu Vincent Fu8Qiang Wei9National Pathogen Resource Center, Chinese Center for Disease Control and Prevention, Beijing 102206, ChinaChina General Microbiological Culture Collection Center (CGMCC), State Key Laboratory of Microbial Diversity and Innovative Utilization, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, ChinaKey Laboratory of Surveillance and Early-Warning on Infectious Disease, National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing 102206, ChinaSchool of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, ChinaNational Pathogen Resource Center, Chinese Center for Disease Control and Prevention, Beijing 102206, ChinaNational Pathogen Resource Center, Chinese Center for Disease Control and Prevention, Beijing 102206, ChinaTuberculosis Prevention and Control Institute, Beijing Center for Disease Control and Prevention, Beijing 100013, ChinaNational Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, ChinaChina General Microbiological Culture Collection Center (CGMCC), State Key Laboratory of Microbial Diversity and Innovative Utilization, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, ChinaNational Pathogen Resource Center, Chinese Center for Disease Control and Prevention, Beijing 102206, ChinaRapid and accurate identification of pathogenic bacteria phenotypic traits, including virulence, drug resistance, and metabolic activity, is essential for clinical diagnosis and infectious disease control. Traditional methods are time-consuming, highlighting the need for more efficient approaches. This study develops a single-cell Raman spectroscopy approach to detect multiple phenotypic traits of <i>Staphylococcus aureus</i> (<i>S. aureus</i>) as a proof of concept. We constructed a single-cell Raman spectral database encompassing 6240 spectra from 10 strains of <i>S. aureus</i> with diverse phenotypic traits and developed a convolutional neural network (CNN) to predict these phenotypes from the Raman spectra. The CNN model achieved 93.90%, 98.73%, and 98.66% accuracy in identifying enterotoxin-producing strains, methicillin-resistant <i>S. aureus</i> (MRSA), and growth stages, respectively. Characteristic Raman peaks for enterotoxin producers mainly appeared at 781, 939, 1161, 1337, 1451, and 1524 cm<sup>−1</sup>, whereas MRSA primarily exhibited peaks at 723, 780, 939, 1095, 1162, 1340, 1451, 1523, and 1660 cm<sup>−1</sup>. During culture, nucleic acid-related peaks weakened, lipid peaks increased, and protein peaks initially increased and subsequently decreased. This integration of Raman spectroscopy and machine learning demonstrates considerable potential for rapid bacterial phenotyping. Future research should expand to a wider range of bacterial species and phenotypes to enhance the diagnosis, prevention, and management of infectious diseases.https://www.mdpi.com/2076-2607/13/6/1333Raman spectroscopymachine learning<i>Staphylococcus aureus</i>phenotypic characteristicsrapid detection |
| spellingShingle | Li Liu Junjing Xue Yang Song Taijie Zhan Yang Liu Xiaohui Song Li Mei Duochun Wang Yu Vincent Fu Qiang Wei A Pilot Study on Single-Cell Raman Spectroscopy Combined with Machine Learning for Phenotypic Characterization of <i>Staphylococcus aureus</i> Microorganisms Raman spectroscopy machine learning <i>Staphylococcus aureus</i> phenotypic characteristics rapid detection |
| title | A Pilot Study on Single-Cell Raman Spectroscopy Combined with Machine Learning for Phenotypic Characterization of <i>Staphylococcus aureus</i> |
| title_full | A Pilot Study on Single-Cell Raman Spectroscopy Combined with Machine Learning for Phenotypic Characterization of <i>Staphylococcus aureus</i> |
| title_fullStr | A Pilot Study on Single-Cell Raman Spectroscopy Combined with Machine Learning for Phenotypic Characterization of <i>Staphylococcus aureus</i> |
| title_full_unstemmed | A Pilot Study on Single-Cell Raman Spectroscopy Combined with Machine Learning for Phenotypic Characterization of <i>Staphylococcus aureus</i> |
| title_short | A Pilot Study on Single-Cell Raman Spectroscopy Combined with Machine Learning for Phenotypic Characterization of <i>Staphylococcus aureus</i> |
| title_sort | pilot study on single cell raman spectroscopy combined with machine learning for phenotypic characterization of i staphylococcus aureus i |
| topic | Raman spectroscopy machine learning <i>Staphylococcus aureus</i> phenotypic characteristics rapid detection |
| url | https://www.mdpi.com/2076-2607/13/6/1333 |
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