Surface-enhanced Raman scattering-based surface chemotaxonomy: combining bacteria extracellular matrices and machine learning for rapid and universal species identification
Rapid, universal, and accurate identification of bacteria in their natural states is necessary for on-site environmental monitoring and fundamental microbial research. Surface-enhanced Raman scattering (SERS) spectroscopy emerges as an attractive tool due to its molecule-specific spectral fingerprin...
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sg-ntu-dr.10356-1733602024-01-30T00:57:02Z Surface-enhanced Raman scattering-based surface chemotaxonomy: combining bacteria extracellular matrices and machine learning for rapid and universal species identification Leong, Shi Xuan Tan, Emily Xi Han, Xuemei Luhung, Irvan Aung, Ngu War Nguyen, Lam Bang Thanh Tan, Si Yan Li, Haitao Phang, In Yee Schuster, Stephan Ling, Xing Yi School of Chemistry, Chemical Engineering and Biotechnology Singapore Centre for Environmental Life Sciences and Engineering (SCELSE) Engineering::Bioengineering Small Molecular Probes Surface Chemistry Rapid, universal, and accurate identification of bacteria in their natural states is necessary for on-site environmental monitoring and fundamental microbial research. Surface-enhanced Raman scattering (SERS) spectroscopy emerges as an attractive tool due to its molecule-specific spectral fingerprinting and multiplexing capabilities, as well as portability and speed of readout. Here, we develop a SERS-based surface chemotaxonomy that uses bacterial extracellular matrices (ECMs) as proxy biosignatures to hierarchically classify bacteria based on their shared surface biochemical characteristics to eventually identify six distinct bacterial species at >98% classification accuracy. Corroborating with in silico simulations, we establish a three-way inter-relation between the bacteria identity, their ECM surface characteristics, and their SERS spectral fingerprints. The SERS spectra effectively capture multitiered surface biochemical insights including ensemble surface characteristics, e.g., charge and biochemical profiles, and molecular-level information, e.g., types and numbers of functional groups. Our surface chemotaxonomy thus offers an orthogonal taxonomic definition to traditional classification methods and is achieved without gene amplification, biochemical testing, or specific biomarker recognition, which holds great promise for point-of-need applications and microbial research. Agency for Science, Technology and Research (A*STAR) Ministry of Education (MOE) Nanyang Technological University National Research Foundation (NRF) This research is supported by Singapore National Research Foundation Investigatorship (NRF-NRFI08-2022-0011), A*STAR AME Individual Research Grant (A20E5c0082), and National Research Foundation Competitive Research Programme (NRF-CRP26-2021-0002). This research is also supported by Academic Research Fund Tier 3, Singapore Ministry of Education (Grant MOE2013-T3-1-013). L.B.T.N. acknowledges scholarship support from Nanyang Technological University, Singapore. 2024-01-30T00:57:02Z 2024-01-30T00:57:02Z 2023 Journal Article Leong, S. X., Tan, E. X., Han, X., Luhung, I., Aung, N. W., Nguyen, L. B. T., Tan, S. Y., Li, H., Phang, I. Y., Schuster, S. & Ling, X. Y. (2023). Surface-enhanced Raman scattering-based surface chemotaxonomy: combining bacteria extracellular matrices and machine learning for rapid and universal species identification. ACS Nano, 17(22), 23132-23143. https://dx.doi.org/10.1021/acsnano.3c09101 1936-0851 https://hdl.handle.net/10356/173360 10.1021/acsnano.3c09101 37955967 2-s2.0-85178132224 22 17 23132 23143 en NRF-NRFI08-2022-0011 A20E5c0082 NRF-CRP26-2021-0002 MOE2013-T3-1-013 ACS Nano © 2023 American Chemical Society. All rights reserved. |
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Engineering::Bioengineering Small Molecular Probes Surface Chemistry Leong, Shi Xuan Tan, Emily Xi Han, Xuemei Luhung, Irvan Aung, Ngu War Nguyen, Lam Bang Thanh Tan, Si Yan Li, Haitao Phang, In Yee Schuster, Stephan Ling, Xing Yi Surface-enhanced Raman scattering-based surface chemotaxonomy: combining bacteria extracellular matrices and machine learning for rapid and universal species identification |
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Rapid, universal, and accurate identification of bacteria in their natural states is necessary for on-site environmental monitoring and fundamental microbial research. Surface-enhanced Raman scattering (SERS) spectroscopy emerges as an attractive tool due to its molecule-specific spectral fingerprinting and multiplexing capabilities, as well as portability and speed of readout. Here, we develop a SERS-based surface chemotaxonomy that uses bacterial extracellular matrices (ECMs) as proxy biosignatures to hierarchically classify bacteria based on their shared surface biochemical characteristics to eventually identify six distinct bacterial species at >98% classification accuracy. Corroborating with in silico simulations, we establish a three-way inter-relation between the bacteria identity, their ECM surface characteristics, and their SERS spectral fingerprints. The SERS spectra effectively capture multitiered surface biochemical insights including ensemble surface characteristics, e.g., charge and biochemical profiles, and molecular-level information, e.g., types and numbers of functional groups. Our surface chemotaxonomy thus offers an orthogonal taxonomic definition to traditional classification methods and is achieved without gene amplification, biochemical testing, or specific biomarker recognition, which holds great promise for point-of-need applications and microbial research. |
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School of Chemistry, Chemical Engineering and Biotechnology |
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School of Chemistry, Chemical Engineering and Biotechnology Leong, Shi Xuan Tan, Emily Xi Han, Xuemei Luhung, Irvan Aung, Ngu War Nguyen, Lam Bang Thanh Tan, Si Yan Li, Haitao Phang, In Yee Schuster, Stephan Ling, Xing Yi |
format |
Article |
author |
Leong, Shi Xuan Tan, Emily Xi Han, Xuemei Luhung, Irvan Aung, Ngu War Nguyen, Lam Bang Thanh Tan, Si Yan Li, Haitao Phang, In Yee Schuster, Stephan Ling, Xing Yi |
author_sort |
Leong, Shi Xuan |
title |
Surface-enhanced Raman scattering-based surface chemotaxonomy: combining bacteria extracellular matrices and machine learning for rapid and universal species identification |
title_short |
Surface-enhanced Raman scattering-based surface chemotaxonomy: combining bacteria extracellular matrices and machine learning for rapid and universal species identification |
title_full |
Surface-enhanced Raman scattering-based surface chemotaxonomy: combining bacteria extracellular matrices and machine learning for rapid and universal species identification |
title_fullStr |
Surface-enhanced Raman scattering-based surface chemotaxonomy: combining bacteria extracellular matrices and machine learning for rapid and universal species identification |
title_full_unstemmed |
Surface-enhanced Raman scattering-based surface chemotaxonomy: combining bacteria extracellular matrices and machine learning for rapid and universal species identification |
title_sort |
surface-enhanced raman scattering-based surface chemotaxonomy: combining bacteria extracellular matrices and machine learning for rapid and universal species identification |
publishDate |
2024 |
url |
https://hdl.handle.net/10356/173360 |
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1789968697793708032 |