AFM-based nanomechanics and machine learning for rapid and non-destructive detection of bacterial viability

Detecting bacterial viability remains a critical necessity across the pharmaceutical, medical, and food sectors. Yet, a rapid, non-destructive approach for distinguishing between intact live and dead bacteria remains elusive. Here, this work introduces a robust and accessible methodology that integr...

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Main Authors: Xu, Xiaoyan, Feng, Haowen, Zhao, Ying, Shi, Yunzhu, Feng, Wei, Loh, Xian Jun, Vancso, Gyula Julius, Guo, Shifeng
Other Authors: School of Materials Science and Engineering
Format: Article
Language:English
Published: 2024
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Online Access:https://hdl.handle.net/10356/179817
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1798172024-08-30T15:45:27Z AFM-based nanomechanics and machine learning for rapid and non-destructive detection of bacterial viability Xu, Xiaoyan Feng, Haowen Zhao, Ying Shi, Yunzhu Feng, Wei Loh, Xian Jun Vancso, Gyula Julius Guo, Shifeng School of Materials Science and Engineering Engineering Atomic force microscopy Bacterial viability Detecting bacterial viability remains a critical necessity across the pharmaceutical, medical, and food sectors. Yet, a rapid, non-destructive approach for distinguishing between intact live and dead bacteria remains elusive. Here, this work introduces a robust and accessible methodology that integrates atomic force microscopy (AFM) imaging, quantitative nano-mechanics, and machine learning algorithms to assess the survival of gram-negative (Escherichia coli [E. coli]) and gram-positive (Staphylococcus aureus [S. aureus]) bacteria. The results reveal distinctive changes in ultraviolet-killed E. coli and S. aureus manifesting intact morphological structures but increased stiffness. Three specific features—bacterial deformation, spring constant, and Young's modulus—extracted from AFM force spectroscopy are established as pivotal inputs for a machine-learning-based stacking classifier. Trained on extensive AFM datasets encompassing known bacterial viability, this methodology demonstrates exceptional predictive accuracy exceeding 95% for both E. coli and S. aureus. These results underscore its universal applicability, rapidity, and non-destructive nature, positioning it as a definitive method for universally detecting bacterial viability. Published version This work was supported in part by the National Natural Science Foundation of China under grants 52071332, U2133213, and 51901242; in part by the Department of Science and Technology of Guangdong Province under grants 2019QN01H430 and 2019TQ05Z654; in part by the Guangdong Basic and Applied Basic Research Foundation under grant 2023B1515120090; in part by the Natural Science Foundation of Guangdong Province under grant 2023B1515040008; and in part by the Science and Technology Innovation Commission of Shenzhen under grants JCYJ20180507182239617, ZDSYS20190902093209795, and JCYJ20220818101215033. 2024-08-26T06:46:26Z 2024-08-26T06:46:26Z 2024 Journal Article Xu, X., Feng, H., Zhao, Y., Shi, Y., Feng, W., Loh, X. J., Vancso, G. J. & Guo, S. (2024). AFM-based nanomechanics and machine learning for rapid and non-destructive detection of bacterial viability. Cell Reports Physical Science, 5(4), 101902-. https://dx.doi.org/10.1016/j.xcrp.2024.101902 2666-3864 https://hdl.handle.net/10356/179817 10.1016/j.xcrp.2024.101902 2-s2.0-85189476738 4 5 101902 en Cell Reports Physical Science © 2024 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering
Atomic force microscopy
Bacterial viability
spellingShingle Engineering
Atomic force microscopy
Bacterial viability
Xu, Xiaoyan
Feng, Haowen
Zhao, Ying
Shi, Yunzhu
Feng, Wei
Loh, Xian Jun
Vancso, Gyula Julius
Guo, Shifeng
AFM-based nanomechanics and machine learning for rapid and non-destructive detection of bacterial viability
description Detecting bacterial viability remains a critical necessity across the pharmaceutical, medical, and food sectors. Yet, a rapid, non-destructive approach for distinguishing between intact live and dead bacteria remains elusive. Here, this work introduces a robust and accessible methodology that integrates atomic force microscopy (AFM) imaging, quantitative nano-mechanics, and machine learning algorithms to assess the survival of gram-negative (Escherichia coli [E. coli]) and gram-positive (Staphylococcus aureus [S. aureus]) bacteria. The results reveal distinctive changes in ultraviolet-killed E. coli and S. aureus manifesting intact morphological structures but increased stiffness. Three specific features—bacterial deformation, spring constant, and Young's modulus—extracted from AFM force spectroscopy are established as pivotal inputs for a machine-learning-based stacking classifier. Trained on extensive AFM datasets encompassing known bacterial viability, this methodology demonstrates exceptional predictive accuracy exceeding 95% for both E. coli and S. aureus. These results underscore its universal applicability, rapidity, and non-destructive nature, positioning it as a definitive method for universally detecting bacterial viability.
author2 School of Materials Science and Engineering
author_facet School of Materials Science and Engineering
Xu, Xiaoyan
Feng, Haowen
Zhao, Ying
Shi, Yunzhu
Feng, Wei
Loh, Xian Jun
Vancso, Gyula Julius
Guo, Shifeng
format Article
author Xu, Xiaoyan
Feng, Haowen
Zhao, Ying
Shi, Yunzhu
Feng, Wei
Loh, Xian Jun
Vancso, Gyula Julius
Guo, Shifeng
author_sort Xu, Xiaoyan
title AFM-based nanomechanics and machine learning for rapid and non-destructive detection of bacterial viability
title_short AFM-based nanomechanics and machine learning for rapid and non-destructive detection of bacterial viability
title_full AFM-based nanomechanics and machine learning for rapid and non-destructive detection of bacterial viability
title_fullStr AFM-based nanomechanics and machine learning for rapid and non-destructive detection of bacterial viability
title_full_unstemmed AFM-based nanomechanics and machine learning for rapid and non-destructive detection of bacterial viability
title_sort afm-based nanomechanics and machine learning for rapid and non-destructive detection of bacterial viability
publishDate 2024
url https://hdl.handle.net/10356/179817
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