Rapid SERS identification of methicillin-susceptible and methicillin-resistant Staphylococcus aureus via aptamer recognition and deep learning

Shu Wang; Hao Dong; Wanzhu Shen; Yong Yang; Zhigang Li; Yong Liu; Chongwen Wang; Bing Gu; Long Zhang

doi:10.1039/d1ra05778b

Rapid SERS identification of methicillin-susceptible and methicillin-resistant Staphylococcus aureus via aptamer recognition and deep learning

RSC Adv. 2021 Oct 25;11(55):34425-34431. doi: 10.1039/d1ra05778b.

Authors

Shu Wang^{1

2}, Hao Dong^{1

2}, Wanzhu Shen³, Yong Yang^{1

2}, Zhigang Li¹, Yong Liu^{1

2}, Chongwen Wang³, Bing Gu⁴, Long Zhang^{1

2}

Affiliations

¹ Hefei Institute of Physical Science, Chinese Academy of Sciences Hefei 230036 P. R China zhanglong@aiofm.ac.cn.
² University of Science and Technology of China Hefei 230036 P. R China.
³ Anhui Agricultural University Hefei 230036 P. R China wangchongwen1987@126.com.
⁴ Laboratory Medicine, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences Guangzhou 510000 P. R China gb20031129@163.com.

Abstract

Here, we report a label-free surface-enhanced Raman scattering (SERS) method for the rapid and accurate identification of methicillin-susceptible Staphylococcus aureus (MSSA) and methicillin-resistant Staphylococcus aureus (MRSA) based on aptamer-guided AgNP enhancement and convolutional neural network (CNN) classification. Sixty clinical isolates of Staphylococcus aureus (S. aureus), comprising 30 strains of MSSA and 30 strains of MRSA were used to build the CNN classification model. The developed method exhibited 100% identification accuracy for MSSA and MRSA, and is thus a promising tool for the rapid detection of drug-sensitive and drug-resistant bacterial strains.