Surface-enhanced Raman spectroscopy for the identification of tigecycline-resistant E. coli strains

Bashir, Saba; Nawaz, Haq; Majeed, Muhammad Irfan; Mohsin, Mashkoor; Nawaz, Ali; Rashid, Nosheen; Batool, Fatima; Akbar, Saba; Abubakar, Muhammad; Ahmad, Shamsheer; Ali, Saqib; Kashif, Mohammad

doi:10.1016/j.saa.2021.119831

Cited by 48 publications

(26 citation statements)

References 81 publications

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“…In specificity, as for E . coli , the unique characteristic peaks identified in its average SERS spectrum include C=O, C-N and ring deformation at 794 cm −1 ( Kubryk et al, 2016 ), Ribose vibration, one of the distinct RNA modes at 910 cm −1 ( Szekeres and Kneipp, 2019 ), v(C-C) at 1034 cm −1 ( Athamneh et al, 2014 ), Amine III of proteins at 1248 cm −1 ( Bashir et al, 2021 ), (C-N) stretch at 1320 cm −1 ( Zeiri et al, 2004 ), and Stretch C=C in the quinoid ring at 1414 cm −1 ( Laska and Widlarz, 2005 ). On the other hand, for Shigella spp., their unique characteristic peaks include C-C chain stretch of cell wall lipids at 1096 cm −1 ( Zheng et al, 2018 ), tryptophan at 1332 cm −1 ( Xie et al, 2013 ), and Ring stretching (Adenine, guanine) at 1594 cm −1 ( Demirel et al, 2009 ).…”

Section: Resultsmentioning

confidence: 99%

Rapid discrimination of Shigella spp. and Escherichia coli via label-free surface enhanced Raman spectroscopy coupled with machine learning algorithms

Liu

Tang²,

Mou³

et al. 2023

Front. Microbiol.

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Shigella and enterotoxigenic Escherichia coli (ETEC) are major bacterial pathogens of diarrheal disease that is the second leading cause of childhood mortality globally. Currently, it is well known that Shigella spp., and E. coli are very closely related with many common characteristics. Evolutionarily speaking, Shigella spp., are positioned within the phylogenetic tree of E. coli. Therefore, discrimination of Shigella spp., from E. coli is very difficult. Many methods have been developed with the aim of differentiating the two species, which include but not limited to biochemical tests, nucleic acids amplification, and mass spectrometry, etc. However, these methods suffer from high false positive rates and complicated operation procedures, which requires the development of novel methods for accurate and rapid identification of Shigella spp., and E. coli. As a low-cost and non-invasive method, surface enhanced Raman spectroscopy (SERS) is currently under intensive study for its diagnostic potential in bacterial pathogens, which is worthy of further investigation for its application in bacterial discrimination. In this study, we focused on clinically isolated E. coli strains and Shigella species (spp.), that is, S. dysenteriae, S. boydii, S. flexneri, and S. sonnei, based on which SERS spectra were generated and characteristic peaks for Shigella spp., and E. coli were identified, revealing unique molecular components in the two bacterial groups. Further comparative analysis of machine learning algorithms showed that, the Convolutional Neural Network (CNN) achieved the best performance and robustness in bacterial discrimination capacity when compared with Random Forest (RF) and Support Vector Machine (SVM) algorithms. Taken together, this study confirmed that SERS paired with machine learning could achieve high accuracy in discriminating Shigella spp., from E. coli, which facilitated its application potential for diarrheal prevention and control in clinical settings.

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Section: Resultsmentioning

confidence: 99%

Rapid discrimination of Shigella spp. and Escherichia coli via label-free surface enhanced Raman spectroscopy coupled with machine learning algorithms

Liu

Tang²,

Mou³

et al. 2023

Front. Microbiol.

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“…For the electronic noise composed of cosmic noise, flicker noise, and thermal noise, it will randomly appear in multiple positions of the spectral curve in an unpredictable form, which has a large impact on the quality of Raman spectroscopy data. Savitzky-Golay (S-G) filtering is one of the most commonly used preprocessing methods in the process of smoothing and denoising Raman spectra [99,100]. This method can keep the shape and width of the signal unchanged while filtering the noise, so as to meet the processing requirements of Raman spectral data in different situations [101].…”

Section: Raman Spectroscopy Preprocessingmentioning

confidence: 99%

“…This method can keep the shape and width of the signal unchanged while filtering the noise, so as to meet the processing requirements of Raman spectral data in different situations [101]. As one of the recognized best processing steps in Raman spectrum analysis preprocessing [96], baseline correction is used to deal with the continuous distortion caused by uncontrollable factors during Raman spectrum acquisition, such as removing substrate-related Raman signals [99] and fluorescence signals generated by the sample itself [102]. Commonly used methods are asymmetric weighted penalized least squares (arPLS) algorithm [103], adaptive iterative weighted penalized least squares (airPLS) algorithm and polynomial fitting [104].…”

Section: Raman Spectroscopy Preprocessingmentioning

confidence: 99%

Recent Progress in the Diagnosis of Staphylococcus in Clinical Settings

Zhang¹,

Gu²,

Usman³

et al. 2023

Infectious Diseases

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Staphylococci are mainly found on the skin or in the nose. These bacteria are typically friendly, causing no harm to healthy individuals or resulting in only minor issues that can go away on their own. However, under certain circumstances, staphylococcal bacteria could invade the bloodstream, affect the entire body, and lead to life-threatening problems like septic shock. In addition, antibiotic-resistant Staphylococcus is another issue because of its difficulty in the treatment of infections, such as the notorious methicillin-resistant Staphylococcus aureus (MRSA) which is resistant to most of the currently known antibiotics. Therefore, rapid and accurate diagnosis of Staphylococcus and characterization of the antibiotic resistance profiles are essential in clinical settings for efficient prevention, control, and treatment of the bacteria. This chapter highlights recent advances in the diagnosis of Staphylococci in clinical settings with a focus on the advanced technique of surface-enhanced Raman spectroscopy (SERS), which will provide a framework for the real-world applications of novel diagnostic techniques in medical laboratories via bench-top instruments and at the bedside through point-of-care devices.

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“…Raman-based technology is an emerging approach for bacterial identification by measuring the spectral differences in bacteria and AST by monitoring bacteria’s spectral response to antibiotic treatment ( Dina et al, 2017 ; Ivleva et al, 2017 ; Fang et al, 2019 ; Ho et al, 2019 ; Thrift et al, 2020 ; Bashir et al, 2021 ). Especially, Raman-based technology using bacterial metabolism as a marker has proven to be a promising alternative for rapid AST ( Tao et al, 2017 ; Hong et al, 2018 ; Yang et al, 2019 ; Bauer et al, 2020 ; Yi et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Compound Raman microscopy for rapid diagnosis and antimicrobial susceptibility testing of pathogenic bacteria in urine

Zhang

Sun

et al. 2022

Front. Microbiol.

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Rapid identification and antimicrobial susceptibility testing (AST) of bacteria are key interventions to curb the spread and emergence of antimicrobial resistance. The current gold standard identification and AST methods provide comprehensive diagnostic information but often take 3 to 5 days. Here, a compound Raman microscopy (CRM), which integrates Raman spectroscopy and stimulated Raman scattering microscopy in one system, is presented and demonstrated for rapid identification and AST of pathogens in urine. We generated an extensive bacterial Raman spectral dataset and applied deep learning to identify common clinical bacterial pathogens. In addition, we employed stimulated Raman scattering microscopy to quantify bacterial metabolic activity to determine their antimicrobial susceptibility. For proof-of-concept, we demonstrated an integrated assay to diagnose urinary tract infection pathogens, S. aureus and E. coli. Notably, the CRM system has the unique ability to provide Gram-staining classification and AST results within ~3 h directly from urine samples and shows great potential for clinical applications.

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Surface-enhanced Raman spectroscopy for the identification of tigecycline-resistant E. coli strains

Cited by 48 publications

References 81 publications

Rapid discrimination of Shigella spp. and Escherichia coli via label-free surface enhanced Raman spectroscopy coupled with machine learning algorithms

Rapid discrimination of Shigella spp. and Escherichia coli via label-free surface enhanced Raman spectroscopy coupled with machine learning algorithms

Recent Progress in the Diagnosis of Staphylococcus in Clinical Settings

Compound Raman microscopy for rapid diagnosis and antimicrobial susceptibility testing of pathogenic bacteria in urine

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