Detection of multi-resistant clinical strains of E. coli with Raman spectroscopy

Amir Nakar,Oleg Ryabchykov,Petra Rösch,Thomas Bocklitz,Jürgen Popp,Aikaterini Pistiki

doi:10.1007/s00216-021-03800-y

Abstract

In recent years, we have seen a steady rise in the prevalence of antibiotic-resistant bacteria. This creates many challenges in treating patients who carry these infections, as well as stopping and preventing outbreaks. Identifying these resistant bacteria is critical for treatment decisions and epidemiological studies. However, current methods for identification of resistance either require long cultivation steps or expensive reagents. Raman spectroscopy has been shown in the past to enable the rapid identification of bacterial strains from single cells and cultures. In this study, Raman spectroscopy was applied for the differentiation of resistant and sensitive strains of Escherichia coli. Our focus was on clinical multi-resistant (extended-spectrum β-lactam and carbapenem-resistant) bacteria from hospital patients. The spectra were collected using both UV resonance Raman spectroscopy in bulk and single-cell Raman microspectroscopy, without exposure to antibiotics. We found resistant strains have a higher nucleic acid/protein ratio, and used the spectra to train a machine learning model that differentiates resistant and sensitive strains. In addition, we applied a majority of voting system to both improve the accuracy of our models and make them more applicable for a clinical setting. This method could allow rapid and accurate identification of antibiotic resistant bacteria, and thus improve public health.Graphical abstract

Highlights

The rise of antimicrobial resistance is a global public health challenge [1]
We measured the Raman spectra of 20 E. coli strains with two spectroscopic approaches: UV resonance Raman (UVRR) spectroscopy with excitation wavelength of 244 nm and Raman microspectroscopy with excitation wavelength of 532 nm
The spectra measured by UV resonance Raman spectroscopy (UVRR) on bulk samples (Fig. 1A) represent primarily bands which are enhanced by the resonance effect of excitation with 244 nm

Summary

Introduction

The rise of antimicrobial resistance is a global public health challenge [1]. The Organisation for Economic Co-operation and Development (OECD) predicts that 2.4 million people in Europe, North America, and Australia will die from infections caused by resistant microorganisms before 2050, Amir Nakar, Aikaterini Pistiki, and Oleg Ryabchykov contributed to this work.Prescribing the correct antibiotic treatment to patients, in time, is of paramount importance for this cause [5]. The routine microbiological techniques used in clinical laboratories require at least 48 h and up to 4 days to deliver results on pathogen resistance [6, 7]. This leads physicians to use empirical treatment based on patient history and resistance rates of healthcare facilities. These treatments are not always appropriate and contribute to further increase antimicrobial resistance, since often last-line antibiotics are used unnecessarily [5, 8]

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Conclusion