Abstract
Raman spectrometry appears to be an opportunity to perform rapid tests in microbiological diagnostics as it provides phenotype-related information from single bacterial cells thus holding the promise of direct analysis of clinical specimens without any time-consuming growth phase. Here, we demonstrate the feasibility of a rapid antibiotic-susceptibility determination based on the use of Raman spectra acquired on single bacterial cells. After a two-hour preculture step, one susceptible and two resistant E. coli strains were incubated, for only two hours, in the presence of different bactericidal antibiotics (gentamicin, ciprofloxacin, amoxicillin) in a range of concentrations that included the clinical breakpoints used as references in microbial diagnostic. Spectra were acquired and processed to isolate spectral modifications associated with the antibiotic effect. We evidenced an “antibiotic effect signature” which is expressed with specific Raman peaks and the coexistence of three spectral populations in the presence of antibiotic. We devised an algorithm and a test procedure that overcome single-cell heterogeneities to estimate the MIC and determinate the susceptibility phenotype of the tested bacteria using only a few single-cell spectra in four hours only if including the preculture step.
Highlights
Raman spectrometry appears to be an opportunity to perform rapid tests in microbiological diagnostics as it provides phenotype-related information from single bacterial cells holding the promise of direct analysis of clinical specimens without any time-consuming growth phase
Based on the same principle, we incubated a series of bacterial suspensions in the presence of antibiotics at increasing concentrations ranging from below low breakpoint to above high breakpoint defined by the EUCAST
We addressed the question of performing an Antibiotic Susceptibility Testing (AST) using Raman spectrometry on single bacterial cells
Summary
Raman spectrometry appears to be an opportunity to perform rapid tests in microbiological diagnostics as it provides phenotype-related information from single bacterial cells holding the promise of direct analysis of clinical specimens without any time-consuming growth phase. One of the fastest approaches for resistance detection consists in molecular biology – mostly PCR-based – assays Such assays only require small numbers of micro-organisms and can be performed directly from a clinical specimen in just hours without requiring any time-consuming bacterial culture. Such tests are based on amplification of known antibiotic resistance genes and inherently fail to detect unknown or unexpected phenotypes[6,7]. We demonstrate, by analyzing these differences through a Support Vector Machine (SVM) with a radial basis kernel, that this differential spectral behavior can be used to determinate the susceptibility phenotype and estimate the MIC of the considered strain, and that this prediction can be done using only a very small number of cells of the considered bacterial population
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