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.

We report on rapid identification of single bacteria using a low-cost, compact, Raman spectroscope. We demonstrate that a 60-s procedure is sufficient to acquire a comprehensive Raman spectrum in the range of 600 to 3300 cm⁻¹. This time includes localization of small bacteria aggregates, alignment on a single individual, and spontaneous Raman scattering signal collection. Fast localization of small bacteria aggregates, typically composed of less than a dozen individuals, is achieved by lensfree imaging over a large field of view of 24 mm². The lensfree image also allows precise alignment of a single bacteria with the probing beam without the need for a standard microscope. Raman scattered light from a 34-mW continuous laser at 532 nm was fed to a customized spectrometer (prototype Tornado Spectral Systems). Owing to the high light throughput of this spectrometer, integration times as low as 10 s were found acceptable. We have recorded a total of 1200 spectra over seven bacterial species. Using this database and an optimized preprocessing, classification rates of ~90% were obtained. The speed and sensitivity of our Raman spectrometer pave the way for high-throughput and nondestructive real-time bacteria identification assays. This compact and low-cost technology can benefit biomedical, clinical diagnostic, and environmental applications.

Abstract. Decreasing turnaround time is a paramount objective in clinical diagnosis. We evaluated the discrimination power of Raman spectroscopy when analyzing colonies from 80 strains belonging to nine bacterial and one yeast species directly on solid culture medium after 24-h (macrocolonies) and 6-h (microcolonies) incubation. This approach, that minimizes sample preparation and culture time, would allow resuming culture after identification to perform downstream antibiotic susceptibility testing. Correct identification rates measured for macrocolonies and microcolonies reached 94.1% and 91.5%, respectively, in a leave-one-strain-out cross-validation mode without any correction for possible medium interference. Large spectral differences were observed between macrocolonies and microcolonies, that were attributed to true biological differences. Our results, conducted on a very diversified panel of species and strains, were obtained by using simple and robust sample preparation and preprocessing procedures, while still confirming published results obtained by using more complex elaborated protocols. Instrumentation is simplified by the use of 532-nm laser excitation yielding a Raman signal in the visible range. It is, to our knowledge, the first side-by-side full classification study of microorganisms in the exponential and stationary phases confirming the excellent performance of Raman spectroscopy for early species-level identification of microorganisms directly from an agar culture. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.