Human health is at
great risk due to the spreading of antimicrobial
resistance (AMR). The lengthy procedure of conventional antimicrobial
susceptibility testing (AST) usually requires a few days. We developed
a fast Raman-assisted antibiotic susceptibility test (FRAST), which
detects single bacterial metabolic activity in the presence of antibiotics,
using Raman single-cell spectroscopy. It was found that single-cell
Raman spectra (SCRS) would show a clear and distinguishable Raman
band at the “silent zone” (2000–2300 cm–1), due to the active incorporation of deuterium from heavy water
(D2O) by antibiotic-resistant bacteria. This pilot study
has compared the FRAST and the conventional AST for six clinical standard
quality controls (four Gram-negative and two Gram-positive bacteria
strains) in response to 38 antibiotics. In total, 3200 treatments
have been carried out and approximately 64 000 SCRS have been
acquired for FRAST analysis. The result showed an overall agreement
of 88.0% between the FRAST and the conventional AST assay. The gram-staining
classification based on the linear discriminant analysis (LDA) model
of SCRS was developed, seamlessly coupling with the FRAST to further
reduce the turnaround time. We applied the FRAST to real clinical
analysis for nine urinary infectious samples and three sepsis samples.
The results were consistent with MALDI-TOF identification and the
conventional AST. Under the optimal conditions, the “sample
to report” of the FRAST could be reduced to 3 h for urine samples
and 21 h for sepsis samples. The FRAST provides fast and reliable
susceptibility tests, which could speed up microbiological analysis
for clinical practice and facilitate antibiotic stewardship.
Accurate and rapid identification of infectious bacteria is important in medicine. Raman microspectroscopy holds great promise in performing label-free identification at the single-cell level. However, due to the naturally weak Raman signal, it is a challenge to build extensive databases and achieve both accurate and fast identification. Here, we used signal-to-noise ratio (SNR) as a standard indicator for Raman data quality and performed bacterial identification using 11, 141 single-cell Raman spectra from nine bacterial strains. Subsequently, using two machine learning methods, a simple filter, and a neural networkbased denoising autoencoder (DAE), we demonstrated 92% (simple filter using 1 s/cell spectra) and 84% (DAE using 0.1 s/ cell spectra) identification accuracy. Our machine learning-aided Raman analysis paves the way for high-speed Raman microspectroscopic clinical diagnostics.
Gram staining (GS) is one of the routine microbiological operations to classify bacteria based on the cell wall structure. Accurate GS classification of pathogens is of great significance since it...
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