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This paper introduces a Raman spectroscopy technique for bacterial identification directly from unopened agar plates, eliminating the need for additional sample preparation. The method achieves single gene-level sensitivity, distinguishing between E. coli variants with and without GFP expression across different media. By integrating DFT-based material computation with machine learning, the approach achieves over 97.7% classification accuracy, outperforming standard measurements from opened plates.
Skip the prep: Raman spectroscopy can now identify bacteria directly on unopened agar plates with single-gene sensitivity, outperforming traditional methods.
Raman spectroscopy is a promising tool for microbial identification, yet its implementation in microbiology and clinical workflow is still restricted due to the accompanying additional preparation required to focus on microbial signals. Here, we demonstrate Raman-based bacterial identification directly from unopened, inverted agar plates, the same conditions used during incubation. Our approach enabled identification with single gene-level sensitivity using two Escherichia coli variants, differing only in green fluorescent protein (GFP) expression, across diverse media and substrate material conditions, despite the interrogation path traversing 3-4 mm thick background material. We integrated traditional density functional theory (DFT)-based material computation with machine learning analysis, achieving over 97.7% classification accuracy, surpassing the performance of standard measurements from opened plates by 10.8% higher mean accuracy and 0.76% less variance. We further demonstrated Raman mapping-based colony identification via Raman peaks characteristic to GFPmut3 chromophore structure generated by DFT. Our approach is robust to changes in algorithms or substrate materials and promises real-time, non-perturbative monitoring of bacterial growth, biofilm formation, and antimicrobial resistance development.