Deep Learning-Enhanced Raman Microspectroscopy Enables Rapid Microbial Classification and Captures Phylogenetic...

Microbial classification and taxonomic information are fundamental to microbiological studies. Raman microspectroscopy, a rapid and non-destructive single-cell analytical technique, captures intrinsic molecular fingerprints reflecting cellular biochemical composition, thereby enabling microbial classification at the single-cell level. However, current Raman-based classification frameworks allow accurate identification only for micro-organisms already represented in reference databases. These approaches often fail or yield errors for uncharacterized microorganisms. To address this limitation, we collected 6600 single-cell Raman spectra from 11 microbial species, including bacteria and fungi, and developed deep learning models for rapid classification. A hierarchical clustering (HC) framework based on Raman features extracted by a one-dimensional convolutional neural network (1D-CNN) was constructed and compared with phylogenetic trees derived from rRNA gene sequences. 1D-CNN achieved high classification performance with an overall accuracy of 99.7%. Notably, the Raman HC tree exhibited clear concordance with phylogenetic structures, particularly at the higher taxonomic levels. Validation using five independent unknown strains demonstrated that the Raman HC tree consistently positioned these strains near their closest phylogenetic relatives, in strong agreement with sequence-based analyses. Collectively, these findings highlight the potential of single-cell Raman spectroscopy with deep learning as an alternative and complementary framework for microbial taxonomic analysis, particularly for previously uncharacterized microorganisms.