Towards high-accuracy bacterial taxonomy identification using phenotypic single-cell Raman spectroscopy data.

Single-cell Raman Spectroscopy (SCRS) emerges as a promising tool for single-cell phenotyping in environmental ecological studies, offering non-intrusive, high-resolution, and high-throughput capabilities. In this study, we obtained a large and the first comprehensive SCRS dataset that captured phenotypic variations with cell growth status for 36 microbial strains, and we compared and optimized analysis techniques and classifiers for SCRS-based taxonomy identification. First, we benchmarked five dimensionality reduction (DR) methods, 10 classifiers, and the impact of cell growth variances using a SCRS dataset with both taxonomy and cellular growth stage labels. Unsupervised DR methods and non-neural network classifiers are recommended for at a balance between accuracy and time efficiency, achieved up to 96.1% taxonomy classification accuracy. Second, accuracy variances caused by cellular growth variance (<2.9% difference) was found less than the influence from model selection (up to 41.4% difference). Remarkably, simultaneous high accuracy in growth stage classification (93.3%) and taxonomy classification (94%) were achievable using an innovative two-step classifier model. Third, this study is the first to successfully apply models trained on pure culture SCRS data to achieve taxonomic identification of microbes in environmental samples at an accuracy of 79%, and with validation via Raman-FISH (fluorescence