Deep Learning-Enabled Raman Spectroscopic Identification of Pathogen-Derived Extracellular Vesicles and the Biogenesis Process

, species, strain, and even down to physiological levels. By training the aNN model with a large Raman data set from six typical pathogen-derived EVs, we achieved the identification of EVs with high accuracies at all levels: exceeding 96% at the Gram and species levels, 93% at the antibiotic-resistant and sensitive strain levels, and still above 87% at the physiological level. aNN enabled Raman spectroscopy to interrogate the bacterial origin of EVs to a much higher level than previous methods. Moreover, spectral markers underpinning EV discrimination were uncovered from subtly different EV spectra via an interpretation algorithm of the integrated gradient. A further comparative analysis of the rich Raman biochemical signatures of EVs and parental pathogens clearly revealed the biogenesis process of EVs, including the selective encapsulation of biocomponents and distinct membrane compositions from the original bacteria. This developed platform provides an accurate and versatile means to identify pathogen-derived EVs, spectral markers, and the biogenesis process. It will promote rapid diagnosis and allow the timely treatment of bacterial infections.