Advancing living <i>Bacillus</i> spore identification: Multi-head self-attention mechanism-enabled deep learning combined with single-cell Raman spectroscopy

Many spore-forming Bacillus species can cause serious human diseases, because of accidental Bacillus spore infection. Thus, developing an identification strategy with both high sensitivity and specificity is greatly in demand. In this work, we proposed a novel approach named multi-head self-attention mechanism-guided neural network Raman platform to identify living Bacillus spores within a single-cell resolution. The multi-head self-attention mechanism-guided neural network Raman platform was created by combining single-cell Raman spectroscopy, convolutional neural network (CNN), and multi-head self-attention mechanism. To address the limited size of the original spectra dataset, Gaussian noise-based spectra augmentation was employed to increase the number of single-cell Raman spectra datasets for CNN training. Owing to the assistance of both spectra augmentation and multi-head self-attention mechanism, the obtained prediction accuracy of five Bacillus spore species was further improved from 92.29 ± 0.82% to 99.43 ± 0.15%. To figure out the spectra differences covered by the multi-head self-attention mechanism-guided CNN, the relative classification weight from typical Raman bands was visualized via multi-head self-attention mechanism curve. In the process of spectra augmentation from 0 to 1000, the distribution of relative classification weight varied from a discrete state to a more concentrated phase. More importantly, these highlighted four Raman bands (1017, 1449, 1576, and 1660[Formula: see text]cm[Formula: see text]) were assigned large weights, showing that the spectra differences in the Raman bands produced the largest contribution to prediction accuracy. It can be foreseen that, our proposed sorting platform has great potential in accurately identifying Bacillus and its related genera species at a single-cell level.