Raman spectroscopy and artificial intelligence open up accurate detection of pathogens from DNA-based sub-species level classification

Genomic deoxyribounucleic acid (DNA) extracted from Brucella and Bacillus genera including Bacillus anthracis was investigated for the first time using Raman spectroscopy coupled with deep learning technique. Since DNA sequence is unique and independent of growth phases of bacteria, Raman spectroscopy can be a potential molecular diagnostic tool to identify different pathogens. Additionally, pure cellular components such as DNA provide pure Raman spectra and are not corrupted by spectral features from other cell components which is usually the case in whole organism detection. In this work, 15 DNA samples (two from Brucella genus and 13 from Bacillus genus) were studied. Raman signatures revealed unique features for Brucella and Bacillus genus bacteria. We propose an artificial intelligence (AI) based method, convolutional neural network (CNN) to discriminate all 15 DNA samples. The results reveal that Bacillus anthracis has distinct Raman DNA signatures compared to Bacillus cereus and Bacillus thuringiensis and could be discriminated from the latter two using principal component analysis (PCA), hierarchical cluster analysis (HCA), principal component-linear discriminant analysis (PC-LDA). In addition to these multivariate analysis techniques, we show that using convolutional neural network (CNN) architecture all 15 DNA samples could be discriminated with 100% accuracy.

Automated summary

Genomic DNA from Brucella and Bacillus genera, including Bacillus anthracis, was studied using Raman spectroscopy and deep learning for the first time. Distinct Raman DNA signatures were found for different bacteria, and a convolutional neural network was able to discriminate between all samples with 100% accuracy.