Rapid discrimination of Shigella spp. and Escherichia coli via label-free surface enhanced Raman spectroscopy coupled with machine learning algorithms.

Shigella and enterotoxigenic Escherichia coli (ETEC) are major bacterial pathogens of diarrheal disease that is the second leading cause of childhood mortality globally. Currently, it is well known that Shigella spp., and E. coli are very closely related with many common characteristics. Evolutionarily speaking, Shigella spp., are positioned within the phylogenetic tree of E. coli. Therefore, discrimination of Shigella spp., from E. coli is very difficult. Many methods have been developed with the aim of differentiating the two species, which include but not limited to biochemical tests, nucleic acids amplification, and mass spectrometry, etc. However, these methods suffer from high false positive rates and complicated operation procedures, which requires the development of novel methods for accurate and rapid identification of Shigella spp., and E. coli. As a low-cost and non-invasive method, surface enhanced Raman spectroscopy (SERS) is currently under intensive study for its diagnostic potential in bacterial pathogens, which is worthy of further investigation for its application in bacterial discrimination. In this study, we focused on clinically isolated E. coli strains and Shigella species (spp.), that is, S. dysenteriae, S. boydii, S. flexneri, and S. sonnei, based on which SERS spectra were generated and characteristic peaks for Shigella spp., and E. coli were identified, revealing unique molecular components in the two bacterial groups. Further comparative analysis of machine learning algorithms showed that, the Convolutional Neural Network (CNN) achieved the best performance and robustness in bacterial discrimination capacity when compared with Random Forest (RF) and Support Vector Machine (SVM) algorithms. Taken together, this study confirmed that SERS paired with machine learning could achieve high accuracy in discriminating Shigella spp., from E. coli, which facilitated its application potential for diarrheal prevention and control in clinical settings. Graphical abstract.

One-step sensitive detection of Salmonella typhimurium by coupling magnetic capture and fluorescence identification with functional nanospheres.

Sensitive, rapid, and reliable detection of bacteria has always been pursued due to the great threat of the bacteria to human health. In this study, a convenient one-step strategy for detecting Salmonella typhimurium was developed. Immunomagnetic nanospheres (IMNS) and immunofluorescent nanospheres (IFNS) were used to specifically capture and recognize S. typhimurium simultaneously. After magnetic separation, the sandwich immune complexes (IMNS-bacteria-IFNS) were detected under a fluorescence microscope with a detection limit as low as ca. 10 CFU/mL. When they were detected by fluorescence spectrometer, a linear range was exhibited at the concentration from 10(5) to 10(7) CFU/mL with R(2) = 0.9994. Compared with the two-step detection strategy, in which the bacteria were first captured with the IMNS and subsequently identified with the IFNS, this one-step strategy simplified the detection process and improved the sensitivity. Escherichia coli and Shigella flexneri both showed negative results with this method, indicating that this method had excellent selectivity and specificity. Moreover, this method had strong anti-interference ability, and it had been successfully used to detect S. typhimurium in synthetic samples (milk, fetal bovine serum, and urine), showing the potential application in practice.