Rapid identification and drug resistance screening of respiratory pathogens based on single-cell Raman spectroscopy - Liu, Ziyu; Xue, Ying; Yang, Chun; Li, Bei; Zhang, Ying (Frontiers in Microbiology, 2023)
Respiratory infections rank fourth in the global economic burden of disease. Lower respiratory infections are the leading cause of death in low-income countries. How to early identify the pathogenic bacteria of lower respiratory tract infection, guide the correct use of antibiotics, can reduce the mortality of patients with lower respiratory tract infection. Single cell Raman spectroscopy is a "whole biological fingerprint" technique that can be used to identify microbial samples. It has the advantages of no marking, fast and non-destructive testing. In this study, single-cell Raman spectroscopy was used to collect spectral data of six respiratory tract pathogens isolates, and tSNE isolation analysis algorithm was used to compare the differences of six respiratory tract pathogens. The XGBoost algorithm was used to establish the Raman phenotype database model, and the classification accuracy of the isolated samples was 93-100%, and the classification accuracy of the clinical samples was more than 80%. Combined with heavy water labeling technology, the drug resistance of respiratory tract pathogens was detected. The study showed that Single cell Raman spectroscopy-D2O （SCRS-D2O）labeling could achieve the rapid identification of drug resistance of respiratory tract pathogens within 2 hours.