Identification of pathogens and detection of antibiotic susceptibility at single-cell resolution by Raman spectroscopy combined with machine learning

Weilai Lu; Haifei Li; Haoning Qiu; Lu Wang; Jie Feng; Yu Vincent Fu

doi:10.3389/fmicb.2022.1076965

Identification of pathogens and detection of antibiotic susceptibility at single-cell resolution by Raman spectroscopy combined with machine learning

Front Microbiol. 2023 Jan 4:13:1076965. doi: 10.3389/fmicb.2022.1076965. eCollection 2022.

Authors

Weilai Lu^{1

2}, Haifei Li¹, Haoning Qiu^{1

2}, Lu Wang^{1

2}, Jie Feng¹, Yu Vincent Fu^{1

3}

Affiliations

¹ State Key Laboratory of Microbial Resources, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China.
² College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China.
³ Savaid Medical School, University of Chinese Academy of Sciences, Beijing, China.

Abstract

Rapid, accurate, and label-free detection of pathogenic bacteria and antibiotic resistance at single-cell resolution is a technological challenge for clinical diagnosis. Overcoming the cumbersome culture process of pathogenic bacteria and time-consuming antibiotic susceptibility assays will significantly benefit early diagnosis and optimize the use of antibiotics in clinics. Raman spectroscopy can collect molecular fingerprints of pathogenic bacteria in a label-free and culture-independent manner, which is suitable for pathogen diagnosis at single-cell resolution. Here, we report a method based on Raman spectroscopy combined with machine learning to rapidly and accurately identify pathogenic bacteria and detect antibiotic resistance at single-cell resolution. Our results show that the average accuracy of identification of 12 species of common pathogenic bacteria by the machine learning method is 90.73 ± 9.72%. Antibiotic-sensitive and antibiotic-resistant strains of Acinetobacter baumannii isolated from hospital patients were distinguished with 99.92 ± 0.06% accuracy using the machine learning model. Meanwhile, we found that sensitive strains had a higher nucleic acid/protein ratio and antibiotic-resistant strains possessed abundant amide II structures in proteins. This study suggests that Raman spectroscopy is a promising method for rapidly identifying pathogens and detecting their antibiotic susceptibility.

Keywords: Raman spectroscopy; antibiotic resistance; machine learning; pathogenic bacteria; single cell.