Rapid serotyping of Salmonella based on matrix assisted laser desorption ionization-time of flight mass spectrometry
10.3760/cma.j.cn112338-20240314-00121
- VernacularTitle:基于基质辅助激光解析电离飞行时间质谱技术的快速沙门菌血清型分型
- Author:
Luyan WANG
1
;
Xinjiao HOU
;
Huiying SUN
;
Baowei DIAO
;
Jie LI
;
Meiying YAN
Author Information
1. 山东省阳信县疾病预防控制中心，滨州 251800
- Keywords:
Matrix assisted laser desorption ionization-time of flight mass spectrometry;
Serotyping;
Salmonella
- From:
Chinese Journal of Epidemiology
2024;45(9):1266-1272
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To establish a matrix assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) assay for the identification of common Salmonella serotypes and provide etiology evidence for the early precise treatment of salmonellosis. Methods:A total of 500 strains were collected from different regions and sources and five predominant Salmonella serotypes ( Salmonella Typhi , Salmonella Paratyphi A , Salmonella Typhimurium , Salmonella Enteritidis , and Salmonella Indiana) of each strain was identified by agglutination test and whole-genome sequencing. The protein complex of the strains was extracted by using optimized pretreatment method to establish the fingerprint database of peptides for each Salmonella serotype. The new serotyping assays were established by using different modules based on the mass spectra database. Additional 155 strains with specified serotypes and variant sources were used to test and evaluate the accuracy of the new typing assays. Results:Five MALDI-TOF MS databases were established, and two new serotyping assays were established via peptide fingerprint mapping/matching and machine learning of the neuronal convolutional network respectively based on the databases. The results showed that the fingerprint matching approach could quickly identify five common Salmonella serotypes in clinical practice compared with the machine learning method, the accuracy of fingerprint matching assay to identify five Salmonella serotypes reached 100.00% and the serotyping can be conducted within a short time (15-20 minutes) and had a good reproducibility, while the machine learning method could not completely identify these serotypes. Moreover the sensitivity and specificity of fingerprint matching assay were all 100.00% respectively, while they were only 82.23% and 95.81% for machine learning method. Conclusion:The established Salmonella serotyping assay based on MALDI-TOF MS in this study can easily, rapidly and accurately identify different serotypes of Salmonella.
