| 王鲁彦,侯心娇,孙慧颖,刁保卫,李杰,闫梅英.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[J].中华流行病学杂志,2024,45(9):1266-1272 |
| 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 |
| Rapid serotyping of Salmonella based on matrix assisted laser desorption ionization-time of flight mass spectrometry |
| 收稿日期:2024-03-14 出版日期:2024-09-14 |
| DOI：10.3760/cma.j.cn112338-20240314-00121 |
| 中文关键词: 基质辅助激光解析电离飞行时间质谱 血清分型 沙门菌 |
| 英文关键词: Matrix assisted laser desorption ionization-time of flight mass spectrometry Serotyping Salmonella |
| 基金项目:基于质谱法的沙门菌分型研究（KFYJ-2021-009）；主要沙门菌血清型荧光PCR鉴定技术（KFYJ-2021-027） |
| 作者 | 单位 | E-mail | | 王鲁彦 | 山东省阳信县疾病预防控制中心, 滨州 251800 | | | 侯心娇 | 传染病溯源预警与智能决策全国重点实验室, 中国疾病预防控制中心传染病预防控制所, 北京 102206
山东大学公共卫生学院, 济南 250061 | | | 孙慧颖 | 传染病溯源预警与智能决策全国重点实验室, 中国疾病预防控制中心传染病预防控制所, 北京 102206 | | | 刁保卫 | 传染病溯源预警与智能决策全国重点实验室, 中国疾病预防控制中心传染病预防控制所, 北京 102206 | | | 李杰 | 传染病溯源预警与智能决策全国重点实验室, 中国疾病预防控制中心传染病预防控制所, 北京 102206 | | | 闫梅英 | 传染病溯源预警与智能决策全国重点实验室, 中国疾病预防控制中心传染病预防控制所, 北京 102206 | yanmeiying@icdc.cn |
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| 中文摘要: |
| 目的 应用基质辅助激光解析电离飞行时间质谱（MALDI-TOF MS）对常见沙门菌血清型进行鉴定，为临床早期精确治疗提供病原学依据。方法 收集不同地区来源的500株菌株，采用血清凝集及基因组测序方法确定5种临床常见的沙门菌（包括伤寒沙门菌、甲型副伤寒沙门菌、鼠伤寒沙门菌、肠炎沙门菌、印第安纳沙门菌）血清型，提取菌株的蛋白复合物，进行图谱采集，构建肽指纹图谱数据库，利用不同模块算法建立沙门菌血清分型方法，然后用155株测试菌株评价该分型方法的准确性。结果 建立了5种沙门菌血清型MALDI-TOF MS数据库，在此基础上建立基于匹配算法和基于神经元卷积网络机器学习方法的2种沙门菌血清型分型方法，通过测试菌株分别评价2种分型方法的鉴定效果，发现基于匹配算法的MALDI-TOF MS能够快速识别、鉴定5种临床常见的沙门菌血清型，对沙门菌血清型鉴定的准确率可达100.00%，分型的敏感性及特异性均达100.00%。而且，该分型方法用时短（15~20 min）、重复性好。而基于机器学习的方法不能准确区分不同的血清型，分型的敏感性及特异性分别为82.23%和95.81%。结论 本研究建立的基于MALDI-TOF MS技术的沙门菌血清分型方法能够简便、快速、准确地进行临床常见沙门菌血清分型。 |
| 英文摘要: |
| 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. |
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