通过通过预期最大化算法的新修改通过干扰去除增强的Tandem质量标签标记多重化微生物的识别
Gelio Alves1, Aleksey Y Ogurtsov1, Harry Porterfield2
1National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland 20894, United States.
概括
这项研究引入了一种使用双重质量标签 (TMT) 和预期最大化算法的新方法,以加快通过质谱测量来识别微生物. 这种方法大大缩短了分析时间,同时保持了对公共卫生和研究应用的高准确性.
科学领域:
- 微生物学 微生物学
- 蛋白质组学是指蛋白质组学.
- 分析化学 分析化学
背景情况:
- 准确的微生物识别对于公共卫生,研究和安全至关重要.
- 基于质谱的蛋白质组学提供了有效的微生物鉴定,但受到长时间分析时间 (大约2个小时) 的限制. 1小时/样本).
- 这种时间限制阻碍了常规的临床诊断和文化学.
研究的目的:
- 开发一种使用质谱仪更快地识别微生物的方法.
- 为了解决当前蛋白质分子识别技术的时间限制.
- 提高对临床和研究环境的微生物分析的效率.
主要方法:
- 在单个MS/MS实验中,采用双重质量标签 (TMT) 进行微生物的多重识别.
- 使用修改后的预期最大化 (EM) 算法来纠正TMT记者离子强度的干扰.
- 使用94个MS/MS实验验验证了该方法,其中有1931个真正阳性和317个真负的TMT标记通道.
主要成果:
- 实现了高物种级别的识别灵敏度为93-97%和100%的特异性.
- 每个样品的质谱仪时间显著减少:从1小时减少到10分钟 (TMT-6) 和6分钟 (TMT-10).
- 展示了与先进的质谱仪 (如Orbitrap Astral) 的兼容性,以进一步缩短时间.
结论:
- 提出的基于TMT的多重复合方法与EM校正大大加快了微生物的识别.
- 这一进步克服了临床诊断和文化经济学的一个主要瓶.
- 该方法为微生物分析提供了更快,更准确和更广泛的解决方案.
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