Automatic identification of mixed bacterial species fingerprints in a MALDI-TOF mass-spectrum
Automatic identification of mixed bacterial species fingerprints in a MALDI-TOF mass-spectrum
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DOI：
10.1093/bioinformatics/btu022
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发表时间：
2014-05-01
期刊：
影响因子：
5.8
通讯作者：
Veyrieras, Jean-Baptiste
中科院分区：
文献类型：
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作者：
Mahe, Pierre;Arsac, Maud;Veyrieras, Jean-Baptiste
动机：基质辅助激光解吸/电离飞行时间质谱仪已被常规临床微生物学实验室广泛采用，用于细菌种类鉴定。目标微生物的隔离菌落是唯一的先决条件。目前，直接从临床标本中进行基于MS的微生物鉴定不能常规进行，因为它提出了两个主要挑战：(I)样品本身的性质可能会增加技术变异性的水平，并带来相对于参考数据库的异质性；(Ii)可能会遇到多个微生物样品，从而产生“混合”的MS指纹。在这篇文章中，我们介绍了一种基于单质谱来推断多个微生物样品组成的新方法。我们的方法依赖于惩罚的非负线性回归框架，利用物种特定的原型，可以直接从常规的纯光谱参考数据库中获得。结果：从体外单微生物和双微生物样品获得的大量光谱数据集使我们能够以综合的方式评估该方法的性能。如果参考基质辅助激光解吸/电离飞行时间质谱仪指纹对于单个物种足够清晰，该方法自动预测样品中存在哪些细菌物种。只有很少的样本(5.3%)被错误识别，高达61.2%的病例正确识别了双微生物样本。该方法可用于临床常规微生物学实践。
Motivation: Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry has been broadly adopted by routine clinical microbiology laboratories for bacterial species identification. An isolated colony of the targeted microorganism is the single prerequisite. Currently, MS-based microbial identification directly from clinical specimens can not be routinely performed, as it raises two main challenges: (i) the nature of the sample itself may increase the level of technical variability and bring heterogeneity with respect to the reference database and (ii) the possibility of encountering polymicrobial samples that will yield a 'mixed' MS fingerprint. In this article, we introduce a new method to infer the composition of polymicrobial samples on the basis of a single mass spectrum. Our approach relies on a penalized non-negative linear regression framework making use of species-specific prototypes, which can be derived directly from the routine reference database of pure spectra.Results: A large spectral dataset obtained from in vitro mono- and bi-microbial samples allowed us to evaluate the performance of the method in a comprehensive way. Provided that the reference matrix-assisted laser desorption/ionization time-of-flight mass spectrometry fingerprints were sufficiently distinct for the individual species, the method automatically predicted which bacterial species were present in the sample. Only few samples (5.3%) were misidentified, and bi-microbial samples were correctly identified in up to 61.2% of the cases. This method could be used in routine clinical microbiology practice.