Abstract:
:Separating Yersinia pseudotuberculosis and Yersinia pestis is an important issue in plague diagnosis but can be extremely difficult because of the high similarity between the two species. MALDI-TOF MS has grown as a diagnostic tool with great potential in bacterial identification. Its application in this field is largely enhanced by multivariate analysis, especially in extracting subtle spectral differences. In this study, we built a complete MALDI-TOF MS data pipeline and found a Y. pestis-specific biomarker at 3063 Da closely related to Y. pestis plasminogen activation factor. Based on this, we achieved almost perfect separation between Y. pseudotuberculosis and Y. pestis (AUC = 0.999) using a supervised linear discriminant analysis (LDA) model. This is significantly better than the conventionally applied unsupervised spectral similarity comparison methods, such as hierarchical clustering analysis (HCA), which gave a separation accuracy of 75.0%. This new computing method paves the way for automatic differentiation between the two highly similar bacterial species with high separation accuracy.
journal_name
Talantajournal_title
Talantaauthors
Feng B,Shi L,Zhang H,Shi H,Ding C,Wang P,Yu Sdoi
10.1016/j.talanta.2021.122640keywords:
["Bacterial identification","Multivariate analysis","Plague","Yersinia pestis","Yersinia pseudotuberculosis"]subject
Has Abstractpub_date
2021-11-01 00:00:00pages
122640eissn
0039-9140issn
1873-3573pii
S0039-9140(21)00561-0journal_volume
234pub_type
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