Dataset Information

Fast and accurate bacterial species identification in biological samples using LC-MS/MS mass spectrometry and machine learning (DDA dataset)

ABSTRACT: We have developed a new strategy for identifying bacterial species in biological samples using specific LC-MS/MS peptidic signatures. In the first training step, deep proteome coverage of bacteria of interest is obtained in Data Independent Acquisition (DIA) mode, followed by the use of machine learning to define the peptides the most susceptible to distinguish each bacterial species from the others. Then, in the second step, this peptidic signature is monitored in biological samples using targeted proteomics. This method, which allows the bacterial identification from clinical specimens in less than 4h, has been applied to 15 species representing 84% of all Urinary Tract Infections (UTI). This dataset contains all the DDA files used to create bacterial spectral libraries prior to DIA analyses.

INSTRUMENT(S): Orbitrap Fusion ETD

ORGANISM(S): Citrobacter Freundii (ncbitaxon:546) Streptococcus Agalactiae 2603v/r (ncbitaxon:208435) Staphylococcus Aureus Subsp. Aureus Nctc 8325 (ncbitaxon:93061) Streptococcus Mitis (ncbitaxon:28037) Klebsiella Oxytoca (ncbitaxon:571) Staphylococcus Epidermidis Atcc 12228 (ncbitaxon:176280) Enterococcus Faecalis V583 (ncbitaxon:226185) Enterobacter Aerogenes Kctc 2190 (ncbitaxon:1028307) Proteus Mirabilis (ncbitaxon:584) Escherichia Coli K-12 (ncbitaxon:83333) Enterobacter Cloacae (ncbitaxon:550) Klebsiella Pneumoniae Subsp. Pneumoniae Mgh 78578 (ncbitaxon:272620) Pseudomonas Aeruginosa Pao1 (ncbitaxon:208964) Staphylococcus Saprophyticus Subsp. Saprophyticus Atcc 15305 (ncbitaxon:342451) Staphylococcus Haemolyticus Jcsc1435 (ncbitaxon:279808)

SUBMITTER: Arnaud Droit

PROVIDER: MSV000083784 | MassIVE | Thu May 16 08:14:00 BST 2019

SECONDARY ACCESSION(S): PXD013885

REPOSITORIES: MassIVE

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Publications

Fast and Accurate Bacterial Species Identification in Urine Specimens Using LC-MS/MS Mass Spectrometry and Machine Learning.

Roux-Dalvai Florence F Gotti Clarisse C Leclercq Mickaël M Hélie Marie-Claude MC Boissinot Maurice M Arrey Tabiwang N TN Dauly Claire C Fournier Frédéric F Kelly Isabelle I Marcoux Judith J Bestman-Smith Julie J Bergeron Michel G MG Droit Arnaud A

Molecular & cellular proteomics : MCP 20191004 12

Fast identification of microbial species in clinical samples is essential to provide an appropriate antibiotherapy to the patient and reduce the prescription of broad-spectrum antimicrobials leading to antibioresistances. MALDI-TOF-MS technology has become a tool of choice for microbial identification but has several drawbacks: it requires a long step of bacterial culture before analysis (≥24 h), has a low specificity and is not quantitative. We developed a new strategy for identifying bacterial ...[more]

PMID: 31585987

Similar Datasets

Project description:Fast identification of microbial species in clinical samples is essential to provide an appropriate antibiotherapy to the patient and reduce the prescription of broad-spectrum antimicrobials leading to antibioresistances. MALDI-TOF-MS technology has become a tool of choice for microbial identification but has several drawbacks: it requires a long step of bacterial culture before analysis (≥24 h), has a low specificity and is not quantitative. We developed a new strategy for identifying bacterial species in urine using specific LC-MS/MS peptidic signatures. In the first training step, libraries of peptides are obtained on pure bacterial colonies in DDA mode, their detection in urine is then verified in DIA mode, followed by the use of machine learning classifiers (NaiveBayes, BayesNet and Hoeffding tree) to define a peptidic signature to distinguish each bacterial species from the others. Then, in the second step, this signature is monitored in unknown urine samples using targeted proteomics. This method, allowing bacterial identification in less than 4 h, has been applied to fifteen species representing 84% of all Urinary Tract Infections. More than 31,000 peptides in 190 samples were quantified by DIA and classified by machine learning to determine an 82 peptides signature and build a prediction model. This signature was validated for its use in routine using Parallel Reaction Monitoring on two different instruments. Linearity and reproducibility of the method were demonstrated as well as its accuracy on donor specimens. Within 4h and without bacterial culture, our method was able to predict the predominant bacteria infecting a sample in 97% of cases and 100% above the standard threshold. This work demonstrates the efficiency of our method for the rapid and specific identification of the bacterial species causing UTI and could be extended in the future to other biological specimens and to bacteria having specific virulence or resistance factors.