Fast and accurate bacterial species identification in biological samples using LC-MS/MS mass spectrometry and machine learning (DIA 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 DIA files that has been used by the machine learnings algorithms to define a peptidic signture for UTI.

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: MSV000083793 | MassIVE | Thu May 16 16:04:00 BST 2019

SECONDARY ACCESSION(S): PXD013888

REPOSITORIES: MassIVE