The fast identification of microbial species in clinical samples is essential to provide an appropriated antiobiotherapy to the patient and to reduce the prescription of broad spectrum antimicrobials leading to antibioresistances. We have developed a new strategy for the fast identification of bacterial species in urine using a specific peptide signature designed by combination of proteomics data and machine learning approaches. Thereby, we have developed a 82 peptides signature which, we monitored by targeted proteomics, is able to distinguish between the 15 species the most frequently found in Urinary Tract Infections (UTIs). Our method allows the bacterial identification in less than 4 hours without culturing.

This PRM dataset contains contains 4 experiments in which the 82 peptides signature has been monitored: - Validation of the peptide signature on a nanoflow chromatography - Orbitrap Fusion system (90 minutes gradient) for the detection of the 4 major bacteria of UTIs artificially inoculated in healthy urine. 5 five bacterial concentrations have been tested in 4 different urines. - Same validation with the same sample on a capillaryflow chromatography - Q-Exactive HF-X system (30 minutes gradient) operating in another lab. - Monitoring of the signature on 27 patient urine specimens for benchmarking with standard method (bacterial culture + MALDI-TOF analysis). - Monitoring of the signature on 15 healthy urines inoculated with the 15 different bacterial species for benchmarking with standard method (bacterial culture + MALDI-TOF analysis).

10 mL healthy urine inoculated with various amount of one the 15 bacterial species of interest or 10 mL of patient urine specimen The samples were diffrentially centrifuged to remove the human cells and pellet the bacterial cells. Proteins were then extracted using 1% sodium deoxycholate in 50mM ammonium bicarbonate and trypsin-digested using a short protocol (1h, 58oC). The resulting peptides were purified on C18 and half of each sample was injected on the LC-MS system.