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Related Concept Videos

MALDI-TOF Mass Spectrometry01:19

MALDI-TOF Mass Spectrometry

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Mass spectrometry is a powerful characterization technique that can identify and separate a wide variety of compounds ranging from chemical to biological entities, based on their mass-to-charge ratio (m/z). The instruments that allow this detection, known as mass spectrometers, have three components: an ion source, a mass analyzer, and a detector. These spectrometers differ based on the nature of their ion source and analyzers.
Matrix-assisted laser desorption ionization (MALDI) is a commonly...
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LC-SRM Combined With Machine Learning Enables Fast Identification and Quantification of Bacterial Pathogens in

Clarisse Gotti1, Florence Roux-Dalvai1, Ève Bérubé2

  • 1Computational Biology Laboratory, CHU de Québec - Université Laval Research Center, Québec City, Quebec, Canada; Proteomics Platform, CHU de Québec - Université Laval Research Center, Québec City, Quebec, Canada.

Molecular & Cellular Proteomics : MCP
|August 23, 2024
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Summary

This study introduces a rapid, culture-free method for detecting urinary tract infections (UTIs) in under 4 hours. The new workflow accurately identifies and quantifies bacterial pathogens, aiding timely antibiotic treatment.

Keywords:
clinical proteomicsinfectious diseasesmachine learningselected reaction monitoringtargeted proteomicsurinary tract infection

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Area of Science:

  • Clinical Microbiology
  • Analytical Chemistry
  • Bioinformatics

Background:

  • Urinary tract infections (UTIs) represent a significant global health challenge, necessitating rapid diagnostics.
  • Current gold-standard methods for bacterial identification in UTIs, involving culture and MALDI-TOF, require 24-48 hours.
  • Delayed diagnosis hinders prompt antibiotic administration and contributes to the rise of antimicrobial resistance.

Purpose of the Study:

  • To develop and validate a rapid, culture-free workflow for bacterial identification and quantification in urine samples.
  • To enable timely and accurate diagnosis of UTIs, facilitating appropriate antibiotherapy.
  • To reduce the time to result for UTI diagnostics to under 4 hours.

Main Methods:

  • A culture-free workflow involving rapid, automated sample preparation from 1 ml of urine.
  • Liquid chromatography-mass spectrometry (LC-MS) utilizing Selected Reaction Monitoring (SRM) mode on a TSQ Altis instrument.
  • Machine learning-defined peptide signature of 82 bacterial peptides for distinguishing 15 common UTI-causing species.

Main Results:

  • The workflow achieved bacterial identification and quantification in less than 4 hours.
  • Demonstrated high accuracy (99.2%) on inoculated healthy urines and robustness for analyzing up to 75 samples daily.
  • Validated on patient samples, showing 87.1% agreement with culture-MALDI for infections requiring therapy (≥ 1 × 10⁵ CFU/ml).

Conclusions:

  • The proposed culture-free LC-MS workflow offers a fast and accurate alternative for UTI diagnostics.
  • This method significantly reduces turnaround time compared to traditional culture-based techniques.
  • The workflow supports efficient management of UTIs and combats antimicrobial resistance through timely treatment.