Rapid identification of bacterial mixtures in urine using MALDI-TOF MS-based algorithm profiling coupled with magnetic enrichment.
Wenmin ChengHaimei ShiMengjing TengMenghuan YuBin FengChuanfan DingShaoning YuFan YangPublished in: The Analyst (2022)
Urinary tract infections (UTIs) are a severe public health problem caused by mono- or poly-bacteria. Culture-based methods are routinely used for the diagnosis of UTIs in clinical practice, but those are time consuming. Rapid and unambiguous identification of each pathogen in UTIs can have a significant impact on timely diagnoses and precise treatment. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) is an alternative method for the identification of pathogens in clinical laboratories. However, a certain number of pure bacteria are required for MALDI-TOF MS analysis. Here, we explored a strategy combining magnetic enrichment and MALDI-TOF MS for the rapid identification of pathogenic bacterial mixtures in urine. Fragment crystallizable mannose-binding lectin-modified Fe 3 O 4 (Fc-MBL@Fe 3 O 4 ) was used for rapid enrichment and the individual-peak-based similarity model as the analytical tool. Within 30 min, a mixture of the four most prevalent UTI-causing bacteria, Escherichia coli, Klebsiella pneumoniae , Staphylococcus aureus , and Pseudomonas aeruginosa , was successfully identified using this method. This rapid MALDI-TOF MS-based strategy has potential applications in the clinical identification of UTI pathogens.
Keyphrases
- mass spectrometry
- escherichia coli
- urinary tract infection
- public health
- klebsiella pneumoniae
- staphylococcus aureus
- pseudomonas aeruginosa
- loop mediated isothermal amplification
- bioinformatics analysis
- clinical practice
- cystic fibrosis
- deep learning
- early onset
- transcription factor
- high resolution
- gram negative
- antimicrobial resistance
- risk assessment
- binding protein
- dna binding
- methicillin resistant staphylococcus aureus
- low density lipoprotein
- sensitive detection