Rapid Species-Level Classification of Urinary Pathogens from Raw LC-MS/MS Signals Using Machine Learning

Abstract

Urinary tract infections are among the most common infections in humans, yet their diagnosis still depends on time-consuming workflows based on microbial culture, followed by MALDI-TOF mass spectrometry. Although LC-MS/MS offers the sensitivity and specificity needed to bypass culture, conventional pipelines depend on lengthy analyses and peptide/protein identification steps, limiting the throughput and hindering its adoption in clinical settings. Here, we introduce a direct, identification-free LC-MS/MS workflow that analyzes raw ion signal and produces species-level microbial identification in about 5 min after preparation, fast enough to meet clinical throughput requirements. Our machine learning-enabled raw-signal pipeline bypasses peptide identification entirely, preserving information and eliminating the traditional interpretation stack. Across 15 independent analytical batches covering 28 clinically relevant pathogens, the method achieved high-confidence classification (MCC = 0.86). Applied to 206 clinical urine specimens across three batches, the approach reached 91% accuracy at clinically actionable microbial loads (greater than 105 CFU/mL) and, critically, 0 false positives in control specimens. The performance was lower for specimens below this threshold. These results show that raw LC-MS/MS spectra contain sufficient biological information for direct microbial diagnosis, establishing an analytical framework for clinical mass spectrometry. This proof-of-concept demonstrates that rapid, culture-free, fast microbial identification is achievable and positions raw signal inference as a promising direction for next-generation diagnostic mass spectrometry.