{
  "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.",
  "abstract_shown": true,
  "affiliations": [
    "Computational Biology Laboratory, CHU de Québec-Université Laval Research Center, Québec City, QuebecG1V 4G2, Canada.",
    "Proteomics Platform, CHU de Québec-Université Laval Research Center, Québec City, QuebecG1V 4G2, Canada.",
    "Centre de Recherche en Infectiologie de l'Université Laval, Axe Maladies Infectieuses et Immunitaires, Centre de Recherche du CHU de Québec-UniversitéLaval, Québec City, QuebecG1V 4G2, Canada.",
    "Evosep Biosystems, OdenseDK-5230, Denmark.",
    "ThermoFisher Scientific, Bremen28199, Germany.",
    "Université Côte d'Azur, Inria, CNRS, I3S, Nice06903, France."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 14,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Simon J Pelletier",
      "orcid": "0000-0003-2515-1633"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Antoine Lacombe-Rastoll"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Florence Roux-Dalvai"
    },
    {
      "affs": [
        0
      ],
      "name": "Mickaël Leclercq",
      "orcid": "0000-0001-6205-888X"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Clarisse Gotti"
    },
    {
      "affs": [
        2
      ],
      "name": "Eve Bérubé"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Pascaline Bories",
      "orcid": "0009-0009-7075-7424"
    },
    {
      "affs": [
        1
      ],
      "name": "Marie-Ève Thibeault"
    },
    {
      "affs": [
        3
      ],
      "name": "Dorte B Bekker-Jensen"
    },
    {
      "affs": [
        3
      ],
      "name": "Nicolai Bache"
    },
    {
      "affs": [
        4
      ],
      "name": "Maciej Bromirski"
    },
    {
      "affs": [
        2
      ],
      "name": "Sandra Isabel"
    },
    {
      "affs": [
        5
      ],
      "name": "Frederic Precioso"
    },
    {
      "affs": [
        0,
        1,
        5
      ],
      "name": "Arnaud Droit"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/dd34fnd4qmr6y.bib",
  "cited_by": 0,
  "doi": "10.1021/acs.analchem.6c02725",
  "fulltext_url": "https://europepmc.org/articles/PMC13647984",
  "id": "dd34fnd4qmr6y",
  "issns": [
    "0003-2700",
    "1520-6882"
  ],
  "landing_url": "https://europepmc.org/article/MED/42915683",
  "language": "eng",
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1308|Bacterial Identification and Susceptibility Testing"
    ]
  ],
  "openalex": "W7213872641",
  "pdf_url": "https://europepmc.org/articles/PMC13647984?pdf=render",
  "pmcid": "PMC13647984",
  "pmid": "42915683",
  "published": "2026-10-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/dd34fnd4qmr6y.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:36Z",
      "native_id": "MED:42915683",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42915683"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:1308|Bacterial Identification and Susceptibility Testing",
      "subfield": "general"
    }
  ],
  "title": "Rapid Species-Level Classification of Urinary Pathogens from Raw LC-MS/MS Signals Using Machine Learning.",
  "title_text": "Rapid Species-Level Classification of Urinary Pathogens from Raw LC-MS/MS Signals Using Machine Learning.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:20:36Z",
  "url": "https://research.draconitic.com/paper/dd34fnd4qmr6y",
  "venue": "Analytical chemistry",
  "versions": []
}