Direct MALDI-TOF MS Identification of Bacterial Mixtures

Yi Yang, Yu Lin*, Liang Qiao

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    55 Citations (Scopus)

    Abstract

    Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) is now widely used to characterize bacterial samples for clinical diagnosis, food safety control, environmental monitoring, and so on. However, existing standard approaches are only applied to analyze single colonies purified by plate culture, which limits the approaches to cultivable bacteria and makes the whole approaches time-consuming. In this work, we propose a new framework to analyze MALDI-TOF spectra of bacterial mixtures and to directly characterize each component without purification procedures. The framework is a combination of a synthetic mixture model based on a non-negative linear combination of candidate reference spectra and a statistical assessment by in silico generated spectra via a jackknife resampling. Ninety-seven model bacterial mixture samples and 8 cocultured blind-coded bacterial mixture samples, containing up to 6 strains in varied ratios in each sample, together with a reference database containing the mass spectra of 1081 strains, were used to validate the framework. High sensitivity (>80%, with error rate <5%) was achieved for balanced binary and ternary mixtures. The sensitivity was >60% for balanced quaternary and pentabasic mixtures, and 48%-71% for asymmetric situation, with error rate <5%. The work can facilitate rapid and reliable characterization of bacterial mixtures without purification procedures, which is of practical value in clinical diagnosis, food safety control, environmental monitoring, and so on. The framework can be further applied to many other spectroscopy-based analytics to interpret spectra from mixed samples.

    Original languageEnglish
    Pages (from-to)10400-10408
    Number of pages9
    JournalAnalytical Chemistry
    Volume90
    Issue number17
    DOIs
    Publication statusPublished - 4 Sept 2018

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