4.5 Article

Sample Preparation Free Mass Spectrometry Using Laser-Assisted Rapid Evaporative Ionization Mass Spectrometry: Applications to Microbiology, Metabolic Biofluid Phenotyping, and Food Authenticity

Journal

Publisher

AMER CHEMICAL SOC
DOI: 10.1021/jasms.0c00452

Keywords

mass spectrometry; ambient ionization mass spectrometry; automation; microbiology; food authenticity; metabolic phenotyping

Funding

  1. WatersCorporation
  2. Biotechnology and Biological Sciences Research Council [BB/L020858/1]
  3. European Research Council [617896]
  4. NIHR BRC
  5. European Research Council (ERC) [617896] Funding Source: European Research Council (ERC)
  6. BBSRC [BB/L020858/1] Funding Source: UKRI

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This study introduces an automated sample-preparation-free mass spectrometry platform utilizing a CO2 laser for sample thermal desorption, achieving high signal-to-noise ratios. The platform is suitable for analyzing three diverse biological materials and has shown promise in clinical microbiology, diabetes research, and food authenticity testing.
Mass spectrometry has established itself as a powerful tool in the chemical, biological, medical, environmental, and agricultural fields. However, experimental approaches and potential application areas have been limited by a traditional reliance on sample preparation, extraction, and chromatographic separation. Ambient ionization mass spectrometry methods have addressed this challenge but are still somewhat restricted in requirements for sample manipulation to make it suitable for analysis. These limitations are particularly restrictive in view of the move toward high-throughput and automated analytical workflows. To address this, we present what we consider to be the first automated sample-preparation-free mass spectrometry platform utilizing a carbon dioxide (CO2) laser for sample thermal desorption linked to the rapid evaporative ionization mass spectrometry (LA-REIMS) methodology. We show that the pulsatile operation of the CO2 laser is the primary factor in achieving high signal-to-noise ratios. We further show that the LA-REIMS automated platform is suited to the analysis of three diverse biological materials within different application areas. First, clinical microbiology isolates were classified to species level with an accuracy of 97.2%, the highest accuracy reported in current literature. Second, fecal samples from a type 2 diabetes mellitus cohort were analyzed with LA-REIMS, which allowed tentative identification of biomarkers which are potentially associated with disease pathogenesis and a disease classification accuracy of 94%. Finally, we showed the ability of the LA-REIMS system to detect instances of adulteration of cooking oil and determine the geographical area of production of three protected olive oil products with 100% classification accuracy.

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