Sample preparation free mass spectrometry using laser-assisted rapid evaporative Ionization mass spectrometry: applications to microbiology, metabolic biofluid phenotyping, and food authenticity

Simon J. S. Cameron*, Alvaro Perdones-Montero, Lieven Van Meulebroek, Adam Burke, Kate Alexander-Hardiman, Daniel Simon, Richard Schaffer, Julia Balog, Tamas Karancsi, Tony Rickards, Monica Rebec, Sara Stead, Lynn Vanhaecke, Zoltán Takáts*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)
224 Downloads (Pure)

Abstract

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.


Original languageEnglish
Pages (from-to)1393–1401
Number of pages9
JournalJournal of the American Society for Mass Spectrometry
Volume32
Issue number6
Early online date13 May 2021
DOIs
Publication statusPublished - 02 Jun 2021

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