Sample Preparation Free Mass Spectrometry Using Laser-Assisted
Rapid Evaporative Ionization Mass Spectrometry: Applications to Microbiology,
Metabolic Biofluid Phenotyping, and Food Authenticity
posted on 2021-05-13, 07:35authored bySimon 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
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.