Scientific Reports (Jul 2024)

Rapid differentiation of cystic fibrosis-related bacteria via reagentless atmospheric pressure photoionisation mass spectrometry

  • Adam Haworth-Duff,
  • Barry L. Smith,
  • Tung-Ting Sham,
  • Cedric Boisdon,
  • Paul Loughnane,
  • Mark Burnley,
  • Daniel B. Hawcutt,
  • Rasmita Raval,
  • Simon Maher

DOI
https://doi.org/10.1038/s41598-024-66851-y
Journal volume & issue
Vol. 14, no. 1
pp. 1 – 13

Abstract

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Abstract Breath analysis is an area of significant interest in medical research as it allows for non-invasive sampling with exceptional potential for disease monitoring and diagnosis. Volatile organic compounds (VOCs) found in breath can offer critical insight into a person’s lifestyle and/or disease/health state. To this end, the development of a rapid, sensitive, cost-effective and potentially portable method for the detection of key compounds in breath would mark a significant advancement. Herein, we have designed, built and tested a novel reagent-less atmospheric pressure photoionisation (APPI) source, coupled with mass spectrometry (MS), utilising a bespoke bias electrode within a custom 3D printed sampling chamber for direct analysis of VOCs. Optimal APPI-MS conditions were identified, including bias voltage, cone voltage and vaporisation temperature. Calibration curves were produced for ethanol, acetone, 2-butanone, ethyl acetate and eucalyptol, yielding R2 > 0.99 and limits of detection < 10 pg. As a pre-clinical proof of concept, this method was applied to bacterial headspace samples of Escherichia coli (EC), Pseudomonas aeruginosa (PSA) and Staphylococcus aureus (SA) collected in 1 L Tedlar bags. In particular, PSA and SA are commonly associated with lung infection in cystic fibrosis patients. The headspace samples were classified using principal component analysis with 86.9% of the total variance across the first three components and yielding 100% classification in a blind-sample study. All experiments conducted with the novel APPI arrangement were carried out directly in real-time with low-resolution MS, which opens up exciting possibilities in the future for on-site (e.g., in the clinic) analysis with a portable system.