Real-time detection of volatile metabolites enabling species level discrimination of bacterial biofilms associated with wound infection

Aims The main aim of this study was to investigate the real-time detection of volatile metabolites for the species level discrimination of pathogens associated with clinically relevant wound infection, when grown in a collagen wound biofilm model. Methods and Results This work shows that Staphylococcus aureus, Pseudomonas aeruginosa and Streptococcus pyogenes produce a multitude of volatile compounds when grown as biofilms in a collagen based biofilm model. The real-time detection of these complex volatile profiles using selected ion flow tube mass spectrometry (SIFT-MS) and the use of multivariate statistical analysis on the resulting data can be used to successfully differentiate between the pathogens studied. Conclusions The range of bacterial volatile compounds detected between the species studied vary and are distinct. Discrimination between bacterial species using real-time detection of volatile metabolites and multivariate statistical analysis was successfully demonstrated. Significance and Impact of Study Development of rapid point-of-care diagnostics for wound infection would improve diagnosis and patient care. Such technological approaches would also facilitate appropriate use of antimicrobials, minimizing the emergence of antimicrobial resistance. This study further develops the use of volatile metabolite detection as a new diagnostic approach for wound infection.