This Is AuburnElectronic Theses and Dissertations

Rapid Detection and Accurate Discrimination of Microorganisms by Liquid Chromatography, High-Resolution Ion Mobility, and Tandem Mass Spectrometry




Olajide, Orobola

Type of Degree

PhD Dissertation


Chemistry and Biochemistry


Determining bacterial identity at the strain level is crucial for public health to enable appropriate medical treatment and reduce hospitalization times and antibiotic resistance. To achieve this goal, we have reported the coupling of ambient ionization techniques with a commercial drift tube ion mobility mass spectrometer and demonstrated their ability to rapidly separate constitutional and geometric isomers. After successful rapid isomer separation, we investigated paper spray – IM – MS/MS to discriminate five Bacillus species. We optimized several parameters, such as the incubation time and the spray solvent. We found that a 4 h-incubation time is sufficient for detection and that isopropyl alcohol (IPA) gives a longer spray time and higher intensities of the observed biomarkers than methanol (MeOH, typical spray solvent in PS – MS experiments). Numerical multivariate statistics (principal component analysis followed by linear discriminant analysis) allowed discrimination at the species level with a prediction rate of 92.4 % and 97.6 % using the negative and positive ion information from PS – MS/MS, respectively. However, when including the corresponding drift times of the biomarkers, i.e. PS – IM – MS/MS, prediction rates of 99.7% and 100% were obtained using the negative and positive ion information, respectively. We attribute the improvement in prediction rates to the ability of IM separations to resolve isomers. When analyzing seven E. coli strains by PS – IM – MS/MS, the prediction rate was 80.5% after numerical data fusion of negative and positive ion modes. Therefore, we combined liquid chromatography with IM – MS/MS as LC – IM - MS/MS to accurately discriminate the seven E. coli strains. Numerical multivariate statistics demonstrated the ability of this method to perform strain-level discrimination with prediction rates of 96.1% and 100% using the negative and positive ion information, respectively. This work demonstrates the great potential to accurately detect pathogenic and antibiotic-resistant bacteria in agrochemical screening, disease diagnostics, etc. Moreover, this work can pave the way for developing standalone ion mobility spectrometers which can be used in several medical, environmental, and security applications.