Date of Award


Degree Type


Degree Name

Doctor of Philosophy (PhD)



Committee Chair

R. Graham Cooks

Committee Member 1

Peter T. Kissinger

Committee Member 2

Matthew Tantama

Committee Member 3

Julia Laskin


Mass spectrometry (MS) provides a high level of sensitivity and specificity to accurately and precisely identify and quantify analytes in a complex matrix. In clinical samples, this instrument is often used to quantify drugs or discover new biomarkers. However, existing workflows routinely use chromatography to separate the components of a sample. These methods lack speed and are expensive, neither of which are ideal characteristics for point of care or high throughput analysis. Paper spray (PS) is an ambient ionization technique that combines the sample preparation and ionization steps, to directly spray a complex sample into a MS. The MS provides the specificity and sensitivity to quantify drugs at low ng/mL levels of detection. Described here are three PS-MS methods demonstrating PS for clinical research. First, a drug is measured for a pharmacokinetic study and demonstrates PS-MS utility for personalized medicine. Then, PS is used to measure whole blood samples collected in a low resource region, demonstrating its compatibility with in-field clinical trial samples. And finally, PS is multiplexed to measure 30 drugs in oral fluid, proving that this methodology can be used for large panels of analytes as traditionally done in the clinical environment. Endogenous metabolites in biofluids can also be measured by MS without prior separation. Multiple reaction monitoring (MRM)- profiling rapidly measures a sample to create a metabolite profile for classifying diseased and healthy samples. This methodology targets biological functional groups in a pooled sample using a library of over 200 precursor (Prec) and neutral loss (NL) scans. All MS signals discovered in these experiments are transformed into ion transitions and are measured in a MRM method. In MRM mode, each transition can be measured on the millisecond time scale allowing for rapid screening of large sample sets. Using univariate and multivariate statistics the sample set can be classified with high accuracy. With diseased sample sets metabolite profiles can be found that classify samples based on signals related to the disease. Since a large variety of functional groups are considered and all signal discovered is collected by MRM, this is considered an unsupervised biomarker discovery methodology. MRM-profiling is described here and demonstrated with over 900 human plasma coronary artery disease samples. First, the metabolite signal was discovered with Prec and NL scans. Then, with a MRM method, the samples were screened in under five days. A metabolite profile was established from this data for the disease. The signals that comprised the MRM-profile were identified and found to be associated with coronary artery disease metabolism. This validates that the methodology generates a useful metabolite profile but is much faster than traditional methodologies. The same methodology is also performed with Parkinson’s disease cerebrospinal fluid samples and discovered signal relevant to the diseased population.