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Faculty of Graduate Studies

Kate McMurray

  • B.Sc. (University of Victoria, 2023)
Notice of the Final Oral Examination for the Degree of Master of Science

Topic

NanoLC-MS for Amino Acid Quantitation in Low-Input Ovarian Cancer and Immune Cell Populations

School of Molecular Life Sciences

Date & location

  • Wednesday, September 9, 2026
  • 10:00 A.M.
  • Clearihue Building, Room B017

Examining Committee

Supervisory Committee

  • Dr. David Goodlett, School of Molecular Life Sciences, University of Victoria (Co-Supervisor)
  • Dr. Julian Lum, School of Molecular Life Sciences, UVic (Co-Supervisor)
  • Dr. Jun Han, School of Medical Sciences, UVic (Outside Member)

External Examiner

  • Dr. Ryland Giebelhaus, Department of Chemistry, UVic

Chair of Oral Examination

  • Dr. Kathy Gaul, School of Exercise Science, Physical and Health Education, UVic

Abstract

Amino acids support diverse and sometimes opposing roles in cancer and immune cells, acting as carbon/nitrogen sources for tumor proliferation, regulators of redox balance, and T-cell function modulators. It is therefore important to analyze these cell populations separately, but these subsets can yield limited cell numbers, often below the 1,000,000 cells required for conventional LC-MS metabolomics. Nanoflow LC-MS (nanoLC-MS) provides a potential solution, but its use in quantitative metabolomics remains underexplored.

This work focused on developing an ultra-sensitive amino acid quantitation assay using nanoLC-MS, improving sensitivity by an average of 95-fold compared to UPLC and enabling quantitation from just 1,000-10,000 cells. Amino acids were derivatized with dansyl chloride to improve chromatographic retention and improve ionization, then analyzed with a custom nanoLC-MS setup to balance sensitivity, isomer resolution, precision, and throughput. This nanoLC-MS method demonstrated high-sensitivity detection in the low femtomole range and excellent linearity (R2 > 0.995) across > 250-fold concentrations for each analyte. Across cell lines tested (SKOV3, OVCAR3, OVCAR8, T cells), two thirds of amino acids at 1,000 cells and up to all amino acids at 10,000 cells showed excellent precision (CV < 15%).

To assess the utility of the assay, it was applied to sorted cells from malignant ascites from primary and recurrent ovarian cancer. While technical and biological variability posed a significant challenge, the limited significant results confirm that the method is suitable for probing metabolic phenotypes in contexts where sample availability is limiting. Overall, this work established a sensitive and reproducible analytical workflow for amino acid quantitation in low-input samples. The platform expands the capabilities of targeted metabolomics and provides a foundation for future studies investigating metabolic processes in constrained or rare biological systems.

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