Discovery and Targeted Approaches for Comparative Label-Free Proteomic Quantification via Mass Spectrometry

• Author / Creator

  Siva Piragasam, Ramanaguru

• Over the last few decades, the field of proteomics has developed rapidly owing to advancements in genomic and transcriptomic technologies contributing to the compilation of vast quantities of protein sequence libraries. Proteomics, the study of global protein composition in a biological system, has become a priority within biological and health sciences for its capability to elucidate the subtle complexity of protein biochemistry on a larger system-wide scale with a relatively lower time cost. Vast protein sequence libraries coupled with advancements in mass spectrometry and liquid chromatography pushed mass spectrometry-based proteomics to the forefront of protein investigation in terms of protein identification and more recently protein quantification.

  Quantification in mass spectrometry is primarily focused on relative approaches where label-free comparative protein quantification has been popular recently compared to label-based methods due to its versatility of applications from a simple study of individual proteins to complex samples covering a wide range of sample types; purified proteins, cell lysates, biological fluids, and tissue and organs. Several label-free quantification techniques have been developed to determine protein abundance. However, label-free quantification has several limitations that require optimization for its implementation. Issues such as data normalization, treatment of missing data, and statistical approach and corrections are all active research areas. Thus, the best strategy for the execution of a label-free quantification analysis is yet to be finalized.

  Additionally, the focus on quantification by mass spectrometry has allowed for the development of several data acquisition methods each with its own advantages and disadvantages with respect to protein identification, quantification accuracy, and sensitivity. Traditionally, proteomics experiments have focused on discovery-based techniques, which try to maximize protein identification such as data-dependent acquisition (DDA). Later, several quantification-focused data acquisition methods were introduced such as selected reaction monitoring (SRM) and parallel reaction monitoring (PRM) primarily quantify protein based on prior knowledge of protein identification. More recently, a more hybrid approach that boasts an excellent protein identification rate while retaining reproducible and sensitive quantification has been introduced, the data-independent acquisition (DIA).
  The practicality and suitability of the label-free quantification method to be applied in a wide range of biological systems including clinical samples have led it to become our lab’s choice for quantification method. Our group has worked to develop a robust, reproducible, and reliable approach toward label-free proteomics analyses. We have focused on sample-specific normalization utilizing multiple data acquisition strategies for label-free proteomics studies over a wide range of biological systems. This thesis focuses on the application of our mass spectrometry-based label-free quantification strategy for comparative proteomics analysis via multiple data acquisition methods in the characterization of:

  1. The proteomics changes in HEK293T cell cultures upon over-expression of miR23~24 miRNA cluster via data-dependent acquisition.
  2. The proteomics differences between blood serum of Myasthenia Gravis patients and healthy control to identify potential biomarker candidates. The initial discovery was based on data-dependent acquisition and further validation by a targeted proteomics approach via parallel reaction monitoring.
  3. The differential expression of ribosomal proteins in mouse tissues and cell lines to investigate ribosomal proteins’ role in the specialization and development of tissues. Ribosomal proteins are quantified primarily by parallel reaction monitoring method.

• Subjects / Keywords

  • Proteomics
  • Mass Spectrometry
  • Label-free proteomics
  • Targeted Proteomics
  • Myasthenia Gravis
  • MicroRNA
  • Ribosome Heterogeneity

• Graduation date

  Spring 2023

• Type of Item

  Thesis

• Degree

  Doctor of Philosophy

• DOI

  https://doi.org/10.7939/r3-fe3e-mg33

• License

  This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.