A Mass Spectrometry-based Investigation of Dynamic Protein Turnover and Interactions in Human iPSC-derived Neurons
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Neurons are postmitotic cells that rely heavily on proteostasis, a network of pathways balancing protein synthesis, maintenance, and degradation. Disruptions in neuronal proteostasis have been linked to the onset and progression of neurodegenerative diseases such as Alzheimer’s and Parkinson’s disease. However, global protein turnover and its dysregulation in human neurons are not well understood. This dissertation explores the development and application of mass spectrometry (MS)-based analytical chemistry methods for studying protein turnover and interactions in human induced pluripotent stem cell (iPSC)-derived neurons. Chapter 1 of this dissertation introduces advances in MS-based proteomics to address biological questions related to protein turnover and protein-protein interactions (PPI). It provides information about the development and optimization of stable isotope labeling by amino acids in cell culture (SILAC) and proximity labeling (PL) MS-based techniques. It further elaborates on common technical challenges and how they can be mitigated. Finally, it discusses how iPSCs have allowed human brain disorders to be studied in a genetic and physiologically relevant model. Chapter 2 describes the development of a protein contaminant library and workflow to mark and remove contaminant proteins from proteomics datasets. Contaminant libraries can improve specificity, enhance protein identification, and reduce false discoveries without influencing quantification accuracy. This contaminant library can benefit all MS-based proteomics workflows and has already been widely adopted in the proteomics field. Chapter 3 presents a systematic benchmarking of ten different data analysis pipelines for static and dynamic SILAC labeling proteomics. Different data analysis workflows are evaluated using twelve performance metrics, including identification, quantification, accuracy, precision, reproducibility, filtering criteria, missing values, false discovery rate, protein half-life measurement, completeness, unique software features, and speed of data analysis. Practical guidelines are provided to assist decision-making in SILAC proteomic study design and data analysis. Chapter 4 develops a deep dynamic SILAC proteomic method that can be used to quantify more than ten thousand protein half-lives in human iPSC-derived neurons. A website (neuronprofile.com) was built to share this exciting resource in the field to allow interactive data searching and visualization. This method was further applied to compare proteostasis rates in different neuron subtypes, including motor and cortical neurons. Chapter 5 develops a custom neuron culture medium that is compatible with SILAC proteomics and supports optimal neuronal health. Different neuron culture medium systems are compared regarding their influences on global protein relative abundance and turnover in neurons. The custom medium was applied to understand phosphorylated protein turnover in a neuronal model. Chapter 6 develops and optimizes a PL proteomic method to investigate PPI interactions surrounding the endolysosomal membrane. Overexpression and endogenous expression-level PL probes are used to quantify endolysosomal proteins and their interactors. Technical challenges of proximity labeling proteomics are systemtically addressed to reduce interference from endogenously biotinylated proteins, include proper control groups, and minimize experimental variations among replicates. Chapter 7 summarizes the work presented in this dissertation and provides future directions for the application of protein turnover and proximity labeling proteomic techniques in neuronal research.
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