Development of mass spectrometry-based multi-omic methods for the study of the brain extracellular matrix
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Abstract
Extracellular matrix (ECM) makes up nearly 20% of the volume of the brain, and includes the interstitial matrix, basement membrane, and perineuronal nets. In addition to functioning as a scaffold, it regulates signaling pathways through interactions with cell-surface molecules and by binding growth factors. The ECM comprises an organized network of proteins and glycans that vary spatially and temporally and are known to be altered during aging and neurodegeneration. Glycosylation is the most abundant post-translational modification and is key to protein folding and secretion, as well as cell adhesion, signaling, and receptor binding. Mass spectrometry (MS) can yield detailed information about the proteome and the glycome of the brain. However, detailed, site-specific glycoproteomic information is often lacking, limiting the ability to understand the alterations in ECM protein glycosylation that occur with healthy aging versus neurodegeneration. In considering the glycoproteome, both the occupancy and type of the glycan need to be taken into account, which vastly increases the difficulty of interpreting the mass spectral data. The heterogeneity also divides signal strength of an individual glycopeptide relative to that of an unmodified peptide. Additionally, the instrument conditions generally used for proteomics experiments do not preserve glycan positional information, which is especially problematic in the case of O-linked glycosylation, which can occur in clusters and lacks the consensus sequon that characterizes N-linked glycosylation. Thus, there is a need for the development of new mass spectrometry-based and bioinformatic tools to examine the glycoproteomic changes that occur in the brain ECM. Chapter 1 of this dissertation summarizes current uses of mass spectrometric methods for characterization of brain ECM to contextualize the research discussed in later chapters. Chapter 2 presents a cohort study of cerebral arteries and adjacent brain tissue to understand the contribution of the vasculature to the progression of Alzheimer’s disease (AD). We find that proteomic changes seen in the arteries are consistent with mechanical and structural alterations observed in previous studies, and that they correlate with metabolic and signaling abnormalities in the adjacent brain tissue. These in turn likely up-regulate processes that contribute to the progression of neurodegeneration. Chapter 3 describes the incorporation of electron transfer-higher energy collisional dissociation (EThcD) into a typical higher-energy collisional dissociation (HCD) experiment, using several proteoglycans as model systems. For N-linked glycosylation, HCD is often sufficient to confidently identify a large number of glycopeptides. We demonstrate that EThcD permits the confident identification of peptides with two O-linked glycans on them and localizes the glycans on the peptide. However, because most glycoproteomics search software programs are optimized for HCD, the glycopeptides identified with EThcD do not score as well. Chapter 4 describes the implementation of the novel Omnitrap platform, which adds the capability for electron-based dissociation (ExD) to an existing instrument. Historically, ExD experiments were performed on less-sensitive instruments with slower scan speeds. We demonstrate the ability for ExD to be performed with chromatographic separation, allowing the identification of glycopeptides in a complex mixture. Additionally, ExD analysis with the Omnitrap provides glycan linkage information, which has previously been lacking in glycoproteomics experiments and is key to understanding glycan heterogeneity. The ExD analyses demonstrate the need for advancement in glycoproteomics search algorithms, as in the case of the Omnitrap, the position assigned by the software is not one for which there is spectral evidence. The development of novel MS-based tools for glycoproteomics offers opportunities to better understand diseases such as AD and potentially uncover novel therapeutics.
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2025