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Review
. 2023 Sep-Oct;42(5):1848-1875.
doi: 10.1002/mas.21792. Epub 2022 Jun 20.

Mass spectrometry methods for analysis of extracellular matrix components in neurological diseases

Affiliations
Review

Mass spectrometry methods for analysis of extracellular matrix components in neurological diseases

Margaret Downs et al. Mass Spectrom Rev. 2023 Sep-Oct.

Abstract

The brain extracellular matrix (ECM) is a highly glycosylated environment and plays important roles in many processes including cell communication, growth factor binding, and scaffolding. The formation of structures such as perineuronal nets (PNNs) is critical in neuroprotection and neural plasticity, and the formation of molecular networks is dependent in part on glycans. The ECM is also implicated in the neuropathophysiology of disorders such as Alzheimer's disease (AD), Parkinson's disease (PD), and Schizophrenia (SZ). As such, it is of interest to understand both the proteomic and glycomic makeup of healthy and diseased brain ECM. Further, there is a growing need for site-specific glycoproteomic information. Over the past decade, sample preparation, mass spectrometry, and bioinformatic methods have been developed and refined to provide comprehensive information about the glycoproteome. Core ECM molecules including versican, hyaluronan and proteoglycan link proteins, and tenascin are dysregulated in AD, PD, and SZ. Glycomic changes such as differential sialylation, sulfation, and branching are also associated with neurodegeneration. A more thorough understanding of the ECM and its proteomic, glycomic, and glycoproteomic changes in brain diseases may provide pathways to new therapeutic options.

Keywords: Alzheimer's disease; Parkinson's disease; Schizophrenia; chondroitin sulfate; extracellular matrix; glycomics; glycoproteomics; glycosaminoglycans; glycosylation; heparan sulfate; mass-spectrometry; proteoglycans; proteomics.

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Conflict of interest statement

Conflict of Interest

The authors declare no conflict of interest.

Figures

Figure 1:
Figure 1:
Summary of major ECM components. (A) Perineuronal nets (PNNs) are highly glycosylated ECM structures that surround neurons and play crucial roles in neuroplasticity. (B) Heparan sulfate proteoglycans (HSPGs) found on the cell surface and pericellular matrix bind growth factors and their receptors and contribute to cell signaling. (C) The cell surface of neurons is populated with highly glycosylated molecules, whose function is modulated by glycans. (D) Chondroitin sulfate proteoglycans (CSPGs) include the hyalectan family (aggrecan, versican, neurocan, and brevican), each of which contains an N-terminal domain that binds hyaluronan, an extracellular GAG polysaccharide, and contributes to the structure of the ECM network.
Figure 2:
Figure 2:
Sample preparation and enrichment methods. (A) Schematic of on-slide digestion of proteins and GAGs from brain tissue slides adapted from (Raghunathan, Sethi, Klein, et al., 2019). (B) Schematic of in-solution digestion of proteins and GAGs from brain tissue or biofluids, adapted from (Sethi et al., 2020). (C) Summary of commonly used enrichment methods for glycopeptides adapted from (J. A. Klein et al., 2018; Riley et al., 2021; Xiao et al., 2016).
Figure 2:
Figure 2:
Sample preparation and enrichment methods. (A) Schematic of on-slide digestion of proteins and GAGs from brain tissue slides adapted from (Raghunathan, Sethi, Klein, et al., 2019). (B) Schematic of in-solution digestion of proteins and GAGs from brain tissue or biofluids, adapted from (Sethi et al., 2020). (C) Summary of commonly used enrichment methods for glycopeptides adapted from (J. A. Klein et al., 2018; Riley et al., 2021; Xiao et al., 2016).
Figure 3:
Figure 3:
Major core ECM changes in brain disorders. Arrows indicate the direction of abundance change (increased or decreased). References: a. (Hondius et al., 2016; Saia-Cereda et al., 2015; van Dijk et al., 2012) b. (Föcking et al., 2015; Muraoka et al., 2020; Raghunathan et al., 2020; Raiszadeh et al., 2012; L. Wang et al., 2010) c. (Föcking et al., 2015) d. (Y. Liu et al., 2015) e. (Hondius et al., 2016; Raghunathan et al., 2020) f. (Manavalan et al., 2013) g. (McKetney et al., 2019) h. (Licker et al., 2014) i.(Y. Liu et al., 2015; Saia-Cereda et al., 2015) j. (McKetney et al., 2019) k. (Jaros et al., 2012) l. (Cho et al., 2019; Gizaw et al., 2016; Schedin-Weiss et al., 2020) m. (Stanta et al., 2010) n. (Raghunathan et al., 2020; Wilkinson et al., 2021) o. (Raghunathan et al., 2020) p. (Wilkinson et al., 2021)

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