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Uncovering Discrete Synaptic Proteomes to Understand Neurological Disorders

Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
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Academic Editors: Angus C. Nairn and Kenneth R. Williams
Proteomes 2018, 6(3), 30; https://doi.org/10.3390/proteomes6030030
Received: 2 June 2018 / Revised: 10 July 2018 / Accepted: 13 July 2018 / Published: 19 July 2018
(This article belongs to the Special Issue Neuroproteomics)
The mammalian nervous system is an immensely heterogeneous organ composed of a diverse collection of neuronal types that interconnect in complex patterns. Synapses are highly specialized neuronal cell-cell junctions with common and distinct functional characteristics that are governed by their protein composition or synaptic proteomes. Even a single neuron can possess a wide-range of different synapse types and each synapse contains hundreds or even thousands of proteins. Many neurological disorders and diseases are caused by synaptic dysfunction within discrete neuronal populations. Mass spectrometry (MS)-based proteomic analysis has emerged as a powerful strategy to characterize synaptic proteomes and potentially identify disease driving synaptic alterations. However, most traditional synaptic proteomic analyses have been limited by molecular averaging of proteins from multiple types of neurons and synapses. Recently, several new strategies have emerged to tackle the ‘averaging problem’. In this review, we summarize recent advancements in our ability to characterize neuron-type specific and synapse-type specific proteomes and discuss strengths and limitations of these emerging analysis strategies. View Full-Text
Keywords: proteomics; basal ganglia; synapses; synapse specificity; neuronal circuits; axons; dendrites; neurodegeneration proteomics; basal ganglia; synapses; synapse specificity; neuronal circuits; axons; dendrites; neurodegeneration
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Wang, Y.-Z.; Savas, J.N. Uncovering Discrete Synaptic Proteomes to Understand Neurological Disorders. Proteomes 2018, 6, 30.

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