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Uncovering Discrete Synaptic Proteomes to Understand Neurological Disorders
- Source :
- Proteomes, Vol 6, Iss 3, p 30 (2018), Proteomes
- Publication Year :
- 2018
- Publisher :
- MDPI AG, 2018.
-
Abstract
- 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.
- Subjects :
- 0301 basic medicine
dendrites
Clinical Biochemistry
lcsh:QR1-502
axons
Review
Biology
neuronal circuits
Proteomics
Biochemistry
lcsh:Microbiology
Synapse
03 medical and health sciences
proteomics
Structural Biology
Basal ganglia
medicine
Molecular Biology
Mammalian nervous system
Neurodegeneration
neurodegeneration
Protein composition
medicine.disease
030104 developmental biology
medicine.anatomical_structure
basal ganglia
Proteome
synapse specificity
synapses
Neuron
Neuroscience
Subjects
Details
- ISSN :
- 22277382
- Volume :
- 6
- Database :
- OpenAIRE
- Journal :
- Proteomes
- Accession number :
- edsair.doi.dedup.....9c05d8e9148e28ea3a62037434374c0e