期刊
ENEURO
卷 8, 期 1, 页码 -出版社
SOC NEUROSCIENCE
DOI: 10.1523/ENEURO.0286-20.2020
关键词
machine learning; multiplexed imaging; synaptic plasticity; synaptic scaling
This study utilized multiplexed imaging and machine learning techniques to identify novel synaptic subtypes within excitatory and inhibitory synapses, and investigated the differences in protein expression within these subtypes. The results revealed changes in synaptic distribution following synaptic plasticity induction, while the distribution of synaptic subtypes remained unchanged under chronic suppression of neuronal activity.
Neuronal synapses contain hundreds of different protein species important for regulating signal transmission. Characterizing differential expression profiles of proteins within synapses in distinct regions of the brain has revealed a high degree of synaptic diversity defined by unique molecular organization. Multiplexed imaging of in vitro rat primary hippocampal culture models at single synapse resolution offers new opportunities for exploring synaptic reorganization in response to chemical and genetic perturbations. Here, we combine 12-color multiplexed fluorescence imaging with quantitative image analysis and machine learning to identify novel synaptic subtypes within excitatory and inhibitory synapses based on the expression profiles of major synaptic components. We characterize differences in the correlated expression of proteins within these subtypes and we examine how the distribution of these synapses is modified following induction of synaptic plasticity. Under chronic suppression of neuronal activity, phenotypic characterization revealed coordinated increases in both excitatory and inhibitory protein levels without changes in the distribution of synaptic subtypes, suggesting concerted events targeting glutamatergic and GABAergic synapses. Our results offer molecular insight into the mechanisms of synaptic plasticity.
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