Ricardo A Valenzuela1, Kristina D Micheva1, Marianna Kiraly1, Dong Li1, Daniel V Madison2. 1. Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA 94305-5345, USA. 2. Department of Molecular and Cellular Physiology, Stanford University School of Medicine, Stanford, CA 94305-5345, USA. Electronic address: madison@stanford.edu.
Abstract
BACKGROUND: The ability to correlate plastic changes in synaptic physiology with changes in synaptic anatomy has been very limited in the central nervous system because of shortcomings in existing methods for recording the activity of specific CNS synapses and then identifying and studying the same individual synapses on an anatomical level. NEW METHOD: We introduce here a novel approach that combines two existing methods: paired neuron electrophysiological recording and array tomography, allowing for the detailed molecular and anatomical study of synapses with known physiological properties. RESULTS: The complete mapping of a neuronal pair allows determining the exact number of synapses in the pair and their location. We have found that the majority of close appositions between the presynaptic axon and the postsynaptic dendrite in the pair contain synaptic specializations. The average release probability of the synapses between the two neurons in the pair is low, below 0.2, consistent with previous studies of these connections. Other questions, such as receptor distribution within synapses, can be addressed more efficiently by identifying only a subset of synapses using targeted partial reconstructions. In addition, time sensitive events can be captured with fast chemical fixation. COMPARISON WITH EXISTING METHODS: Compared to existing methods, the present approach is the only one that can provide detailed molecular and anatomical information of electrophysiologically-characterized individual synapses. CONCLUSIONS: This method will allow for addressing specific questions about the properties of identified CNS synapses, even when they are buried within a cloud of millions of other brain circuit elements.
BACKGROUND: The ability to correlate plastic changes in synaptic physiology with changes in synaptic anatomy has been very limited in the central nervous system because of shortcomings in existing methods for recording the activity of specific CNS synapses and then identifying and studying the same individual synapses on an anatomical level. NEW METHOD: We introduce here a novel approach that combines two existing methods: paired neuron electrophysiological recording and array tomography, allowing for the detailed molecular and anatomical study of synapses with known physiological properties. RESULTS: The complete mapping of a neuronal pair allows determining the exact number of synapses in the pair and their location. We have found that the majority of close appositions between the presynaptic axon and the postsynaptic dendrite in the pair contain synaptic specializations. The average release probability of the synapses between the two neurons in the pair is low, below 0.2, consistent with previous studies of these connections. Other questions, such as receptor distribution within synapses, can be addressed more efficiently by identifying only a subset of synapses using targeted partial reconstructions. In addition, time sensitive events can be captured with fast chemical fixation. COMPARISON WITH EXISTING METHODS: Compared to existing methods, the present approach is the only one that can provide detailed molecular and anatomical information of electrophysiologically-characterized individual synapses. CONCLUSIONS: This method will allow for addressing specific questions about the properties of identified CNS synapses, even when they are buried within a cloud of millions of other brain circuit elements.
Authors: Forrest Collman; JoAnn Buchanan; Kristen D Phend; Kristina D Micheva; Richard J Weinberg; Stephen J Smith Journal: J Neurosci Date: 2015-04-08 Impact factor: 6.167
Authors: Sumit Nanda; Hanbo Chen; Ravi Das; Shatabdi Bhattacharjee; Hermann Cuntz; Benjamin Torben-Nielsen; Hanchuan Peng; Daniel N Cox; Erik De Schutter; Giorgio A Ascoli Journal: Sci Data Date: 2018-01-23 Impact factor: 6.444