Long-term potentiation expands information content of hippocampal dentate gyrus synapses

Bromer, Cailey; Bartol, Thomas M.; Bowden, Jared B.; Hubbard, Dusten D.; Hanka, Dakota C; Gonzalez, Paola V; Kuwajima, Masaaki; Mendenhall, John M.; Parker, Patrick H.; Abraham, Wickliffe C.; Sejnowski, Terrence J.; Harris, Kristen M.

doi:10.1073/pnas.1716189115

Cited by 60 publications

(76 citation statements)

References 54 publications

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“…Another difference between the cerebral cortex and the CA1 region of the hippocampus concerns the distribution of synaptic sizes. In the cerebral cortex and several other brain regions, including CA3 and dentate gyrus, synaptic size and amplitude of evoked EPSP follow a log-normal distribution (Song et al, 2005;Sarid et al, 2007;Lefort et al, 2009;Loewenstein et al, 2011;Ikegaya et al, 2013;Bromer et al, 2018). In our CA1 samples, however, ASI sizes followed a bimodal distribution, with the largest synapses, most of which are perforated and contain many AMPA and NMDA receptors (Nusser et al, 1998;Takumi et al, 1999;Ganeshina et al, 2004;Nicholson et al, 2006;Nicholson and Geinisman, 2009), accounting for a greater proportion of all synapses than in a log-normal distribution.…”

Section: Sleep/wake Effects In Hippocampus and Cortexmentioning

confidence: 67%

Sleep Deprivation by Exposure to Novel Objects Increases Synapse Density and Axon–Spine Interface in the Hippocampal CA1 Region of Adolescent Mice

et al. 2019

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Sleep has been hypothesized to rebalance overall synaptic strength after ongoing learning during waking leads to net synaptic potentiation. If so, because synaptic strength and size are correlated, synapses on average should be larger after wake and smaller after sleep. This prediction was recently confirmed in mouse cerebral cortex using serial block-face electron microscopy (SBEM). However, whether these findings extend to other brain regions is unknown. Moreover, sleep deprivation by gentle handling was reported to produce hippocampal spine loss, raising the question of whether synapse size and number are differentially affected by sleep and waking. Here we applied SBEM to measure axon-spine interface (ASI), the contact area between pre-synapse and post-synapse, and synapse density in CA1 stratum radiatum. Adolescent YFP-H mice were studied after 6-8 h of sleep (S ϭ 6), spontaneous wake at night (W ϭ 4) or wake enforced during the day by novelty exposure (EW ϭ 4; males/females balanced). In each animal Ն425 ASIs were measured and synaptic vesicles were counted in ϳ100 synapses/mouse. Reconstructed dendrites included many small, nonperforated synapses and fewer large, perforated synapses. Relative to S, ASI sizes in perforated synapses shifted toward higher values after W and more so after EW. ASI sizes in nonperforated synapses grew after EW relative to S and W, and so did their density. ASI size correlated with presynaptic vesicle number but the proportion of readily available vesicles decreased after EW, suggesting presynaptic fatigue. Thus, CA1 synapses undergo changes consistent with sleep-dependent synaptic renormalization and their number increases after extended wake.

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Section: Sleep/wake Effects In Hippocampus and Cortexmentioning

confidence: 67%

Sleep Deprivation by Exposure to Novel Objects Increases Synapse Density and Axon–Spine Interface in the Hippocampal CA1 Region of Adolescent Mice

et al. 2019

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“…Since synaptic strength has been shown to be correlated to the size of synaptic specializations (postsynaptic density, spine head volume, (Harris and Stevens, 1988, 1989) and axon-spine interface (ASI) area (de Vivo et al, 2017)), the analysis of the similarity of synaptic size for pairs of pre-and postsynaptic neurites that establish more than one joint synapse can be used to obtain evidence for previous episodes of synaptic weight assimilation between these joint synapses. In the hippocampus, where activity-dependent synaptic weight increase is consistently found (LTP), this argument has been employed to investigate the storage capacity of synapses (Bartol et al, 2015; Bromer et al, 2018), and joint-synapse size homogeneity has been described for clustered synaptic input (Bloss et al, 2018). Here, we wanted to determine whether such synaptic size homogenization can be quantitatively determined in cortical layer 4, in which only long-term synaptic depression has been found (Egger et al, 1999), and to determine upper and lower bounds on the fraction of the circuit that can have recently undergone such synaptic weight adaptation.…”

Section: Resultsmentioning

confidence: 99%

“…The extraction of connectomic evidence for synaptic size consistency requires further discussion. While the similarity of synaptic size has been previously used for arguments about synaptic plasticity and learning (Bartol et al, 2015; Bromer et al, 2018), an alternative source of decreased variance of synapse size for defined pairs of axons and dendrites is the establishment of consistently weaker or stronger synaptic connections between certain subtypes of pre-and postsynaptic neurons. While in the L4 circuit, the majority of excitatory connections is known to be established between local spiny neurons, and the observed synaptic size effects (Fig.…”

Section: Discussionmentioning

confidence: 99%

Dense connectomic reconstruction in layer 4 of the somatosensory cortex

Motta

Berning

Boergens

et al. 2018

Preprint

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21The dense circuit structure of the mammalian cerebral cortex is still unknown. With 22 developments in 3-dimensional (3D) electron microscopy, the imaging of sizeable 23 volumes of neuropil has become possible, but dense reconstruction of connectomes 24 from such image data is the limiting step. Here, we report the dense reconstruction 25 of a volume of about 500,000 µm 3 from layer 4 of mouse barrel cortex, about 300 26 times larger than previous dense reconstructions from the mammalian cerebral 27 cortex. Using a novel reconstruction technique, FocusEM, we were able to 28 reconstruct a total of 0.9 meters of dendrites and about 1.8 meters of axons investing 29 only about 4,000 human work hours, about 10-25 times more efficient than previous 30 dense circuit reconstructions. We find that connectomic data alone allows the 31 definition of inhibitory axon types that show established principles of synaptic 32 specificity for subcellular postsynaptic compartments. We find that also a fraction of 33 excitatory axons exhibit such subcellular target specificity. Only about 34 35% of inhibitory and 55% of excitatory synaptic subcellular innervation can be 35 predicted from the geometrical availability of membrane surface, revoking coarser 36 models of random wiring for synaptic connections in cortical layer 4. We furthermore 37 find evidence for enhanced variability of synaptic input composition between neurons 38 at the level of primary dendrites in cortical layer 4. Finally, we obtain evidence for 39 Hebbian synaptic weight adaptation in at least 24% of connections; at least 35% of 40 connections show no sign of such previous plasticity. Together, these results 41 establish an approach to connectomic phenotyping of local dense neuronal circuitry 42 in the mammalian cortex. 43 44 109First, we reconstructed those neurons, which had their cell bodies in the tissue 110 volume ( Fig. 1h,i, Supplementary Material 1, n=125 cell bodies, of these 97 111 neuronal, of these 89 reconstructed with dendrites in the dataset) using a set of 112 simple growth rules for automatically connecting neurite pieces based on the 113 segment-to-segment neighborhood graph and the connectivity and neurite type 114 classifiers ( Fig. 1f, see Methods). As a result, we obtained fully automated 115 reconstructions of the neuron's soma and dendritic processes. Notably with a 116 minimal additional manual correction investment of 9.7 hours for 89 cells (54.5 mm 117 6 dendritic and 2.1 mm axonal path length), the dendritic shafts of these neurons could 118 be reconstructed without merge errors, but 37 remaining split errors, at 87.3% 119 dendritic length recall ( Fig. 1h,i, Supplementary Material 1, see Methods). This 120 reconstruction efficiency compares favorably to recent reports of automated 121 segmentation of neurons in 3D EM data from the bird brain obtained at about 2-fold 122 higher imaging resolution (Januszewski et al., 2018), which reports soma-based 123 neuron reconstruction at an error rate of beyond 100 errors per 66 mm dendritic 124 ...

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“…Collectively these tools enable the facile mathematical modeling of biological systems using realistic geometries. The GAMer workflow has been previously applied in several works (25,(31)(32)(33)(34)(35)(36). Several examples are described in the online GAMer 2 documentation.…”

Section: Resultsmentioning

confidence: 99%

An Open Source Mesh Generation Platform for Biophysical Modeling Using Realistic Cellular Geometries

Lee¹,

Laughlin²,

Moody³

et al. 2019

Preprint

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Advances in imaging methods such as electron microscopy, tomography and other modalities are enabling high-resolution reconstructions of cellular and organelle geometries. Such advances pave the way for using these geometries for biophysical and mathematical modeling once these data can be represented as a geometric mesh, which, when carefully conditioned, enables the discretization and solution of partial differential equations. In this study, we outline the steps for a naïve user to approach GAMer 2, a mesh generation code written in C++ designed to convert structural datasets to realistic geometric meshes, while preserving the underlying shapes. We present two example cases, 1) mesh generation at the subcellular scale as informed by electron tomography, and 2) meshing a protein with structure from x-ray crystallography. We further demonstrate that the meshes generated by GAMer are suitable for use with numerical methods. Together, this collection of libraries and tools simplifies the process of constructing realistic geometric meshes from structural biology data. SIGNIFICANCE As biophysical structure determination methods improve, the rate of new structural data is increasing. New methods that allow the interpretation, analysis, and reuse of such structural information will thus take on commensurate importance. In particular, geometric meshes, such as those commonly used in graphics and mathematics, can enable a myriad of mathematical analysis. In this work, we describe GAMer 2, a mesh generation library designed for biological datasets. Using GAMer 2 and associated tools PyGAMer and BlendGAMer, biologists can robustly generate computer and algorithm friendly geometric mesh representations informed by structural biology data. We expect that GAMer 2 will be a valuable tool to bring realistic geometries to biophysical models.

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Long-term potentiation expands information content of hippocampal dentate gyrus synapses

Cited by 60 publications

References 54 publications

Sleep Deprivation by Exposure to Novel Objects Increases Synapse Density and Axon–Spine Interface in the Hippocampal CA1 Region of Adolescent Mice

Sleep Deprivation by Exposure to Novel Objects Increases Synapse Density and Axon–Spine Interface in the Hippocampal CA1 Region of Adolescent Mice

Dense connectomic reconstruction in layer 4 of the somatosensory cortex

An Open Source Mesh Generation Platform for Biophysical Modeling Using Realistic Cellular Geometries

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