Presynaptic Boutons That Contain Mitochondria Are More Stable

Lees, Robert M.; Johnson, James D.; Ashby, Michael C.

doi:10.3389/fnsyn.2019.00037

Cited by 37 publications

(29 citation statements)

References 49 publications

(73 reference statements)

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“…The size of postsynaptic spines is associated with spine stability over time and spine size has a strong, positive linear relationship with the magnitude of synaptic currents (Holler‐Rickauer et al., 2019). Mitochondria in the bouton serve potentially important roles in vesicle recycling, enhancing short‐term plasticity, and stabilizing synaptic connections over time and often suggest highly active terminals (Cserép et al., 2018; Feldman & Peters, 1978; Lees et al., 2020). The presence of a spine apparatus in spine is correlated with potentiated synapses (Deller et al., 2003; Jedlicka et al., 2009).…”

Section: Discussionmentioning

confidence: 99%

An ultrastructural connectomic analysis of a higher‐order thalamocortical circuit in the mouse

Sampathkumar

Miller‐Hansen

Sherman

et al. 2021

Eur J of Neuroscience

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Many studies exist of thalamocortical synapses in primary sensory cortex, but much less in known about higher‐order thalamocortical projections to higher‐order cortical areas. We begin to address this gap using genetic labeling combined with large volume serial electron microscopy (i.e., “connectomics”) to study the projection from the thalamic posterior medial nucleus to the secondary somatosensory cortex in a mouse. We injected into this thalamic nucleus a cocktail combining a cre‐expressing virus and one expressing cre‐dependent ascorbate peroxidase that provides an electron dense cytoplasmic label. This “intersectional” viral approach specifically labeled thalamocortical axons and synapses, free of retrograde labeling, in all layers of cortex. Labeled thalamocortical synapses represented 14% of all synapses in the cortical volume, consistent with previous estimates of first‐order thalamocortical inputs. We found that labeled thalamocortical terminals, relative to unlabeled ones: were larger, were more likely to contain a mitochondrion, more frequently targeted spiny dendrites and avoided aspiny dendrites, and often innervated larger spines with spine apparatuses, among other differences. Furthermore, labeled terminals were more prevalent in layers 2/3 and synaptic differences between labeled and unlabeled terminals were greatest in layers 2/3. The laminar differences reported here contrast with reports of first‐order thalamocortical connections in primary sensory cortices where, for example, labeled terminals were larger in layer 4 than layers 2/3 (Viaene et al., 2011a). These data offer the first glimpse of higher‐order thalamocortical synaptic ultrastructure and point to the need for more analyses, as such connectivity likely represents a majority of thalamocortical circuitry.

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Section: Discussionmentioning

confidence: 99%

An ultrastructural connectomic analysis of a higher‐order thalamocortical circuit in the mouse

Sampathkumar

Miller‐Hansen

Sherman

et al. 2021

Eur J of Neuroscience

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“…The spine apparatus plays a role in synaptic plasticity since synaptopodin-deficient mice that lack these organelles show deficits in long-term potentiation and spatial learning (Jedlicka et al, 2008). Similarly, presynaptic mitochondria are found in axon terminals that are stable and less prone to change in size (Lees et al, 2019). Therefore, synapses characterized by the presence of a spine apparatus or a presynaptic mitochondrion might hold different plastic abilities from those that do not have these organelles.…”

Section: Discussionmentioning

confidence: 99%

Characterization of Subcellular Organelles in Cortical Perisynaptic Astrocytes

Alaoui

Jackson

Fabri

et al. 2021

Front. Cell. Neurosci.

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Perisynaptic astrocytic processes (PAPs) carry out several different functions, from metabolite clearing to control of neuronal excitability and synaptic plasticity. All these functions are likely orchestrated by complex cellular machinery that resides within the PAPs and relies on a fine interplay between multiple subcellular components. However, traditional transmission electron microscopy (EM) studies have found that PAPs are remarkably poor of intracellular organelles, failing to explain how such a variety of PAP functions are achieved in the absence of a proportional complex network of intracellular structures. Here, we use serial block-face scanning EM to reconstruct and describe in three dimensions PAPs and their intracellular organelles in two different mouse cortical regions. We described five distinct organelles, which included empty and full endosomes, phagosomes, mitochondria, and endoplasmic reticulum (ER) cisternae, distributed within three PAPs categories (branches, branchlets, and leaflets). The majority of PAPs belonged to the leaflets category (~60%), with branchlets representing a minority (~37%). Branches were rarely in contact with synapses (<3%). Branches had a higher density of mitochondria and ER cisternae than branchlets and leaflets. Also, branches and branchlets displayed organelles more frequently than leaflets. Endosomes and phagosomes, which accounted for more than 60% of all the organelles detected, were often associated with the same PAP. Likewise, mitochondria and ER cisternae, representing ~40% of all organelles were usually associated. No differences were noted between the organelle distribution of the somatosensory and the anterior cingulate cortex. Finally, the organelle distribution in PAPs did not largely depend on the presence of a spine apparatus or a pre-synaptic mitochondrion in the synapse that PAPs were enwrapping, with some exceptions regarding the presence of phagosomes and ER cisternae, which were slightly more represented around synapses lacking a spine apparatus and a presynaptic mitochondrion, respectively. Thus, PAPs contain several subcellular organelles that could underlie the diverse astrocytic functions carried out at central synapses.

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“…On a short time scale, they have more stable vesicle release than synapses without stationary mitochondria (Sun et al, 2013). On a longer time scale (at least weeks), there is less turnover of boutons in the mouse neocortex, which contain mitochondria (Lees et al, 2020). These studies suggests a link of synaptic energy demand and stability.…”

Section: Discussionmentioning

confidence: 99%

Presynaptic Stochasticity Improves Energy Efficiency and Alleviates the Stability-Plasticity Dilemma

Schug

Benzing²,

Steger³

2021

Preprint

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When an action potential arrives at a synapse there is a large probability that no neurotransmitter is released. Surprisingly, simple computational models suggest that these synaptic failures enable information processing at lower metabolic costs. However, these models only consider information transmission at single synapses ignoring the remainder of the neural network as well as its overall computational goal. Here, we investigate how synaptic failures affect the energy efficiency of models of entire neural networks that solve a goal-driven task. We find that presynaptic stochasticity and plasticity improve energy efficiency and show that the network allocates most energy to a sparse subset of important synapses. We demonstrate that stabilising these synapses alleviates the stability-plasticity dilemma, thus connecting a presynaptic notion of importance to a computational role in lifelong learning. Overall, our findings present a set of hypotheses for how presynaptic plasticity and stochasticity contribute to sparsity, energy efficiency and improved trade-offs in the stability-plasticity dilemma.

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Presynaptic Boutons That Contain Mitochondria Are More Stable

Cited by 37 publications

References 49 publications

An ultrastructural connectomic analysis of a higher‐order thalamocortical circuit in the mouse

An ultrastructural connectomic analysis of a higher‐order thalamocortical circuit in the mouse

Characterization of Subcellular Organelles in Cortical Perisynaptic Astrocytes

Presynaptic Stochasticity Improves Energy Efficiency and Alleviates the Stability-Plasticity Dilemma

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