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Uytiepo M, Zhu Y, Bushong E, Polli F, Chou K, Zhao E, Kim C, Luu D, Chang L, Quach T, Haberl M, Patapoutian L, Beutter E, Zhang W, Dong B, McCue E, Ellisman M, Maximov A. Synaptic architecture of a memory engram in the mouse hippocampus. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.23.590812. [PMID: 38712256 PMCID: PMC11071366 DOI: 10.1101/2024.04.23.590812] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Memory engrams are formed through experience-dependent remodeling of neural circuits, but their detailed architectures have remained unresolved. Using 3D electron microscopy, we performed nanoscale reconstructions of the hippocampal CA3-CA1 pathway following chemogenetic labeling of cellular ensembles with a remote history of correlated excitation during associative learning. Projection neurons involved in memory acquisition expanded their connectomes via multi-synaptic boutons without altering the numbers and spatial arrangements of individual axonal terminals and dendritic spines. This expansion was driven by presynaptic activity elicited by specific negative valence stimuli, regardless of the co-activation state of postsynaptic partners. The rewiring of initial ensembles representing an engram coincided with local, input-specific changes in the shapes and organelle composition of glutamatergic synapses, reflecting their weights and potential for further modifications. Our findings challenge the view that the connectivity among neuronal substrates of memory traces is governed by Hebbian mechanisms, and offer a structural basis for representational drifts.
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Paulussen I, Beckert H, Musial TF, Gschossmann LJ, Wolf J, Schmitt M, Clasadonte J, Mairet-Coello G, Wolff C, Schoch S, Dietrich D. SV2B defines a subpopulation of synaptic vesicles. J Mol Cell Biol 2024; 15:mjad054. [PMID: 37682518 PMCID: PMC11184983 DOI: 10.1093/jmcb/mjad054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 04/03/2023] [Accepted: 09/07/2023] [Indexed: 09/09/2023] Open
Abstract
Synaptic vesicles can undergo several modes of exocytosis, endocytosis, and trafficking within individual synapses, and their fates may be linked to different vesicular protein compositions. Here, we mapped the intrasynaptic distribution of the synaptic vesicle proteins SV2B and SV2A in glutamatergic synapses of the hippocampus using three-dimensional electron microscopy. SV2B was almost completely absent from docked vesicles and a distinct cluster of vesicles found near the active zone. In contrast, SV2A was found in all domains of the synapse and was slightly enriched near the active zone. SV2B and SV2A were found on the membrane in the peri-active zone, suggesting the recycling from both clusters of vesicles. SV2B knockout mice displayed an increased seizure induction threshold only in a model employing high-frequency stimulation. Our data show that glutamatergic synapses generate molecularly distinct populations of synaptic vesicles and are able to maintain them at steep spatial gradients. The almost complete absence of SV2B from vesicles at the active zone of wildtype mice may explain why SV2A has been found more important for vesicle release.
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Affiliation(s)
- Isabelle Paulussen
- Synaptic Neuroscience Team, Department of Neurosurgery, University Hospital Bonn, Bonn 53127, Germany
- Synaptic Neuroscience Team, Department of Neuropathology, University Hospital Bonn, Bonn 53127, Germany
| | - Hannes Beckert
- Microscopy Core Facility, Medical Faculty, University of Bonn, Bonn 53127, Germany
| | - Timothy F Musial
- Microscopy Core Facility, Medical Faculty, University of Bonn, Bonn 53127, Germany
| | - Lena J Gschossmann
- Synaptic Neuroscience Team, Department of Neurosurgery, University Hospital Bonn, Bonn 53127, Germany
- Synaptic Neuroscience Team, Department of Neuropathology, University Hospital Bonn, Bonn 53127, Germany
| | - Julia Wolf
- Synaptic Neuroscience Team, Department of Neurosurgery, University Hospital Bonn, Bonn 53127, Germany
- Synaptic Neuroscience Team, Department of Neuropathology, University Hospital Bonn, Bonn 53127, Germany
| | | | | | | | | | - Susanne Schoch
- Synaptic Neuroscience Team, Department of Neuropathology, University Hospital Bonn, Bonn 53127, Germany
| | - Dirk Dietrich
- Synaptic Neuroscience Team, Department of Neurosurgery, University Hospital Bonn, Bonn 53127, Germany
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3
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Pushkin AN, Kay Y, Herring BE. Protein 4.1N Plays a Cell Type-Specific Role in Hippocampal Glutamatergic Synapse Regulation. J Neurosci 2023; 43:8336-8347. [PMID: 37845032 PMCID: PMC10711697 DOI: 10.1523/jneurosci.0185-23.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 09/14/2023] [Accepted: 10/02/2023] [Indexed: 10/18/2023] Open
Abstract
Many glutamatergic synapse proteins contain a 4.1N protein binding domain. However, a role for 4.1N in the regulation of glutamatergic neurotransmission has been controversial. Here, we observe significantly higher expression of protein 4.1N in granule neurons of the dentate gyrus (DG granule neurons) compared with other hippocampal regions. We discover that reducing 4.1N expression in rat DG granule neurons of either sex results in a significant reduction in glutamatergic synapse function that is caused by a decrease in the number of glutamatergic synapses. By contrast, we find reduction of 4.1N expression in hippocampal CA1 pyramidal neurons has no impact on basal glutamatergic neurotransmission. We also find 4.1N's C-terminal domain (CTD) to be nonessential to its role in the regulation of glutamatergic synapses of DG granule neurons. Instead, we show that 4.1N's four-point-one, ezrin, radixin, and moesin (FERM) domain is essential for supporting synaptic AMPA receptor (AMPAR) function in these neurons. Altogether, this work demonstrates a novel, cell type-specific role for protein 4.1N in governing glutamatergic synapse function.SIGNIFICANCE STATEMENT Glutamatergic synapses exhibit immense molecular diversity. In comparison to heavily studied Schaffer collateral, CA1 glutamatergic synapses, significantly less is known about perforant path-dentate gyrus (DG) synapses. Our data demonstrate that compromising 4.1N function in CA1 pyramidal neurons produces no alteration in basal glutamatergic synaptic transmission. However, in DG granule neurons, compromising 4.1N function leads to a significant decrease in the strength of glutamatergic neurotransmission at perforant pathway synapses. Together, our data identifies 4.1N as a cell type-specific regulator of synaptic transmission within the hippocampus and reveals a unique molecular program that governs perforant pathway synapse function.
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Affiliation(s)
- Anna N Pushkin
- Neuroscience Graduate Program, University of Southern California, Los Angeles, California 90089
| | - Yuni Kay
- Neuroscience Graduate Program, University of Southern California, Los Angeles, California 90089
| | - Bruce E Herring
- Neuroscience Graduate Program, University of Southern California, Los Angeles, California 90089
- Department of Biological Sciences, Neurobiology Section, Dornslife College of Letters, Arts, and Sciences, University of Southern California, Los Angeles, California 90089
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Ramsay HJ, Gookin SE, Ramsey AM, Kareemo DJ, Crosby KC, Stich DG, Olah SS, Actor-Engel HS, Smith KR, Kennedy MJ. AMPA and GABAA receptor nanodomains assemble in the absence of synaptic neurotransmitter release. Front Mol Neurosci 2023; 16:1232795. [PMID: 37602191 PMCID: PMC10435253 DOI: 10.3389/fnmol.2023.1232795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 07/17/2023] [Indexed: 08/22/2023] Open
Abstract
Postsynaptic neurotransmitter receptors and their associated scaffolding proteins assemble into discrete, nanometer-scale subsynaptic domains (SSDs) within the postsynaptic membrane at both excitatory and inhibitory synapses. Intriguingly, postsynaptic receptor SSDs are mirrored by closely apposed presynaptic active zones. These trans-synaptic molecular assemblies are thought to be important for efficient neurotransmission because they concentrate postsynaptic receptors near sites of presynaptic neurotransmitter release. While previous studies have characterized the role of synaptic activity in sculpting the number, size, and distribution of postsynaptic SSDs at established synapses, it remains unknown whether neurotransmitter signaling is required for their initial assembly during synapse development. Here, we evaluated synaptic nano-architecture under conditions where presynaptic neurotransmitter release was blocked prior to, and throughout synaptogenesis with tetanus neurotoxin (TeNT). In agreement with previous work, neurotransmitter release was not required for the formation of excitatory or inhibitory synapses. The overall size of the postsynaptic specialization at both excitatory and inhibitory synapses was reduced at chronically silenced synapses. However, both AMPARs and GABAARs still coalesced into SSDs, along with their respective scaffold proteins. Presynaptic active zone assemblies, defined by RIM1, were smaller and more numerous at silenced synapses, but maintained alignment with postsynaptic AMPAR SSDs. Thus, basic features of synaptic nano-architecture, including assembly of receptors and scaffolds into trans-synaptically aligned structures, are intrinsic properties that can be further regulated by subsequent activity-dependent mechanisms.
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Affiliation(s)
- Harrison J. Ramsay
- Anschutz Medical Campus, Department of Pharmacology, University of Colorado, Aurora, CO, United States
| | - Sara E. Gookin
- Anschutz Medical Campus, Department of Pharmacology, University of Colorado, Aurora, CO, United States
| | - Austin M. Ramsey
- Anschutz Medical Campus, Department of Pharmacology, University of Colorado, Aurora, CO, United States
| | - Dean J. Kareemo
- Anschutz Medical Campus, Department of Pharmacology, University of Colorado, Aurora, CO, United States
| | - Kevin C. Crosby
- Anschutz Medical Campus, Department of Pharmacology, University of Colorado, Aurora, CO, United States
| | - Dominik G. Stich
- Anschutz Medical Campus, Advanced Light Microscopy Core, University of Colorado, Aurora, CO, United States
| | - Samantha S. Olah
- Anschutz Medical Campus, Department of Pharmacology, University of Colorado, Aurora, CO, United States
| | - Hannah S. Actor-Engel
- Anschutz Medical Campus, Department of Pharmacology, University of Colorado, Aurora, CO, United States
| | - Katharine R. Smith
- Anschutz Medical Campus, Department of Pharmacology, University of Colorado, Aurora, CO, United States
| | - Matthew J. Kennedy
- Anschutz Medical Campus, Department of Pharmacology, University of Colorado, Aurora, CO, United States
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5
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Yang S, Park JH, Lu HC. Axonal energy metabolism, and the effects in aging and neurodegenerative diseases. Mol Neurodegener 2023; 18:49. [PMID: 37475056 PMCID: PMC10357692 DOI: 10.1186/s13024-023-00634-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 06/08/2023] [Indexed: 07/22/2023] Open
Abstract
Human studies consistently identify bioenergetic maladaptations in brains upon aging and neurodegenerative disorders of aging (NDAs), such as Alzheimer's disease, Parkinson's disease, Huntington's disease, and Amyotrophic lateral sclerosis. Glucose is the major brain fuel and glucose hypometabolism has been observed in brain regions vulnerable to aging and NDAs. Many neurodegenerative susceptible regions are in the topological central hub of the brain connectome, linked by densely interconnected long-range axons. Axons, key components of the connectome, have high metabolic needs to support neurotransmission and other essential activities. Long-range axons are particularly vulnerable to injury, neurotoxin exposure, protein stress, lysosomal dysfunction, etc. Axonopathy is often an early sign of neurodegeneration. Recent studies ascribe axonal maintenance failures to local bioenergetic dysregulation. With this review, we aim to stimulate research in exploring metabolically oriented neuroprotection strategies to enhance or normalize bioenergetics in NDA models. Here we start by summarizing evidence from human patients and animal models to reveal the correlation between glucose hypometabolism and connectomic disintegration upon aging/NDAs. To encourage mechanistic investigations on how axonal bioenergetic dysregulation occurs during aging/NDAs, we first review the current literature on axonal bioenergetics in distinct axonal subdomains: axon initial segments, myelinated axonal segments, and axonal arbors harboring pre-synaptic boutons. In each subdomain, we focus on the organization, activity-dependent regulation of the bioenergetic system, and external glial support. Second, we review the mechanisms regulating axonal nicotinamide adenine dinucleotide (NAD+) homeostasis, an essential molecule for energy metabolism processes, including NAD+ biosynthetic, recycling, and consuming pathways. Third, we highlight the innate metabolic vulnerability of the brain connectome and discuss its perturbation during aging and NDAs. As axonal bioenergetic deficits are developing into NDAs, especially in asymptomatic phase, they are likely exaggerated further by impaired NAD+ homeostasis, the high energetic cost of neural network hyperactivity, and glial pathology. Future research in interrogating the causal relationship between metabolic vulnerability, axonopathy, amyloid/tau pathology, and cognitive decline will provide fundamental knowledge for developing therapeutic interventions.
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Affiliation(s)
- Sen Yang
- The Linda and Jack Gill Center for Biomolecular Sciences, Indiana University, Bloomington, IN, 47405, USA
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, 47405, USA
- Program in Neuroscience, Indiana University, Bloomington, IN, 47405, USA
| | - Jung Hyun Park
- The Linda and Jack Gill Center for Biomolecular Sciences, Indiana University, Bloomington, IN, 47405, USA
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, 47405, USA
- Program in Neuroscience, Indiana University, Bloomington, IN, 47405, USA
| | - Hui-Chen Lu
- The Linda and Jack Gill Center for Biomolecular Sciences, Indiana University, Bloomington, IN, 47405, USA.
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, 47405, USA.
- Program in Neuroscience, Indiana University, Bloomington, IN, 47405, USA.
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6
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Wilson A, Babadi M. SynapseCLR: Uncovering features of synapses in primary visual cortex through contrastive representation learning. PATTERNS 2023; 4:100693. [PMID: 37123442 PMCID: PMC10140600 DOI: 10.1016/j.patter.2023.100693] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 11/23/2022] [Accepted: 01/25/2023] [Indexed: 03/09/2023]
Abstract
3D electron microscopy (EM) connectomics image volumes are surpassing 1 mm3, providing information-dense, multi-scale visualizations of brain circuitry and necessitating scalable analysis techniques. We present SynapseCLR, a self-supervised contrastive learning method for 3D EM data, and use it to extract features of synapses from mouse visual cortex. SynapseCLR feature representations separate synapses by appearance and functionally important structural annotations. We demonstrate SynapseCLR's utility for valuable downstream tasks, including one-shot identification of defective synapse segmentations, dataset-wide similarity-based querying, and accurate imputation of annotations for unlabeled synapses, using manual annotation of only 0.2% of the dataset's synapses. In particular, excitatory versus inhibitory neuronal types can be assigned with >99.8% accuracy to individual synapses and highly truncated neurites, enabling neurite-enhanced connectomics analysis. Finally, we present a data-driven, unsupervised study of synaptic structural variation on the representation manifold, revealing its intrinsic axes of variation and showing that representations contain inhibitory subtype information.
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Kleinjan MS, Buchta WC, Ogelman R, Hwang IW, Kuwajima M, Hubbard DD, Kareemo DJ, Prikhodko O, Olah SL, Gomez Wulschner LE, Abraham WC, Franco SJ, Harris KM, Oh WC, Kennedy MJ. Dually innervated dendritic spines develop in the absence of excitatory activity and resist plasticity through tonic inhibitory crosstalk. Neuron 2023; 111:362-371.e6. [PMID: 36395772 PMCID: PMC9899020 DOI: 10.1016/j.neuron.2022.11.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 07/13/2022] [Accepted: 10/31/2022] [Indexed: 11/17/2022]
Abstract
Dendritic spines can be directly connected to both inhibitory and excitatory presynaptic terminals, resulting in nanometer-scale proximity of opposing synaptic functions. While dually innervated spines (DiSs) are observed throughout the central nervous system, their developmental timeline and functional properties remain uncharacterized. Here we used a combination of serial section electron microscopy, live imaging, and local synapse activity manipulations to investigate DiS development and function in rodent hippocampus. Dual innervation occurred early in development, even on spines where the excitatory input was locally silenced. Synaptic NMDA receptor currents were selectively reduced at DiSs through tonic GABAB receptor signaling. Accordingly, spine enlargement normally associated with long-term potentiation on singly innervated spines (SiSs) was blocked at DiSs. Silencing somatostatin interneurons or pharmacologically blocking GABABRs restored NMDA receptor function and structural plasticity to levels comparable to neighboring SiSs. Thus, hippocampal DiSs are stable structures where function and plasticity are potently regulated by nanometer-scale GABAergic signaling.
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Affiliation(s)
- Mason S Kleinjan
- Department of Pharmacology, University of Colorado School of Medicine, Aurora, CO 80045, USA
| | - William C Buchta
- Department of Pharmacology, University of Colorado School of Medicine, Aurora, CO 80045, USA
| | - Roberto Ogelman
- Department of Pharmacology, University of Colorado School of Medicine, Aurora, CO 80045, USA
| | - In-Wook Hwang
- Department of Pharmacology, University of Colorado School of Medicine, Aurora, CO 80045, USA
| | - Masaaki Kuwajima
- Department of Neuroscience, University of Texas at Austin, Austin, TX 78712, USA
| | - Dusten D Hubbard
- Department of Neuroscience, University of Texas at Austin, Austin, TX 78712, USA
| | - Dean J Kareemo
- Department of Pharmacology, University of Colorado School of Medicine, Aurora, CO 80045, USA
| | - Olga Prikhodko
- Department of Pharmacology, University of Colorado School of Medicine, Aurora, CO 80045, USA
| | - Samantha L Olah
- Department of Pharmacology, University of Colorado School of Medicine, Aurora, CO 80045, USA
| | - Luis E Gomez Wulschner
- Department of Pharmacology, University of Colorado School of Medicine, Aurora, CO 80045, USA
| | - Wickliffe C Abraham
- Department of Psychology, Brain Health Research Centre, University of Otago, Dunedin, New Zealand
| | - Santos J Franco
- Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO 80045, USA
| | - Kristen M Harris
- Department of Neuroscience, University of Texas at Austin, Austin, TX 78712, USA
| | - Won Chan Oh
- Department of Pharmacology, University of Colorado School of Medicine, Aurora, CO 80045, USA.
| | - Matthew J Kennedy
- Department of Pharmacology, University of Colorado School of Medicine, Aurora, CO 80045, USA.
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8
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Su F, Wei M, Sun M, Jiang L, Dong Z, Wang J, Zhang C. Deep learning-based synapse counting and synaptic ultrastructure analysis of electron microscopy images. J Neurosci Methods 2023; 384:109750. [PMID: 36414102 DOI: 10.1016/j.jneumeth.2022.109750] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 11/11/2022] [Accepted: 11/18/2022] [Indexed: 11/21/2022]
Abstract
BACKGROUND Synapses are the connections between neurons in the central nervous system (CNS) or between neurons and other excitable cells in the peripheral nervous system (PNS), where electrical or chemical signals rapidly travel through one cell to another with high spatial precision. Synaptic analysis, based on synapse numbers and fine morphology, is the basis for understanding neurological functions and diseases. Manual analysis of synaptic structures in electron microscopy (EM) images is often limited by low efficiency and subjective bias. NEW METHOD We developed a multifunctional synaptic analysis system based on several advanced deep learning (DL) models. The system achieved synapse counting in low-magnification EM images and synaptic ultrastructure analysis in high-magnification EM images. RESULTS The synapse counting system based on ResNet18 and a Faster R-CNN model had a mean average precision (mAP) of 92.55%. For synaptic ultrastructure analysis, the Faster R-CNN model based on ResNet50 achieved a mAP of 91.60%, the DeepLab v3 + model based on ResNet50 enabled high performance in presynaptic and postsynaptic membrane segmentation with a global accuracy of 0.9811, and the Faster R-CNN model based on ResNet18 achieved a mAP of 91.41% for synaptic vesicle detection. CONCLUSIONS The proposed multifunctional synaptic analysis system may help to overcome the experimental bias inherent in manual analysis, thereby facilitating EM image-based synaptic function studies.
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Affiliation(s)
- Feng Su
- Department of Neurobiology, School of Basic Medical Sciences, Beijing Key Laboratory of Neural Regeneration and Repair, Capital Medical University, Beijing 100069, China; Chinese Institute for Brain Research, Beijing 102206, China; State Key Laboratory of Translational Medicine and Innovative Drug Development, Nanjing 210000, Jiangsu, China; Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Mengping Wei
- Department of Neurobiology, School of Basic Medical Sciences, Beijing Key Laboratory of Neural Regeneration and Repair, Capital Medical University, Beijing 100069, China
| | - Meng Sun
- Department of Neurobiology, School of Basic Medical Sciences, Beijing Key Laboratory of Neural Regeneration and Repair, Capital Medical University, Beijing 100069, China
| | - Lixin Jiang
- Peking University Institute of Mental Health (Sixth Hospital), No. 51 Huayuanbei Road, Haidian District, Beijing 100191, China
| | - Zhaoqi Dong
- Department of Neurobiology, School of Basic Medical Sciences, Beijing Key Laboratory of Neural Regeneration and Repair, Capital Medical University, Beijing 100069, China
| | - Jue Wang
- Department of Neurobiology, School of Basic Medical Sciences, Beijing Key Laboratory of Neural Regeneration and Repair, Capital Medical University, Beijing 100069, China
| | - Chen Zhang
- Department of Neurobiology, School of Basic Medical Sciences, Beijing Key Laboratory of Neural Regeneration and Repair, Capital Medical University, Beijing 100069, China; Chinese Institute for Brain Research, Beijing 102206, China; State Key Laboratory of Translational Medicine and Innovative Drug Development, Nanjing 210000, Jiangsu, China.
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9
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Angular gyrus: an anatomical case study for association cortex. Brain Struct Funct 2023; 228:131-143. [PMID: 35906433 DOI: 10.1007/s00429-022-02537-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Accepted: 07/05/2022] [Indexed: 01/07/2023]
Abstract
The angular gyrus is associated with a spectrum of higher order cognitive functions. This mini-review undertakes a broad survey of putative neuroanatomical substrates, guided by the premise that area-specific specializations derive from a combination of extrinsic connections and intrinsic area properties. Three levels of spatial resolution are discussed: cellular, supracellular connectivity, and synaptic micro-scale, with examples necessarily drawn mainly from experimental work with nonhuman primates. A significant factor in the functional specialization of the human parietal cortex is the pronounced enlargement. In addition to "more" cells, synapses, and connections, however, the heterogeneity itself can be considered an important property. Multiple anatomical features support the idea of overlapping and temporally dynamic membership in several brain wide subnetworks, but how these features operate in the context of higher cognitive functions remains for continued investigations.
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10
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Konietzny A, Wegmann S, Mikhaylova M. The endoplasmic reticulum puts a new spin on synaptic tagging. Trends Neurosci 2023; 46:32-44. [PMID: 36428191 DOI: 10.1016/j.tins.2022.10.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 10/12/2022] [Accepted: 10/31/2022] [Indexed: 11/23/2022]
Abstract
The heterogeneity of the endoplasmic reticulum (ER) makes it a versatile platform for a broad range of homeostatic processes, ranging from calcium regulation to synthesis and trafficking of proteins and lipids. It is not surprising that neurons use this organelle to fine-tune synaptic properties and thereby provide specificity to synaptic inputs. In this review, we discuss the mechanisms that enable activity-dependent ER recruitment into dendritic spines, with a focus on molecular mechanisms that mediate transport and retention of the ER in spines. The role of calcium signaling in spine ER, synaptopodin 'tagging' of active synapses, and the formation of the spine apparatus (SA) are highlighted. Finally, we discuss the role of liquid-liquid phase separation as a possible driving force in these processes.
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Affiliation(s)
- Anja Konietzny
- AG Optobiology, Institute of Biology, Humboldt Universität zu Berlin, Berlin, Germany; Guest Group 'Neuronal Protein Transport', Center for Molecular Neurobiology, ZMNH, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Susanne Wegmann
- German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany
| | - Marina Mikhaylova
- AG Optobiology, Institute of Biology, Humboldt Universität zu Berlin, Berlin, Germany; Guest Group 'Neuronal Protein Transport', Center for Molecular Neurobiology, ZMNH, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.
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11
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Dubes S, Soula A, Benquet S, Tessier B, Poujol C, Favereaux A, Thoumine O, Letellier M. miR
‐124‐dependent tagging of synapses by synaptopodin enables input‐specific homeostatic plasticity. EMBO J 2022; 41:e109012. [PMID: 35875872 PMCID: PMC9574720 DOI: 10.15252/embj.2021109012] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 06/11/2022] [Accepted: 06/27/2022] [Indexed: 12/26/2022] Open
Abstract
Homeostatic synaptic plasticity is a process by which neurons adjust their synaptic strength to compensate for perturbations in neuronal activity. Whether the highly diverse synapses on a neuron respond uniformly to the same perturbation remains unclear. Moreover, the molecular determinants that underlie synapse‐specific homeostatic synaptic plasticity are unknown. Here, we report a synaptic tagging mechanism in which the ability of individual synapses to increase their strength in response to activity deprivation depends on the local expression of the spine‐apparatus protein synaptopodin under the regulation of miR‐124. Using genetic manipulations to alter synaptopodin expression or regulation by miR‐124, we show that synaptopodin behaves as a “postsynaptic tag” whose translation is derepressed in a subpopulation of synapses and allows for nonuniform homeostatic strengthening and synaptic AMPA receptor stabilization. By genetically silencing individual connections in pairs of neurons, we demonstrate that this process operates in an input‐specific manner. Overall, our study shifts the current view that homeostatic synaptic plasticity affects all synapses uniformly to a more complex paradigm where the ability of individual synapses to undergo homeostatic changes depends on their own functional and biochemical state.
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Affiliation(s)
- Sandra Dubes
- University of Bordeaux CNRS Interdisciplinary Institute for Neuroscience UMR 5297 Bordeaux France
| | - Anaïs Soula
- University of Bordeaux CNRS Interdisciplinary Institute for Neuroscience UMR 5297 Bordeaux France
| | - Sébastien Benquet
- University of Bordeaux CNRS Interdisciplinary Institute for Neuroscience UMR 5297 Bordeaux France
| | - Béatrice Tessier
- University of Bordeaux CNRS Interdisciplinary Institute for Neuroscience UMR 5297 Bordeaux France
| | - Christel Poujol
- University of Bordeaux CNRS INSERM Bordeaux Imaging Center BIC UMS 3420, US 4 Bordeaux France
| | - Alexandre Favereaux
- University of Bordeaux CNRS Interdisciplinary Institute for Neuroscience UMR 5297 Bordeaux France
| | - Olivier Thoumine
- University of Bordeaux CNRS Interdisciplinary Institute for Neuroscience UMR 5297 Bordeaux France
| | - Mathieu Letellier
- University of Bordeaux CNRS Interdisciplinary Institute for Neuroscience UMR 5297 Bordeaux France
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12
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Pekkurnaz G, Wang X. Mitochondrial heterogeneity and homeostasis through the lens of a neuron. Nat Metab 2022; 4:802-812. [PMID: 35817853 PMCID: PMC11151822 DOI: 10.1038/s42255-022-00594-w] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 05/23/2022] [Indexed: 12/12/2022]
Abstract
Mitochondria are vital organelles with distinct morphological features and functional properties. The dynamic network of mitochondria undergoes structural and functional adaptations in response to cell-type-specific metabolic demands. Even within the same cell, mitochondria can display wide diversity and separate into functionally distinct subpopulations. Mitochondrial heterogeneity supports unique subcellular functions and is crucial to polarized cells, such as neurons. The spatiotemporal metabolic burden within the complex shape of a neuron requires precisely localized mitochondria. By travelling great lengths throughout neurons and experiencing bouts of immobility, mitochondria meet distant local fuel demands. Understanding mitochondrial heterogeneity and homeostasis mechanisms in neurons provides a framework to probe their significance to many other cell types. Here, we put forth an outline of the multifaceted role of mitochondria in regulating neuronal physiology and cellular functions more broadly.
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Affiliation(s)
- Gulcin Pekkurnaz
- Neurobiology Department, School of Biological Sciences, University of California San Diego, La Jolla, CA, USA.
| | - Xinnan Wang
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA.
- Wu Tsai Neurosciences Institute, Stanford University School of Medicine, Stanford, CA, USA.
- Maternal & Child Health Research Institute, Stanford University School of Medicine, Stanford, CA, USA.
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Turner NL, Macrina T, Bae JA, Yang R, Wilson AM, Schneider-Mizell C, Lee K, Lu R, Wu J, Bodor AL, Bleckert AA, Brittain D, Froudarakis E, Dorkenwald S, Collman F, Kemnitz N, Ih D, Silversmith WM, Zung J, Zlateski A, Tartavull I, Yu SC, Popovych S, Mu S, Wong W, Jordan CS, Castro M, Buchanan J, Bumbarger DJ, Takeno M, Torres R, Mahalingam G, Elabbady L, Li Y, Cobos E, Zhou P, Suckow S, Becker L, Paninski L, Polleux F, Reimer J, Tolias AS, Reid RC, da Costa NM, Seung HS. Reconstruction of neocortex: Organelles, compartments, cells, circuits, and activity. Cell 2022; 185:1082-1100.e24. [PMID: 35216674 PMCID: PMC9337909 DOI: 10.1016/j.cell.2022.01.023] [Citation(s) in RCA: 51] [Impact Index Per Article: 25.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Revised: 07/26/2021] [Accepted: 01/27/2022] [Indexed: 12/31/2022]
Abstract
We assembled a semi-automated reconstruction of L2/3 mouse primary visual cortex from ∼250 × 140 × 90 μm3 of electron microscopic images, including pyramidal and non-pyramidal neurons, astrocytes, microglia, oligodendrocytes and precursors, pericytes, vasculature, nuclei, mitochondria, and synapses. Visual responses of a subset of pyramidal cells are included. The data are publicly available, along with tools for programmatic and three-dimensional interactive access. Brief vignettes illustrate the breadth of potential applications relating structure to function in cortical circuits and neuronal cell biology. Mitochondria and synapse organization are characterized as a function of path length from the soma. Pyramidal connectivity motif frequencies are predicted accurately using a configuration model of random graphs. Pyramidal cells receiving more connections from nearby cells exhibit stronger and more reliable visual responses. Sample code shows data access and analysis.
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Affiliation(s)
- Nicholas L Turner
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Computer Science Department, Princeton University, Princeton, NJ 08544, USA
| | - Thomas Macrina
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Computer Science Department, Princeton University, Princeton, NJ 08544, USA
| | - J Alexander Bae
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Electrical and Computer Engineering Department, Princeton University, Princeton, NJ 08544, USA
| | - Runzhe Yang
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Computer Science Department, Princeton University, Princeton, NJ 08544, USA
| | - Alyssa M Wilson
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | | | - Kisuk Lee
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Brain & Cognitive Sciences Department, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Ran Lu
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Jingpeng Wu
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Agnes L Bodor
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | | | - Emmanouil Froudarakis
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA; Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX 77030, USA
| | - Sven Dorkenwald
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Computer Science Department, Princeton University, Princeton, NJ 08544, USA
| | | | - Nico Kemnitz
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Dodam Ih
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | | | - Jonathan Zung
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Computer Science Department, Princeton University, Princeton, NJ 08544, USA
| | - Aleksandar Zlateski
- Electrical Engineering and Computer Science Department, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Ignacio Tartavull
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Szi-Chieh Yu
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Sergiy Popovych
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Computer Science Department, Princeton University, Princeton, NJ 08544, USA
| | - Shang Mu
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - William Wong
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Chris S Jordan
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Manuel Castro
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - JoAnn Buchanan
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Marc Takeno
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Russel Torres
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Leila Elabbady
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Yang Li
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Erick Cobos
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA; Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX 77030, USA
| | - Pengcheng Zhou
- Department of Statistics, Columbia University, New York, NY 10027, USA; Center for Theoretical Neuroscience, Columbia University, New York, NY 10027, USA; Grossman Center for the Statistics of Mind, Columbia University, New York, NY 10027, USA
| | - Shelby Suckow
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Lynne Becker
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Liam Paninski
- Department of Statistics, Columbia University, New York, NY 10027, USA; Center for Theoretical Neuroscience, Columbia University, New York, NY 10027, USA; Grossman Center for the Statistics of Mind, Columbia University, New York, NY 10027, USA; Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA; Department of Neuroscience, Columbia University, New York, NY 10027, USA; Kavli Institute for Brain Science at Columbia University, New York, NY 10027, USA
| | - Franck Polleux
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA; Department of Neuroscience, Columbia University, New York, NY 10027, USA; Kavli Institute for Brain Science at Columbia University, New York, NY 10027, USA
| | - Jacob Reimer
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA; Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX 77030, USA
| | - Andreas S Tolias
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA; Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX 77030, USA; Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA
| | - R Clay Reid
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - H Sebastian Seung
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Computer Science Department, Princeton University, Princeton, NJ 08544, USA.
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