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Takács V, Bardóczi Z, Orosz Á, Major A, Tar L, Berki P, Papp P, Mayer MI, Sebők H, Zsolt L, Sos KE, Káli S, Freund TF, Nyiri G. Synaptic and dendritic architecture of different types of hippocampal somatostatin interneurons. PLoS Biol 2024; 22:e3002539. [PMID: 38470935 PMCID: PMC10959371 DOI: 10.1371/journal.pbio.3002539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Revised: 03/22/2024] [Accepted: 02/06/2024] [Indexed: 03/14/2024] Open
Abstract
GABAergic inhibitory neurons fundamentally shape the activity and plasticity of cortical circuits. A major subset of these neurons contains somatostatin (SOM); these cells play crucial roles in neuroplasticity, learning, and memory in many brain areas including the hippocampus, and are implicated in several neuropsychiatric diseases and neurodegenerative disorders. Two main types of SOM-containing cells in area CA1 of the hippocampus are oriens-lacunosum-moleculare (OLM) cells and hippocampo-septal (HS) cells. These cell types show many similarities in their soma-dendritic architecture, but they have different axonal targets, display different activity patterns in vivo, and are thought to have distinct network functions. However, a complete understanding of the functional roles of these interneurons requires a precise description of their intrinsic computational properties and their synaptic interactions. In the current study we generated, analyzed, and make available several key data sets that enable a quantitative comparison of various anatomical and physiological properties of OLM and HS cells in mouse. The data set includes detailed scanning electron microscopy (SEM)-based 3D reconstructions of OLM and HS cells along with their excitatory and inhibitory synaptic inputs. Combining this core data set with other anatomical data, patch-clamp electrophysiology, and compartmental modeling, we examined the precise morphological structure, inputs, outputs, and basic physiological properties of these cells. Our results highlight key differences between OLM and HS cells, particularly regarding the density and distribution of their synaptic inputs and mitochondria. For example, we estimated that an OLM cell receives about 8,400, whereas an HS cell about 15,600 synaptic inputs, about 16% of which are GABAergic. Our data and models provide insight into the possible basis of the different functionality of OLM and HS cell types and supply essential information for more detailed functional models of these neurons and the hippocampal network.
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Affiliation(s)
- Virág Takács
- Laboratory of Cerebral Cortex Research, HUN-REN Institute of Experimental Medicine, Budapest, Hungary
| | - Zsuzsanna Bardóczi
- Laboratory of Cerebral Cortex Research, HUN-REN Institute of Experimental Medicine, Budapest, Hungary
| | - Áron Orosz
- Laboratory of Cerebral Cortex Research, HUN-REN Institute of Experimental Medicine, Budapest, Hungary
- János Szentágothai Doctoral School of Neurosciences, Semmelweis University, Budapest, Hungary
| | - Abel Major
- Laboratory of Cerebral Cortex Research, HUN-REN Institute of Experimental Medicine, Budapest, Hungary
| | - Luca Tar
- Laboratory of Cerebral Cortex Research, HUN-REN Institute of Experimental Medicine, Budapest, Hungary
- Roska Tamás Doctoral School of Sciences and Technology, Pázmány Péter Catholic University, Budapest, Hungary
| | - Péter Berki
- Laboratory of Cerebral Cortex Research, HUN-REN Institute of Experimental Medicine, Budapest, Hungary
- János Szentágothai Doctoral School of Neurosciences, Semmelweis University, Budapest, Hungary
| | - Péter Papp
- Laboratory of Cerebral Cortex Research, HUN-REN Institute of Experimental Medicine, Budapest, Hungary
| | - Márton I. Mayer
- Laboratory of Cerebral Cortex Research, HUN-REN Institute of Experimental Medicine, Budapest, Hungary
- János Szentágothai Doctoral School of Neurosciences, Semmelweis University, Budapest, Hungary
| | - Hunor Sebők
- Laboratory of Cerebral Cortex Research, HUN-REN Institute of Experimental Medicine, Budapest, Hungary
| | - Luca Zsolt
- Laboratory of Cerebral Cortex Research, HUN-REN Institute of Experimental Medicine, Budapest, Hungary
| | - Katalin E. Sos
- Laboratory of Cerebral Cortex Research, HUN-REN Institute of Experimental Medicine, Budapest, Hungary
- János Szentágothai Doctoral School of Neurosciences, Semmelweis University, Budapest, Hungary
| | - Szabolcs Káli
- Laboratory of Cerebral Cortex Research, HUN-REN Institute of Experimental Medicine, Budapest, Hungary
| | - Tamás F. Freund
- Laboratory of Cerebral Cortex Research, HUN-REN Institute of Experimental Medicine, Budapest, Hungary
| | - Gábor Nyiri
- Laboratory of Cerebral Cortex Research, HUN-REN Institute of Experimental Medicine, Budapest, Hungary
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Wheeler DW, Kopsick JD, Sutton N, Tecuatl C, Komendantov AO, Nadella K, Ascoli GA. Hippocampome.org 2.0 is a knowledge base enabling data-driven spiking neural network simulations of rodent hippocampal circuits. eLife 2024; 12:RP90597. [PMID: 38345923 PMCID: PMC10942544 DOI: 10.7554/elife.90597] [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] [Indexed: 02/15/2024] Open
Abstract
Hippocampome.org is a mature open-access knowledge base of the rodent hippocampal formation focusing on neuron types and their properties. Previously, Hippocampome.org v1.0 established a foundational classification system identifying 122 hippocampal neuron types based on their axonal and dendritic morphologies, main neurotransmitter, membrane biophysics, and molecular expression (Wheeler et al., 2015). Releases v1.1 through v1.12 furthered the aggregation of literature-mined data, including among others neuron counts, spiking patterns, synaptic physiology, in vivo firing phases, and connection probabilities. Those additional properties increased the online information content of this public resource over 100-fold, enabling numerous independent discoveries by the scientific community. Hippocampome.org v2.0, introduced here, besides incorporating over 50 new neuron types, now recenters its focus on extending the functionality to build real-scale, biologically detailed, data-driven computational simulations. In all cases, the freely downloadable model parameters are directly linked to the specific peer-reviewed empirical evidence from which they were derived. Possible research applications include quantitative, multiscale analyses of circuit connectivity and spiking neural network simulations of activity dynamics. These advances can help generate precise, experimentally testable hypotheses and shed light on the neural mechanisms underlying associative memory and spatial navigation.
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Affiliation(s)
- Diek W Wheeler
- Center for Neural Informatics, Structures, & Plasticity, Krasnow Institute for Advanced Study, George Mason UniversityFairfaxUnited States
- Bioengineering Department and Center for Neural Informatics, Structures, & Plasticity, College of Engineering and Computing, George Mason UniversityFairfaxUnited States
| | - Jeffrey D Kopsick
- Center for Neural Informatics, Structures, & Plasticity, Krasnow Institute for Advanced Study, George Mason UniversityFairfaxUnited States
- Interdisciplinary Program in Neuroscience, College of Science, George Mason UniversityFairfaxUnited States
| | - Nate Sutton
- Center for Neural Informatics, Structures, & Plasticity, Krasnow Institute for Advanced Study, George Mason UniversityFairfaxUnited States
- Bioengineering Department and Center for Neural Informatics, Structures, & Plasticity, College of Engineering and Computing, George Mason UniversityFairfaxUnited States
| | - Carolina Tecuatl
- Center for Neural Informatics, Structures, & Plasticity, Krasnow Institute for Advanced Study, George Mason UniversityFairfaxUnited States
- Bioengineering Department and Center for Neural Informatics, Structures, & Plasticity, College of Engineering and Computing, George Mason UniversityFairfaxUnited States
| | - Alexander O Komendantov
- Center for Neural Informatics, Structures, & Plasticity, Krasnow Institute for Advanced Study, George Mason UniversityFairfaxUnited States
- Bioengineering Department and Center for Neural Informatics, Structures, & Plasticity, College of Engineering and Computing, George Mason UniversityFairfaxUnited States
| | - Kasturi Nadella
- Center for Neural Informatics, Structures, & Plasticity, Krasnow Institute for Advanced Study, George Mason UniversityFairfaxUnited States
- Bioengineering Department and Center for Neural Informatics, Structures, & Plasticity, College of Engineering and Computing, George Mason UniversityFairfaxUnited States
| | - Giorgio A Ascoli
- Center for Neural Informatics, Structures, & Plasticity, Krasnow Institute for Advanced Study, George Mason UniversityFairfaxUnited States
- Bioengineering Department and Center for Neural Informatics, Structures, & Plasticity, College of Engineering and Computing, George Mason UniversityFairfaxUnited States
- Interdisciplinary Program in Neuroscience, College of Science, George Mason UniversityFairfaxUnited States
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3
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Wheeler DW, Kopsick JD, Sutton N, Tecuatl C, Komendantov AO, Nadella K, Ascoli GA. Hippocampome.org v2.0: a knowledge base enabling data-driven spiking neural network simulations of rodent hippocampal circuits. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.05.12.540597. [PMID: 37425693 PMCID: PMC10327012 DOI: 10.1101/2023.05.12.540597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
Hippocampome.org is a mature open-access knowledge base of the rodent hippocampal formation focusing on neuron types and their properties. Hippocampome.org v1.0 established a foundational classification system identifying 122 hippocampal neuron types based on their axonal and dendritic morphologies, main neurotransmitter, membrane biophysics, and molecular expression. Releases v1.1 through v1.12 furthered the aggregation of literature-mined data, including among others neuron counts, spiking patterns, synaptic physiology, in vivo firing phases, and connection probabilities. Those additional properties increased the online information content of this public resource over 100-fold, enabling numerous independent discoveries by the scientific community. Hippocampome.org v2.0, introduced here, besides incorporating over 50 new neuron types, now recenters its focus on extending the functionality to build real-scale, biologically detailed, data-driven computational simulations. In all cases, the freely downloadable model parameters are directly linked to the specific peer-reviewed empirical evidence from which they were derived. Possible research applications include quantitative, multiscale analyses of circuit connectivity and spiking neural network simulations of activity dynamics. These advances can help generate precise, experimentally testable hypotheses and shed light on the neural mechanisms underlying associative memory and spatial navigation.
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Affiliation(s)
- Diek W. Wheeler
- Center for Neural Informatics, Structures, & Plasticity; Krasnow Institute for Advanced Study; George Mason University, Fairfax, VA, USA
- Bioengineering Department and Center for Neural Informatics, Structures, & Plasticity; College of Engineering and Computing; George Mason University, Fairfax, VA, USA
| | - Jeffrey D. Kopsick
- Center for Neural Informatics, Structures, & Plasticity; Krasnow Institute for Advanced Study; George Mason University, Fairfax, VA, USA
- Interdisciplinary Program in Neuroscience; College of Science; George Mason University, Fairfax, VA, USA
| | - Nate Sutton
- Center for Neural Informatics, Structures, & Plasticity; Krasnow Institute for Advanced Study; George Mason University, Fairfax, VA, USA
- Bioengineering Department and Center for Neural Informatics, Structures, & Plasticity; College of Engineering and Computing; George Mason University, Fairfax, VA, USA
| | - Carolina Tecuatl
- Center for Neural Informatics, Structures, & Plasticity; Krasnow Institute for Advanced Study; George Mason University, Fairfax, VA, USA
- Bioengineering Department and Center for Neural Informatics, Structures, & Plasticity; College of Engineering and Computing; George Mason University, Fairfax, VA, USA
| | - Alexander O. Komendantov
- Center for Neural Informatics, Structures, & Plasticity; Krasnow Institute for Advanced Study; George Mason University, Fairfax, VA, USA
- Bioengineering Department and Center for Neural Informatics, Structures, & Plasticity; College of Engineering and Computing; George Mason University, Fairfax, VA, USA
| | - Kasturi Nadella
- Center for Neural Informatics, Structures, & Plasticity; Krasnow Institute for Advanced Study; George Mason University, Fairfax, VA, USA
- Bioengineering Department and Center for Neural Informatics, Structures, & Plasticity; College of Engineering and Computing; George Mason University, Fairfax, VA, USA
| | - Giorgio A. Ascoli
- Center for Neural Informatics, Structures, & Plasticity; Krasnow Institute for Advanced Study; George Mason University, Fairfax, VA, USA
- Interdisciplinary Program in Neuroscience; College of Science; George Mason University, Fairfax, VA, USA
- Bioengineering Department and Center for Neural Informatics, Structures, & Plasticity; College of Engineering and Computing; George Mason University, Fairfax, VA, USA
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Ecker A, Romani A, Sáray S, Káli S, Migliore M, Falck J, Lange S, Mercer A, Thomson AM, Muller E, Reimann MW, Ramaswamy S. Data-driven integration of hippocampal CA1 synaptic physiology in silico. Hippocampus 2020; 30:1129-1145. [PMID: 32520422 PMCID: PMC7687201 DOI: 10.1002/hipo.23220] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2019] [Revised: 04/20/2020] [Accepted: 05/07/2020] [Indexed: 12/31/2022]
Abstract
The anatomy and physiology of monosynaptic connections in rodent hippocampal CA1 have been extensively studied in recent decades. Yet, the resulting knowledge remains disparate and difficult to reconcile. Here, we present a data‐driven approach to integrate the current state‐of‐the‐art knowledge on the synaptic anatomy and physiology of rodent hippocampal CA1, including axo‐dendritic innervation patterns, number of synapses per connection, quantal conductances, neurotransmitter release probability, and short‐term plasticity into a single coherent resource. First, we undertook an extensive literature review of paired recordings of hippocampal neurons and compiled experimental data on their synaptic anatomy and physiology. The data collected in this manner is sparse and inhomogeneous due to the diversity of experimental techniques used by different groups, which necessitates the need for an integrative framework to unify these data. To this end, we extended a previously developed workflow for the neocortex to constrain a unifying in silico reconstruction of the synaptic physiology of CA1 connections. Our work identifies gaps in the existing knowledge and provides a complementary resource toward a more complete quantification of synaptic anatomy and physiology in the rodent hippocampal CA1 region.
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Affiliation(s)
- András Ecker
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Campus Biotech, Geneva, Switzerland
| | - Armando Romani
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Campus Biotech, Geneva, Switzerland
| | - Sára Sáray
- Institute of Experimental Medicine, Budapest, Hungary.,Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
| | - Szabolcs Káli
- Institute of Experimental Medicine, Budapest, Hungary.,Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary
| | - Michele Migliore
- Institute of Biophysics, National Research Council, Palermo, Italy
| | - Joanne Falck
- UCL School of Pharmacy, University College London, London, UK
| | - Sigrun Lange
- UCL School of Pharmacy, University College London, London, UK.,School of Life Sciences, University of Westminster, London, UK
| | - Audrey Mercer
- UCL School of Pharmacy, University College London, London, UK
| | - Alex M Thomson
- UCL School of Pharmacy, University College London, London, UK
| | - Eilif Muller
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Campus Biotech, Geneva, Switzerland
| | - Michael W Reimann
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Campus Biotech, Geneva, Switzerland
| | - Srikanth Ramaswamy
- Blue Brain Project, École Polytechnique Fédérale de Lausanne, Campus Biotech, Geneva, Switzerland
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Munro Krull E, Sakata S, Toyoizumi T. Theta Oscillations Alternate With High Amplitude Neocortical Population Within Synchronized States. Front Neurosci 2019; 13:316. [PMID: 31037053 PMCID: PMC6476345 DOI: 10.3389/fnins.2019.00316] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2018] [Accepted: 03/20/2019] [Indexed: 12/16/2022] Open
Abstract
Synchronized states are marked by large-amplitude low-frequency oscillations in the cortex. These states can be seen during quiet waking or slow-wave sleep. Within synchronized states, previous studies have noted a plethora of different types of activity, including delta oscillations (0.5-4 Hz) and slow oscillations (<1 Hz) in the neocortex and large- and small- irregular activity in the hippocampus. However, it is not still fully characterized how neural populations contribute to the synchronized state. Here we apply independent component analysis to parse which populations are involved in different kinds of neocortical activity, and find two populations that alternate throughout synchronized states. One population broadly affects neocortical deep layers, and is associated with larger amplitude slower neocortical oscillations. The other population exhibits theta-frequency oscillations that are not easily observed in raw field potential recordings. These theta oscillations apparently come from below the neocortex, suggesting hippocampal origin, and are associated with smaller amplitude faster neocortical oscillations. Relative involvement of these two alternating populations may indicate different modes of operation within synchronized states.
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Affiliation(s)
- Erin Munro Krull
- RIKEN Center for Brain Science, Tokyo, Japan
- Beloit College, Beloit, WI, United States
| | - Shuzo Sakata
- Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow, United Kingdom
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6
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Barsy B, Szabó GG, Andrási T, Vikór A, Hájos N. Different output properties of perisomatic region-targeting interneurons in the basal amygdala. Eur J Neurosci 2017; 45:548-558. [PMID: 27977063 DOI: 10.1111/ejn.13498] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2016] [Revised: 11/24/2016] [Accepted: 11/27/2016] [Indexed: 01/06/2023]
Abstract
The perisomatic region of principal neurons in cortical regions is innervated by three types of GABAergic interneuron, including parvalbumin-containing basket cells (PVBCs) and axo-axonic cells (AACs), as well as cholecystokinin and type 1 cannabinoid receptor-expressing basket cells (CCK/CB1BCs). These perisomatic inhibitory cell types can also be found in the basal nucleus of the amygdala, however, their output properties are largely unknown. Here, we performed whole-cell recordings in morphologically identified interneurons in slices prepared from transgenic mice, in which the GABAergic cells could be selectively targeted. Investigating the passive and active membrane properties of interneurons located within the basal amygdala revealed that the three interneuron types have distinct single-cell properties. For instance, the input resistance, spike rate, accommodation in discharge rate, or after-hyperpolarization width at the half maximal amplitude separated the three interneuron types. Furthermore, we performed paired recordings from interneurons and principal neurons to uncover the basic features of unitary inhibitory postsynaptic currents (uIPSCs). Although we found no difference in the magnitude of responses measured in the principal neurons, the uIPSCs originating from the distinct interneuron types differed in rise time, failure rate, latency, and short-term dynamics. Moreover, the asynchronous transmitter release induced by a train of action potentials was typical for the output synapses of CCK/CB1BCs. Our results suggest that, despite the similar uIPSC magnitudes originating from the three perisomatic inhibitory cell types, their distinct release properties together with the marked differences in their spiking characteristics may contribute to accomplish specific functions in amygdala network operation.
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Affiliation(s)
- Boglárka Barsy
- 'Lendület' Laboratory of Network Neurophysiology, Institute of Experimental Medicine, Hungarian Academy of Sciences, Budapest, H-1450, Hungary
| | - Gergely G Szabó
- 'Lendület' Laboratory of Network Neurophysiology, Institute of Experimental Medicine, Hungarian Academy of Sciences, Budapest, H-1450, Hungary
| | - Tibor Andrási
- 'Lendület' Laboratory of Network Neurophysiology, Institute of Experimental Medicine, Hungarian Academy of Sciences, Budapest, H-1450, Hungary.,János Szentágothai School of Neurosciences, Semmelweis University, Budapest, Hungary
| | - Attila Vikór
- 'Lendület' Laboratory of Network Neurophysiology, Institute of Experimental Medicine, Hungarian Academy of Sciences, Budapest, H-1450, Hungary
| | - Norbert Hájos
- 'Lendület' Laboratory of Network Neurophysiology, Institute of Experimental Medicine, Hungarian Academy of Sciences, Budapest, H-1450, Hungary
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