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Poirier J, Beninger J, Naud R. Computational protocol for modeling and analyzing synaptic dynamics using SRPlasticity. STAR Protoc 2025; 6:103652. [PMID: 40029747 PMCID: PMC11915160 DOI: 10.1016/j.xpro.2025.103652] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2024] [Revised: 12/09/2024] [Accepted: 02/05/2025] [Indexed: 03/21/2025] Open
Abstract
Transient changes in synaptic strength, known as short-term plasticity (STP), play a fundamental role in neuronal communication. Here, we present a protocol for using SRPlasticity, a software package that implements a computational model of STP. SRPlasticity supports automatic characterization of electrophysiological data and simulation of synaptic responses. We describe steps for installing and utilizing SRPlasticity, preprocessing data, fitting models, and simulating responses. We then detail procedures for analyzing spike response plasticity (SRP) model parameters to infer functional groupings of STP. For complete details on the use and execution of this protocol, please refer to Rossbroich et al.1 and Beninger et al.2.
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Affiliation(s)
- Jade Poirier
- Center for Neural Dynamics and Artificial Intelligence, uOttawa Brain and Mind Research Institute, Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, ON K1H 8M5, Canada
| | - John Beninger
- Center for Neural Dynamics and Artificial Intelligence, uOttawa Brain and Mind Research Institute, Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, ON K1H 8M5, Canada.
| | - Richard Naud
- Center for Neural Dynamics and Artificial Intelligence, uOttawa Brain and Mind Research Institute, Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, ON K1H 8M5, Canada; Department of Physics, University of Ottawa, Ottawa, ON K1H 8M5, Canada.
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Chamberland S, Grant G, Machold R, Nebet ER, Tian G, Stich J, Hanani M, Kullander K, Tsien RW. Functional specialization of hippocampal somatostatin-expressing interneurons. Proc Natl Acad Sci U S A 2024; 121:e2306382121. [PMID: 38640347 PMCID: PMC11047068 DOI: 10.1073/pnas.2306382121] [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: 04/23/2023] [Accepted: 02/27/2024] [Indexed: 04/21/2024] Open
Abstract
Hippocampal somatostatin-expressing (Sst) GABAergic interneurons (INs) exhibit considerable anatomical and functional heterogeneity. Recent single-cell transcriptome analyses have provided a comprehensive Sst-IN subpopulations census, a plausible molecular ground truth of neuronal identity whose links to specific functionality remain incomplete. Here, we designed an approach to identify and access subpopulations of Sst-INs based on transcriptomic features. Four mouse models based on single or combinatorial Cre- and Flp- expression differentiated functionally distinct subpopulations of CA1 hippocampal Sst-INs that largely tiled the morpho-functional parameter space of the Sst-INs superfamily. Notably, the Sst;;Tac1 intersection revealed a population of bistratified INs that preferentially synapsed onto fast-spiking interneurons (FS-INs) and were sufficient to interrupt their firing. In contrast, the Ndnf;;Nkx2-1 intersection identified a population of oriens lacunosum-moleculare INs that predominantly targeted CA1 pyramidal neurons, avoiding FS-INs. Overall, our results provide a framework to translate neuronal transcriptomic identity into discrete functional subtypes that capture the diverse specializations of hippocampal Sst-INs.
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Affiliation(s)
- Simon Chamberland
- New York University Neuroscience Institute, New York University Grossman School of Medicine, New York University, New York, NY10016
- Department of Neuroscience and Physiology, New York University, New York, NY10016
| | - Gariel Grant
- New York University Neuroscience Institute, New York University Grossman School of Medicine, New York University, New York, NY10016
- Department of Neuroscience and Physiology, New York University, New York, NY10016
| | - Robert Machold
- New York University Neuroscience Institute, New York University Grossman School of Medicine, New York University, New York, NY10016
- Department of Neuroscience and Physiology, New York University, New York, NY10016
| | - Erica R. Nebet
- New York University Neuroscience Institute, New York University Grossman School of Medicine, New York University, New York, NY10016
- Department of Neuroscience and Physiology, New York University, New York, NY10016
| | - Guoling Tian
- New York University Neuroscience Institute, New York University Grossman School of Medicine, New York University, New York, NY10016
- Department of Neuroscience and Physiology, New York University, New York, NY10016
| | - Joshua Stich
- New York University Neuroscience Institute, New York University Grossman School of Medicine, New York University, New York, NY10016
- Department of Neuroscience and Physiology, New York University, New York, NY10016
| | - Monica Hanani
- New York University Neuroscience Institute, New York University Grossman School of Medicine, New York University, New York, NY10016
- Department of Neuroscience and Physiology, New York University, New York, NY10016
| | - Klas Kullander
- Developmental Genetics, Department of Neuroscience, Uppsala University, Uppsala, Uppsala län752 37, Sweden
| | - Richard W. Tsien
- New York University Neuroscience Institute, New York University Grossman School of Medicine, New York University, New York, NY10016
- Center for Neural Science, New York University, New York, NY10003
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