Integration of Within-Cell Experimental Data With Multi-Compartmental Modeling Predicts H-Channel Densities and Distributions in Hippocampal OLM Cells

Vladislav Sekulić; Feng Yi; Tavita Garrett; Alexandre Guet-McCreight; J Josh Lawrence; Frances K Skinner

doi:10.3389/fncel.2020.00277

Integration of Within-Cell Experimental Data With Multi-Compartmental Modeling Predicts H-Channel Densities and Distributions in Hippocampal OLM Cells

Front Cell Neurosci. 2020 Sep 17:14:277. doi: 10.3389/fncel.2020.00277. eCollection 2020.

Authors

Vladislav Sekulić^{1

2}, Feng Yi³, Tavita Garrett⁴, Alexandre Guet-McCreight^{1

2}, J Josh Lawrence^{5

6

7}, Frances K Skinner^{1

8}

Affiliations

¹ Krembil Research Institute, University Health Network, Toronto, ON, Canada.
² Department of Physiology, University of Toronto, Toronto, ON, Canada.
³ Department of Biomedical and Pharmaceutical Sciences, Center for Biomolecular Structure and Dynamics, Center for Structural and Functional Neuroscience, University of Montana, Missoula, MT, United States.
⁴ Neuroscience Graduate Program and Vollum Institute, Oregon Health & Science University, Portland, OR, United States.
⁵ Department of Pharmacology and Neuroscience, Texas Tech University Health Sciences Center, Lubbock, TX, United States.
⁶ Center for Translational Neuroscience and Therapeutics, Texas Tech University Health Sciences Center, Lubbock, TX, United States.
⁷ Garrison Institute on Aging, Texas Tech University Health Sciences Center, Lubbock, TX, United States.
⁸ Departments of Medicine (Neurology) and Physiology, University of Toronto, Toronto, ON, Canada.

Abstract

Determining biophysical details of spatially extended neurons is a challenge that needs to be overcome if we are to understand the dynamics of brain function from cellular perspectives. Moreover, we now know that we should not average across recordings from many cells of a given cell type to obtain quantitative measures such as conductance since measures can vary multiple-fold for a given cell type. In this work we examine whether a tight combination of experimental and computational work can address this challenge. The oriens-lacunosum/moleculare (OLM) interneuron operates as a "gate" that controls incoming sensory and ongoing contextual information in the CA1 of the hippocampus, making it essential to understand how its biophysical properties contribute to memory function. OLM cells fire phase-locked to the prominent hippocampal theta rhythms, and we previously used computational models to show that OLM cells exhibit high or low theta spiking resonance frequencies that depend respectively on whether their dendrites have hyperpolarization-activated cation channels (h-channels) or not. However, whether OLM cells actually possess dendritic h-channels is unknown at present. We performed a set of whole-cell recordings of OLM cells from mouse hippocampus and constructed three multi-compartment models using morphological and electrophysiological parameters extracted from the same OLM cell, including per-cell pharmacologically isolated h-channel currents. We found that the models best matched experiments when h-channels were present in the dendrites of each of the three model cells created. This strongly suggests that h-channels must be present in OLM cell dendrites and are not localized to their somata. Importantly, this work shows that a tight integration of model and experiment can help tackle the challenge of characterizing biophysical details and distributions in spatially extended neurons. Full spiking models were built for two of the OLM cells, matching their current clamp cell-specific electrophysiological recordings. Overall, our work presents a technical advancement in modeling OLM cells. Our models are available to the community to use to gain insight into cellular dynamics underlying hippocampal function.

Keywords: dendrite; electrophysiology; h-channels; hippocampus; inhibitory cell; interneuron; multi-compartment model.

Abstract

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