1
|
Ghovanloo MR, Tyagi S, Zhao P, Kiziltug E, Estacion M, Dib-Hajj SD, Waxman SG. High-throughput combined voltage-clamp/current-clamp analysis of freshly isolated neurons. CELL REPORTS METHODS 2023; 3:100385. [PMID: 36814833 PMCID: PMC9939380 DOI: 10.1016/j.crmeth.2022.100385] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Revised: 11/11/2022] [Accepted: 12/15/2022] [Indexed: 01/15/2023]
Abstract
The patch-clamp technique is the gold-standard methodology for analysis of excitable cells. However, throughput of manual patch-clamp is slow, and high-throughput robotic patch-clamp, while helpful for applications like drug screening, has been primarily used to study channels and receptors expressed in heterologous systems. We introduce an approach for automated high-throughput patch-clamping that enhances analysis of excitable cells at the channel and cellular levels. This involves dissociating and isolating neurons from intact tissues and patch-clamping using a robotic instrument, followed by using an open-source Python script for analysis and filtration. As a proof of concept, we apply this approach to investigate the biophysical properties of voltage-gated sodium (Nav) channels in dorsal root ganglion (DRG) neurons, which are among the most diverse and complex neuronal cells. Our approach enables voltage- and current-clamp recordings in the same cell, allowing unbiased, fast, simultaneous, and head-to-head electrophysiological recordings from a wide range of freshly isolated neurons without requiring culturing on coverslips.
Collapse
Affiliation(s)
- Mohammad-Reza Ghovanloo
- Department of Neurology, Yale University School of Medicine, New Haven, CT, USA
- Center for Neuroscience & Regeneration Research, Yale University, West Haven, CT, USA
- Neuro-Rehabilitation Research Center, Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Sidharth Tyagi
- Department of Neurology, Yale University School of Medicine, New Haven, CT, USA
- Center for Neuroscience & Regeneration Research, Yale University, West Haven, CT, USA
- Neuro-Rehabilitation Research Center, Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
- Medical Scientist Training Program, Yale University School of Medicine, New Haven, CT, USA
| | - Peng Zhao
- Department of Neurology, Yale University School of Medicine, New Haven, CT, USA
- Center for Neuroscience & Regeneration Research, Yale University, West Haven, CT, USA
- Neuro-Rehabilitation Research Center, Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Emre Kiziltug
- Department of Neurology, Yale University School of Medicine, New Haven, CT, USA
| | - Mark Estacion
- Department of Neurology, Yale University School of Medicine, New Haven, CT, USA
- Center for Neuroscience & Regeneration Research, Yale University, West Haven, CT, USA
- Neuro-Rehabilitation Research Center, Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Sulayman D. Dib-Hajj
- Department of Neurology, Yale University School of Medicine, New Haven, CT, USA
- Center for Neuroscience & Regeneration Research, Yale University, West Haven, CT, USA
- Neuro-Rehabilitation Research Center, Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| | - Stephen G. Waxman
- Department of Neurology, Yale University School of Medicine, New Haven, CT, USA
- Center for Neuroscience & Regeneration Research, Yale University, West Haven, CT, USA
- Neuro-Rehabilitation Research Center, Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA
| |
Collapse
|
2
|
Agrawal A, Wang K, Polonchuk L, Cooper J, Hendrix M, Gavaghan DJ, Mirams GR, Clerx M. Models of the cardiac L-type calcium current: A quantitative review. WIREs Mech Dis 2023; 15:e1581. [PMID: 36028219 PMCID: PMC10078428 DOI: 10.1002/wsbm.1581] [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: 03/25/2022] [Revised: 06/16/2022] [Accepted: 07/19/2022] [Indexed: 01/31/2023]
Abstract
The L-type calcium current (I CaL ) plays a critical role in cardiac electrophysiology, and models ofI CaL are vital tools to predict arrhythmogenicity of drugs and mutations. Five decades of measuring and modelingI CaL have resulted in several competing theories (encoded in mathematical equations). However, the introduction of new models has not typically been accompanied by a data-driven critical comparison with previous work, so that it is unclear which model is best suited for any particular application. In this review, we describe and compare 73 published mammalianI CaL models and use simulated experiments to show that there is a large variability in their predictions, which is not substantially diminished when grouping by species or other categories. We provide model code for 60 models, list major data sources, and discuss experimental and modeling work that will be required to reduce this huge list of competing theories and ultimately develop a community consensus model ofI CaL . This article is categorized under: Cardiovascular Diseases > Computational Models Cardiovascular Diseases > Molecular and Cellular Physiology.
Collapse
Affiliation(s)
- Aditi Agrawal
- Computational Biology & Health Informatics, Department of Computer ScienceUniversity of OxfordOxfordUK
| | - Ken Wang
- Pharma Research and Early Development, Innovation Center BaselF. Hoffmann‐La Roche Ltd.BaselSwitzerland
| | - Liudmila Polonchuk
- Pharma Research and Early Development, Innovation Center BaselF. Hoffmann‐La Roche Ltd.BaselSwitzerland
| | - Jonathan Cooper
- Centre for Advanced Research ComputingUniversity College LondonLondonUK
| | - Maurice Hendrix
- Centre for Mathematical Medicine & Biology, School of Mathematical SciencesUniversity of NottinghamNottinghamUK
- Digital Research Service, Information SciencesUniversity of NottinghamNottinghamUK
| | - David J. Gavaghan
- Computational Biology & Health Informatics, Department of Computer ScienceUniversity of OxfordOxfordUK
| | - Gary R. Mirams
- Centre for Mathematical Medicine & Biology, School of Mathematical SciencesUniversity of NottinghamNottinghamUK
| | - Michael Clerx
- Centre for Mathematical Medicine & Biology, School of Mathematical SciencesUniversity of NottinghamNottinghamUK
| |
Collapse
|
3
|
Lei CL, Mirams GR. Neural Network Differential Equations For Ion Channel Modelling. Front Physiol 2021; 12:708944. [PMID: 34421652 PMCID: PMC8371386 DOI: 10.3389/fphys.2021.708944] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Accepted: 07/05/2021] [Indexed: 11/13/2022] Open
Abstract
Mathematical models of cardiac ion channels have been widely used to study and predict the behaviour of ion currents. Typically models are built using biophysically-based mechanistic principles such as Hodgkin-Huxley or Markov state transitions. These models provide an abstract description of the underlying conformational changes of the ion channels. However, due to the abstracted conformation states and assumptions for the rates of transition between them, there are differences between the models and reality-termed model discrepancy or misspecification. In this paper, we demonstrate the feasibility of using a mechanistically-inspired neural network differential equation model, a hybrid non-parametric model, to model ion channel kinetics. We apply it to the hERG potassium ion channel as an example, with the aim of providing an alternative modelling approach that could alleviate certain limitations of the traditional approach. We compare and discuss multiple ways of using a neural network to approximate extra hidden states or alternative transition rates. In particular we assess their ability to learn the missing dynamics, and ask whether we can use these models to handle model discrepancy. Finally, we discuss the practicality and limitations of using neural networks and their potential applications.
Collapse
Affiliation(s)
- Chon Lok Lei
- Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macau, China
- Department of Biomedical Sciences, Faculty of Health Sciences, University of Macau, Macau, China
- School of Mathematical Sciences, Faculty of Science and Engineering, University of Nottingham, Ningbo, China
| | - Gary R. Mirams
- Centre for Mathematical Medicine & Biology, School of Mathematical Sciences, University of Nottingham, Nottingham, United Kingdom
| |
Collapse
|