Name: Dynamic Clamp Methods to Investigate Impaired Neuronal Excitability Associated with Autism (Video) | JoVE Journal
Uploaded: 2025-10-17T14:20:00.000+00:00
Duration: 8 min 44 s
Description: Learn dynamic clamp electrophysiology to investigate impaired neuronal excitability in autism using mouse cerebellar Purkinje neurons. This method reveals how ion channel dysfunction affects firing patterns.

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5 minutes

We would like to thank Telly Galiatsatos and Josh Stanford of Sutter Instrument for their assistance in technical inquiries. We also acknowledge and thank Jan Dolzer for help in initiating dPatch dynamic clamp studies.

The authors declare that they have no conflicts of interest.

Cerebellar circuits function in proprioception and motor control37,38 but are also critical for complex behaviors such as social interactions and emotional processing39,40,41. Pathology in cerebellar structure and function is often linked to ASD42,43,44,45,46. The simple architecture of cerebellar circuits47 and the cerebellum's clear role in ASD pathology make it an attractive brain region to investigate the molecular and cellular drivers of ASD behavioral deficits, as well as potential therapeutics. Dynamic clamp electrophysiology offers an important tool for these investigations, enabling investigators to directly test the causal relationships between ASD-related changes in ion channel properties/ionic conductances and the firing of cerebellar neurons. While dynamic clamp methods are particularly suitable for investigations of ASD-related changes in neuronal excitability, this technique is relevant for investigating a range of pathological mechanisms that affect cells with excitable membranes. Additionally, dynamic clamp remains an excellent tool for dissecting the complex processes by which neurons and muscle cells regulate and maintain appropriate action potential firing.
The accurate execution of dynamic clamp experiments requires careful control of technical parameters. Specifically, experimenters should know and understand the consequences of the potential variability in the delay between voltage-sampling and current injection, often referred to as jitter29, as well as electrode passive properties, space-clamp limitations, and the physiological fidelity of the experimental preparation under investigation. Bettencourt et al. (2008)48 and Butera et al. (2001)29 previously showed that achieving high sampling rates of at least 50 kHz is critical for accurately capturing fast ionic conductances, with feedback latencies ideally maintained below 25% (≤ 5 µs) of the sampling interval. Nominal sampling rates reported by commercial systems, particularly Windows-based platforms, can be misleading; hence, independent performance verification is often necessary, which is discussed in more detail by Milescu et al. (2008)25 and Clausen et al. (2012)49. Space-clamp constraints must also be considered, particularly when working with intact neurons that have complex morphologies and non-isopotential membrane properties. For instance, attempting to apply (or subtract) a conductance with an electrode that is distal to the integrating site, such as the AIS, will result in an inaccurate portrayal of the effects of the applied conductance. Poor (high) electrode resistance, inadequate capacitance compensation, and/or failing to correct junction potential error may also cause a misrepresentation of voltage measurements (input signal), and consequently, incorrect application of modeled ionic conductances50,51.
Thousands of Markov and HH models are freely available to researchers in online databases such as ModelDB52 and Channelpedia53, supported by the Human Brain Project and The Laboratory of Neural Microcircuitry, respectively. These databases include models for synaptic conductances, ion channel conductances, and conductance-based neuron models, which are primarily written in NEURON, MATLAB, or Python. Database files typically provide the necessary model equations as well as any miscellaneous information about the cell type(s) and experiments that informed the model.
Both Markov and HH conductance models contain conducting (simulating channels-open/activated) and non-conducting (simulated channels-closed) gating states/variables. However, these model types differ in the way in which the conductance value is calculated at each sampled voltage, with Markov models requiring more computational power but providing more detail in the gating properties of simulated channels. For instance, in HH models, the conducting and non-conducting states are independent variables, each determined by the voltage-input and gating kinetics of the respective state54,55. In contrast with Markov models, HH models do not simulate or constrain the transitions between gating states, such as the transiting of simulated channels from the activated/open kinetic state directly into the fast-inactivated kinetic state in a voltage-gated sodium conductance model25. This makes HH models less computationally complex54,55 and enables investigators to add multiple fast-conductance HH models simultaneously, while maintaining high sampling and update rates.
Markov kinetic state models are composed of discrete kinetic states, which may be conducting or non-conducting, and these states are interconnected in a fixed topology. In Markov model simulations, conductance is calculated at each sampled voltage by the proportion of simulated channels occupying the conducting kinetic state(s). The proportion of simulated channels occupying each kinetic state (over time) is determined by rate constants, which are parameters that govern the rate at which state occupancy transitions from one kinetic state to another. Rate constants have units of inverse seconds (s-1), representing a transition probability per unit of time. Depending on the model, rate constant values may depend on dynamic factors such as voltage or temperature.
In Markov models, ordinary differential equations are used to describe the time-dependent occupancy of each kinetic state. These equations govern how the probability of being in each state evolves over time, based on the transition rate constants. This is represented by a rate matrix Q: <img alt="Differential equation for quantum state dynamics, formula representation, educational use." src="/files/ftp_upload/69155/69155eq1.jpg" style="margin: auto;" data-altupdatedat="1783396841000" data-alt="Equation 1">56. As discussed above, transition rate constants (r), which determine the movement between states, may be influenced by the input voltage (V) and parameters α and β, which reflect the kinetics of the conductance across voltages. These equations can be described as: <img alt="Reaction rate formula \(r_x = a_x \times \exp(V/\beta_x)\); kinetic analysis equation." src="/files/ftp_upload/69155/69155eq2.jpg" style="margin: auto;" data-altupdatedat="1783396841000" data-alt="Equation 2">, and can be represented visually in the form of graphs that include connections (transition rate constants) into and out of each kinetic state of the model56,57,58. A helpful discussion on the development of Markov models and their topology can be found in Schoening and Silva (2024)56.
Since the inception of dynamic clamp in its current form, in which non-linear ionic conductances are modeled and applied (via current-injecting electrode) to living cells in real-time19,59, the methods and resources for this technique have evolved, and are increasingly accessible and cost-effective due to innovations such as microcontroller-based, low-cost systems60 and free, open-source software23,25,26. Commercial systems, such as Sutter Instrument's dPatch or Cytocybernetics's Cybercyte, further facilitate rapid implementation of dynamic clamp experiments through integrated hardware and software solutions27,61.
The versatility of dynamic clamp enables diverse applications, including the creation of virtual chemical and electrical synapses, embedding real neurons within artificial networks (or vice-versa), and adding virtual ion channels62. Importantly, the voltage-dependence, kinetic properties, and amplitudes of an applied conductance can be rapidly adjusted, allowing investigators to directly test how scaling one or more parameters affects action potential firing and membrane excitability31. Today's systems offer high sampling rates, enabling the accurate application of fast-transient conductances and can be easily integrated with conventional electrophysiological equipment60,63, solidifying dynamic clamp as an ideal method for advancing our understanding of neuronal function and dysfunction.

Previous investigations into the molecular and cellular drivers of autism spectrum disorder (ASD) often emphasized synaptic dysfunction as central to the disorder&#39;s pathophysiology1,2. Numerous studies have demonstrated increased excitatory synapse density2,3,4, impaired synaptic pruning5, and alterations in postsynaptic density proteins6,7 in ASD models. However, recent work has shown that changes in intrinsic neuronal excitability are also drivers of ASD pathology. For instance, ASD transgenic mouse models and patient-derived cell lines have revealed impaired action potential generation in cerebellar Purkinje8,9,10 and cortical pyramidal neurons11, as well as attenuated dendritic excitability and synaptic integration in subclasses of cortical pyramidal neurons12. Channelopathies involving loss-of-function mutations in SCN2A, encoding the voltage-gated sodium (Nav) channel Nav1.2 pore-forming &#945; subunit, is now a well-known monogenic cause of ASD13, and has been shown to cause deficits in the intrinsic excitability of various classes of central neurons14,15,16,17. As these types of investigations progress, it is important to establish causal links between pathogenic changes in ionic conductances and the effects of these changes on neuron and circuit function. Dynamic clamp electrophysiology provides an effective approach for this task. Sharp et al. (1992 and 1993)18,19, along with Robinson and Kawai (1993)20 initially described dynamic clamp methods in which a simulated non-linear ionic conductance, with or without voltage-dependent properties, is injected/added into living neurons, allowing investigators to assess in real-time how the properties or putative changes in a voltage-gated ionic conductance affect neuronal firing21,22.
The implementation of dynamic clamp experiments has been facilitated by several freely available software systems such as RTXI23, QuB24,25, and StdpC26; however, recent advancements have simplified dynamic clamp implementation with commercial plug-and-play systems that enable model conductances to be developed, modified, and applied on a single computer and within a single software package. Here, we provide protocols to perform dynamic clamp using a Sutter dPatch system27 and provide a sample investigation in which we test how the targeted deletion of tuberous sclerosis 1 (Tsc1)9, which is a prevalent locus of mutations for ASD28, affects the intrinsic firing of cerebellar Purkinje neurons as a result of changes in the expression of the voltage-gated sodium conductance8.

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dynamic clamp methods to investigate impaired neuronal excitability associated with autism

Dynamic Clamp Methods to Investigate Impaired Neuronal Excitability Associated with Autism

Autism spectrum disorder (ASD) arises from a wide range of genetic and environmental factors. While numerous ASD-linked mutations disrupt synapse development or plasticity, an increasing number have been shown to alter the expression and functioning of voltage-gated ion channels, resulting in deficits in neuronal intrinsic excitability. Whole-cell voltage-clamp recordings can be used to characterize how ASD-related mutations affect ion channel function. However, these experiments fail to directly assess how an altered ionic conductance affects neuronal action potential firing. Dynamic clamp electrophysiology bridges this gap by enabling real-time injection of user-defined ionic conductances into living neurons. This allows causal testing of how changes in ion channel properties affect the electrical activity of a given cell type. In this methods article, we describe how to implement dynamic clamp electrophysiology in adult mouse Purkinje neurons recorded under physiological conditions in acutely prepared cerebellar brain slices. Purkinje neurons are a particularly relevant model for this work because they have an intrinsic capacity to fire repetitive, high-frequency (20-100 Hz) action potentials and are consistently implicated in ASD-related cerebellar circuit dysfunction. We focus on Tsc1, a gene whose loss-of-function mutations are among the most common monogenic causes of ASD. In mouse Purkinje neurons, Tsc1 deletion has also been linked to reduced voltage-gated sodium (Nav) channel expression. We utilize Markov kinetic state models to simulate and reproduce Purkinje neuron Nav conductance properties and go on to use dynamic clamp to directly assess how changes in the Nav conductance impact the intrinsic firing of intact cerebellar Purkinje neurons. We provide instructions and resources for modifying and tuning ionic conductance models. By integrating ionic conductance modeling, dynamic clamp, and conventional patch-clamp techniques, this approach provides a powerful and flexible framework for linking genetic perturbations to physiological outcomes in ASD-relevant neurons.

In the presented experiments, we apply, using dynamic clamp, a modeled voltage-gated sodium (Nav) conductance to adult (6-7 week-old) mouse cerebellar Purkinje neurons that are acutely isolated in a parasagittal cerebellar slice preparation. The studies are performed on wild-type (control) mice and transgenic mice, in which Cre-loxP recombination is used to selectively delete tuberous sclerosis 1 (Tsc1) from cerebellar Purkinje neurons. Tuberous sclerosis complex (TSC) is a multisystem disorder caused by loss-of-function mutations in either TSC1 or TSC232,28,33. Individuals with TSC are commonly diagnosed with epilepsy disorders, cognitive impairment, and autism spectrum disorder34,35. Mice with Purkinje neuron-specific Tsc1 deletion, referred to here as Tsc1mut/mut, exhibit several ASD-related behavioral phenotypes, including impairments in motor function, social interaction behavior, and vocalizations, as well as exaggerated repetitive behaviors9,36. Tsc1mut/mut Purkinje neurons have also been shown to have attenuated action potential firing (Figure 2A,B), which is linked to significantly reduced Nav current amplitudes (Figure 2C) and Nav channel expression at the axon initial segments of Tsc1mut/mut Purkinje neurons8. Nav currents inTsc1mut/mut Purkinje neurons have similar kinetic and voltage-dependent properties as wild-type Purkinje neurons8. In this transgenic Tsc1mut/mut mouse model, we used dynamic clamp to test if adding Nav conductance to Tsc1mut/mut Purkinje neurons can rescue repetitive firing. We go on to test if subtracting Nav conductance in wild-type Purkinje neurons causes a similar attenuation in Purkinje neuron firing as measured in Tsc1mut/mut Purkinje neurons (Figure 2B). To subtract the natively expressed Nav conductance, we applied the Nav conductance using dynamic clamp; however, the polarity of the conductance is reversed.
The Markov kinetic state model (Figure 1A) used to simulate Purkinje neuron Nav conductance contains nine kinetic states, eight of which are non-conducting, which are labeled as closed (C1, C2, and C3), inactivated-closed (IC1 and IC2), fast-inactivated (IF1 and IF2), and slow-inactivated (IS). There is one open/conducting (O) kinetic state30,31. Transition rate constants, shown between the labeled kinetic states (Figure 1A) determine the proportion of simulated channels/conductance occupying each kinetic state. Membrane voltage affects each of the model's rate constants. Simulated Nav currents produced by this model (in silico) in simulated voltage-clamp experiments are shown (in red) to the right of Nav currents measured from acutely isolated Purkinje neurons, which are shown in black (Figure 1A, lower). The voltage-command evoking these simulated (red) and Purkinje neuron (black) Nav currents is presented above the current traces in black. Note, in the voltage-command, an initial depolarizing step (to 0 mV) results in a fast-transient inward sodium current, which, in the simulated trace, reflects channels transiting from the non-conducting closed states into the open kinetic state, allowing brief inward current before open-state channels accumulate into the fast inactivated (IF1 and IF2) kinetic states. This depolarizing voltage-step is brief (5 ms) and the membrane potential is subsequently stepped to an intermediate repolarized voltage of -45 mV, at which the potential is held for 80 ms (Figure 1A, lower). During this repolarization voltage step, it is notable that there is a resurgence of inward sodium current, a current component referred to as the resurgent sodium current (INaR). During this 80 ms step to -45 mV, INaR exhibits a slow (compared to INaT) decay in amplitude, eventually reaching a steady-state of inward current, which is the persistent Nav current component (INaP). Activation of modeled INaR (Figure 1A, lower, red) during membrane repolarization reflects simulated channels recovering from the fast-inactivated (IF1 + IF2) kinetic states into the open/conducting state. Over time (holding at -45 mV), a portion of the simulated channels occupying the open state accumulate into an absorbing slow-inactivated (IS) kinetic state, which is reflected as INaR decay, and a portion of these channels remain in the open/conducting state, reflected as the steady-state INaP current component30,31.
In the representative experiments/results, the Nav Markov conductance model (Figure 1A) was added to adult Purkinje neurons in acutely isolated cerebellar slices. A cartoon depiction of this experimental setup is presented in Figure 3A. To apply the modeled Nav conductance via dynamic clamp current injection, we used the dPatch amplifier system (shown in Figure 3B), which has fully integrated analog-to-digital (A/D) and digital-to-analog (D/A) conversion. This system also handles signal transformations via integrated ARM core processors (external to the PC) and has an integrated FPGA (field-programmable array) circuit that enables near instantaneous processing of input signals and feedback (sending of output signals) to the current-injecting electrode27. The experiments here involved the application (in dynamic clamp) of a complex Markov conductance model, which we were able to apply with current injection update rates of up to 500 kHz.
Adding the simulated Nav conductance (400 nS) to Tsc1mut/mut Purkinje neurons during gap-free current-clamp recordings resulted in clear increases in cells' repetitive firing frequencies (Figure 3C1, D1), indicating the addition of Nav conductance in these cells may be sufficient to rescue deficits in Tsc1mut/mut Purkinje neuron excitability, although we did not examine the full repertoire of deficits reported in Tsc1mut/mut Purkinje neuron excitability8. As is evident from the representative traces shown in Figure 3C2 (upper), wild-type Purkinje neurons have an intrinsic capacity to fire repetitive action potentials at high frequencies. Using dynamic clamp, we subtracted the modeled Nav conductance from wild-type Purkinje neurons by applying the modeled Nav conductance with a reversed (negative) polarity (-400 nS). Subtracting the modeled Nav conductance resulted in an immediate and obvious reduction in repetitive firing (Figure 3C2, lower), which is also consistent with the hypothesis that reduced Nav currents measured in Tsc1mut/mut Purkinje neurons contribute to the attenuated firing properties measured in these cells8. Across several Tsc1mut/mut or wild-type Purkinje neurons, these dynamic clamp experiments, in which the Nav conductance was added or subtracted, respectively, resulted in consistent effects on firing frequency. The addition of Nav conductance significantly (P = 0.029) increased Tsc1mut/mut Purkinje neuron firing frequency (Figure 3D1), and in wild-type Purkinje neurons, the subtraction of the Nav conductance significantly (P = 0.031) reduced firing frequency (Figure 3D2) (Student's paired t-test).
<img alt="Dynamic clamp method; conductance model; transition rate matrix; electrophysiology diagram; software setup." class="xfigimg" src="/files/ftp_upload/69155/69155fig1.jpg" data-altupdatedat="1783396841000" data-alt="Figure 1"> 
Figure 1: Setting up a Markov model in dynamic clamp software. (A) A Markov state transition diagram is shown for a model that reproduces Nav conductance properties measured in mouse cerebellar Purkinje neurons30. This model includes nine kinetic states. Of these, 8 are non-conducting states: three closed (C), two inactivated-closed (IC), two fast-inactivated (IF), and one slow-inactivated (IS). The model includes one open (O) kinetic state, which is conducting. Between adjacent kinetic states, connections/state occupancy transitioning over time is defined by transition rate constants, shown as S0-S15. Note that each kinetic state is also assigned a numerical value (red), which allows users to define the Markov model and its kinetic state topology in a gating state matrix (shown in panel C, described below). (B) Panels/graphical interfaces that users work with in the SutterPatch Dynamic Clamp Editor are presented. Note, the arrangement of these Dynamic Clamp Editor panels is not consistent with the arrangement as it appears within the dynamic clamp software. Additionally, not all panels associated with the Dynamic Clamp Editor are shown. (C) (upper panel) An example of a gating state matrix is shown with variables entered that correspond to the topology of the state transition diagram shown in panel A. Red boxes on the gating state matrix highlight row and column 7, which correspond to the model's open (O) kinetic state. Within column 7, all rate constants that define transitions into the open (O) kinetic state are listed, and within row 7, all rate constants that define transitions that exit the open (O) kinetic state are listed. With this organization, the gating state matrix describes the number of kinetic states, the rate constant variables, and the topology (interconnections between kinetic states) of the model. Shown in (D) are the SutterPatch panels/user interfaces associated with loading dynamic clamp conductance models, along with the interfaces involved in creating current clamp routines. User interactions with these models are described in Protocol steps 2.17-2.20. <a href="https://www.jove.com/files/ftp_upload/69155/69155fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Electrophysiological analysis diagram; neuron firing, voltage-clamp, wild type vs. Tsc1^mut/mut." class="xfigimg" src="/files/ftp_upload/69155/69155fig2.jpg" data-altupdatedat="1783396841000" data-alt="Figure 2"> 
Figure 2: Tsc1mut/mut Purkinje neurons have attenuated membrane excitability compared to wild-type controls. (A) Whole-cell current-clamp recordings from wild type (upper, black) and Tsc1mut/mut (lower, blue) Purkinje neurons reveal attenuated firing in the Tsc1 mutant cell. (B) Reduced firing frequencies in Tsc1mut/mut Purkinje neurons, compared to wild type controls, is consistent across cells. The mean (±± SEM) spontaneous firing frequency of Tsc1mut/mutPurkinje neurons is significantly (P &lt; 0.0001) lower compared to wild type cells (Welch's unpaired t-test; control N = 13, n = 32; Tsc1mut/mut N = 6, n = 21). (C) Voltage-clamp measurements of Nav currents in adult Purkinje neurons reveal that the mean (±± SEM) fast-transient Nav current (INaT) peak is significantly reduced in Tsc1mut/mut cells, compared to wild-type controls. (P = 0.007, RM two-way ANOVA; wild type control: N = 6, n = 22, black; Tsc1mut/mut: N = 6, n = 16, blue squares). Example INaT records, evoked by a -35 mV depolarizing step, are shown as an inset panel to the right of panel C. Data previously published in Brown et al.8. <a href="https://www.jove.com/files/ftp_upload/69155/69155fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Neuronal firing dynamics using Nav conductance model and amplifier setup; graphs show firing frequency." class="xfigimg" src="/files/ftp_upload/69155/69155fig3.jpg" data-altupdatedat="1783396841000" data-alt="Figure 3"> 
Figure 3: Using dynamic clamp to determine the effects of an ionic conductance on Purkinje neuron excitability. Dynamic clamp involves sampling membrane voltage (via a recording electrode) and computing, based on the sampled voltage, the current output of a model conductance. The computed current is then applied to a biological cell using a current-injecting electrode. Panel (A) depicts this experimental arrangement. If voltage sampling is performed at a sufficient frequency, and there is minimal delay (latency) between voltage sampling and applying the computed current, this technique can be used to assess how adding or subtracting a modeled conductance affects neuronal firing and membrane excitability. (B) In representative experiments, dynamic clamp recordings were acquired using SutterPatch software and the dPatch amplifier system. dPatch is a patch-clamp amplifier that has external signal processors and a fully integrated digitizer, which makes it well-suited for dynamic clamp recordings (see Discussion). (C) Representative current clamp records of spontaneous Purkinje neuron firing, before and after using dynamic clamp to add or subtract 400 nS of the modeled Nav conductance (presented in Figure 1A). In C1 (left), records are from a Tsc1mut/mut Purkinje neuron with attenuated firing. The addition of 400 nS Nav conductance results in a clear increase in this cell's firing frequency. Alternatively, in C2, records are from a wild-type Purkinje neuron. In this experiment, the modeled Nav conductance is applied with a reversed polarity (-400 nS), resulting in Nav conductance subtraction, and a clear reduction in repetitive firing frequency. Dynamic clamp current injection records, shown in grey, are presented below the action potential firing (voltage) records, shown in black. (D) Results from these dynamic clamp experiments, presented as the changes in spontaneous firing frequency after adding Nav conductance to Tsc1mut/mut Purkinje neurons (D1, n = 3) or subtracting Nav conductance from wild type Purkinje neurons (D2, n = 3), reveals consistent and significant effects of adding or subtracting this conductance across Tsc1mut/mut (P = 0.029) and wild type (P = 0.031) cells, respectively (paired Student's t-test). <a href="https://www.jove.com/files/ftp_upload/69155/69155fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Dynamic clamp electrophysiology can establish causal links between neuronal firing deficits and ion channel dysfunction. We describe dynamic clamp methods and protocols in mouse cerebellar Purkinje neurons, noting useful resources and technical considerations. In a representative study, we add modeled sodium conductance to Tsc1-/- Purkinje neurons to rescue repetitive firing.

Autism spectrum disorder (ASD) arises from a wide range of genetic and environmental factors. While numerous ASD-linked mutations disrupt synapse ...

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Neuroscience

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Learn dynamic clamp electrophysiology to investigate impaired neuronal excitability in autism using mouse cerebellar Purkinje neurons. This method reveals how ion channel dysfunction affects firing patterns.

Dynamic Clamp Methods to Investigate Impaired Neuronal Excitability Associated with Autism

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