Membrane voltage fluctuations in human breast cancer cells

Cancer cells feature a resting membrane potential (Vm) that is depolarized compared to normal cells, and express active ionic conductances, which factor directly in their pathophysiological behavior. Despite similarities to 'excitable' tissues, relatively little is known about cancer cell Vm dynamics. With high-throughput, cellular-resolution Vm imaging, we characterized Vm fluctuations of hundreds of human triple-negative breast cancer MDA-MB-231 cells and compared to non-cancerous breast epithelial MCF-10A cells. By quantifying their Dynamic Electrical Signatures (DESs) through an unsupervised machine-learning protocol, we identified four classes ranging from "noisy" to "blinking/waving". The Vm of MDA-MB-231 cells exhibited spontaneous, transient hyperpolarizations that were inhibited by the voltage-gated sodium channel blocker tetrodotoxin. The Vm of MCF-10A cells was comparatively static, but fluctuations increased following treatment with transforming growth factor-β1, a canonical inducer of the epithelial-to-mesenchymal transition. These data suggest that the ability to generate Vm fluctuations is acquired during transformation and may participate in oncogenesis.

A dynamic clamp protocol to artificially modify cell capacitance

Dynamics of excitable cells and networks depend on the membrane time constant, set by membrane resistance and capacitance. Whereas pharmacological and genetic manipulations of ionic conductances are routine in electrophysiology, experimental control over capacitance remains a challenge. Here, we present capacitance clamp, an approach that allows to mimic a modified capacitance in biological neurons via an unconventional application of the dynamic clamp technique. We first demonstrate the feasibility to quantitatively modulate capacitance in a mathematical neuron model and then confirm the functionality of capacitance clamp in in vitro experiments in granule cells of rodent dentate gyrus with up to threefold virtual capacitance changes. Clamping of capacitance thus constitutes a novel technique to probe and decipher mechanisms of neuronal signaling in ways that were so far inaccessible to experimental electrophysiology.

Spike-frequency dependent coregulation of multiple ionic conductances in fast-spiking cells forces a metabolic tradeoff

ABSTRACTHigh-frequency action potentials (APs) allow rapid information acquisition and processing in neural systems, but create biophysical and metabolic challenges for excitable cells. The electric fish Eigenmannia virescens images its world and communicates with high-frequency (200-600 Hz) electric organ discharges (EODs) produced by synchronized APs generated at the same frequency in the electric organ cells (electrocytes). We cloned three previously unidentified Na+-activated K+ channel isoforms from electroctyes (eSlack1, eSlack2, and eSlick1). In electrocytes, mRNA transcript levels of the rapidly-activating eSlick, but not the slower eSlack1 or eSlack2, correlated with EOD frequency across individuals. In addition, transcript levels of an inward-rectifier K+ channel, a voltage-gated Na+ channel, and Na+,K+-ATPases also correlated with EOD frequency while a second Na+ channel isoform did not. Computational simulations showed that maintaining electrocyte AP waveform integrity as firing rates increase requires scaling conductances in accordance with these mRNA expression correlations, causing AP metabolic costs to increase exponentially.

The Adrenal Medulla Modulates Mechanical Allodynia in a Rat Model of Neuropathic Pain

We have investigated whether the stress response mediated by the adrenal medulla in rats subjected to chronic constriction injury of the sciatic nerve (CCI) modulates their nocifensive behavior. Treatment with SK29661 (300 mg/kg; intraperitoneal (I.P.)), a selective inhibitor of phenylethanolamine N-methyltransferase (PNMT) that converts noradrenaline (NA) into adrenaline (A), fully reverted mechanical allodynia in the injured hind paw without affecting mechanical sensitivity in the contralateral paw. The effect was fast and reversible and was associated with a decrease in the A to NA ratio (A/NA) in the adrenal gland and circulating blood, an A/NA that was elevated by CCI. 1,2,3,4-tetrahydroisoquinoline-7-sulfonamide (SKF29661) did not affect exocytosis evoked by Ca2+ entry as well as major ionic conductances (voltage-gated Na+, Ca2+, and K+ channels, nicotinic acetylcholine receptors) involved in stimulus-secretion coupling in chromaffin cells, suggesting that it acted by changing the relative content of the two adrenal catecholamines. Denervation of the adrenal medulla by surgical splanchnectomy attenuated mechanical allodynia in neuropathic animals, hence confirming the involvement of the adrenal medulla in the pathophysiology of the CCI model. Inhibition of PNMT appears to be an effective and probably safe way to modulate adrenal medulla activity and, in turn, to alleviate pain secondary to the injury of a peripheral nerve.

Inferring neuronal ionic conductances from membrane potentials using CNNs

The neuron is the fundamental unit of computation in the nervous system, and different neuron types produce different temporal patterns of voltage fluctuations in response to input currents. Understanding the mechanism of single neuron firing patterns requires accurate knowledge of the spatial densities of diverse ion channels along the membrane. However, direct measurements of these microscopic variables are difficult to obtain experimentally. Alternatively, one can attempt to infer those microscopic variables from the membrane potential (a mesoscopic variable), or features thereof, which are more experimentally tractable. One approach in this direction is to infer the ionic densities as parameters of a neuronal model. Traditionally this is done using a Multi-Objective Optimization (MOO) method to minimize the differences between features extracted from a simulated neuron’s membrane potential and the same features extracted from target data. Here, we use Convolutional Neural Networks (CNNs) to directly regress generative parameters (e.g., ionic conductances, membrane resistance, etc.,) from simulated time-varying membrane potentials in response to an input stimulus. We simulated diverse neuron models of increasing complexity (Izikivich: 4 parameters; Hodgkin-Huxley: 7 parameters; Mainen-Sejnowski: 10 parameters) with a large range of variation in the underlying parameter values. We show that hyperparameter optimized CNNs can accurately infer the values of generative variables for these neuron models, and that these results far surpass the previous state-of-the-art method (MOO). We discuss the benefits of optimizing the CNN architecture, improvements in accuracy with additional training data, and some observed limitations. Based on these results, we propose that CNNs may be able to infer the spatial distribution of diverse ionic densities from spatially resolved measurements of neuronal membrane potentials (e.g. voltage imaging).

Viscometric and Conductometric Studies of Solvation Behaviour of Tetraalkylammonium Salts in the Binary Mixtures of Dimethylsulfoxide and Methanol at 298.15 K

Molar conductance and viscosity of some tetraalkylammonium perchlorates (R4NClO4 where R = Methyl, Ethyl, Propyl, Butyl) have been measured in the concentration range (30–500) × 10−4 mol kg−1 at 298.15 K in the binary mixtures of dimethylsulfoxide (DMSO) + methanol (MeOH) containing 0, 20, 40, 50, 60, 80 and 100 mol% methanol. Conductance data has been analyzed using the Shedlovsky equation and the viscosity data by Jones-Dole equation. The limiting ionic conductances ($\lambda_{\pm}^{o}$) were used to calculate the solvated radii (ri) of the ions. The A and B coefficients of the Jones-Dole equation are positive in all salts. The A coefficients are in reasonable good agreement with the limiting theoretical values (Aη) calculated using Falkenhagen-Vernon equation. The variation of the ionic B± coefficients as well as the actual solvated radii (ri) with solvent composition in DMSO + MeOH mixtures show the preferential solvation of tetraalkylammonium ions by MeOH and MeOH-rich region of the mixtures. The tetraalkylammonium ions exhibit solvation in the order Me4N+ > Et4N+ > Pr4N+ > Bu4N+.