Distal Spike Initiation Zone Location Estimation by Morphological Simulation of Ionic Current Filtering Demonstrated in a Novel Model of an Identified Drosophila Motoneuron

Abstract

Studying ion channel currents generated distally from the recording site is difficult because of artifacts caused by poor space clamp and membrane filtering. A computational model can quantify artifact parameters for correction by simulating the currents only if their exact anatomical location is known. We propose that the same artifacts that confound current recordings can help pinpoint the source of those currents by providing a signature of the neuron’s morphology. This method can improve the recording quality of currents initiated at the spike initiation zone (SIZ) that are often distal to the soma in invertebrate neurons. Drosophila being a valuable tool for characterizing ion currents, we estimated the SIZ location and quantified artifacts in an identified motoneuron, aCC/MN1-Ib, by constructing a novel multicompartmental model. Initial simulation of the measured biophysical channel properties in an isopotential Hodgkin-Huxley type neuron model partially replicated firing characteristics. Adding a second distal compartment, which contained spike-generating Na+ and K+ currents, was sufficient to simulate aCC’s in vivo activity signature. Matching this signature using a reconstructed morphology predicted that the SIZ is on aCC’s primary axon, 70 μm after the most distal dendritic branching point. From SIZ to soma, we observed and quantified selective morphological filtering of fast activating currents. Non-inactivating K+ currents are filtered ∼3 times less and despite their large magnitude at the soma they could be as distal as Na+ currents. The peak of transient component (NaT) of the voltage-activated Na+ current is also filtered more than the magnitude of slower persistent component (NaP), which can contribute to seizures. The corrected NaP/NaT ratio explains the previously observed discrepancy when the same channel is expressed in different cells. In summary, we used an in vivo signature to estimate ion channel location and recording artifacts, which can be applied to other neurons.

Computational implications of biophysical diversity and multiple timescales in neurons and synapses for circuit performance

“…Database and model reconstruction approaches have been used to fit ion channel distributions on anatomical reconstructions of known neurons [30–32, 33•]. A recent study showed that, to maintain functional properties along the dendritic tree of a neuron, mechanisms that tune the number of all ion channels collectively are more likely than those that tune the number of individual ion channels – this would not have been seen in single-compartment models [34•].…”

Quantitative neuroanatomy for connectomics inDrosophila

“…2b-g). We first considered larval motor neurons aCC and RP2 (Landgraf et al, 1997), which have functional and structural similarities to vertebrate neurons (Sánchez-Soriano et al, 2005;Nicolaï et al, 2010;Günay et al, 2015).…”

A single-cell level and connectome-derived computational model of the Drosophila brain

“…As the information about the thickness of each branch will soon be available in the database, we will be able to more accurately calculate the area of the membrane and thus derive better estimates for related parameters. Adopting a multi-compartmental model will also help to improve the accuracy of the simulations (Günay et al 2015). Moreover, some neurons in the visual system conduct signals by graded potentials or by mixed graded and action potentials (Mu et al 2012;Baden et al 2013).…”