In this issue, I have selected one paper on building a cheap lab with 3D-printed equipment (1), and 4 papers related to axonal excitability and plasticity. Williams et al. (2) formalize and analyze a model of intracellular trafficking and its regulation, which might apply to axonal plasticity and protein turn-over. I picked an old paper by Stanford (3) suggesting that there might be in particular axonal plasticity for timing, in a way that equalizes the conduction time from different retinal ganglion cells and their targets. A review by Wefelmeyer et al. (4) summarizes recent findings about the structural plasticity of the axonal initial segment. Finally, I selected a paper by Gouwens and Wilson (5), where they used theory and experiments to study the geometry of spike initiation in Drosophila neurons.
1. Chagas AM, Godino LP, Arrenberg AB, Baden T (2017). The 100 € lab: A 3-D printable open source platform for fluorescence microscopy, optogenetics and accurate temperature control during behaviour of zebrafish, Drosophila and C. elegans. (Comment on PubPeer).
This is quite exciting: the authors demonstrate the use of a 3D printed platform, with some basic electronics (Arduino, Raspberry Pi), which includes a microscope, manipulators, Peltier heating, and everything necessary to do optogenetics and fluorescence imaging, and behavioral tracking, all of this for about 200 €. The optics are apparently not great (about 10 µm precision) but could be replaced. This could be a way to convince theoreticians to do their own experiments!
2. Williams AH, O’Donnell C, Sejnowski TJ, O’Leary T (2016). Dendritic trafficking faces physiologically critical speed-precision tradeoffs. (Comment on PubPeer).
Plasticity and protein turnover require intracellular transport of molecules. How can molecules be delivered at the right place? A popular model is the “sushi belt” model: material moves along a belt (microtubules) and synapses pick from it at a variable rate. There are different ways to regulate the amount of material that is delivered at different places, for example to regulate the rate of capture, or to regulate trafficking rates (the speed of the belt; although here the analogy with the sushi belt does not work so well). This model, which is not a mathematical model but rather a vague conceptual model, raises very interesting theoretical questions, which are examined in this paper. For example, how is it possible to ensure that material is delivered at different sites in appropriate amounts, based only on local demand signals? If a synapse picks from the belt, wouldn’t that affect delivery to all downstream synapses? The authors formalize the sushi belt model mathematically, and examine essentially two variations, one where the trafficking rates are regulated, another where capture rates are regulated. The study shows that it is in fact not at all trivial to make the model functional, in terms of precision (delivering the right amount of material) and delivery speed. I suspect that there are better ways to regulate the trafficking and capture rates than proposed there, but in any case this study has the merit of formalizing the model and some of the functional problems. Although the model was conceived for dendritic trafficking, I suppose it should also apply to the axon, for example for the maintenance of excitability via protein turn-over. Note that there are other theoretical studies on intracellular trafficking, in particular by Paul Bressloff (e.g. Bressloff and Levien, 2015).
3. Stanford LR (1987). Conduction Velocity Variations Minimize Conduction Time DIfferences Among Rednal Ganglion Cell Axons. (Comment on PubPeer).
This 30 years-old paper is not very well known, but I find it fascinating. In the retina, axons of ganglion cells converge to the optic disk where they then form the optic nerve. The optic nerve is myelinated, but the part of the axons within the retina is not. Because all axons first meet at the optic disk, there is a conduction delay that depends on how far the cell is from the optic disk. The surprising result in this paper is that the conduction time in the optic nerve (from the retina to the LGN) is inversely correlated to the conduction time in the retina, so that the total conduction time is invariant (arguably, there are not so many data points, just 12 cells). This suggests the existence of developmental plasticity mechanisms that adjust axon size (or distance between Ranvier nodes) for synchrony.
4. Wefelmeyer W, Puhl CJ, Burrone J (2016). Homeostatic Plasticity of Subcellular Neuronal Structures- From Inputs to Outputs. (Comment on PubPeer)
This review highlights recent findings on structural plasticity of synapses and the axonal initial segment (AIS). I was especially interested in the AIS part. Several recent studies show that the AIS can change position and length with different manipulations, for example photogenetic stimulation or high potassium depolarization. These structural changes are associated with changes in excitability, which the authors present as homeostatic, although they recognize the results are not so clear. In particular, structural plasticity depends on cell type (distal displacement in some cell types, proximal displacement in others) and other plastic changes (eg expression of ionic channels) occur and act as confounding factors. For example, high potassium depolarization makes the AIS of cultured hippocampal neurons move distally (Grubb & Burrone, 2010). I have shown that this displacement should in principle make the neuron (slightly) more excitable (Brette, 2013), but the opposite is seen in those neurons. There were however strong changes in membrane properties, so the causal relations are not so obvious, all the more that other changes, such as Nav channel phosphorylation might have occurred too. The authors cite Gulledge & Bravo (2016) to point out that attenuation between the soma and AIS could be responsible for the decreased excitability, but that paper was a simulation study were axon diameter was fixed (1 µm) while somatodendritic morphology was changed, but in reality small neurons also have small axons, so that the situation analyzed in (Brette, 2013) still applies, in principle. Another interesting finding reviewed in this paper is that GABAergic synapses on the AIS do not move when the AIS moves, and therefore the number of synapses between the soma and initiation site can change, which changes the effect of inhibitory inputs. All these observations call for theoretical studies, where the relation between geometrical factors and excitability is analyzed. Finally, I would like to point out that one of our recent studies (Hamada et al., 2016) shows that structural plasticity of the AIS can have a homeostatic effect not on excitability per se, but on the transmission of the axonal spike to the soma.
5. Gouwens, NW and Wilson, RI (2009). Signal Propagation in Drosophila Central Neurons. (Comment on PubPeer)
Spike initiation in invertebrate neurons is quite different from vertebrate neurons. In the typical vertebrate neuron, synaptic currents from the dendrites are gathered at the soma, and spikes are initiated in the axon, which starts from the soma. In the typical invertebrate neuron, as the one studied here (Drosophila central neuron), a neurite emerges from the soma, then bifurcates into a dendritic tree and an axon. There is immunochemical evidence of an initial segment-like structure in Drosophila neurons near the bifurcation point (Trunova et al. 2011). This study confirms it with electrophysiological evidence and modeling. Morphologies are reconstructed, and passive responses to currents are measured at the soma. Optimization finds values for the passive properties – there are significant sources of uncertainty, but these are well addressed in the paper. Then they show that spikes in the soma are small, implying that the initiation zone is distal, and they use the model plus recordings of larger action potentials in other types of Drosophila neurons to get an estimate of the spike initiation site, which is found to be near the axon-dendrite bifurcation. Finally, they show that the resting potential is due mainly to Na+ and K+, as in other invertebrate neurons (Marmor, 1975).
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