Identifying Long-range Synaptic Inputs Using Genetically Encoded Labels and Volume Electron Microscopy

Enzymes that facilitate the local deposition of electron dense reaction products have been widely used as labels in electron microscopy (EM). Peroxidases, in particular, can efficiently metabolize 3,3′-diaminobenzidine tetrahydrochloride hydrate (DAB) to produce precipitates with high contrast under EM following heavy metal staining, and can be genetically encoded to facilitate the labeling of specific cell-types or organelles. Nevertheless, the peroxidase/DAB method has so far not been reported to work in combination with 3D volume EM techniques (e.g. Serial blockface electron microscopy, SBEM; Focused ion beam electron microscopy, FIBSEM) because the surfactant treatment needed for efficient reagent penetration disrupts tissue ultrastructure and because these methods require the deposition of large amounts of heavy metals that can obscure DAB precipitates. However, a recently described peroxidase with enhanced enzymatic activity (dAPEX2) appears to successfully deposit EM-visible DAB products in thick tissue without surfactant treatment. Here we demonstrate that multiplexed dAPEX2/DAB tagging is compatible with both FIBSEM and SBEM volume EM approaches and use them to map long-range genetically identified synaptic inputs from the anterior cingulate cortex to the periaqueductal gray in the mouse brain.

Emergence of synaptic organization and computation in dendrites

Single neurons in the brain exhibit astounding computational capabilities, which gradually emerge throughout development and enable them to become integrated into complex neural circuits. These capabilities derive in part from the precise arrangement of synaptic inputs on the neurons’ dendrites. While the full computational benefits of this arrangement are still unknown, a picture emerges in which synapses organize according to their functional properties across multiple spatial scales. In particular, on the local scale (tens of microns), excitatory synaptic inputs tend to form clusters according to their functional similarity, whereas on the scale of individual dendrites or the entire tree, synaptic inputs exhibit dendritic maps where excitatory synapse function varies smoothly with location on the tree. The development of this organization is supported by inhibitory synapses, which are carefully interleaved with excitatory synapses and can flexibly modulate activity and plasticity of excitatory synapses. Here, we summarize recent experimental and theoretical research on the developmental emergence of this synaptic organization and its impact on neural computations.

Identifying long-range synaptic inputs using genetically encoded labels and volume electron microscopy

Enzymes that facilitate the local deposition of electron dense reaction products have been widely used as labels in electron microscopy (EM). Peroxidases, in particular, can efficiently metabolize 3,3′-diaminobenzidine tetrahydrochloride hydrate (DAB) to produce precipitates with high contrast under EM following heavy metal staining, and can be genetically encoded to facilitate the labeling of specific cell-types or organelles. Nevertheless, the peroxidase/DAB method has so far not been reported to work in combination with 3D volume EM techniques (e.g. Serial blockface electron microscopy, SBEM; Focused ion beam electron microscopy, FIBSEM) because the surfactant treatment needed for efficient reagent penetration disrupts tissue ultrastructure and because these methods require the deposition of large amounts of heavy metals that can obscure DAB precipitates. However, a recently described peroxidase with enhanced enzymatic activity (dAPEX2) appears to successfully deposit EM-visible DAB products in thick tissue without surfactant treatment. Here we demonstrate that multiplexed dAPEX2/DAB tagging is compatible with both FIBSEM and SBEM volume EM approaches and use them to map long-range genetically identified synaptic inputs from the anterior cingulate cortex to the periaqueductal gray in the mouse brain.

Non-monotonic effects of GABAergic synaptic inputs on neuronal firing

GABA is canonically known as the principal inhibitory neurotransmitter in the nervous system, usually acting by hyper-polarizing membrane potential. However, GABAergic currents can also exhibit non-inhibitory effects, depending on the brain region, developmental stage or pathological condition. Here, we investigate the diverse effects of GABA on the firing rate of several single neuron models, using both analytical calculations and numerical simulations. We find that the relationship between GABAergic synaptic conductance and output firing rate exhibits three qualitatively different regimes as a function of GABA reversal potential, νGABA: monotonically decreasing for sufficiently low νGABA (inhibitory), monotonically increasing for νGABA above firing threshold (excitatory); and a non-monotonic region for intermediate values of νGABA. In the non-monotonic regime, small GABA conductances have an excitatory effect while large GABA conductances show an inhibitory effect. We provide a phase diagram of different GABAergic effects as a function of GABA reversal potential and glutamate conductance. We find that noisy inputs increase the range of νGABA for which the non-monotonic effect can be observed. We also construct a micro-circuit model of striatum to explain observed effects of GABAergic fast spiking interneurons on spiny projection neurons, including non-monotonicity, as well as the heterogeneity of the effects. Our work provides a mechanistic explanation of paradoxical effects of GABAergic synaptic inputs, with implications for understanding the effects of GABA in neural computation and development.

A Fast Markovian Method For Modeling Channel Noise In Neurons

Channel noise results from rapid transitions of protein channels from closed to open state and is generally considered as the most dominant source of electrical noise causing membrane-potential fluctuations even in the absence of synaptic inputs. The simulation of a realistic channel noise remains a source of possible error. Although the Markovian method is considered as the golden standard for appropriate description of channel noise, its computation time increasing exponentially with numbers of channels, it is poorly suitable to simulate realistic features. We describe here a novel algorithm for simulating ion channel noise based on Markov chains (MC). Although this new algorithm refers to a Monte-Carlo process, it only needs few random numbers whatever the number of channels involved. Our fast MC (FMC) model does not exhibit the drawbacks due to approximations based on stochastic differential equations. In fact, we show here, that these drawbacks can be highlighted even for a high number of channels.

Burst control: Synaptic conditions for burst generation in cortical layer 5 pyramidal neurons

The output of neocortical layer 5 pyramidal cells (L5PCs) is expressed by a train of single spikes with intermittent bursts of multiple spikes at high frequencies. The bursts are the result of nonlinear dendritic properties, including Na+, Ca2+, and NMDA spikes, that interact with the ~10,000 synapses impinging on the neuron’s dendrites. Output spike bursts are thought to implement key dendritic computations, such as coincidence detection of bottom-up inputs (arriving mostly at the basal tree) and top-down inputs (arriving mostly at the apical tree). In this study we used a detailed nonlinear model of L5PC receiving excitatory and inhibitory synaptic inputs to explore the conditions for generating bursts and for modulating their properties. We established the excitatory input conditions on the basal versus the apical tree that favor burst and show that there are two distinct types of bursts. Bursts consisting of 3 or more spikes firing at < 200 Hz, which are generated by stronger excitatory input to the basal versus the apical tree, and bursts of ~2-spikes at ~250 Hz, generated by prominent apical tuft excitation. Localized and well-timed dendritic inhibition on the apical tree differentially modulates Na+, Ca2+, and NMDA spikes and, consequently, finely controls the burst output. Finally, we explored the implications of different burst classes and respective dendritic inhibition for regulating synaptic plasticity.

Differences in spike generation instead of synaptic inputs determine the feature selectivity of two retinal cell types.

The output of spiking neurons depends both on their synaptic inputs and on their intrinsic properties. Retinal ganglion cells (RGCs), the spiking projection neurons of the retina, comprise over 40 different types in mice and other mammals, each tuned to different features of visual scenes. The circuits providing synaptic input to different RGC types to drive feature selectivity have been studied extensively, but there has been substantially less research aimed at understanding how the intrinsic properties of RGCs differ and how those differences impact feature selectivity. Here, we introduce an RGC type in the mouse, the Bursty Suppressed-by-Contrast (bSbC) RGC, whose contrast selectivity is shaped by its intrinsic properties. Surprisingly, when we compare the bSbC RGC to the OFF sustained alpha (OFFsA) RGC that receives similar synaptic input, we find that the two RGC types exhibit starkly different responses to an identical stimulus. We identified spike generation as the key intrinsic property behind this functional difference; the bSbC RGC undergoes depolarization block in conditions where the OFFsA RGC maintains a high spike rate. Pharmacological experiments, imaging, and compartment modeling demonstrate that these differences in spike generation are the result of differences in voltage-gated sodium channel conductances. Our results demonstrate that differences in intrinsic properties allow these two RGC types to detect and relay distinct features of an identical visual stimulus to the brain.