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. 2016 Nov 16;11(11):e0165887.
doi: 10.1371/journal.pone.0165887. eCollection 2016.

Whole-Cell Properties of Cerebellar Nuclei Neurons In Vivo

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Whole-Cell Properties of Cerebellar Nuclei Neurons In Vivo

Cathrin B Canto et al. PLoS One. .

Abstract

Cerebellar nuclei neurons integrate sensorimotor information and form the final output of the cerebellum, projecting to premotor brainstem targets. This implies that, in contrast to specialized neurons and interneurons in cortical regions, neurons within the nuclei encode and integrate complex information that is most likely reflected in a large variation of intrinsic membrane properties and integrative capacities of individual neurons. Yet, whether this large variation in properties is reflected in a heterogeneous physiological cell population of cerebellar nuclei neurons with well or poorly defined cell types remains to be determined. Indeed, the cell electrophysiological properties of cerebellar nuclei neurons have been identified in vitro in young rodents, but whether these properties are similar to the in vivo adult situation has not been shown. In this comprehensive study we present and compare the in vivo properties of 144 cerebellar nuclei neurons in adult ketamine-xylazine anesthetized mice. We found regularly firing (N = 88) and spontaneously bursting (N = 56) neurons. Membrane-resistance, capacitance, spike half-width and firing frequency all widely varied as a continuum, ranging from 9.63 to 3352.1 MΩ, from 6.7 to 772.57 pF, from 0.178 to 1.98 ms, and from 0 to 176.6 Hz, respectively. At the same time, several of these parameters were correlated with each other. Capacitance decreased with membrane resistance (R2 = 0.12, P<0.001), intensity of rebound spiking increased with membrane resistance (for 100 ms duration R2 = 0.1503, P = 0.0011), membrane resistance decreased with membrane time constant (R2 = 0.045, P = 0.031) and increased with spike half-width (R2 = 0.023, P<0.001), while capacitance increased with firing frequency (R2 = 0.29, P<0.001). However, classes of neuron subtypes could not be identified using merely k-clustering of their intrinsic firing properties and/or integrative properties following activation of their Purkinje cell input. Instead, using whole-cell parameters in combination with morphological criteria revealed by intracellular labelling with Neurobiotin (N = 18) allowed for electrophysiological identification of larger (29.3-50 μm soma diameter) and smaller (< 21.2 μm) cerebellar nuclei neurons with significant differences in membrane properties. Larger cells had a lower membrane resistance and a shorter spike, with a tendency for higher capacitance. Thus, in general cerebellar nuclei neurons appear to offer a rich and wide continuum of physiological properties that stand in contrast to neurons in most cortical regions such as those of the cerebral and cerebellar cortex, in which different classes of neurons operate in a narrower territory of electrophysiological parameter space. The current dataset will help computational modelers of the cerebellar nuclei to update and improve their cerebellar motor learning and performance models by incorporating the large variation of the in vivo properties of cerebellar nuclei neurons. The cellular complexity of cerebellar nuclei neurons may endow the nuclei to perform the intricate computations required for sensorimotor coordination.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Intrinsic properties of CNNs.
(A) Three representative CNNs recorded from in vivo in anesthetized mice. Top two traces: Voltage clamp recordings of the three representative CNNs in vivo in whole-cell mode. Estimations for access resistance and membrane resistance were obtained by applying a square 10 mV voltage pulse (lower black trace) to the neuron. Top trace: Current trace for each example neuron with the time point for measuring the access resistance (Ra), membrane resistance (Rm) and membrane time constant (τ) indicated. Third trace: Spontaneous activity of a CNN in current clamp. Neurons in the CN often fire spontaneously with varying firing frequencies. Some neurons are regular firing neurons (A1 and A2), others fire in bursts (A3). Fourth trace: Peak-aligned averages of spikes. Current response (fifth graph) in response to a 100 pA square pulse (sixth black trace). Bottom traces: Current response to a - 100pA step. CNNs can show a strong hyperpolarization-activated depolarizing current, which can lead to a rebound burst of action potentials. (B-G) Plots representing the wide distribution of intrinsic CNNs properties. (B) Membrane resistance versus (vs) capacitance. (C) Spike half-width vs capacitance. (D) Firing frequency versus capacitance. (E) Length membrane time constant vs membrane resistance. (F) Spike half-width vs membrane resistance. (G) Short rebound vs long rebound. Values on the x-axis are the averages of firing frequencies during the first 50 ms after current offset (rebound) divided by the last 100 ms before current onset (baseline). Similarly, values on the y-axis are calculated by dividing the firing rate during the first 100 ms (blue) or 250 ms (green) after current offset by the spike rate during the last 100 ms before the current onset. Therefore, values above 1 indicate post-inhibitory rebound, while values lower than 1 indicate a decreased firing rate after the inhibition. The color coding in B-F corresponds to the neurons presented in Fig 1A (different colours of purple; dots are also encircled), to the neurons presented in Fig 2B (red, black and blue neuron; crosses are encircled), and finally all morphologically identified neurons presented in Fig 3 (the colours of the dots have the same colour as the reconstructions of the neurons). Crosses indicate the values of light-activated neurons.
Fig 2
Fig 2. Evoked CNN activity in response to optical stimulation of PCs in L7-ChR2(H134R)-eYFP transgenic mice.
The schemes represent the experimental set-up. We recorded from a neuron in vivo, while we shined with three blue 465 nm LED lights (indicated with blue stripes) from the outside of the brain on (A) PCs to stimulate their activity (green traces). (B) The graphs show responses of 3 CNNs to 1 second (top traces) and 10 ms (bottom traces) light stimulation (indicated with blue stripes) recorded at current clamp, illustrating the diversity in responses. Neurons with a comparable resistance (B2 and B3) can show differences in the time to reach steady state after inhibition as well as in their decay time.
Fig 3
Fig 3. Properties of morphologically and physiologically identified CNNs.
(A) Neurolucida reconstructions of cells. The scale bar (25 μm) applies to all reconstructions and is indicated at the bottom right. (B) Relation between somatic area and several morphological and electrophysiological measures. (C) Morphology and physiology of one representative large and one small CNN. Top left: schematic drawing of the experimental set-up with the morphology of the recorded neuron on the right (scale bar 25 μm). Top right: Voltage clamp recordings of the two CNNs in vivo in whole-cell mode. The average current response (top trace) of the corresponding neuron to ten 10 mV pulses (lower trace). Bottom graphs: Current clamp recordings of the two CNNs in vivo in whole-cell mode. Bottom left graphs: Top trace presents the voltage response of the reconstructed neuron to -100 pA current injection (lower trace). These traces demonstrate the IH and postinhibitory rebound firing for the corresponding neuron. The middle graphs show spontaneous activity of those CNNs. The right traces show the peak-aligned average of the spikes, highlighting the differences in the spike half-width comparing small and large neurons.

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Grants and funding

This work was supported by the Dutch Organization for Medical Science [ZonMW; CIDZ], NWO-VENI Fellowship [CC], Life Sciences (NWO-ALW; CDIZ), Senter (Neuro-Bsik; CIDZ), ERC-Advanced Grant [CIDZ] and ERC-POC [CIDZ] programs of the European Community.