Skip to main content

Identification of an inhibitory neuron subtype, the L-stellate cells of the cochlear nucleus

Cite this dataset

Ngodup, Tenzin; Romero, Gabriel; Trussell, Laurence (2020). Identification of an inhibitory neuron subtype, the L-stellate cells of the cochlear nucleus [Dataset]. Dryad.


Auditory processing depends upon inhibitory signaling by interneurons, even at its earliest stages in the ventral cochlear nucleus (VCN). Remarkably, to date only a single subtype of inhibitory neuron has been documented in the VCN, a projection neuron termed the D-stellate cell. With the use of a transgenic mouse line, optical clearing and imaging techniques, combined with electrophysiological tools, we revealed a population of glycinergic cells in the VCN distinct from the D-stellate cell. These novel multipolar glycinergic cells were smaller in soma size and dendritic area, but over 10-fold more numerous than D-stellate cells. They were activated by auditory nerve fibers and T-stellate cells, and made local inhibitory synaptic contacts on principal cells of the VCN. Given their abundance, combined with their narrow dendritic fields and axonal projections, it is likely that these neurons, here termed L-stellate cells, play a significant role in frequency-specific processing of acoustic signals.


All procedures were approved by the Oregon Health and Science University’s Institutional Animal Care and Use Committee. GlyT2-GFP mice (Zeilhofer et al., 2005) and Somatostatin(Sst)-Cre tdTomato::GlyT2-GFP of either sex, postnatal days(P) 17-30 were used in this study. Mice were deeply anesthetized with isoflurane, then perfused transcardially with 0.9% warm saline followed by 4% paraformaldehyde buffered in PBS. Brains were removed and post-fixed overnight at 4 °C. The entire cochlear nucleus (450-500 mm) was cut from the brainstem using a vibratome (Leica VT1000S). Because the cochlear nucleus sits at the lateral edge of the brainstem, only a single cut was required to prepare the specimen. Samples were washed in 0.1 M PBS before clearing for 72 h at room temperature on a shaker table with an accelerated ‘clear unobstructed brain imaging cocktail’ (CUBIC)-mount solution (Lee et al., 2016) containing sucrose (50 %, w/v), urea (25%, w/v), and N,N,N′,N′-tetrakis (2- hydroxypropyl)ethylenediamine (25%, w/v) dissolved in 30 ml of dH2O. Once each sample was cleared and almost transparent, it was then mounted on a glass slide with 0.5 mm deep silicone spacers (EMS) in CUBIC-mount. The images were acquired within 24 h to reduce volume changes in CUBIC-mount (10-20% increase in volume after incubation). The samples were imaged using a fast Airyscan LSM880 microscope with a 25X objective. Tiles and z-stack of whole cochlear nucleus were imaged at 5-mm step size with the exception of one sample where images were obtained at 2-mm step size. Images were post-processed and individual tiles were stitched together using Zeiss Zen Black software. Analysis of cell count and soma volume were quantified using Imaris software (Bitplane 12.1). VCN was selected for quantification by drawing regions of interest around the VCN followed by applying a mask outside the region of interest. The dorsal border of the VCN was defined by the granule cell lamina, and the medial border by the absence of glycinergic somata (Muniak et al., 2013).  For cell count quantification, we used the built-in “spot detection” algorithm in Imaris in which the program places a “spot” on the soma of each cell. The spots are used for counting of GFP positive cells in the VCN. Automatically detected spots were verified and corrected manually. Thus, if the computer failed to detect a cell or erroneously placed a spot on a background bright spot, we manually added spots and deleted spots respectively. To test the validity of automated spot function, certain regions of VCN were manually counted and compared against the spot function. The spot function detected more than 95% of all cells present in that area. For volume measurements, we used the in-built “surface” rendering function in Imaris. The program rendered 3D surfaces on the soma of GFP positive cells. Rendered surfaces were used to extract the volume statistics. Surfaces were also verified and corrected manually.

Spike waveforms were analyzed using IgorPro8. Threshold was defined as the voltage at 5% of the peak of the spike’s first derivative, measured after subtracting the baseline dV/dt generated by the neuron’s electronic charging. This method was found empirically to identify the visually identified spike ‘onset’ much more reliably than the oft-used peak of the 3rd derivative. The latter method, when applied to diverse neurons in our dataset, often failed when the spike had a prominent initial segment ‘hump’ on the rising phase, or when the 3rd derivative was noisy. Spike overshoot was calculated as spike peak voltage relative to zero. Absolute amplitude of the spike was calculated from threshold to peak. Undershoot was the negative-most excursion of voltage, relative to threshold, and AHP latency was the time delay from spike peak to undershoot peak. Spike AHP was an arbitrary definition that measured the difference between voltage at the peak of the undershoot to the voltage one ms later. This time point captured well differences among neurons, since the D-stellate cells, and not the smaller cells, had extremely fast decaying AHPs. Membrane input resistance (Rin) was measured by injecting a small hyperpolarizing current in voltage clamp mode, Rin was calculated offline using Ohm’s law. Average spike rate was calculated in response to depolarizing 500 pA current injections.


Hearing Health Foundation, Award: TN

National Cancer Institute, Award: R01 NS028901 and DC004450 to LT

NINDS, Award: P30NS061800 to Imaging center, OHSU

Howard Hughes Medical Institute, Award: GR