Veratridine (Tocris Bioscience #2918) was suspended in DMSO and applied to the axonal compartment at a final concentration of 10 µM for 10 min in the absence of axotomy/injury. Local ABS, which includes low-Ca2+, high-Mg2+, and TTX (0.5 mM CaCl2, 10 mM MgCl2, 1 µM TTX) was applied solely to the axonal compartment for 1 h during axotomy (15 min prior and 45 min after axotomy). Dantrolene (Tocris Bioscience #0507) and (-)- Xestospongin C (Tocris Bioscience #1280), stock concentrations prepared in DMSO, were diluted in HBS or ABS solution and added to axonal compartment at a final concentration of 20 and 1 µM respectively for 1 h during axotomy (with 15 min pre-treatment and 45 min treatment post-axotomy). DMSO or HBS was used as vehicles. Media stored from the axonal compartment prior to treatment was added back to the axonal compartment after treatment and washes with pre-warmed fresh neuron culture media.

### Microscopy and Image Analysis

FM and fixed imaging was performed using CSU-X1 (Yokogawa) spinning disk confocal imaging unit configured for an Olympus IX81 microscope (Andor Revolution XD). Excitation for the spinning disk confocal imaging system was provided by 405 nm, 488 nm, 561 nm, and/or 640 nm lasers. The following bandpass emission filters (BrightLine, Semrock) were used for the spinning disk: 447/60 nm (TRF447-060), 525/30 nm (TRF525-030), 607/36 nm (TR-F607-036), and 685/40 nm (TR-F685-040). Zeiss LSM 780 (63 × 1.4 NA or 40 × 1.4 NA oil immersion objective) or the spinning disk system above (60 × 1.3 NA silicon oil immersion objective) was used to capture high-resolution images of mCherry or eGFP labeled live neurons as previously described (Nagendran et al., 2017).

For FM imaging, the spinning disk confocal imaging system was used with excitation at 561 nm and the 685/40 nm emission filter. We used 2 × 2 binning to reduce the laser intensity and acquisition time for each frame; each z-stack was obtained in ∼5 s. For dendritic spine analysis, before and 24 h post-axotomy confocal z-stack images of live G-deleted Rabies-mCherry or eGFP virus labeled neurons were captured to create montages of neurons extending axons into the axonal compartment. Fluorescent protein labels the entire neuron including small protrusions like dendritic spines. Calibrated z-stack montages were analyzed for all dendrite and spine parameters. Primary dendrites were traced using the semiautomatic neurite tracing tool, Neuron J (Meijering et al., 2004). Dendritic spines were quantified based on their size and shape. The number of spines on all primary dendrites of each neuron was manually counted and spine density was calculated for 10 µm length of dendrite as [(# of spines/dendrite length) × 10] (Nagendran et al., 2017).

#### Statistical Analysis

Statistical analyses were performed using GraphPad Prism 6. Spine density before and after treatment or injury was analyzed using paired two-tailed t-test. FM unloading curves were analyzed by two-way ANOVA, using a Bonferroni post hoc test. FM decay time constants were analyzed using either unpaired two-tailed t-test. vGat puncta per area were analyzed by unpaired two-tailed t-test. For sample size, p-value, and statistical test, refer to the figure legends.

## DISCUSSION

Hyper-excitability following acquired brain injury leads to longterm effects, such as persistent seizures, chronic pain, and spasticity. Intrinsic injury signaling within damaged neurons likely plays a key role in induced hyper-excitability.

The main conclusion of our results is that axonal ER signaling plays a critical role in regulating axotomy-induced retrograde presynaptic release in hippocampal neurons. Using our in vitro microfluidic model, our data show that while NCXs at the site of injury mediate retrograde dendritic spine loss, they do not mediate the delayed retrograde changes in presynaptic release rate. Rather than influx of extracellular calcium into the cytosol, release from internal ER stores within the axon at the site of injury appears to be a critical signaling component that triggers changes in retrograde presynaptic neurotransmitter release rate.

Dantrolene or Xestospongin C applied at the time and location of axotomy, both prevented the injury-induced retrograde release

changes. Both receptors are localized to hippocampal neurons. RyRs are predominant in hippocampal CA3 and DG with less expression in CA1 (Sharp et al., 1993). IP3Rs show the opposite trend, though there is evidence of immunoreactivity of both receptors in the same neuron. Interestingly, RyRs are more prominently seen in hippocampal axons than IP3Rs (Sharp et al., 1993).

RyRs play a critical role in injury-dependent neuronal signaling. Inhibiting RyRs with Dantrolene reduced neuronal injury in an ischemic gerbil model (Wei and Perry, 1996). RyR are also necessary for CICR, suggesting the involvement of this mechanism in presynaptic release changes following axotomy (Verkhratsky, 2005). Ca2+ release from IP3Rs may induce RyRmediated Ca2+ release. Xestospongin C blocks SERCA pumps in addition to IP3R, preventing Ca2+ uptake into the ER (Smet et al., 1999; Castonguay and Robitaille, 2002; Solovyova et al., 2002). Thus, it is also possible that the non-specific effect of this drug may alter ER calcium availability via mechanisms not involving IP3Rs and may explain why the retrograde release changes were prevented with local application of this drug. Nonetheless, a major conclusion of this data is the dependence of Ca2+ release from ER, and not extracellular Ca2+ influx, for the axotomyinduced enhancement in retrograde presynaptic release rate.

Our previously published data support that both axotomyinduced dendritic spine loss and presynaptic release changes are mediated by rapid transcription (Nagendran et al., 2017). Our data show that ER changes in Ca2+ buffering and sequestration are observed in the soma following axotomy (**Figure 6**), supporting axon-to-soma ER-dependent Ca2+ signaling. In peripheral neurons, locally initiated calcium waves can propagate to the nucleus to induce a transcriptional response (Cho et al., 2013). Other studies suggest that local influx of calcium may be a priming effect for retrograde microtubule-based transport of signaling complexes required to initiate transcription (Rishal and Fainzilber, 2014). We previously identified netrin-1, a known synaptogenic cue, as downregulated within the somatodendritic compartment both in vivo and in vitro following axon damage (Nagendran et al., 2017). Further application of exogenous netrin-1 after axotomy normalized both dendritic spine density and inhibitory terminals, suggesting netrin-1 signaling may regulate axotomy-induced synapse loss. Interestingly, ER stress activates IRE1, a sensor that activates UPR, to degrade netrin-1 mRNA (Binet et al., 2013).

Critical involvement in ER signaling is not only implicated in acute neural injuries, but also in multiple neurological

#### REFERENCES

disorders (Ozcan and Tabas, 2012), including Alzheimer's disease, Parkinson's disease, multiple sclerosis, amyotrophic lateral sclerosis, and prion diseases. In these diseases, axon damage is a first site of pathology, suggesting signaling from the axon may be a key early event and clear target for future therapeutics.

#### DATA AVAILABILITY STATEMENT

The datasets generated for this study are available on request to the corresponding author.

#### ETHICS STATEMENT

The animal study was reviewed and approved by The University of North Carolina at Chapel Hill Institutional Animal Care and Use Committee (IACUC).

#### AUTHOR CONTRIBUTIONS

TN designed the experiments, acquired the data, analyzed the data, and wrote the manuscript. AT designed the experiments and wrote the manuscript.

## FUNDING

The authors received financial support from the National Institute of Mental Health (R42 MH097377), the National Institute of Neurological Disorders and Stroke (R41 NS108895), the American Heart Association (17GRNT33700108), and Xona Microfluidics, LLC. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Viral resources were supported by the GT3 Core Facility of the Salk Institute with funding from NIH-NCI CCSG: P30 014195, an NINDS R24 Core Grant and funding from NEI. Imaging was partially performed at the Neuroscience Center Microscopy Core Facility, supported, in part, by funding from the NIH-NINDS Neuroscience Center Support Grant P30 NS045892 and the NIH-NICHD Intellectual and Developmental Disabilities Research Center Support Grant U54 D079124.

axotomized spinal motor neurons. BMC Neurosci. 6:19. doi: 10.1186/1471- 2202-6-19

**Conflict of Interest:** AT is an inventor of the multi-compartment microfluidic device (US 7419822 B2, EPO 1581612, and EPO 2719756), and is Chief Scientist and a Member of Xona Microfluidics, LLC.

The remaining author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2019 Nagendran and Taylor. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Scaling of the AIS and Somatodendritic Compartments in α S RGCs

#### Vineeth Raghuram1,2,3† , Paul Werginz2,4† and Shelley I. Fried1,2 \*

1 Rehabilitation Research & Development Service, Boston VA Healthcare System, Boston, MA, United States, 2 Department of Neurosurgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States, 3 Department of Biomedical Engineering, Tufts University, Medford, MA, United States, 4 Institute for Analysis and Scientific Computing, Vienna University of Technology, Vienna, Austria

The anatomical properties of the axon initial segment (AIS) are tailored in certain types of CNS neurons to help optimize different aspects of neuronal function. Here, we questioned whether the AISs of retinal ganglion cells (RGC) were similarly customized, and if so, whether they supported specific RGC functions. To explore this, we measured the AIS properties in alpha sustained RGCs (α S RGCs) of mouse; α S RGCs sizes vary systematically along the nasal temporal axis of the retina, making these cells an attractive population with which to study potential correlations between AIS properties and cell size. Measurements of AIS length as well as distance from the soma revealed that both were scaled to cell size, i.e., cells with large dendritic fields had long AISs that were relatively far from the soma. Within the AIS, the percentage of Nav1.6 voltagegated sodium channels remained highly consistent, regardless of cell size or other AIS properties. Although ON RGCs were slightly larger than OFF cells at any given location of the retina, the level of scaling and relative distribution of voltage-gated sodium channels were highly similar. Computational modeling revealed that AIS scaling influenced spiking thresholds, spike rate as well as the kinetics of individual action potentials, Interestingly, the effect of individual features of the AIS varied for different neuronal functions, e.g., AIS length had a larger effect on the efficacy by which the AIS initiated spike triggered the somatic spike than it did on repetitive spiking. The polarity of the effect varied for different properties, i.e., increases to soma size increased spike threshold while increases to AIS length decreased threshold. Thus, variations in the relative level of scaling for individual components could fine tune threshold or other neuronal functions. Light responses were highly consistent across the full range of cell sizes suggesting that scaling may post-synaptically shape response stability, e.g., in addition to several well-known pre-synaptic contributors.

Keywords: axon initial segment, retinal ganglion cells, morphological scaling, axonal geometry, action potential

## INTRODUCTION

The axon initial segment (AIS) is a specialized portion of the proximal axon that mediates the initiation and propagation of action potentials in CNS neurons. Structurally, the AIS is comprised of densely packed voltage-gated sodium (Nav) and potassium channels (Kv) that are held in place by a complex network of structural proteins and tethering molecules. Much recent evidence suggests

#### Edited by:

Dominique Debanne, INSERM U1072 Neurobiologie des Canaux Ioniques et de la Synapse, France

#### Reviewed by:

Matthew s Grubb, King's College London, United Kingdom Bela Volgyi, University of Pécs, Hungary

\*Correspondence:

Shelley I. Fried<EMAIL_ADDRESS>
†These authors have contributed equally to this work

#### Specialty section:

This article was submitted to Cellular Neurophysiology, a section of the journal Frontiers in Cellular Neuroscience

Received: 25 July 2019 Accepted: 13 September 2019 Published: 27 September 2019

#### Citation:

Raghuram V, Werginz P and Fried SI (2019) Scaling of the AIS and Somatodendritic Compartments in α S RGCs. Front. Cell. Neurosci. 13:436. doi: 10.3389/fncel.2019.00436

**183**

that AISs are tailored to help optimize specific elements of neuronal function. For example, neurons of the chick nucleus laminaris that are sensitive to low auditory frequencies have AISs that are shorter and further from the soma than the AISs of high-frequency neurons; the differences in the length and location are thought to help maximize the sensitivity to the interaural time differences (ITD) associated with each frequency range (Kuba et al., 2006). In contrast, the length of the AIS does not vary systematically in thick-tufted pyramidal neurons of the somatosensory cortex but instead, the distance between the AIS and the soma is inversely correlated to the size of the apical dendrite; this arrangement helps maintain consistency in the amplitude of the back-propagated action potential across a wide range of cellular morphologies (Hamada et al., 2016).

Within the AIS, two spatially and functionally distinct subtypes of voltage-gated sodium channels are thought to play complementary roles in the generation and back-propagation of spiking (Boiko et al., 2003; Van Wart et al., 2007; Hu et al., 2009). Nav1.6 channels are confined to the distal portion of the AIS and activate at lower levels of membrane depolarization; consistent with their higher sensitivity and greater separation from the soma (i.e., more decoupled), spikes are initiated in this portion of the AIS. In contrast, Nav1.2 channels are confined to the proximal portion of the AIS and have higher activation thresholds; these channels remain largely unactivated by the initial depolarization of the soma and thus are well-suited to effectively facilitate penetration of the AIS-initiated spike into the soma. In a previous study in rat retinal ganglion cells (RGCs) (Van Wart et al., 2007) the Nav1.6 portion was reported to comprise ∼2/3 of the total AIS length although details were not provided as to whether the ratio was for a single cell type or an average across the population of all RGCs tested. Given the specificity of the AIS in different cell types, it seems likely that the composition of the AIS, e.g., the distribution of Nav channels may also be tailored for individual cells or cell types.

Somewhat surprisingly, neither the structure nor the function of the AIS has been well studied in retinal neurons. Most mammalian retinas contain over 30 different types of ganglion cells (retinal output neurons); each type extracts different features of the visual world and uses distinct patterns of spiking to convey information to higher visual centers (Devries and Baylor, 1997; Roska and Werblin, 2001; Baden et al., 2016). Given the functional specificity of the AIS in other types of CNS neurons, it seems reasonable to question whether the AISs of individual RGC types are also customized to meet specific functional requirements. For example, the AISs of cell types that generate long-duration, high-frequency bursts of spikes may be different from the AISs of types that generate short-duration, low-frequency spike trains. Potential support for AIS specificity in RGCs comes from an earlier study that found longer AISs in brisk-transient (BT) vs. directionally selective (DS) RGCs of the rabbit retina (Fried et al., 2009). Surprisingly however, there was a great deal of overlap between the two populations with the result that the length of the AIS in many BT cells was actually shorter than that of some DS cells. This variability is somewhat curious and raises the possibility that the absolute length of the AIS may be less critical than its length relative to other cellular features.

The alpha Sustained (α S) RGCs of the mouse retina is a particularly attractive population with which to study AIS structure and function because the size of these cells varies systematically across the retina (Bleckert et al., 2014), i.e., the relationship between AIS properties and cell size can be directly evaluated. Further, because RGCs remain intact in the flatmount preparation, AIS properties can be directly correlated to a wide range of other cellular features. The fact that α S RGCs consist of both ON and OFF sub-types (Pang et al., 2003) is also attractive because AIS properties can be compared across two, functionally similar populations. Finally, the light responses of α S RGCs provide a measure of neuronal function, allowing correlations between AIS structure and cellular function to be explored. Much previous work with the α S population has led to well characterized neuronal features that facilitate the rapid and unequivocal identification of targeted cells.

Here, we measured the anatomical properties of the AIS and of other cellular features in a large number of ON- and OFF-α S RGCs that spanned the full extent of the mouse retina. We found that many cellular features, including AIS length and location, varied systematically across the retina and were generally scaled to one another. The observed scaling was similar in both ONand OFF-α S RGCs although ON cells were generally larger at any given retinal location. Unlike other cellular properties, the composition of the AIS was highly consistent across all cells but differed from previously reported values. The functional role(s) of the anatomical variations were explored using a series of morphologically- and biophysically realistic computational models; scaling was shown to influence neuronal sensitivity, spike rate and the effectiveness with which AIS initiated spikes trigger somatic spikes. Scaling may also help to stabilize responses across the population.

## MATERIALS AND METHODS

#### Immunohistochemistry

GFP line M (Thy-1 promoter) mice were chosen for anatomical experiments due to the sparse fluorescent labeling of individual RGC cell bodies as well as their complete dendritic trees (Feng et al., 2000), which enabled the ability to perform detailed morphological analyses (see below). Retinal whole-mounts were surgically isolated from Thy-1 GFP line M mice (Jackson Laboratory), fixed in 4% paraformaldehyde (w/v) solution for 30 min, washed in PBS, pH 7.4, for 1 h, and subsequently placed into 12-well clear multiwell plates (Corning Falcon) for processing through free-floating immunochemistry. Retinas were then blocked with 5% normal donkey serum (NDS) in 0.3% PBTX (10x PBS + Triton X) solution for 2 h, and then incubated with chicken anti-GFP (1/200; Abcam), mouse anti-AnkyrinG (1/200; NeuroMab clone N 106-36), rabbit anti-Nav1.6 (1/200; Millipore Sigma), and goat anti-ChAT (1/200; Millipore Sigma) in 1% NDS with 0.3% PBTX solution for 5 overnights. Following incubation in primaries, samples were washed in PBS for 2 h (4 × 30 min), and placed in Alexa Fluor 488-conjugated donkey anti-chicken (1:200; Jackson Immuno), Cy5-conjugated donkey anti-mouse

(1:200; Jackson ImmunoResearch), Alexa Fluor 405-conjugated donkey anti-rabbit (1:200; Abcam), and Alexa Fluor 594 conjugated donkey anti-goat (1:200; Invitrogen) secondary antibodies in 0.3% PBTX solution for 1 overnight. Finally, samples were washed in PBS for 1 h (2 × 30 min), mounted with Prolong gold anti-fade mounting medium (Invitrogen), cover-slipped, and placed in 4◦C refrigeration until further confocal imaging.

#### Image Acquisition and Analysis

Fluorescence imaging was performed with a laser scanning confocal microscope (Zeiss LSM 880 with Airyscan) using Zeiss Efficient Navigation (ZEN Pro) software to acquire and export images. Images of the full RGC dendritic field morphology were acquired with 20x magnification at a x/y/z resolution of 0.15 × 0.15 × 0.40 µm, while images of the soma, as well as proximal axon with AIS labeling were acquired with a 63x oil immersion objective (NA = 1.4) at a x/y/z resolution of 0.09 × 0.09 × 0.40 µm. The use of a higher magnification (63x) as well as the Thy-1 line M GFP mouse (Feng et al., 2000), which provides complete labeling of RGC morphologies, allowed for the ability to perform precise tracing around the cell body and dendritic tree. Anatomical measurements and image post-processing of confocal scans were performed in NIH Image J/FIJI package (Schindelin et al., 2012). Calculation of cell volume was performed in NIH Image J/FIJI by first converting the raw image stack into a binary image (63x magnification) using median filters and local auto thresholding via the Bernsen method (**Figure 1C**) (Bernsen, 1986); additional image processing was performed using the NIH Image J/FIJI binary processing toolbox. Separately, a region of interest (ROI) is determined by creating a maximum intensity projection of the cell in the z-dimension and then manually tracing a contour around the cell body. The ROI is then applied to the processed binary image file, and the area outside the cell body is cleared to isolate the soma. This cropped binary image of the soma is then displayed in the 3D viewer (Schmid et al., 2010) where the cell volume is calculated using the voxel counter plugin.

Confirmation of cell type was obtained using choline acetyltransferase (ChAT), an immunochemical marker which labels the processes of ON and OFF starburst amacrine cells (Baughman and Bader, 1977). These are otherwise referred to as (ONand OFF-) ChAT bands and can be used to delineate the specific sublamina in which the terminal dendrites of tentatively identified RGCs stratified (Jeon et al., 1998). Consistent with previous reports, the terminal dendrites of putative ON and OFFα S RGCs stratified above and below the ON- and OFF-ChAT bands (van Wyk et al., 2009).

The anatomical properties (e.g., start and end position, distance from the soma, length) of the AIS in confocal image stacks (63x magnification) were measured by manually identifying and precisely tracing along the region of colocalization between AnkyrinG/Nav1.6 and the green fluorescent signal of the native GFP expressed within the axons of labeled RGCs using NIH Image J/FIJI package. Measurements of AIS length and distance were performed independently from calculations of cell size (dendritic field diameter, soma diameter, soma volume). Measurements were reliable between 3 independent experimenters. In a few cases (<10), AISs were obscured, either because they overlapped with other AISs or because of interfering axon bundles, making it difficult to obtain an accurate measurement and to get agreement between examiners. In these cases, the AIS measurements were excluded from further analysis.

Simple 3-d tracing of dendritic architecture and Sholl analysis was performed in NIH ImageJ/FIJI toolbox Simple Neurite Tracer (Longair et al., 2011). Full tracing, including compartment diameters of dendritic arbors, was performed in NeuronStudio (20x magnification) (Wearne et al., 2005). Tracing of diameters of the proximal axon was performed at higher magnification (63x).

#### Electrophysiology

The care and use of animals followed all federal and institutional guidelines and all protocols were approved by the Institutional Animal Care and Use Committee (IACUC) of the Massachusetts General Hospital. Wild type (C57BL/6J) mice (Charles River Laboratories) were anesthetized with isofluorane (Henry Schein) and subsequently euthanized by cervical dislocation. Eyeballs were harvested, retinas were dissected from the eyecup and mounted, photoreceptor side down, onto a recording chamber. The retina was subsequently perfused with oxygenated Ames medium (Sigma-Aldrich) buffered to pH 7.4 at a flow rate of 2– 3 ml/min for the duration of the experiment. Temperature was maintained at ∼34◦C. Small holes were made in the inner limiting membrane in order to obtain access to RGC somata. Spiking responses were obtained using loose or whole cell patch recordings. Intracellular solution consisted of (in mM, all Sigma-Aldrich): 125 K-gluconate, 10 KCl, 10 Hepes, 10 EGTA, 4 Mg-ATP, 1 Na-GTP. Morphological analysis was allowed by adding Neurobiotin (0.25 mM, Vector Laboratories) and Alexa 488 (0.5%, Invitrogen) to the intracellular solution. Further processing of filled RGCs was performed as described above (Immunohistochemistry), but primary/secondary incubation to recover cell morphology was done with Alexa Fluor 488-conjugated streptavidin (1:200; Invitrogen).

Visual stimulation consisted of bright spots on neutral (gray) background with diameters ranging from 100–1500 µm and presented for 1 s. ON-α S cells were targeted by their large somata (diameter > 15 µm) and identified by their strong sustained light responses (Pang et al., 2003; van Wyk et al., 2009; Krieger et al., 2017).

Stimulus control and data acquisition were performed with custom software written in LabView (National Instruments) and Matlab (Mathworks). Data were recorded using an Axopatch 700B amplifier (Molecular Devices) and digitized by a data acquisition card (PCI-MIO-16E-4, National Instruments). The timing of individual spikes was detected as the depolarization (loose patch = negative; whole cell patch = positive) peak of each spike in the raw trace. Firing rate was computed by pooling

from flat mount preparations of different Thy-1 GFP-M mice. Polygons drawn around the periphery of the ON cell's terminal dendrites (dotted line) and the soma of the OFF cell (inset) were used to estimate the size of each (see text). Scale bars: 100 µm. (Bottom) Cross-sectional views of the confocal image stack reveal the stratification level of the terminal dendrites (green) relative to that of the ON and OFF ChAT bands (red). (B) Each point is a plot of soma diameter vs. dendritic field diameter for an individual ON- (unfilled) or OFF-α S RGC (filled). Blue triangles (n = 7) are from ON-α S cells for which light responses were also obtained; blue traces on the top left are representative light responses from 3 of the cells (Scale bar: 200 ms/20 mV). Gray and black lines are the best-fit linear regressions for ON- (p = 2 × 10-8) and OFF- (p = 5 × 10-6) α S cell populations, respectively. Box plots (right and top) reveal statistically significant differences in soma diameter (p = 2 × 10-4), and dendritic field diameter (p = 0. 0164) between ON- vs. OFF-α S RGCs. (C) The volume of the soma was calculated by (1,2) obtaining the raw image stack, (3) converting it into a binary image file, (4) using the maximum intensity projection to create a region of interest (ROI) around the cell body, (5) clearing the area outside of the ROI, (6) displaying the result in the NIH Image J/FIJI 3D viewer and using the voxel counter plugin. (D) Each point (n = 12) is a plot of the volume-based soma diameters were plotted vs. dendritic field diameter. The solid gray line is the best-fit linear regression (p = 0.0014). (E) Similar to D, each point is a plot of soma volume calculated from 2-d measurements vs. soma volume measured from 3-d reconstructions (see text). The solid grey line is the best-fit linear regression (p = 0.0016). (F) Expanded view of the soma and proximal axon region from two ON-α S RGCs reveals that axons (arrows) can emerge from the soma (left) or from a primary dendrite (right). Scale bars: 20 µm. (G) Box plots reveal statistically significant differences in soma diameter (p = 3 × 10-5), and dendritic field diameter (p = 8 × 10-5) for axo-somatic vs. axo-dendritic cells.

responses from multiple trials (≥3) and subsequent convolution with a 50 ms sliding window.

#### Computational Modeling

fncel-13-00436 September 26, 2019 Time: 18:4 # 5

To simulate the response of model RGCs to somatic current injection we used the approach of compartment modeling, similar to much previous work from our group (Jeng et al., 2011; Werginz et al., 2014). Thereby, current flow along the stimulated fiber as well as current flow across the neuronal membrane is simulated by a network of compartments with given electrical (membrane) properties. We used membrane dynamics from Fohlmeister (Fohlmeister et al., 2010) and ion channel densities for the five different sections along the neuron [dendrites, soma, axon hillock (soma to AIS), AIS, axon] can be found in **Supplementary Table S1**. Consistent with previous work in the retina (Jeng et al., 2011; Guo et al., 2013), the levels of voltage-gated sodium and potassium ion channels in the proximal axon were approximately 7x greater than those in the soma. Intracellular (axial) resistivity was set to 143.2 ·cm (Fohlmeister et al., 2010), specific membrane capacitance 1 µF/cm2 and model temperature was set to 35◦C similar to experimental conditions. The model was solved in Matlab (Mathworks) using a custom written implicit (backward) Euler solver. Time step was set to 0.005 ms. Cell morphologies of traced cells consisted of 1388–2455 compartments depending on cell size; compartment length ranged from 3 to 5 µm. Axonal geometries were reconstructed based on trendlines derived from anatomical measurements (**Figure 2D**).

Single-spike thresholds were determined as the current amplitude necessary to elicit an action potential by a 1 ms rectangular current injection. Action potentials were detected within 3 ms after pulse offset. Spike trains were generated by constant current injections (100 pA) 400 ms in length.

#### Statistical Analysis

We measured linear correlation between two variables using Pearson's correlation coefficient. For comparison of means, we used a two-sample t-test. Significance levels were set as follows: n.s. p ≥ 0.05, ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001. Numerical values are presented as mean ± standard deviation of mean (SD). Boxplots use standard notation (1st Quartile, Median, 3rd Quartile); outliers are indicated outside 1.5x the inter-quartile range (3rd Quartile - 1st Quartile). All statistical analysis was performed in Matlab (Mathworks).

#### RESULTS

#### α S RGCs Have Readily Identifiable Anatomical Properties

The sparse labeling of the GFP M mouse model (Feng et al., 2000) allowed us to tentatively identify α S RGCs based on previously described morphological features (Völgyi et al., 2009; Baden et al., 2016; Bae et al., 2018) such as soma size >15 µm and dendritic field diameter >200 µm (**Figure 1A**, top panels). Total dendritic length, computation of mean segment length

FIGURE 2 | AIS features scale with cell size in α S RGCs. (A) Expanded view of the soma and proximal axon region from two typical ON-α S RGC; a small RGC with a soma diameter of ∼17 µm (top) and a large RGC with a soma diameter of ∼24 µm (bottom). Staining for AnkyrinG (blue) was used as a marker for the AIS and allowed both length (AIS L, distance between the middle and right vertical lines), as well as distance from the soma (AIS D, distance between the left and middle lines) to be determined. Scale bars: 20 µm. (B) Each point is a plot of soma diameter vs. AIS distance (left) or soma diameter vs. AIS length (right), for individual ON- (unfilled) or OFF-α S RGCs (filled). Gray and black lines are the best-fit linear regressions for ON (AIS D: p = 0.0045, AIS L: p = 0.0015) and OFF (AIS D: p = 0.0043,

#### FIGURE 2 | Continued

fncel-13-00436 September 26, 2019 Time: 18:4 # 6

AIS L: p = 0.0220) α S cell populations, respectively. Box plots (right) reveal statistically significant differences in AIS D (left, p = 0.0078), and AIS L (right, p = 7 × 10-4) for ON vs. OFF cells. (C) The diameter of the proximal axon was measured at the soma-axon border [red vertical line in (D)], the proximal edge of the AIS (blue vertical line) and the distal edge of the AIS (green vertical line) in 39 α S RGCs. Dashed lines indicate best fit linear regressions. The diameter of the axon varied with cell size at the soma-axon border (red, p = 0.0001) but not at the other two locations (p = 0.5012 and p = 0.0541, respectively). (D) Schematic representation of the soma and proximal axon of an α S RGC depicting the relationship between AIS properties and soma diameter; equations at top were derived from the best-fit plots in (B).

and Sholl analysis (Sholl, 1953) in presumed α S RGCs were also highly similar to previous reports (Bleckert et al., 2014) (**Supplementary Figure S1**), providing additional confirmation of cell type. Final verification was obtained from the stratification level of terminal dendrites (van Wyk et al., 2009): ON-α S RGCs stratified just below the ON-ChAT band (**Figure 1A** bottom, left) while OFF cells stratified just above the OFF-ChAT band (**Figure 1A** bottom, right). Using this approach, we identified a total of 47 ON-α S and 27 OFF-α S RGCs obtained from 11 different mice. Light responses were measured in seven additional putative ON-α S RGCs (**Figure 1B**, top left, blue traces) and found to be highly similar to those described previously (Pang et al., 2003; van Wyk et al., 2009; Krieger et al., 2017). Subsequent morphological analysis of these seven additional cells, using the approach described above, were consistent with the findings from the original 47 ON cells, adding additional support to the notion that the cells studied here were indeed the same population of ON-α S RGCs described in previous anatomical and physiological studies (Pang et al., 2003; van Wyk et al., 2009; Krieger et al., 2017).

Consistent with earlier studies (Bleckert et al., 2014), the spatial extent of the dendritic field was quantified by drawing a polygon around the edges of the cell's terminal dendrites (**Figure 1A**, left) and then calculating the diameter of the circle whose area matched that of the drawn polygon. A similar approach was also used to calculate the diameter of the soma (**Figure 1A**, inset), but unlike prior studies (Bleckert et al., 2014) which used maximum intensity projections at low magnification (20x) to estimate the perimeter of the soma, we were able to more clearly delineate the boundaries by manually tracing the cell body along the entire z-dimension using high (63x) magnification (see section Materials and Methods). We plotted soma size vs. dendritic field diameter for all ON- and OFF-α S cells (**Figure 1B**), unfilled (n = 47) and filled (n = 27), respectively; blue triangles represent the 7 ON cells for which light responses were also obtained). Calculating the best-fit linear regressions for the ON and OFF populations (gray and black lines, respectively) revealed a highly significant positive correlation in both populations (ON: p = 2 × 10-8; OFF: p = 5 × 10-6), suggesting that the size of the dendritic field scales with the size of the soma. This correlation differs from the findings of an earlier study with this same RGC population in which the size of the soma did not vary systematically with the size of the dendritic field (Bleckert et al., 2014). While the difference in methodology is likely to contribute to the discrepancy, we nevertheless sought to more precisely estimate the soma size of each cell using numerical integration. Briefly, we determined the somatic voxel contribution from each individual confocal slice and then summed the contribution from all slices to estimate the total somatic '3-d' volume (**Figure 1C**, see section Materials and Methods). The calculated diameter of the soma from the 3-d cell volume (n = 12) was highly correlated to its dendritic field size (**Figure 1D**), providing additional support that soma size does indeed scale with cell size. Comparison of the soma diameter calculated using the 3-d volume to the soma diameter estimated from the 2-d projection across the z-stack (**Figure 1E**) suggested the 2-d approach overestimates the somatic volume and is consistent with our observation that most α S somas appeared 'flattened,' i.e., not perfectly spherical (Bae et al., 2018). The linear correlation between the two estimates (**Figure 1E**) nevertheless suggests that soma size does increase with dendritic field size across the entire population of α S RGCs, i.e., not just the 12 cells for which were able to perform the full 3-d estimates.

The finding that soma size is correlated to dendritic field size in α S RGCs of mouse is consistent with previous reports in other types of RGCs, including α RGCs of rabbit retina (Peichl et al., 1987) as well as midget and parasol RGCs of the primate retina (Watanabe and Rodieck, 1989). This type of scaling may be cell type specific as there were significant variations in dendritic field diameter but not soma diameter in dorsal vs. ventral OFFalpha transient (α T) RGCs of the mouse (Warwick et al., 2018). Both the size of the soma as well as the size of the dendritic field were smaller in OFF-α S RGCs than in neighboring ON cells (**Supplementary Figure S2** and **Figure 1B**, box plots at right and top, respectively), consistent with previous reports in both human and primate RGCs (Dacey and Petersen, 1992; Chichilnisky and Kalmar, 2002).

We also measured the diameters of the dendritic sections from the 7 filled ON-α S RGCs to see if they too were correlated to cell size (see section Materials and Methods). A histogram of the dendritic diameters (**Supplementary Figure S3A**) revealed that 95% were between 0.36 µm and 1.48 µm (mean of 0.92 µm). A probability distribution function (PDF) was fit to each histogram (red trace) and then PDFs from all cells were overlaid (**Supplementary Figure S3B**, red lines); the distribution of dendritic diameters was similar for all cells, i.e., it was not dependent on cell size. Consequently, total dendritic surface area was found to scale linearly with dendritic field diameter (**Supplementary Figure S3C**, p = 0.0028).

In most α S RGCs (n = 50/74) the axon emerged directly from the soma, but in other cells, it emerged instead from a primary dendrite (**Figure 1F**, compare left and right panels). This arrangement is similar to that reported in other types of CNS neurons (Hamada et al., 2016; Höfflin et al., 2017) although in some of these other types, axons can emerge from secondary or even tertiary dendritic processes while here, axo-dendritic axons emerged only from primary dendrites. α S RGCs with axo-somatic axons tended to be smaller than RGCs with axodendritic axons (mean soma diameters of 19.1 vs. 21.6 µm,

p = 3 × 10-5, and mean dendritic field diameter of 287 vs. 354 µm, p = 8 × 10-5) (**Figure 1G**). Most axo-dendritic α S RGCs were ON cells (n = 21/24).

#### AIS Properties Also Scale With Cell Size

The AIS is typically the site of spike initiation in CNS neurons (Stuart et al., 1997). The high density of voltage-gated sodium channels in this region (Kole et al., 2008) combined with its much smaller surface area (vs. that of the soma) leads to more rapid depolarization of membrane voltage with a corresponding earlier onset of spiking, even though excitatory synaptic inputs typically result in a slightly stronger initial depolarization at the soma. Both the size as well as the location of the AIS can influence neuronal function (Kuba et al., 2006; Evans et al., 2015; Hamada et al., 2016) although interestingly, AIS properties mediate distinct functions in different types of neurons. For example, the length and location of the AIS is customized in individual neurons of the nucleus laminaris to help optimize sensitivity to specific auditory frequencies (Kuba et al., 2006) while the location of the AIS is tailored in thick-tufted L5 pyramidal neurons to help normalize spike amplitude across a wide range of cell sizes (Hamada et al., 2016).

Given the importance of AIS properties in shaping neuronal output in other types of CNS neurons, we questioned whether AIS properties might similarly influence neuronal function in RGCs. If so, we hypothesized that similar to other CNS neurons, one or more features of the AIS would be correlated to other morphological features of the cell; the systematic variations in cell size across the α S population (**Figure 1** and **Supplementary Figure S2**) provide an ideal substrate from which to explore this hypothesis. Variability in both the length and the location of the AIS has been reported previously in two types of rabbit RGCs (Fried et al., 2009) although any correlation between AIS properties and other RGC features was not reported in this earlier work.

Similar to previous approaches (Boiko et al., 2003; Van Wart et al., 2007; Fried et al., 2009) we used immunochemical labeling of AnkyrinG, a cytoskeletal anchoring protein (Kordeli et al., 1995) that enables dense packing of voltage-gated sodium channels in the AIS (Zhou et al., 1998), as a marker for the size and location of the AIS. The length of AnkyrinG staining (**Figure 2A**, distance between middle and right vertical lines), hereafter referred to as AIS length or AIS L, did indeed vary considerably across the population of α S RGCs (**Figure 2B**, right). Further, the length of the AIS displayed a highly significant positive correlation to soma diameter (**Figure 2B**, right, unfilled and filled circles for ON and OFF RGCs, respectively; p = 1 × 10- 6). The distance from the soma to the AIS, hereafter referred to as AIS distance or AIS D (**Figure 2A**, distance between left and middle lines), also varied across the population of α S RGCs with a similarly strong correlation to soma size (**Figure 2B**, left, p = 2 × 10-6). A comparison between the slopes of the best fit lines suggested that AIS length increases with soma size at a slightly faster rate (1.38 µm for every 1 µm increase in soma diameter) than does AIS distance (1.22 µm for every 1 µm increase in soma diameter); the equations describing the rates of increase are indicated above the schematic in **Figure 2D**.

In addition to the systematic scaling of AIS size and location, careful observation revealed variability in the diameter of the proximal edge of the axon. To quantify this, we measured the diameter of the proximal axon at three locations (**Figures 2C,D**, see section Materials and Methods): (1) the border between the soma and axon (**Figure 2D**, bottom, red vertical line), (2) the proximal edge of the AIS (blue line) and (3) the distal edge of the AIS (green line). Linear regressions of the measured points (n = 39) revealed that the diameter at the start of the axon was indeed correlated to soma size (**Figure 2C**, left, red, p = 0.0001) while the diameter at the proximal and distal ends of the AIS were not (**Figure 2C**, middle, blue, p = 0.5012; **Figure 2C**, right, green, p = 0.0541); diameters at these two locations were ∼0.6 and 1.0 µm, respectively. At locations >100 µm from the soma, the diameter of the distal axon remained largely constant at ∼1 µm, consistent with previous results in amphibian retina (Carras et al., 1992). Thus, consistent with the scaling of other neuronal features found earlier, the diameter of the proximalmost portion of the axon is also wider in larger AIS α S RGCs. Further, because the diameter of more distal portions of the axon does not scale with cell size, the angular taper between the AIS and the soma is also wider in large cells. Previous modeling work in neocortical pyramidal neurons found that the degree of angular taper in this region can influence the efficacy with which the AIS-initiated spike invades the soma (Mainen et al., 1995), suggesting a functional role for this variability (see below).

## The Ratio of Nav Channel Sub-Components Remains Constant in α S RGCs

The AIS is comprised of two distinct sodium channel sub-types (Boiko et al., 2003; Van Wart et al., 2007; Hu et al., 2009): Nav1.6 channels cluster at the distal end of the AIS and have a relatively low activation threshold while Nav1.2 channels cluster at the proximal end and have a higher activation threshold (Hu et al., 2009). Note that in the retina, Nav1.1 channels are found in the proximal axon (Van Wart et al., 2007). Action potential initiation occurs in the Nav1.6 (low threshold) portion of the AIS (Hu et al., 2009; Kole and Stuart, 2012) while the lower sensitivity of the Nav1.2 portion is thought to facilitate the effectiveness with which the AIS-initiated spike triggers a somatic spike and may also regulate the amplitude of the back-propagated (somatic) spike (Hu et al., 2009). Given the distinct functional role of each sodium channel sub-type, we questioned whether their relative distribution within the AIS might also vary across the population of α S RGCs. Antibodies specific to Nav1.6 channels have been characterized previously in unclassified rodent RGCs (Van Wart et al., 2007; Damiani et al., 2012) as well as in other types of CNS neurons (Boiko et al., 2003; Hu et al., 2009), and were used here to selectively stain this portion of the AIS in α S RGCs (**Figure 3A**, see section Materials and Methods). Although we could not selectively stain the Nav1.1 component directly (but see Van Wart et al., 2007), the Nav1.1 and Nav1.6 components do not overlap (Boiko et al., 2003; Van Wart et al., 2007) and thus the length of the non-Nav1.6 + (presumed to be Nav1.1) component could be estimated by subtracting the Nav1.6 component from

vertical lines); staining for Nav1.6 (red) was used to determine the length of the Nav1.6 component (distance between the rightmost solid and dashed vertical lines). Scale bar: 20 µm. (B) Each row corresponds to a single ON- or OFF-α S cell and depicts the composition of the AIS. The length of the red portion of the line corresponds to the length of the Nav1.6 component. The length of the blue portion (non-Nav1.6+) corresponds to the portion stained by AnkyrinG but not by Nav1.6 and therefore likely to be the Nav1.1 component (see text). The shading of the circle to the left of each line corresponds to the size of the soma diameter for that cell (scale at left). (C) The calculated ratio of Nav1.6 length to total AIS (AnkyrinG) length for each cell (scale at bottom); the shading of filled circles is the same as in (B). (D) Scatterplot of soma size vs. Nav1.6/AIS ratio (p = 0.0817). The gray arrow/data point indicates an outlier which was not used in correlation analysis. (E) Box plot reveals no statistically significant differences in soma diameter (p = 0.9366).

the length of the entire AIS. **Figure 3B** shows the distribution of each of the two sub-types of sodium channels across the population of all α S RGCs tested (n = 29). The red portion of each horizontal line corresponds to the measured length of the Nav1.6 component while the blue portion corresponds to the calculated length of the non-Nav1.6 + region component (the length of the Nav1.6 component subtracted from the length of the AnkyrinG staining). The ratio of Nav1.6 length to total AIS length was calculated for each cell and ranged narrowly around a mean of 0.88 ± 0.04 (**Figure 3C**, range 0.81–0.96 with one outlier at 0.68). Plotting soma size vs. the Nav1.6/AIS ratio revealed a weak and slightly negative correlation that was not significant (**Figure 3D**, p = 0.0817). The ratio did not co-vary with any other morphological property of the cell (not shown).

The mean ratio found here (0.88) is higher than the twothirds value described previously for unclassified types of RGCs in the rat retina (Van Wart et al., 2007). Few quantification details are provided in the earlier study and thus it is difficult to reach any definitive conclusions regarding the discrepancy in AIS composition between the two studies. It is possible of course that differences in species and/or cell-type contribute to the variability, but further work will be required to elucidate the actual reason(s). Nevertheless, the high level of consistency in AIS composition across the large number of α S RGCs studied here suggests that the relative proportion of channel sub-types may be essential for optimizing one or more functions of the AIS. There was no significant difference between the ratios for ON- vs. OFFα S RGCs (**Figure 3E**, ON: 0.88 ± 0.04, range 0.81–0.96; OFF: 0.88 ± 0.04, range 0.81–0.95; p = 0.9366).

#### Light Responses Are Independent of Cell Size

The wide range of morphological variability across α S RGCs raises the question as to whether light responses would remain consistent across such a wide variation. Previous studies have shown that light responses from nearby cells of the same type (e.g., those found within the spatial spread of a single multielectrode array) are highly consistent but it is not known whether this consistency persists across the full extent of cell variability. We therefore measured light responses from a large number of ON-α S RGCs across the full extent of the nasaltemporal axis (**Figure 4**).

Light stimuli consisted of 1-s flashes that ranged in size from 100 to 1500 µm (see section Materials and Methods). Consistent with much previous work (Pang et al., 2003; Margolis and Detwiler, 2007; van Wyk et al., 2009), response strength varied considerably with stimulus size but was strongest when the size of the stimulus closely matched the size of the dendritic field (**Supplementary Figure S4**). Thus, for example, the strongest response from a small cell, located at the temporal edge of the retina, arose when the diameter of the flashed spot was 200 µm (**Figure 4A**, top) while the strongest response from a large cell, found at the nasal edge of the retina, was elicited by a much larger (500 µm) spot (**Figure 4A**, bottom). Despite the large size difference between the two cells, their responses were quite similar. Indeed, the strongest response of each ONα S RGC appeared similar across the entire population of cells tested (**Figure 4C**, n = 39). To quantitatively compare responses, we calculated the peak firing rate, the sustained firing rate, the sustained firing rate normalized by the peak firing rate, and, the time to peak and plotted each as a function of the size of the stimulus that produced the strongest response (**Figures 4D**1−4). The differences across cells were not statistically significant (p = 0.5557, p = 0.8660, p = 0.5438, and p = 0.4263, respectively) suggesting that the peak responses of individual ON-α S RGCs remain similar across the population, despite a wide range of cell sizes.

## AIS Size and Location Influence RGC Excitability

Because scaling was so prevalent in α S RGCs, we sought to understand whether it contributed to cell function. In particular,

because the AIS mediates spike initiation, we questioned whether this aspect of scaling influenced the sensitivity of spike generation and propagation. To explore this, we ran a series of computer simulations in which individual neuronal properties of model cells were systematically varied while exploring the effects on (1) spike threshold, (2) the frequency and consistency of spike trains, and (3) the effectiveness with which AIS-initiated spikes triggered somatic spikes.

Seven model cells were created, each from an actual ON-α S RGC for which the entire morphological structure of the cell had been captured (see section Materials and Methods). Dendritic field diameters of the 7 model cells ranged from 231 to 422 µm, approximating the extent of sizes found naturally. In addition to replicating the anatomy of individual cells, the density of voltagegated ion channels was customized for individual neuronal compartments using previously established values (Fohlmeister et al., 2010) (**Supplementary Table S1**, see section Materials and Methods). Prior to exploring the role of individual neuronal features, the functionality of model cells was first verified by comparing the kinetics of simulated action potentials to that from physiological measurements (**Supplementary Figure S5A**, middle column). The close match between simulated and actual spikes suggested the spike generation mechanism in model cells was accurately capturing key elements of normal physiology. In addition, action potentials in model cells were always initiated in the AIS, suggesting that the underlying mechanisms of activation in the model replicated that of normal physiology.

Threshold in model cells was defined as the minimum amplitude of a 1 ms somatic current injection required to elicit an action potential. Individual AIS features were varied systematically and a separate calculation of threshold made for each iteration of each parameter. Because the size of individual features typically varied across the seven model cells, all variations were made relative to the measured feature size in each cell, e.g., the length of the AIS was varied from 50–200% of the nominal (measured) value for that cell. The effect reported is the mean across all seven cells.

Reducing the length of the AIS resulted in higher activation thresholds (**Figure 5A**1); the largest increase occurred when AIS length was set to its minimum value (increase of 2.9 ± 1.1% for a length of 50%). Conversely, increasing the length of the AIS lowered activation threshold with the largest reduction occurred when the length of the AIS was set to its maximum value (8.9 ± 4.2% reduction for an AIS length of 200%). Because the observed variations in AIS length (**Figure 2B**) are slightly smaller than the 50–200% range tested here, the simulations suggest that the AIS variability arising naturally produces only a modest change in threshold but nevertheless, that AIS length modulates the sensitivity with which the cell responds to a given stimulus. Changes to the distance between the soma and the AIS (AIS D) also influenced threshold but the effect was smaller (**Figure 5A**2, 50%: +0.7 ± 0.6%; 200%: −2.0 ± 1.3%). When threshold was mapped across the full range of AIS lengths and distances (**Supplementary Figure S5B**), the iso-threshold lines were near vertical, further suggesting the stronger influence of AIS length over distance in modulating activation threshold.

Changes to soma size also influenced sensitivity with reductions in soma diameter leading to lower thresholds (**Figure 5A**3); the smallest diameter tested (75% of nominal) led to a threshold reduction of 4.0 ± 0.9% while the largest diameter (125% of nominal) led to a threshold increase of 5.1 ± 0.6%. Note that the sensitivity to size changes was opposite for soma size vs. that of AIS length (or AIS distance), i.e., larger AISs resulted in lower thresholds while larger somas resulted in higher thresholds. This is not surprising given that larger somas require larger amounts of current injection to depolarize to a given

level but nevertheless indicates that uniform increases of size in all cellular features may not result in uniform and predictable changes in threshold levels (see below). The final parameter tested, the diameter of the initial portion of the axon, had only a negligible effect on threshold (**Figure 5A**4, 75%: −0.2 ± 0.1%; 125%: +0.2 ± 0.1%).

To gain insight as to how scaling of all parameters simultaneously might influence thresholds across the population of α S RGCs, we constructed two new models in which all neuronal features (AIS L, AIS D, Soma Ø and Axon Ø) were varied together. The LARGE model had all values set to maximum while the SMALL model used all minimum values. Comparison of the sensitivity of these two cells allowed the effect of comprehensive scaling of all features to be evaluated. Threshold for the SMALL model cell increased by 1.9 ± 1.6% (over nominal) while threshold in the LARGE cell decreased by 3.7 ± 2.3% (**Figure 5A**5). As mentioned above, increases to some neuronal elements reduced threshold while increases to others lowered threshold, e.g., increased soma size raised threshold in the LARGE model while the increased AIS length caused a reduction and therefore, the range of thresholds across the SMALL to LARGE models was smaller than the range induced by some neuronal elements in isolation. This suggests the possibility that scaling may be to help stabilize response sensitivity across the population of a given cell type even across a wide range of feature sizes.

## AIS Properties Influence the Properties of Spike Trains

The second modeling experiments explored the influence of the AIS in shaping the properties of elicited spike trains, i.e., not just the threshold for single spikes. In previous studies, the loss of Nav1.6 channels resulted in an inability to produce highrate spike trains in mouse RGCs (Van Wart and Matthews, 2006). Similarly, in cultured hippocampal pyramidal neurons from newborn mice, the loss of the structural proteins needed to maintain a high density of sodium channels in the AIS reduced the precision of spike timing (Lazarov et al., 2018). To explore the relationship between individual properties of the AIS and properties of the cell's spike trains, we used the same models and general approach described above but increased the duration of the current injection from 1 to 400 ms, so that multiple spikes would be generated from each stimulus (100 pA).

Analogous to the threshold simulations above, variations in the AIS had small but significant effects on firing rate (**Figure 5B**). For example, when AIS length was set to 50% of nominal, the mean reduction in firing rate was 3.9 ± 1.4%; doubling the length of the AIS increase firing rate by an average of 6.0 ± 2.0% (**Figure 5B**1). The effects of varying AIS distance were also analogous to the single spike threshold findings with a 50% reduction in AIS D leading to a 1.2 ± 1.5% reduction of firing rate while a doubling of AIS D led to a 2.5 ± 2.2% increase (**Figure 5B**2). Soma diameter also influenced firing rate (**Figure 5B**3) however its effect was smaller than it was for singlespike threshold (75%: +1.6 ± 1.5%; 125%: −1.9 ± 1.4%). The effect was smaller in the single spike experiments because the size of the soma was linearly related to the amount of charge needed to depolarize the cell membrane and thus produce the spike while for the longer duration stimulus in the spike rate experiments, the cell membrane remained depolarized once it was charged and thus less charge was required to generate subsequent spikes. Also similar to the results from the threshold experiments, changes to the diameter of the initial axon had

only a minimal effect on firing rate (**Figure 5B**4). After testing the sensitivity of all parameters in isolation, we re-created the SMALL and LARGE models and tested the resultant firing rates (**Figure 5B**5). Interestingly, the increase in firing in the LARGE model (vs. nominal) was 8.2 ± 4.1%, larger than the effect due to any one parameter in isolation and also nearly twice that for single-spike threshold (3.7 ± 2.3%). This larger difference arose because AIS length and AIS distance both had a stronger effect on firing rate while soma size did not and is in contrast to the single-spike threshold results which were strongly affected by soma size (and AIS length). The results, taken together, therefore suggest that the influence of specific neuronal elements as well as the interplay between multiple elements is not constant for all modes of operation. Firing rate in the SMALL model was lowered by 2.0 ± 2.3%, comparable to the 1.9 ± 1.6% reduction for single spike thresholds. It is worth noting that the increase firing rates described in the large model cells were not matched by physiological responses – at least not for light responses (**Figure 4**). This suggests that other factors, not incorporated into the model, are likely contributing to spike rate (see section Discussion).

#### Scaling Influences the Infiltration of AIS Spikes Into the Soma

In the simulations above, variations in the diameter of the initial portion of the axon were found to have little effect on either threshold or pulse rate. What then was the functional role of variability in this part of the neuron? Results from a previous modeling study in neocortical pyramidal neurons suggested that the taper of the proximal axon influences the effectiveness with which AIS-initiated spikes trigger somatic spikes (Mainen et al., 1995). The results of **Figure 5A** suggest that taper does not directly influence the sensitivity of spike threshold but the shape of the action potential was altered by some of the changes in the previous simulations (**Supplementary Figure S5A**), including taper, leading us to question the specifics of this effect and, whether the effectiveness of back-propagation was also influenced.

Plots of membrane voltage (V) vs. the time derivative of membrane potential (1V/1t) are referred to as phase plots and allow the fine details of action potential kinetics to be examined. They are especially useful for comparing specific time segments of the action potential during periods where either V or 1V/1t are changing rapidly. **Figure 6A** shows phase plot overlays of 10 spikes from an actual cell (left) and 10 spikes from a model cell (right). As expected, individual spikes were highly consistent from trial to trial and many aspects of the spike kinetics were matched by the model cell. Overlay of individual spikes from 10 different α S RGCs (**Figure 6B**, left) revealed high consistency across the population and many of the subtle variations in spike kinetics could be largely replicated in the different model cells (right). Although phase plots report somatic potentials, the AISinduced spike can be observed in the waveform as the portion of the curve prior to the inflection point (arrows in panels A and B); the inflection point, referred to as the initial segmentsomatodendritic (IS-SD) break, occurs at the transition between the initial depolarization arising from the AIS-initiated spike and the onset of the (somatic) action potential (Coombs et al., 1957; Yu et al., 2008) suggesting that the axial current arising from the AIS-initiated spike helps to trigger the somatic spike. The presence of an IS-SD break in all recorded cells is a marker that indicates that spikes are always initiated in the AIS suggesting the process is very reliable in α S RGCs.

As in previous simulations, we varied individual neuronal elements in all 7 model cells and examined the effect on membrane dynamics via phase plots (**Figure 6C**). The largest differences occurred in response to variations in AIS length and were most noticeable around the IS-SD break with a longer AIS (blue) leading to a sharper rise during the initial phase of the action potential. This leads to a more pronounced IS-SD break for longer AISs while the IS-SD break is almost eliminated for shortened AISs (red). To quantitatively compare changes in phase plots, we calculated onset rapidness (Lazarov et al., 2018), defined as the slope in 1V/1t when a rate of 25 V/s was reached; the red lines in **Figure 6C** (right) depict the onset rapidness calculations for two of the sample traces from the main panel with the one on the left corresponding to an increased AIS length (200%) and the one on the right to a reduced AIS length of (50%). Reduction of AIS length to 50% of nominal led to a decrease of onset rapidness (−33.6 ± 19.0%) whereas doubling led to an increase (479 ± 117%) (**Figure 6D**1). This occurs because the increased length of the AISs results in a stronger inward current during the AIS spike and thus stronger axial currents are delivered to the soma. Changes to AIS distance (**Figure 6D**2) had a similar but less pronounced effect as onset rapidness was reduced by 21.7 ± 13.0% for distance reduction to 50% and increased by 190 ± 41% for a distance of 200%. Soma diameter had only a small influence on action potential shape as onset rapidness changed by less than 3% (**Figure 6D**3, 75%: −0.5 ± 7.8%; 125%: +2.7 ± 8.4%). Thus, in contrast to their relatively small effect on single-spike threshold and firing rate, changes to the initial axon diameter (**Figure 6D**4) had a more substantial effect on spike kinetics with an onset rapidness increase of 30.0 ± 18.3% for a diameter reduction to 75% of nominal and an onset rapidness decrease of 11.7 ± 3.7% when diameter was increased to 125%. Because reducing the size of most elements (relative to that of nominal) resulted in a reduction in onset rapidness while reducing the size of the initial axon diameter resulted in an increase in onset rapidness, these model results suggest that adjustments to the initial axon diameter might be essential to optimize the consistency of the somatic spike both within spikes from the same cell as well as across cells of the same cell type. Use of the LARGE and SMALL models revealed that, similar to the effects with threshold, the simultaneous increase in size of all neuronal elements mitigates the effect of changes to any one parameter change and thus provides further support for the notion that scaling may help to maintain consistency of many response features across a wide range of morphological variations (**Figure 6D**5).

The plots of **Figures 6C,D** suggest that the shape of the somatic action potential is sensitive to the properties of at least several neuronal elements. The most noticeable change in spike shape was the IS-SD break, suggesting that changes to

the neuronal elements in question alter not only the timing of individual spikes but also the strength and effectiveness with which the AIS-initiated spike triggers the somatic spike (Yu et al., 2008). As with the previous modeling experiments, we found here that multiple elements could have an effect and that the polarity could vary for individual elements. Therefore, similar to the experiments with threshold sensitivity, the effect on somatic spike initiation was smaller when multiple parameters were changed simultaneously vs. when the effect of individual parameters. Whereas the diameter of the initial axon (and the corresponding taper of the initial axon segment) had only small effects on threshold and spike rate, it had a more substantial effect on the triggering of the somatic spike, and had the strongest counter-balancing effect to changes in AIS properties. It is of course possible that factors not considered in the model, e.g., the density and/or expression patterns of specific types of voltagegated potassium channels, could also play a role in normalizing spike thresholds and/or the effectiveness with which the AISinitiated spike triggers the somatic spike and it will be interesting to explore such effects in future studies.

The change in kinetics of the action potential as a function of AIS length (**Figure 6C**), suggest that part of the reason for customizing AIS length is to optimize the initiation of the somatic spike. Note that in **Figure 6C**, the longer AIS (blue) resulted in a faster initial change in voltage (1V/1t) but a lower level in overall depolarization (lower maximum level of V). While our results do not explain why this occurs, it is likely that a 'toofast' delivery of current to the soma is simply not effective for charging the large capacitance of the somatic membrane; it leads to inactivation of some portion of somatic voltage-gated sodium channels with the net result that the somatic action potential has a weaker rise and reaches a lower peak voltage. At the other end of the spectrum, a 'too-small' delivery of current (from an AIS that is too short) offers no boost to the somatic spike (no IS-SD break), i.e., the AIS-spike does little to help trigger the somatic spike.

## DISCUSSION

#### Summary

Detailed anatomical measurements revealed systematic correlations in size between a wide range of morphological features in α S RGCs of the mouse. For example, cells with large dendritic fields tended to also have large somas. In addition, the AISs of large cells were longer and further from the soma than those of smaller cells. Because the AIS is the portion of the cell that mediates spike initiation, our results suggest that this function is not mediated by a uniform structure across all cells of a given type but instead is sized relative to the rest of the cell. To explore the functional role of this scaling, we developed morphologically- and biophysically realistic computational models that allowed individual aspects of the AIS to be systematically varied. The models suggest that scaling helps to reduce sensitivity variations for the initiation of spikes across the cells of an individual RGC type. This helps to further stabilize the temporal properties of elicited spike trains across the population, and ensure consistency in the conversion of the AIS-initiated spike to a somatic spike. Thus, in addition to a myriad of previously identified pre-synaptic processes that shape the responses of RGCs, here we identify a novel post-synaptic mechanism that completes these other processes. Further, scaling is also likely to contribute to the strong similarities in both the kinetics of individual spikes as well as the spike trains arising in response to light during physiological experiments.

#### Somatodendritic and AIS Scaling

fncel-13-00436 September 26, 2019 Time: 18:4 # 13

Our finding that both soma size and dendritic field diameter increase systematically along the nasal temporal axis in α S RGCs (**Figure 1B**) differs from an earlier study of these same cells which found that only dendritic field extent (not soma size) increases along this axis (Bleckert et al., 2014). To verify our findings, we performed additional measurements of somatic volume using a detailed 3-dimensional approach in a subset of cells; these measurements confirmed that soma size does indeed vary across cells and that it is strongly correlated to dendritic field size (**Figure 1D**). The 3-d measurements also revealed that the shape of the soma in α S RGCs was slightly 'flattened' along the z-axis (perpendicular to the plane of the retina, **Figures 1C,E**) and thus the 2-d measurements systematically overestimated the actual soma size (**Figure 1E**). Nevertheless, there was a linear correlation between the two estimation techniques, and thus, taken together, our results show that soma size is indeed variable across the α S RGC population and that it scales with dendritic field size. We found similar levels of scaling in both ON and OFF sub-types of α S RGCs (**Figure 1B** and **Supplementary Figure S2**). Scaling in these cells is consistent with previous reports in brisk transient (BT) RGCs of the rabbit retina (Peichl et al., 1987) as well as in midget and parasol RGCs of the primate retina (Watanabe and Rodieck, 1989). While this indicates that scaling is not unique to α S RGCs at least one well studied cell type of the mouse retina does not exhibit scaling (Warwick et al., 2018). At any given location in the mouse retina, ON-α S RGCs were slightly larger than neighboring OFF-α S RGCs (**Supplementary Figure S2**), analogous to previous reports in ON and OFF midget and parasol RGCs in both human and non-human primate retinas (Dacey and Petersen, 1992; Chichilnisky and Kalmar, 2002).

Previous studies have shown that the properties of the AIS can be variable, both within (Hamada et al., 2016) and across individual cell types (Kuba et al., 2006); such variability is thought to help optimize one or more aspects of neuronal function. These previous findings outside the retina led us to analyze the variability in the AIS properties of α S RGCs and we found that both the length of the AIS as well as its distance from the soma increased as a function of cell size (**Figure 3B**), i.e., AIS properties were scaled to the overall size of the cell. AIS scaling in α s RGCs is intriguing because it suggests that nature tailors the spike initiating region of each cell based on the size of other cellular features. Interestingly, the relative proportion of the Nav components of the AIS both remained remarkably constant across the entire population (**Figure 3**). The relative proportion of Nav1.6 channels within the AIS (88 ± 4%) was nearly identical in both ON- and OFF-α S cells suggesting that whatever role served by this particular ratio, it is similar in both cell populations. Interestingly, the ratio measured here, from a single cell type in mouse retina, was different from that of a previous study in which the Nav1.6 portion comprised only ∼67% of the total length across unspecified RGC types in rat retina (Van Wart et al., 2007). It will be interesting in future studies to learn the functional reason for the ratio difference, and to determine whether the ratio varies for different types of RGCs and/or across different species. Finally, our anatomical measurements revealed that the cross-sectional diameter of the axon at the point from which it emerges from the soma, also increased linearly with cell size. Thus, there was a wider taper that persisted over a longer extent of the proximal-most portion of the axon in larger cells. Because this portion of the axon serves as the direct link between AIS and soma, the fact that it too was scaled provides additional support for the notion that AIS properties are functionally linked to the size of the soma and perhaps the overall size of the cell.

#### Scaling and AIS Function

To gain insight into the functional role of the anatomical scaling observed here, we developed a series of morphologicallyand biophysically realistic computational models. Each model matched the precise morphology of a single α S RGC that had been dye-filled, stained and captured via confocal imaging. The models allowed individual features of the cell to be systematically varied and thus could provide insight as to the functional role of each. Simulations were run to test all cellular features that been anatomically characterized in the first part of the study (soma size, dendritic field, AIS length, AIS distance and the angular taper of the proximal axon). Not surprisingly, alterations to each of the individual features led to changes in neuronal function, particularly in the initiation and/or propagation of action potentials (**Figures 5**, **6**).

Prior to exploring the role of AIS scaling, we sought to first gain insight into the functional role of the scaling observed between the soma and dendritic field diameter (**Figure 1B**). In preliminary simulations (not shown), we compared the synaptic input arriving at the soma between two cells that were identical except for the size of their dendritic fields. Because the density of synaptic inputs along the dendrites was the same in both cells, a larger amount of synaptic current could be delivered to the soma of the cell with the larger dendritic field. Further, because soma size was identical in the two cells, the larger amount of synaptic input in the cell with the larger dendritic field produced a stronger depolarization in its soma. Increasing the soma size of the cell with the larger dendritic field (as occurs with scaling) reduces the level of somatic depolarization and thus helps to normalize the peak level of depolarization between the different size cells. This therefore suggests that the scaling between soma size and dendritic field diameter observed here (**Figure 1B**) may help to normalize responses across the full population of cells and is consistent with the highly similar physiological responses found in neighboring cells of the same type as well as across cells of the same type that are located much further apart (**Figure 4**).

Simulations that explored the functional role of the AIS revealed that its scaling may also help to normalize responses across the population of α S RGCs. Changes to both the length and the location of the AIS did not alter the absolute level of somatic depolarization but instead modulated the level of somatic depolarization required to elicit an action potential, i.e., spike threshold. Interestingly, the longer and more distal AISs found in larger α S RGCs acted to reduce the threshold for spike initiation, suggesting an opposite relationship for some features, i.e., while increased soma size reduced the level of

somatic depolarization (and thus increases threshold), increased length of the AIS as well as the increased distance from the soma (as found in large cells) both acted to reduce threshold. Thus, scaling of multiple features may act to fine tune sensitivity in each cell and ultimately, to normalize sensitivity across the entire population. The ability to fine tune sensitivity via scaling of multiple features might also be particularly attractive during development as it would allow the properties of one feature to be adjusted after other features were already sized, e.g., AIS properties could be fine-tuned after the soma and dendritic field sizes had already been established. The demonstration of plasticity of both the length and location of the AIS (Grubb and Burrone, 2010; Kuba et al., 2010) lends support to this possibility. Our results do not preclude the possibility that other features of the cell, e.g., those not explored in this study, also shape sensitivity as well.

Scaling had an analogous influence on the generation of spike trains with synergistic interactions between different neuronal elements again working to stabilize spike rates. The effects of soma size were again opposed by those of AIS length and distance although soma size had a smaller effect on spike rate (than it did on single spike thresholds). Thus, individual neuronal features do not exert the same influence on all aspects of cell function and the resulting interplay between different elements varies as well. The simulations also revealed that changes to the angular taper of the proximal-most portion of the axon had little effect on either spike threshold or the rate of spiking but instead altered the efficacy with which the AIS-initiated spike triggered the somatic spike. The wider tapers associated with larger cells (**Figure 2C**), resulted in a slower rise in somatic depolarization (slower onset rapidness, **Figure 6D**4), and thus a smaller IS-SD break. This was in contrast to the effects of longer AISs in large cells, which increased onset rapidness (**Figures 6C,D**1). Thus, for all aspects of neuronal function tested here, increasing the size of some neuronal elements drove the response in one direction while increasing the size of others drove the response in the opposite direction. Taken together, this suggests that slight changes to the size of individual elements can be used to fine tune the performance of a wide range of spiking-related functions. We did not probe the role of onset rapidness or the IS-SD break in detail, but changes to the waveforms (**Figure 6C**) suggest that if onset rapidness is too steep, the AIS-initiated depolarization of the soma occurs faster than the soma can respond (e.g., faster than the large somatic capacitance can be charged), and thus the somatic spike is delayed; further delays eventually led to failure of the somatic spike. This suggests the rate of increase may influence the efficacy with which the AIS-initiated spike helps to trigger the somatic spike and thus, analogous to the simulations above, the simultaneous scaling of multiple components helps to optimize this aspect of cell function. It is worth nothing that spike kinetics were highly similar across all measured α S RGCs (**Figure 6B**), even when, for example, axo-somatic and axo-dendritic axons were compared, suggesting that nature tries to keep activation thresholds constant and maintain response consistency across the population of α S RGCs.

#### Analysis of Model Sensitivity

Our simulation results were dependent upon the underlying anatomy of the cell as well as the distribution and kinetics of the channels used in the model. As such, we evaluated the sensitivity of the results to many different model parameters to ensure that the results were not being artificially driven (biased) by any one (or few) feature of the model. Anatomy of individual cells closely matched those of actual cells and we chose cells that spanned the full range of sizes found in nature. The consistency of results across this population suggests that the model was not inappropriately sensitive to any one specific cell size or morphological feature. Channel kinetics largely matched those developed by Fohlmeister et al. (2010) and are similar to those used previously by our group. The close match between physiological action potentials and those from the model (**Figure 6A** and **Supplementary Figure S5A**) suggests key elements of normal physiology were captured by the model, including the fact that action potentials were always initiated in the AIS in both actual and model cells. Further, similar to physiological measurements across the population of α S RGCs, the action potential kinetics of all model cells were also highly similar, suggesting physiological responses of model cells were not inappropriately sensitive to any specific aspect of the model. Nevertheless, estimates for channel densities in RGCs have varied considerably across previous studies (Fohlmeister et al., 2010; Jeng et al., 2011; Werginz et al., 2014; Guo et al., 2016) and the timing of individual spikes could vary if channel conductances were altered significantly. For example, the incorporation of both low- and high-voltage activated calcium channels into the dendrites of model cells (Margolis et al., 2010) could alter spike timing and thus their effect, especially in relationship to the effect of varying AIS properties, will need to be further investigated, including the possibility that the effects are different for ON vs. OFF sub-types.

#### α S RGC Properties Influence the Sensitivity to Artificial Stimulation

Our findings may help to improve strategies for selective targeting of specific RGC types with electric stimulation, e.g., for use in basic research studies or as part of a retinal prosthesis. Previous studies have shown that the AIS is the portion of the RGC that is most sensitive to electric stimulation (Sekirnjak et al., 2008; Fried et al., 2009) and further, that differences in AIS properties can influence sensitivity (Jeng et al., 2011). Thus, our findings that show that at any given retinal eccentricity ON-α S RGCs are slightly larger than OFF-α S RGCs (**Supplementary Figure S2**), including the fact that the AIS is longer and more distant from the soma in ON cells (**Figure 2B**), coupled with previous work that found that increases to both AIS length and distance reduce activation threshold in response to electrical stimulation (Jeng et al., 2011), suggests that at any given location ON cells have a slightly lower threshold than do OFF cells. This is important as the ability to selectively target ON vs. OFF pathways would allow natural signaling patterns to be

more accurately reproduced with electric stimulation and thus might lead to better quality percepts (Freeman et al., 2011; Werginz et al., 2015; Weiland et al., 2016). Interestingly, previous studies have reported that ON and OFF cells have similar thresholds to electric stimulation (Fried et al., 2009; Tsai et al., 2012), but such studies did not carefully account for comparisons at similar eccentricities.

Note that the ability to capitalize on these small differences in sensitivity will require knowledge of the precise location of each AIS as well as the ability to selectively target each from a nearby electrode; this could be accomplished, for example, with high-density arrays of electrodes along with detailed measurements of the responses from the array (Jepson et al., 2014). Developing strategies that are clinically relevant will also require confirmation that the AIS size differences found here in mouse α S RGCs persist in human RGCs. This may be the case however as it has already been established that ON midget and parasol cells in the human retina have larger somas and dendritic fields, raising the possibility that the AIS scaling found here will persist there as well.

The AIS size difference for ON vs. OFF RGCs could also explain earlier findings in which more complex patterns of electric stimulation were able to differentially activate the two populations (Cai et al., 2013; Twyford et al., 2014). While the mechanism underlying differential sensitivity of the two types has yet to be unequivocally identified (but see Kameneva et al., 2016; Guo et al., 2019), the sensitivity differences between ON and OFF cells persist in the presence of synaptic blockers, suggesting that the differences arise from features intrinsic to the two cell types, most likely within the AIS themselves. The improved understanding of the AIS anatomy found here may lead to a better understanding of the mechanism underlying preferential selectivity and may also help to identify stimulation paradigms that provide even higher levels of selectivity.

#### DATA AVAILABILITY STATEMENT

The raw datasets generated for this study are available on request to the corresponding author.

#### REFERENCES

#### ETHICS STATEMENT

The animal study was reviewed and approved by Institutional Animal Care and Use Committee (IACUC) of the Massachusetts General Hospital.

#### AUTHOR CONTRIBUTIONS

VR, PW, and SF conceived the study, analyzed the results, wrote the manuscript, and prepared the figures. VR performed the immunochemistry and confocal imaging experiments. PW performed the electrophysiology experiments and computational modeling.

## FUNDING

Research was supported by the Veterans Administration - Rehabilitation Research and Development Service (1I01RX001663), by the National Institute of Neurological Disorders and Stroke (U01-NS099700 and R01-NS110575), and by the Austrian Science Fund (FWF J3947).

## ACKNOWLEDGMENTS

We would like to thank Steve Massey (UT Houston) for help with immunohistochemistry, Molly McGoldrick (Boston College) for assistance with immunohistochemistry experiments, and Aditya Datye (MGH) and Hannah Potter (UMass Amherst) for assistance with image processing and analysis. We would also like to thank Russell Huber (Boston University, VA Boston), Tim McKenna (Harvard University, VA Boston) for their assistance with confocal imaging, and Brian Timko (Tufts University) for helpful discussions during the preparation of the manuscript.

#### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fncel. 2019.00436/full#supplementary-material

**Conflict of Interest:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2019 Raghuram, Werginz and Fried. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Impact of Auditory Experience on the Structural Plasticity of the AIS in the Mouse Brainstem Throughout the Lifespan

#### Eun Jung Kim1 , Chenling Feng2 , Fidel Santamaria2 and Jun Hee Kim1 \*

1 The Department of Cellular and Integrative Physiology, UT Health San Antonio, San Antonio, TX, United States, 2 The Department of Biology, University of Texas, San Antonio, TX, United States

Sound input critically influences the development and maintenance of neuronal circuits in the mammalian brain throughout life. We investigate the structural and functional plasticity of auditory neurons in response to various auditory experiences during development, adulthood, and aging. Using electrophysiology, computer simulation, and immunohistochemistry, we study the structural plasticity of the axon initial segment (AIS) in the medial nucleus of the trapezoid body (MNTB) from the auditory brainstem of the mice (either sex), in different ages and auditory environments. The structure and spatial location of the AIS of MNTB neurons depend on their functional topographic location along the tonotopic axis, aligning high- to low-frequency sound-responding neurons (HF or LF neurons). HF neurons dramatically undergo structural remodeling of the AIS throughout life. The AIS progressively shortens during development, is stabilized in adulthood, and becomes longer in aging. Sound inputs are critically associated with setting and maintaining AIS plasticity and tonotopy at various ages. Sound stimulation increases the excitability of auditory neurons. Computer simulation shows that modification of the AIS length, location, and diameter can affect firing properties of MNTB neurons in the developing brainstem. The adaptive capability of axonal structure in response to various auditory experiences at different ages suggests that sound input is important for the development and maintenance of the structural and functional properties of the auditory brain throughout life.

#### Edited by:

Dominique Debanne, INSERM U1072 Neurobiologie des canaux Ioniques et de la Synapse, France

#### Reviewed by:

Maren Engelhardt, University of Heidelberg, Germany Claire Cheetham, University of Pittsburgh, United States Matthews Grubb, King's College London, United Kingdom

> \*Correspondence: Jun Hee Kim<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Cellular Neurophysiology, a section of the journal Frontiers in Cellular Neuroscience

Received: 24 May 2019 Accepted: 25 September 2019 Published: 15 October 2019

#### Citation:

Kim EJ, Feng C, Santamaria F and Kim JH (2019) Impact of Auditory Experience on the Structural Plasticity of the AIS in the Mouse Brainstem Throughout the Lifespan. Front. Cell. Neurosci. 13:456. doi: 10.3389/fncel.2019.00456 Keywords: axon initial segment, auditory brainstem, MNTB, auditory experience, mouse

## INTRODUCTION

Continuous auditory input is necessary for the proper development and maintenance of the auditory system. Peripheral hearing deficits can occur during development (congenital deafness) and through aging (presbycusis), lead to reduced auditory input, and generate a reorganization of central auditory circuits (Butler and Lomber, 2013; Lin et al., 2014). Alterations in the structure and function of the central auditory system impair auditory processing even after peripheral sound sensitivity is restored, such as with hearing aids (Sweetow, 2005; Atcherson et al., 2015). To restore proper circuit processing, it is essential to understand how sound-evoked activity refines and modulates the structural plasticity, and thereby the functional connectivity of the central auditory system. In animal models,

sound deprivation in congenital deafness or by surgical ablation of the cochlea resulted in anatomical and functional changes in subcortical nuclei (Russell and Moore, 1995; Grande et al., 2014). In the auditory brainstem, congenital deafness is associated with a reduction in the number and size of neurons in the cochlear nucleus (Hashisaki and Rubel, 1989; Saada et al., 1996), decreased inhibitory inputs on medial superior olivary neurons (Kapfer et al., 2002; Tirko and Ryugo, 2012), and disruption of the tonotopic organization in the MNTB (von Hehn et al., 2004; Leao et al., 2006). Although the structural consequences of congenital and early-onset deafness have been studied in several animal models, the physiological consequences or homeostatic adaptation processes in the central auditory system in response to sound experiences throughout life are not yet determined.

As the key axonal domain for generating action potentials (APs), the AIS determines neuronal excitability, modulates neuronal output, and controls auditory processing along the ascending auditory pathway (Kuba et al., 2006; Gründemann and Häusser, 2010). In the chick brainstem, structural properties of the AIS depend on its tonotopic location - the spatial arrangement of where sounds of different frequencies are processed in the brain (Kuba et al., 2006, 2014). The AIS of chick neurons elongates to increase excitability when synaptic inputs are removed by cochlear ablation, suggesting a contribution of the AIS to the homeostatic control of neural activity (Kuba, 2012). In cultured hippocampal neurons, the AIS is located farther from the soma when neuronal activity is increased (Grubb and Burrone, 2010). When the AIS is located proximal to the soma, APs are easier to initiate, thus increasing neuronal excitability, whereas a more distal location reduces neuronal excitability (Grubb and Burrone, 2010; Kole and Stuart, 2012). Thus, adaptive changes in AIS structure and position have been thought to alter intrinsic excitability in a homeostatic direction (Kole and Stuart, 2012; Wefelmeyer et al., 2016). However, depending on somatodendritic morphology in various neurons, the distal shift of the AIS location theoretically reduces the threshold and rheobase of AP and promotes AP generation (Gulledge and Bravo, 2016; Kole and Brette, 2018). Importantly, the structural properties and plasticity of the AIS vary among individual neurons across brain regions (Chand et al., 2015; Höfflin et al., 2017). Although the structural plasticity of the AIS has been studied in the chick brainstem, concerning the heterogeneity of AIS structure across species, it is important to explore AIS plasticity in response to auditory input within a mammalian system. Understanding how in vivo auditory experience alters the AIS of auditory neurons throughout the lifespan of the mammalian brain from development to aging is critically important for developing targeted strategies to remedy potential long-term deficits of the auditory brain at specific ages.

Here, we investigated the spatial distribution and location of the AIS in the mouse auditory brainstem at different ages, focusing on AIS length, position, and tonotopic differentiation. Sound modifications alter the structural properties of AIS and affect tonotopy in the auditory brainstem. Electrophysiological and computer modeling studies indicate that there is a relationship between AIS structural plasticity and neuronal excitability in the MNTB. The results demonstrate that structural plasticity of the AIS in auditory neurons is influenced by soundevoked activity.

#### MATERIALS AND METHODS

#### Animals

Either sex of C57BL/6 mice and whirler mice with a C57BL/6 background were used in accordance with the guidelines approved by the University of Texas Health Science Center, San Antonio (UTHSCSA) Institutional Animal Care and Use Committee protocols. All mice were housed in the institutional animal facilities on a 12-h light/dark cycle.

## Sound Modification in Mice

Mice in the sound stimulation group were given acoustic stimulation with 80dB single tones of 16 kHz for 7 days, 3 h per day from P13 to P19 (in postnatal development) or from P38 to P44 (in early adulthood) in a sound attenuation chamber (Med Associates, Albans, VT). Acoustic stimuli were generated by an auditory evoked potentials workstation [Tucker-Davis Technologies (TDT), Alachua, FL]. The signals consisted of a series of amplitude-modulated square waves (duration 0.1 ms, repeat rate 16/s) through TDT multifield magnetic speakers. Whirler mice, a model of congenital deafness (Xu et al., 2017), were used for the sound deprivation model and were provided by Dr. M. A. Bhat's laboratory (UTHSCSA). As a model of hearing loss during adulthood, mice (at P70) were exposed to highpressure air (13 psi shock wave, ∼183 dB) generated by a large (17-inch diameter) compressed-air shock tube (Cho et al., 2013; Choi et al., 2015). ABR test and immunostaining were performed in 1 week after the blast exposure (at P80).

#### Slice Preparation

After rapid decapitation, the brains were quickly removed from the skull and immediately immersed in ice-cold low-calcium artificial cerebrospinal fluid (aCSF) containing (in mM): 125 NaCl, 2.5 KCl, 3 MgCl2, 0.1 CaCl2, 25 glucose, 25 NaHCO3, 1.25 NaH2PO4, pH 7.3–7.4 bubbled with carbogen (95% O2, 5% CO2; osmolarity of 310–320 mOsm). For c-fos staining, animal was sacrificed within 1 h after sound stimulation. Then, transverse 200-µm-thick brainstem slices containing the MNTB were collected using a Vibratome (VT1200S, Leica, Germany). Collected slices were prepared for electrophysiology or immunohistochemistry experiments.

#### Electrophysiology

After vibratome sectioning, slices were further incubated in a chamber containing normal aCSF bubbled with carbogen at 35◦C for 30 min and then were kept at room temperature. The normal aCSF was the same as the low-calcium aCSF, except that 3 mM MgCl2 and 0.1 mM CaCl2 were replaced with 1 mM MgCl2 and 2 mM CaCl2. Whole-cell patch-clamp recording was carried out on postsynaptic principal neurons in the MNTB nuclei at room temperature (∼24◦C). Action potentials (APs) were recorded in normal aCSF using the voltage or current-clamp mode of the EPC-10 (HEKA Electronik, Lambrecht/Pfalz, Germany). The

pipettes were filled with an internal solution containing (in mM) 125 K-gluconate, 20 KCl, 5 Na2-phosphocreatine, 10 HEPES, 4 Mg-ATP, 0.2 EGTA, and 0.3 GTP, pH adjusted to 7.3 with KOH. The holding potential was −70 mV in the voltage-clamp mode. Current-clamp protocols were 200 ms in duration with current steps from −50 to 250 pA (50 pA increments). Patch electrodes had resistances of 4–5 M . Series resistance was < 20 M , with 80% compensation. In our solution composition (extracellular K + concentration of 2.5 mM and intracellular K+ concentration of 150 mM), the calculated equilibrium potential for potassium is around −106 mV, and liquid junction potential is ∼20 mV. Membrane potentials were not corrected for this constant liquid junction potential between the extracellular and pipette solution. In the current clamp recordings, the baseline potential was around −70 mV with ∼ −20 pA injection, and the amplitude of action potential was defined as the potential between the baseline potential to the peak. The threshold of action potential (AP) was determined by the point where dV/dt exceeds 10V/s and the amplitude of AP from this threshold to the AP peak in the plot of dV/dt and voltage. Data were analyzed and displayed with Igor Pro (Wavemetrics, Lake Oswego, OR, United States).

#### Immunohistochemistry

Brainstem slices were fixed with 4% (w/v) paraformaldehyde in phosphate-buffered saline (PBS) for 20 min and were washed with PBS three times. Free-floating slices were blocked in 4% goat serum and 0.3% (w/v) Triton X-100, 0.1% Tween 20 in PBS for a 1 h and then were incubated with primary antibody overnight at 4◦C. The following primary antibodies were used: rabbit anti β4 spectrin (1:250) as previously described (Saifetiarova et al., 2018), mouse monoclonal anti-MAP2 (1:200; Millipore Cat# MAB3418, RRID:AB94856), mouse monoclonal anti-ankyrinG (AnkG; 1:100; UC Davis/NIH NeuroMab Facility Cat# 75-146, RRID:AB\_10673030), mouse monoclonal anti-Kv1.2 (1:250; UC Davis/NIH NeuroMab Facility Cat# 75-008, RRID:AB\_10673030), mouse monoclonal anti-Na-pan (1:100; Sigma-Aldrich Cat# S8809, RRID:AB\_10673030), and rabbit polyclonal anti-c-Fos (1:100; Synaptic System Cat# 226003). After 3 washes with PBS containing 0.1% Tween 20, slices were incubated with different Alexa-488 goat anti-mouse IgG1 or 568 goat anti-mouse IgG2b or 568 goat anti-rabbit or 647 goat antiguinea pig secondary antibodies (1:1000; Invitrogen) accordingly for 2 h at room temperature. Slices were then rinsed with PBS containing 0.1% Tween 20 and were coverslipped using the mounting medium (Vectashield; Vector Laboratories). Stained slices were viewed on a confocal laser-scanning microscope (Carl Zeiss LSM-710) at 488, 568, and 633 nm using a pinhole of 1 AU and 20x (0.8 NA), 40x (oil-immersion, 1.30 NA) objectives. Z-stack images were acquired at a digital size of 1,024 × 1,024 pixels with optical section separation (z interval) of 0.5 um. The images were imported into Fiji (Image J, Schindelin et al., 2012), ZEN (Carl Zeiss) and Amira software (FEI, Netherlands) for the analysis. To analyze AIS structure, either the 2D compressed or the 3D reconstructed Z-stack confocal images of MNTB neurons were utilized. Most analyses were performed using the 2D compressed images because there was no significant difference between the results from the 2D- and 3D- analysis (the AIS from normal mice, 16.89 ± 0.245, n = 18 for 2D vs. 17.17 ± 0.36, n = 18 for 3D, p = 0.5245). The AIS was determined as the axonal domain where the fluorescence intensity of β4 spectrin was > 10% of the peak signal. We did not perform background subtraction of images, because the signal of β4 spectrin is very specific to axonal domains including the AIS. For the analysis of AIS structure, we utilized a line profile of AIS from the end of distal edge of the AIS, which was defined by a paranodal protein, caspr, to the end of the proximal edge of the AIS, where the signal of β4 spectrin was < 10% of the peak signal. 3D image (Amira) confirmed the AIS structure including length, volume, and diameter obtained from 2D images by Fiji or ZEN software. For analyzing the AIS with a strong curvature, we utilized the segmental line profile in the ImageJ. For c-fos analysis, the double staining against MAP2 and c-Fos were performed from normal hearing group (P20) and sound stimulation group (P20) simultaneously to exclude the difference of fluorescence intensity. MAP2- and c-fos-positive cells were counted using cell counter plugin of Fiji software. Only the cells with c-fos-positive nucleus were counted and the constant threshold level of fluorescence intensity was used in each slices. The percentage of c-fos-positive cells were calculated by dividing the number of MAP2 -positive MNTB neurons in each slices.

#### In vivo Auditory Brain Stem Response (ABR) Test

ABR recordings were conducted as described previously (Kim et al., 2013). Briefly, mice were anesthetized with 4% isoflurane and maintained with 2% isoflurane during recording (1 l/min O2 flow rate). During ABR recordings, the body temperature was maintained between 35 and 37◦C using a non-electric heating pad and equally controlled in different experimental groups. ABR recordings were performed in a sound attenuation chamber (Med Associates, Albans, VT). Subdermal needle electrodes (Rochester Electro-Medical, Lutz, FL, United States) were placed on the top of the head, ipsilateral mastoid, and contralateral mastoid as the active, reference, and ground electrodes, respectively. The signal differences in the ABRs between the vertex and the mastoid electrodes were amplified and filtered (100–5,000 Hz). Acoustic stimuli were generated by an auditory evoked potentials workstation [Tucker-Davis Technologies (TDT), Alachua, FL, United States]. Closed-field click stimuli were presented to the left ear. The signals consisted of a series of amplitudemodulated square waves (duration 0.1 ms, repeat rate 16/s) through TDT multifield magnetic speakers. The sound stimuli were delivered through a 10-cm plastic tube (Tygon; 3.2-mm outer diameter) at a repeat rate of 16/s. Sound intensities ranged from 90 to 20 dB, with 5-dB decrements, and responses to 512 sweeps were averaged.

#### Computer Simulation

A compartmental model of an MNTB cell was implemented based on previous publications (Leão et al., 2008; Wang et al., 1998). The structure of the cell consisted of one primary dendrite with a length of 40 µm and a uniform diameter of 3 µm. The dendrite was connected to a spherical soma with a diameter of 30 µm. The axonal segment was divided between the hillock and the initial segment (AIS), both with a diameter of 2 µm. The length of the hillock was 16 µm and the length of the AIS was 13 µm (**Figure 3**). The model had active conductance. The density and kinetics are described in the following **Tables 1**, **2**.

Simulations consisted of injecting constant current to the soma and quantifying the shape and number of spikes generated over a period of 700 ms. The model was implemented in Python-NEURON (ver 7.3) and analyzed with custom routines in Matlab (Mathworks, Natick, MA, United States).

#### Statistical Analysis

fncel-13-00456 October 14, 2019 Time: 15:14 # 4

All statistical analyses were performed in Prism (GraphPad Software). Normality of datasets was analyzed using the D'Agostino and Pearson's omnibus test. Parametric or nonparametric tests were carried out accordingly. α values were set to 0.05, and all comparisons were two-tailed. To compare two groups, unpaired t-test or Mann–Whitney U test was carried out, respectively. For three or more groups, the Kruskal–Wallis test with Dunn's post hoc test or one-way ANOVA with Turkey's multiple comparison test was used. For the comparison of the AIS of HF and LF MNTB neurons from three groups, we analyzed the mean value from individual animal as the experimental unit, and utilized 2-way ANOVA with Sidak's multiple comparison test. The significance was determined at P-values < 0.05. To analyze the tonotopy of AIS structure, we analyzed the mean value from individual animal as the experimental unit using 2 way ANOVA with Sidak's multiple comparison test. Data were shown as the mean ± standard error of the mean (s.e.m.) with n values representing the number of animals per experimental group or the number of neurons per group where indicated.

TABLE 2 | Kinetics of active conductance.

In box and whisker plot, boxes indicate 25–75% interquartile range and horizontal lines in boxes indicate the median. Whiskers show 5% ∼ 95% range and dots show outliers that reside outside the whisker range.

## RESULTS

#### The AIS of Auditory Neurons Are Differentiated by Structure and Function Along the Tonotopic Axis in the Mouse MNTB

We characterized structural properties of the AIS in the mouse MNTB (P20) using β4 spectrin immunostaining, which was present along the AIS and co-localized with ankyrin G, represented the AIS of MNTB neurons (**Figure 1A**). β4 spectrin was also co-localized with voltage-activated Na+ and K + channels at the AIS of MNTB neurons (**Figure 1B**). To analyze AIS structure, either the 2D compressed or the 3D reconstructed Z-stack confocal images of MNTB neurons were utilized (**Figure 1C**). We selected the AIS with the distinct distal end, which was defined by a paradonal protein, Caspr (Xu et al., 2017), and with the proximal end, where β4 spectrin intensity was < 10% of the peak intensity, for the analysis of AIS structure (**Figure 1D**). To quantify AIS length and position, we measured the length of the region, where the intensity of β4 spectrin was > 10% of the peak signal, and the distance from soma to the proximal end of the AIS (**Figures 1E,F**).

To quantify AIS length and location along the medial-lateral axis in the MNTB from individual mouse brainstems (n = 6 mice at P20), the MNTB was proportionally defined by the percent of the total distance from medial to lateral edges of the MNTB, where 0% represents the medial edge and 100% represents the lateral edge (**Figure 2A**). High-frequency sound responding neurons (HF neurons) were defined as neurons located within 0–30% of the total distance from medial to lateral edges, and low-frequency responding neurons (LF neurons) were defined as neurons located within 70–100% of the distance from the medial to the lateral edge (**Figures 2A,B**). AIS length was gradually increased from medial to lateral MNTB neurons. Plotting AIS length against the proportional distance showed the distribution of AIS length as estimated by linear regression analysis (R 2 = 0.293, n = 24 cells, p = 0.0063; **Figure 2C**). AIS length was significantly shorter in HF neurons than LF neurons. AIS length in HF and LF neurons was 15.1 ± 0.23 µm (n = 79 cells) and 19.9 ± 0.37 µm (n = 64 cells), respectively (p < 0.0001, Mann–Whitney U test; **Figure 2D**). The spatial location of the AIS was quantified by the distance between the proximal end of the AIS and the soma, which was gradually decreased along the medial-lateral axis with a linear correlation (R 2 = 0.342, n = 22 cells, p = 0.0042; **Figure 2E**). The AIS of HF neurons were located more distally from the soma than the AIS of LF neurons, which were located proximal to the soma. Thus, AIS distance was significantly longer in HF than LF neurons (10.4 ± 0.34 µm, n = 63 cells in HF neurons vs. 6.2 ± 0.24 µm, n = 74 cells in LF neurons, p < 0.0001, Mann–Whitney U-test; **Figure 2F**).

images (left) and 3D reconstructed Z-stack images using Zen (middle) and Amir (right) of the same MNTB neuron immunostained with MAP2, β4 spectrin. Yellow arrows indicate AIS length. (D) Immunostaining of the MNTB with β4 spectrin and caspr. White arrows indicate the AIS proximal and distal ends. Capsr (magenta, yellow arrows) was located at the next of the AIS distal end. (E) (Top) Diagram of the MNTB neuron with soma (green), AIS (cyan), and paranode (magenta) shows the AIS length and distance from the soma. (Bottom) MNTB neurons immunotained with MAP2 (green), β4 spectrin (magenta) and caspr (cyan). A dotted line indicates the length (L, white) and distance (D, yellow) of AIS structure. (F) The intensity profiles of β4 spectrin (magenta) and caspr (cyan) immunostaining along the AIS of MNTB neuron indicated by length and distance in E.

To examine how structural differentiation of the AIS along the tonotopic map influences intrinsic properties of MNTB neurons, we recorded action potentials from HF (n = 11 cells from 5 mice) and LF neurons (n = 7 cells from 3 mice), which were evoked by step-current injections in whole-cell recording (**Figure 2G**). There was no difference in the threshold and half-width of APs, and the rheobase current (threshold: -45.3 ± 2.53 mV in HF neurons vs. -48.3 ± 0.74 mV in LF neurons, p = 0.3189; halfwidth: 0.5 ± 0.04 ms in HF neurons vs. 0.5 ± 0.03 ms in LF neurons, p = 0.8917, the rheobase current: 106.5 ± 11.47 pA in HF neurons vs. 107.1 ± 27.66 pA in LF neurons, p = 0.8573, Mann–Whitney U test; **Figures 2H–J**). However, HF neurons displayed smaller APs with amplitudes of 94.1 ± 2.66 mV (n = 10 cells), while APs from LF neurons had larger amplitudes of 102.8 ± 1.12 mV (n = 7 cells, p = 0.0250, Mann–Whitney U-test; **Figure 2K**). Taken together, HF neurons with a shorter AIS distally located from the soma displayed a smaller AP, whereas LF neurons with a longer AIS proximally located from the soma exhibited a larger AP in the MNTB. This indicates that MNTB neurons have structural and functional differentiation of the AIS along the tonotopic axis, which is associated with differentiation of their firing properties.

### AIS Tonotopy Is Initiated After Hearing Onset, Progressively Developed, and Stabilized in Adulthood

In hearing mammals, a rough tonotopy is established before hearing onset, then later refined by sound-evoked activity, generating the sophisticated tonotopy of the mature

system (Kandler et al., 2009). To determine when tonotopic differentiation of AIS structure initiates in the mouse auditory brainstem, we examined AIS length and location throughout postnatal development into early adulthood (P9: n = 4 mice, P16: n = 3 mice, P40: 4 mice, P60: 3 mice and P80: 4 mice). At P9 (before hearing onset at P12), the AIS was distinctly detectable and its length was similar in HF and LF neurons (20.2 ± 0.40 mm, n = 61 cells in HF neurons vs. 20.1 ± 0.39 mm, n = 47 cells in LF neurons, p = 0.8867, Mann–Whitney U test; **Figures 3A,B**). The AIS from both groups was closely located to the soma, and the distance between the proximal end and the soma was 3.8 ± 0.24 mm (n = 61 cells) in HF neurons and 4.7 ± 0.37 mm (n = 45 cells) in LF neurons (p = 0.0505, Mann–Whitney U test; **Figures 3A,C**). There was no significant difference in AIS length and location between HF and LF neurons at P9. At P16 after hearing onset, a significant difference in AIS length and location between HF and LF neurons was observed. The AIS of HF neurons was significantly shorter than the AIS of LF neurons (16.3 ± 0.30 mm, n = 84 cells in HF neurons vs. 20.2 ± 0.58 mm, n = 59 cells in LF neurons, p < 0.0001, unpaired t-test; **Figures 3A,B**). In parallel with changes in length, the AIS of HF neurons moved further away from the soma during postnatal development, resulting in a significant difference between HF and LF neurons in the distance

Kim et al. Structural Plasticity of the AIS

of the AIS from the soma at P16 (10.69 ± 0.64 mm, n = 44 cells in HF neurons vs. 6.9 ± 0.31 mm, n = 33 cells in LF neurons, p < 0.0001, unpaired t-test; **Figures 3A,C**). The refinement of AIS length and location was progressively enhanced through P40 and stabilized in adulthood at P80. Notably, the progressive shortening of the AIS occurred specifically in HF neurons during postnatal development. The AIS of HF neurons was shortened by ∼29.30% by P40 (from P9 to P40, p < 0.001, Kruskal–Wallis test with Dunn's post hoc test; **Figure 3D**). However, there was no significant change in the AIS length of LF neurons, which maintained AIS length throughout adulthood from when the AIS began to be detected at an early postnatal age (from p9 to P80, p = 0.2264, Kruskal–Wallis test with Dunn's post hoc test; **Figure 3D**). The AIS of both HF and LF neurons were relocated more distally from the soma during postnatal development and maintained their spatial distance from the soma in adulthood (HF neurons from P9 to P40, p < 0.0001 and LF neurons from P9 to P40, p < 0.001, Kruskal–Wallis test with Dunn's post hoc test; **Figure 3E**). However, the distance of the AIS from the soma was much longer in HF neurons than LF neurons, thus resulting in the tonotopic difference of AIS location in the MNTB. The AIS of HF neurons was located distally, whereas the LF neuron AIS was located closer to the soma. The refinement of AIS length and location progressively developed in postnatal ages. Around P40, AIS length and distance reached a plateau and then stabilized in adulthood, maintaining tonotopic differentiation. The AIS of HF neurons preferentially and dynamically underwent developmental refinement, resulting in the tonotopy of the AIS in the auditory brainstem.

## Sound Modification During Early Postnatal Development Influences AIS Length, Location, and Tonotopic Differentiation

The structural refinement of the AIS was initiated and progressively developed after hearing onset. To determine whether in vivo auditory experience contributes to the initiation and development of structural plasticity and tonotopy of the AIS in the auditory nervous system, we tested the effects of sound deprivation or stimulation on AIS plasticity in the MNTB neurons compared to the age-matched normal hearing mice during postnatal development. As a model of sound deprivation, we utilized a mouse with congenital deafness (hereafter, Deaf) caused by a stereocilia defect due to a mutation of whirlin (DFNB3, Lane, 1963; Xu et al., 2017). For mild sound stimulation, we established a mouse model (hereafter, Sound) exposed to additional sound inputs (80 dB, 16kHz, 3 h./day for 7 days) immediately after hearing onset from P13 to P19, which were chosen to determine whether early sound exposure sped developmental changes in MNTB neurons. We utilized 16 kHz tone sound to target HF neurons and determine if the AIS length during development would be altered by sound experience, because HF neurons displayed a change in AIS length during development. In vivo ABRs (n = 11 mice in Normal, n = 12 mice in Sound) and c-fos staining of the MNTB (n = 3 mice for each group) indicated that neuronal activity was significantly increased in the MNTB of the auditory brainstem in the Sound. The amplitude of ABRs was significantly increased in the Sound (wave I: 0.8 ± 0.13 mV in normal mice vs. 1.8 ± 0.13 mV in Sound, p = 0.0003; wave II: 1.3 ± 0.15 mV in Normal vs. 2.6 ± 0.35 mV in Sound, p = 0.0047; wave III: 0.7 ± 0.24 mV in Normal vs. 2.4 ± 0.42 mV in Sound, p = 0.0052, unpaired t-test; **Figures 4A–C**). In the Sound, the number of c-fos + cells was significantly increased (31.7 ± 3.55% in Normal vs. 90.1 ± 3.83% in Sound, p = 0.0286, unpaired t-test; **Figure 4C**). Sound deprivation and stimulation significantly altered AIS length and location in HF neurons (**Figures 4D,E**). Compared to mice in a normal auditory environment (n = 6 mice), AIS length was significantly longer as a result of sound deprivation in the Deaf (n = 5 mice), whereas AIS length was significantly shorter in the Sound (n = 8 mice) compared to the Normal (21.5 ± 0.52 mm, n = 67 cells in Deaf vs. 15.1 ± 0.23 mm, n = 79 cells in Normal vs. 13.4 ± 0.20 mm, n = 94 cells in Sound, p < 0.0001, oneway ANOVA with Turkey's multiple comparison test; **Figure 4F**). In the developmental time course of AIS length (as shown in **Figure 3D**), sound deprivation seemed to inhibit the shortening of AIS, while sound stimulation enhanced the shortening of AIS specifically in HF neurons (**Figure 4G**). However, LF neurons did not exhibit any changes in AIS length in response to either sound deprivation or stimulation (20.9 ± 0.57 mm, n = 50 cells in Deaf vs. 19.9 ± 0.37 mm, n = 64 cells in Normal vs. 19.7 ± 0.34 mm, n = 54 cells in Sound, p = 0.3188, Kruskal–Wallis with Dunn's post hoc test; **Figure 4H**). The AIS structural plasticity of the HF neurons to sound inputs consequently influenced the tonotopic differentiation of AIS length. The analysis of AIS length of HF and LF neurons using animal as the experimental unit indicated the MNTB did not display the tonotopic differentiation of AIS length in Deaf (n = 5 mice), whereas in Sound, the tonotopy of AIS length was enhanced (n = 8 mice, 2-way ANOVA with Sidak's multiple comparison test).

In terms of AIS location, the AIS of HF neurons in the Deaf and the Sound were significantly different from the Normal (13.7 ± 0.71 mm, n = 54 cells in Deaf vs. 10.4 ± 0.34 mm, n = 63 cells in Normal vs. 5.9 ± 0.23 mm, n = 57 cells in Sound, p < 0.001, Kruskal–Wallis test with Dunn's post hoc test; **Figure 4I**). The AIS in Deaf mice is more distally located from the soma compared to those in the Normal. However, in the Sound, the location of the AIS was more proximal to the soma. This proximal shift of the AIS position resulted in an increase in the spatial distance between the proximal edge of AIS and the soma in the Deaf, whereas there was a significant decrease in this distance in the Sound compared to the normal age-matched mice (as shown in **Figure 3E**) in the natural auditory environment (**Figure 4J**). AIS distance was increased in LF neurons of the Deaf, but no significant change was observed in the Sound (6.2 ± 0.24 mm, n = 74 in Normal vs. either 10.1 ± 0.51 mm, n = 43 cells in Deaf, p < 0.001, or 6.6 ± 0.38 mm, n = 67 cells in Sound, p > 0.05, Kruskal–Wallis test with Dunn's post hoc test; **Figure 4K**). In the analysis of AIS location of HF and LF neurons using animal as the experimental unit, there was no tonotopic differentiation of AIS location in the Sound whereas in Deaf the tonotopy of AIS location

FIGURE 4 | Sound deprivation and stimulation impact developmental refinement of AIS structure, position, and tonotopy in the MNTB. (A) Examples of the ABRs of normal (black) and sound stimulated mice (blue) are recorded in response to a click stimulus of sound (80 and 85 dB). Roman numerals indicate peak waves I to IV (B) Summary of the amplitude of waves I to III in response to click stimulus (85 dB) in normal and sound stimulated mice at P20. ∗∗p < 0.01, unpaired t-test. (C) Immunostaining of the MNTB with c-fos and MAP2 from normal and sound stimulated mouse with the tonotopic axis (M: medial MNTB, L: lateral MNTB). Note, the number of c-fos positive cells is increased in the MNTB of the sound stimulation group. (D) HF neurons, immunostained with MAP2 and β4 spectrin from deaf mice (Deaf), age-matched normal hearing mice (Normal), and sound stimulation group (Sound). The dotted line and white arrow indicate AIS length and distance, respectively. (E) Diagram of AIS length and location in HF neurons from Deaf (red), Normal (black), and Sound (blue). (F) Summary of AIS length in HF neurons from Deaf, Normal, and Sound at P20. ∗∗∗p < 0.0001, one-way ANOVA with Turkey's multiple comparison test. (G) The plot of AIS length of HF neurons from normal mice from P9 to P80 (black, as shown in Figure 3D), and those from Deaf and Sound at P20. (H) Tonotopic differentiation of AIS length between HF and LF neurons in Deaf, Normal, and Sound. ∗∗∗p < 0.0001, Mann–Whitney U test for HF and LF neurons; ns, non-significant, Kruskal–Wallis with Dunn's post hoc test for AIS length of LF neurons from Deaf, Normal, and Sound. (I) Summary of AIS location, as defined by the distance from the soma, of HF neurons in Deaf, Normal, and Sound at P20. ∗∗∗p < 0.001, Kruskal–Wallis with Dunn's post hoc test. (J) The plot of AIS location changes during postnatal development in normal mice from P9 to P80 (black, as shown in Figure 3E), and those in Deaf and Sound at P20. (K) Tonotopic differentiation of AIS location between HF and LF neurons in Deaf, Normal, and Sound at P20. ∗∗∗p < 0.0001, either Mann–Whitney U test or unpaired t-test for HF and LF neurons, Kruskal–Wallis test with Dunn's post hoc test for AIS distance of LF neurons from Deaf, Normal, and Sound.

existed (n = 8 mice vs. n = 6 mice for Normal, and n = 5 mice for Deaf groups, 2-way ANOVA with Sidak's multiple comparison test). These results indicate that excessively altered sound inputs in different auditory environments critically impact AIS structure, location, and tonotopy in the mouse auditory brainstem during development.

## After the Stabilization of AIS Structure in Adulthood, Alteration of Sound Input Disrupts the Structural and Spatial Stability of the AIS Tonotopy

We next questioned whether auditory neurons maintained structural plasticity of the AIS following changes in the auditory environment in adulthood, even after AIS length, location, and tonotopy are stabilized. We tested adult mice (4 mice per each group) that experienced sudden hearing loss after blast exposure (Choi et al., 2015, see in method section) and those that experienced additional mild sound stimulation for a week (16kHz, 3 h/day, for 7 days). The AIS of HF neurons was significantly elongated in adult mice that experienced hearing loss (at P80), comparing to age-matched normal mice. Interestingly, there was no alteration in AIS length in the Sound (14.2 ± 0.30 mm, n = 66 cells in the Normal vs. either 19.0 ± 3.53 mm, n = 64 cells in hearing loss, p < 0.001 or 14.1 ± 0.34 mm, n = 57 cells in Sound, p > 0.05, Kruskal– Wallis test with Dunn's post hoc test; **Figures 5A,B**). In the developmental plot of AIS length (as shown in **Figure 3D**), the length at around P40 reached a steady state of ∼15 mm, which could be the maximum shortening of AIS during development (**Figure 5C**). Thus, further mild sound stimulation cannot enhance the shortening of the AIS beyond this plateau of AIS length after development. Consistent with the results seen in postnatal development, AIS length in LF neurons was not affected by either hearing loss or the Sound in adulthood (20.8 ± 0.41 mm, n = 55 cells in hearing loss vs. 19.7 ± 0.43 mm, n = 57 cells in Normal vs. 20.4 ± 0.42 mm, n = 56 cells in Sound; p = 0.2092, Kruskal–Wallis test with Dunn's post hoc test; **Figure 5D**). Thus, elongated AIS specifically in HF neurons in the hearing loss model resulted in the loss of tonotopic segregation of AIS length in the MNTB (n = 4 mice for each group, 2-way ANOVA with Sidak's multiple comparison test; **Figure 5D**).

AIS location of HF neurons in the hearing loss model was not changed, whereas the AIS was relocated close to the soma in the Sound, compared to the Normal (12.3 ± 0.62 mm, n = 40 cells in the Normal vs. either 10.6 ± 0.57 mm, n = 53 cells in hearing loss, p > 0.05 or 6.1 ± 0.32 mm, n = 37 cells in the Sound, p < 0.0001, one-way ANOVA with Turkey's multiple comparison test; **Figure 5E**). It is also interpreted that adult HF neurons have reached a maximum distance between the AIS and the soma (∼13 mm; **Figure 5F**). This plateau of the developmental plot of the spatial distance between the AIS and soma might be the limit for the physical distance to generate APs by the integration of somatic and dendritic signals (Hamada et al., 2016). AIS location of LH neurons was dynamically altered in the Sound and affected the tonotopy of AIS location (2-way ANOVA with Sidak's multiple comparison test; **Figure 5G**). The results demonstrated that, after developmental refinement, AIS plasticity in adulthood following auditory experience occurred only within the structural and spatial boundaries of the AIS.

## AIS Structural Alterations Are Distinctively Observed in Age-Related Hearing Loss

Age-related hearing loss critically changes auditory brain structure in humans (Lin et al., 2014; Peelle et al., 2011). In the elderly, a decline in hearing sensitivity is one prominent phenomenon of aging. Thus, we examined how age-related hearing loss impacts AIS structure in the auditory nervous system using aged mice (14-month-old; 14M, C57BL/6 mice), which showed an elevated threshold and reduced amplitude of ABRs (Hunter and Willott, 1987). In aged mice (n = 4 mice), AIS length in HF neurons was significantly increased, compared to those in normal adult mice (14.5 ± 0.29 mm, n = 96 cells in adult mice at P80, as shown **Figure 3** vs. 19.9 ± 0.59 mm, n = 60 cells in aged mice (14M), p < 0.0001, Mann–Whitney U-test; **Figures 6A,B**). However, LF neurons in aged mice did not show any difference in AIS length compared to normal adult mice (19.7 ± 0.36 mm, n = 84 cells in adult mice vs. 20.9 ± 0.53 mm, n = 57 cells in aged mice, p = 0.0745, Mann– Whitney U-test; **Figure 6B**). Although there was a significant difference in AIS length between HF and LF neurons, this elongated AIS of HF neurons lessened the tonotopic segregation of AIS length in aged mice (HF vs. LF neurons of either adult mice, p < 0.0001 or aged mice, p = 0.0669, Mann–Whitney U-test; **Figure 6C**).