All the animal experiments were performed following institutional and governmental regulations approved by the Stanford University Institutional Animal Care and Use Committee. A triple transgenic mouse line was generated by systematically crossing three lines: Ai14-tdTomato (Jax:007908) mice were crossed with Bhlhb5-cre mice, a neuronal-specific transcriptional factor (Lu et al., 2011). These mice were subsequently crossed with peripherin (Prph)-GFP mice (McLenachan et al., 2008; Huang et al., 2014) to generate triple transgenic Ai14-tdTomato, Bhlhb5-cre, Prph-GFP mice. Peripherin is a type III intermediate filament protein expressed in SGNIIs (Hafidi et al., 1993). In this scheme, SGNI cells are labeled in red, and SGNII is labeled in red and green. We have used a similar approach to create the Lmx1a reporter line. We have crossed a Lmx1a-cre (Chizhikov et al., 2010) to Ai14-tdTomato and Prph-GFP mouse line.

For single-cell experiments, 4–6 of each of the postnatal ages P3, P8 or P12 cochleae were incubated in digestion solution [50 μM kynurenic acid (Sigma–Aldrich, K3375), 10 mM MgCl₂, 10 mM glucose in MEM Hanks (Life Technologies, 11575-032)] with 50 μg/ml collagenase (Roche, 10269638001) and 6 μg/ml DNAseI (Worthington, LS002004) for 15 min at 37°C with continuous shaking at 50 rpm (Excella E24 Incubator Shaker Series, New Brunswick Scientific). Tissue was dissociated with gentle pipetting four to six times during digestion. Subsequently, trypsin (Gibco, 15090046) was added to a final concentration of 0.05%, and tissues were and incubate for another 15 min at 37°C and 50 rpm. For P12 cochleae, we replaced trypsin with milder recombinant enzyme 0.05% TrypLE Select (Gibco, A12177-01) for better cell viability. After digestion, the cell suspension was placed on ice and remaining clumps were dissociated by pipetting. The enzymatic digestion was stopped using the fetal bovine serum. The samples were centrifuged at 0.8× g for 5 min at 4°C, and cells were resuspended in 500 μl HBSS (Hyclone, ADD20159) and passed through a 35 μm cell strainer (Corning, 352235) and used directly for fluorescence-activated cell sorting (FACS) analysis or culture.

To prepare neuronal cultures, the cells were resuspended in Neurobasal-A media supplemented with glutamax (Gibco, 35050079), 1× B27 (Gibco, 17504-044), 10 ng/ml BDNF (Sigma, B3795) and 10 ng/ml NT-3 (Sigma, N1905), and cultured overnight on 0.5 mg/ml poly-D-lysine (Sigma, P6407) coated coverslip in a 35 mm cell culture dish.

Cells cultured overnight were fixed with 4% paraformaldehyde in PBS for 30 min at room temperature, then were washed three times for 10 min in room temperature PBS. Cells were blocked with 5% BSA/0.5% Triton-X 100/PBS for 1 h at room temperature, then washed three times in PBS. Cells were incubated overnight with the TUJ1 antibody (BioLegend, 801202) at a 1:500 dilution at 4°C, then washed three times with 0.1% Tween20 in PBS for 10 min at room temperature, before incubating with secondary antibody for 1 h and repeating wash steps. Slides were mounted with anti-fade mounting media with DAPI (Invitrogen, 1010789). Cells were manually counted from different areas on coverslip under a 20× fluorescent microscope.

Cochleae were dissected out from triple transgenic animals and enzymatically dissociated as described in the cochlea dissociation section. Cells were then stained with Sytox red (Life Technologies, S34859) and sorted on the FACS Aida and FACS Falstaff (BD Biosciences) at the Stanford FACS core facility. Cells debris and dead cells were removed by gating forward scatter area (FSC-A) and side scatter area (SSC-A; Supplementary Figures S1D–H for FACS gating strategies). Finally, tdTomato and GFP positive cells were gated and high tomato positive cells were sorted into 96-wells plate, with each well, containing 5 μl of 2× reaction mix (Invitrogen, CellsDirect, 1753-500) mixed with 0.05 units of SUPERase-In RNase inhibitor (Ambion, PN AM2696) and stored at −80°C until use. The total time from animal sacrifice to single-cell sorting was ~2.5 h. A subset of cells was always kept for culture to ensure minimal cell stress to the cells being analyzed by single-neuron qRT-PCR.

Single-neuron multiplex qRT-PCR assays were performed on sorted cells following the manufacturer’s guidelines (Fluidigm manual-PN 68000088 L) and as previously described (Durruthy-Durruthy et al., 2014). Briefly, each cell was placed in a well containing CellsDirect reagents (Invitrogen, CellsDirect, 11753-500) to isolate RNA. RNA was then transcribed to cDNA, and specific target genes were pre-amplified with one step PCR using SuperScript III RT Platinum Taq Mix and 500 nM primer (DELTAgene). Samples were treated with ExoI (NEB, M0293L) to cleave off single-stranded DNA. Exo-treated samples were diluted five times with nuclease-free water. Samples were then prepared for qRT-PCR analysis as per manufacturer specifications (Fluidigm, 85000736). qRT-PCR experiments were performed on the Biomark HD (Fluidigm manual, PN 68000088 L1) with pre-defined protocol GE96.96 Fast PCR+Melt v2.PCI for 30 cycles using the 96.96 dynamic arrays integrated fluidic circuit chip (IFC, Fluidigm). Data are available in Supplementary Table S1. For P3, the data represent 203 single cells aggregated from three independent runs. For P8, the data represent 383 single cells aggregated from seven independent runs, and for P12, data represents 230 single cells aggregated from three runs. Each run consists of four to six pups pooled from a litter.

A series of preliminary experiments were conducted to validate that: (a) the primers amplify single amplicons in the expected size range; (b) the target mRNA is indeed expressed in neonatal and young cochlea; and (c) to determine the limit of detection (LOD), which is the cycle threshold (Ct value) for each primer/gene combination. Quantitative single-cell RT-PCR cannot be normalized to a single housekeeping gene or groups of genes, but rather to the individually determined detection limit for each primer pair. Ultimately, quantitative gene expression for each primer pair and cell is presented as expression level above detection limit on a log scale using Log2Ex values [Log2Ex = Ct(LOD) − Ct(measured); Durruthy-Durruthy et al., 2014]. In simple terms, Log2Ex for a gene represents transcript level above background in log base 2. LOD Ct values for each primer pair were determined in dilutions of bulk cochlear cDNA (neonatal and P21) over 16 orders of magnitude. Primers that did not meet the three above stated validation requirements were eliminated. A list of 96 genes (Supplementary Table S1) was used for gene expression profiling, and the Log2Ex values were used for downstream analysis.

Before clustering, the data set was cleaned by removing cells with low expression of housekeeping genes Gapdh and Actb, as well as removing any non-neuronal contaminants by selecting cells, which expressed Map2 and Tubb3. We used HDBSCAN, implemented in python, to cluster single cells obtained from P3, P8, and P12 cochlea, and obtained six clusters. In selecting our clustering method, we sought to find a method that would agree with our underlying assumptions about the cell populations, namely that: (1) the variance between different cell populations might not be the same; (2) the size of each subpopulation may be different; (3) some rare cell populations might not be sufficiently sampled; and (4) variance in the data can be introduced by additional factors, such as RNA degradation. HDBSCAN (McInnes et al., 2017) has many advantages over traditional k-means clustering, including its ability to deal with data with variable density and variance, fulfilling goals (1) and (2), and the ability to deal with noisy data by assigning some points to no cluster, fulfilling conditions (3) and (4). We restricted our analysis to five of these six clusters, as the sixth was found not to express any of the selected genes. To visualize the cells, we utilized UMAP, a dimensionality-reducing algorithm, to project the cells into 2D space and mapped the HDBSCAN called cluster identities. Here, we present all cells, including those cells that HDBSCAN did not cluster due to low confidence about their identity. Enrichment for a particular marker gene was tested using a one-factor ANOVA with correction for multiple hypothesis testing (alpha = 0.05/96), and then each significant ANOVA was tested using the post hoc Tukey test. Statistical analysis was performed in Python and Prism (Graphpad).

We also repeated the same clustering process using PCA analysis plus K-means clustering and hierarchical clustering. K-means clustering was applied in each data sets using the algorithm “Hartigan-Wong” with one thousand iterations (iter.max = 1,000) in R. Hierarchical clustering was computed by Ward’s minimum variance method (Ward.D2). The numbers of stable clusters generated were assessed by gap statistic (Tibshirani et al., 2001).

RNAscope in situ hybridization was performed according to RNAscope guidelines (ACD, document number, 320293; Wang et al., 2012). Briefly, temporal bones were removed in ice-cold PBS and cleaned. Cochleae were placed in 4% PFA at 4°C for 22–24 h with gentle shaking. After fixation, samples were washed 2× with PBS and dehydrated overnight with 30% sucrose at 4°C, for 24 h, the mounted in OCT. Fourteen micrometer cochlear section was cut using a cryostat. The manufacturer designed probes were used for double fluorescent labeling (Cacna1a: 493141-C2, Mfap4: 421391, Kcnd2: 452581-C3) according to manufacturer specifications. Conventional in situ hybridization procedures for Cacna1a and Nefm were performed as described (Mendus et al., 2014). Briefly, in situ probes were cloned into the pGEM-T vector (Promega, Madison, WI, USA), with the following primers: Cacna1a: GAGAGAATTCGGGCGCACTGCAAATGATAA and GAGAAAGCTTGTCCCAAGCCCACGTTTTTC. In situ hybridization was carried out as previously described (Schwander et al., 2007).

Images were acquired on a confocal microscope (Zeiss, LMS700) as previously described (Mendus et al., 2014). A 0.5 μm z-stack of images was collected. The signals for specific genes such as Cacna1a, Mfap4, and Kcnd2 in each cell were visualized in Velocity 3D image analysis software (PerkinElmer, Inc., Waltham, MA, USA). The boundary of a cell was defined by merging fluorescent images with bright-field images and manually tracing cell borders. The numbers of fluorescent signals in each defined boundary were quantified manually.

To prepare single-cell suspensions of SGNs from the cochlea, we utilized a triple transgenic mouse model in which Type I and II SGNs are uniquely labeled with different fluorescent reporters. Briefly, we crossed Ai14-tdTomato to Bhlhb5-cre mice and Prph-GFP mice (see “Materials and Methods” section). This resulted in cochlear tissues where SGNIs are labeled red and SGNIIs are marked red and green (Figures 1A–E). We then isolated pure cell populations by FACS for subsequent single-cell transcriptome analysis. To validate our dissociation and sorting strategy, we immunostained cells with the neuronal marker TUJ1 (Supplementary Figures S1A,B). Our dissociation protocol resulted in 85.6% cell viability after sorting (Supplementary Figure S1C). As expected, tdTomato/GFP double-positive cells (SGNIIs) compose only 0.05% of the final viable fraction (Supplementary Figures S1D–H). Cells were directly sorted into 96-well plates and analyzed using the Fluidigm single-cell platform (Supplementary Figures S2A–F). The panel of 96 genes (Supplementary Table S1) was preselected using microarray expression profiles generated from the same mouse model and contained genes hypothesized to be either selectors or effectors for SGNI subpopulations.

After quality control and filtering (see “Materials and Methods” section), the single-cell data was visualized using the UMAP projection, which visualizes high-dimensional data on a 2D axis and whose utility for single-cell data has been recently shown (McInnes et al., 2018; Becht et al., 2019). We analyzed 203 cells at P3, 383 cells at P8, and 230 cells at P12.

We first sought to determine the broad molecular features separating Type I and Type II SGNs at postnatal day 8 (P8), before the onset of hearing. Mapping the FACs gating information onto the UMAP projection of SGNs, we observed that Cluster I is enriched for tdTomato/GFP expressing cells, suggesting that this cluster corresponds to Type II SGNs (Supplementary Figure S3A). Classically, SGNII has been defined by the expression of Prph (Hafidi et al., 1993). Cells in Cluster I were enriched for Prph expression (Figure 2A) as well as Mafb and Gata3 (Figures 2B,C), which also have been suggested as markers of postnatal Type II cells (Petitpré et al., 2018; Shrestha et al., 2018; Sun et al., 2018). We also found Gata3/Mfab positive cells that are not Prph positive (Figures 2A–C), as has been observed by other studies (Petitpré et al., 2018; Shrestha et al., 2018; Sun et al., 2018). The ambiguity of this expression highlights the need for better molecular markers to distinguish between Type I and Type II SGNs.

With this goal, we focused on the transcription factors in our panel to find those that might act as selectors to designate Type II vs. Type I cells. We observed that Type I cells were strongly enriched for Pax6, Nfix, and Zic1 (Figures 2E–G), while Type II cells were enriched for Foxg1 (Figure 2D). In contrast, Zic5, although highly expressed in Type I cells, was also mildly expressed by Type II cells (Figure 2H). We observed that while Zic1 expression was highest in early development (P3) and decreased slightly with time, Pax6 and Nfix expression stayed constant from P3 to P12 (Figures 2E–H), suggesting they would be optimal markers to identify Type I and II cells over time. In addition to these transcription factors, we find that Cadps2 and Tmem178 are broadly enriched in Type I SGNs (Supplementary Figure S3B), as well as Cacna1g, which although expressed in Type II SGNs, is more strongly expressed in Type I cells (Supplementary Figure S3B) in contrast to previous findings. Collectively, these data provide us with novel genetic markers for Type I and II SGNs (Figures 2I,J).

We next focused our attention on identifying subsets of Type I SGNs at post-natal day 8 (P8). Previous work has shown a variety of physiologically distinct SGNI cells (Taberner and Liberman, 2005; Davis and Liu, 2011) and we hypothesize that these cells should have unique molecular signatures. Therefore, we used machine learning to cluster all the Type I SGNs based on their 96-dimensional gene expression profiles. We chose to use HDBSCAN clustering because it can deal with data of variable density and variance (see “Materials and Methods” section). After data transformation, we selected cells that expressed the housekeeping genes B-actin and Gapdh, as well as high levels of neuronal markers Map2 and Tubb3 (Supplementary Figures S3C–F). HDBSCAN was run on this set of Type I SGNs and provided five high confidence clusters (II-VI), of varying sizes (Supplementary Figures S3G–I). Three of these clusters contained a substantial number of cells with clear delineating markers, which we termed Type IA, IB, and IC (Figure 3A). Similar delineations were found using k-means clustering following PCA analysis (Supplementary Figure S4) as well as hierarchical clustering (Supplementary Figure S5), validating our findings. Type IA, B, and C cells were distinguished by their expression of the Lmx1a (Figure 3B), Slc4a4 (Figure 3E), and Mfap4 and Fzd2 (Figures 3F,G), respectively. Both Type IA and IB cells express Cacna1a (Figure 3C) and Kcnd2 (Figure 3D). The additional two subtypes were characterized by a complex pattern of gene expression (Supplementary Figure S6). For completeness, we projected all cells, including those that were not assigned to any subset, onto the UMAP axes when displaying gene expression.

Some of these marker genes have been previously implicated in SGN biology, in particular Lmx1a and Cacna1a. Lmx1a belongs to the family of LIM-domain containing transcription factors (Rétaux and Bachy, 2002) and is known to play roles in regulating fate decisions, and defining neural boundaries and domains in both the central and peripheral nervous system and the inner ear (Millonig et al., 2000; Chizhikov and Millen, 2004; Nichols et al., 2008; Koo et al., 2009). Cacna1a (encoding CAV2.1) has been shown to control fast excitatory synaptic transmission and low threshold exocytosis in the CNS (Jun et al., 1999; Pagani et al., 2004; Weiss and Zamponi, 2013), and its expression in SGNs has been determined by whole-cell and single-channel recordings (Lv et al., 2012, 2014; Stephani et al., 2019). Less is known about Slc4a4 in SGNs, however, mutations in the human SLC4A4 gene have been associated with neurosensory disorders including glaucoma and hereditary sensory neuropathy type I (Kok et al., 2003; Dinour et al., 2004). Although the Fzd2 gene has not been characterized in SGNs, its ligand WNT5A plays an important role in planar cell polarity and cochlear development (Qian et al., 2007; Munnamalai and Fekete, 2013). Therefore, our identified marker genes are instrumental to distinguish functional subsets of SGN cells.

We next sought to trace the number and fate of these subpopulations over-development by performing single-cell qPCR on SGNIs, before and at the onset of hearing from P3 and P12 cochlea, respectively. We clustered these cells together with P8 neurons and projected them onto the same UMAP embedding. At P3 and P12, we can see cells belonging to all the clusters we described (Figure 4A). However, we observed age-related changes in their abundance. IA cells are abundant at pre-hearing stages (P3, P8) representing 85% and 38% of the total SGNs analyzed, and decrease after the onset of hearing to 9% (Figure 4B). On the other hand, IB and IC cells are low at P3 (0.9% and 2.8%) and increase significantly at P8 (20% and 32%), stabilizing by P12 (35% and 35%; Figure 4B). The expression markers genes for the IB and IC subtypes stayed fairly constant from P8 to P12, after the large increase in these cell populations (Figures 4D–H). In contrast, expression levels of Lmx1a also decreased mildly with the concurrent loss of IA cells (Figure 4C). The loss of the IA population may either reflect differentiation or death with development or increased sensitivity of these cells to manipulation with age. Intriguingly, Lmx1a expression was not found by single-cell SGN studies focusing on the adult cochlea (Petitpré et al., 2018; Shrestha et al., 2018; Sun et al., 2018).

We next sought to understand the unique functions or properties of the Type I SGN subtypes by analyzing the expression patterns of the other genes beyond those defining the subtypes in our assay. As demonstrated by our PCA analysis (Supplementary Figure S4D), none of these genes has a role in exclusively defining a particular subtype, but several are enriched in only one or two of the subtypes. Genes were classified by their broad functions: transcription factor, signaling, physiology, guidance, and adhesion (Supplementary Figure S6). Overall, we observed that transcription factor expression (beside Lmx1a and Zeb1) from our selected gene set was generally homogenous between all three identified subsets of Type I SGNs, while some of the signaling and physiology related genes have more distinct expression patterns among the subtypes.

We found that subtype IA cells were enriched for a variety of sodium channels, including Scn1a (Nav1.1) Scn2b (Nav1.5), and Scn9a (Nav1.7; Figures 5A,G, Supplementary Figure S6). These channels activate at a more negative membrane potential than the other Nav channels and therefore may contribute to making fibers sensitive to the lower intensity of sound (Royeck et al., 2008; Fryatt et al., 2009; Browne et al., 2017). In addition to these channels, the IA subtype differentially express channels that mediate SGNI resting membrane potential and control neuronal excitability such as hyperpolarization-activated cyclic nucleotide-gated channel α-subunit 2 and 4 (HCN2 and HCN4; Figures 5A,C) and the K+-selective leak channels (KCNK9; Figure 5E; Welker and Woolsey, 1974; Mo and Davis, 1997; Kim and Holt, 2013; Liu et al., 2014).

IA cells were also enriched for markers related to neural branching and patterning, suggesting that these cells may still be migrating or differentiating into their mature forms between P3 and P12. In IA cells, we find enrichment for Plxna1 and Plxnb1 (Figure 5A). Plxna1, the receptor for class 3 semaphorin (Sema3a) was recently shown to be involved in SGNI branching and refinement during postnatal synapse maturation (Katayama et al., 2013; Jung et al., 2019). Plxnb1 also plays a role in axonal guidance through Sema2a in the CNS (Ayoob et al., 2006). Finally, IA cells are enriched in Nrp1 (Figures 5A,F), a receptor involved in neural pathfinding, survival, and maintenance (Cariboni et al., 2011; Guaiquil et al., 2014). Together these genes expressed by the IA subtype, in addition to Lmx1a, maybe representing a subset of mid or low threshold neurons that are refining their final connections with target cells.

Subtype IB was enriched in physiological markers involved in increased neural excitability such as Kcnd2 (Kv4.2; Figures 4E, 5A), a potassium voltage-gated channel. Interestingly, Kv4.2 expression in the SGNs is regulated by neurotrophins (Adamson et al., 2002). This channel is activated at membrane potentials that are below the threshold for action potentials (Shibata et al., 2000; Chen et al., 2006; Granados-Fuentes et al., 2012). Kv4.2 functions downstream of the metabotropic glutamate receptor GRM5 and plays a role in nociception mediated by activation of GRM5 (Hu et al., 2007). Grm5 is enriched in both type IA and IB as compare to IC (Figures 5A,G). IB cells are also enriched for Gria4 at P8 (Figure 5A), another glutamate receptor known to control the frequency, amplitude, and kinetics of the spontaneous excitatory postsynaptic channels of the reticular thalamic nucleus (nRT) neurons (Paz et al., 2011, p. 4). Taken together with the highest expression of Cacna1 in type IB SGNI, this subtype may be a representative of the mid to low-threshold SGNI.

Additionally, subtype IB cells had the highest expression of Ca²⁺-dependent activator protein for secretion 2 (Cadps2; Figure 5A), which is involved in cell survival and the activity-dependent release of the brain-derived neurotrophic factor (BDNF; Sadakata et al., 2004; Shinoda et al., 2011). BDNF is involved in neuronal maturation and synaptic plasticity (Sadakata et al., 2007, 2013). Intriguingly, Bdnf, and Ntf3 expression, as well as their receptor Ntrk2, are high in type IB, although present in the other subtypes (Figure 5A, Supplementary Figure S7). Previous studies have shown a graded expression of neurotrophins in the cochlea, with BDNF expression being highest at the basal turn, while NTF3 is highest at the apical edge (Adamson et al., 2002; Schimmang et al., 2003), suggesting that type IB cells originate from many tonotopic areas. Although expression analysis cannot definitively establish electrophysiological properties, these patterns suggest that IA and IB are more closely related molecularly than IC and may the low or mid-low neurons.

Subtype IC cells are defined by their specific expression of adhesion and signaling molecules Mfap4 and Fzd2. They are also enriched for two members of the Tcf transcription factor family: Zeb1 (Tcf8) andTcf12 (Figure 5A), which may be involved in establishing the IC cluster during early development. Several members of the hedgehog-signaling pathway, Ptch1, Ptch2, and Yes1, are also enriched in this subtype (Figure 5A). Ptch2 expression (Figure 5I) has previously been shown to influence neural cell fate decisions and regulate synaptic plasticity and neuronal activity in the CNS (Konířová et al., 2017, p. 2; Herholt et al., 2018). Type IC cells also expressed high platelet-derived growth factor receptor alpha (Pdgrfa; Figure 5J). PDGF receptors and their ligands play essential roles in neuronal differentiation during embryonic stages and in adult neuronal maintenance (Funa and Sasahara, 2014). PDGF receptors were elevated in cochlear tissues, including SGNs, following noise injury (Fetoni et al., 2014; Bas et al., 2015), suggesting their role in cochlear tissue protection following noise trauma. These expression patterns indicate that IC neurons may be the high-threshold SGNIs. Collectively, our findings suggest that the three SGNI subtypes defined by their distinctive transcriptional profiles represent physiologically distinct subpopulations.

After identifying IA, IB, and IC to be the main three subpopulations arising from our single-cell dataset, we sought to validate these three populations in vivo. First, we designed in situ probes against Cacna1a, which we observed to be enriched in both the IA and IB subtypes. Indeed, we see a strong signal of Cacna1a in cochlear tissues at both P3 and P8 (Supplementary Figures S6D,E). However, due to the low resolution of traditional in situ, we were not able to localize the expression of more than one gene to individual populations of neurons. Therefore, we opted to use RNA-scope technology, an in situ hybridization approach with low background signal, that allows us to visualize and/or co-localize two or more probes (Wang et al., 2012). We designed and tested probes against several of the defining population markers (see “Materials and Methods” section). For the validated probes, we were able to observe subpopulations of SGNs that exclusively expressed Cacna1a, representing Type IA and Type IB cells and Mfap4 expressing cells, representing Type IC cells (Figures 6A,B). We also tested the co-localization of Cacna1a and Kcnd2. From the single-cell data, we expected to observe two populations, one that would only express the Cacna1a representing Type IA cells without Kcnd2 (Figure 4G) and those expressing Cacna1a and Kcnd2, pooled both from Type IA and Type IB. In concordance with this data, in RNA-scope images of the cochlea, we observed both cells only expressing Cacna1a and others expressing both Cacna1a and Kcnd2 (Figures 6C,D).

Previous studies had established a role for Lmx1a in inner ear cell fate decisions (Nichols et al., 2008; Koo et al., 2009). However, we were unable to establish robust probes for Lmx1a. Therefore, to validate the existence of SGNI cells which expressed Lmx1a, we established a new triple transgenic mouse line using a Lmx1a-cre line (Chizhikov et al., 2010) crossed to the Ai14-tdTomato reporter line and Peripherin-GFP lines. Our in vivo data revealed that only a subset of SGNIs (i.e., cells not showing GFP expression) was red at P8 (Figure 7A), validating that IA cells are indeed a unique subset of SGNIs distinct from the SGNII (Figure 7B). Further studies need to be performed on this mouse model to analyze the dynamics of this cell population over time (Figure 7C).

We next sought to compare our study to the previously published results from whole-genome single-cell studies (Petitpré et al., 2018; Shrestha et al., 2018; Sun et al., 2018). Of these, two focused primarily on adult SGNs, and therefore were not suitable for comparison with our study, as neuronal properties are known to change from neonate to adult (Crozier and Davis, 2014). We thus chose to compare our work to the Pvalb-cre P3 neurons sequenced by Petitpré et al. (2018) although the two studies use different transgenic mouse models for sorting.

In both the adult and P3 data, Petitpré et al. (2018) found three subtypes of SGNs, characterized by their expression of Calb1, Runx1, and Calb2 (Type IA), Lypd1, Grm8 and Runx1 (Type IB) and, Trim54, Pcdh20, Rxrg and Calb2 (Type IC). We did not have any of these markers in our targeted panel, except Calb1, whose expression we compared (Supplementary Figures S7A,B). We see a more limited expression of Calb1, although it is enriched in Type IA (Lmx1a positive) neurons at P3, suggesting that these two subtypes may be similar (Supplementary Figure S7B). However, by P8, we observe the majority of the Calb1 expressing cells to be Type II cells (Supplementary Figure S7C).

We next wanted to determine if we could sort out our subtypes using the Petitpré et al.’s (2018) data. Thus, we selected the same 96 genes found in our panel, and projected and clustered the cells as before, but were not able to form the same clusters. We noted several differences between our data and Petitpré et al. (2018): (1) SGNI markers Zic1 and Zic5 were not expressed (Supplementary Figures S7D,E), although they are expressed in previous studies of bulk SGN tissues at P0 and P6 (Supplementary Figure S7F, Lu et al., 2011). (2) Two of our marker genes (Lmx1a and Mfap4) were not expressed (Supplementary Figures S7G,H). Fzd2 was lowly expressed by a few of their Type IC cells, but these did not cluster together in our analysis (Supplementary Figure S7F). Finally, Slc4a4 and Cacna1a were broadly expressed across all subtypes (Supplementary Figures S7J,K).

These discrepancies in the two datasets may suggest that transcripts like Lmx1a and Mfap4 may be low-abundance and therefore need to be pre-amplified to be detected. The limited detection of low-abundance transcripts is a known problem with both the 10× and Smart-seq2 pipelines, as these transcripts are not efficiently converted into cDNA. Thus, our dataset complements previous whole sequencing studies that set the outlines of SGN subtypes with the detection of these low abundance transcripts.