Revealing heterogeneity and damage response in the adult human utricle

Domain: neuroscience
Published: 2025-12-11
Editorial outcome: major_revision

Abstract
The human utricle is a vestibular organ essential for balance, a function that declines with age. With the aging population projected to double to 2 billion by 2050 and no pharmaceutical or biological treatments available, balance disorders represent a significant unmet medical need. The utricle is composed of sensory and non-sensory cells, which are closely related. Non-sensory cells have limited capacity to regenerate sensory cells and, therefore, are a relevant therapeutic target. In this work, we profile the cellular and transcriptional landscape of the adult human utricle and its early response to ototoxic damage using bulk and single-cell RNA-sequencing of patient-derived samples. We identify six transcriptionally distinct non-sensory cell types, including a previously uncharacterized supporting cell-like population, demonstrating utricular heterogeneity. Following aminoglycoside-induced damage, we detect early transcriptional changes consistent with a capacity to respond to ototoxic damage within 24 hours and potentially initiate a regenerative response via an early-responding cell population, providing a foundation for regenerative strategies for balance recovery.

Introduction
The utricle is the organ responsible for balance and the vestibulo-ocular reflex, one of the most critical human reflexes, which allows both high- and slow-velocity visual fixation. This function, together with other sensory inputs, enables us to navigate our environment. This balance organ is part of the vestibular system located within the inner ear (Fig. 1). The sensory epithelium of the utricle consists of mechanosensory hair cells (HCs), which detect movement and convert mechanical signals into electrical signals that are eventually processed by the vestibular cortex, as well as non-sensory supporting cells (SCs) that orchestrate the organ’s function, providing structural support for hair cells, mediating their survival, and helping to maintain extracellular homeostasis1,2. Defining the transcriptome of hair cells and supporting cells is essential for understanding physiological, pathological, and regenerative conditions of this understudied balance organ. Several factors, including aging, infection, and ototoxic drugs, can damage the cellular components within the utricle. Although limited hair cell turnover may occur spontaneously in adult human utricles3,4, this is not sufficient to recover function, resulting in progressive and permanent balance disorders as no treatments are available. Altogether, this leads to increased fall risk, morbidities associated with injuries, and considerably reduces quality of life5. Identifying cell types, markers, therapeutic candidates, and druggable targets in the adult human utricle is challenging as adult balance organ availability is limited to scarce cadaveric tissues and individuals with vestibular schwannoma. This results in a knowledge gap that hampers the investigation of potential solutions for this unmet medical need.

Mounting evidence suggests that supporting cells are a potential target for endogenous regeneration as they survive after damage and hair cell loss; therefore, identifying the supporting cell transcriptome is crucial. In embryonic and newborn mouse utricles, supporting cells can regenerate hair cells by proliferation or direct SC-to-HC transdifferentiation during homeostasis and after ototoxic damage6–8, and this ability drastically declines with age6,9. Single-cell RNA-sequencing (scRNA-seq) of the mouse utricle has improved our understanding of utricle biology during development10–12, at maturity13,14, and after damage15,16. In humans, information on the utricle is minimal, despite the rise in balance disorders. A limited degree of spontaneous regeneration has been reported in adult utricles after ototoxic damage3,4 and via overexpression of ATOH1, a master regulator of hair cell development17–19. Until today, only three studies, one bulk20 and two single cell21,22, sequenced adult human utricles. Therefore, characterizing the cellular and transcriptional profiles of the adult human utricle sensory epithelium, unravelling supporting cell heterogeneity, and examining their response to damage, including any transcriptional changes related to their capacity to regenerate hair cells, is essential for designing regenerative strategies.

Here, we show transcriptional heterogeneity and damage-induced response by conducting bulk and scRNA-seq analyses on 12 patient-derived utricles, six for bulk analysis (control and gentamicin-treated; n=3 per condition) and six for scRNA-seq (untreated, control, gentamicin-treated; n=2 per condition); Supplementary Data 1a. Our study generates a single-cell atlas of the human utricle sensory epithelium, revealing, in addition to the already known two hair cell types, six types of supporting cells, leading to the discovery of SC-like cells. We also investigate the response to ototoxic damage, providing insight into the early transcriptional changes following 24 h gentamicin exposure and detecting an early-responding supporting cell population. We identify over 400 differentially expressed genes, including druggable targets and transcription factors, many associated with proliferation, survival, regeneration, and epithelial integrity. Cross-species comparisons between human and mouse utricles, identify human-specific or enriched genes as putative markers for sensory and non-sensory cells, highlighting species-specific differences in the balance organ.

Results
Transcriptional heterogeneity of the utricle sensory epithelium
Before mapping the cellular and molecular features of the inner ear balance organ (Fig. 1a), we morphologically illustrated its structure via histology (Fig. 1b). We also show the intactness of the collected organs (Fig. 1c), and their features, demonstrating areas of flat epithelium with no sensory cells (Fig. 1d), with new hair cells (Fig. 1e–g) and their established cell type markers and hair cell shapes (Fig. 1h). Then, using the 10x Genomics Chromium system, we performed scRNA-seq and obtained 1,134 cells from one adult utricle sensory epithelium (a median of 31,163 reads per cell and a median of 991 genes per cell) collected from an individual with vestibular schwannoma (scUntr-2, Supplementary Data 1a). After quality control (Supplementary Table 1), we profiled the transcriptome and heterogeneity of 967 cells. Unsupervised cell clustering yielded eight transcriptionally distinct clusters, which were visualized using UMAP (Unsupervised Uniform Manifold Approximation and Projection, Fig. 2a, Supplementary Fig. 6a). We employed an additional dimensionality reduction method, SWNE (Similarity Weighted Nonnegative Embedding)23, to validate the UMAP output, and clustering was consistent. SWNE was also utilized for visualizing the local and global structure among cell types (Supplementary Fig. 1a). To annotate the eight clusters, in addition to the differentially expressed genes in each cluster (Fig. 2b, Supplementary Data 2a), we used known mammalian hair cell and supporting cell marker genes like MYO7A for hair cells and SOX2 for supporting cells and type II hair cells4 (Fig. 2c). Two groups of hair cells had specific expression for established markers: type I hair cells expressed subtype marker OCM24, and type II hair cells were marked by SOX2. To further confirm supporting cell populations, we also used the pan-SC marker OTOL125. Notably, Calb1, a mouse type I hair cell marker26, was specific for human type II hair cells (Fig. 2d).

In this utricle, we identified five transcriptionally distinct supporting cell clusters and a previously uncharacterized SC-like cell population. All of the supporting cell clusters expressed established pan-SC markers like SOX2 and OTOL1, while the SC-like cells expressed cluster-specific genes such as CRYAB and ANKRD1 (Fig. 2d). To validate supporting cell cluster-defining genes (dot plot Fig. 2e, Supplementary Data 2a), we performed in situ hybridization using CYR61 for SC-2, MANSC4 for SC-3, and PIGR for SC-5 (Fig. 2e). Moreover, we investigated the expression patterns of vestibulopathy genes and genes associated with syndromic and nonsyndromic sensorineural hearing loss using a list of 158 genes21. Of the 68 vestibulopathy-related genes present in the list, 35% (24/68) were expressed in this utricle, 11 in supporting cells, including SC-like cells, and 13 in hair cells (Supplementary Fig. 1b). Similarly, for hearing loss genes, 30% (47/158) were expressed in our dataset, of which 36% (17/47) were in supporting cells and SC-like cells, and the remaining in hair cells (Supplementary Fig. 1c).

To confirm the cluster-defining genes for the main clusters, we used a recently published dataset (vestibular schwannoma 2, VS-2)22 and integrated it with our scUntr-2 dataset (Supplementary Figs. 2a, 6b). We reannotated and colour-coded the integrated clusters in four main clusters: types I and II hair cells, supporting cells, and SC-like cells (Supplementary Fig. 2b). To evaluate the similarity between the two human datasets across the main clusters, we measured the Euclidean distances between these expression profiles. Distance values were zero for all four clusters, confirming the perfect concordance between them (Supplementary Fig. 2c). In addition, using their integrated datasets of human utricles (n=6, including VS-2)22, we demonstrate that pan-HC markers, hair cell subtype-specific markers, and pan-SC markers are consistent across human datasets (Supplementary Fig. 2d, e).

Putative markers identified in the utricular transcriptome
To discover unknown and enriched genes, we compared sensory hair cells (types I and II) with non-sensory supporting cells (SC1-5, excluding SC-like cells); 473 differentially expressed genes were identified (Padj < 0.05, log2FC > |1|), including eight transcription factors for hair cells and 25 for supporting cells (Fig. 3a, Supplementary Data 2b). We found GFI1, IRX2, LHX3, and POU4F3 expressed among the known transcription factors for hair cells. We also found transcription factors like SMARCE1, RARB and three members of the zinc-finger family (ZNF385A, ZNF397, ZNF688) that are poorly characterized in the inner ear. Among the HC-enriched genes, we found previously undescribed HC-specific markers like secretoglobin (SCGB) 2A1 and MINDY4B (Fig. 3b, Supplementary Data 2b). Among the identified 187 SC-defining genes, in addition to well-known inner ear transcription factors such as SOXs, ELF3, XPA, and IDs, we found NFIB (nuclear factor IB) enriched in supporting cells. In line with the recently published integrated human datasets22 (Supplementary Fig. 2f, SWNE plot), we also detected ATOH1 (Supplementary Fig. 2f, UMAP plot), a master transcription factor essential for hair cell development, in 10.3% (9/87) hair cells and 1.4% (12/843) supporting cells (Supplementary Data 2b; P=3.08×10-5, Fisher’s exact test).

To profile the transcriptome of hair cell subtypes, including subtype-specific markers, we examined differentially expressed genes between type I and II hair cells (Padj < 0.05, log2FC > |1|, Fig. 3c, Supplementary Data 2c). Among the 30 differentially expressed genes, we found known markers like OCM for type I and SOX2 for type II. We also found, among others, ADAM11, PPP1R27, ENC1 to be specific for type I, and ZNF385B, AC025244.1, ELFN1, and CXCL14 for type II (Fig. 3d). Furthermore, we validated the expression of CALB1 to be specific for human type II hair cell (Fig. 3e), and confirmed this subtype specificity in the human-integrated datasets available on gEAR22 (Supplementary Fig. 2d), as well as in the integrated scUntr-2 and VS-2 datasets (Supplementary Fig. 2g, h).

Cross-species comparisons highlighted differences in utricular genes
We performed three cross-species transcriptomic analyses to assess any gene expression divergences between human and mouse utricles and identify species-specific signature genes for hair cells and supporting cells. Using Reciprocal Principal Component Analysis (RPCA)27 we integrated our human utricle dataset (scUntr-2) with the adult mouse utricle dataset14 (Fig. 4a–d, Supplementary Figs. 3a, 6c, Supplementary Data 3a, b). To identify human-enriched hair cell and supporting cell genes, we performed differential expression analysis between human and mouse hair cell subtypes (Fig. 4e, f, Supplementary Data 3c, d) and supporting cells (Fig. 4g, Supplementary Data 3e, f). We used two external datasets for in silico validations. The first was a P6 dataset containing both hair cell and supporting cell information10 (Supplementary Figs. 3b–f, 7d, Supplementary Data 3g–i), while the second was a P100 dataset comprising only hair cell data. However, due to the limited number of adult cells analyzed (25 hair cells), we also included hair cell data from the next most mature stage (P12) available from the same study13. By combining both datasets, we obtained 76 hair cells for subsequent differential expression analysis with human hair cells. We found 248 differentially expressed genes for hair cell type I, including 15 transcription factors (Supplementary Fig. 3g, Supplementary Data 3j), and 273 differentially expressed genes for hair cell type II, including 23 transcription factors (Supplementary Fig. 3h, Supplementary Data 3k). Differentially expressed genes identified from hair cell and supporting cell comparisons showed a high degree of consistency across all the three datasets.

Our results revealed significant SCGB2A1 expression, specifically in human hair cells (Fig. 4h). SCGBs are exclusive to mammals28, and SCGB2A1 encodes a secreted protein with an unknown function in the inner ear. Interestingly, the mouse orthologue of this gene, Scgb2a2, was not found to be expressed in hair cells of inner ear sensory epithelia and utricle datasets publicly available on gEAR29 or published, suggesting it is a gene unique to human utricle hair cells. We validated SCGB2A1 expression in patient-derived samples (n≥3) at the transcript and protein levels using in situ hybridization (Fig. 4i) and immunofluorescence (Fig. 4j, k), respectively. SCGB2A1 is an uncharacterized human-specific hair cell gene, and the encoded protein was observed to localize either in the cytoplasm or the intercellular space. We also confirmed that SCGB2A1 was expressed in the integrated datasets recently published22 (Supplementary Fig. 2d) and in the human-integrated datasets, scUntr-2 and VS-2, where its expression was restricted to hair cells (Supplementary Fig. 3i, j). Altogether, these results indicate some divergence in the transcriptomes of sensory and non-sensory cells, and their subtypes, between mice and humans.

To further investigate species-specific hair cell markers, we analyzed the integrated scUntr-2 and VS-2 datasets, focusing on the expression of a hair cell marker previously established in mice. As we described earlier, Calb1, which marks type I hair cells in mice, is instead enriched in human type II hair cells. We confirmed that CALB1 expression was consistently specific to type II hair cells across both human datasets (Supplementary Fig. 2g, h). This finding highlights that the HC-subtype marker CALB1 exhibits species-specific divergence between mouse and human.

A unique SC-like cell population
To assess how distinct the SC-like cell, supporting cell, and hair cell populations are from one another, we calculated the average expression level of each gene within each cluster, and used the Euclidean distances to compare these average expression vectors. We found that SC-like cells had a more similar transcriptional profile to supporting cells than hair cells (Figs. 2a, and 5a). These results also confirm that SC-like cells differ from the rest of the supporting cell population (distance value of 14.53) as they present unique gene signatures and potential biological features. Among the 24 cluster-defining differentially expressed genes, one-third of the genes are involved in the organization or remodelling of the cytoskeleton, like keratins and thymosin family genes, VIM, RRAS, and PALLD. To further elucidate the transcriptomic landscape of the SC-like population, we compared it with the other five supporting cell clusters, identifying 21 differentially expressed genes (Fig. 5b; Supplementary Data 2d), including ATF3, a well-known transcription factor involved in cellular stress responses30 and a recently proposed candidate for leading supporting cell proliferation and likely hair cell regeneration in the avian inner ear31. In addition, this analysis revealed that SC-like cells also expressed KRT7 (Keratin-7) and SFRP4 (Fig. 5c), the secreted frizzled-related protein 4 that acts as an inhibitor of Wnt signalling through direct interaction, antagonizing both canonical and noncanonical Wnt pathways32,33, regulating cell growth and differentiation. We also found IL-32 (Fig. 5c), LGALS3 (galectin-3), and ANX family genes, ANXA1 and ANXA2, among the upregulated genes. IL-32 may contribute to the modulation of the inflammatory response by regulating intracellular Ca2+ in the inner ear34, increasing calcium levels. Galectin-3 has anti-apoptotic activity, and Annexin A1 and A2 are known to be involved in the anti-inflammatory response. Additionally, ANXA1 is a pro-resolving mediator. The four members of the S100 genes (A1, A6, A10, and A11) were also significantly upregulated in SC-like cells. These genes encode a family of proteins that contain two EF-hand (helix E-loop-helix F) calcium-binding motifs, and the S100A10 member is also the ligand of ANXA235.

Furthermore, our analysis highlighted the expression of cluster-specific genes like ANKRD1 (Ankyrin Repeat Domain 1) and CRYAB (Alpha-crystallin B), Fig. 5b–d. ANKRD1 is a downstream YAP signalling gene involved in the murine utricle’s development and is expressed from embryonic stages until P236. CRYAB is a small heat shock protein with neuroprotective (otoprotective) and anti-inflammatory activities37. In mouse utricles, it is expressed in supporting cells10 and found to be upregulated after heat shock15. It is involved in proliferation, regeneration, and regulation of intracellular apoptosis signalling with an anti-apoptotic function. CRYAB is a stem/progenitor cell marker involved in regeneration of human interlaminar astrocytes38 and induces osteogenesis differentiation of human bone marrow stem cells, stabilizing β-catenin and promoting Wnt signalling39. We validated this SC-like cell type in adult human utricles (n≥3) using RNA probes for ANKRD1 and CRYAB (Fig. 5d). In accordance with our sequencing data, we observed co-localization of these transcripts in a subset of cells; both transcripts are enhanced near areas of missing cells (Fig. 5d, insets).

Furthermore, we conducted a cluster similarity analysis between SC-like cells in scUntr-2 and VS-2 datasets using ClusterFoldSimilarity40, a statistical method that computes similarity coefficients based on gene fold changes between clusters from different datasets. This analysis confirmed the high similarity between the two clusters and their presence across human utricles (Supplementary Fig. 2i).

Overall, our computational analyses, supported by experimental validation, demonstrate the adult human utricle cell heterogeneity, leading to the discovery of SC-like cells and enriched markers for human hair cells and supporting cell subtypes.

Bulk RNA-seq revealed early transcriptional changes after ototoxic damage
To examine the early response of the human utricle to ototoxic damage, we sequenced RNA from the sensory epithelia of six utricles. Briefly, we cultured whole organotypic utricles for 24h, three with vehicle control and three treated with an aminoglycoside (gentamicin) to induce hair cell damage. Then, we isolated the sensory epithelium and performed bulk RNA-seq (Fig. 6a). We used principal component analysis (PCA) to visualize variance between the two groups and similarity within sample replicates. This analysis revealed two distinct clusters representing each group. Samples undergoing the same treatment clustered together and were well-separated along PC1, indicating that the difference in gentamicin treatment was the primary source of variance across all the samples (Fig. 6b).

We profiled the transcriptome of the two sample groups (n=3 per condition) and identified 47 differentially expressed genes, 28 upregulated and 19 downregulated in the treated group, including five transcription factors and 14 druggable targets (Fig. 6c, Supplementary Data 4a). Among the upregulated genes, we identified FDZ8 receptor for WNT proteins and WNT activation target genes, ATF3 and JUN, both of which are known to be upregulated in response to injury41; COCH, responsible for cochleo-vestibular dysfunction42,43 in Deafness Autosomal Dominant 9 (DFNA9) and DFNA31 nonsyndromic forms of hearing loss; TCERG1, a candidate gene for nonsyndromic human deafness in the interval of DFNA42 locus44 and within mapped deafness loci DFNA54, DNFB6045; and CSF1, a cytokine that regulates macrophage activation and immune responses in the inner ear, that is necessary for the survival of otic macrophages46, although the source(s) of CSF1 in the ear have not been identified. We also identified matrix metalloproteinase (MMP) ADAMTS4; AC058791.1, an uncharacterized long intergenic non-coding RNA (lincRNA), and lncRNAs EPB41L4A-AS1, SNHG15. Among the downregulated genes, we found and validated SLPI (Secretory Leukocyte Protease Inhibitor), which is known to be involved in the innate immune response47,48, transcription factors ZBTB8B and CSRNP3, MMPER MP1, and lncRNA NBAT1. These results profiled the early response to damage of the human utricle, highlighting a transcriptional difference after 24h of gentamicin exposure. In summary, the expression of early responding genes (including upregulated and downregulated genes involved in immune responses, metalloproteases, and lncRNAs) suggests that the utricular damage triggers multiple cellular responses.

Single-cell analysis uncovers detailed early damage response
To define the transcriptional landscape following ototoxic damage at the single-cell resolution, we cultured utricles for 24h with either vehicle control or gentamicin and performing scRNA-seq, n=2 per condition (Fig. 6a). In one control sample cultured for 24h (scCtrl-1), we found type I and type II hair cells, SC-like cells, and six clusters of supporting cells (Fig. 7a, b, and Supplementary Fig. 6e, Supplementary Data 5a), similar to the uncultured utricle (scUntr-2). Next, we clustered and annotated the cells for one gentamicin-treated sample (scGent-2); we detected a cluster of damaged hair cells (type I and II clustered together), five transcriptionally distinct populations of supporting cells, one SC-like cell cluster, and a distinct cluster of early-responding SC (Fig. 7c, d, Supplementary Fig. 6f, Supplementary Data 5b).

We also performed differential expression analysis between these utricles. After 24h of damage, we identified 365 genes with significant transcriptional changes (114 upregulated and 251 downregulated), including 103 druggable targets and seven transcription factors (Fig. 7e, Supplementary Data 5c). Among the downregulated genes (Fig. 7f, upper panel), we found SLPI, CRYAB, ELF3, a transcription factor regulator of mesenchymal to epithelial transition49, ID4, a transcriptional target of Notch signalling, and NFIB, which is involved in retinal neuron regeneration processes in zebrafish, chicks, and mice50. Among the upregulated genes (Fig. 7f, lower panel), we found ANKRD1 and transcription factors NR1D1, a circadian gene that suppresses inflammation51, and HMGA1, which encodes for a nonhistone architectural chromosomal protein involved in epigenetic regulation, cell differentiation and proliferation, and DNA repair52. We also found FZD7, a gene related to the activation of Wnt/β-catenin signalling, and MIR4435-2HG, an immune-related lncRNA53. CDH11 was also upregulated; cadherin-11 binds to PDGFRβ and enhances cell proliferation and tissue regeneration via the PDGFR-AKT signalling axis54. Some genes of the S100A family were upregulated (1A, A2, and A14) and downregulated (A6, A11) after gentamicin treatment. Previous studies have shown that gentamicin can induce ferroptosis55–57, an iron-dependent non-apoptotic cell death. In the gentamicin-treated sample, the top three genes with the highest fold change are key iron metabolism-associated ferroptosis-resistant genes that encode for iron storage proteins FTH1 and FTL, and ion transporter LCN2: all downregulated. Also, TIMP1, an inhibitor of MMPs, was downregulated.

To validate some identified post-damage genes, we performed in situ hybridization using anti-SLPI and anti-ANKRD1 probes in control and damaged utricles. SLPI is a gene that was also significantly downregulated in the bulk data set after damage (Fig. 6c). ANKRD1, in addition to being a SC-like cell cluster-defining gene, was significantly upregulated in the damaged utricle (Fig. 7f, lower panel). In situ hybridization, supported by statistical analysis, confirmed significant downregulation of SLPI transcripts in treated utricle vs control (Fig. 7g) and upregulation of ANKRD1 (Fig. 7h), confirming in silico analysis, n=5 (Supplementary Data 5d). Altogether, these data suggest an intrinsic capacity of the adult human utricle to activate a transcriptional response within 24h of gentamicin exposure.

Multiple sample integration validated cluster-defining markers
To further investigate utricle heterogeneity across different treatment conditions at the single-cell level, we employed RPCA, to pool, integrate, and batch-correct all six samples (Supplementary Fig. 6g–i). We observed a robust alignment of cells across samples and experimental conditions (Supplementary Fig. 5a–f), indicating successful integration. Cluster markers (Supplementary Data 6a–c) remained consistent with non-integrated datasets, validating previous analyses (Figs. 2b, and 7b, d). Importantly, the consistency of cellular responses across diverse human samples subjected to the same conditions (untreated, 24 h control and gentamicin-treated) suggests shared underlying biological mechanisms. Although some degree of clustering by treatment was observed, this likely reflects treatment-induced transcriptional differences rather than residual batch effects. These results indicate that treatment-dependent transcriptional changes are a key driver of clustering, reinforcing the biological relevance of these differences.

An early-responding supporting cell population
Deciphering the immediate response to ototoxic damage, including any evidence for a regenerative response, is of fundamental importance for elucidating whether the adult human utricle has regenerative capacity. After only 24h of gentamicin treatment, we detected a cluster of 122 supporting cells (Fig. 7c) with unique transcriptomic features (Fig. 8a, Supplementary Data 5b). We performed differential expression analysis between early-responding SC and supporting cell clusters, excluding SC-like cells, yielding 126 differentially expressed genes, finding 27 transcription factors, 21 upregulated and six downregulated (Fig. 8b, Supplementary Data 5e). Among the latter, we found NFIB, which maintains and restores quiescence, acting as a suppressor gene for proliferation and differentiation, and found to be downregulated immediately after damage50; representative transcription factors are shown (Fig. 8c).

We also compared early-responding SC to SC-like cells and found 54 differentially expressed genes (Fig. 8d, Supplementary Data 5f). To determine the gene regulatory network of early-responding SC, we performed a network analysis using STRING (string-db.org58, P<0.05, Fig. 8e, Supplementary Data 5e). The resulting network consisted of five subnetworks, including JUN and EGR families of transcription-regulatory factors; integrin superfamily members (ITGs) with THBS2 ligand; MMP9 and its inhibitor TIMP3. MMP9 participates in the shedding of membrane-anchored signalling molecules, like proHBEGF. When HBEGF is cleaved, it binds its receptor EGFR, the epidermal growth factor receptor, inducing cellular proliferation59,60. MMPs and TIMPs have a critical role in extracellular matrix (ECM) remodelling during regeneration of any tissue61–64. Loss of attachment to the ECM and neighbouring cells might be the first step of SC-to-HC transdifferentiation65.

Furthermore, to enable pseudotemporal reconstruction, we first performed unsupervised clustering of early-responding SC and identified four distinct subclusters. We showed that subcluster-4, composed of 13 outlier cells, and one outlier cell from subcluster-3 were isolated from the others subclusters. Therefore, we excluded them from the trajectory analysis with Monocle366 (Supplementary Fig. 4a). The remaining 108 cells were used to identify differentially expressed genes unique for each subcluster, discovering 60 transcription factors and 145 druggable targets (Fig. 8f, Supplementary Data 5g). We also conducted pseudotime trajectory analysis using Slingshot and examined the gene expression dynamics in early-responding SC, with subcluster-1, closest to supporting cells, set as the origin of the trajectory (Fig. 8g). In subcluster-1 (early genes), we detected early upregulated genes involved in the stress response, as well as JUND and JUN, also essential for proliferation, differentiation, and regeneration, including in the sensory organs67. Subcluster-2 (intermediate genes) exhibited upregulated genes such as EGR1, a downstream gene of JUN associated with tissue repair68. The upregulation of MMP9, EDNRB, GAP43, and APOE, among others, characterized subcluster-3 (late genes, Supplementary Fig. 4b). Mmp-9 regulates photoreceptor regeneration in adult zebrafish, promoting the survival of regenerated cones69; EDNRB (endothelin receptor) is a homolog of chicken EDNRB2, an early marker of SC-to-HC transdifferentiation in the avian cochlea70, a species where hair cells regenerate; GAP43 is a well-known gene involved in axonal growth and regeneration71; lastly, APOE promotes axonal regeneration in the mammalian central nervous system following injury, via the MAPK signalling pathway72. We carried out in situ hybridization to validate the ERSC cluster. We observed that early-responding supporting cells expressing EDNRB or MMP9 were located at the basal region of the sensory epithelium (Fig. 8h), adjacent to the mesenchymal tissue, where nascent hair cells (MYO7A+/SOX2-) were also present (Fig. 1f–h).

Methods
Sample cohort, collections, and dissection
We enrolled 46 patients (21 to 75 y.o.) for this study. Patient cohort includes 57% (26/46) females (average age of 43.4 years) and 43% (20/46) males (average age of 48.6 years); Supplementary Data 1a. Samples ID, age, sex, affected side, and clinical information regarding hearing and vestibular symptoms were reported (Supplementary Data 1a). Utricles were collected from individuals with vestibular schwannomas undergoing surgical tumour resection via a translabyrinthine approach. A thorough lateral exposure was achieved to ensure clear visualization of the utricle. The utricle and its ampullary attachments were detached from the elliptical recess using a curved Rosen needle and a curved Fisch dissector. Then, tissue was carefully removed with fine forceps and transferred into 45 mL cold Tis-U-Sol® solution (Baxter Healthcare Corporation, Deerfield, IL 60015 USA) for bulk RNA-seq, or HBSS+/+ supplemented with 10 mM HEPES for scRNA-seq. The time between collecting and receiving the tissue in our laboratory was less than 10 minutes. Utricles were dissected in a black sylgard-coated dish immersed in the above solutions. Any bone fragments, residual blood, or otoconia were gently flushed away using a 1mL syringe/30G 1/2’’ needle filled with the above solutions.

Organ culture, treatments, and tissue preparation
Utricles were cultured free-floating for 24h in DMEM/F-12 GlutaMAXTM medium (Thermo Fisher Scientific, 10565042) supplemented with 10% FBS (Invitrogen, 12484028), 10 mM HEPES (Gibco, 15630-080), 2.5 µg/mL fungizone (Gibco, 15290018), 10 µg/mL ciprofloxacin (Sigma-Aldrich, 17850-5G-F) at 37°C in a humidified incubator with 5% CO2. Tissues were treated with 2mM gentamicin (Gibco 15750-060 Gentamicin Reagent Solution, 50 mg/mL) or distilled water as vehicle control. After 24h, samples were washed in HBSS+/+ supplemented with 10 mM HEPES, then incubated for 5-10min at 37°C in a glass vial with Rubber-Lined Cap, 1 dram, 15x45mm (Fisher, 03-339-25B) containing an enzymatic solution, 2mL of HBSS+/+ with 1mg/mL of thermolysin (Sigma-Aldrich, P1512), pre-heated for 10min at 37°C. For RNA-seq, sensory epithelia were isolated from the underlying mesenchymal stroma using forceps and a 1mL insulin syringe/30 G 1/2’’ needle.

For hematoxylin and eosin staining, immunofluorescence, and in situ hybridization, after dissection, whole utricles were immediately fixed with freshly prepared 4% formaldehyde in 1x PBS – derived from a 16% (w/v) solution of paraformaldehyde in distilled/deionized water – overnight (o.n.) at 4°C; then processed for sectioning or used as whole mounts. For cryosectioning, tissues were washed in 1x PBS, underwent a serial sucrose gradient (10%, 20%, 30%), cryo-embedded in Tissue Plus O.C.T. compound (Fisher Health Care), and stored at -80°C in a sealed plastic bag. After cryopreservation, 10-μm-thick sections were sectioned and stored at -80°C in a sealed box with desiccant (Humidity Sponge, VWR 61161-319).

Bulk RNA sequencing and analysis
Six human utricle samples were processed for bulk RNA-seq, comprising three biological replicates per condition (control: n=3; gentamicin-treated: n=3). RNA from delaminated sensory epithelia was extracted using Quick-RNA™ Microprep kit according to the manufacturer’s guidelines. Libraries were prepared following ribosomal RNA depletion using the Illumina Truseq Stranded mRNA Library Prep kit and then sequenced with the HiSeq 2000 platform (Illumina) using the 100-bp paired-end strategy. Quality control of raw reads was performed using RNA-SeQC v1.1.786 for all six RNA-seq samples, and adapters were trimmed with cutadapt v1.9.187. Reads were then aligned to the appropriate reference genome (Human GRCh38, GENCODE v32/Ensembl 98) using STAR v2.5.288. Gene abundances were quantified with RSEM v1.3.089 using default settings. All estimated gene abundances were imported into R v4.1.1 with tximport, and genes with total counts <10 across all samples were filtered out before downstream analyses. The R package DESeq2 v1.28.190 was used for variance stabilizing transformation, normalization (DESeq2’s median of ratios method), and differential gene expression. Confounding effects (i.e., sex and age) were accounted for in the generalized linear model. Differential genes were defined using an FDR (false discovery rate) threshold of 0.05. Variance-stabilized counts were used for principal component analysis and sample-to-sample distance assessments to evaluate batch effects and sample separation.

Sensory epithelia dissociation for scRNA-seq
Six human utricle samples were processed for scRNA-seq, comprising two biological replicates per condition (untreated: n=2; control: n=2; gentamicin-treated: n=2). Utricles were transferred with a mini perforated spoon (FST) in 1mL cold DMEM/F-12 GlutaMAXTM (Thermo Fisher Scientific, 10565042) supplemented with 10% FBS for a washing step. Sensory epithelia were delaminated using fine Dumont Forceps (FST) and a 1mL syringe/30G 1/2’’ needle in a clean sylgard-coated dish with the solution as above. Then, using a spoon microcurette (FST), they were transferred to the bottom of a low binding/DNase-free tube with 200µL of cold DMEM/F-12 GlutaMAXTM supplemented with 10% FBS. Media was removed, and a fresh-prepared dissociating mix was added: 200µL (10mg/mL) Native Bacillus licheniformis Protease – Subtilisin, (Creative Enzymes NATE-0633); 1µL (125U/mL) Deoxyribonuclease I from bovine pancreas (DNase I-Sigma-Aldrich D5025-15KU); 1µL (10µg/mL) N-Acetyl-L-cysteine (Sigma-Aldrich A9165-5G); 1µL (10µg/mL) 2-Phospho-L-ascorbic acid trisodium salt (Sigma-Aldrich 49752-10G). Tissues were incubated for 30–40 min at 4°C and triturated every 5 min with a P200 pipette set at 50 µL (wide-bore tip) until complete dissociation. Vertex Filter Tips, Clear, Graduated, and NoStick®, were used for the entire procedure. 500µL of DMEM/F-12 GlutaMAXTM medium supplemented with 10% FBS and 1% MACS® BSA Stock Solution (Miltenyi Biotech) was added to the enzymatic dissociation mix. Cell viability for each sample is reported in Supplementary Table 1.

Microfluidic capture of single cells and generation of cDNA
Sample preparation and cell capture were performed using the 10x Genomics Chromium Single Cell 3’ v3 Reagent Kits user guide (10x Genomics, Pleasanton, CA). Briefly, two washes were done to exchange sample buffer for one recommended for loading on the Chromium Controller. Samples were spun at 0.3G for 6min at 4oC and resuspended in 250µL RPMI-1640 Medium with 0.04% BSA and 5 U/µL RNase Inhibitor. Cell viability was assessed via Trypan Blue (Thermo Fisher) and using a hemocytometer (Thermo Fisher). A second spin was performed under identical conditions, and cells were resuspended in 55µL final volume. Following counting, cell suspension (46.6µL) was loaded onto the Chromium Single Cell B Chip Kit. After droplet generation, samples were transferred onto a pre-chilled 96-well plate (Eppendorf), heat-sealed, and incubated overnight in a Veriti 96-well thermal cycler (Thermo Fisher) for the reverse transcriptase reaction. Following this reaction, cDNA was recovered using the Recovery Agent provided by 10x Genomics and subsequently cleaned up using a Silane DynaBead (Thermo Fisher) as outlined by the user guide. Purified cDNA was amplified as follows: 12 cycles for control utricles and 15 cycles for uncultured and gentamicin-treated utricles before cleaning up using SPRIselect beads (Beckman). Samples were run neat on a Bioanalyzer (Agilent Technologies) to determine cDNA concentration. cDNA libraries were prepared as outlined by the Single Cell 3’ Reagent Kits v3 user guide with 16 cycles of PCR amplification based on the calculated cDNA concentration. A final clean-up followed this with SPRIselect beads (Beckman). Sample quantity was assessed by NanoDrop (Thermo Scientific) and normalized to ~5ng/µL.

scRNA-seq library construction and sequencing
The molarity of each library was calculated based on library size as measured by BioAnalyzer (Agilent Technologies), qPCR amplification data (Sigma) or Illumina MiSeq clustering data (Illumina). Details for each sample are listed in Supplementary Table 2. Briefly, samples were normalized, then diluted using elution buffer (Qiagen) with 0.1% Tween® 20 (Sigma-Aldrich), and each pool denaturated at RT. Library pools were further diluted before being diluted to a final loading concentration. 100µL from each pool were loaded into each well of an 8-well strip tube and loaded onto a cBot (Illumina) for cluster generation. All samples were sequenced with the following run parameters: read 1–28 cycles, read 2–91 cycles, and index 1–8 cycles. A median sequencing depth of 60,000 reads/cell was targeted for each sample.

scRNA-seq data analysis
Quality control metrics for all FASTQ reads were obtained using RNA-SeQC v1.1.7. The Cell Ranger v3.0.2 pipeline was used for primary data processing and the production of gene-barcode matrices, with settings as recommended by the manufacturer (10x Genomics). The raw gene-barcode matrices for all groups (Uncultured, Control, and Damaged; two samples per group) were processed as follows: first, cell quality was determined by examining the distribution of library sizes, the number of expressed genes across cells, and the fraction of reads mapped to mitochondrial (MT) genes. Then, we filtered out doublets using DoubletFinder v2.091. Cells with log-library sizes that were more than three median absolute deviations (MADs) below the median log-library size were also filtered out92. The MAD approach was also used to determine mitochondrial expression cutoffs for each group (Supplementary Table 1). Genes detected in less than three cells were removed. The total cell counts after QC are provided in Supplementary Table 1. Batch effects were regressed from the integrated data using Seurat’s (v5.1.0) reciprocal principal component analysis. In brief, each sample was individually normalized and scaled with Seurat’s SCTransform function. The top 3000 highly variable genes (HVGs) were used as the integration features for each sample (nfeatures= 3000), SelectIntegrationFeatures function. RPCA integration anchors were estimated by setting k.anchor to 25, and all samples were integrated using the IntegrateData function with k-nearest neighbors (k.weight) set to 56. This integrated RPCA object was then used for downstream analysis. Clusters were identified using an iterative SNN-Cliq-inspired clustering approach (Seurat’s FindCluster function), with dims=PC1:PC18 for the integrated data. Optimal clustering resolution values were decided using the Clustree93 R package (Supplementary Fig. 6a–i), as well as by exploring the biological significance of the differentially expressed genes between all clusters (using Seurat’s FindMarkers function; two-sided Wilcoxon rank-sum test, with a Bonferroni adjusted p-value threshold of 0.05). We identified the transcription factors among the differentially expressed genes using AnimalTFDB94 and druggable targets using DGIdb95. The RunUMAP function was used to generate UMAP plots. UMAP was selected as the primary projection technique due to its superior preservation of the data’s global structure96. Clusters were visualized by plotting the reduction values on a Cartesian graph. The following well-characterized marker genes were used to identify known cell types: GFI1, MYOSIN (6, 7A, 15A), OCM, OTOF, POU4F3, and TMC1 for hair cells; GFAP, KREMEN1, OTOL1, PLP1, SOX2, and SPARCL1 for supporting cells. Cell-type identification was also facilitated by using the differentially expressed genes (upregulated marker genes) in each cluster. We calculated the Euclidean distances between the clusters (HC, SC and SC-like cell) in the subspace of PC1:PC50 when annotating the SC-like cell cluster.

Trajectory analysis
Trajectory analysis for the early-responding supporting cell (ERSC) cluster in the gentamicin-treated sample was performed using Monocle3 v1.3.466 and Slingshot v1.6.097, setting subcluster-1 as the starting point. To gain deeper insights into the cellular dynamics across the clusters, we visualized the expression changes of marker genes for each subcluster (P<0.05; log2FC > 0.25) along with the trajectory.

Human datasets integration and comparisons
For human-specific comparisons, the datasets in Wang et al. 22, were used. We employed the Seurat (v5.0.3) scRNAseq workflow for normalization (SCTransform method), data integration using RPCA (IntegrateLayers function), clustering, and creating visualizations for the two patient-derived datasets scUntr-2 (967 cells) and VS-2 (1124 cells). Clustree v0.5.1, in addition to the differentially expressed genes between clusters, was used to select the optimal clustering resolution, with a final value of 0.6 (Supplementary Fig. 6b). Canonical markers were found using the FindAllMarkers function using the following parameters: returning all positive markers, return.thresh = 1, and min.pct=0.25. Correlation analysis between clusters was performed using ClusterFoldSimilarity v1.0.0. We also used an integrated dataset of human utricles (n=6), which was recently published22 and available on gEAR, and performed cluster-defining gene comparisons for hair cells, their subtypes, and supporting cells.

Comparison between human and mouse datasets
For cross-species comparison, three scRNA-seq datasets for mouse utricles were downloaded from NCBI Gene Expression Omnibus (GEO): GSM771038314; GSE11593413; GSE15596610. For GSM7710383 cells with >20% mitochondrial UMIs or >25,000 total UMIs were excluded during quality control. Data were normalized using SCTransform v2. Clusters representing the main sensory epithelium cell types (HCs, SCs, TECs) were identified using canonical markers (EPCAM, MYO7A, SOX2, ISLR). To enable cross-species integration, mouse gene symbols were matched to human orthologs using OrthoIntegrate98. The renamed adult mouse dataset (909 hair cells – 470 type I, 439 type II, 923 supporting cells, 441 transitional epithelial cells) was merged with human scUntr-2 dataset (87 hair cells – 68 type I, 19 type II, 843 supporting cells, 37 SC-like cells). Integration was performed using Seurat v5’s IntegrateLayers with RPCA and k.anchor=25 and clustering resolution of 0.1. Cell types were annotated based on marker expression, and cluster-specific differentially expressed genes were identified using FindAllMarkers and FindMarkers using MAST test. Conserved markers across species were determined with FindConservedMarkers. The lists of differentially expressed genes were filtered using the Housekeeping Transcript Atlas to remove housekeeping genes, and low-expression genes using pct_diff≤0.25. Mouse genes without human orthologues retained their original gene symbols. Top markers per cluster were used to generate heatmaps and volcano plots highlighting species-specific differences. For GSE115934 (P12+P100 datasets), we generated the gene count matrix from the FASTQ files using kallisto v0.46.099 (reference transcriptome: mm10 RefSeq genes). For GSE155966 (P6 dataset), we utilized the processed data downloaded from GEO. Next, we applied quantile normalization100 to both mouse datasets. Afterward, to compensate for sequencing depth differences, we normalized human and mouse datasets using SCTransform v0.3.3101 in the Seurat v4.1.027 workflow. To convert mouse gene symbols to corresponding human ortholog gene symbols, we first identified orthologs between human and mouse based on the vertebrate ortholog list from the Mouse Genome Informatics database (informatics.jax.org), downloaded in June 2022. If multiple orthologs were mapped between species, we chose the genes expressed in the human dataset. If multiple orthologs were present in the human dataset, we did not use those orthologs. Then, we selected the top 3000 common variable genes and integrated the human dataset and either of the two mouse datasets based on the RPCA method27. We visualized integrated datasets using PCA and UMAP to find the similarities and differences of cell types between species. We performed clustering with the resolution parameter of 0.4 determined by the Clustree R package. Then, we annotated the new clusters based on the proportion of original cell types. We applied log-normalization to raw count matrices for differential expression analysis and performed a two-sided Wilcoxon rank-sum test between species, with a Bonferroni Padj < 0.05, log2FC > |2|. Results were visualized using the EnhancedVolcano R package (v1.12.0).

Hematoxylin and eosin (H&E) staining and immunofluorescence
H&E staining of 10 μm-thick cross-sections was conducted using standard protocol for hematoxylin Gill II (Leica, 3801522), slides were mounted with Eukitt® mounting medium (Sigma-Aldrich, 03989), n>3. For immunofluorescence, utricle cross-sections were permeabilized with 1x PBS with 0.5% Triton X-100 (Fisher #BP151-500) for 30min, followed by 30min of antigen blocking with 10% donkey serum in 1x PBS 0.5% Triton X-100 containing 0.3 M glycine. Primary antibodies were incubated o.n. at 4°C (Supplementary Table 2); we performed heat-induced epitope retrieval using ACD (Advanced Cell Diagnostics) Target Retrieval Reagent for SCGB2A1. Species-specific Alexa-conjugated secondary antibodies (1:1000; Invitrogen) and DAPI (1 µg/mL; Invitrogen #D1306) were applied for 1h at RT in 1x PBS with 0.5% Triton X-100. ProLong™ Gold Antifade Mountant was used for mounting the slides.

Fluorescent in situ hybridization
RNAscope® assay (ACD) validated the candidate marker for hair cells and cluster-specific supporting cell markers. Briefly, antigen retrieval was performed at 99°C for 10min, followed by protease III treatment for 20min at 40°C. Probe hybridizations and amplification steps were carried out following the manufacturer’s protocol (RNAscope Multiplex Fluorescent Reagent kit v2 Assay, 323100-USM). Positive and negative control probes were used with every sample during every run. Sections were counterstained with DAPI and mounted, n≥3.

Transcripts quantification and statistical analysis
For each condition, control and gentamicin-treated samples (n=5, Supplementary Data 5d), a 200 µm segment of the sensory epithelium was selected for quantifying SLPI and ANKRD1 transcripts as well as nuclei. Nuclei counts were performed manually, and automated transcript quantification was conducted using Fiji software and ACD protocol to analyze RNAscope data. Briefly, the fluorescent image of the target probe was converted to a binary image (“Image > Adjust > Threshold”) using Otsu’s method to isolate the probe signal. The area of a single transcript (“Analyze > Measure”) was used as the lower threshold for automated particle analysis (“Analyze > Analyze Particles”). Clusters of probe signals were quantified by dividing the cluster area by the area of a single transcript. Data were normalized to the number of nuclei. Statistical analysis was performed with GraphPad Prism software. Comparisons between conditions were conducted using a two-tailed Welch’s t-test, with significance set to P<0.05. Data are presented as the mean ± standard deviation.

Scanning electron microscopy
Dissected utricles were fixed (2.5% glutaraldehyde in 0.1 M cacodylate buffer, pH 7.4) o.n. at 4°C. Tissues were washed in PBS for 15min and immersed in 1% buffered OsO4 for 1.5h at RT, washed again and dehydrated using a series of ethanol with increasing concentration (30%, 50%, 70%, 90%, 95%, and 100%) in a rotating rack. Utricles were fully immersed in ethanol during this process, as exposing the samples to the air can damage the architectural structures. Then, samples were transferred into microporous capsules for sample protection during critical point drying, filled with 100% ethanol. Samples were rinsed three times in a CO2 aqueous solution and maintained at a pressure of 800 psi. When the pressure increased to 1500 psi at 42°C, samples were critical-point dried. Then, utricles were mounted on stubs covered with adhesive carbon sheets, gold-sputtered for 2min, and stored at 37°C until imaging; n=3.

Imaging
Imaging was carried out using a Leica DIM6000 SP8 Lightning Confocal/STED microscope, with gating function engaged, and a Nikon A1 confocal. For each sample, positive and negative controls were used to set the background threshold, laser power, and gain. These settings were then applied to capture pictures using 20x/0.75 NA, 40x/1.3 NA and 63x/1.4 NA oil immersion objectives. Images were captured using z-projection from a stack covering the entire depth of the utricle section. Z-stack step size was 0.35 µm and 0.2 µm for SCGB2A1. For whole-mount utricle, Z-stack step size was 0.15 µm. For the analysis of SLPI and ANKRD1 transcripts in control and gentamicin-treated samples, representative images from in situ experiments were captured using a Leica DMi8 inverted microscope with a 40x/0.95 DRY objective. H&E stained image was acquired using a Nikon Eclipse Ti microscope equipped with a DS-Ri2 camera and a 20×/0.75 NA objective under brightfield illumination and tiling mode. Scanning electron micrographs were acquired with Hitachi S-3400N microscope. Image processing was performed using Fiji.

Statistics & reproducibility
For this exploratory study, no statistical method was used to predetermine sample size, and sex- and gender-based analyses were not performed. However, the findings apply to both sexes, and sex information was collected via self-report. All experiments included multiple replicates and, unless otherwise specified, were performed in at least triplicate. Statistical analyses are detailed in the relevant sections. A P-value < 0.05 was considered statistically significant. Samples were randomly assigned to experimental groups, ensuring representation across both sexes and a range of ages (21–75 years). Investigators were not blinded to group allocation during experiments or outcome assessment. Data excluded from the analyses were limited to poor-quality cells identified and removed during scRNA-seq quality control filtering.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Luca et al. present a manuscript detailing a single-cell transcriptome analysis of the human adult utricle utilizing the 10X platform. The study incorporates cluster analysis to identify clusters and their respective identities. The researchers distinguish two types of hair cells termed as type I and type II HCs, along with multiple clusters of supporting cells. To gain further insights, they conduct a cross-species comparative analysis by comparing their dataset with mouse data, revealing some notable differences. Additionally, employing the same scRNAseq method, the authors characterize the transcriptome of utricle cells following in vitro gentamicin treatment. A comparison is made between the treated samples and control conditions. The findings suggest damage-induced molecular changes in supporting cell identity, potentially contributing to the regenerative capacity of the organ.\nWhile the initial concept of the study holds great promise for the field, the overall quality of the datasets, analysis, and data presentation is suboptimal, preventing robust conclusions about the identified cell types and expressed molecules in both control and treated conditions. A significant drawback is the notably small sample size, particularly the limited data from only one utricle during the initial characterization, hindering a comprehensive analysis of cell types. Furthermore, there is a recurring need for clarification and a cohesive representation of marker genes, scattered across various panels and figures or, at times, absent. The absence of an original table detailing the normalized expression levels of all genes in the cells, per cluster, is also limiting appreciation of gene expression. Additionally, there is minimal confirmation of their clusters in situ. Lastly, it is worth noting that a related publication from the lab of Alan Cheng, available on Biorxiv for over a year (doi.org/10.1101/2022.10.29.514378), features a larger number of cells and genes per cell, with large effort for in situ confirmation, but goes unacknowledged in this study; moreover, it potentially impacts the novelty of the current research.\nSpecific comments:\nLine 91 and 101: a recent study generating single cell data from human utricle is available on Biorxiv, Wang et al. (doi.org/10.1101/2022.10.29.514378); the authors should refer to it and compare their data.\nLine 130: the DEGs are not obvious, questioning the cluster identification. Also, Mapt is not clearly expressed in SCs and HC-I in Suppl. Data 1c; these are too few positive cells to be considered as “being expressed”. Likewise, given the limited gene coverage, the absence of a marker such as Spp1 in the current dataset should not be considered definitive evidence of its non-expression.\nLine 136: the authors should be more cautious with such conclusion, based on the above comment.\nLine 140: The majority of genes specific to each cluster lack representation in figures or tables. Additionally, it is unclear whether genes such as Id1 or Zbtb20 are genuinely cluster-specific. The depiction of Sox2 expression in Figure 2e is also not straightforward. The authors should be more convincing concerning their new marker genes and their confirmation in situ. A potential limitation stems from the low gene coverage and the reliance on a single sample for the analysis. Moreover, it would be beneficial for the authors to conduct a comparative analysis or integrate their data with that presented in doi.org/10.1101/2022.10.29.514378, where several clusters of supporting cells in the human utricle were identified. This might help provide a more solid i/ cell identity characterization and ii/ list of marker genes.\nLine 153: in this paragraph, with regard to panels fig.2f(i), the very limited number of HC-II (19 cells), coupled with the general low gene coverage in the dataset, most likely limit any comprehensive cell signaling analysis between SCs and HC-II.\nLine 158: Do the authors find Ncl to be expressed in HC-I, which would support the possibility of a Ptn-Ncl interaction between SCs and HC-I.\nLine 189: Cross-data analysis is limited due to the disparate platforms/methods employed for generating the datasets, along with variations in the sequencing depth achieved. Any difference in expression may potentially be artefacts resulting from these divergences. The authors should provide additional information and essential graphs for proper integration of mouse and human datasets.\nLine 196: P6 dataset is hardly comparable to adult, given the expected differences in gene expression, therefore a comparative analysis is questionable.\nLine 334: Data are not well integrated, in contrast to the authors’ claim. Moreover, there is considerable variability in gene expression coverage across datasets, with notably extremely low coverage in two samples, averaging only 105 and 346 detected genes per cell.\nLine 344: The ERSC cluster is exclusive to scGENT2, characterized by notably low gene coverage, indicating lower data quality. ERSC is not present in scGENT1, which has a higher gene coverage. It is unclear whether the ERSC cluster therefore represents a true population or results from artifacts associated with low quality samples.", "reviewer_2": "In the current manuscript, Luca et al. have performed transcriptomics analysis of the adult human utricle to define cell types, gene expression patterns and responses to treatment with an ototoxic aminoglycoside antibiotic. The data are comprehensive and importantly use human tissue, which has been evaluated in the past only in a very limited manner. The data provide a rich resource for the research community, highlight differences between mice and humans and reveal transcriptional changes in the utricle that are caused by aminoglycosides, which might be useful for further investigation into the mechanisms of ototoxicity.\nMajor comments:\n1.In general, the data are documented comprehensively and with appropriate bioinformatics tools. However, I am not sure it is appropriate to use the velocity analysis in the way done here. True, the analysis informs about differentiation trajectories, but many parameters come into play. Most importantly, in Fig. 7 velocity analysis is used to compare controls and gentamicin treated samples. I do not think that the data can be compared (for example length of arrows) without a detailed statistical analysis and consideration of variation between the samples. I am not sure if these panels add useful information and they could perhaps be eliminated.\n2.Fig. 2F and the supplement, the authors analyze cell communication and infer signaling pathways. This type of analysis always leads to a result but the meaning of the result is unclear. Also, the values that are reported seem very low, further reducing confidence in the conclusions. Without experimental validation, I suggest to eliminate the panels and associated text, which remains highly speculative. This would also help to streamline the manuscript a bit and help to focus on the basic message.\n3. In Fig. 6b, the three samples for controls cluster nicely and segregate away from the three treated samples. However, the three treated samples also completely segregate from each other. Why is this? In the subsequent analysis, the authors focus on one sample each for control and treated. Are the data replicated in the other samples? Given the variation in the three treated samples, one wonders if comparisons to controls lead to comparable results for the three samples. Otherwise, the differences between the two analyzed samples could just be random variation or due to some technical issues.\n4. The authors state “…..potentially initiate a regenerative response via an early-responding supporting cell population…” (p2 and similar statements elsewhere). The conclusions about an early response population and regeneration are not supported by any data. I suggest that the authors rephrase this throughout the text. In the discussion, they may want to speculate on this point, but it is at this point hypothetical. Alternatively, if the authors wish to show that this is a regenerative response, they should do additional experiments (for example at least some rudimentary lineage tracing in their explants using BrdU and staining of the cells with the identified molecular markers; if regeneration happens and is non-replicative, they still would have to demonstrate trans-differentiation)\n5. I do not find the discussion particularly illuminating. It is entirely speculative with little reference to the published literature to put findings into context. I would suggest to briefly summarize the key findings and then discuss in a few paragraphs key points, as well as comparison to already published data.\nMinor comments\nLine 100: 12 patient-derived samples. The authors may want to explain here in more detail what the 12 samples are. Only one was used for scRNAseq it seems.\nLine 116: the authors briefly mention Fig. 1. The figure should be discussed in more detail in the text. What exactly is panel (b)?\nLine 130 fand following: cite references for all cell type markers\nLine 148: “..marker for SC-5 (Fig. 2d-e)”: describe what is shown in the panels\nLine 168: this seems like a trivial statement and no literature is cited to support it.\nLine 186: “..human type II HC….”. How do you know these are type II HCs? Define your criteria how you established this. Do all type II HCs express CALB1? Some quantification would be important\nLine 201: it is “4d” not “4D”\nLine 209: I do not find the data in Fig. 4g-i, ii convincing. It is not clear to me especially from panel “I” that SCGB2A1 is expressed.\nLine 250: something is wrong with the image in insert 5d; maybe it happened during PDF conversion?\nLine 330: can you quantify the data in Fib. 7g; while the data look convincing, it would be far more so with quantification\nLine 348: the authors talk about “regenerative” features. That is purely speculative and should be changed; as outlined in the major comments, the entire paragraph needs reworking to just summarize the data;\nLine 383/384: purely speculative and should be removed", "reviewer_3": "Human utricle scRNASeq. Dabdoub and Luca Nat. Communications 2024\nThe authors performed scRNASeq on human patient-derived utricles at homeostasis and after damage. They discovered six support cell types, one of which has not previously described, and characterize the transcriptional responses in support cells to hair cell death that suggest a potential for hair cell regeneration. This study is the first to describe a single-cell atlas of the human utricle sensory epithelium and the molecular events following hair cell damage in human ear sensory epithelia. Further, they conducted cross-species comparisons between human and mouse utricles, identified human-specific or enriched genes as putative new markers for sensory and non-sensory cells, highlighting species-specific differences of the balance organ. The study is very interesting to the hair cell regeneration field and groundbreaking, as it provides proof of principle that human sensory tissues can be and should be studied, as mouse tissues are not identical and show transcriptional differences. However, I would have liked to see a better characterization of the support cell clusters in the intact tissue using in situ hybridization. Are the different SC populations intermingled or spatially restricted? What are the ratios of the different cell types in the tissue etc. Also, the figures should be better explained in the text and figure legends.\nMajor critique:\n1) The identification of six different SC clusters is very interesting but needs to be validated by in situ hybridization with cluster markers. This is the first study describing different support cell types based on transcriptomic data. Unfortunately, the cellular composition/histology of the human utricle is not well described. Are these different SC populations intermingled or spatially segregated? Are some of the populations sparser than others? scRNA-seq data is not always reliable when it comes to estimating the size of a population.\n2) Line: 158: The authors state: ‘SC-3 and SC-4 interact with type I (but not type II) HCs via PTN signaling, and SC-4 also via MK (midkine) signalling (Fig. 2f-i).\nThe authors should show by in situ hybridization the location of SC-2 and SC-4 and their relationship to type I HCs. Does the cell-cell communication (L-R pairs) occur in adjacent cells or are the signals diffusible?\n3) SCLCs: As SCLCs are in the vicinity of dead cells, could they be a cell state, rather than a different SC type? The authors seems to agree alter in the manuscript that this population is a state change in an existing population but they still refer to these cells as a different cell type.\n4) Line 202: DEG between mouse and human data sets. In situ hybridization with some human specific genes should be performed in the mouse to validate that they are indeed missing or much more lowly expressed. Technical reasons could explain why some genes are detected by scRNA-seq only in only one of the species.\n5) Please discuss similarities and differences between the results of the bulk and scRNASeq analyses after gentamycin treatment.\n6) It is a bit surprising that integration of different data sets (mouse and human; treated, untreated, control, Extended Data 5) does not lead to a better mixing of the cells. As the authors state, cells still cluster by treatment. One would expect that some support cells do not respond to gentamycin and therefore these cells should intermingle, irrespective of from which sample they came from. Maybe the authors should try a different integration method as validation that cells from the different samples show transcriptional differences, even though they are the same cell population.\nMinor Critique:\n1) ‘Fig. S1’ should be referred to as Extended Data Fig. 1 etc.\n2) This is a comment for the authors and the journal: The extended data files (power point files) are not labeled (please add Figure numbers to all your Figure files) and the titles also do not reveal the Figure numbers in the ‘Zip of files for the reviewer’.\n3) Lines 137- 146: which Figures correspond to this paragraph? Fig. 2C is not cited.\n4) Line 155: ‘SC-3and SC-4 express the ligand PTN (pleiotrophin), and SC-5 expresses the receptor SDC4 (syndecan-4)’. I cannot find the figure that shows that.\n5) Line 207: ‘We validated SCGB2A1 expression in patient-derived samples (n≥3) at the transcript and protein levels using in situ hybridization (Fig. 4g-i) and IF, respectively.\nShould be called (Fig. 4g (i) and 4g (ii)).\n6) This figure also needs a better description. (i) does not show which cells express SCGB2A1. Do the authors conclude that they are hair cells because of the absence of Sox2? Likewise, (ii) the protein does not appear to be restricted to hair cells.\n7) Line 296: Additionally, this analysis did not infer any cellular state transition (Fig. 7a inset).\nPlease explain. How would a state transition look like?\n8) Line 347: After only 24h of gentamicin treatment, we detected a cluster of 122 supporting cells with unique transcriptomic regenerative features (Fig. 8a, Table S6).\nI cannot tell where in the figure I can see the 122 cell cluster. Is it the one called ERSC? If yes, please label/cite better.\n9) Extended Data Fig. 5: Figure legend is too short. What are the samples in a, c, e? Title: What does top 3000 HVG mean?\n10) The authors conclude that human utricle tissue has the potential to regenerate based on the upregulation of genes after 14h (such as early response genes). However, it is possible that these genes are always upregulated in response to injury, even in the absence of subsequent regeneration. The authors should keep this in mind and point out the genes that are truly only induced in regeneration contexts."}, "author_response": ""}, {"round": 2, "reviewer_comments": {"reviewer_1": "I appreciate the opportunity to review this revised manuscript and acknowledge the effort put into addressing previous concerns. However, I remain concerned about the quality of the original RNA-seq dataset, which, like any scientific method, must meet established standards to ensure reliability for both present and future comparative analyses. High-quality datasets are essential, particularly when serving as reference landmarks in a field. It is surprising indeed that, despite years of advancements in single-cell RNA sequencing, datasets with such low gene detection rates continue to be used for large-scale analyses and major conclusions. In this case, it is critical to limit interpretations strictly to what can be supported by the data. Below, I outline a few specific concerns that highlight these limitations.\nCOMMENTS:\nIn reference to a previous comment, Line 130: the DEGs are not obvious, questioning the cluster identification. Also, Mapt is not clearly expressed in SCs and HC-I in Suppl. Data 1c; these are too few positive cells to be considered as “being expressed”. Likewise, given the limited gene coverage, the absence of a marker such as Spp1 in the current dataset should not be considered definitive evidence of its non-expression.\nThe response from Authors: According to 10x Genomics, gene detection in the range of 500-5000 is considered normal, and our median gene counts per sample ranged from 475-1944 genes for uncultured (scUntr-2), control (scCtrl-1), and gentamicin-treated (scGent-2) samples; thus, we have enough information to draw conclusions from this data. Moreover, optimal clustering parameters were verified by exploring each cluster’s stability using the Clustree algorithm (Please see Extended Data Figs. 7a-h). In addition to clarifying the DEGs on line 130, we edited statements and relative feature UMAP plots regarding the specificity of MAPT and SPP1 in human samples, as we found that in Wang et al., dataset these mouse genes were also not specific, confirming that our outcome is consistent between human datasets and is not affected by gene coverage (see Extended Data Fig. 3f, Lines 224-227).", "reviewer_2": "The authors have done a good job in revising the manuscript. Analytic tools and marker genes are described more comprehensively making it easier to follow the flow of the text. An important and interesting addition is the integration of the data presented here with a published dataset, enhancing confidence in the robustness of the findings. I think that the manuscript is a valuable resource for the research field because it presents one of only a few datasets that are available from the human utricle.", "reviewer_3": "The authors have addressed all my comments, added new figures and clarified the text. I only have one minor comment:\nFigure 2 legend:\nPlease explain how the proportions in cell numbers were calculated. Eg., in Fig2e” say that '57' is the total number of SC5 cells as shown in legend in Fig. 2a."}, "author_response": "“A significant drawback is the notably small sample size, ...”\nThank you for the insightful comments. I hope you will find that we have addressed all\nof them.\nSeveral studies on RNA-seq corroborate that human variability is not a limitation for de\nnovo discoveries and/or for detecting differentially expressed genes, even with a small\ncohort per condition (Basile et al., Genome Med. 2021, DOI: 10.1186/s13073-021-00941-\n8; Slyper et al., Nat Med. 2020, DOI: 10.1038/s41591-020-0844-1). In fact, we discovered\nand validated a novel support cell-like cell population (SCLC), identified human-enriched\ncell-type-specific genes, and validated differentially expressed genes between control\nand treated samples. Furthermore, although the initial analysis was done using a small\ncohort, all the validation experiments were conducted with n ≥ 3 (e.g., Figs. 7g,g’),\novercoming any initial sample size drawback.\nAdditionally, we have now integrated our dataset with the one from Wang et al., and\nshow that we obtained similar results in terms of main cell type clusters (i.e., hair cells,\nsupporting cells, SC-like cells) as well as cluster-defining genes; please see Extended\nData Figs. 3a-i, 4d,e.\n“Furthermore, there is a recurring need for clarification and a cohesive representation of\nmarker genes …”\nTo ensure a clear and cohesive representation of marker genes throughout the\nmanuscript, we have revised the text, figures, and legends. Specifically, we have ensured\nthat marker genes are consistently represented and appropriately cross-referenced in all\nrelevant sections.\n“The absence of an original table …”\nWe now provide the Reviewers access to the datasets on GEO, where the original data is\ndeposited. Data includes the normalized expression levels of all genes within the cells,\ndelineated by cluster.\n“Additionally, there is minimal confirmation of their clusters in situ.”\nWe also performed additional in situ experiments (RNAscope) to confirm other clusters,\nstrengthening the robustness of our findings; see Figs. 2e-e’’.\n“Lastly, it is worth noting that a related publication from the lab of Alan Cheng, …”\nWe do not usually cite non-peer-reviewed publications. We were aware of Alan Cheng’s\ngroup study as both of our groups presented the results at the Association for Research\nin Otolaryngology conference in the past and had many discussions about our studies.\nFurthermore, the data from Cheng’s group was not available during that time for\nanalysis or comparison. However, while our manuscript was in revision, Dr. Cheng’s\ngroup published their study (Wang et al., 2024), and thus in our revised manuscript, we\nnow cite it and refer to the publication where appropriate, including in the Discussion\n(Lines 95, 151, 157, 173, 184, 217, 434, 442, 461, and 966). Our research offers distinct\ncontributions. Specifically, our study is the first to focus on the human utricle using a\ndamage paradigm, providing bulk and single-cell transcriptomic datasets that\ninvestigate the response to damage. These novel datasets reveal early damage-induced\nmolecular changes occurring after only 24 hours, enhancing our understanding of the\nhuman utricle’s damage response and paving the way for future studies targeting\ntherapeutic interventions for balance disorders. Furthermore, we validate different\nsupporting cell populations, discovering a supporting cell-like cell population.\nSpecific comments:\nLine 91 and 101: a recent study generating single cell data from human utricle is\navailable on Biorxiv, Wang et al. (doi.org/10.1101/2022.10.29.514378); the authors\nshould refer to it and compare their data.\nSince 2022, the dataset in Wang et al.,’s preprint was not available on bioRxiv nor in any\nother public database repository (e.g., GEO); therefore, we could not perform any\ncomparative analyses at the time of our submission in Dec 2023. However, while our\nmanuscript was in revision, Wang et al., published their study, and the data became\naccessible. We have now referred to this publication, integrated and compared the\ndatasets as requested, validating results and marker genes (see Figs. 3a-i and 4d,e). In\naddition to the two populations of hair cells (type I and type II), we could also detect the\nSC-like cells cluster in their dataset. The new analysis is shown in Extended Data Figs. 3a-\ni.\nLine 130: the DEGs are not obvious, questioning the cluster identification. Also, Mapt is\nnot clearly expressed in SCs and HC-I in Suppl. Data 1c; these are too few positive cells\nto be considered as “being expressed”. Likewise, given the limited gene coverage, the\nabsence of a marker such as Spp1 in the current dataset should not be considered\ndefinitive evidence of its non-expression.\nAccording to 10x Genomics, gene detection in the range of 500-5000 is considered\nnormal, and our median gene counts per sample ranged from 475-1944 genes for\nuncultured (scUntr-2), control (scCtrl-1), and gentamicin-treated (scGent-2) samples;\nthus, we have enough information to draw conclusions from this data. Moreover,\noptimal clustering parameters were verified by exploring each cluster’s stability using\nthe Clustree algorithm (Please see Extended Data Figs. 7a-h). In addition to clarifying the\nDEGs on line 130, we edited statements and relative feature UMAP plots regarding the\nspecificity of MAPT and SPP1 in human samples, as we found that in Wang et al., dataset\nthese mouse genes were also not specific, confirming that our outcome is consistent\nbetween human datasets and is not affected by gene coverage (see Extended Data Fig.\n3f, Lines 224-227).\nLine 136: the authors should be more cautious with such conclusion, based on the\nabove comment.\nWe have rephrased our conclusions at the end of the paragraph, now Lines 228-229.\nLine 140: The majority of genes specific to each cluster lack representation in figures or\ntables. Additionally, it is unclear whether genes such as Id1 or Zbtb20 are genuinely\ncluster-specific. The depiction of Sox2 expression in Figure 2e ns is also not\nstraightforward. The authors should be more convincing concerning their new marker\ngenes and their confirmation in situ. A potential limitation stems from the low gene\ncoverage and the reliance on a single sample for the analysis. Moreover, it would be\nbeneficial for the authors to conduct a comparative analysis or integrate their data with\nthat presented in doi.org/10.1101/2022.10.29.514378, where several clusters of\nsupporting cells in the human utricle were identified. This might help provide a more\nsolid i/ cell identity characterization and ii/ list of marker genes.\nWe used cluster-defining genes CYR61 and MANSC4 to validate SC2 and SC3 clusters via\nin situ hybridization (RNAscope), confirming in silico outcomes. RNA expression in the\nsensory epithelium shows the co-expression of these SC markers with SOX2, a pan-SC\nmarker. The new data is in Figs. 2e-e’’.\nWe conducted dataset integration as well as comparative analyses between our data\nand the one in Wang et al., finding that cluster-defining genes between supporting cells,\nSC-like cells, and hair cell subtypes were considerably overlapping, confirming our cell\nidentity characterization; please see Extended Data Figs. 3c-h, and 4d,e. Again, the data\nfrom the Wang preprint was not available at the time of our submission.\nLine 153: in this paragraph, with regard to panels fig.2f(i), the very limited number of\nHC-II (19 cells), coupled with the general low gene coverage in the dataset, most likely\nlimit any comprehensive cell signaling analysis between SCs and HC-II.\nWe used CellChat which allows cell-cell communication where the minimum number of\ncells required in each cell group for cell-cell communication is 10\n(https://htmlpreview.github.io/?https://github.com/jinworks/CellChat/blob/master/tutori\nal/CellChat-vignette.html.). However, to streamline the manuscript, we removed CellChat\noutcomes as suggested by Reviewer #2 (Major comments #2).\nLine 158: Do the authors find Ncl to be expressed in HC-I, which would support the\npossibility of a Ptn-Ncl interaction between SCs and HC-I.\nThank you for the remarks. Yes, the three receptors (SDC4, PTPRZ1, and NCL) are\nexpressed in HC type I; however, as mentioned in the above comment, we removed\nCellChat analysis.\nLine 189: Cross-data analysis is limited due to the disparate platforms/methods\nemployed for generating the datasets, along with variations in the sequencing depth\nachieved. Any difference in expression may potentially be artefacts resulting from these\ndivergences. The authors should provide additional information and essential graphs for\nproper integration of mouse and human datasets.\nWe acknowledge that differences in platforms, methods, or sequencing depths present\nlimitations for cross-species comparisons. However, at the time of this submission, only\ntwo mouse datasets were available for human-mouse comparisons (Jan et al., Cell\nReports 2021, DOI: 10.1016/j.celrep.2021.109358; McInturff et al., Biol Open 2018, DOI:\n10.1242/bio.038083). One dataset (P100) included a very small number of cells (25) and\nwas confined to hair cells, while the other dataset provided data for both hair cells and\nsupport cells but was restricted to P6 as the highest age available. The lack of\ncomprehensive mouse datasets covering later developmental or adult stages can\nrepresent a limitation for direct comparisons. However, we were able to detect and\nvalidate human-enriched genes such as CALB1 (Figs. 3c,e and 4e-g) and SCGB2A1 (Figs.\n4f-g’ and Extended Data Figs. 4d,e).\nWe also re-ran the Reciprocal PCA method using the latest package and changed the\nhyperparameter for the intensity of cell mixing. We also modified the integration plot’s\ncolours to enhance visualization. In the new graphs, the integration of hair cells and\nsupporting cells is more evident, while human SC-like cell and mouse transitional\nepithelial cell (TEC) clusters remain distinct (see Figs. 4a-c and Extended Data Fig. 4a).\nLine 196: P6 dataset is hardly comparable to adult, given the expected differences in\ngene expression, therefore a comparative analysis is questionable.\nAt the time of this submission, Jan et al., was the only published mouse dataset\ncontaining both hair cell and supporting cell information. While developmental\ncomponents could play a role in the comparison, there is a lack of information about\nhuman-enriched genes in the utricle. Our goal was to perform a cross-species\ncomparative analysis to identify some of those, regardless of age. Despite the age\ndifferences between mouse P6 and adult human hair cells, we validated the analysis on\nthe mouse P100 dataset (Mclnturff et al.,) by identifying in silico genes enriched in\nhuman hair cells, such as SCGB2A1. This gene was confirmed using in situ hybridization\nand immunofluorescence (n ≥ 3), Figs. 4f-g’. Additionally, we validated that CALB1, a\nmarker known to be specific for mouse hair cell type I, is uniquely expressed in hair cell\ntype II in human (Fig. 3e).\nLine 334: Data are not well integrated, in contrast to the authors’ claim. Moreover, there\nis considerable variability in gene expression coverage across datasets, with notably\nextremely low coverage in two samples, averaging only 105 and 346 detected genes per\ncell.\nTo address this, we have now employed the Reciprocal PCA integration method,\nreplacing the original Harmony algorithm. Reciprocal PCA led to a robust batch\ncorrection and addressed the issue of cell mixing across different datasets (see Extended\nData Figs. 6a-c’).\nRegarding gene expression variability, we have taken measures to mitigate this\nlimitation by carefully processing the data and filtering low-quality cells.\nLine 344: The ERSC cluster is exclusive to scGENT2, characterized by notably low gene\ncoverage, indicating lower data quality. ERSC is not present in scGENT1, which has a\nhigher gene coverage. It is unclear whether the ERSC cluster therefore represents a true\npopulation or results from artifacts associated with low quality samples.\nWe performed a damage paradigm to investigate whether there are supporting cells\nthat could immediately respond to an ototoxic insult and which genes are potentially\ninvolved. While scGent-1 exhibited a higher gene coverage, the total number of cells\ncaptured is approximately 10-fold lower (283 cells) than scGent-2 (2670 cells). Given that\nthe early-responding SC (ERSC) cluster is represented by a relatively small population,\nrepresenting 4.6% (122/2670) of the cells, its detection in scGent-1 poses a challenge.\nDespite the low percentage of ERSCs in the whole utricle sensory epithelium, we\nidentified a few cells stained with cluster-specific markers such as EDNRB or MMP9 in\nutricle cross-sections, validating the ERSC cluster via in situ hybridization (RNAscope).\nThe newly generated data are in Fig. 8h.\nReviewer #2 (Remarks to the Author):\nIn the current manuscript, Luca et al. have performed transcriptomics analysis of the\nadult human utricle to define cell types, gene expression patterns and responses to\ntreatment with an ototoxic aminoglycoside antibiotic. The data are comprehensive and\nimportantly use human tissue, which has been evaluated in the past only in a very\nlimited manner. The data provide a rich resource for the research community, highlight\ndifferences between mice and humans and reveal transcriptional changes in the utricle\nthat are caused by aminoglycosides, which might be useful for further investigation into\nthe mechanisms of ototoxicity.\nWe thank the Reviewer for emphasizing the scarcity of human inner ear data and the\ncritical importance of conducting experiments on the human vestibular organs.\nMajor comments:\n1.In general, the data are documented comprehensively and with appropriate\nbioinformatics tools. However, I am not sure it is appropriate to use the velocity analysis\nin the way done here. True, the analysis informs about differentiation trajectories, but\nmany parameters come into play. Most importantly, in Fig. 7 velocity analysis is used to\ncompare controls and gentamicin treated samples. I do not think that the data can be\ncompared (for example length of arrows) without a detailed statistical analysis and\nconsideration of variation between the samples. I am not sure if these panels add useful\ninformation and they could perhaps be eliminated.\nWe accepted the Reviewer’s suggestion and have now removed velocity analysis.\n2.Fig. 2F and the supplement, the authors analyze cell communication and infer\nsignaling pathways. This type of analysis always leads to a result but the meaning of the\nresult is unclear. Also, the values that are reported seem very low, further reducing\nconfidence in the conclusions. Without experimental validation, I suggest to eliminate\nthe panels and associated text, which remains highly speculative. This would also help to\nstreamline the manuscript a bit and help to focus on the basic message.\nWe removed CellChat outcomes as suggested and agree with the Reviewer that it helps\nstreamline the study.\n3. In Fig. 6b, the three samples for controls cluster nicely and segregate away from the\nthree treated samples. However, the three treated samples also completely segregate\nfrom each other. Why is this? In the subsequent analysis, the authors focus on one\nsample each for control and treated. Are the data replicated in the other samples?\nGiven the variation in the three treated samples, one wonders if comparisons to controls\nlead to comparable results for the three samples. Otherwise, the differences between the two\nanalyzed samples could just be random variation or due to some technical issues.\nIn the bulk RNA-seq analysis, we included six samples – three for each group (control\nand treated, n = 3) – with age of the donors spanning between 21 and 75 years old (Fig.\n6c). Although in the analysis, we accounted for confounding effects like sex and age by\nperforming regression analysis, age is one of the main factors affecting the hair cell\npopulation in the sensory epithelium. Therefore, the variance observed among treated\nsamples is likely due to individual responses to ototoxic treatment since gentamicin\ndirectly damages the hair cells. Despite this variability, the gentamicin-treated samples\nhave very similar transcriptomic profiles and show similar differences compared to\ncontrol samples; please see Fig. 6c.\nDue to the millimetric size of the utricular sensory epithelium (see Fig. 1b), at the time of\nthis study, performing bulk and single-cell RNA sequencing in the same sample donor\nwas not feasible with the technologies available as the cell input/output will be very low.\nTherefore, for the scRNA-seq analysis, we used two additional patient-derived utricles of\nsimilar ages, one for each condition (control and treated), and performed differential\nexpression analysis. Among the differentially expressed genes, we found SLPI to be\nsignificantly downregulated in both bulk and single-cell datasets. We validated this\noutcome via in situ hybridization (RNAscope, n=5). Please see Figs. 7g,g’ and the answer\nto Line 330 below.\n4. The authors state “…..potentially initiate a regenerative response via an early-\nresponding supporting cell population…” (p2 and similar statements elsewhere). The\nconclusions about an early response population and regeneration are not supported by\nany data. I suggest that the authors rephrase this throughout the text. In the discussion,\nthey may want to speculate on this point, but it is at this point hypothetical.\nAlternatively, if the authors wish to show that this is a regenerative response, they\nshould do additional experiments (for example at least some rudimentary lineage\ntracing in their explants using BrdU and staining of the cells with the identified\nmolecular markers; if regeneration happens and is non-replicative, they still would have\nto demonstrate trans-differentiation)\nWe rephrased such statements throughout the text and only mentioned this point in the\nDiscussion.\n5. I do not find the discussion particularly illuminating. It is entirely speculative with little\nreference to the published literature to put findings into context. I would suggest to\nbriefly summarize the key findings and then discuss in a few paragraphs key points, as\nwell as comparison to already published data.\nWe have modified the Discussion as suggested.\nMinor comments\nLine 100: 12 patient-derived samples. The authors may want to explain here in more\ndetail what the 12 samples are. Only one was used for scRNAseq it seems.\nThe samples are detailed in Supp. Table 1 where samples are grouped by experiments.\nWe now also describe them better in the paragraph and in the Methods. We used six\nsamples for bulk RNA-seq (n = 3 for control and treated) and six for scRNA-seq: two for\neach condition (untreated, control and gentamicin-treated) – Figs. 6b,c and Extended\nData Figs. 6a-c’.\nLine 116: the authors briefly mention Fig. 1. The figure should be discussed in more\ndetail in the text. What exactly is panel (b)?\nWe expanded the description of Fig. 1 in the text, including panel (b), which shows a\ndissected adult human utricle, the whole balance organ.\nLine 130 fand following: cite references for all cell type markers\nWe added the references for all cell markers mentioned here, now Lines 133-137.\nLine 148: “..marker for SC-5 (Fig. 2d-e)”: describe what is shown in the panels\nWe have improved the description of all the panels, including the one for this figure,\nnow Fig. 2e’’.\nLine 168: this seems like a trivial statement and no literature is cited to support it.\nWe have removed this statement.\nLine 186: “..human type II HC….”. How do you know these are type II HCs? Define your\ncriteria how you established this. Do all type II HCs express CALB1? Some quantification\nwould be important\nWe have performed triple immunofluorescence staining with antibodies against CALB1\n(novel HC type II marker), MYO7A and SOX2, two well-known markers for all hair cells\nand type II HCs, respectively. We clarified this in the manuscript and added in silico\nCALB1/SOX2 quantification. The data showed that 95% (18/19) of type II hair cells\nexpress CALB1, 58% (11/19) express SOX2, and 53% (10/19) express both markers (see\nUMAP FeaturePlots in Fig. 3e, left panel. These data support the immunofluorescence\nresults in Fig. 3e right panel and suggest that CALB1 is a specific marker for human HC\ntype II.\nLine 201: it is “4d” not “4D”\nWe have changed this.\nLine 209: I do not find the data in Fig. 4g-i, ii convincing. It is not clear to me especially\nfrom panel “I” that SCGB2A1 is expressed.\nWe performed additional immunofluorescence staining on utricle cross-sections (n ≥ 3),\nusing SCGB2A1, the pan-HC marker MYO7A, and the SOX2 marker for type II hair cells\nand supporting cells. In particular, we also conducted the experiment on the serial\nsection corresponding to the in situ experiment (RNAscope) in Fig. 4f’. The new data in\nFig. 4g’ demonstrates the co-expression of these markers in hair cells, confirming the\nfindings from the RNAscope experiments. We added UMAP plots to show in silico co-\nexpression of SCGB2A1 with SOX2 and MYO7A and relative counting; see Figs. 4f,g.\nLine 250: something is wrong with the image in insert 5d; maybe it happened during\nPDF conversion?\nTo convey the finding better, we have modified the left bottom inset and replaced the\nDIC picture (right upper inset) with the UMAP plots showing gene co-expression and\nrelative counting. The image has been updated in the revised manuscript.\nLine 330: can you quantify the data in Fib. 7g; while the data look convincing, it would\nbe far more so with quantification\nWe have included in situ quantification data for SLPI and ANKRD1 transcripts, now\npresented in Figs. 7g,g’; please see also below. Statistical analysis confirms that the\nobserved RNA expression levels are significantly different between control and\ngentamicin-treated samples for both transcripts.\nQuantification of SLPI and ANKRD1 transcripts per cell in control and treated sensory epithelia (n = 5)\ndepict significant downregulation of SLPI and upregulation of ANKRD1 transcripts following gentamicin\ntreatment. * Indicates P < .05 by Welch’s t-test. Graphs show the mean with standard deviation.\nLine 348: the authors talk about “regenerative” features. That is purely speculative and\nshould be changed; as outlined in the major comments, the entire paragraph needs\nreworking to just summarize the data;\nWe have modified the paragraph and summarized the findings.\nLine 383/384: purely speculative and should be removed\nWe have removed this sentence.\nReviewer #3 (Remarks to the Author):\nHuman utricle scRNASeq. Dabdoub and Luca Nat. Communications 2024\nThe authors performed scRNASeq on human patient-derived utricles at homeostasis\nand after damage. They discovered six support cell types, one of which has not\npreviously described, and characterize the transcriptional responses in support cells to\nhair cell death that suggest a potential for hair cell regeneration. This study is the first to\ndescribe a single-cell atlas of the human utricle sensory epithelium and the molecular\nevents following hair cell damage in human ear sensory epithelia. Further, they\nconducted cross-species comparisons between human and mouse utricles, identified\nhuman-specific or enriched genes as putative new markers for sensory and non-sensory\ncells, highlighting species-specific differences of the balance organ. The study is very\ninteresting to the hair cell regeneration field and groundbreaking, as it provides proof of\nprinciple that human sensory tissues can be and should be studied, as mouse tissues are\nnot identical and show transcriptional differences. However, I would have liked to see a\nbetter characterization of the support cell clusters in the intact tissue using in situ\nhybridization. Are the different SC populations intermingled or spatially restricted? What\nare the ratios of the different cell types in the tissue etc. Also, the figures should be\nbetter explained in the text and figure legends.\nThank you for your comments and insightful suggestions.\nIn addition to characterizing the human utricle supporting cell heterogeneity, this study\npresents the first transcriptomic dataset of the human utricle using a damage paradigm,\nrepresenting a significant advancement in the field. By providing this new dataset, we\nhave established a foundational resource for future studies exploring the effects of\naminoglycosides on the adult human utricle. We agree with the Reviewer about the\nimportance of performing experiments on human sensory inner ear organs, which are\nindeed understudied.\n“However, I would have liked to see a better characterization of …”\nWe further characterized the support cell clusters within the intact tissue using in situ\nhybridization (RNAscope) and marker genes that define the support cell clusters\nidentified in our analysis, validating their transcriptional profiles. The results of these\nexperiments have been incorporated into the manuscript, along with corresponding\nupdates to the figures and text, strengthening the characterization of the supporting cell\nclusters; please see Figs. 2e-e’’. From the RNAscope outcome, supporting cell\npopulations are intermingled in the tissue.\n“Also, the figures should be better explained in the text and figure legends.”\nWe revised all figure legends and updated the main text to provide more\ncomprehensive and detailed descriptions, ensuring that each figure effectively conveys\nour findings.\nMajor critique:\n1) The identification of six different SC clusters is very interesting but needs to be\nvalidated by in situ hybridization with cluster markers. This is the first study describing\ndifferent support cell types based on transcriptomic data. Unfortunately, the cellular\ncomposition/histology of the human utricle is not well described. Are these different SC\npopulations intermingled or spatially segregated? Are some of the populations sparser\nthan others? scRNA-seq data is not always reliable when it comes to estimating the size\nof a population.\nIn addition to clusters SC5 (Fig. 2e’’) and SC-like cells (Fig. 5d), we performed\nexperiments to validate SC2 and SC3 via in situ hybridization (RNAscope). Based on the\nRNA expression, these two populations look intermingled in the sensory epithelium. In\naddition, we now included an H&E staining of a utricle cross-section to show the\nhistology better (Extended Data Fig. 1a). Furthermore, while our study was in revision, a\nstudy was published by Dr. Alan Cheng’s group looking only at the homeostatic state of\nthe human utricle. We have integrated and analyzed these human datasets, and our\ncluster-defining genes for supporting cells and type I and II hair cells agree with their\nfindings (Extended Data Figs. 3a-i). However, the characterization of a supporting cell-\nlike cell population, although present in their dataset, remains a novelty of our study.\n2) Line: 158: The authors state: ‘SC-3 and SC-4 interact with type I (but not type II) HCs\nvia PTN signaling, and SC-4 also via MK (midkine) signalling (Fig. 2f-i).\nThe authors should show by in situ hybridization the location of SC-2 and SC-4 and their\nrelationship to type I HCs. Does the cell-cell communication (L-R pairs) occur in adjacent\ncells or are the signals diffusible?\nTo focus on the primary message, we removed the CellChat data as suggested by\nReviewer #2 and agree that this merits further future investigations.\n3) SCLCs: As SCLCs are in the vicinity of dead cells, could they be a cell state, rather than\na different SC type? The authors seems to agree alter in the manuscript that this\npopulation is a state change in an existing population but they still refer to these cells as\na different cell type.\nWe would like to clarify that SCLCs are located in surrounding areas with missing cells,\nnot areas in the proximity of dead cells (Fig. 5d). This distinction is important, and to\nensure clarity, we have revised the text throughout the manuscript to reflect this\ndistinction accurately and address any potential misunderstandings regarding the\nnature of SC-like cells.\n4) Line 202: DEG between mouse and human data sets. In situ hybridization with some\nhuman specific genes should be performed in the mouse to validate that they are\nindeed missing or much more lowly expressed. Technical reasons could explain why\nsome genes are detected by scRNA-seq only in only one of the species.\nBoth mouse datasets have a higher gene coverage than the human dataset, and some\ngenes, e.g., SCGB2A1, were not detected. In fact, Scgb2a2, the corresponding ortholog\nof SCGB2A1, was not expressed in any mouse utricle dataset available on gEAR (gene\nExpression Analysis Resource) portal for inner ear multiomic data exploration, as well as\nin the mouse hair cells of other sensory organs like cochlea and crista ampullaris.\nTherefore, SCGB2A1, among others, is a reliable utricular human-enriched gene.\n5) Please discuss similarities and differences between the results of the bulk and\nscRNASeq analyses after gentamycin treatment.\nWe implemented bulk deconvolution methods Bisque and SCDC (Single Cell Differential\nComposition) to tackle this request. However, since our corresponding bulk and single-\ncell samples do not originate from the same individual (i.e., the bulk samples do not\nhave a matched single-cell sample), both deconvolution methods were not effective.\nDue to the utricle size (see Fig. 1b), we could not perform both types of sequencing with\nthe same vestibular organ.\nHowever, in both bulk and scRNA-seq datasets (differential expression analysis -\ncontrol vs gentamicin-treated samples), we found modulation of immune response\ngenes, matrix metalloproteinase genes (see Fig. 8e), and significant downregulation of\nthe epithelial marker SLPI, validation and statistical analysis are shown in Fig. 7g.\nFurthermore, scRNA-seq analysis allowed us to identify an early-responding supporting\ncells (ERSCs) cluster that we validated in situ (RNAscope), Fig. 8h. We have edited the\nmanuscript accordingly.\n6) It is a bit surprising that integration of different data sets (mouse and human; treated,\nuntreated, control, Extended Data 5) does not lead to a better mixing of the cells. As the\nauthors state, cells still cluster by treatment. One would expect that some support cells\ndo not respond to gentamycin and therefore these cells should intermingle, irrespective\nof from which sample they came from. Maybe the authors should try a different\nintegration method as validation that cells from the different samples show\ntranscriptional differences, even though they are the same cell population.\nWe revisited our integration strategy and employed Reciprocal Principal Component\nAnalysis instead of the initial Harmony integration approach, resulting in better\nintegration of transcriptionally similar cells irrespective of their source. The Reciprocal\nPCA method is specifically designed to minimize batch effects while preserving\nbiologically relevant variations. This approach allowed us to achieve a more robust\nalignment of cells across samples (reduction of batch effects) and experimental\nconditions, as demonstrated in Extended Data Fig. 6. By applying Reciprocal PCA, we\nobserved improved cell mixing, particularly among cell populations expected to be\ntranscriptionally similar, such as hair cells and support cells across uncultured, treated,\nand control conditions, and we still observed some degree of clustering by treatment.\nThis outcome likely reflects transcriptional differences induced by experimental\nconditions rather than residual batch effects. These findings suggest that treatment-\ndependent transcriptional changes are a significant clustering driver, underscoring the\nbiological relevance of these differences.\nWe re-ran the Reciprocal PCA methods for cross-species comparison using the latest\npackage version, adjusting the hyperparameters to refine the intensity of cell mixing. We\nalso updated the integration plot’s colour scheme to enhance clarity and visualization. In\nthe revised graphs, the integration of interspecies hair cells and supporting cells shows a\nclearer overlap. At the same time, the human SC-like cell and mouse TEC clusters remain\ndistinct, as shown in Figs. 4b,c.\nMinor Critique:\n1) ‘Fig. S1’ should be referred to as Extended Data Fig. 1 etc.\nWe have modified the text.\n2) This is a comment for the authors and the journal: The extended data files (power\npoint files) are not labeled (please add Figure numbers to all your Figure files) and the\ntitles also do not reveal the Figure numbers in the ‘Zip of files for the reviewer’.\nWe labeled all the Figures, including the Extended Data files, with numerical identifiers.\n3) Lines 137- 146: which Figures correspond to this paragraph? Fig. 2C is not cited.\nWe have edited and added the figure (now Fig. 2d) to the paragraph and edited the text\naccordingly.\n4) Line 155: ‘SC-3and SC-4 express the ligand PTN (pleiotrophin), and SC-5 expresses\nthe receptor SDC4 (syndecan-4)’. I cannot find the figure that shows that.\nHowever, we edited the figure by removing the CellChat outcome, as suggested by\nReviewer #2.\n5) Line 207: ‘We validated SCGB2A1 expression in patient-derived samples (n≥3) at the\ntranscript and protein levels using in situ hybridization (Fig. 4g-i) and IF, respectively.\nShould be called (Fig. 4g (i) and 4g (ii)).\nWe have corrected this.\n6) This figure also needs a better description. (i) does not show which cells express\nSCGB2A1. Do the authors conclude that they are hair cells because of the absence of\nSox2? Likewise, (ii) the protein does not appear to be restricted to hair cells.\nWe have extended the description of this panel. We have also performed additional\nimmunofluorescences on utricle cross-sections – including a serial section of RNAscope\nvalidation experiment in Fig. 4f’ – co-staining SCGB2A1 with MYO7A, a well-known pan-\nHC marker, as well as SOX2, a pan-SC marker and a well-known HC type II marker (Fig.\n4g’). SCGB2A1 is expressed in the hair cells; however, since this protein forms\nextracellular vesicles, it is also visible in the intercellular space of the utricle whole mount\n(Fig. 4g). Furthermore, we conducted comparative analyses with another recently\npublished human utricle dataset and found that SCGB2A1 expression is restricted to the\nhair cells (Extended Data Figs. 4d,e).\n7) Line 296: Additionally, this analysis did not infer any cellular state transition (Fig. 7a\ninset).\nPlease explain. How would a state transition look like?\nWe eliminated all the velocity analyses, as requested by Reviewer #2.\n8) Line 347: After only 24h of gentamicin treatment, we detected a cluster of 122\nsupporting cells with unique transcriptomic regenerative features (Fig. 8a, Table S6).\nI cannot tell where in the figure I can see the 122 cell cluster. Is it the one called ERSC? If\nyes, please label/cite better.\nmain text (Lines 366-367), updated the legend, and clearly labelled Fig. 8a, which shows\nonly the early-responding SC transcriptomic features as a dot plot.\n9) Extended Data Fig. 5: Figure legend is too short. What are the samples in a, c, e? Title:\nWhat does top 3000 HVG mean?\nWe have expanded the figure legend, clarified the samples used, and updated the title.\nSamples in a, b, and c represent the integrated UMAP plots of the control and treated\nsamples used in the control vs treated analysis (a), the main three samples used for\nperforming the analysis described in the manuscript (b), all the six samples used for\nscRNA-seq (c) to confirm cluster-defining genes. Our initial analysis used Harmony to\nperform dataset integrations, which have now been replaced with the Reciprocal PCA\nmethod, and the top 3000 Highly Variable Genes (HVGs) as the integration features for\neach sample; please see Extended Data Fig. 6.\n10) The authors conclude that human utricle tissue has the potential to regenerate\nbased on the upregulation of genes after 14h (such as early response genes). However,\nit is possible that these genes are always upregulated in response to injury, even in the\nabsence of subsequent regeneration. The authors should keep this in mind and point\nout the genes that are truly only induced in regeneration contexts.\nWe have revised our conclusions explicitly highlighting genes upregulated as a general\ninjury response (i.e., MMP9) and those potentially induced during regenerative processes\n(i.e., EDNRB). To this end, we have identified and discussed genes exclusively associated\nwith regenerative contexts, supported by additional literature and our supplementary\nanalyses (please see Fig. 8h and Extended Data Fig. 5b).\nI appreciate the opportunity to review this revised manuscript and acknowledge the\neffort put into addressing previous concerns. However, I remain concerned about the\nquality of the original RNA-seq dataset, which, like any scientific method, must meet\nestablished standards to ensure reliability for both present and future comparative\nanalyses. High-quality datasets are essential, particularly when serving as reference\nlandmarks in a field. It is surprising indeed that, despite years of advancements in\nsingle-cell RNA sequencing, datasets with such low gene detection rates continue to be\nused for large-scale analyses and major conclusions. In this case, it is critical to limit\ninterpretations strictly to what can be supported by the data. Below, I outline a few\nspecific concerns that highlight these limitations.\nWe thank Reviewer #1 for their continued engagement with our work and appreciate\ntheir acknowledgment of our efforts to address previously raised concerns. Although we\naddressed all feasible requests (e.g., integration with the human dataset Wang et al.,\nand in situ validations), we recognize that no dataset is without limitations, and we\nrespectfully disagree with the conclusion that our data are of insufficient quality to\nsupport the conclusions drawn. In the updated revised manuscript, all our\ninterpretations are supported by validation (n≥3), and we excluded conclusions related\nto the absence or specificity of genes such as MAPT and SPP1 (please see the response to\nthe first comment below).\nSingle-cell RNA-seq studies can yield meaningful biological insights even when gene\ndetection per cell is relatively low. Several recent and influential studies have been\npublished using datasets with gene detection levels comparable to or lower genes than\nours. For example, these two published in Nature Communications:\n• Wang et al., 2024 (DOI: 10.1038/s41467-024-48491-y || Single-cell\ntranscriptomic atlas reveals increased regeneration in diseased human inner\near balance organs) integrated human utricle datasets with <1,000 genes/cell\nand similar mitochondrial content to our patient-derived samples.\n• Quach et al., 2024 (DOI: 10.1038/s41467-024-50281-5 || Early human fetal\nlung atlas reveals the temporal dynamics of epithelial cell plasticity) used\ncutoffs of 200 genes/cell, significantly lower than in our study, and 15%\nmitochondrial content.\nThese examples demonstrated that datasets of similar quality to ours are considered\nacceptable in the inner ear field and human-derived samples and have been used to draw\nnovel and meaningful biological insights, moving the field forward. Our validated\ndatasets with in situ hybridizations and immunofluorescences adhere to accepted\nscRNA-seq norms and provide meaningful insights into an under-characterized human\norgan.\nCOMMENTS:\nIn reference to a previous comment, Line 130: the DEGs are not obvious, questioning\nthe cluster identification. Also, Mapt is not clearly expressed in SCs and HC-I in Suppl.\nData 1c; these are too few positive cells to be considered as “being expressed”. Likewise,\nPage 1 of 7\ngiven the limited gene coverage, the absence of a marker such as Spp1 in the current\ndataset should not be considered definitive evidence of its non-expression.\nIn response to Reviewer #1’s comments in the previous round of review, we revised\nthe paragraph and used established cluster-defining genes for annotation (Figs. 2c, d),\nin addition to cluster validations (Figs. 2e-e’’, 3e, 5e). Furthermore, we also compared\nour dataset with the Wang et al., dataset confirming the annotation for hair cell\nsubtypes and supporting cells (Ext. Data Figs. 3c, d) and found that MAPT and SPP1\nare also weakly or non-specifically expressed in their data, suggesting that the limited\ndetection is not unique to our dataset but rather reflects a shared biological feature.\nNonetheless, as noted above in our prior response, we accepted the Reviewer’s\nsuggestion to remove all the panels and conclusions related to these genes; please see\nthe revised manuscript and figures.\nThe response from Authors: According to 10x Genomics, gene detection in the range of\n500-5000 is considered normal, and our median gene counts per sample ranged from\n475-1944 genes for uncultured (scUntr-2), control (scCtrl-1), and gentamicin-treated\n(scGent-2) samples; thus, we have enough information to draw conclusions from this\ndata. Moreover, optimal clustering parameters were verified by exploring each cluster’s\nstability using the Clustree algorithm (Please see Extended Data Figs. 7a-h). In addition\nto clarifying the DEGs on line 130, we edited statements and relative feature UMAP\nplots regarding the specificity of MAPT and SPP1 in human samples, as we found that in\nWang et al., dataset these mouse genes were also not specific, confirming that our\noutcome is consistent between human datasets and is not affected by gene coverage (see\nExtended Data Fig. 3f, Lines 224-227).\nResponse:\n<1,000 genes/cell: Low-quality or dead cells.\nThe 1,000 genes/cell benchmark is not a universal quality cutoff. Its interpretation must\nbe contextualized by tissue type and platform used. Notably, Wang et al., and Baek et al.,\n(Dev Cell, 2022, DOI: 10.1016/j.devcel.2022.03.001 || Single-cell transcriptome\nanalysis reveals three sequential phases of gene expression during zebrafish sensory\nhair cell regeneration) reported median gene counts <1,000/cell in their inner ear organs\nvalidated datasets. These studies, like ours, worked with quiescent, post-mitotic cells,\nsuch as sensory hair cells, which naturally exhibit lower transcript abundance. Also, in\nvan der Valk et al., (Cell Rep, 2023, DOI: 10.1016/j.celrep.2023.112623 || A single-cell\nlevel comparison of human inner ear organoids with the human cochlea and vestibular\norgans) for the adult and fetal human utricle the cutoffs used were nUMI ≥ 500 &\nnGene ≥ 750, and for human pluripotent stem cell-derived inner ear organoids cutoffs\nwere even nUMI ≥ 500 & nGene ≥ 250. Therefore, these numbers do not reflect low-\nquality or dead cells. In fact, in our datasets, the average cell viability for uncultured\nsamples was >90%, while for the samples undergoing the damage paradigm was >75%.\nWe included these percentages in Supplementary Table 2.\nPage 2 of 7\n1,500–3,000 genes/cell: Typical for most cell types in large datasets (yet, still low in\naverage)\nWhile 1,500–3,000 genes/cell may be ideal for certain cell types, it is not a strict\nrequirement; see also data.humancellatlas.org. In addition, 10X Genomics documents a\nrange of 500–5,000 genes/cell as normal. Many published studies, including Quach et\nal., (Nat. Commun., 2024), Kolla et al., (Nat. Commun., 2020 || Characterization of the\ndevelopment of the mouse cochlear epithelium at the single cell level), and Seurat\ntutorials work with cells in the 200–3,000 range as well. Furthermore, our datasets\nmeet 10X’s recommended read depth (20,000–50,000 reads/cell), and the average\nsequencing saturation exceeds 65%. We included the saturation percentages in the\nrevised Supplementary Table 2.\nHuman cells are usually bigger, more mRNA molecule in cytoplasm, and increased\nlikelihood for more genes to be detected.\nWhile this might be true for some cells including proliferative tissues, it does not apply\nuniversally. Adult human utricle cells are post-mitotic, and this biological feature\ncontributes to lower gene detection rates in inner ear tissues. This is consistent with\nfindings from Wang et al., and aligns with scRNA-seq datasets from the adult human\nbrain, where neurons frequently exhibit <1,500 genes/cell (see Allen Brain Atlas:\nportal.brain-map.org).\nOther comments:\nGene coverage in Wang et al. is not high neither, with value as small as 600 genes per\ncell in average in some samples, and integrated value below 1000 genes. +\nmitochondrial content is high. This is not to undermine the work done but to maintain\nscientific standards for drawing conclusions. One mut be very cautious with data\ninterpretation when the data are limited, and when these are low quality. Therefore, I\ndisagree with the authors' statement that \"we have enough information to draw\nconclusions from this data,\" particularly when concluding that a gene is not expressed or\nlacks marker potential due to scattered expression across clusters. Given the low\nsequencing depth, many genes will be undetected or only barely visible, a limitation well\nunderstood in the field.\nAs noted in the comments above, the gene coverage reported by Wang et al., is typical\nfor datasets from patient-derived inner ear organs, and we agree that careful\ninterpretation is essential. As requested, we removed conclusions related to MAPT and\nSPP1 expressions to avoid overinterpretation. In the newly revised manuscript, all our\nconclusions are supported by validated markers (in situ hybridization,\nimmunofluorescence) and statistical analyses.\nFigures 1 and 2 should be merged, there is no data in Fig. 1, and main figures should\ncontain new data with conclusion.\nPage 3 of 7\nFigure 1 provides a clear and engaging overview with contextual background to help\norient the reader within the framework of a limitedly characterized human organ, in\naddition to data regarding the quality of patient-derived utricle samples and features of\nthe balance organ. Furthermore, in the previous round of review, Reviewer #1 did not\nhave such a request and Reviewer #2 requested a detailed discussion of Figure 1, which\nwill likely capture the attention of a broad audience and better orient the general\naudience, including non-specialists. However, if the Editor(s) thinks we should merge\nthe two figures, we will accept this new request from Reviewer #1 and make the changes.\nscUntr-2 and VS-2 datasets do not seem to integrate very well in your figure. I find it\npuzzling to see only few scUntr-2 (blue) cells identified as type II hair cells. There could\neasily be artifact-driven clustering. Similarly, human and mouse data do not integrate\nwell at all in your analysis.\nThe cluster of type II hair cells in Extended Data Fig. 3a (1st panel) includes 66 cells,\nwith 29 (blue) originating from scUntr-2 and 37 (yellow) from VS-2 dataset (2nd\npanel). Unsupervised clustering revealed that type II HCs from scUntr-2 and VS-2\ncontributed similarly (66% and 68%, respectively) to the integrated cluster,\nsupporting that the clustering is not “artifact-driven” (3rd panel). Furthermore,\nalthough we adjusted the alpha parameter in the 2D UMAP plot to render the cells\nsemi-transparent and better visualize the overlap between the two integrated\nsamples, fully resolving overlapping cells remains a common challenge and is a\nknown limitation of the integration analysis. The 3D UMAP plot (4th panel) of the\ntype II hair cell cluster elucidates this point even further. In addition, as reported by\nWang et al., adult patient-derived utricles contain fewer type II hair cells.\nWe have performed a new integration analysis using a recently published adult\nmouse utricle dataset, again showing successful integration between main cell types\nand similar differential expression analysis outcomes (e.g., SCGB2A1, a gene we\nvalidated, is specific for human hair cells). Please see the comment below and Fig. 4,\nExt. Data Fig. 4. Please note that we have now used a different method,\nOrthoIntegrate, to improve the integration of single-cell transcriptomes from\ndifferent species.\nThe integration analysis between mouse and human datasets remains unsuccessful,\nlikely due to technical limitations that cannot be resolved here. Additionally, the\ndatasets represent different developmental stages (e.g., P6 in mice corresponds to a\nhuman fetal stage), while the P100 dataset is insufficient to capture the full diversity of\nPage 4 of 7\ncell types and gene expression. These constraints severely limit cross-species\ncomparisons and preclude drawing definitive conclusions or using this dataset as a\nreference; it represents only a tiny fragment of the broader biological landscape. It\nremains unclear to me why the authors chose not to generate their own mouse adult\ndataset.\nCross-species integration is known to face limitations due to biological divergence and\ndataset compatibility (e.g., DOIs: 10.1093/nar/gkae1316; 10.1101/gr.276868.122;\n10.1371/journal.pbio.3002633). Furthermore, the generation of an adult mouse dataset\nis beyond the scope of this study, which focuses on human tissue. At the time of our\nanalysis, only two relevant mouse utricle datasets, P6 and P100 (only for sensory hair\ncells), were publicly available. Despite this limitation, our cross-species comparisons\nallowed us to validate a human-specific gene, SCGB2A1 (n≥3), at both the\ntranscriptomic and proteomic levels using in situ hybridization (RNAscope, Figs. 4 f-g)\nand immunofluorescence (Fig. g’). Recently, and while our manuscript was under\nrevision, a new adult mouse utricle dataset was published (DOI: 10.1038/s41467-024-\n53153-0), and we performed a new cross-species integration analysis (Revised Figs. 4 a-\ne, and Ext. Data Fig. 4a).\nNotably, the data were consistent across all three cross-species integrations and\ncomparisons and were not confounded by developmental stage differences (adult\nhuman vs P6 mouse). Furthermore, as previously reported by Wang et al., all our cross-\nspecies analyses aligned with their analysis, confirming that the transcriptome profiles\nof inner ear cells only partially overlap between mice and human.\nFurthermore, the detection of only 105–346 genes per cell falls well below the minimum\ncriterium that are accepted in the field, regardless of the analysis performed. These low\ncounts suggest dying or damaged cells, which raises concerns about the dataset's\nreliability for comparative analysis.\nThe sample with a median 105 genes/cell was used only for in silico marker validation\n(Ext. Data Fig. 6c–c′). The gentamicin-treated sample with a median 346 genes/cell\nreflects biological changes following ototoxic insult and not technical failure and\nremains above 200 genes/cell cutoff suggested by Seurat and commonly used.\nReviewer #2 (Remarks to the Author):\nThe authors have done a good job in revising the manuscript. Analytic tools and marker\ngenes are described more comprehensively making it easier to follow the flow of the\ntext. An important and interesting addition is the integration of the data presented here\nwith a published dataset, enhancing confidence in the robustness of the findings. I think\nthat the manuscript is a valuable resource for the research field because it presents one\nof only a few datasets that are available from the human utricle.\nWe thank Reviewer #2 for their constructive feedback during the review process, for\nendorsing the revised manuscript, and for recognizing the value of our data.\nReviewer #3 (Remarks to the Author):\nThe authors have addressed all my comments, added new figures and clarified the text. I\nPage 5 of 7\nonly have one minor comment:\nFigure 2 legend:\nPlease explain how the proportions in cell numbers were calculated. Eg., in Fig2e” say\nthat '57' is the total number of SC5 cells as shown in legend in Fig. 2a.\nWe thank Reviewer #3 for their thoughtful and valuable feedback throughout the review\nprocess and for fully accepting the revisions made.\nTo address this comment, we have revised all the legends where cell numbers were\nmentioned, including Fig. 2.\nPage 6 of 7\nReviewer #1 (Remarks to the Author):\nPlease report the average number of genes per cell, as median values are not particularly\ninformative. For transparency, these values should also be displayed in a table or graph\nin main Fig. 2 or Supplementary Fig. 2 for easy access.\nThe median number of genes per cell reported for each sample was obtained directly\nfrom the 10x Genomics pipeline, prior to quality control. We subsequently calculated the\naverage and the median number of genes per cell for each dataset after applying the\nquality control criteria described in the Methods. The reported values excluded cells that\ndid not meet these thresholds; therefore, they reflect the quality-filtered data used in the\nmanuscript. We have now edited Supplementary Table 1 (formerly Table 2) to show the\naverage and median number of genes per cell. As suggested, we also included a table\n(inset) in Supplementary Fig. 5, which displays all six samples.\nAlso, in response to “We thank Reviewer #1 for their continued engagement …”, thank\nyou for the clarifications and references. I acknowledge that datasets with relatively low\ngene detection per cell have been published, particularly in rare or degraded human\ntissues. However, while common pipelines allow thresholds as low as 200-500\ngenes/cell, this is permissive and not a quality benchmark, as typical datasets average\n2,000-4,000 genes/cell. This highlights a distinction between what may be acceptable\nfor exploratory studies and what constitutes a robust reference dataset for future\nintegration and comparative work. Anyhow, while the biological insights supported by\nin situ validation are valuable, the relatively low detection rates limit the dataset’s long-\nterm utility as a reference. It would be more transparent to note this explicitly in the\nmanuscript, ideally in a dedicated paragraph (end of conclusion) or early in the results,\nsince most readers will not be familiar with single-cell data and its key metrics.\nWhile our detection rates are sufficient for the biological insights presented and are\nconsistent with prior studies on adult human utricles, they are lower than those of\ntypical reference datasets. We now include a clear statement regarding this limitation in\na dedicated paragraph at the end of the Discussion.\nFig. 1: An overview does not preclude showing data. Panel b adds little information, and\nthe panels overall are oversized.\nWe would like to clarify that panel c (formerly panel b) is a representative image\nillustrating the quality and intactness of a dissected patient-derived sample, showing the\nentire utricle obtained after surgery. This data is essential to show: during the sample\ncollection via the translabyrinthine approach, the tissue quality of the balance organ can\nbe compromised, affecting downstream experiments and analyses.\nWe have resized most of the panels as recommended.\nFig. 2: There should be, in the main fig., a clear visualization of DEG lists per cluster or\ncell type linked to the UMAP, e.g. dot or violin plots to show both relative levels and\nabundance, in reference to markers used in the rest of the ms (not in ref. to the\nheatmap). The small, unclear UMAPs in panel e are hard to see.\nPage 1 of 2\nWe thank the Reviewer for this suggestion. To improve clarity, we revised Fig. 2e to\ninclude a new dot plot displaying the expression levels and relative abundance of\nsupporting cell cluster-specific genes, providing a more precise visualization of DEGs\nacross clusters and cell types. Therefore, we have removed the small UMAP plots in\npanel e. All other validated markers mentioned in the manuscript are also shown as dot\nor violin plots.\nTo this response: “The sample with a median 105 genes/cell was used only for in silico\nmarker validation (Ext. Data Fig. 6c–c′). The gentamicin-treated sample with a median\n346 genes/cell reflects biological changes following ototoxic insult and not technical\nfailure and remains above 200 genes/cell cutoff suggested by Seurat and commonly\nused.”:\nThank you for clarifying. While the gentamicin-treated sample may reflect biological\ninjury, a median of 346 genes/cell is still far below typical datasets and limits\ndownstream analyses. The 200 genes/cell cutoff is only a minimum for including single\ncells, not a benchmark for median sample quality. I suggest, once again, making this\nclear in the manuscript so readers understand both the interpretation and the\nlimitations.\nWhile the gentamicin-treated sample likely reflects biological injury, the relatively low\nmedian gene detection might impose limitations for downstream analyses. As\nsuggested, we have revised the manuscript to recognise this limitation, ensuring readers\nare aware. Please see the updated Discussion.\nPage 2 of 2"}]

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