Comparison of expression patterns of converted HC clusters to P1 wild-type OHCs and other naive OHC stages.

(A) PAGA trajectory analysis of wild-type cochlear cells across different developmental stages. L.PsCs, Lateral pro-sensory cells; IPC: Inner pillar cells; DC/OPCs: Deiters’ cells/Outer pillar cells. (B) UMAP analysis of eight different cell types, including cHC1-3 and OHC_P33 from Yamashita et al. [7], and OHC at different stages of natural hair cell development [1]. iHC_E14: immature hair cell at E14; iOHC_E16: immature outer hair cell at E16. (C) The SCT-normalization method effectively reduced the influence of technical factors in the combined dataset. (D) Heatmap showing the top 10 (or all, if less than 10) markers for each cluster. (E) Correlation plot of cell types revealed that cHC3 is more similar to OHC_P1 (r = 0.32) than to other naive OHC stages (iHC_E14:r = 0.1; iOHC_E16: r = 0.2; OHC_P7: r = 0.032; OHC_P33: r = 0.031).</p

Transcription factors identified using VIPER in the comparison of the expression profiles of cHC3 versus naive OHCs at P1.

VIPER plot shows the top 10 up-regulated (red) and down-regulated (blue) transcription factors. The strength of protein activity (Act) and RNA expression (Exp) are represented by color intensity, with darker color indicating higher values. Prox1 is shown to be insignificant when using 100 bootstraps. The lines in the middle of the plot are the individual gene expression values for each cell in the dataset and provide additional information about the variability in gene expression within each cell type.</p

Regulators in WGCNA-derived co-expression modules.

Regulators in WGCNA-derived co-expression modules.</p

Single-cell gene expression profiling of adult mouse cochleae during ectopic Atoh1-induced conversion.

(A) tSNE plot of the SCANPY clusters. Cell types were annotated based on previous cell annotations [7]. (B) tSNE mapping of normalised count expression levels of specific markers. Slc17a8 and Ocm were highly expressed in mature IHCs and OHCs, respectively, but not in cHCs (annotated as Atoh1+ in the figure) or SCs (annotated as DC/PCs, IPs/IB+ in the figure). HA (Atoh1-HA) and Fgfr3 were highly expressed in cHCs clusters. (C) Top 25 differential genes when comparing IHCs with OHCs. (D) Ranking for highly differentially expressed genes in the Atoh1-HA+ cHC, DC/PC, IHC and OHC clusters, respectively. RM: Reisner membrane; DCs: Deiters’ cells; PCs: pillar cells; HCs: hair cells; Iphs: inner phalangeal cells; IBs: inner border cells; SGs: spiral ganglia.</p

S2 Table -

Regeneration of mammalian cochlear hair cells (HCs) by modulating molecular pathways or transcription factors is a promising approach to hearing restoration; however, immaturity of the regenerated HCs in vivo remains a major challenge. Here, we analyzed a single cell RNA sequencing (scRNA-seq) dataset during Atoh1-induced supporting cell (SC) to hair cell (HC) conversion in adult mouse cochleae (Yamashita et al. (2018)) using multiple high-throughput sequencing analytical tools (WGCNA, SCENIC, ARACNE, and VIPER). Instead of focusing on differentially expressed genes, we established independent expression modules and confirmed the existence of multiple conversion stages. Gene regulatory network (GRN) analysis uncovered previously unidentified key regulators, including Nhlh1, Lhx3, Barhl1 and Nfia, that guide converted HC differentiation. Comparison of the late-stage converted HCs with the scRNA-seq data from neonatal mouse cochleae (Kolla et al. (2020)) revealed that they closely resemble postnatal day 1 wild-type OHCs, in contrast to other developmental stages. Using ARACNE and VIPER, we discovered multiple key regulators likely to promote conversion to a more mature OHC-like state, including Zbtb20, Nfia, Zmiz1, Gm14418, Bhlhe40, Six2, Fosb and Klf9. Our findings provide insights into the regulation of HC regeneration in adult mammalian cochleae in vivo and demonstrate an approach for analyzing GRNs in large scRNA-seq datasets.</div

Gene co-regulation network for the four co-expression modules.

(A-D) The network was derived using the ARACNE program and constructed using Cytoscape. The key regulators (in red) are connected with their targets (in purple). (ZIP)</p

Functional analysis of gene modules associated with hair cell conversion.

(A) Gene ontology (GO) analysis of genes in the blue module (SC—cHC1) showed enrichment of biological processes related to early-stage hair cell development, such as Notch signaling pathway, regulation of mechanoreceptor differentiation, and epithelial proliferation (B) GO analysis of the red module (cHC1—cHC2) revealed pathways related to organ growth and morphogenesis, cell and tissue migration, indicating the dynamic regulation of conversion-associated genes in this middle stage. (C) GO analysis of the yellow module (cHC1—cHC2) showed enrichment of genes associated with similar processes as the red module, supporting their potential role in the middle stage of hair cell conversion. (D) GO analysis of the turquoise module (cHC2 –cHC3) suggested pathways such as synapse organization and neuroepithelial cell differentiation, indicating their potential role in the later stages of hair cell conversion. (ZIP)</p

Gene regulatory network (GRN) inference using WGCNA and SCENIC.

(A) WGCNA analysis of cHCs and endogenous HCs, showing the correlation of each cell type and the expression pattern of eigengenes in each module. Each spike represents one cell of the cell types indicated below. (B) Correlation matrix depicting the correlation coefficients of different modules, with all seven modules included and self-comparisons removed from the graph. The size of the box and its color gradient indicate the correlation coefficient. (C) SCENIC identified cell-type-specific GRNs during SC-to-cHC conversion. The binary regulon activity matrix shows correlated regulons (absolute correlation > 0.3) active in at least 1% of cells. Cells are colored according to cluster identity, and genes are colored based on expression level. Regulons exceeding a manually adjusted AUC threshold are shown in black, while inactive regulons are white. Each stage of the conversion is shown at the top of the graph in different colours. (D) Venn diagram displaying the five overlapping key regulators identified by both WGCNA turquoise module (cHC3) and SCENIC in cHC2/3, compared to regulons in OHCs.</p

VIPER analysis identified the TFs active in OHC_P7 and cHCs.

Table showing the top 10 up- and down-regulated transcription factors found by VIPER with corresponding parameters (normalized enrichment score (NES), p-value, false discovery rate (FDR), and bootstraps). (TIF)</p

WGCNA analysis in cHCs and endogenous HCs.

(A) Sample clustering was conducted to detect outliers. This analysis was based on the expression data of top 4000 variable genes in the total 251 cells with 161 Atoh1-HA+ cHCs and 90 SCs available in Yamashita et al [7] (S2 Table). Genes were clustered based on a dissimilarity measure. The branches correspond to modules of highly interconnected groups of genes. Colors in the horizontal bar represent the five cell types assigned by scRNA-seq analysis. (B) Selection of the soft-thresholding powers for scale-free co-expression network. The left panel showed the scale-free fit index versus soft-thresholding power. The right panel displayed the mean connectivity versus soft-thresholding power. Power 4 was used. (ZIP)</p