Mapping the regulatory landscape of auditory hair cells from single-cell multi-omics data

Abstract

Auditory hair cells transduce sound to the brain, and in mammals, these cells reside together with supporting cells in the sensory epithelium of the cochlea, called the organ of Corti. To establish the organ's delicate function during development and differentiation, spatiotemporal gene expression is strictly controlled by chromatin accessibility and cell type–specific transcription factors, jointly representing the regulatory landscape. Bulk sequencing technology and cellular heterogeneity obscured investigations on the interplay between transcription factors and chromatin accessibility in inner ear development. To study the formation of the regulatory landscape in hair cells, we collected single-cell chromatin accessibility profiles accompanied by single-cell RNA data from genetically labeled murine hair cells and supporting cells after birth. Using an integrative approach, we predicted cell type–specific activating and repressing functions of developmental transcription factors. Furthermore, by integrating gene expression and chromatin accessibility data sets, we reconstructed gene regulatory networks. Then, using a comparative approach, 20 hair cell–specific activators and repressors, including putative downstream target genes, were identified. Clustering of target genes resolved groups of related transcription factors and was used to infer their developmental functions. Finally, the heterogeneity in the single-cell data allowed us to spatially reconstruct transcriptional as well as chromatin accessibility trajectories, indicating that gradual changes in the chromatin accessibility landscape are lagging behind the transcriptional identity of hair cells along the organ's longitudinal axis. Overall, this study provides a strategy to spatially reconstruct the formation of a lineage-specific regulatory landscape using a single-cell multi-omics approach.

Depicting pseudotime-lagged causality across single-cell trajectories for accurate gene-regulatory inference

“…We devised a pipeline for multi-omic transfer learning to infer the gene-regulatory network for developing mouse outer hair cells (mOHCs), the mechanical amplifiers of the inner ear. We used both gene-expression 30 and chromatin-accessibility 31 datasets to fine-tune our network. By first calculating the cell-by-gene accessibility scores across annotated genes, we were able to generate lagged input matrices for the scATAC-seq dataset (Fig.…”

“…This procedure revealed two distinct developmental modules corresponding to prosensory genes such as Sox2 , Id2 , Hes1 , and Prox1 and hair cell genes such as Atoh1 , Pou4f3 , Gfi1 , Lhx3 , and Barhl1 . We sought to validate the predicted interactions by comparing the locations of known transcription factor-binding sites 34, 35 to the locations of open-chromatin peaks 31 within 50 kb of target genes’ transcription start sites. Twenty-two of 28 predicted interactions were confirmed by the accessibility of transcription-factor binding sites.…”

“…For the scATAC-seq dataset, we first collected the cell-by-peak chromatin-accessibility data from the original study 31 . Then, we used the function createGmatFromMat in SnapATAC 72 (v1.0.0) to calculate the cell-by-gene accessibility scores as the counts of each 5 kb bin per gene in the UCSC mouse genome mm10 73 (TxDb.Mmusculus.UCSC.mm10.knownGene; v3.4.4) that contained at least one open-chromatin peak for a given cell.…”

“…Of the six remaining interactions, three were instances of predicted target-gene regulation by transcriptional cofactors lacking true DNA-binding domains. We additionally identified 13 reported interactions that were not detected by DELAY 31,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54 .…”

“…For the scATAC-seq dataset, we first collected the cell-by-peak chromatin-accessibility data from the original study 31 . Then, we used the function createGmatFromMat in SnapATAC 72 and applied GAMs to the raw accessibility counts to select the differentially accessible genes across pseudotime.…”