Multi-omic Analysis of Developing Human Retina and Organoids Reveals Cell-Specific Cis-Regulatory Elements and Mechanisms of Non-Coding Genetic Disease Risk

Abstract

Cis-regulatory elements (CREs) play a critical role in the development, maintenance, and disease-states of all human cell types. In the human retina, CREs have been implicated in a variety of inherited retinal disorders. To characterize cell-class-specific CREs in the human retina and elucidate their potential functions in development and disease, we performed single-nucleus (sn)ATAC-seq and snRNA-seq on the developing and adult human retina and on human retinal organoids. These analyses allowed us to identify cell-class-specific CREs, enriched transcription factor binding motifs, putative target genes, and to examine how these features change over development. By comparing DNA accessibility between the human retina and retinal organoids we found that CREs in organoids are highly correlated at the single-cell level, validating the use of organoids as a model for studying disease-associated CREs. As a proof of concept, we studied the function of a disease-associated CRE at 5q14.3 in organoids, identifying its principal target gene as the miR-9-2 primary transcript and demonstrating a dual role for this CRE in regulating neurogenesis and gene regulatory programs in mature glia. This study provides a rich resource for characterizing cell-class-specific CREs in the human retina and showcases retinal organoids as a model in which to study the function of retinal CREs that influence retinal development and disease.

Gene regulatory networks controlling temporal patterning, neurogenesis, and cell-fate specification in mammalian retina

“…To identify evolutionarily conserved regulatory elements and TFs controlling retinal neurogenesis and cell-fate specification, we compared our mouse data to scATAC-seq and scRNA-seq data obtained from whole human fetal retinas at six developmental time points, ranging from 7.5 to 19 gestational weeks (Thomas et al, 2021). As in the mouse, UMAP analysis identified each major cell type (Figures S3A and S3B; Mendeley dataset) and resembled an aggregate UMAP plot of scRNA-seq analysis of developing human retina (Figure S3C).…”

“…To annotate cell types corresponding to each cluster, we used existing mouse and human scRNA-seq data (Clark et al, 2019;Thomas et al, 2021) to interpret our scATAC-seq cell types using the CCA (canonical correlation analysis) integration method in the Seurat package. First, we downloaded the mouse scRNA-seq data (''https://github.com/gofflab/developing_mouse_ retina_scRNASeq'') and converted them to Seurat objects.…”

“…To address this, we generated chromatin accessibility profiles of developing mouse retina using sinlge cell assay for transposon accessible chromatin (scATAC)-seq. We identified cis-regulatory elements and putative TF binding sites from scATAC-seq data and integrated these with existing, age-matched scRNA-seq data from mouse (Clark et al, 2019) and newly generated scRNA-seq and scATAC-seq data from developing human retina (Thomas et al, 2021) to identify evolutionarily conserved GRNs that control developmental transitions and cell-fate specification. Cell-type-specific TFs activate expression of other TFs within these GRNs, although often also inhibiting (or more rarely activating) expression of TFs in other networks.…”

Single-cell multiome of the human retina and deep learning nominate causal variants in complex eye diseases

“…Using linked gene expression data, we also predicted rs17421627 to act on LINC00461, a long noncoding RNA. Consistent with these predictions, deletion of the locus containing rs17421627 in human retinal organoids has been shown to significantly downregulate LINC00461 with the strongest effect in Müller glia 29 . These examples illustrate how single-cell multiomics can reveal the cellular targets of noncoding variants in the retina and nominate how they might contribute to eye disorders.…”

Multi-omics profiling, in vitro and in vivo enhancer assays dissect the cis-regulatory mechanisms underlying North Carolina macular dystrophy, a retinal enhanceropathy

“…Data generated by single-nucleus RNA-seq of embryonic (53,59,74,78,113, and 132 days) and adult (25,50, and 54 years old) human retinal cells performed by Thomas et al (2021) were processed for evaluating PRDM13 and IRX1 expression at single-cell level. 44 Expression matrices derived from nine post-mortem donor neural retinal samples (GSE183684) were imported into R (v4.0.5) and processed using the Seurat single-cell analysis package (v4.0). 45 Pre-processing and quality control was conducted to remove outlier cells.…”