Single-cell analysis of the epigenome and 3D chromatin architecture in the human retina

Ying Yuan; Pooja Biswas; Nathan R Zemke; Kelsey Dang; Yue Wu; Matteo D’Antonio; Yang Xie; Qian Yang; Keyi Dong; Pik Ki Lau; Daofeng Li; Chad Seng; Weronika Bartosik; Justin Buchanan; Lin Lin; Ryan Lancione; Kangli Wang; Seoyeon Lee; Zane Gibbs; Joseph Ecker; Kelly Frazer; Ting Wang; Sebastian Preissl; Allen Wang; Radha Ayyagari; Bing Ren

doi:10.1101/2024.12.28.630634

This is a preprint.

It has not yet been peer reviewed by a journal.

The National Library of Medicine is running a pilot to include preprints that result from research funded by NIH in PMC and PubMed.

[Preprint]. 2025 Apr 2:2024.12.28.630634. Originally published 2024 Dec 29. [Version 2] doi: 10.1101/2024.12.28.630634

Single-cell analysis of the epigenome and 3D chromatin architecture in the human retina

Ying Yuan ¹, Pooja Biswas ⁵, Nathan R Zemke ³, Kelsey Dang ³, Yue Wu ⁷, Matteo D’Antonio ⁴, Yang Xie ², Qian Yang ³, Keyi Dong ³, Pik Ki Lau ³, Daofeng Li ⁶, Chad Seng ⁶, Weronika Bartosik ³, Justin Buchanan ³, Lin Lin ³, Ryan Lancione ³, Kangli Wang ², Seoyeon Lee ², Zane Gibbs ², Joseph Ecker ^8,⁹, Kelly Frazer ^10,¹¹, Ting Wang ⁶, Sebastian Preissl ³, Allen Wang ³, Radha Ayyagari ^5,^*, Bing Ren ^2,^3,^*

PMCID: PMC11703273 PMID: 39764062

Abstract

Most genetic risk variants linked to ocular diseases are non-protein coding and presumably contribute to disease through dysregulation of gene expression, however, deeper understanding of their mechanisms of action has been impeded by an incomplete annotation of the transcriptional regulatory elements across different retinal cell types. To address this knowledge gap, we carried out single-cell multiomics assays to investigate gene expression, chromatin accessibility, DNA methylome and 3D chromatin architecture in human retina, macula, and retinal pigment epithelium (RPE)/choroid. We identified 420,824 unique candidate regulatory elements and characterized their chromatin states in 23 sub-classes of retinal cells. Comparative analysis of chromatin landscapes between human and mouse retina cells further revealed both evolutionarily conserved and divergent retinal gene-regulatory programs. Leveraging the rapid advancements in deep-learning techniques, we developed sequence-based predictors to interpret non-coding risk variants of retina diseases. Our study establishes retina-wide, single-cell transcriptome, epigenome, and 3D genome atlases, and provides a resource for studying the gene regulatory programs of the human retina and relevant diseases.

Introduction

Retinal diseases, including age-related macular degeneration (AMD), diabetic retinopathy, glaucoma, and retinal vein occlusion, are major causes of vision loss in the United States, particularly affecting older adults and individuals with diabetes (1). AMD impacts 1.8 million Americans, while diabetic retinopathy affects 4.1 million (1–3). As these conditions are expected to increase with an aging population and the rising prevalence of diabetes, there is a critical need to develop effective early detection, prevention, and treatment strategies. The retina plays a dual role as a sensory interface and a processor of visual information (4–6). Genome-wide association studies (GWAS) have further revealed a strong genetic component to retinal diseases, identifying a large number of risk variants, the majority of which reside in noncoding regions of the genome (7). These noncoding variants are thought to modulate disease risks by altering the function of cis-regulatory elements (CREs) and gene expression patterns in retinal cell types (8–10). However, the lack of comprehensive annotation of CREs and their target genes across diverse retinal cell types remains a significant barrier to understanding the mechanisms by which these variants contribute to disease pathogenesis.

Recent advancements in single-cell genomic technologies have enabled detailed exploration of cellular heterogeneity within complex tissues, including the retina. Methodologies such as snRNA-seq/scRNA-seq and snATAC-seq/scATAC-seq (11–16), or 10x multiome single cell ATAC/RNA-seq (7, 17) have been employed to investigate the complex regulatory mechanisms and disease pathology. Further, leveraging 3D genome data from HiChIP and Hi-C assays unraveled the cis-regulatory interactions (18, 19). By integrating single-cell approaches with GWAS data, researchers are beginning to link noncoding variants to specific regulatory sequences in distinct retinal cell types, providing new insights into disease mechanisms. However, cell types of retinal tissues from younger donors remain unexplored(20, 21). Additionally, knowledge of cell-type-specific methylation patterns, which can be influenced by environment (22), diet (23), and age (24), is still incomplete, hindering deeper understanding of the role of epigenetic processes in eye development and disease (25, 26).

In this study, we comprehensively characterized the epigenome and 3D chromatin architecture of human retinal cell types using fresh post-mortem retinal tissues, collected within two hours of donor death, from three donors aged 20 to 40. We performed single-nucleus multiome (snATAC-seq/snRNA-seq) and single-nucleus methyl-3C sequencing (snm3C-seq) experiments (29, 30), profiling gene expression, chromatin accessibility, DNA methylation, and chromatin conformation in over 58,000 retinal cells. Integrative multi-omic analysis of these datasets identified 420,824 candidate CREs (cCREs), revealing their cell-type-specific usage and potential target genes across 23 retinal cell subtypes from retina, macula and RPE/choroid tissues. Leveraging GWAS data, we identified cell types relevant to a spectrum of eye diseases, and determined likely causal SNPs for AMD and Macular telangiectasia (MacTel). Comparative analysis between human and mouse chromatin landscapes uncovered rapid turnover of gene regulatory elements during evolution. Additionally, we developed a deep neural network model to predict the regulatory functions of disease risk variants, and validated the predictions using CRISPR editing and hTERT-RPE1 cells. These data are publicly accessible and can be visualized via a custom built Web Portal (https://epigenome.wustl.edu/EyeEpigenome/) (27, 28).

Results

A single-cell epigenome atlas of human retina

To create a comprehensive single-cell atlas of the human retinal epigenome, we performed single-nucleus 10x multiome (10x Genomics snRNA-seq and snATAC-seq) and snm3C-seq experiments on retina, macula, and retinal pigment epithelium (RPE)/choroid tissues from three phenotypically healthy donors (Fig. 1A). These methodologies allowed us to simultaneously profile transcriptomes alongside chromatin accessibility or DNA methylation with 3D genome organization in the same cells (29, 30). In total, we analyzed 34,230 nuclei from the retina and macula using 10x multiome, with an additional 4,579 nuclei from the same tissues using snm3C-seq (Fig. 1B). For RPE/choroid tissues, 18,176 nuclei were profiled with 10x multiome, and 2,045 nuclei with snm3C-seq (Fig. 1C). The four modalities of Muller glia and RPE cell types are shown as examples (Fig. 1D).

Fig. 1. — (A) Illustration of the human retina (left), specific cell type locations (middle), and experimental design. Tissues from three donors were used for 10x-multiome and snm3C-seq experiments (right). (B) Uniform manifold approximation and projection (UMAP) embeddings of 10x multiome RNA and snm3C-seq DNA methylation from human retina and macula. See method for details of clustering and integration of the two types of datasets(C) UMAP embeddings of 10x multiome and snm3C-seq data from human RPE. See methods for details of the integration and clustering of two types of datasets (D) Visualization of the pseudo bulk signals of gene expression, chromatin accessibility, DNA methylation and chromosome conformation using the WashU EyeEpigenome Browser. Muller glia and RPE cell types are shown as examples.

We conducted unsupervised clustering with the snRNA-seq component of the 10x multiome data from the retina and macula, resulting in 13 distinct cell clusters. We annotated the cell identity of each cluster according to known cell-type marker gene expression (7), including Rods, Cones, rod bipolar cells, OFF cone bipolar cells, ON cone bipolar cells, Muller glia, horizontal cells, GABA amacrine cells, Glycine amacrine cells, Retinal Ganglion Cells (RGC), All amacrine cells, astrocytes, and microglia (fig. S1, A, B).

For DNA methylome clustering (snm3C-seq), we annotated 13 cell types across 4,579 nuclei in the retina and macula tissues, guided by hypomethylation levels of marker genes and integration with the snRNA-seq dataset (methods). The RPE/Choroid dataset was grouped into 10 distinct cell types across 2,045 nuclei (Fig. 1B, C).

Next, we assigned cell-type identity to each cell cluster based on expression of known marker genes (Fig.2A, table S1). Across 30,293 detected genes, we identified 14,390 differentially expressed genes (Fig. 2B, table S2, methods). We also identified the top 50 marker genes per cell type using Seurat (31) (fig. S1C, methods). Gene Ontology (GO) enrichment analysis of these markers revealed expected functional terms, such as “detection of visible light” for Rods (Fig. 2C) and “visual perception” for Cones (Fig. 2D) (4).

In RPE/choroid tissue, we identified 10 distinct cell types (RPE, Schwann, Melanocyte, T/NK cell, Macrophage, Mast cell, Pericytes, Endothelial cells, Fibroblasts, and VSMC) based on snRNA-seq data and known marker genes (fig. S1D, Fig. 2E, table S3). Among the 30,037 genes profiled in the RNA assay, 12,007 were identified as differentially expressed (Fig. 2F, table S4, methods). The top 50 marker genes per cell type from RPE/choroid were determined using Seurat (31) (fig. S1E), with consistent expression across donors. GO analysis of these top 50 RPE cell marker genes revealed functional categories consistent with cell type identity, such as “gamma-aminobutyric acid transport” for RPE cells (Fig. 2G) and “glomerulus vasculature development” for Endothelial cells (Fig. 2H) (4).

Notably, identified marker genes RLBP1 (Retinaldehyde Binding Protein 1) in Müller glia and RPE65 in RPE are associated with the visual cycle (32) and human retinal disease (33), highlighting their homogeneous high expression cross different donors in Muller glia or RPE than other cell types (fig. S1, F, G, with additional data in fig. S7 and fig. S8).

Identification and characterization of candidate cis-regulatory elements in retina cell types

To define gene-regulatory programs across distinct retina and RPE/choroid cell types, we identified candidate cis-regulatory elements (cCREs) by profiling open chromatin in 22 cell types (excluding microglia due to a low cell number). For each cell type, we aggregated snATAC-seq fragments and used MACS2 (34) to identify accessible chromatin regions. Previous research indicates that cluster size and read depth can impact MACS2 peak scores (35), with approximately 1,000 nuclei required to capture over 80% of accessible regions. Accordingly, we set a q-value of 0.05 for clusters with more than 1,000 nuclei and q-value of 0.1 for clusters with fewer nuclei. We iteratively merged the open chromatin regions identified from every cell type and kept the summits with the highest MACS2 peak score for overlapped regions.

Across retina and macula cells, we detected 10,408 to 198,843 open chromatin regions per cell type (500 bp span), with a combined total of 302,856 unique regions across 12 cell types. Of these, 113,996 regions showed significant cell-type specific chromatin accessibility (Fig. 3A, table S5, methods). In RPE/choroid tissues, we identified 12,929 to 142,274 open chromatin regions per cell type, with a union of 229,320 regions across 10 cell types. Among these, 67,299 displayed significant cell-type specificity (Fig.3B, table S6, methods). Notably, 111,352 accessible regions are shared between RPE/choroid and retina/macula tissues. Altogether, we identified 420,824 unique cCREs from retina, macula, and RPE/choroid tissues, most of which displayed highly cell type-specific patterns of accessibility (Fig. 3C–G).

Fig. 3. — (A)Heatmap showing chromatin accessibility of cell-type-specific cCREsof retina and macula tissues. CPM, counts per million. Each CRE is ordered by the cell type with the highest accessibility level. The color bar shows the color change for the accessibility change, a score over 2.5 is colored with 2.5. See method for details of the computation. (B) Heatmap showing chromatin accessibility of cell-type-specific cCREs of RPE/choroid tissue. (C) Stacked bar chart showing the contribution of each donor to each cell type in the retina/macular tissue. (D) Genome browser tracks chromatin accessibility profiles for each cell type of retina and macular tissues at selected marker gene loci. (E) Stacked bar chart representing the relative contribution of retina and macula regions to each cell type. (F) Stacked bar chart showing the contribution of each donor to the cell counts of each cell type of RPE/choroid tissue. (G) Genome browser tracks of chromatin accessibility profiles for each cell type at selected marker gene loci that were used for cell cluster annotation of RPE/choroid tissue. (H and I) Enrichment of TF motifs in cell-type-specific cCREs of retina and macula tissues (H) and RPE/choroid tissue (I). (J and K) Heatmap showing the DMRs of retina and macula tissues (J) and RPE/choroid tissue(K). Each DMR is ordered corresponding to the existing cCREs. The max value is 1. (L and M) Violin plot of mCG, mCH, CisLongRatio and TransRatio of each cell type in retina and macula tissues (L) and RPE/choroid tissue (M).

To explore the potential regulators of these cCREs, we performed motif enrichment analysis on the accessible chromatin regions for each cell type. In retina and macula tissues, 142 known transcription factor (TF) motifs were enriched within the cCREs across the 12 cell types, most of which displayed cell-type-specific enrichment patterns (Fig.3H, table S7, methods). In RPE/choroid tissue, 197 known motifs were enriched among cCREs across 10 cell types, most with cell-type-specific enrichment (Fig. 3I, table S8, methods). For retina and macula, certain TFs, such as Otx2, are known to play roles in both rod cells and rod bipolar cells (36). In RPE/choroid tissue, many of these TF motifs, such as Otx2 and CRX in RPE cells, have been previously implicated in cell-type-specific gene regulation (37).

This comprehensive list of candidate TF regulators provides a valuable resource for understanding gene-regulatory networks in human retinal and RPE cell types, offering insights for further studies into the regulatory elements that drive cell-type-specific functions in the retina and their implications for retinal diseases.

DNA methylomes across the retina cell types

DNA methylation, or 5-methylcytosine (5mC), frequently present at cytosine-guanine dinucleotides (CpGs), is a key epigenetic modification involved in gene regulation and cell-type-specific functions within the retina (38). Differentially methylated regions (DMRs) across cell types are enriched at cCREs (39, 40) and have been used for identifying cCREs. In addition to CpG methylation (mCG), methylation in non-CG (mCH, where H = A, C, or T) contexts is abundant in certain cell types in particular neurons and plays a crucial role in cell-type-specific gene regulation (41). The DNA methylation profile of the human retina can also aid in identifying genetic variations associated with specific retinal diseases (26).

Using the methylation modality of snm3C-seq, we generated a single-cell atlas of DNA methylation in the human eye, spanning cell types from the retina, macula, and RPE/choroid (fig. S1, H, I). This atlas provides cell-type-specific DNA methylation patterns, offering insights into the regulatory landscape of each retinal cell type. DMRs were identified using the ALLCools software (38), resulting in 102,410 unique CRE-associated DMRs across the 12 cell types in retina and macula tissues (Fig.3J). In RPE/choroid tissue, we identified 151,817 unique CRE-associated DMRs across 10 cell types (Fig.3K). These DMRs typically displayed an anti-correlated pattern with cCREs, reinforcing the cell-type specificity of DNA methylation in the retina. Consistent with previous findings (42), we observed that neuronal cell types (e.g., Rods, Cones, Horizontal, Rod bipolar, ON cone bipolar, OFF cone bipolar, All amacrine, GABA amacrine, Gly amacrine, and RGC) exhibited significantly higher levels of non-CG methylation (mCH) compared to non-neuronal cell types (e.g., Müller glia, Astrocytes, RPE, Melanocytes, T/NK cells, Mast cells, Pericytes, Fibroblasts, Endothelial cells, Schwann cells, VSMC, and Macrophages) (Fig. 3L, M).

3D genome architecture across retinal cell types

The three-dimensional (3D) organization of chromatin plays a critical role in gene regulation, with chromatin structures including active (A) and repressive (B) compartments, topologically associating domains (TADs), and chromatin loops influencing interactions between gene promoters and distal regulatory elements (43). These structures are also involved in essential nuclear processes, including DNA repair, homologous recombination, and replication (42, 44). Eva D’haene et al. had explored the role of tissue-specific 3D genomic structures in establishing retinal disease gene expression patterns in the neural retina and RPE/choroid (18). Here, we sought to explore the cell-type-specific 3D genomic architecture of retinal cell types and reveal specific regulation mechanisms.

To investigate cell-type-specific genome folding, we first examined chromatin contact frequencies across different genomic distances, which is usually relevant to the cell cycle (45). In both retina and macula tissues, most cell types displayed an enrichment of chromatin contacts at mid-range (200 kb to 2 Mb) and long-range (20 Mb to 50 Mb) scales (Fig. 4A). However, the ratio of mid-to long-range contacts varied across cell types, indicating differences in chromatin organization (Fig.4B). Similarly, RPE/choroid cell types showed enrichment of contacts in both mid- and long-range categories, with distinct mid- to long-range ratios across cell types (Fig.4C, 4D). These contact profiles could provide insights into gene regulation in different cell types. We analyzed chromatin compartments and TADs in various retinal and RPE/choroid cell types, defining compartments at a 100-kb resolution (Fig.4E, 4G) and TAD boundaries at a 25-kb resolution. The boundary probability of a genomic bin, representing its frequency as a TAD boundary across cells, aligned closely with insulation scores derived from cell-type pseudo-bulk contact maps (Fig.4F, 4H). Notably, cell-type-specific compartment and TAD boundary patterns were evident, particularly around cell-type marker genes like RLBP1 in Müller glia cells and RPE65 in RPE cells. In regions approximately 2 Mb upstream of the transcription start site (TSS) and downstream of the transcription end site (TES) of these marker genes, we observed distinct domain boundary patterns across cell types, reflecting cell-type-specific chromatin organization (Fig.4F, 4H).

For retina and macula tissues, we identified 1,792 variable TAD boundaries across the 12 cell types (Fig.4I, fig. S2A). In RPE/choroid tissues, we found 216 variable TAD boundaries across the 10 cell types (Fig.4J, fig. S2B). These differences in TAD boundaries may reveal cell-type-specific regulatory landscapes, emphasizing the distinct 3D genome organization in each retinal cell type.

We next examined the relationship between TAD boundaries and other epigenetic modalities, such as open chromatin, mCG, and mCH methylation. In retina and macula cell types, both mCG and mCH methylation were generally anti-correlated with TAD boundary probabilities (Fig.4K). In contrast, open chromatin signals showed positive correlations with boundary probabilities, indicating that accessible chromatin DNA hypomethylation is often enriched at TAD boundaries. Similarly, in RPE/choroid cell types, mCG and mCH remained anti-correlated with boundary probabilities, while open chromatin showed a weaker positive correlation (Fig.4L).

Linking distal cCREs to target genes

To understand the transcriptional regulatory programs underlying cell-type-specific gene expression in the human retina, we used the activity-by-contact (ABC) method (46) to link distal cCREs to their potential target genes. By integrating chromatin accessibility and contact maps across retina cell types, we identified 302,856 distal cCREs linked to 32,766 potential target genes, resulting in 207,616 cCRE-gene pairs (table S9, fig. S1M and table S10, methods). Among these, 39,218 pairs with the highest ABC scores exhibited strong cell-type specificity, revealing distinct regulatory landscapes for each cell type (fig. S1L). In RPE/choroid cell types, we connected 229,320 distal cCREs to 32,815 target genes, resulting in a total of 197,458 cCRE-gene pairs, with 49,210 pairs showing clear cell-type-specific patterns based on high ABC scores (fig. S1O and table S11, fig. S1P and table S12, methods). Additionally, DMRs associated with these cCREs exhibited cell-type specificity, reinforcing the unique regulatory networks in each retinal and RPE/choroid cell type (fig. S1, N, Q).

Our findings provide a comprehensive map of cCRE-gene interactions in human retina and RPE cell types, highlighting the intricate regulatory networks that drive cell-specific gene expression. The varied correlation patterns between cCRE accessibility, target gene expression, and ABC scores suggest that multiple regulatory factors contribute to gene expression, underscoring the need for further investigation into the complex mechanisms underlying retinal cell-type-specific gene regulation.

Single-cell epigenome analysis of mouse retina

Studying gene conservation across species provides insights into the evolutionary origins of key genes, identifies essential developmental pathways, and helps guide the selection of suitable animal models for studying human diseases (47). To explore the evolutionary dynamics of cCREs in human retinal cells and evaluate the suitability of mouse models for studying human retinal diseases, we performed snRNA-seq and snATAC-seq separately on retina tissues from four adult mice (two each at 2.5 and 5 months of age, with one male and one female per age group). This approach enabled us to profile the transcriptome and chromatin accessibility assays at single cell resolution separately.

Using unsupervised clustering based on single-cell RNA-seq data, we identified 13 distinct cell types in the mouse retina (fig. S2C, methods). Each cluster was annotated according to known cell-type markers in the retina (7, 47). After removing doublets, the annotated cell types included Rods, OFF cone bipolar cells, Müller glia, ON cone bipolar cells, Rod bipolar cells, Cones, GABA amacrine cells, Horizontal cells, Gly amacrine cells, Retinal Ganglion Cells (RGC), All amacrine cells, Astrocytes, and Microglia. We observed cell-type-specific expression of known cell type marker genes (fig. S2D, table S13). Label transfer from RNA-seq data onto ATAC-seq clusters enabled chromatin accessibility profiling within the same 13 cell types across 27,359 nuclei (fig. S2E, methods).

In the mouse retina, we identified a union of 124,056 open chromatin regions across the 13 cell types. Among these, 53,899 were significant cell type-specific cCREs (fig. S2F, methods) and likely regulate cell-specific gene expression within the retina. Additionally, we detected 23,295 expressed genes in the RNA-seq assay, of which 12,274 displayed significant cell type specific expression (fig. S2G, methods). This epigenomic and transcriptomic atlas enriches our understanding of cell-specific functions within the mouse retina.

To identify potential transcriptional regulators, we performed motif analysis on accessible regions across the 13 retinal cell types. A total of 168 known TF motifs were enriched among the cCREs from different cell types, most of which displayed cell-type-specific patterns (fig. S2H, table S14). Notable cell-type-specific motifs included ZNF143|STAF in All-amacrine cells; Sp1 in Astrocytes; CTCF in Cones; Tgif2 in GABA amacrine cells; Usf2 in Gly amacrine cells; ELF5 in Müller glia; Ronin in OFF cone bipolar cells; YY1 in ON cone bipolar cells; RORgt in RGC; Elk4 in Rod bipolar cells; and BORIS in Rods. These enriched motifs suggest regulatory roles for these TFs in specific retinal cell types, supporting distinct gene regulatory programs essential for retinal function.

Comparative analysis of gene expression programs in human and mouse retinal cell types

To explore the conservation of gene-regulatory landscapes between human and mouse retinas, we performed a comparative analysis of chromatin states and gene expression in retinal cell types. Given that mouse eyes lack macula tissue (48), our analysis focused on merged data from human retina and macula tissues and mouse retina tissue. We profiled 34,230 cells from human retina and macula RNA-seq data and 27,359 cells from mouse retina RNA-seq data, using joint clustering based on gene activity scores to align cell types across species (Fig. 5A–C, fig. S1J–K, methods).

Fig. 5. — (A) UMAP co-embedding of single nuclei RNA-seq data, with each cell colored by species. (B) UMAP embedding of 10x multiome RNA-seq data of human retina and macula tissues, annotated by cell type. (C) UMAP embedding of single-cell RNA-seq of mouse retina tissue, annotated by cell type. (D) Comparison of human and mouse retinal cCREs. Left, pie chart showing fraction of three categories of cCREs, including human specific, human divergent, and human conserved cCREs. The human conserved cCREs are those both with DNA sequences conserved across species and in open chromatin in orthologous regions. The human divergent cCREs are sequences conserved but the orthologous regions have not been identified as open chromatin regions in mouse retina. The human specific cCREs are those without orthologous sequences in the mouse genome. Right, bar plot showing three categories of cCREs in corresponding cell types from human and mouse. (E) The differential gene expression between human and mouse in Muller glia. Gray dots represent all the orthologous genes across human and mouse in this cell type; green dots represent the genes paired with human divergent cCREs; red dots represent the genes paired with human conserved cCREs; blue dots represent the genes paired with human specific cCREs. (F) Top significant GO terms for significantly differentially expressed genes in human Muller glia. (G) Top significant GO terms for significant differentially expressed genes in mouse Muller glia. **(H)** Heatmap showing chromatin accessibility of putative human conserved enhancers of retina and macula tissues for human and mouse. **(I)** Heatmap showing chromatin accessibility of putative human divergent enhancers of retina and macula tissues for human and mouse. **(J)** Dot plot of conserved TF motif enrichment of human and mouse in Muller glia. (K) Scatter plot of TF motif conservation of human and mouse for all retinal cell types. X axis represents Pearson R, y axis represents -log(P-value).

We next investigated the conservation of chromatin accessibility at cCREs between corresponding cell types in the human and mouse retina. For 51.44% of human cCREs, we identified mouse genome sequences with high similarity (defined as >50% of bases lifted over to the human genome) (Fig.5D). Among these conserved sequences, 12.18% also showed chromatin accessibility in at least one cell type from the mouse retina. We termed these cCREs with both DNA sequence similarity and chromatin accessibility as “chromatin accessibility conserved cCREs.” The remaining 39.26% of human cCREs showed sequence similarity without conserved chromatin accessibility, which we termed “human divergent cCREs.” Additionally, 48.56% of human cCREs lacked orthologous sequences in the mouse genome and were classified as “human-specific cCREs,” although they may be conserved in other primates or mammals (Fig.5D, left). This general pattern aligns with previous reports on chromatin conservation across species (49).

Breaking down these categories by cell type, we observed consistent proportions of conserved, divergent, and human-specific cCREs across different retinal cell types (Fig. 5D, right). This cell-type-specific breakdown provides insight into the evolutionary dynamics of chromatin accessibility in the retina.

To examine species-specific and conserved gene expression programs, we performed differential expression and chromatin accessibility analyses using edgeR (75) for each cell type. In Müller glia, we observed notable differences in RNA expression levels, with 1,191 genes showing higher expression in human cells and 472 genes showing higher expression in mouse cells (Fig.5E). Genes paired with human-specific cCREs were primarily found in regions with high expression in humans or nonsignificant regions, while genes paired with conserved cCREs spanned all expression regions (Fig.5E). Human divergent cCREs were mostly associated with genes exhibiting high expression in humans, although some orthologous genes with mouse divergent cCREs showed elevated expression in mice, potentially reflecting cell-type-specific functions in the mouse retina.

GO analysis of differentially expressed genes revealed distinct functional profiles, with genes highly expressed in humans (Fig.5F) and in mice (Fig.5G) enriched for specific processes. This functional divergence underscores potential species-specific roles in retinal biology, even among orthologous cell types. For example, compared with mice, highly expressed genes of human Muller glia cell type have functions relevant to synaptic transmission and GABAergic (Fig.5F), but murine Muller glia have functions relevant to membrane lipid catabolic process (Fig.5G). Additional cell type comparisons and their specific gene functions are provided in fig. S3.

For the 35,768 human-conserved cCRE regions, heatmaps revealed consistent cell-type-specific accessibility patterns across species (Fig.5H). In contrast, the 118,499 human-divergent cCRE regions displayed a clear cell-type-specific accessibility pattern in human retina cell types, while corresponding mouse sequences showed minimal chromatin accessibility (Fig.5I). This divergence in chromatin accessibility highlights epigenetic differences between species that may reflect adaptation to unique visual demands.

To investigate conservation at the level of TF motifs, we selected conserved TF motifs for each cell type and calculated the PCC of motif enrichment scores. For example, in Müller glia, we observed a Pearson correlation coefficient (R) of 0.86, indicating strong TF motif conservation between species (Fig. 5J). Similar analyses for other cell types (fig. S9A–K) demonstrated varying degrees of conservation. Overall, cell types with higher Pearson correlation coefficients tend to have lower p-values. For example, Horizontal and RGC cell types exhibit the lowest Pearson correlation coefficients and the highest p-values, suggesting a lower conservation of TF motifs between human and mouse in these cell types. In contrast, GABA amacrine cells show the highest conservation, as indicated by their higher Pearson correlation coefficient and lower p-value (Fig.5K). The conservation of TF motifs across species not only supports the use of mouse models to explore mechanisms underlying human retinal pathology but also strengthens their relevance in translational research.

Epigenome maps facilitate the interpretation of non-coding risk variants

Mapping non-coding risk variants to specific cell types provides critical insights into the regulatory mechanisms underlying ocular diseases. By integrating identified cCREs and their associated transcription factor motifs, we can better understand chromatin accessibility across retinal cell types and assess the functional roles of non-coding regions linked to disease, particularly those identified by genome-wide association studies (GWAS).

GWAS have revealed genetic variants associated with numerous ocular diseases and traits, yet most of these variants are located in non-coding regions of the genome (50). Previous studies have shown that non-coding risk variants often overlap with cCREs active in disease-relevant cell types (51). Leveraging the newly annotated, cell-type-specific human retina cCREs, we first aimed to predict cell types associated with various eye diseases. Using linkage disequilibrium score regression (LDSC), we assessed whether genetic heritability of DNA variants linked to retinal diseases is significantly enriched within cCREs active in specific retinal cell types. This analysis revealed significant associations between seven eye diseases and cell-type-specific open chromatin profiles in the retina and macula tissues, and five in RPE tissue (Fig. 6A, 6B).

Specifically, LDSC analysis using human-specific regulatory elements showed an association between MacTel and Müller glia cells (Fig.6A), suggesting that MacTel-related risk variants might reside within human-specific regulatory elements that drive gene regulation in Müller glia (7, 52). Similarly, an association between age-related macular degeneration (AMD) and RPE cell types was observed (Fig.6B), indicating that AMD-related risk variants could influence gene regulatory programs specific to the human RPE (53).

To refine our understanding of disease-associated variants within cCREs, we conducted fine mapping for MacTel and AMD traits to prioritize lists of potential causal SNPs. For MacTel, we identified nine loci on chromosomes 1, 2, 3, 5, 7, 9, and 10, consistent with previous reports (52). Detailed visualization of these loci (fig. S6, K–S) revealed 118 likely causal SNPs. For AMD, we identified ten loci on chromosomes 1, 6, 8, 10, 16, and 19, also consistent with previous findings (53). Fine mapping with susieR (54) yielded a total of 88 likely causal SNPs across these AMD loci (fig. S6, A–J).

Given the strong association between Müller glia and MacTel in humans (Fig.6A), we hypothesized a similar relationship in the mouse retina. Surprisingly, no overlap was found between potential causal SNPs for MacTel and conserved human-mouse peaks. This lack of conservation indicates that, while Müller glia cells are present in both species, the causal gene regulatory variants linked to MacTel are probably human-specific. These findings underscore the necessity of using human or primate models to investigate the mechanisms underlying this disorder.

To further characterize SNP-target gene relationships, we examined overlaps between potential causal SNPs identified through fine mapping and peak regions from the ABC model, supported by Hi-C data. For MacTel, only one causal SNP directly overlapped with Müller glia peak regions (Fig.6C). For AMD, three RPE peaks overlapped with AMD-associated causal SNPs, including rs9391734, which overlapped with the peak region at chr6: 32,130,132–32,130,631 in RPE cells, paired with the target gene ATF6B/FKBPL (Fig.6D). Hi-C analysis further refined target gene predictions, demonstrating how the combination of single-cell multiomics with chromatin interaction data enhances our ability to interpret non-coding variants in ocular disease.

Deep neural network predicts the effects of risk variants

To investigate how risk variants affect the function of regulatory elements, we adopted Basenji (55) to train a deep neural network (DNN) to predict chromatin accessibility from DNA sequences. We trained the DNN model on normalized pseudo-bulk ATAC-seq profiles from human RPE and melanocyte, selecting the model with the highest average PCC on validation dataset for each cell type. Specifically, our best model achieved PCC values of 0.8331 for the RPE dataset and 0.8353 for the melanocyte dataset (fig. S10, A, B, methods). We conducted in silico mutagenesis on RPE-specific cCREs associated with potential AMD-risk variants to assess their effects on chromatin accessibility across different scenarios. In the first case (Fig. 6D–G), we showed that the potential causal variant rs9391734, located within a peak region, slightly reduced the predicted accessibility of the ATF6B/FKBPL promoter, with high prediction accuracy (PearsonR = 0.915). In the second case (fig. S4, A–D), two potential variants (rs943079 and rs943080) within an enhancer, but without paired target genes, were predicted to either increase or decrease accessibility. In the third case (fig. S4, E–H), two potential variants (rs2672600 and rs3750847) located near but outside a peak region were found to have subtle regulatory effects on accessibility, with proximity to the peak summit correlating with their impact. In the fourth case (fig. S5, A–G), we observed that the same mutation within an enhancer could have different effects on accessibility across cell types, as demonstrated by divergent impacts in RPE and melanocyte cells. Additionally, deletion of enhancer sequences disrupted accessibility and altered neighboring enhancer activity, likely due to motif disruption (fig. S5H). These findings highlight the nuanced regulatory effects of both causal and noncausal variants on chromatin states across contexts.

To validate the deep neural network’s predictions for the effects of non-coding variants, we used CRISPR editing to genetically engineer the hTERT-RPE1 cell line. TMEM216 is known to be associated with retinal degeneration and syndromes such as Joubert and Meckel (56–60). It is a component of cilia, specifically localized between the basal body and ciliary axoneme (61, 62). Firstly using the model trained on the pseudo bulk chromatin accessibility profile of RPE cell type, we performed in silico mutageneisis, and predicted a decrease in chromatin accessibility following the c.−69G>A mutation (Fig. 7A,B). To evaluate the performance of our deep learning model, we used CRISPR editing to engineer two RPE1 cell lines harboring TMEM216 c.−69G>A variant in homozygous state (D4 and F6), and heterozygous state (G1), and performed bulk ATAC-seq (Fig.7C). In agreement with the prediction, chromatin accessibility at this site was reduced in both homozygous cell lines D4, F6, and also in the heterozygous G1 cell line compared to the wild-type hTERT-RPE1 cells (Fig.7C). These findings are consistent with reduced levels of TMEM216 expression reported in cells with c.−69G>A change (56). This result supports the utility of our deep learning model in predicting functional consequences of non-coding risk variants.

Fig. 7. — (A) Predicted chromatin accessibility after *in silico* mutagenesis within region chr11: 61392322–61393674 (0-based) of pseudo bulk RPE cell type. (B) Zoom in *in silico* nucleotide mutagenesis within region chr11: 61392547–61392566. Lower accessibility was predicted on *TMEM216* enhancer with risk loci chr11 61392562 G>A. (C) Genome browser tracks (GRCh38) display chromatin accessibility profiles of CRISPR edited RPE cell lines. Two replicates were shown for each cell line.

Discussions

Analysis of the transcriptome, epigenome and 3D genome features of 23 cell types from human retina tissues enhances our knowledge of gene regulatory programs in the human retina. Compared with other recent works, our study introduces several unique features:

First, by utilizing freshly collected retina tissues from young donors aged around 20–40 years, our study minimizes postmortem artifacts.

Second, we incorporated three tissues—retina, macula, and RPE/choroid in our analysis. The inclusion of RPE/choroid, rarely in previous studies, provides a more holistic view of retinal biology and its regulatory mechanisms. Leveraging four molecular modalities—gene expression, chromatin accessibility, DNA methylation, and chromatin conformation across 23 cell types, we report a resource for studying the cell type specific gene regulation of human retinal diseases analysis.

Third, we also provided detailed, cell-type-specific data, including values for CREs, genes, transcription factor motifs, and CRE-gene pairs, all presented in comprehensive tables. These resources are further enriched with cross-species information, offering a unique comparative perspective. Additionally, our extensive list of candidate transcription factors and their motifs, particularly those enriched in rare cell populations such as those in the RPE, lays a foundation for reconstructing detailed gene-regulatory networks and conducting targeted studies on retinal cell function and disease mechanisms. These curated datasets could facilitate research in retinal biology.

Fourth, our study represents the first report of cell-type-specific DNA single cell methylation data in the human retina, providing a valuable resource for linking DMRs with cCRE-gene pairs and disease-related loci. Future studies incorporating age-diverse and patient-derived samples could build on this work to reveal further insights into the role of cell-type-specific single cell DNA methylation in eye aging and disease.

Fifth, our deep cross-species comparisons highlight conserved enhancers and transcriptional programs, providing a framework to assess the utility of mouse models for human retina diseases.

Sixth, by integrating GWAS data, deep neural network predictions, and CRISPR validation, our analysis uncovers the genetic underpinnings of several eye diseases, highlighting the role of conserved and human-specific noncoding regulatory elements in polygenic traits. Using deep neural networks, we captured the gene-regulatory code and interpreted the effects of risk variants associated with complex traits and diseases. In silico mutagenesis enabled the identification of “high-effect” SNPs, offering insights into causal variants across various cell types. This integrative approach bridges computational predictions and experimental validation, facilitating the functional annotation of noncoding disease risk variants and advancing precision medicine for ocular diseases.

Finally, our study introduces a user-friendly web portal that enables researchers to explore the single cell multiomic data from 23 cell types across three eye tissues. This portal, enriched with cell type-specific information on cCREs, transcription factors, and 3D genome contacts, ensures accessibility and utility for a broad range of research applications.

Our study is limited by the small sample size, which includes only three individuals of varying ages. This limitation may impact the generalizability of our findings across broader demographic groups, including diverse ethnicities, genders, and age ranges. Expanding sample diversity and size in future studies will be crucial for refining our understanding of chromatin landscape variability across populations. Additionally, integrating single-cell multiomics with spatial transcriptomics will aid in identifying rare cell types and elucidating complex gene-regulatory networks, further enhancing our ability to pinpoint mechanisms through which genetic variants influence disease phenotypes.

In conclusion, our integrated approach advances the understanding of the genetic architecture of retinal diseases, demonstrating the potential of cutting-edge technologies to unravel complex gene regulation in polygenic human traits. As these findings are validated and expanded, the potential for significant clinical impact grows, promising to transform patient care through more personalized, genetically informed therapies. Our work also provides a strong foundation for future research on human retinal aging and the identification of retinal disease-associated variants.

Supplementary Material

NIHPP2024.12.28.630634V2-supplement-1.pdf^{(4.8MB, pdf)}

Acknowledgments:

We thank Zhaoning Wang for bulk-ATAC seq experiment discussion and guidance; thank Jingtian Zhou for discussion; We would like to thank the families of donors and San Diego Eye Bank for providing the eye globes within 2 hours of enucleation.

Funding:

This study is founded in part by the 4DN project (UM1HG011585 to B.R.), the Foundation Fighting Blindness (RA); Unrestricted funds from Research to Prevent Blindness to the Viterbi family department of Ophthalmology, UCSD; NIH-RO1EY21237 (RA), RO1EY030591 (RA), RO1EY031663 (RA), T32EY026590 (RA), P30-EY22589 (RA).

Funding Statement

Footnotes

Competing interests: B.R. is a co-founder of Epigenome Technologies and has equity in Arima Genomics.

Code availability: Code to perform the analyses in this study is accessible at GitHub (https://github.com/yingyuan830/human_retina).

Supplementary Materials

Materials and Methods

Figs. S1 to S10

Tables S1 to S14

Data and materials availability:

Data produced in this study are available at the NCBI GEO under accession number GSE277326 (human eye 10x multiome), GSE277361 (human eye sn-m3C-seq), GSE276851 (human RPE cell lines bulk ATAC-seq), GSE276864 (mouse retina snRNA-seq), GSE276923 (mouse retina snATAC-seq). All the datasets are also available at the 4DN portal (https://data.4dnucleome.org/ren-lab-single-cell-epigenome-and-hic-analysis-in-human-mouse-retina). Datasets of human eye 10x multiome and sn-m3C-seq have been uploaded for viewing on the Eye Epigenomics Web Portal (https://epigenome.wustl.edu/EyeEpigenome/).

References and Notes

1.Lundeen E. A., Burke-Conte Z., Rein D. B., Wittenborn J. S., Saaddine J., Lee A. Y., Flaxman A. D., Prevalence of Diabetic Retinopathy in the US in 2021. JAMA Ophthalmol. 141, 747–754 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]
2.Cheng Q., “Age-related Macular Degeneration and Vascular and Renal Comorbidities in Adults Aged 40 Years or Older: NHANES 2005–2008,” thesis, Georgia State University; (2014). [Google Scholar]
3.Lim L. S., Mitchell P., Seddon J. M., Holz F. G., Wong T. Y., Age-related macular degeneration. Lancet 379, 1728–1738 (2012). [DOI] [PubMed] [Google Scholar]
4.How the Retina Works (pdf) by Helga Kolb.
5.Dowling J. E., The Retina: An Approachable Part of the Brain (Harvard University Press, 1987). [Google Scholar]
6.Ptito M., Bleau M., Bouskila J., The Retina: A Window into the Brain. Cells 10, 3269 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
7.Wang S. K., Nair S., Li R., Kraft K., Pampari A., Patel A., Kang J. B., Luong C., Kundaje A., Chang H. Y., Single-cell multiome of the human retina and deep learning nominate causal variants in complex eye diseases. Cell Genomics 2, 100164 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
8.Preissl S., Gaulton K. J., Ren B., Characterizing cis-regulatory elements using single-cell epigenomics. Nat. Rev. Genet. 24, 21–43 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]
9.Gusev A., Lee S. H., Trynka G., Finucane H., Vilhjálmsson B. J., Xu H., Zang C., Ripke S., Bulik-Sullivan B., Stahl E., Schizophrenia Working Group of the Psychiatric Genomics Consortium, SWE-SCZ Consortium, Kähler A. K., Hultman C. M., Purcell S. M., McCarroll S. A., Daly M., Pasaniuc B., Sullivan P. F., Neale B. M., Wray N. R., Raychaudhuri S., Price A. L., Schizophrenia Working Group of the Psychiatric Genomics Consortium, SWE-SCZ Consortium, Partitioning heritability of regulatory and cell-type-specific variants across 11 common diseases. Am. J. Hum. Genet. 95, 535–552 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]
10.Finucane H. K., Reshef Y. A., Anttila V., Slowikowski K., Gusev A., Byrnes A., Gazal S., Loh P.-R., Lareau C., Shoresh N., Genovese G., Saunders A., Macosko E., Pollack S., Brainstorm Consortium, Perry J. R. B., Buenrostro J. D., Bernstein B. E., Raychaudhuri S., McCarroll S., Neale B. M., Price A. L., Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types. Nat. Genet. 50, 621–629 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
11.Liang Q., Cheng X., Wang J., Owen L., Shakoor A., Lillvis J. L., Zhang C., Farkas M., Kim I. K., Li Y., DeAngelis M., Chen R., A multi-omics atlas of the human retina at single-cell resolution. Cell Genom 3, 100298 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]
12.Li J., Wang J., Ibarra I. L., Cheng X., Luecken M. D., Lu J., Monavarfeshani A., Yan W., Zheng Y., Zuo Z., Colborn S. L. Z., Cortez B. S., Owen L. A., Tran N. M., Shekhar K., Sanes J. R., Stout J. T., Chen S., Li Y., DeAngelis M. M., Theis F. J., Chen R., Integrated multiomics single cell atlas of the human retina. Res. Sq., doi: 10.21203/rs.3.rs-3471275/v1 (2023). [DOI] [Google Scholar]
13.Lopez Soriano V., Dueñas Rey A., Mukherjee R., Genomics England Research Consortium, Coppieters F., Bauwens M., Willaert A., De Baere E., Multi-omics analysis in human retina uncovers ultraconserved cis-regulatory elements at rare eye disease loci. Nat. Commun. 15, 1600 (2024). [DOI] [PMC free article] [PubMed] [Google Scholar]
14.Cherry T. J., Yang M. G., Harmin D. A., Tao P., Timms A. E., Bauwens M., Allikmets R., Jones E. M., Chen R., De Baere E., Greenberg M. E., Mapping the cis-regulatory architecture of the human retina reveals noncoding genetic variation in disease. Proc. Natl. Acad. Sci. U. S. A. 117, 9001–9012 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
15.Thomas E. D., Timms A. E., Giles S., Harkins-Perry S., Lyu P., Hoang T., Qian J., Jackson V. E., Bahlo M., Blackshaw S., Friedlander M., Eade K., Cherry T. J., Cell-specific cis-regulatory elements and mechanisms of non-coding genetic disease in human retina and retinal organoids. Dev. Cell 57, 820–836.e6 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
16.Finkbeiner C., Ortuño-Lizarán I., Sridhar A., Hooper M., Petter S., Reh T. A., Single-cell ATAC-seq of fetal human retina and stem-cell-derived retinal organoids shows changing chromatin landscapes during cell fate acquisition. Cell Rep. 38, 110294 (2022). [DOI] [PubMed] [Google Scholar]
17.Mullin N. K., Voigt A. P., Boese E. A., Liu X., Stone E. M., Tucker B. A., Mullins R. F., Transcriptomic and chromatin accessibility analysis of the human macular and peripheral retinal pigment epithelium at the single-cell level. Am. J. Pathol. 193, 1750–1761 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]
18.D’haene E., López-Soriano V., Martínez-García P. M., Kalayanamontri S., Rey A. D., Sousa-Ortega A., Naranjo S., Van de Sompele S., Vantomme L., Mahieu Q., Vergult S., Neto A., Gómez-Skarmeta J. L., Martínez-Morales J. R., Bauwens M., Tena J. J., De Baere E., Comparative 3D genome analysis between neural retina and retinal pigment epithelium reveals differential cis-regulatory interactions at retinal disease loci. Genome Biol. 25, 123 (2024). [DOI] [PMC free article] [PubMed] [Google Scholar]
19.Marchal C., Singh N., Batz Z., Advani J., Jaeger C., Corso-Díaz X., Swaroop A., High-resolution genome topology of human retina uncovers super enhancer-promoter interactions at tissue-specific and multifactorial disease loci. Nat. Commun. 13, 5827 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
20.Mondal A. K., Gaur M., Advani J., Swaroop A., Epigenome-metabolism nexus in the retina: implications for aging and disease. Trends Genet. 40, 718–729 (2024). [DOI] [PMC free article] [PubMed] [Google Scholar]
21.Campello L., Singh N., Advani J., Mondal A. K., Corso-Díaz X., Swaroop A., Aging of the retina: Molecular and metabolic turbulences and potential interventions. Annu. Rev. Vis. Sci. 7, 633–664 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
22.Waterland R. A., Is epigenetics an important link between early life events and adult disease? Horm. Res. 71 Suppl 1, 13–16 (2009). [DOI] [PubMed] [Google Scholar]
23.Niculescu M. D., Zeisel S. H., Diet, methyl donors and DNA methylation: interactions between dietary folate, methionine and choline. J. Nutr. 132, 2333S–2335S (2002). [DOI] [PubMed] [Google Scholar]
24.Bollati V., Schwartz J., Wright R., Litonjua A., Tarantini L., Suh H., Sparrow D., Vokonas P., Baccarelli A., Decline in genomic DNA methylation through aging in a cohort of elderly subjects. Mech. Ageing Dev. 130, 234–239 (2009). [DOI] [PMC free article] [PubMed] [Google Scholar]
25.Otteson D. C., Eyes on DNA methylation: current evidence for DNA methylation in ocular development and disease. J. Ocul. Biol. Dis. Infor. 4, 95–103 (2011). [DOI] [PMC free article] [PubMed] [Google Scholar]
26.Advani J., Mehta P. A., Hamel A. R., Mehrotra S., Kiel C., Strunz T., Corso-Díaz X., Kwicklis M., van Asten F., Ratnapriya R., Chew E. Y., Hernandez D. G., Montezuma S. R., Ferrington D. A., Weber B. H. F., Segrè A. V., Swaroop A., QTL mapping of human retina DNA methylation identifies 87 gene-epigenome interactions in age-related macular degeneration. Nat. Commun. 15, 1972 (2024). [DOI] [PMC free article] [PubMed] [Google Scholar]
27.Li D., Hsu S., Purushotham D., Sears R. L., Wang T., WashU Epigenome Browser update 2019. Nucleic Acids Res. 47, W158–W165 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
28.Li D., Purushotham D., Harrison J. K., Hsu S., Zhuo X., Fan C., Liu S., Xu V., Chen S., Xu J., Ouyang S., Wu A. S., Wang T., WashU Epigenome Browser update 2022. Nucleic Acids Res. 50, W774–W781 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
29.Lee D.-S., Luo C., Zhou J., Chandran S., Rivkin A., Bartlett A., Nery J. R., Fitzpatrick C., O’Connor C., Dixon J. R., Ecker J. R., Simultaneous profiling of 3D genome structure and DNA methylation in single human cells. Nat. Methods 16, 999–1006 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
30.Li G., Liu Y., Zhang Y., Kubo N., Yu M., Fang R., Kellis M., Ren B., Joint profiling of DNA methylation and chromatin architecture in single cells. Nat. Methods 16, 991–993 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
31.Hao Y., Stuart T., Kowalski M. H., Choudhary S., Hoffman P., Hartman A., Srivastava A., Molla G., Madad S., Fernandez-Granda C., Satija R., Dictionary learning for integrative, multimodal and scalable single-cell analysis. Nat. Biotechnol. 42, 293–304 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]
32.Scimone C., Donato L., Esposito T., Rinaldi C., D’Angelo R., Sidoti A., A novel RLBP1 gene geographical area-related mutation present in a young patient with retinitis punctata albescens. Hum. Genomics 11, 18 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]
33.Cai X., Conley S. M., Naash M. I., RPE65: role in the visual cycle, human retinal disease, and gene therapy. Ophthalmic Genet. 30, 57–62 (2009). [DOI] [PMC free article] [PubMed] [Google Scholar]
34.Zhang Y., Liu T., Meyer C. A., Eeckhoute J., Johnson D. S., Bernstein B. E., Nusbaum C., Myers R. M., Brown M., Li W., Liu X. S., Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, R137 (2008). [DOI] [PMC free article] [PubMed] [Google Scholar]
35.Li Y. E., Preissl S., Hou X., Zhang Z., Zhang K., Qiu Y., Poirion O. B., Li B., Chiou J., Liu H., Pinto-Duarte A., Kubo N., Yang X., Fang R., Wang X., Han J. Y., Lucero J., Yan Y., Miller M., Kuan S., Gorkin D., Gaulton K. J., Shen Y., Nunn M., Mukamel E. A., Behrens M. M., Ecker J. R., Ren B., An atlas of gene regulatory elements in adult mouse cerebrum. Nature 598, 129–136 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
36.Wang S., Sengel C., Emerson M. M., Cepko C. L., A gene regulatory network controls the binary fate decision of rod and bipolar cells in the vertebrate retina. Dev. Cell 30, 513–527 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]
37.Samuel A., Housset M., Fant B., Lamonerie T., Otx2 ChIP-seq reveals unique and redundant functions in the mature mouse retina. PLoS One 9, e89110 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]
38.Liu H., Zhou J., Tian W., Luo C., Bartlett A., Aldridge A., Lucero J., Osteen J. K., Nery J. R., Chen H., Rivkin A., Castanon R. G., Clock B., Li Y. E., Hou X., Poirion O. B., Preissl S., Pinto-Duarte A., O’Connor C., Boggeman L., Fitzpatrick C., Nunn M., Mukamel E. A., Zhang Z., Callaway E. M., Ren B., Dixon J. R., Behrens M. M., Ecker J. R., DNA methylation atlas of the mouse brain at single-cell resolution. Nature 598, 120–128 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
39.Luo C., Keown C. L., Kurihara L., Zhou J., He Y., Li J., Castanon R., Lucero J., Nery J. R., Sandoval J. P., Bui B., Sejnowski T. J., Harkins T. T., Mukamel E. A., Behrens M. M., Ecker J. R., Single-cell methylomes identify neuronal subtypes and regulatory elements in mammalian cortex. Science 357, 600–604 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]
40.He Y., Hariharan M., Gorkin D. U., Dickel D. E., Luo C., Castanon R. G., Nery J. R., Lee A. Y., Zhao Y., Huang H., Williams B. A., Trout D., Amrhein H., Fang R., Chen H., Li B., Visel A., Pennacchio L. A., Ren B., Ecker J. R., Spatiotemporal DNA methylome dynamics of the developing mouse fetus. Nature 583, 752–759 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
41.de Mendoza A., Poppe D., Buckberry S., Pflueger J., Albertin C. B., Daish T., Bertrand S., de la Calle-Mustienes E., Gómez-Skarmeta J. L., Nery J. R., Ecker J. R., Baer B., Ragsdale C. W., Grützner F., Escriva H., Venkatesh B., Bogdanovic O., Lister R., The emergence of the brain non-CpG methylation system in vertebrates. Nat Ecol Evol 5, 369–378 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
42.Tian W., Zhou J., Bartlett A., Zeng Q., Liu H., Castanon R. G., Kenworthy M., Altshul J., Valadon C., Aldridge A., Nery J. R., Chen H., Xu J., Johnson N. D., Lucero J., Osteen J. K., Emerson N., Rink J., Lee J., Li Y. E., Siletti K., Liem M., Claffey N., O’Connor C., Yanny A. M., Nyhus J., Dee N., Casper T., Shapovalova N., Hirschstein D., Ding S.-L., Hodge R., Levi B. P., Keene C. D., Linnarsson S., Lein E., Ren B., Behrens M. M., Ecker J. R., Single-cell DNA methylation and 3D genome architecture in the human brain. Science 382, eadf5357 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]
43.Dekker J., Mirny L., The 3D Genome as Moderator of Chromosomal Communication. Cell 164, 1110–1121 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]
44.Zemke N. R., Armand E. J., Wang W., Lee S., Zhou J., Li Y. E., Liu H., Tian W., Nery J. R., Castanon R. G., Bartlett A., Osteen J. K., Li D., Zhuo X., Xu V., Chang L., Dong K., Indralingam H. S., Rink J. A., Xie Y., Miller M., Krienen F. M., Zhang Q., Taskin N., Ting J., Feng G., McCarroll S. A., Callaway E. M., Wang T., Lein E. S., Behrens M. M., Ecker J. R., Ren B., Conserved and divergent gene regulatory programs of the mammalian neocortex. Nature 624, 390–402 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]
45.Nagano T., Lubling Y., Várnai C., Dudley C., Leung W., Baran Y., Mendelson Cohen N., Wingett S., Fraser P., Tanay A., Cell-cycle dynamics of chromosomal organization at single-cell resolution. Nature 547, 61–67 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]
46.Fulco C. P., Nasser J., Jones T. R., Munson G., Bergman D. T., Subramanian V., Grossman S. R., Anyoha R., Doughty B. R., Patwardhan T. A., Nguyen T. H., Kane M., Perez E. M., Durand N. C., Lareau C. A., Stamenova E. K., Aiden E. L., Lander E. S., Engreitz J. M., Activity-by-contact model of enhancer–promoter regulation from thousands of CRISPR perturbations. Nat. Genet. 51, 1664–1669 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
47.Hahn J., Monavarfeshani A., Qiao M., Kao A. H., Kölsch Y., Kumar A., Kunze V. P., Rasys A. M., Richardson R., Wekselblatt J. B., Baier H., Lucas R. J., Li W., Meister M., Trachtenberg J. T., Yan W., Peng Y.-R., Sanes J. R., Shekhar K., Evolution of neuronal cell classes and types in the vertebrate retina. Nature 624, 415–424 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]
48.Fletcher E. L., Jobling A. I., Greferath U., Mills S. A., Waugh M., Ho T., de Iongh R. U., Phipps J. A., Vessey K. A., Studying age-related macular degeneration using animal models. Optom. Vis. Sci. 91, 878–886 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]
49.Minnoye L., Taskiran I. I., Mauduit D., Fazio M., Van Aerschot L., Hulselmans G., Christiaens V., Makhzami S., Seltenhammer M., Karras P., Primot A., Cadieu E., van Rooijen E., Marine J.-C., Egidy G., Ghanem G.-E., Zon L., Wouters J., Aerts S., Cross-species analysis of enhancer logic using deep learning. Genome Res. 30, 1815–1834 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
50.Cano-Gamez E., Trynka G., From GWAS to Function: Using Functional Genomics to Identify the Mechanisms Underlying Complex Diseases. Front. Genet. 11, 424 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
51.Maurano M. T., Humbert R., Rynes E., Thurman R. E., Haugen E., Wang H., Reynolds A. P., Sandstrom R., Qu H., Brody J., Shafer A., Neri F., Lee K., Kutyavin T., Stehling-Sun S., Johnson A. K., Canfield T. K., Giste E., Diegel M., Bates D., Hansen R. S., Neph S., Sabo P. J., Heimfeld S., Raubitschek A., Ziegler S., Cotsapas C., Sotoodehnia N., Glass I., Sunyaev S. R., Kaul R., Stamatoyannopoulos J. A., Systematic localization of common disease-associated variation in regulatory DNA. Science 337, 1190–1195 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]
52.Bonelli R., Jackson V. E., Prasad A., Munro J. E., Farashi S., Heeren T. F. C., Pontikos N., Scheppke L., Friedlander M., Egan C. A., Allikmets R., Ansell B. R. E., Bahlo M., Identification of genetic factors influencing metabolic dysregulation and retinal support for MacTel, a retinal disorder. Communications Biology 4, 1–14 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
53.Winkler T. W., Grassmann F., Brandl C., Kiel C., Günther F., Strunz T., Weidner L., Zimmermann M. E., Korb C. A., Poplawski A., Schuster A. K., Müller-Nurasyid M., Peters A., Rauscher F. G., Elze T., Horn K., Scholz M., Cañadas-Garre M., McKnight A. J., Quinn N., Hogg R. E., Küchenhoff H., Heid I. M., Stark K. J., Weber B. H. F., Genome-wide association meta-analysis for early age-related macular degeneration highlights novel loci and insights for advanced disease. BMC Med. Genomics 13, 120 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
54.Wang G., Sarkar A., Carbonetto P., Stephens M., A simple new approach to variable selection in regression, with application to genetic fine mapping. J. R. Stat. Soc. Series B Stat. Methodol. 82, 1273–1300 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
55.Kelley D. R., Cross-species regulatory sequence activity prediction. PLoS Comput. Biol. 16, e1008050 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
56.Malka S., Biswas P., Berry A.-M., Sangermano R., Ullah M., Lin S., D’Antonio M., Jestin A., Jiao X., Quinodoz M., Sullivan L., Gardner J. C., Place E. M., Michaelides M., Kaminska K., Mahroo O. A., Schiff E., Wright G., Cancellieri F., Vaclavik V., Santos C., Rehman A. U., Mehrotra S., Azhar Baig H. M., Iqbal M., Ansar M., Santos L. C., Sousa A. B., Tran V. H., Matsui H., Bhatia A., Naeem M. A., Akram S. J., Akram J., Riazuddin S., Ayuso C., Pierce E. A., Hardcastle A. J., Riazuddin S. A., Frazer K. A., Hejtmancik J. F., Rivolta C., Bujakowska K. M., Arno G., Webster A. R., Ayyagari R., Substitution of a single non-coding nucleotide upstream of TMEM216 causes non-syndromic retinitis pigmentosa and is associated with reduced TMEM216 expression. Am. J. Hum. Genet. 111, 2012–2030 (2024). [DOI] [PMC free article] [PubMed] [Google Scholar]
57.Valente E. M., Logan C. V., Mougou-Zerelli S., Lee J. H., Silhavy J. L., Brancati F., Iannicelli M., Travaglini L., Romani S., Illi B., Adams M., Szymanska K., Mazzotta A., Lee J. E., Tolentino J. C., Swistun D., Salpietro C. D., Fede C., Gabriel S., Russ C., Cibulskis K., Sougnez C., Hildebrandt F., Otto E. A., Held S., Diplas B. H., Davis E. E., Mikula M., Strom C. M., Ben-Zeev B., Lev D., Sagie T. L., Michelson M., Yaron Y., Krause A., Boltshauser E., Elkhartoufi N., Roume J., Shalev S., Munnich A., Saunier S., Inglehearn C., Saad A., Alkindy A., Thomas S., Vekemans M., Dallapiccola B., Katsanis N., Johnson C. A., Attié-Bitach T., Gleeson J. G., Mutations in TMEM216 perturb ciliogenesis and cause Joubert, Meckel and related syndromes. Nat. Genet. 42, 619–625 (2010). [DOI] [PMC free article] [PubMed] [Google Scholar]
58.Hartill V., Szymanska K., Sharif S. M., Wheway G., Johnson C. A., Meckel-Gruber syndrome: An update on diagnosis, clinical management, and research advances. Front. Pediatr. 5, 244 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]
59.Edvardson S., Shaag A., Zenvirt S., Erlich Y., Hannon G. J., Shanske A. L., Gomori J. M., Ekstein J., Elpeleg O., Joubert syndrome 2 (JBTS2) in Ashkenazi Jews is associated with a TMEM216 mutation. Am. J. Hum. Genet. 86, 93–97 (2010). [DOI] [PMC free article] [PubMed] [Google Scholar]
60.Valente E. M., Salpietro D. C., Brancati F., Bertini E., Galluccio T., Tortorella G., Briuglia S., Dallapiccola B., Description, nomenclature, and mapping of a novel cerebello-renal syndrome with the molar tooth malformation. Am. J. Hum. Genet. 73, 663–670 (2003). [DOI] [PMC free article] [PubMed] [Google Scholar]
61.Garcia-Gonzalo F. R., Corbit K. C., Sirerol-Piquer M. S., Ramaswami G., Otto E. A., Noriega T. R., Seol A. D., Robinson J. F., Bennett C. L., Josifova D. J., García-Verdugo J. M., Katsanis N., Hildebrandt F., Reiter J. F., A transition zone complex regulates mammalian ciliogenesis and ciliary membrane composition. Nat. Genet. 43, 776–784 (2011). [DOI] [PMC free article] [PubMed] [Google Scholar]
62.Gogendeau D., Lemullois M., Le Borgne P., Castelli M., Aubusson-Fleury A., Arnaiz O., Cohen J., Vesque C., Schneider-Maunoury S., Bouhouche K., Koll F., Tassin A.-M., MKS-NPHP module proteins control ciliary shedding at the transition zone. PLoS Biol. 18, e3000640 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
63.Wang A., Chiou J., Poirion O. B., Buchanan J., Valdez M. J., Verheyden J. M., Hou X., Kudtarkar P., Narendra S., Newsome J. M., Guo M., Faddah D. A., Zhang K., Young R. E., Barr J., Sajti E., Misra R., Huyck H., Rogers L., Poole C., Whitsett J. A., Pryhuber G., Xu Y., Gaulton K. J., Preissl S., Sun X., NHLBI LungMap Consortium, Single-cell multiomic profiling of human lungs reveals cell-type-specific and age-dynamic control of SARS-CoV2 host genes. Elife 9, e62522 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
64.Hocker J. D., Poirion O. B., Zhu F., Buchanan J., Zhang K., Chiou J., Wang T.-M., Zhang Q., Hou X., Li Y. E., Zhang Y., Farah E. N., Wang A., McCulloch A. D., Gaulton K. J., Ren B., Chi N. C., Preissl S., Cardiac cell type-specific gene regulatory programs and disease risk association. Sci. Adv. 7, eabf1444 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
65.Preissl S., Fang R., Huang H., Zhao Y., Raviram R., Gorkin D. U., Zhang Y., Sos B. C., Afzal V., Dickel D. E., Kuan S., Visel A., Pennacchio L. A., Zhang K., Ren B., Single-nucleus analysis of accessible chromatin in developing mouse forebrain reveals cell-type-specific transcriptional regulation. Nat. Neurosci. 21, 432–439 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
66.Fang R., Preissl S., Li Y., Hou X., Lucero J., Wang X., Motamedi A., Shiau A. K., Zhou X., Xie F., Mukamel E. A., Zhang K., Zhang Y., Behrens M. M., Ecker J. R., Ren B., Comprehensive analysis of single cell ATAC-seq data with SnapATAC. Nat. Commun. 12, 1337 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
67.Zheng G. X. Y., Terry J. M., Belgrader P., Ryvkin P., Bent Z. W., Wilson R., Ziraldo S. B., Wheeler T. D., McDermott G. P., Zhu J., Gregory M. T., Shuga J., Montesclaros L., Underwood J. G., Masquelier D. A., Nishimura S. Y., Schnall-Levin M., Wyatt P. W., Hindson C. M., Bharadwaj R., Wong A., Ness K. D., Beppu L. W., Deeg H. J., McFarland C., Loeb K. R., Valente W. J., Ericson N. G., Stevens E. A., Radich J. P., Mikkelsen T. S., Hindson B. J., Bielas J. H., Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8, 14049 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]
68.Hao Y., Hao S., Andersen-Nissen E., Mauck W. M. 3rd, Zheng S., Butler A., Lee M. J., Wilk A. J., Darby C., Zager M., Hoffman P., Stoeckius M., Papalexi E., Mimitou E. P., Jain J., Srivastava A., Stuart T., Fleming L. M., Yeung B., Rogers A. J., McElrath J. M., Blish C. A., Gottardo R., Smibert P., Satija R., Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587.e29 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
69.Zhou J., Ma J., Chen Y., Cheng C., Bao B., Peng J., Sejnowski T. J., Dixon J. R., Ecker J. R., Robust single-cell Hi-C clustering by convolution- and random-walk–based imputation. Proceedings of the National Academy of Sciences 116, 14011–14018 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
70.Shin H., Shi Y., Dai C., Tjong H., Gong K., Alber F., Zhou X. J., TopDom: an efficient and deterministic method for identifying topological domains in genomes. Nucleic Acids Res. 44, e70 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]
71.Hinrichs A. S., Karolchik D., Baertsch R., Barber G. P., Bejerano G., Clawson H., Diekhans M., Furey T. S., Harte R. A., Hsu F., Hillman-Jackson J., Kuhn R. M., Pedersen J. S., Pohl A., Raney B. J., Rosenbloom K. R., Siepel A., Smith K. E., Sugnet C. W., Sultan-Qurraie A., Thomas D. J., Trumbower H., Weber R. J., Weirauch M., Zweig A. S., Haussler D., Kent W. J., The UCSC Genome Browser Database: update 2006. Nucleic Acids Res. 34, D590–8 (2006). [DOI] [PMC free article] [PubMed] [Google Scholar]
72.Schultz M. D., He Y., Whitaker J. W., Hariharan M., Mukamel E. A., Leung D., Rajagopal N., Nery J. R., Urich M. A., Chen H., Lin S., Lin Y., Jung I., Schmitt A. D., Selvaraj S., Ren B., Sejnowski T. J., Wang W., Ecker J. R., Human body epigenome maps reveal noncanonical DNA methylation variation. Nature 523, 212–216 (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]
73.Chen E. Y., Tan C. M., Kou Y., Duan Q., Wang Z., Meirelles G. V., Clark N. R., Ma’ayan A., Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics 14, 128 (2013). [DOI] [PMC free article] [PubMed] [Google Scholar]
74.Fang Z., Liu X., Peltz G., GSEApy: a comprehensive package for performing gene set enrichment analysis in Python. Bioinformatics 39, btac757 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
75.Robinson M. D., McCarthy D. J., Smyth G. K., edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010). [DOI] [PMC free article] [PubMed] [Google Scholar]
76.Heinz S., Benner C., Spann N., Bertolino E., Lin Y. C., Laslo P., Cheng J. X., Murre C., Singh H., Glass C. K., Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010). [DOI] [PMC free article] [PubMed] [Google Scholar]
77.Bulik-Sullivan B. K., Loh P.-R., Finucane H. K., Ripke S., Yang J., Patterson N., Daly M. J., Price A. L., Neale B. M., LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]
78.Fleming S. J., Chaffin M. D., Arduini A., Akkad A.-D., Banks E., Marioni J. C., Philippakis A. A., Ellinor P. T., Babadi M., Unsupervised removal of systematic background noise from droplet-based single-cell experiments using CellBender. Nat. Methods 20, 1323–1335 (2023). [DOI] [PubMed] [Google Scholar]
79.Korsunsky I., Millard N., Fan J., Slowikowski K., Zhang F., Wei K., Baglaenko Y., Brenner M., Loh P.-R., Raychaudhuri S., Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

NIHPP2024.12.28.630634V2-supplement-1.pdf^{(4.8MB, pdf)}

Data Availability Statement

[R1] 1.Lundeen E. A., Burke-Conte Z., Rein D. B., Wittenborn J. S., Saaddine J., Lee A. Y., Flaxman A. D., Prevalence of Diabetic Retinopathy in the US in 2021. JAMA Ophthalmol. 141, 747–754 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R2] 2.Cheng Q., “Age-related Macular Degeneration and Vascular and Renal Comorbidities in Adults Aged 40 Years or Older: NHANES 2005–2008,” thesis, Georgia State University; (2014). [Google Scholar]

[R3] 3.Lim L. S., Mitchell P., Seddon J. M., Holz F. G., Wong T. Y., Age-related macular degeneration. Lancet 379, 1728–1738 (2012). [DOI] [PubMed] [Google Scholar]

[R4] 4.How the Retina Works (pdf) by Helga Kolb.

[R5] 5.Dowling J. E., The Retina: An Approachable Part of the Brain (Harvard University Press, 1987). [Google Scholar]

[R6] 6.Ptito M., Bleau M., Bouskila J., The Retina: A Window into the Brain. Cells 10, 3269 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R7] 7.Wang S. K., Nair S., Li R., Kraft K., Pampari A., Patel A., Kang J. B., Luong C., Kundaje A., Chang H. Y., Single-cell multiome of the human retina and deep learning nominate causal variants in complex eye diseases. Cell Genomics 2, 100164 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R8] 8.Preissl S., Gaulton K. J., Ren B., Characterizing cis-regulatory elements using single-cell epigenomics. Nat. Rev. Genet. 24, 21–43 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R9] 9.Gusev A., Lee S. H., Trynka G., Finucane H., Vilhjálmsson B. J., Xu H., Zang C., Ripke S., Bulik-Sullivan B., Stahl E., Schizophrenia Working Group of the Psychiatric Genomics Consortium, SWE-SCZ Consortium, Kähler A. K., Hultman C. M., Purcell S. M., McCarroll S. A., Daly M., Pasaniuc B., Sullivan P. F., Neale B. M., Wray N. R., Raychaudhuri S., Price A. L., Schizophrenia Working Group of the Psychiatric Genomics Consortium, SWE-SCZ Consortium, Partitioning heritability of regulatory and cell-type-specific variants across 11 common diseases. Am. J. Hum. Genet. 95, 535–552 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R10] 10.Finucane H. K., Reshef Y. A., Anttila V., Slowikowski K., Gusev A., Byrnes A., Gazal S., Loh P.-R., Lareau C., Shoresh N., Genovese G., Saunders A., Macosko E., Pollack S., Brainstorm Consortium, Perry J. R. B., Buenrostro J. D., Bernstein B. E., Raychaudhuri S., McCarroll S., Neale B. M., Price A. L., Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types. Nat. Genet. 50, 621–629 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R11] 11.Liang Q., Cheng X., Wang J., Owen L., Shakoor A., Lillvis J. L., Zhang C., Farkas M., Kim I. K., Li Y., DeAngelis M., Chen R., A multi-omics atlas of the human retina at single-cell resolution. Cell Genom 3, 100298 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R12] 12.Li J., Wang J., Ibarra I. L., Cheng X., Luecken M. D., Lu J., Monavarfeshani A., Yan W., Zheng Y., Zuo Z., Colborn S. L. Z., Cortez B. S., Owen L. A., Tran N. M., Shekhar K., Sanes J. R., Stout J. T., Chen S., Li Y., DeAngelis M. M., Theis F. J., Chen R., Integrated multiomics single cell atlas of the human retina. Res. Sq., doi: 10.21203/rs.3.rs-3471275/v1 (2023). [DOI] [Google Scholar]

[R13] 13.Lopez Soriano V., Dueñas Rey A., Mukherjee R., Genomics England Research Consortium, Coppieters F., Bauwens M., Willaert A., De Baere E., Multi-omics analysis in human retina uncovers ultraconserved cis-regulatory elements at rare eye disease loci. Nat. Commun. 15, 1600 (2024). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R14] 14.Cherry T. J., Yang M. G., Harmin D. A., Tao P., Timms A. E., Bauwens M., Allikmets R., Jones E. M., Chen R., De Baere E., Greenberg M. E., Mapping the cis-regulatory architecture of the human retina reveals noncoding genetic variation in disease. Proc. Natl. Acad. Sci. U. S. A. 117, 9001–9012 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R15] 15.Thomas E. D., Timms A. E., Giles S., Harkins-Perry S., Lyu P., Hoang T., Qian J., Jackson V. E., Bahlo M., Blackshaw S., Friedlander M., Eade K., Cherry T. J., Cell-specific cis-regulatory elements and mechanisms of non-coding genetic disease in human retina and retinal organoids. Dev. Cell 57, 820–836.e6 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R16] 16.Finkbeiner C., Ortuño-Lizarán I., Sridhar A., Hooper M., Petter S., Reh T. A., Single-cell ATAC-seq of fetal human retina and stem-cell-derived retinal organoids shows changing chromatin landscapes during cell fate acquisition. Cell Rep. 38, 110294 (2022). [DOI] [PubMed] [Google Scholar]

[R17] 17.Mullin N. K., Voigt A. P., Boese E. A., Liu X., Stone E. M., Tucker B. A., Mullins R. F., Transcriptomic and chromatin accessibility analysis of the human macular and peripheral retinal pigment epithelium at the single-cell level. Am. J. Pathol. 193, 1750–1761 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R18] 18.D’haene E., López-Soriano V., Martínez-García P. M., Kalayanamontri S., Rey A. D., Sousa-Ortega A., Naranjo S., Van de Sompele S., Vantomme L., Mahieu Q., Vergult S., Neto A., Gómez-Skarmeta J. L., Martínez-Morales J. R., Bauwens M., Tena J. J., De Baere E., Comparative 3D genome analysis between neural retina and retinal pigment epithelium reveals differential cis-regulatory interactions at retinal disease loci. Genome Biol. 25, 123 (2024). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R19] 19.Marchal C., Singh N., Batz Z., Advani J., Jaeger C., Corso-Díaz X., Swaroop A., High-resolution genome topology of human retina uncovers super enhancer-promoter interactions at tissue-specific and multifactorial disease loci. Nat. Commun. 13, 5827 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R20] 20.Mondal A. K., Gaur M., Advani J., Swaroop A., Epigenome-metabolism nexus in the retina: implications for aging and disease. Trends Genet. 40, 718–729 (2024). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R21] 21.Campello L., Singh N., Advani J., Mondal A. K., Corso-Díaz X., Swaroop A., Aging of the retina: Molecular and metabolic turbulences and potential interventions. Annu. Rev. Vis. Sci. 7, 633–664 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R22] 22.Waterland R. A., Is epigenetics an important link between early life events and adult disease? Horm. Res. 71 Suppl 1, 13–16 (2009). [DOI] [PubMed] [Google Scholar]

[R23] 23.Niculescu M. D., Zeisel S. H., Diet, methyl donors and DNA methylation: interactions between dietary folate, methionine and choline. J. Nutr. 132, 2333S–2335S (2002). [DOI] [PubMed] [Google Scholar]

[R24] 24.Bollati V., Schwartz J., Wright R., Litonjua A., Tarantini L., Suh H., Sparrow D., Vokonas P., Baccarelli A., Decline in genomic DNA methylation through aging in a cohort of elderly subjects. Mech. Ageing Dev. 130, 234–239 (2009). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R25] 25.Otteson D. C., Eyes on DNA methylation: current evidence for DNA methylation in ocular development and disease. J. Ocul. Biol. Dis. Infor. 4, 95–103 (2011). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R26] 26.Advani J., Mehta P. A., Hamel A. R., Mehrotra S., Kiel C., Strunz T., Corso-Díaz X., Kwicklis M., van Asten F., Ratnapriya R., Chew E. Y., Hernandez D. G., Montezuma S. R., Ferrington D. A., Weber B. H. F., Segrè A. V., Swaroop A., QTL mapping of human retina DNA methylation identifies 87 gene-epigenome interactions in age-related macular degeneration. Nat. Commun. 15, 1972 (2024). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R27] 27.Li D., Hsu S., Purushotham D., Sears R. L., Wang T., WashU Epigenome Browser update 2019. Nucleic Acids Res. 47, W158–W165 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R28] 28.Li D., Purushotham D., Harrison J. K., Hsu S., Zhuo X., Fan C., Liu S., Xu V., Chen S., Xu J., Ouyang S., Wu A. S., Wang T., WashU Epigenome Browser update 2022. Nucleic Acids Res. 50, W774–W781 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R29] 29.Lee D.-S., Luo C., Zhou J., Chandran S., Rivkin A., Bartlett A., Nery J. R., Fitzpatrick C., O’Connor C., Dixon J. R., Ecker J. R., Simultaneous profiling of 3D genome structure and DNA methylation in single human cells. Nat. Methods 16, 999–1006 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R30] 30.Li G., Liu Y., Zhang Y., Kubo N., Yu M., Fang R., Kellis M., Ren B., Joint profiling of DNA methylation and chromatin architecture in single cells. Nat. Methods 16, 991–993 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R31] 31.Hao Y., Stuart T., Kowalski M. H., Choudhary S., Hoffman P., Hartman A., Srivastava A., Molla G., Madad S., Fernandez-Granda C., Satija R., Dictionary learning for integrative, multimodal and scalable single-cell analysis. Nat. Biotechnol. 42, 293–304 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R32] 32.Scimone C., Donato L., Esposito T., Rinaldi C., D’Angelo R., Sidoti A., A novel RLBP1 gene geographical area-related mutation present in a young patient with retinitis punctata albescens. Hum. Genomics 11, 18 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R33] 33.Cai X., Conley S. M., Naash M. I., RPE65: role in the visual cycle, human retinal disease, and gene therapy. Ophthalmic Genet. 30, 57–62 (2009). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R34] 34.Zhang Y., Liu T., Meyer C. A., Eeckhoute J., Johnson D. S., Bernstein B. E., Nusbaum C., Myers R. M., Brown M., Li W., Liu X. S., Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, R137 (2008). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R35] 35.Li Y. E., Preissl S., Hou X., Zhang Z., Zhang K., Qiu Y., Poirion O. B., Li B., Chiou J., Liu H., Pinto-Duarte A., Kubo N., Yang X., Fang R., Wang X., Han J. Y., Lucero J., Yan Y., Miller M., Kuan S., Gorkin D., Gaulton K. J., Shen Y., Nunn M., Mukamel E. A., Behrens M. M., Ecker J. R., Ren B., An atlas of gene regulatory elements in adult mouse cerebrum. Nature 598, 129–136 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R36] 36.Wang S., Sengel C., Emerson M. M., Cepko C. L., A gene regulatory network controls the binary fate decision of rod and bipolar cells in the vertebrate retina. Dev. Cell 30, 513–527 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R37] 37.Samuel A., Housset M., Fant B., Lamonerie T., Otx2 ChIP-seq reveals unique and redundant functions in the mature mouse retina. PLoS One 9, e89110 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R38] 38.Liu H., Zhou J., Tian W., Luo C., Bartlett A., Aldridge A., Lucero J., Osteen J. K., Nery J. R., Chen H., Rivkin A., Castanon R. G., Clock B., Li Y. E., Hou X., Poirion O. B., Preissl S., Pinto-Duarte A., O’Connor C., Boggeman L., Fitzpatrick C., Nunn M., Mukamel E. A., Zhang Z., Callaway E. M., Ren B., Dixon J. R., Behrens M. M., Ecker J. R., DNA methylation atlas of the mouse brain at single-cell resolution. Nature 598, 120–128 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R39] 39.Luo C., Keown C. L., Kurihara L., Zhou J., He Y., Li J., Castanon R., Lucero J., Nery J. R., Sandoval J. P., Bui B., Sejnowski T. J., Harkins T. T., Mukamel E. A., Behrens M. M., Ecker J. R., Single-cell methylomes identify neuronal subtypes and regulatory elements in mammalian cortex. Science 357, 600–604 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R40] 40.He Y., Hariharan M., Gorkin D. U., Dickel D. E., Luo C., Castanon R. G., Nery J. R., Lee A. Y., Zhao Y., Huang H., Williams B. A., Trout D., Amrhein H., Fang R., Chen H., Li B., Visel A., Pennacchio L. A., Ren B., Ecker J. R., Spatiotemporal DNA methylome dynamics of the developing mouse fetus. Nature 583, 752–759 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R41] 41.de Mendoza A., Poppe D., Buckberry S., Pflueger J., Albertin C. B., Daish T., Bertrand S., de la Calle-Mustienes E., Gómez-Skarmeta J. L., Nery J. R., Ecker J. R., Baer B., Ragsdale C. W., Grützner F., Escriva H., Venkatesh B., Bogdanovic O., Lister R., The emergence of the brain non-CpG methylation system in vertebrates. Nat Ecol Evol 5, 369–378 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R42] 42.Tian W., Zhou J., Bartlett A., Zeng Q., Liu H., Castanon R. G., Kenworthy M., Altshul J., Valadon C., Aldridge A., Nery J. R., Chen H., Xu J., Johnson N. D., Lucero J., Osteen J. K., Emerson N., Rink J., Lee J., Li Y. E., Siletti K., Liem M., Claffey N., O’Connor C., Yanny A. M., Nyhus J., Dee N., Casper T., Shapovalova N., Hirschstein D., Ding S.-L., Hodge R., Levi B. P., Keene C. D., Linnarsson S., Lein E., Ren B., Behrens M. M., Ecker J. R., Single-cell DNA methylation and 3D genome architecture in the human brain. Science 382, eadf5357 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R43] 43.Dekker J., Mirny L., The 3D Genome as Moderator of Chromosomal Communication. Cell 164, 1110–1121 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R44] 44.Zemke N. R., Armand E. J., Wang W., Lee S., Zhou J., Li Y. E., Liu H., Tian W., Nery J. R., Castanon R. G., Bartlett A., Osteen J. K., Li D., Zhuo X., Xu V., Chang L., Dong K., Indralingam H. S., Rink J. A., Xie Y., Miller M., Krienen F. M., Zhang Q., Taskin N., Ting J., Feng G., McCarroll S. A., Callaway E. M., Wang T., Lein E. S., Behrens M. M., Ecker J. R., Ren B., Conserved and divergent gene regulatory programs of the mammalian neocortex. Nature 624, 390–402 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R45] 45.Nagano T., Lubling Y., Várnai C., Dudley C., Leung W., Baran Y., Mendelson Cohen N., Wingett S., Fraser P., Tanay A., Cell-cycle dynamics of chromosomal organization at single-cell resolution. Nature 547, 61–67 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R46] 46.Fulco C. P., Nasser J., Jones T. R., Munson G., Bergman D. T., Subramanian V., Grossman S. R., Anyoha R., Doughty B. R., Patwardhan T. A., Nguyen T. H., Kane M., Perez E. M., Durand N. C., Lareau C. A., Stamenova E. K., Aiden E. L., Lander E. S., Engreitz J. M., Activity-by-contact model of enhancer–promoter regulation from thousands of CRISPR perturbations. Nat. Genet. 51, 1664–1669 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R47] 47.Hahn J., Monavarfeshani A., Qiao M., Kao A. H., Kölsch Y., Kumar A., Kunze V. P., Rasys A. M., Richardson R., Wekselblatt J. B., Baier H., Lucas R. J., Li W., Meister M., Trachtenberg J. T., Yan W., Peng Y.-R., Sanes J. R., Shekhar K., Evolution of neuronal cell classes and types in the vertebrate retina. Nature 624, 415–424 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R48] 48.Fletcher E. L., Jobling A. I., Greferath U., Mills S. A., Waugh M., Ho T., de Iongh R. U., Phipps J. A., Vessey K. A., Studying age-related macular degeneration using animal models. Optom. Vis. Sci. 91, 878–886 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R49] 49.Minnoye L., Taskiran I. I., Mauduit D., Fazio M., Van Aerschot L., Hulselmans G., Christiaens V., Makhzami S., Seltenhammer M., Karras P., Primot A., Cadieu E., van Rooijen E., Marine J.-C., Egidy G., Ghanem G.-E., Zon L., Wouters J., Aerts S., Cross-species analysis of enhancer logic using deep learning. Genome Res. 30, 1815–1834 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R50] 50.Cano-Gamez E., Trynka G., From GWAS to Function: Using Functional Genomics to Identify the Mechanisms Underlying Complex Diseases. Front. Genet. 11, 424 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R51] 51.Maurano M. T., Humbert R., Rynes E., Thurman R. E., Haugen E., Wang H., Reynolds A. P., Sandstrom R., Qu H., Brody J., Shafer A., Neri F., Lee K., Kutyavin T., Stehling-Sun S., Johnson A. K., Canfield T. K., Giste E., Diegel M., Bates D., Hansen R. S., Neph S., Sabo P. J., Heimfeld S., Raubitschek A., Ziegler S., Cotsapas C., Sotoodehnia N., Glass I., Sunyaev S. R., Kaul R., Stamatoyannopoulos J. A., Systematic localization of common disease-associated variation in regulatory DNA. Science 337, 1190–1195 (2012). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R52] 52.Bonelli R., Jackson V. E., Prasad A., Munro J. E., Farashi S., Heeren T. F. C., Pontikos N., Scheppke L., Friedlander M., Egan C. A., Allikmets R., Ansell B. R. E., Bahlo M., Identification of genetic factors influencing metabolic dysregulation and retinal support for MacTel, a retinal disorder. Communications Biology 4, 1–14 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R53] 53.Winkler T. W., Grassmann F., Brandl C., Kiel C., Günther F., Strunz T., Weidner L., Zimmermann M. E., Korb C. A., Poplawski A., Schuster A. K., Müller-Nurasyid M., Peters A., Rauscher F. G., Elze T., Horn K., Scholz M., Cañadas-Garre M., McKnight A. J., Quinn N., Hogg R. E., Küchenhoff H., Heid I. M., Stark K. J., Weber B. H. F., Genome-wide association meta-analysis for early age-related macular degeneration highlights novel loci and insights for advanced disease. BMC Med. Genomics 13, 120 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R54] 54.Wang G., Sarkar A., Carbonetto P., Stephens M., A simple new approach to variable selection in regression, with application to genetic fine mapping. J. R. Stat. Soc. Series B Stat. Methodol. 82, 1273–1300 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R55] 55.Kelley D. R., Cross-species regulatory sequence activity prediction. PLoS Comput. Biol. 16, e1008050 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R56] 56.Malka S., Biswas P., Berry A.-M., Sangermano R., Ullah M., Lin S., D’Antonio M., Jestin A., Jiao X., Quinodoz M., Sullivan L., Gardner J. C., Place E. M., Michaelides M., Kaminska K., Mahroo O. A., Schiff E., Wright G., Cancellieri F., Vaclavik V., Santos C., Rehman A. U., Mehrotra S., Azhar Baig H. M., Iqbal M., Ansar M., Santos L. C., Sousa A. B., Tran V. H., Matsui H., Bhatia A., Naeem M. A., Akram S. J., Akram J., Riazuddin S., Ayuso C., Pierce E. A., Hardcastle A. J., Riazuddin S. A., Frazer K. A., Hejtmancik J. F., Rivolta C., Bujakowska K. M., Arno G., Webster A. R., Ayyagari R., Substitution of a single non-coding nucleotide upstream of TMEM216 causes non-syndromic retinitis pigmentosa and is associated with reduced TMEM216 expression. Am. J. Hum. Genet. 111, 2012–2030 (2024). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R57] 57.Valente E. M., Logan C. V., Mougou-Zerelli S., Lee J. H., Silhavy J. L., Brancati F., Iannicelli M., Travaglini L., Romani S., Illi B., Adams M., Szymanska K., Mazzotta A., Lee J. E., Tolentino J. C., Swistun D., Salpietro C. D., Fede C., Gabriel S., Russ C., Cibulskis K., Sougnez C., Hildebrandt F., Otto E. A., Held S., Diplas B. H., Davis E. E., Mikula M., Strom C. M., Ben-Zeev B., Lev D., Sagie T. L., Michelson M., Yaron Y., Krause A., Boltshauser E., Elkhartoufi N., Roume J., Shalev S., Munnich A., Saunier S., Inglehearn C., Saad A., Alkindy A., Thomas S., Vekemans M., Dallapiccola B., Katsanis N., Johnson C. A., Attié-Bitach T., Gleeson J. G., Mutations in TMEM216 perturb ciliogenesis and cause Joubert, Meckel and related syndromes. Nat. Genet. 42, 619–625 (2010). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R58] 58.Hartill V., Szymanska K., Sharif S. M., Wheway G., Johnson C. A., Meckel-Gruber syndrome: An update on diagnosis, clinical management, and research advances. Front. Pediatr. 5, 244 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R59] 59.Edvardson S., Shaag A., Zenvirt S., Erlich Y., Hannon G. J., Shanske A. L., Gomori J. M., Ekstein J., Elpeleg O., Joubert syndrome 2 (JBTS2) in Ashkenazi Jews is associated with a TMEM216 mutation. Am. J. Hum. Genet. 86, 93–97 (2010). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R60] 60.Valente E. M., Salpietro D. C., Brancati F., Bertini E., Galluccio T., Tortorella G., Briuglia S., Dallapiccola B., Description, nomenclature, and mapping of a novel cerebello-renal syndrome with the molar tooth malformation. Am. J. Hum. Genet. 73, 663–670 (2003). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R61] 61.Garcia-Gonzalo F. R., Corbit K. C., Sirerol-Piquer M. S., Ramaswami G., Otto E. A., Noriega T. R., Seol A. D., Robinson J. F., Bennett C. L., Josifova D. J., García-Verdugo J. M., Katsanis N., Hildebrandt F., Reiter J. F., A transition zone complex regulates mammalian ciliogenesis and ciliary membrane composition. Nat. Genet. 43, 776–784 (2011). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R62] 62.Gogendeau D., Lemullois M., Le Borgne P., Castelli M., Aubusson-Fleury A., Arnaiz O., Cohen J., Vesque C., Schneider-Maunoury S., Bouhouche K., Koll F., Tassin A.-M., MKS-NPHP module proteins control ciliary shedding at the transition zone. PLoS Biol. 18, e3000640 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R63] 63.Wang A., Chiou J., Poirion O. B., Buchanan J., Valdez M. J., Verheyden J. M., Hou X., Kudtarkar P., Narendra S., Newsome J. M., Guo M., Faddah D. A., Zhang K., Young R. E., Barr J., Sajti E., Misra R., Huyck H., Rogers L., Poole C., Whitsett J. A., Pryhuber G., Xu Y., Gaulton K. J., Preissl S., Sun X., NHLBI LungMap Consortium, Single-cell multiomic profiling of human lungs reveals cell-type-specific and age-dynamic control of SARS-CoV2 host genes. Elife 9, e62522 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R64] 64.Hocker J. D., Poirion O. B., Zhu F., Buchanan J., Zhang K., Chiou J., Wang T.-M., Zhang Q., Hou X., Li Y. E., Zhang Y., Farah E. N., Wang A., McCulloch A. D., Gaulton K. J., Ren B., Chi N. C., Preissl S., Cardiac cell type-specific gene regulatory programs and disease risk association. Sci. Adv. 7, eabf1444 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R65] 65.Preissl S., Fang R., Huang H., Zhao Y., Raviram R., Gorkin D. U., Zhang Y., Sos B. C., Afzal V., Dickel D. E., Kuan S., Visel A., Pennacchio L. A., Zhang K., Ren B., Single-nucleus analysis of accessible chromatin in developing mouse forebrain reveals cell-type-specific transcriptional regulation. Nat. Neurosci. 21, 432–439 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R66] 66.Fang R., Preissl S., Li Y., Hou X., Lucero J., Wang X., Motamedi A., Shiau A. K., Zhou X., Xie F., Mukamel E. A., Zhang K., Zhang Y., Behrens M. M., Ecker J. R., Ren B., Comprehensive analysis of single cell ATAC-seq data with SnapATAC. Nat. Commun. 12, 1337 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R67] 67.Zheng G. X. Y., Terry J. M., Belgrader P., Ryvkin P., Bent Z. W., Wilson R., Ziraldo S. B., Wheeler T. D., McDermott G. P., Zhu J., Gregory M. T., Shuga J., Montesclaros L., Underwood J. G., Masquelier D. A., Nishimura S. Y., Schnall-Levin M., Wyatt P. W., Hindson C. M., Bharadwaj R., Wong A., Ness K. D., Beppu L. W., Deeg H. J., McFarland C., Loeb K. R., Valente W. J., Ericson N. G., Stevens E. A., Radich J. P., Mikkelsen T. S., Hindson B. J., Bielas J. H., Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8, 14049 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R68] 68.Hao Y., Hao S., Andersen-Nissen E., Mauck W. M. 3rd, Zheng S., Butler A., Lee M. J., Wilk A. J., Darby C., Zager M., Hoffman P., Stoeckius M., Papalexi E., Mimitou E. P., Jain J., Srivastava A., Stuart T., Fleming L. M., Yeung B., Rogers A. J., McElrath J. M., Blish C. A., Gottardo R., Smibert P., Satija R., Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587.e29 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R69] 69.Zhou J., Ma J., Chen Y., Cheng C., Bao B., Peng J., Sejnowski T. J., Dixon J. R., Ecker J. R., Robust single-cell Hi-C clustering by convolution- and random-walk–based imputation. Proceedings of the National Academy of Sciences 116, 14011–14018 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R70] 70.Shin H., Shi Y., Dai C., Tjong H., Gong K., Alber F., Zhou X. J., TopDom: an efficient and deterministic method for identifying topological domains in genomes. Nucleic Acids Res. 44, e70 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R71] 71.Hinrichs A. S., Karolchik D., Baertsch R., Barber G. P., Bejerano G., Clawson H., Diekhans M., Furey T. S., Harte R. A., Hsu F., Hillman-Jackson J., Kuhn R. M., Pedersen J. S., Pohl A., Raney B. J., Rosenbloom K. R., Siepel A., Smith K. E., Sugnet C. W., Sultan-Qurraie A., Thomas D. J., Trumbower H., Weber R. J., Weirauch M., Zweig A. S., Haussler D., Kent W. J., The UCSC Genome Browser Database: update 2006. Nucleic Acids Res. 34, D590–8 (2006). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R72] 72.Schultz M. D., He Y., Whitaker J. W., Hariharan M., Mukamel E. A., Leung D., Rajagopal N., Nery J. R., Urich M. A., Chen H., Lin S., Lin Y., Jung I., Schmitt A. D., Selvaraj S., Ren B., Sejnowski T. J., Wang W., Ecker J. R., Human body epigenome maps reveal noncanonical DNA methylation variation. Nature 523, 212–216 (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R73] 73.Chen E. Y., Tan C. M., Kou Y., Duan Q., Wang Z., Meirelles G. V., Clark N. R., Ma’ayan A., Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics 14, 128 (2013). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R74] 74.Fang Z., Liu X., Peltz G., GSEApy: a comprehensive package for performing gene set enrichment analysis in Python. Bioinformatics 39, btac757 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R75] 75.Robinson M. D., McCarthy D. J., Smyth G. K., edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R76] 76.Heinz S., Benner C., Spann N., Bertolino E., Lin Y. C., Laslo P., Cheng J. X., Murre C., Singh H., Glass C. K., Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R77] 77.Bulik-Sullivan B. K., Loh P.-R., Finucane H. K., Ripke S., Yang J., Patterson N., Daly M. J., Price A. L., Neale B. M., LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015). [DOI] [PMC free article] [PubMed] [Google Scholar]

[R78] 78.Fleming S. J., Chaffin M. D., Arduini A., Akkad A.-D., Banks E., Marioni J. C., Philippakis A. A., Ellinor P. T., Babadi M., Unsupervised removal of systematic background noise from droplet-based single-cell experiments using CellBender. Nat. Methods 20, 1323–1335 (2023). [DOI] [PubMed] [Google Scholar]

[R79] 79.Korsunsky I., Millard N., Fan J., Slowikowski K., Zhang F., Wei K., Baglaenko Y., Brenner M., Loh P.-R., Raychaudhuri S., Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

This is a preprint.

Single-cell analysis of the epigenome and 3D chromatin architecture in the human retina

Ying Yuan

Pooja Biswas

Nathan R Zemke

Kelsey Dang

Yue Wu

Matteo D’Antonio

Yang Xie

Qian Yang

Keyi Dong

Pik Ki Lau

Daofeng Li

Chad Seng

Weronika Bartosik

Justin Buchanan

Lin Lin

Ryan Lancione

Kangli Wang

Seoyeon Lee

Zane Gibbs

Joseph Ecker

Kelly Frazer

Ting Wang

Sebastian Preissl

Allen Wang

Radha Ayyagari

Bing Ren

Abstract

Introduction

Results

A single-cell epigenome atlas of human retina

Fig. 1. Single-cell multiomics analysis of the human retina.

Fig. 2. Transcriptional profiles from different cell types of the human retina and macula.

Identification and characterization of candidate cis-regulatory elements in retina cell types

Fig. 3. Identification and characterization of cCREs, TF motifs and DMRs across human retina cell types.

DNA methylomes across the retina cell types

3D genome architecture across retinal cell types

Fig. 4. 3D genome architecture across different retinal and choroidal cell types.

Linking distal cCREs to target genes

Single-cell epigenome analysis of mouse retina

Comparative analysis of gene expression programs in human and mouse retinal cell types

Fig. 5. Comparative analyses of chromatin accessibility and gene expression between human and mouse retinal cell types.

Epigenome maps facilitate the interpretation of non-coding risk variants

Fig. 6. Interpreting noncoding risk variants of ocular disorders and traits.

Deep neural network predicts the effects of risk variants

Fig. 7. CRISPR editing experiments validate a prediction of the deep neural network model.

Discussions

Supplementary Material

Acknowledgments:

Funding:

Funding Statement

Footnotes

Data and materials availability:

References and Notes

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles

Links to NCBI Databases