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Single-cell atlas of the transcriptome and chromatin accessibility in the human retina

Nature Genetics (2026)Cite this article

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Abstract

Single-cell sequencing has revolutionized the scale and resolution of molecular profiling of tissues and organs. Here we present an integrated dual-modal reference atlas of the most accessible portion of the mammalian central nervous system, the retina. We compiled around 3.9 million cells from 125 donors of diverse ancestral backgrounds, including 8 published studies and 2.7 million unpublished data points, to create a comprehensive human retina cell atlas (HRCA) with more than 130 cell types identified. We annotated each cluster, identified marker genes and characterized cis-regulatory elements and gene regulatory networks. Our analysis uncovered differences in transcriptome, chromatin and gene regulatory networks across cell types. We modeled changes in gene expression and chromatin accessibility across age, ancestry and tissue region. This integrated atlas enhanced the fine-mapping of genome-wide association study and expression quantitative trait loci variants. Accessible through interactive browsers, this multimodal multidonor and multilab HRCA can facilitate a better understanding of retinal function and pathology.

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Fig. 1: Overview of single-cell atlas of the human retina.
Fig. 2: BC types are highly conserved across human, macaque and mouse retinas.
Fig. 3: AC and RGC types show increasing divergence among human, macaque, and mouse retinas.
Fig. 4: A high-resolution chromatin accessibility cell atlas of the human retina.
Fig. 5: Regulon of the human BC types.
Fig. 6: Differential gene expression associated with age and tissue region.
Fig. 7: Transcriptomic and chromatin accessibility differences associated with ancestral backgrounds.
Fig. 8: Leveraging multi-omics data to study GWAS and eQTL loci.

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Data availability

The landing page of the HRCA data resources is accessible at https://rchenlab.github.io/resources/human-atlas.html. Raw sequencing data files, processed Cell Ranger data files and sample metadata information files of HRCA have been deposited in the HCA Data Portal (https://data.humancellatlas.org/hca-bio-networks/eye). The datasets used in HRCA are summarized in Supplementary Table 1. The raw sequencing reads of the newly generated data have been deposited in GEO under accessions GSE265774 and GSE281526, and are included in the SuperSeries GSE265801, which contains all the human eye data produced in our lab. Additionally, raw and normalized count matrices, cell-type annotations and embeddings are also publicly available through the CELLxGENE collection (https://cellxgene.cziscience.com/collections/4c6eaf5c-6d57-4c76-b1e9-60df8c655f1e). HRCA is also accessible at the UCSC Cell Browser (https://retina.cells.ucsc.edu) and the Single Cell Portal (SCP2805-SCP2808).

Code availability

All code used for the HRCA project can be found in the HRCA reproducibility Zenodo repository (https://doi.org/10.5281/zenodo.17265527 (ref. 99)). The pipeline to process the unpublished and collected public datasets is accessible at https://doi.org/10.5281/zenodo.8137510 (ref. 100). Scripts related to the benchmark study are available at https://doi.org/10.5281/zenodo.17412073 (ref. 101). A tutorial on training the HRCA reference model using scArches and performing label transfer with the trained model is available at https://doi.org/10.5281/zenodo.17265527 (ref. 99). The reference model can be downloaded from https://doi.org/10.5281/zenodo.14014720 (ref. 102).

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Acknowledgements

This project was supported by the Chan Zuckerberg Initiative (CZI; 2019-002425 and 2021-239847 to R.C. and 2021-237885 to J.T.S.). Additionally, next-generation sequencing was performed on instrument supported by the National Institutes of Health (NIH) and shared instrument grant S10OD023469 (to R.C.). This work was also supported by NIH (grants EY028633 to J.R.S. and U01 MH105960 to J.R.S.), CZI award (CZF-2019-002459 to J.R.S.), NIH/NEI (EY012543 to S.C. and EY002687 to WU-DOVS), NIMH BRAIN (RF1MH132662 to M.H.) and CIRM (DISC0-14514 to M.H.). The authors acknowledge support from the Gavin Herbert Eye Institute at the University of California, Irvine, from an unrestricted grant from Research to Prevent Blindness and from NIH (core grant P30 EY034070). The authors thank J. Zamanian (Department of Biomedical Data Science, Stanford University) and the Lattice team at Stanford for their support with data dissemination. This publication is part of the HCA (http://www.humancellatlas.org/publications/). The authors would also like to acknowledge the feedback and discussions from the members of the HCA Eye Biological Network.

Author information

Author notes

  1. These authors contributed equally: Jin Li, Jun Wang, Ignacio L. Ibarra, Xuesen Cheng.

Authors and Affiliations

  1. Brunson Center for Translational Vision Research, Gavin Herbert Eye Institute, Department of Ophthalmology and Visual Sciences, University of California, Irvine, School of Medicine, Irvine, CA, USA

    Jin Li, Jiaxiong Lu, Xuan Bao, Yumei Li & Rui Chen

  2. Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA

    Jin Li, Jun Wang, Xuesen Cheng, Jiaxiong Lu, Zhen Zuo, Nicholas M. Tran, Yumei Li & Rui Chen

  3. Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA

    Jin Li, Jun Wang, Xuesen Cheng, Jiaxiong Lu, Yumei Li & Rui Chen

  4. Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany

    Ignacio L. Ibarra, Malte D. Luecken & Fabian J. Theis

  5. Institute of Lung Health & Immunity, Helmholtz Munich, German Center for Lung Research (DZL), Munich, Germany

    Malte D. Luecken

  6. Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, TX, USA

    Jiaxiong Lu & Rui Chen

  7. Center for Brain Science and Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA

    Aboozar Monavarfeshani, Wenjun Yan & Joshua R. Sanes

  8. Department of Ophthalmology and Visual Sciences, Washington University in St. Louis, Saint Louis, MO, USA

    Yiqiao Zheng & Shiming Chen

  9. Lions Eye Bank of Texas, Baylor College of Medicine, Houston, TX, USA

    Samantha Lynn Zayas Colborn & Berenice Sarahi Cortez

  10. John A. Moran Eye Center, Department of Ophthalmology and Visual Sciences, University of Utah, Salt Lake City, UT, USA

    Leah A. Owen

  11. Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, USA

    Brittney Wick & Maximilian Haeussler

  12. Department of Chemical and Biomolecular Engineering; Helen Wills Neuroscience Institute; Center for Computational Biology; California Institute for Quantitative Biosciences, QB3, University of California, Berkeley, Berkeley, CA, USA

    Karthik Shekhar

  13. Department of Ophthalmology, Cullen Eye Institute, Baylor College of Medicine, Houston, TX, USA

    J. Timothy Stout

  14. Department of Developmental Biology, Washington University in St. Louis, Saint Louis, MO, USA

    Shiming Chen

  15. Department of Ophthalmology, Ross Eye Institute, Jacobs School of Medicine and Biomedical Sciences, State University of New York at Buffalo, Buffalo, NY, USA

    Margaret M. DeAngelis

Authors
  1. Jin Li
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  2. Jun Wang
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  3. Ignacio L. Ibarra
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  4. Xuesen Cheng
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  5. Malte D. Luecken
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  6. Jiaxiong Lu
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  7. Aboozar Monavarfeshani
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  8. Wenjun Yan
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  9. Yiqiao Zheng
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  10. Zhen Zuo
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  11. Samantha Lynn Zayas Colborn
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  12. Berenice Sarahi Cortez
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  13. Leah A. Owen
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  14. Brittney Wick
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  15. Xuan Bao
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  16. Maximilian Haeussler
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  17. Nicholas M. Tran
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  18. Karthik Shekhar
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  19. Joshua R. Sanes
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  20. J. Timothy Stout
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  21. Shiming Chen
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  22. Yumei Li
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  23. Margaret M. DeAngelis
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  24. Fabian J. Theis
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  25. Rui Chen
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Contributions

J. Li, J.W. and R.C. conceptualized and designed the study. R.C. supervised the work. S.L.Z.C., B.S.C., L.A.O., X.B., M.M.D. and J.T.S. collected the samples. X.C. and Y.L. generated the snRNA-seq and snATAC–seq data in this study. I.L.I. performed the benchmark study for data integration of RNA-seq datasets in this study and label transfer analyses. J. Li and J.W. compiled the dataset collection for public snRNA-seq/scRNA-seq and snATAC–seq datasets. J. Li performed the data integration for RNA-seq datasets. J.W. performed the data integration for ATAC–seq datasets. J. Li performed the construction, annotation and data dissemination of the atlas. J.W. and Z.Z. conducted the multi-omics analysis. J.W. conducted the gene expression across covariates and genetic variant analysis. I.L.I., M.D.L. and F.J.T. provided input to various analysis methods. N.M.T. and K.S. provided input for various annotations. A.M., W.Y. and J.R.S. collected, analyzed and provided the processed data from an unpublished dataset. J. Lu, Y.Z. and S.C. advised and performed the MPRAs in mouse retina. B.W. and M.H. hosted the HRCA on the UCSC Cell Browser. J. Li and J.W. wrote the first draft of the paper. All authors edited the paper and contributed to the critical revisions of the paper.

Corresponding authors

Correspondence to Fabian J. Theis or Rui Chen.

Ethics declarations

Competing interests

F.J.T. consults for Immunai, CytoReason, Cellarity and Omniscope, and owns interests in Dermagnostix GmbH and Cellarity. M.D.L. has received research contracts from the CZI and speaker fees from Pfizer and Janssen Pharmaceuticals. The other authors declare no competing interests.

Peer review

Peer review information

Nature Genetics thanks Anand Swaroop and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Additional information

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Extended data

Extended Data Fig. 1 Overview of the HRCA.

a, Cell proportion distribution of major classes among donors. The x-axis corresponds to each donor, and the y-axis is the cell proportion of major classes. The last bar is the cell proportion across total cells. b, A pie chart illustrating the number of cells for major classes and their proportions. c, Integration of datasets from snRNA-seq and scRNA-seq datasets. The cells are colored by major classes. d, The atlas is colored by the two technologies: snRNA-seq (in coral) and scRNA-seq (in blue). e, The distribution of transcriptomic data for 280 samples obtained from snRNA-seq and scRNA-seq technologies. Each sample is colored by the technology used. f, The atlas of scRNA-seq data, with major classes represented using different colors. g, Dot plot illustrating the distribution of expression levels of marker genes for major cell classes in scRNA-seq data.

Extended Data Fig. 2 Comparison between single-nuclei and single-cell technologies.

a, Cell proportion of major class of samples between snRNA-seq and scRNA-seq in fovea, macular, and periphery tissue regions. The red bar represents cell proportions of major classes in snRNA-seq samples, and the blue bar represents cell proportions of scRNA-seq samples. b, Enriched GO BPs of 1,815 overexpressed genes in snRNA-seq data. c, Enriched GO BPs of 5,473 overexpressed genes in scRNA-seq data. Statistical significance for b and c was calculated using a one-sided Fisher’s exact test and adjusted for FDR using the clusterProfiler package.

Extended Data Fig. 3 Transcriptomic signature of bipolar cells.

a, UMAP visualization of BC cells based on single-cell transcriptome data. b, Dot plot of the distribution of marker gene expression by the single-cell measurements. c, Co-embedding between snRNA-seq and scRNA-seq cells. The label names are prefixed by ‘n’ for snRNA and ‘c’ for scRNA. d, Volcano plot of differentially expressed genes between GB and BB of the snRNA-seq datasets. Differentially expressed genes were identified under |log2 fold change| > 1 and q-value < 0.05. e, Predicted markers per BC type by the binary classification analysis using snRNA-seq datasets. Rows are BC types, and columns represent new markers.

Extended Data Fig. 4 Annotation of amacrine cells.

a, Dot plot of AC cell clusters by markers to identify AC subclasses for GABAergic, glycinergic, and both. PAX6 and TFAP2B were used as AC pan-markers. GAD1/GAD2 were used for GABAergic ACs, and SLC6A9 was used for the glycinergic ACs. MEIS2, TCF4, and EBF1 were also included in the dot plot. b, Dot plot of predicted markers for AC types.

Extended Data Fig. 5 Cross-mapping for human amacrine cells.

a, SATURN co-embedding visualization of AC types between snRNA-seq and scRNA-seq. ACs are colored by the two technologies. b, The same SATURN co-embedding with AC type labels color-coded on top of clusters. Labels are prefixed with ‘n’ for snRNA-seq datasets and ‘c’ for scRNA-seq data. c, SATURN co-embedding visualization of AC types across human, macaque and mouse species. AC labels for the three species are overlaid on clusters. Labels are prefixed with ‘h’ for human, ‘a’ for macaque, and ‘m’ for mouse.

Extended Data Fig. 6 Annotation of retinal ganglion cells.

a, UMAP visualization of RGC clusters, excluding two midget types MG_OFF and MG_ON. b, Dot plot of RGC clusters with existing markers. c, The proportion of parasol RGCs within the RGC population in the samples. Samples enriched by NeuN experiments are highlighted in green. d, Sankey diagram depicting the relationship between RGC clusters from snRNA-seq datasets and the public labeling of RGC types from scRNA-seq datasets. The width of the lines is proportional to the number of cells in the mapping. e, Sankey diagram illustrating RGC types alignment between humans (top row) and mice (bottom row).

Extended Data Fig. 7 A high-resolution snATAC-seq cell atlas of the human retina.

a, Venn diagram showing the overlapped OCRs detected by retinal snATAC-seq and bulk ATAC-seq. b, Pie chart showing cell type specificity of OCRs identified from retinal snATAC-seq (left) and bulk ATAC-seq (right). The color codes the number of cell types where the OCRs were observed. c, Heatmap showing the chromatin accessibility of differential accessible regions (DARs) identified in major retinal cell classes. Rows represented chromatin regions and columns corresponded to cell classes. d, Genome track of the RHO locus showing the cell class-specific chromatin accessibility in the promoter and linked cis-regulatory elements of this gene. e, Heatmap showing chromatin accessibility (left) and gene expression (right) of 129,636 significantly linked CRE–gene pairs identified by the correlation between gene expression and OCR accessibility. Rows represent cis-regulatory element (CRE)–gene pairs, which are clustered into 25 groups using k-means clustering. Columns represent cell groups that were grouped using the K nearest neighbor (KNN) method. f, Density plot showing the activity (log2(FC) value of comparison of the activities between a tested sequence and a basal CRX promoter) distribution of the tested sequences by MPRAs. IRD CREs, n = 1,714 (green); control CREs with a variety of activities, n = 20 (red); scrambled CREs, n = 300 (blue). g, Box plots showing phastCons20way score distribution of validated enhancers, silencers and inactive elements. P-values were calculated using a one-sided Wilcoxon rank-sum test to assess differences among groups (n = 1,714 CREs) without adjustment for multiple comparisons. Centerline represents the median phastCons20way score, upper and lower hinges represent the upper and lower quartiles, respectively, and whiskers represent 1.5× IQR. h, Box plots showing phyloP20way score distribution of validated enhancers, silencers and inactive elements. P-values were calculated using a one-sided Wilcoxon rank-sum test to assess differences among groups (n = 1,714 CREs) without adjustment for multiple comparisons. Centerline represents the median phyloP20way score, upper and lower hinges represent the upper and lower quartiles, respectively, and whiskers represent 1.5× IQR. i, Scatterplot showing eRegulon specificity score of each transcription factor (TF) regulon across cell classes. The top five TFs are highlighted in red.

Extended Data Fig. 8 Multi-omics atlas of the human retinal cell subclasses and cell types.

a, Dot plot showing marker gene expression measured by snRNA-seq and their activity score derived from snATAC-seq in the corresponding bipolar cell types. b, UMAP showing the co-embedding of amacrine cells (AC) from snRNA-seq and snATAC-seq were clustered into AC types. c, Dot plot showing marker gene expression measured by snRNA-seq and their activity score derived from snATAC-seq in the corresponding AC subclasses. d, UMAP showing the co-embedding of horizontal cells (HC) from snRNA-seq and snATAC-seq were clustered into HC types. e, Dot plot showing marker gene expression measured by snRNA-seq and their activity score derived from snATAC-seq in the corresponding HC types. f, UMAP showing the co-embedding of cone cells from snRNA-seq and snATAC-seq were clustered into cone subclasses. g, Dot plot showing marker gene expression measured by snRNA-seq and their activity score derived from snATAC-seq in the corresponding cone subclasses. h, Heatmap showing the identified regulons where TF expression highly correlated with cell type identity. Color scale indicates gene expression level of TF. Dot size indicates enrichment of TF target regions. The rows represent BC cell types, and the columns represent the identified regulons.

Extended Data Fig. 9 Regulon of the human retinal cell subclasses and cell types.

a, Heatmap showing the gene expression and the target site enrichment of transcription factors that regulate cone subclasses. b, Heatmap showing the gene expression and the target site enrichment of transcription factors that regulate AC subclasses. c, Heatmap showing the gene expression and the target site enrichment of transcription factors that regulate HC types. d, Box plot showing the AUC value distribution of the regulon modules identified in bipolar cell (BC) types. For each regulon module, the BC types with the average AUC value higher than 0.2 were labeled. Centerline represents the median AUC value, upper and lower hinges represent the upper and lower quartiles, respectively, and whiskers represent 1.5× the IQR.

Extended Data Fig. 10 Cell type enrichment analysis of GWAS traits.

a, Cell class enrichment analysis of GWAS loci based on open chromatin regions across different cell classes, using LDSC. b, Cell class enrichment analysis of GWAS loci based on differentially accessible regions across different cell classes, using LDSC. c, Cell class enrichment analysis of GWAS loci based on open chromatin regions across different cell classes and ancestral backgrounds, using LDSC. d, Cell class enrichment analysis of GWAS loci based on gene expression across different cell classes. The dot size indicates −log10P. Significant results (FDR < 0.05) are shown in blue in Fig. 10a–c and in red in Fig. 10d. Non-significant results (FDR ≥ 0.05) are shown in grey.

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Li, J., Wang, J., Ibarra, I.L. et al. Single-cell atlas of the transcriptome and chromatin accessibility in the human retina. Nat Genet (2026). https://doi.org/10.1038/s41588-025-02454-1

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