Single Cell Multiome Atlas of the Human Fetal Retina
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ABSTRACT:
PROVIDER: PRJNA1117936 | ENA |
REPOSITORIES: ENA
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Project description:As the light-sensing part of the visual system, the retina is comprised of five classes of neurons, including photoreceptors, horizontal, amacrine, bipolar, and retinal ganglion cells, along with several non-neuronal cell types such as Muller glia. These major cell classes can be further classified into hundreds of distinct cell subtypes. The development of the retina is under tight temporal control where multipotent progenitor cells differentiate into specific mature cell types in a sequential, but overlapping, order. Additionally, the developmental process is under tight spatial control, with cells at the central retina developing earlier than cells at the periphery. To provide a comprehensive view of the human fetal retina at the molecular level and investigate transcriptional regulatory mechanisms controlling the differentiation process, we profiled more than 300,000 single nuclei of the human fetal retina from 12 donors spanning post conception week 10 and 23 with Multiome RNA-seq and ATAC-seq.
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Project description:Single cell atlas of the human retina
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Project description:Mouse retina is heterogeneous, composed of multiple neuronal and non-neuronal cell types. Among them, bipolar cells (BC), which connect photoreceptors (cones and rods) to inner retina, are traditionally dissected into rare subtypes of subtle functional and morphological differences. While high-resolution single-cell transcriptomic profiles of BCs are availabl, little is known about the corresponding single-cell chromatin landscapes. Although it is now possible to directly generate multiome data, there are often restrictions on cost, feasibility, and data quality. Therefore, integrating single-cell ATAC and RNA profiles obtained independently from the same retina sample may provide an exciting opportunity to comprehensively characterize these rare cell subtypes and discover transcription factors (TFs) important in establishing or maintaining the cell identities
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Project description:Single cell RNA sequencing (scRNA-seq) has advanced the assessment of cellular heterogeneity at the single-cell resolution by identifying transcriptional similarities and differences. Data resources of scRNA-seq have been largely produced and extensively studied for the mouse retina. They serve as a powerful tool to study cellular components, transcriptome relationships, and regulatory mechanisms underlying various retinal diseases and biological processes. The large volume of mouse retinal scRNA-seq data has been released in separate repositories, limiting their widespread use in mouse retina communities. In this work, we are presenting a unified single-cell atlas for adult wild-type mouse retina using our in-house generated single-cell RNA-seq data complementing public datasets. The collected data account for over 323,000 single cells. After data integration, cell clustering, and cell type annotation, we have annotated 11 major classes and over 120 retinal cell types to form a unified single-cell reference for the mouse retina. To facilitate the public use of the reference, we have deposited it on CELLxGENE, UCSC Cell Browser, and Single Cell Portal for visualization and gene expression exploration. The unified atlas is also released to annotate new mouse retinal cells using scArches utilities. This unified reference serves an easy-to-use data resource of mouse retina communities.
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Project description:This SuperSeries is composed of the SubSeries listed below. As the light sensing part of the visual system, the human retina is composed of five classes of neuron, including photoreceptors, horizontal cells, amacrine, bipolar, and retinal ganglion cells. Each class of neuron can be further classified into subgroups with the abundance varying three orders of magnitude. Therefore, to capture all cell types in the retina and generate a complete single cell reference atlas, it is essential to scale up from currently published single cell profiling studies to improve the sensitivity. In addition, to gain a better understanding of gene regulation at single cell level, it is important to include sufficient scATAC-seq data in the reference. To fill the gap, we performed snRNA-seq and snATAC-seq for the retina from healthy donors. To further increase the size of the dataset, we then collected and incorporated publicly available datasets. All data underwent a unified preprocessing pipeline and data integration. Multiple integration methods were benchmarked by scIB, and scVI was chosen. To harness the power of multiomics, snATAC-seq datasets were also preprocessed, and scGlue was used to generate co-embeddings between snRNA-seq and snATAC-seq cells. To facilitate the public use of references, we employ CELLxGENE and UCSC Cell Browser for visualization. By combining previously published and newly generated datasets, a single cell atlas of the human retina that is composed of 2.5 million single cells from 48 donors has been generated. As a result, over 90 distinct cell types are identified based on the transcriptomics profile with the rarest cell type accounting for about 0.01% of the cell population. In addition, open chromatin profiling has been generated for over 400K nuclei via single nuclei ATAC-seq, allowing systematic characterization of cis-regulatory elements for individual cell type. Integrative analysis reveals intriguing differences in the transcriptome, chromatin landscape, and gene regulatory network among cell class, subgroup, and type. In addition, changes in cell proportion, gene expression and chromatin openness have been observed between different gender and over age. Accessible through interactive browsers, this study represents the most comprehensive reference cell atlas of the human retina to date. As part of the human cell atlas project, this resource lays the foundation for further research in understanding retina biology and diseases.
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Project description:We present a joint single-cell atlas of gene expression and chromatin accessibility of the adult human retina. We integrate this atlas with a HiChIP enhancer connectome, expression quantitative trait loci (eQTL) data, and base-resolution deep learning models to predict noncoding variants with causal roles in eye disease.
2022-03-09 | GSE196235 | GEO
Project description:To study the development of human retina, we used single cell RNAseq at key fetal stages and followed the development of the major cell types, as well as populations of transitional cells. We also analyzed stem cell (hPSC)-derived retinal organoids; although organoids have a very similar cellular composition at equivalent ages to the fetal retina, there are some differences in gene expression of particular cell types. Moreover, the inner retinal lamination is disrupted in more advanced stages of organoids when compared with fetal retina. To determine whether the disorganization in the inner retina was due to the culture conditions, we analyzed retinal development in fetal retina maintained under similar conditions. These retinospheres develop for at least 6 months, displaying better inner retinal lamination than retinal organoids. Our scRNAseq comparisons between fetal retina, retinal organoids and retinospheres provide a new resource for developing better in vitro models for retinal disease.
2020-01-10 | GSE142526 | GEO
Project description:Cell types in the human retina are highly heterogeneous with their abundance varies by several orders of magnitude. To decipher the complexity of gene expression and regulation of the human retinal cell types, we generated a multi-omics single-cell atlas of the adult human retina, including over 250K nuclei for single-nuclei RNA-seq and 150K nuclei for single-nuclei ATAC-seq. Over 60 cell subtypes have been identified based on their transcriptomic profiles, reaching a sensitivity of 0.01%. Integrative analysis of this single-cell multi-omics dataset identified gene regulatory elements across the genome for each cell subtype. In addition, when combined with other data modalities, such as eQTL, potential causal variants can be identified through fine mapping. Taken together, this new dataset represents the most comprehensive single-cell multi-omics profiling for the human retina that enables in-depth molecular characterization of most cell subtypes.
2023-03-14 | GSE226108 | GEO
Project description:mouse retina 10x Genomics Multiome ATAC+RNA data
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