Single cell of human retina
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ABSTRACT:
PROVIDER: PRJNA649248 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Single cell RNA-seq of adult human neural retina
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Project description:Retinal RNA profiles from macula and periphery of each eye were generated by single-cell sequencing. M11-M14 are macula retina and P11-P14 are peripheral retina from the same 78 year old donor. M21-M24 are macula retina and P21-P24 are peripheral retina from the same 90-year-old donor.
2021-08-11 | GSE155288 | GEO
Project description:Single cell sequencing of mouse retina
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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.
2024-04-25 | GSE265801 | GEO
Project description:Single cell transcriptomic analysis of human retina and RPE/choroid
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Project description:To comprehensively profile cell types in the human retina, we performed single cell RNA-sequencing on 20,009 cells obtained post-mortem from three donors and compiled a reference transcriptome atlas. Using unsupervised clustering analysis, we identified 18 transcriptionally distinct clusters representing all known retinal cells: rod photoreceptors, cone photoreceptors, Müller glia cells, bipolar cells, amacrine cells, retinal ganglion cells, horizontal cells, retinal astrocytes and microglia.
2019-08-21 | E-MTAB-7316 | biostudies-arrayexpress
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:Here we perform massively parallel single-cell RNA sequencing (scRNA-seq) of human retinas using two independent platforms, and report the single-cell transcriptomic atlas of the human retina. Using a multi-resolution network-based analysis, we identify all major retinal cell types, and their corresponding gene expression signatures.
2019-09-21 | GSE137537 | GEO
Project description:Here we perform massively parallel single-cell RNA sequencing (scRNA-seq) of human retinas using two independent platforms, and report the single-cell transcriptomic atlas of the human retina. Using a multi-resolution network-based analysis, we identify all major retinal cell types, and their corresponding gene expression signatures.
2019-09-23 | GSE137846 | GEO
Project description:Single cell genomic analyses provide a new perspective on the development of complex tissues, like the vertebrate neural retina. We previously used single cell transcriptomic analysis to characterize human fetal retinal development and assessed the degree to which retinal organoids recapitulate normal fetal development. In this study we extend the transcriptomic analyses of fetal human retina to incorporate single nuclear scATAC-seq, a powerful new method to characterize potential gene regulatory networks through the changes in accessible chromatin that accompany cell state changes. The combination of scATAC-seq and sc-RNA-seq can be used to study the process of neurogenesis in the developing fetal human retina at unprecedented resolution. We identify the key transcription factors relevant to specific fates and the order of the transcription factor cascades that define each of the major retinal cell types. These transcriptomic and epigenomic features are largely recapitulated in retinal organoids; however, there are differences in Notch signaling and amacrine cell gene regulation, which are not as robust in organoids. The datasets we generated constitute an excellent resource for the continued improvement of retinal organoid technology, and have the potential to inform and accelerate regenerative medicine approaches to retinal disease.
2021-12-30 | GSE184386 | GEO