iScience · 2024 · 69 citations · 45 references
Life SciencesCell BiologyMolecular BiologyGene ExpressionGenomicsTranscriptomicsCell Type AnnotationRare Cell Population DetectionSingle-cell AnalysisSingle-cell GenomicsSingle-cell Rna Sequencing AnalysisSingle-cell TranscriptomicsEnrichment Of Rare CellsHeterogeneityIntegration Of Single-cell DatasetsRare Cell DetectionRare Cell Population IdentificationRare Cell Type DetectionRare Cell Type DiscoverySingle-cell Rna Sequencing
Single-cell RNA sequencing (scRNA-seq) has advanced our understanding of cellular heterogeneity by characterizing cell types across tissues and species. While several mouse retinal scRNA-seq datasets exist, each dataset is either limited in cell numbers or focused on specific cell classes, thereby hindering comprehensive gene expression analysis across all retina types. To fill the gap, we generated the largest retinal scRNA-seq dataset to date, comprising approximately 190,000 single cells from C57BL/6J mouse retinas, enriched for rare population cells via antibody-based magnetic cell sorting. Integrating this dataset with public datasets, we constructed the Mouse Retina Cell Atlas (MRCA) for wild-type mice, encompassing over 330,000 cells, characterizing 12 major classes and 138 cell types. The MRCA consolidates existing knowledge, identifies new cell types, and is publicly accessible via CELLxGENE, UCSC Cell Browser, and the Broad Single Cell Portal, providing a user-friendly resource for the mouse retina research community.
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Integrated analysis of multimodal single-cell data
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