Comprehensive single-cell atlas of the mouse retina

Jin Li; Jongsu Choi; Xuesen Cheng; Justin Ma; Shahil Pema; Joshua R Sanes; Graeme Mardon; Benjamin J Frankfort; Nicholas M Tran; Yumei Li; Rui Chen

doi:10.1016/j.isci.2024.109916

Comprehensive single-cell atlas of the mouse retina

iScience. 2024 May 8;27(6):109916. doi: 10.1016/j.isci.2024.109916. eCollection 2024 Jun 21.

Authors

Jin Li^{1

2}, Jongsu Choi³, Xuesen Cheng^{1

2}, Justin Ma⁴, Shahil Pema¹, Joshua R Sanes⁵, Graeme Mardon^{1

4

6}, Benjamin J Frankfort⁶, Nicholas M Tran¹, Yumei Li^{1

2}, Rui Chen^{1

2

3}

Affiliations

¹ Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA.
² Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA.
³ Department of Biochemistry and Molecular Biology, Baylor College of Medicine, Houston, TX 77030, USA.
⁴ Department of Pathology and Immunology, Baylor College of Medicine, Houston, TX 77030, USA.
⁵ Center for Brain Science and Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02130, USA.
⁶ Departments of Ophthalmology and Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA.

Abstract

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.

Keywords: Bioinformatics; Neuroscience; Omics; Transcriptomics.

Abstract

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