bioRxivMolecular BiologyPreprint
Integrated multi-omics single cell atlas of the human retina
Jin LiiD, Jun WangiD, Ignacio L. IbarraiD, Xuesen ChengiD, Malte D. LueckeniD, Jiaxiong Lu, Aboozar MonavarfeshaniiD, Wenjun YaniD, Yiqiao ZhengiD, Zhen C. ZuoiD, Samantha Lynn Zayas Colborn, Berenice Sarahi Cortez, Leah A. OweniD, Nicholas M. TraniD, Karthik ShekhariD, Joshua R. SanesiD, John Timothy StoutiD, Shiming CheniD, Yumei LiiD, Margaret M. DeAngelisiD, Fabian Joachim TheisiD, Rui CheniD
Abstract
Single-cell sequencing has revolutionized the scale and resolution of molecular profiling of tissues and organs. Here, we present an integrated multimodal reference atlas of the most accessible portion of the mammalian central nervous system, the retina. We compiled around 2.4 million cells from 55 donors, including 1.4 million unpublished data points, to create a comprehensive human retina cell atlas (HRCA) of transcriptome and chromatin accessibility, unveiling over 110 types. Engaging the retina community, we annotated each cluster, refined the Cell Ontology for the retina, identified distinct marker genes, and characterized cis-regulatory elements and gene regulatory networks (GRNs) for these cell types. Our analysis uncovered intriguing differences in transcriptome, chromatin, and GRNs across cell types. In addition, we modeled changes in gene expression and chromatin openness across gender and age. This integrated atlas also enabled the fine-mapping of GWAS and eQTL variants. Accessible through interactive browsers, this multimodal cross-donor and cross-lab HRCA, can facilitate a better understanding of retinal function and pathology.
- organism: human
- assay: scRNA-seq
- assay: scATAC-seq
- research focus: cell type annotation
- research focus: gene regulatory network inference
- research focus: GWAS / association mapping
- research focus: fine-mapping
- approach: statistical model
- approach: machine learning (classical)
- Citations
- 30
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