Single‐cell RNA sequencing: A new opportunity for retinal research

Overview

Mengling You, Rong Rong, Zhou Zeng, Haibo Li, Xiaobo Xia, Dan Ji

Published Online: Mar 23 2021

DOI: 10.1002/wrna.1652

Full article on Wiley Online Library: HTML PDF

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Abstract Single‐cell RNA sequencing (scRNA‐seq) is a technology for single‐cell transcriptome analysis that can be used to characterize complex dynamics of various retinal cell types. It provides deep scrutiny into the gene expression character of diverse cell types, lending insight into all the biological processes being carried out. The scRNA‐seq is an alternative to regular RNA‐seq, which does not achieve cellular heterogeneity. The retina, is a part of the central nervous system (CNS) and consists of six types of neurons and several types of glial cells. Studying retinal cell heterogeneity is important for understanding retinal diseases. Currently, scRNA‐seq is employed to assess retina development and retinal disease pathogenesis and has improved our understanding of the relationship between the retina, its visual pathways, and the brain. Moreover, this technology provides new ideas on the sensitivity and molecular mechanisms of cell subtypes involved in retinal‐related diseases. The application of scRNA‐seq technology has given us a deeper understanding of the latest advancements and challenges in retinal development and diseases. We advocate scRNA‐seq as one of the important tools for developing novel therapies for retinal diseases. This article is categorized under: RNA Methods > RNA Analyses in Cells RNA in Disease and Development > RNA in Development RNA in Disease and Development > RNA in Disease

General workflow of scRNA‐seq. Organs and tissues are harvested and prepared as a single‐cell suspension. A suitable method is selected for single‐cell capture. After cell lysis, RNA is reverse transcribed to cDNA and amplified by PCR to generate a cDNA library for sequencing. The resulting sequence reads are assigned to cells via cell‐specific barcodes incorporated into the cDNA through the primers used for reverse transcription and are aligned to specific genes. Finally, specific analysis of the obtained data is carried out

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Data analysis process. (a) Flow chart of sequencing data analysis; (b1) single‐cell RNA sequencing; (b2) sequencing data; (b3) single‐cell expression profile obtained after quality control; (b4) cell assignment: this mainly includes cell trajectory analysis and cluster analysis; (b5) gene heat map that can be used to determine gene expression

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Single‐cell transcriptome atlas for the mammalian/human retina (a) structure of the retina; (b) mouse retina: Cluster of 44,808 mouse retinal single cells divided into 39 groups (Macosko et al., 2015); (c) human retina: 20,009 single cells from human retina divided into 18 clusters by t‐SNE (Lukowski et al., 2019); (d) BC: 27,499 bipolar cells divided into 26 clusters by t‐SNE (Shekhar et al., 2016); (e) photoreceptors: Photoreceptor cells from human retinal cells divided into 13 different clusters by t‐SNE (Lukowski et al., 2019); (f) RGC: 6225 RGCs divided into 40 subtypes by clustering (Rheaume et al., 2018); (g) AC: The 32,000 ACs from mice were divided into 63 types (Yan et al., 2020); (h,i) HCs (peripheral and foveal): The fovea and peripheral HC are mainly divided into two clusters (Peng et al., 2019); (j,k) non‐neuronal (peripheral and foveal): Four types of non‐neuronal cells were found in both the fovea and the periphery, including MG, pericytes, endothelial cells, and microglia (Peng et al., 2019)

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