Generation of human neural retina transcriptome atlas by single cell RNA sequencing

Abstract

The retina is a highly specialized neural tissue that senses light and initiates image processing. Although the functional organisation of specific cells within the retina has been well-studied, the molecular profile of many cell types remains unclear in humans. 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. Notably, our data captured molecular profiles for healthy and early degenerating rod photoreceptors, and revealed a novel role of MALAT1 in putative rod degeneration. We also demonstrated the use of this retina transcriptome atlas to benchmark pluripotent stem cell-derived cone photoreceptors and an adult Müller glia cell line. This work provides an important reference with unprecedented insights into the transcriptional landscape of human retinal cells, which is fundamental to our understanding of retinal biology and disease.

Single-cell analysis of human retina identifies evolutionarily conserved and species-specific mechanisms controlling development

“…The emergence of single-cell RNA sequencing (scRNA-Seq) technologies provides a powerful tool to comprehensively classify cell types of the central nervous system and the gene regulatory networks that control their development (Fan et al, 2018; Farrell et al, 2018; Liu et al, 2017; Tasic et al, 2018; Wagner et al, 2018; Zeisel et al, 2018; Zhong et al, 2018). Studies in both macaque and human have examined the diversity of cellular subtypes within the mature retina (Cowan et al, 2019; Lukowski et al, 2019; Peng et al, 2019; Voigt et al, 2019). Furthermore, recent studies in mouse using scRNA-Seq analysis have identified changes in gene expression across retinal development, including progenitor maturation and specification/differentiation of each of the major classes of retinal cell types (Buenaventura et al, 2019; Clark et al, 2019; Lo Giudice et al, 2019).…”

Single cell RNA-seq reveals cellular diversity and developmental characteristics of human infant retina

“…Due to the highly homologous sequence between OPN1MW and OPN1LW , we were only capable of distinguishing the S-cones (C2) from M/L-cones (C0, C1) using opsins encoding genes (Figure 3c). In this cone PR dataset (1569 cone PRs), M/L-cones constitute 94.77% of the total cells and the remaining 5.23% are S-cones (Figure 3b), which was a bit higher than the proportion (3.19%) from a scRNA-seq study into human adult retina 4 . Next, we conducted gene differential expression analysis to reveals new genetic characteristics for S-cones and M/L-cones.…”

“…In order to assess the cellular composition in retina, we calculated the percentage of each cluster in the whole dataset (Figure 1c). The constitution of infant retina showed high similarity when compared with human adult retina dataset 4 . Rod photoreceptors took up the majority (75.32%) of the whole dataset while cone photoreceptors occupied a very small portion (3.1%).…”

“…In recent years, the boost of scRNA-seq techniques allow us to zoom in to retina and have discovered tremendous subtypes in traditionally defined classification. In human, 20009 single cells from three post-mortem donors were divided into 18 distinct clusters, including well-known retinal cells and novel rod cells subpopulations 4 . In mouse, 40 subtypes of retinal ganglion cells and their markers were identified using scRNA-seq 5 .…”

Ocular Tissue for Research in Australia: Strategies for Potential Research Utility of Surplus and Transplant-Ineligible Deceased Donations

“…Similarly, the development of new genetic engineering approaches and single-cell RNA sequencing has created a greater requirement for access to fresh tissue to assess expression profiles of individual or cell-specific profiles. 6 These innovations enable "big data" approaches, with concomitant ability to undertake analysis of very large datasets to discover statistically significant associations through bioinformatic approaches, for example, Kuiper et al 7 and Lin et al 8 To fully make use of these scenarios, researchers will be reliant on having access to donor tissue from hundreds or even thousands of individuals. Increased access to OTR might prove a catalyst to such advancements in the biomedical space.…”