Abstract
Purpose :
Based on single cell transcriptomic profiling, more than 90 different cell types have been identified in human retina. However, a major drawback of the current technologies is the loss of spatial information, as tissue dissociation is required. This study aims to establish the first single cell spatial atlas of the human retina with spatial transcriptome technology.
Methods :
Based on transcriptomics profile of human retinal cells via single cell RNA-seq (scRNA-seq), a probe pool against 460 cell type marker genes were designed and synthesized to capture the diversity of the cell type. Multiplexed error-robust fluorescence in situ hybridization (MERFISH) will be optimized and used to profile the retina from healthy human donors. To achieve accurate cell segmentation in the highly packed retina, a set of oligo-conjugated antibodies specific to a cell membrane protein was co-stained with MEFISH probes. Deep-learning segmentation algorithms are developed for proper cell segmentation that allows accurate assignment of transcripts to single cells.
Results :
Over 100,000 cells from multiple donor retina will be profiled. By leveraging scRNA-seq data through data co-embedding, major cell classes and cell types in the retina can be identified. Spatial location of each cell type in the retina are determined. Additional spatial proximity analysis is further conducted to reveal distribution pattern of cell classes and types.
Conclusions :
Our study is the first spatial single cell atlas of the human retina, an essential foundation for better understanding the mechanism of retinal function as well as disease.
This abstract was presented at the 2023 ARVO Annual Meeting, held in New Orleans, LA, April 23-27, 2023.