26Gene expression profiling is an effective way to provide insights into cell function. 27 However, for heterogeneous tissues, bulk RNA-Seq can only provide the average gene 28 expression profile for all cells from the tissue, making the interpretation of the sequencing 29 result challenging. Single-cell RNA-seq, on the other hand, generates transcriptomic 30 profiles of individual cell and cell types, making it a powerful method to decode the 31 heterogeneity in complex tissues. 32 The retina is a heterogeneous tissue composed of multiple cell types with distinct 33 functions. Here we report the first single-nuclei RNA-seq transcriptomic study on human 34 neural retinal tissue to identify transcriptome profile for individual cell types. Six retina 35 samples from three healthy donors were profiled and RNA-seq data with high quality was 36 obtained for 4730 single nuclei. All seven major cell types were observed from the dataset 37 and signature genes for each cell type were identified by differential gene express 38 analysis. The gene expression of the macular and peripheral retina was compared at the 39 cell type level, showing significant improvement from previous bulk RNA-seq studies.
40Furthermore, our dataset showed improved power in prioritizing genes associated with 41 human retinal diseases compared to both mouse single-cell RNA-seq and human bulk 42 RNA-seq results. In conclusion, we demonstrated that feasibility of obtaining single cell 43 transcriptome from human frozen tissues to provide additional insights that is missed by 44 either the human bulk RNA-seq or the animal models. 45 46 48 cell type and state, and investigating human diseases 1-3 . Transcriptome can be more 49 powerful when combined with other 'omics' data to build prediction models on human 50 diseases 4 . For example, by combining transcriptomic data and proteomic data, a list of 51 candidate disease genes can be predicted with high specificity 5 . However, until recently, 52 vast majority transcriptome profiles are generated from profiling tissue samples 53 containing thousands to millions of cells. Thus, gene expression information of individual 54 cells would be lost. For tissues with high cellular heterogeneity, knowing the transcriptome 55 profiles of each cell type would be important for both identification of novel cell types and 56 understanding the functional organization of the tissue. Cell sorting would be required to 57 obtain transcriptome of a single cell type; not only was it not always practical, but also the 58 heterogeneities of many tissues were not fully revealed. This gap was met by the 59 development of the high throughput single-cell RNA-seq technology 6-8 . 60 61 Transcriptomic studies on the single cell level was first performed decades ago 9,10 , while 62 the first single-cell transcriptome study based on Next-Generation Sequencing was 63 reported ten years ago 11 . Since then, technologies have been dramatically improved in 64 scale and sensitivity. Development in single-cell isolation, such as microf...