Retina is composed of a heterogeneous population of cell types, each with a unique biological function. Even if the same type of cells, due to genetic heterogeneity will lead to cell function differences. In the past, traditional molecular biological methods cannot resolve variations in their functional roles that arise from these differences, and some cells are difficult to define due to the lack of specific molecular markers or the scarcity of numbers, which hindered the understanding and research of these cells. With the development of biotechnology, single-cell RNA sequencing can analyze and resolve differences in single-cell transcriptome expression profiles, characterize intracellular population heterogeneity, identify new and rare cell subtypes, and more definitely define the characteristics of each cell type. It clarifies the origin, function, and variations in cell phenotypes. Other attributes include pinpointing both disease-related characteristics of cell subtypes and specific differential gene expression patterns, to deepen our understanding of the causes and progression of diseases, as well as to aid clinical diagnosis and targeted therapy.
Single-cell transcriptome sequencing (scRNA-seq) can resolve the expression characteristics of cells in tissues with single-cell precision, enabling researchers to quantify cellular heterogeneity within populations with higher resolution, revealing potentially heterogeneous cell populations and the dynamics of complex tissues. However, the presence of a large number of technical zeros in scRNA-seq data will have an impact on downstream analysis of cell clustering, differential genes, cell annotation, and pseudotime, hindering the discovery of meaningful biological signals. The main idea to solve this problem is to make use of the potential correlation between cells and genes, and to impute the technical zeros through the observed data. Based on this, this paper reviewed the basic methods of imputing technical zeros in the scRNA-seq data and discussed the advantages and disadvantages of the existing methods. Finally, recommendations and perspectives on the use and development of the method were provided.
Diabetic retinopathy (DR) is one of the main causes of vision loss and irreversible blindness in the working-age population, closely regarded as the destruction of the retinal neurovascular unit (NVU). As an important component of the NVU, retinal microglia (RMG) plays a vital role in the progression of DR. In recent years, single-cell RNA sequencing (scRNA-seq) technology has emerged as an important tool in transcriptomic analysis. This latest method reveals the heterogeneity and complexity of RNA transcriptional profiles within individual cells, as well as the composition of different cell types and functions. Utilizing scRNA-seq technology, researchers have further revealed the role of RMG in the occurrence and development of DR, discovering phenotypic heterogeneity, regional heterogeneity, and cell-to-cell communication in RMG. It is anticipated that in the future, more omics technologies and multi-omics correlation analysis methods will be applied to DR and even other ophthalmic diseases, exploring potential diagnostic and therapeutic targets, providing different perspectives for the clinical diagnosis, treatment, and scientific research of DR, and truly promoting clinical translation through technological innovation, thereby benefiting patients with DR diseases.
Single cell RNA sequencing technique provides a strong technical support for the analysis of cell heterogeneity in biological tissues, and has been widely used in biomedical research. In recent years, considerable scRNA-seq data have been accumulated in the research of ocular fundus diseases. The ocular fundus is abundant for the network of vessel and neuron, which leads to the complicated pathogenesis of fundus diseases. Through single cell RNA sequencing technique, the expression of thousands of genes of certain cell types or even subtypes can be obtained in the disease environment. Single cell RNA sequencing technique accurately reveals the pathogenic cell types and pathogenic mechanisms of ocular fundus diseases such as neovascular retinopathy, which provides a theoretical basis for the birth of new diagnosis and treatment targets. The construction of multi-omics single-cell database of ocular fundus diseases will enable high-quality data to be further explored and provide an analysis platform for ophthalmic researchers.