Biological and Medical Importance of Cellular Heterogeneity Deciphered by Single-Cell RNA Sequencing

Abstract

The present review discusses recent progress in single-cell RNA sequencing (scRNA-seq), which can describe cellular heterogeneity in various organs, bodily fluids, and pathologies (e.g., cancer and Alzheimer's disease). We outline scRNA-seq techniques that are suitable for investigating cellular heterogeneity that is present in cell populations with very high resolution of the transcriptomic landscape. We summarize scRNA-seq findings and applications of this technology to identify cell types, activity, and other features that are important for the function of different bodily organs. We discuss future directions for scRNA-seq techniques that can link gene expression, protein expression, cellular function, and their roles in pathology. We speculate on how the field could develop beyond its present limitations (e.g., performing scRNA-seq in situ and in vivo). Finally, we discuss the integration of machine learning and artificial intelligence with cutting-edge scRNA-seq technology, which could provide a strong basis for designing precision medicine and targeted therapy in the future.

Keywords: artificial intelligence; cell-to-cell heterogeneity; machine learning; scRNA-seq; transcriptomics.

Figure 1

Overview of single-cell RNA sequencing (scRNA-seq) methodology. Single-cell RNA sequencing technology is used to explore transcriptomic profiles of single cells that are isolated from cell lines, organisms, or tissue/blood samples of clinical material. Massive datasets can be generated and analyzed by a specific algorithm that allows the discernment of cell-to-cell heterogeneity, lineage tracing, and stochastic gene expression at the single-cell level.

Figure 2

Overview of scRNA-seq technology integrated into machine learning and artificial intelligence. Single-cell RNA sequencing technology allows quantification of the expression of each gene in a cell. The integration of scRNA-seq technology with artificial intelligence enables the identification of cell-to-cell heterogeneity more accurately and would open a window to better applications.