Understanding the complexity of biological systems requires a comprehensive analysis of their cell populations. Ideally, this should be done at the single cell level, because bulk analysis of the full population obscured many critical details due to artifacts introduced by averaging. However, this has been technically challenging due to the cumbersome procedure, low throughput, and high costs of performing analysis on a single-cell basis. Excitingly, technical improvements in single-cell RNA sequencing are making it economically practical to profile the transcriptomics of large populations of cells at the single-cell level, and have yielded numerous results that address important biological and medical questions. Further development of the technology and data analysis will significantly benefit the biomedical field by unraveling the function of individual cells in their microenvironments and modeling their transcriptional dynamics.