Immune cells are highly heterogeneous and plastic with regard to gene expression and cell phenotype. In this study, we categorized genes into those with low and high gene plasticity, and those categories revealed different functions and applications. We proposed that highly plastic genes could be suited for the labeling of immune cell subpopulations; thus, novel immune cell subpopulations could be identified by gene plasticity analysis. For this purpose, we systematically analyzed highly plastic genes in human and mouse immune cells. In total, 1,379 human and 883 mouse genes were identified as being extremely plastic. We also expanded our previous immunoinformatic method, electronic sorting, which surveys big data to perform virtual analysis. This approach used correlation analysis and took dosage changes into account, which allowed us to identify the differentially expressed genes. A test with human CD4(+) T cells supported the method's feasibility, effectiveness, and predictability. For example, with the use of human nonregulatory T cells, we found that FOXP3(hi)CD4(+) T cells were highly expressive of certain known molecules, such as CD25 and CTLA4, and that this process of investigation did not require isolating or inducing these immune cells in vitro. Therefore, the sorting process helped us to discover the potential signature genes or marker molecules and to conduct functional evaluations for immune cell subpopulations. Finally, in human CD4(+) T cells, 747 potential immune cell subpopulations and their candidate signature genes were identified, which provides a useful resource for big data-driven knowledge discoveries.