Single-cell transcriptomic and TCR profiling of peripheral double-positive T cells in healthy human blood [scRNA-seq]

In addition, we developed a machine learning-based classifier that can accurately distinguish DPT cells from conventional T cell subsets using transcriptomic features. This approach enabled the identification of key marker genes and molecular pathways underlying the functional specialization of DPT cells. This dataset provides a valuable resource for the immunology community to further investigate peripheral DPT cells in health and disease.

Generated from healthy individuals, it serves as a high-quality reference map of normal peripheral T cell states. In our study, this dataset was further integrated with publicly available single-cell datasets derived from patients with immune-related diseases, enabling comparative analyses to explore how the transcriptional identity and abundance of DPT cells may be altered under pathological conditions. By making this healthy reference dataset publicly available, we aim to support future investigations into T cell heterogeneity, immune dysregulation, and disease-specific perturbations of the T cell compartment.