Nature Communications · 2025 · 12 citations · 50 references
Life SciencesImmunologyOncologyAutoimmunityCancer ImmunologyInflammationAntitumor T Cell ResponsesCancer ImmunotherapyHelper T Cell SubsetsSingle-cell Rna Sequencing AnalysisSingle-cell TranscriptomicsT Cell Responses To TumorsCd4 Positive T CellsCd4+ T Helper Cell ResponsesNaive T CellsSingle-cell Rna SequencingT Helper 1 DifferentiationT Helper Cell HeterogeneityT Helper CellsTh1 Immune Response
Naïve CD4 T cells are traditionally viewed as a quiescent, homogeneous, resting population, but emerging evidence reveals their heterogeneity, which can be crucial for understanding disease contexts and therapeutic outcomes. In this study, we identify distinct subpopulations within both murine and human naïve CD4 T cells by single cell-RNA-sequencing (scRNA-seq), particularly focusing on a subpopulation that expresses super-high levels of interleukin-7 receptor (IL-7Rsup-hi), along with CD97, IL-18R, and Ly6C. This subpopulation, absent in the thymus and peripherally induced, exhibits type 1 helper T cell (Th1)-poised characteristics and contributes to the inhibition of cancer progression in B16F10 tumor-bearing mice. In humans, this IL-7Rsup-hi subpopulation expressing CD97 correlates with the responsiveness to anti-PD-1 therapy in cancer patients and the disease state of multiple sclerosis. By elucidating the heterogeneity of naive CD4 T cells and identifying a Th1-poised subpopulation capable of robust type 1 responses, we highlight the importance of this heterogeneity in inflammatory conditions for defining the disease states and predicting drug responsiveness. The naïve CD4 T cell compartment is traditionally deemed as homogenous. Here, by leveraging scRNA-seq analysis, the authors reveal the role of naive CD4 T cell heterogeneity in disease states and therapeutic outcomes by characterizing a Th1-poised naive CD4 T cell subpopulation, linked to anti-tumor immunity in mice and responsiveness to anti-PD-1 therapy in humans.
50
Integrated analysis of multimodal single-cell data
Yuhan Hao, Stephanie Hao, Erica Andersen‐Nissen et al. · Cell · 2021 · 16K citations · Full text
Fast, sensitive and accurate integration of single-cell data with Harmony
Ilya Korsunsky, Nghia Millard, Jean Fan et al. · Nature Methods · 2019 · 10.5K citations · Full text