γδ T lymphocytes represent ∼1% of human peripheral blood mononuclear cells and even more cells in most tissues of vertebrates. Although they have important anticancer functions, most current single-cell RNA sequencing (scRNA-seq) studies do not identify γδ T lymphocytes because their transcriptomes at the single-cell level are unknown. Here we show that high-resolution clustering of large scRNA-seq datasets and a combination of gene signatures allow the specific detection of human γδ T lymphocytes and identification of their T cell receptor (TCR)Vδ1 and TCRVδ2 subsets in large datasets from complex cell mixtures. In t-distributed stochastic neighbor embedding plots from blood and tumor samples, the few γδ T lymphocytes appear collectively embedded between cytotoxic CD8 T and NK cells. Their TCRVδ1 and TCRVδ2 subsets form close yet distinct subclusters, respectively neighboring NK and CD8 T cells because of expression of shared and distinct cytotoxic maturation genes. Similar pseudotime maturation trajectories of TCRVδ1 and TCRVδ2 γδ T lymphocytes were discovered, unveiling in both subsets an unattended pool of terminally differentiated effector memory cells with preserved proliferative capacity, a finding confirmed by in vitro proliferation assays. Overall, the single-cell transcriptomes of thousands of individual γδ T lymphocytes from different CMV + and CMV − donors reflect cytotoxic maturation stages driven by the immunological history of donors. This landmark study establishes the rationale for identification, subtyping, and deep characterization of human γδ T lymphocytes in further scRNA-seq studies of complex tissues in physiological and disease conditions. γδ T lymphocyte | transcriptome | single-cell RNA-sequencing | human immunology | cancer S ingle-cell level mRNA-sequencing (scRNA-seq) of heterogeneous cell populations has become the reference tool for establishing cellular lineages and composition of tissues from the human body (1). In addition, the development of novel and open-source computational tools for processing scRNA-seq datasets enables delineation of both broad and subtle differences in rare cell subsets present in complex mixtures, such as peripheral blood mononuclear cells (PBMC) (2, 3). Such developments are expected to build more knowledge about the cellular composition and states composing tumors. Recent scRNA-seq analyses in melanoma and colorectal cancer evidenced the various tumor microenvironments and exhaustion patterns of the tumor-infiltrating lymphocytes (4). Most of such studies currently focus on the cytotoxic CD8 T lymphocytes, which represent the main target of immune checkpoint blockade therapies, and are readily detected by their scRNA-seq profile. Other subsets of cytolytic T cells are also critical in this therapeutic perspective; however, currently they have never been characterized by scRNA-seq and are therefore missing from all current tumor microenvironments mapped by these technologies.Human γδ T lymphocytes represent a peculiar lymphoid cell subset displaying hall...

The momentum of scRNA-seq datasets prompts for simple and powerful tools exploring their meaningful signatures. Here we present Single-Cell_Signature_Explorer (https://sites.google.com/site/fredsoftwares/products/single-cell-signature-explorer), the first method for qualitative and high-throughput scoring of any gene set-based signature at the single cell level and its visualization using t-SNE or UMAP. By scanning datasets for single or combined signatures, it rapidly maps any multi-gene feature, exemplified here with signatures of cell lineages, biological hallmarks and metabolic pathways in large scRNAseq datasets of human PBMC, melanoma, lung cancer and adult testis.