Project description:While immune signaling has emerged as a defining feature of the glioma microenvironment, local selection of responding T cells and their anti-tumor potential as a population are difficult to measure directly in patients. High-throughput sequencing of T cell receptor repertoires (TCRseq) provides a population-wide statistical description of how T cells respond to disease. Here, we define new immunophenotypes in glioma based on TCRseq and RNA-Seq of tumor tissue, non-neoplastic brain tissue, and peripheral blood from patients. Using information theory, we characterize antigen-driven selection in glioma and its relationship with the expression of distinct immune-functional pathways in the tumor microenvironment. Finally, we identify a strong relationship between usage of certain TCR in peripheral blood and the divergence of the infiltrating T cell population from the peripheral repertoire. We anticipate that these immunophenotypes will be foundational to monitoring and predicting response to anti-glioma vaccines and immunotherapy. We characterized the T cell receptor (TCR) repertoires of 11 high-grade glioma patients, three low-grade glioma patients, and thee non-glioma patients by TCRseq of brain-infiltrating T cells and matching peripheral blood. In addition, we obtained gene expression profiles from brain tissue of each patient by RNA-Seq. We additionally measured the TCR repertoires exclusively from peripheral blood of one additional non-glioma patient.
Project description:Immunotherapy is becoming a mainstay in the treatment of NSCLC. We profiled immune cells of 35 early stage NSCLC lesions using multiscale single cell sequencing, including scRNAseq, CITEseq, and TCRseq.
Project description:This is the raw TCRseq data for the manuscript T cell receptor repertoire sequencing reveals chemotherapy-driven clonal expansion in colorectal liver metastases.
Project description:While immune signaling has emerged as a defining feature of the glioma microenvironment, local selection of responding T cells and their anti-tumor potential as a population are difficult to measure directly in patients. High-throughput sequencing of T cell receptor repertoires (TCRseq) provides a population-wide statistical description of how T cells respond to disease. Here, we define new immunophenotypes in glioma based on TCRseq and RNA-Seq of tumor tissue, non-neoplastic brain tissue, and peripheral blood from patients. Using information theory, we characterize antigen-driven selection in glioma and its relationship with the expression of distinct immune-functional pathways in the tumor microenvironment. Finally, we identify a strong relationship between usage of certain TCR in peripheral blood and the divergence of the infiltrating T cell population from the peripheral repertoire. We anticipate that these immunophenotypes will be foundational to monitoring and predicting response to anti-glioma vaccines and immunotherapy.
Project description:Virus-specific CD8 T cells from PBMCs of healthy donors and hepatitis patients were enriched for recognition of a panel of feature barcode-labeled pMHC multimers. The phenotypic description was made using gene expression and TCRseq. Samples were pooled and sequenced on the same 10x Genomics sequencing chip and deconvoluted using ADT HashTag antibodies,
Project description:Human γδ T cells take a small but important part in the immune system. Human γδ T cells are usually categorized by the V gene of their T cell receptor (TCR) δ chain; most of which are Vδ1+ or Vδ2+. Naive γδ T cells are often found from neonates and cytotoxic γδ T cells (γδCTLs) are frequent in adults. However, phenotypes and the developmental programs of human γδ T cells are not fully clear. Here, by single-cell RNA sequencing (sc-RNAseq) of transcriptome and T cell receptor (sc-TCRseq) on γδ T cells from neonates and adults, we revealed human γδ T cells can be divided as naïve T cells, γδCTLs, and γδNKT cells which are featured by pro-Vδ1, Vδ1, and Vδ2 TCR repertoire, respectively. γδNKT cells can be further described as γδNKT-1, γδNKT-2, and γδNKT-17 cells.