Checkpoint blockade immunotherapy reshapes the high-dimensional phenotypic heterogeneity of murine intratumoural neoantigen-specific CD8+ T cells
The analysis of neoantigen-specific CD8+ T cells in tumour-bearing individuals is challenging due to the small pool of tumour antigen-specific T cells. Here we show that mass cytometry with multiplex combinatorial tetramer staining can identify and characterize neoantigen-specific CD8+ T cells in mice bearing T3 methylcholanthrene-induced sarcomas that are susceptible to checkpoint blockade immunotherapy. Among 81 candidate antigens tested, we identify T cells restricted to two known neoantigens simultaneously in tumours, spleens and lymph nodes in tumour-bearing mice. High-dimensional phenotypic profiling reveals that antigen-specific, tumour-infiltrating T cells are highly heterogeneous. We further show that neoantigen-specific T cells display a different phenotypic profile in mice treated with anti-CTLA-4 or anti-PD-1 immunotherapy, whereas their peripheral counterparts are not affected by the treatments. Our results provide insights into the nature of neoantigen-specific T cells and the effects of checkpoint blockade immunotherapy.
A High-Dimensional Atlas of Human T Cell Diversity Reveals Tissue-Specific Trafficking and Cytokine Signatures.
Depending on the tissue microenvironment, T cells can differentiate into highly diverse subsets expressing unique trafficking receptors and cytokines. Studies of human lymphocytes have primarily focused on a limited number of parameters in blood, representing an incomplete view of the human immune system. Here, we have utilized mass cytometry to simultaneously analyze T cell trafficking and functional markers across eight different human tissues, including blood, lymphoid, and non-lymphoid tissues. These data have revealed that combinatorial expression of trafficking receptors and cytokines better defines tissue specificity. Notably, we identified numerous T helper cell subsets with overlapping cytokine expression, but only specific cytokine combinations are secreted regardless of tissue type. This indicates that T cell lineages defined in mouse models cannot be clearly distinguished in humans. Overall, our data uncover a plethora of tissue immune signatures and provide a systemic map of how T cell phenotypes are altered throughout the human body.
Mass cytometry: blessed with the curse of dimensionality
Immunologists are being compelled to develop new high-dimensional perspectives of cellular heterogeneity and to determine which applications best exploit the power of mass cytometry and associated multiplex approaches.
Deep Profiling Human T Cell Heterogeneity by Mass Cytometry.
Advances of mass cytometry and high-dimensional single-cell data analysis have brought cellular immunological research into a new generation. By coupling these two powerful technology platforms, immunologists now have more tools to resolve the tremendous diversity of immune cell subsets, and their heterogeneous functionality. Since the first introduction of mass cytometry, many reports have been published using this novel technology to study a range of cell types. At the outset, studies of human hematopoietic stem cell and peripheral CD8(+) T cells using mass cytometry have shad the light of future experimental approach in interrogating immune cell phenotypic and functional diversity. Here, we briefly revisit the past and present understanding of T cell heterogeneity, and the technologies that facilitate this knowledge. In addition, we review the current progress of mass cytometry and high-dimensional cytometric analysis, including the methodology, panel design, experimental procedure, and choice of computational algorithms with a special focus on their utility in exploration of human T cell immunology.
Categorical Analysis of Human T Cell Heterogeneity with One-Dimensional Soli-Expression by Nonlinear Stochastic Embedding.
Rapid progress in single-cell analysis methods allow for exploration of cellular diversity at unprecedented depth and throughput. Visualizing and understanding these large, high-dimensional datasets poses a major analytical challenge. Mass cytometry allows for simultaneous measurement of >40 different proteins, permitting in-depth analysis of multiple aspects of cellular diversity. In this article, we present one-dimensional soli-expression by nonlinear stochastic embedding (One-SENSE), a dimensionality reduction method based on the t-distributed stochastic neighbor embedding (t-SNE) algorithm, for categorical analysis of mass cytometry data. With One-SENSE, measured parameters are grouped into predefined categories, and cells are projected onto a space composed of one dimension for each category. In contrast with higher-dimensional t-SNE, each dimension (plot axis) in One-SENSE has biological meaning that can be easily annotated with binned heat plots. We applied One-SENSE to probe relationships between categories of human T cell phenotypes and observed previously unappreciated cellular populations within an orchestrated view of immune cell diversity. The presentation of high-dimensional cytometric data using One-SENSE showed a significant improvement in distinguished T cell diversity compared with the original t-SNE algorithm and could be useful for any high-dimensional dataset.