12 research outputs found

    CellSighter: a neural network to classify cells in highly multiplexed images

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    Abstract Multiplexed imaging enables measurement of multiple proteins in situ, offering an unprecedented opportunity to chart various cell types and states in tissues. However, cell classification, the task of identifying the type of individual cells, remains challenging, labor-intensive, and limiting to throughput. Here, we present CellSighter, a deep-learning based pipeline to accelerate cell classification in multiplexed images. Given a small training set of expert-labeled images, CellSighter outputs the label probabilities for all cells in new images. CellSighter achieves over 80% accuracy for major cell types across imaging platforms, which approaches inter-observer concordance. Ablation studies and simulations show that CellSighter is able to generalize its training data and learn features of protein expression levels, as well as spatial features such as subcellular expression patterns. CellSighter’s design reduces overfitting, and it can be trained with only thousands or even hundreds of labeled examples. CellSighter also outputs a prediction confidence, allowing downstream experts control over the results. Altogether, CellSighter drastically reduces hands-on time for cell classification in multiplexed images, while improving accuracy and consistency across datasets

    The immune system profoundly restricts intratumor genetic heterogeneity

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    International audienceTumors develop under the selective pressure of the immune system. However, it remains critical to establish how the immune system affects the clonal heterogeneity of tumors that often display cell-to-cell variation in genetic alterations and antigenic expression. To address these questions, we introduced a multicolor barcoding strategy to study the growth of a MYC-driven B cell lymphoma harboring a large degree of intratumor genetic diversity. Using intravital imaging, we visualized that lymphoma subclones grow as patches of sessile cells in the bone marrow, creating a spatially compartmentalized architecture for tumor diversity. Using multicolor barcoding and whole-exome sequencing, we demonstrated that immune responses strongly restrict intratumor genomic diversity and favor clonal dominance, a process mediated by the selective elimination of more immunogenic cells and amplified by epitope spreading. Anti-PD-1 treatment also narrowed intratumor diversity. Our results provide direct evidence that immune pressure shapes the level of intratumor genetic heterogeneity and have important implications for the design of therapeutic strategies

    Bystander IFN-Îł activity promotes widespread and sustained cytokine signaling altering the tumor microenvironment

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    International audienceThe cytokine interferon (IFN)-Îł produced by tumor-reactive T cells is a key effector molecule with pleiotropic effects during anti-tumor immune responses. Although IFN-Îł production is targeted at the immunologic synapse, its spatiotemporal activity within the tumor remains elusive. In the present study, we report that, although IFN-Îł secretion requires local antigen recognition, IFN-Îł diffuses extensively to alter the tumor microenvironment in distant areas. Using intravital imaging and a reporter for STAT1 translocation, we provide evidence that T cells mediate sustained IFN-Îł signaling in remote tumor cells. Furthermore, tumor phenotypic alterations required several hours of exposure to IFN-Îł, a feature that disfavored local IFN-Îł activity over diffusion and bystander activity. Finally, single-cell RNA-sequencing data from melanoma patients also suggested bystander IFN-Îł activity in human tumors. Thus, tumor-reactive T cells act collectively to create large cytokine fields that profoundly modify the tumor microenvironment
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