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      Contribution of resident and circulating precursors to tumor-infiltrating CD8+ T cell populations in lung cancer

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          Abstract

          Tumor-infiltrating lymphocytes (TILs), in general, and especially CD8+ TILs, represent a favorable prognostic factor in non–small cell lung cancer (NSCLC). The tissue origin, regenerative capacities, and differentiation pathways of TIL subpopulations remain poorly understood. Using a combination of single-cell RNA and T cell receptor (TCR) sequencing, we investigate the functional organization of TIL populations in primary NSCLC. We identify two CD8+ TIL subpopulations expressing memory-like gene modules: one is also present in blood (circulating precursors) and the other one in juxtatumor tissue (tissue-resident precursors). In tumors, these two precursor populations converge through a unique transitional state into terminally differentiated cells, often referred to as dysfunctional or exhausted. Differentiation is associated with TCR expansion, and transition from precursor to late-differentiated states correlates with intratumor T cell cycling. These results provide a coherent working model for TIL origin, ontogeny, and functional organization in primary NSCLC.

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          Most cited references40

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          Comprehensive Integration of Single-Cell Data

          Single-cell transcriptomics has transformed our ability to characterize cell states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, a key analytical challenge is to integrate these datasets to better understand cellular identity and function. Here, we develop a strategy to "anchor" diverse datasets together, enabling us to integrate single-cell measurements not only across scRNA-seq technologies, but also across different modalities. After demonstrating improvement over existing methods for integrating scRNA-seq data, we anchor scRNA-seq experiments with scATAC-seq to explore chromatin differences in closely related interneuron subsets and project protein expression measurements onto a bone marrow atlas to characterize lymphocyte populations. Lastly, we harmonize in situ gene expression and scRNA-seq datasets, allowing transcriptome-wide imputation of spatial gene expression patterns. Our work presents a strategy for the assembly of harmonized references and transfer of information across datasets.
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            Is Open Access

            Metascape provides a biologist-oriented resource for the analysis of systems-level datasets

            A critical component in the interpretation of systems-level studies is the inference of enriched biological pathways and protein complexes contained within OMICs datasets. Successful analysis requires the integration of a broad set of current biological databases and the application of a robust analytical pipeline to produce readily interpretable results. Metascape is a web-based portal designed to provide a comprehensive gene list annotation and analysis resource for experimental biologists. In terms of design features, Metascape combines functional enrichment, interactome analysis, gene annotation, and membership search to leverage over 40 independent knowledgebases within one integrated portal. Additionally, it facilitates comparative analyses of datasets across multiple independent and orthogonal experiments. Metascape provides a significantly simplified user experience through a one-click Express Analysis interface to generate interpretable outputs. Taken together, Metascape is an effective and efficient tool for experimental biologists to comprehensively analyze and interpret OMICs-based studies in the big data era.
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              Is Open Access

              SCANPY : large-scale single-cell gene expression data analysis

              Scanpy is a scalable toolkit for analyzing single-cell gene expression data. It includes methods for preprocessing, visualization, clustering, pseudotime and trajectory inference, differential expression testing, and simulation of gene regulatory networks. Its Python-based implementation efficiently deals with data sets of more than one million cells (https://github.com/theislab/Scanpy). Along with Scanpy, we present AnnData, a generic class for handling annotated data matrices (https://github.com/theislab/anndata).
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                Author and article information

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                Journal
                Science Immunology
                Sci. Immunol.
                American Association for the Advancement of Science (AAAS)
                2470-9468
                January 29 2021
                January 29 2021
                January 29 2021
                January 29 2021
                : 6
                : 55
                : eabd5778
                Affiliations
                [1 ]PSL Research University, Institut Curie Research Center, INSERM U932, Paris, France.
                [2 ]Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA.
                [3 ]Institut Curie Genomics of Excellence (ICGex) Platform, Institut Curie Research Center, Paris, France.
                [4 ]Centre d’Investigation Clinique Biothérapie, Institut Curie, Paris, France.
                [5 ]Institut Curie, Institut du Thorax Curie Montsouris, Paris, France.
                [6 ]Institut Montsouris, Surgery Department, Institut du Thorax Curie Montsouris, Paris, France.
                [7 ]Paris 13 University, Sorbonne Paris Cité, Faculty of Medicine SMBH, Bobigny, France.
                [8 ]PSL Research University, Laboratoire de physique statistique, CNRS, Sorbonne Université, Université Paris Diderot, Paris, France.
                [9 ]PSL Research University, Laboratoire de physique théorique, CNRS, Sorbonne Université, Université Paris Diderot, and École normale supérieure, Paris, France.
                [10 ]PSL Research University, Institut Curie Research Center, INSERM U830, Paris, France.
                [11 ]PSL Research University, Institut Curie Research Center, Department of Translational Research, Paris, France.
                Article
                10.1126/sciimmunol.abd5778
                33514641
                da0a529a-d1a1-488e-99a8-8f90e7d07752
                © 2021

                https://www.sciencemag.org/about/science-licenses-journal-article-reuse

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