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      Ligand-receptor interaction atlas within and between tumor cells and T cells in lung adenocarcinoma

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          Abstract

          Purpose: Lung adenocarcinoma (LUAD) is the leading cause of cancer-related deaths worldwide. Although tumor cell-T cell interactions are known to play a fundamental role in promoting tumor progression, these interactions have not been explored in LUAD.

          Methods: The 10x genomics single-cell RNA sequencing (scRNA-seq) and gene expression data of LUAD patients were obtained from ArrayExpress, TCGA, and GEO databases. scRNA-seq data were analyzed and infiltrating tumor cells, epithelial cells, and T cells were identified in the tumor microenvironment. Differentially expressed ligand-receptor pairs were identified in tumor cells/normal epithelial cells and tumor T cells/non-tumor T cells based on corresponding scRNA-seq and gene expression data, respectively. These important interactions inside/across cancer cells and T cells in LUAD were systematically analyzed. Furthermore, a valid prognostic machine-learning model based on ligand-receptor interactions was built to predict the prognosis of LUAD patients. Flow cytometry and qRT-PCR were performed to validate the significantly differently expressed ligand-receptor pairs.

          Results: Overall, 39,692 cells in scRNA-seq data were included in our study after quality filtering. A total of 65 ligand-receptor pairs (17 upregulated and 48 downregulated), including LAMB1-ITGB1, CD70-CD27, and HLA-B-LILRB2, and 96 ligand-receptor pairs (41 upregulated and 55 downregulated), including CCL5-CCR5, SELPLG-ITGB2, and CXCL13-CXCR5, were identified in LUAD cancer cells and T cells, respectively. To explore the crosstalk between cancer cells and T cells, 114 ligand-receptor pairs, including 11 ligand-receptor pair genes that could significantly affect survival outcomes, were identified in our research. A machine-learning model was established to accurately predict the prognosis of LUAD patients and ITGB4, CXCR5, and MET were found to play an important role in prognosis in our model. Flow cytometry and qRT-PCR analyses indicated the reliability of our study.

          Conclusion: Our study revealed functionally significant interactions within and between cancer cells and T cells. We believe these observations will improve our understanding of potential mechanisms of tumor microenvironment contributions to cancer progression and help identify potential targets for immunotherapy in the future.

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          Most cited references24

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          Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage

          Tissue fibrosis is a major cause of mortality that results from the deposition of matrix proteins by an activated mesenchyme. Macrophages accumulate in fibrosis, but the role of specific subgroups in supporting fibrogenesis has not been investigated in vivo. Here we used single-cell RNA sequencing (scRNA-seq) to characterize the heterogeneity of macrophages in bleomycin-induced lung fibrosis in mice. A novel computational framework for the annotation of scRNA-seq by reference to bulk transcriptomes (SingleR) enabled the subclustering of macrophages and revealed a disease-associated subgroup with a transitional gene expression profile intermediate between monocyte-derived and alveolar macrophages. These CX3CR1+SiglecF+ transitional macrophages localized to the fibrotic niche and had a profibrotic effect in vivo. Human orthologues of genes expressed by the transitional macrophages were upregulated in samples from patients with idiopathic pulmonary fibrosis. Thus, we have identified a pathological subgroup of transitional macrophages that are required for the fibrotic response to injury.
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            Innate Immune Landscape in Early Lung Adenocarcinoma by Paired Single-Cell Analyses

            To guide the design of immunotherapy strategies for patients with early stage lung tumors, we developed a multiscale immune profiling strategy to map the immune landscape of early lung adenocarcinoma lesions to search for tumor-driven immune changes. Utilizing a barcoding method that allows a simultaneous single cell analysis of the tumor, non-involved lung and blood cells together with multiplex tissue imaging to assess spatial cell distribution, we provide a detailed immune cell atlas of early lung tumors. We show that stage I lung adenocarcinoma lesions already harbor significantly altered T cell and NK cell compartments. Moreover, we identified changes in tumor infiltrating myeloid cell (TIM) subsets that likely compromise anti-tumor T cell immunity. Paired single cell analyses thus offer valuable knowledge of tumor-driven immune changes, providing a powerful tool for the rational design of immune therapies. Comparing single tumor cells with adjacent normal tissue and blood from patients with lung adenocarcinoma charts early changes in tumor immunity and provides insights to guide immunotherapy design.
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              The technology and biology of single-cell RNA sequencing.

              The differences between individual cells can have profound functional consequences, in both unicellular and multicellular organisms. Recently developed single-cell mRNA-sequencing methods enable unbiased, high-throughput, and high-resolution transcriptomic analysis of individual cells. This provides an additional dimension to transcriptomic information relative to traditional methods that profile bulk populations of cells. Already, single-cell RNA-sequencing methods have revealed new biology in terms of the composition of tissues, the dynamics of transcription, and the regulatory relationships between genes. Rapid technological developments at the level of cell capture, phenotyping, molecular biology, and bioinformatics promise an exciting future with numerous biological and medical applications.
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                Author and article information

                Journal
                Int J Biol Sci
                Int. J. Biol. Sci
                ijbs
                International Journal of Biological Sciences
                Ivyspring International Publisher (Sydney )
                1449-2288
                2020
                18 May 2020
                : 16
                : 12
                : 2205-2219
                Affiliations
                [1 ]Department of Thoracic Surgery, Zhongshan Hospital, Fudan University, No. 180, Fenglin Road, Shanghai, 200032, China
                [2 ]Shanghai Medical College, Fudan University, Shanghai 200032, China
                [3 ]Department of Thoracic Surgery, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou 215001, China
                Author notes
                ✉ Corresponding authors: Cheng Zhan & Wei Jiang, Department of Thoracic Surgery, Zhongshan Hospital, Fudan University, No. 180, Fenglin Road, Shanghai, 200032, China; Phone: 86-21-64041990; Fax: 86-21-64041990; Email: czhan10@ 123456fudan.edu.cn ; jiang.wei1@ 123456zs-hospital.sh.cn

                #These authors contributed equally: Zhencong Chen, Xiaodong Yang, and Guoshu Bi.

                Competing Interests: The authors have declared that no competing interest exists.

                Article
                ijbsv16p2205
                10.7150/ijbs.42080
                7294944
                32549766
                53b94b60-5374-4b0e-8a66-ef49556988d1
                © The author(s)

                This is an open access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/). See http://ivyspring.com/terms for full terms and conditions.

                History
                : 26 February 2020
                : 2 May 2020
                Categories
                Research Paper

                Life sciences
                lung adenocarcinoma,single-cell rna-seq,cell-to-cell interactions,machine learning,survival

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