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      Single-cell transcriptomic analysis of the tumor ecosystems underlying initiation and progression of papillary thyroid carcinoma

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          Abstract

          The tumor ecosystem of papillary thyroid carcinoma (PTC) is poorly characterized. Using single-cell RNA sequencing, we profile transcriptomes of 158,577 cells from 11 patients’ paratumors, localized/advanced tumors, initially-treated/recurrent lymph nodes and radioactive iodine (RAI)-refractory distant metastases, covering comprehensive clinical courses of PTC. Our data identifies a “cancer-primed” premalignant thyrocyte population with normal morphology but altered transcriptomes. Along the developmental trajectory, we also discover three phenotypes of malignant thyrocytes (follicular-like, partial-epithelial-mesenchymal-transition-like, dedifferentiation-like), whose composition shapes bulk molecular subtypes, tumor characteristics and RAI responses. Furthermore, we uncover a distinct BRAF-like-B subtype with predominant dedifferentiation-like thyrocytes, enriched cancer-associated fibroblasts, worse prognosis and promising prospect of immunotherapy. Moreover, potential vascular-immune crosstalk in PTC provides theoretical basis for combined anti-angiogenic and immunotherapy. Together, our findings provide insight into the PTC ecosystem that suggests potential prognostic and therapeutic implications.

          Abstract

          The characterisation of the papillary thyroid carcinoma (PTC) tumour microenvironment remains crucial. Here, the authors perform single-cell RNA sequencing in 11 patients and identify potential opportunities for the use of immunotherapy and its combination with anti-angiogenic therapy in PTC.

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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            clusterProfiler: an R package for comparing biological themes among gene clusters.

            Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis. Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters. The analysis module and visualization module were combined into a reusable workflow. Currently, clusterProfiler supports three species, including humans, mice, and yeast. Methods provided in this package can be easily extended to other species and ontologies. The clusterProfiler package is released under Artistic-2.0 License within Bioconductor project. The source code and vignette are freely available at http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html.
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              fastp: an ultra-fast all-in-one FASTQ preprocessor

              Abstract Motivation Quality control and preprocessing of FASTQ files are essential to providing clean data for downstream analysis. Traditionally, a different tool is used for each operation, such as quality control, adapter trimming and quality filtering. These tools are often insufficiently fast as most are developed using high-level programming languages (e.g. Python and Java) and provide limited multi-threading support. Reading and loading data multiple times also renders preprocessing slow and I/O inefficient. Results We developed fastp as an ultra-fast FASTQ preprocessor with useful quality control and data-filtering features. It can perform quality control, adapter trimming, quality filtering, per-read quality pruning and many other operations with a single scan of the FASTQ data. This tool is developed in C++ and has multi-threading support. Based on our evaluation, fastp is 2–5 times faster than other FASTQ preprocessing tools such as Trimmomatic or Cutadapt despite performing far more operations than similar tools. Availability and implementation The open-source code and corresponding instructions are available at https://github.com/OpenGene/fastp.
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                Author and article information

                Contributors
                xmzhang@ips.ac.cn
                yulongwang@fudan.edu.cn
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                18 October 2021
                18 October 2021
                2021
                : 12
                : 6058
                Affiliations
                [1 ]GRID grid.452404.3, ISNI 0000 0004 1808 0942, Department of Head and Neck Surgery, , Fudan University Shanghai Cancer Center, ; Shanghai, 200032 China
                [2 ]GRID grid.8547.e, ISNI 0000 0001 0125 2443, State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, , Fudan University, ; Shanghai, 200438 China
                [3 ]GRID grid.8547.e, ISNI 0000 0001 0125 2443, Department of Oncology, Shanghai Medical College, , Fudan University, ; Shanghai, 200032 China
                [4 ]GRID grid.9227.e, ISNI 0000000119573309, The Center for Microbes, Development and Health, Key Laboratory of Molecular Virology & Immunology, Institut Pasteur of Shanghai, , Chinese Academy of Sciences, ; Shanghai, 200031 China
                [5 ]GRID grid.412528.8, ISNI 0000 0004 1798 5117, Department of Pathology, , Shanghai Jiao Tong University Affiliated Sixth People’s Hospital, ; Shanghai, 200233 China
                [6 ]GRID grid.452404.3, ISNI 0000 0004 1808 0942, Department of Medical Oncology, , Fudan University Shanghai Cancer Center, ; Shanghai, 200032 China
                [7 ]GRID grid.452404.3, ISNI 0000 0004 1808 0942, Phase I Clinical Trial Center, , Fudan University Shanghai Cancer Center, ; Shanghai, 200032 China
                [8 ]GRID grid.452404.3, ISNI 0000 0004 1808 0942, Department of Pathology, , Fudan University Shanghai Cancer Center, ; Shanghai, 200032 China
                [9 ]GRID grid.239573.9, ISNI 0000 0000 9025 8099, Brain Tumor Center, Division of Experimental Hematology and Cancer Biology, , Cincinnati Children’s Hospital Medical Center, ; Cincinnati, OH 45229 USA
                [10 ]GRID grid.452344.0, Department of Clinical Research & Development, , Jiangsu Hengrui Pharmaceuticals Co., Ltd., ; Shanghai, 201210 China
                [11 ]GRID grid.8547.e, ISNI 0000 0001 0125 2443, Human Phenome Institute, , Fudan University, ; Shanghai, 200438 China
                Author information
                http://orcid.org/0000-0001-9668-7756
                http://orcid.org/0000-0003-0647-0153
                http://orcid.org/0000-0003-2765-0620
                http://orcid.org/0000-0001-6732-9132
                http://orcid.org/0000-0002-3830-7257
                Article
                26343
                10.1038/s41467-021-26343-3
                8523550
                34663816
                7293fd00-85bf-42ec-a891-39f3fd236215
                © The Author(s) 2021

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 9 February 2021
                : 30 September 2021
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100001809, National Natural Science Foundation of China (National Science Foundation of China);
                Award ID: 81772851
                Award ID: 81972501
                Award Recipient :
                Categories
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                Custom metadata
                © The Author(s) 2021

                Uncategorized
                rna sequencing,cancer genomics,thyroid cancer,tumour heterogeneity
                Uncategorized
                rna sequencing, cancer genomics, thyroid cancer, tumour heterogeneity

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