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      A human multi-cellular model shows how platelets drive production of diseased extracellular matrix and tissue invasion

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          Summary

          Guided by a multi-level “deconstruction” of omental metastases, we developed a tetra (four cell)-culture model of primary human mesothelial cells, fibroblasts, adipocytes, and high-grade serous ovarian cancer (HGSOC) cell lines. This multi-cellular model replicated key elements of human metastases and allowed malignant cell invasion into the artificial omental structure. Prompted by findings in patient biopsies, we used the model to investigate the role of platelets in malignant cell invasion and extracellular matrix, ECM, production. RNA (sequencing and quantitative polymerase-chain reaction), protein (proteomics and immunohistochemistry) and image analysis revealed that platelets stimulated malignant cell invasion and production of ECM molecules associated with poor prognosis. Moreover, we found that platelet activation of mesothelial cells was critical in stimulating malignant cell invasion. Whilst platelets likely activate both malignant cells and mesothelial cells, the tetra-culture model allowed us to dissect the role of both cell types and model the early stages of HGSOC metastases.

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          Highlights

          • Platelets are associated with a poor prognosis tissue composition

          • A 3D tetra-culture tissue model enables dissection of platelet action in metastasis

          • Platelets stimulate mesothelial and tumor cells to produce a diseased matrisome

          • Platelet activation of the mesothelium permits tumor invasion

          Abstract

          Biological sciences; Cancer systems biology; Cell biology; Methodology in biological sciences; Molecular biology;

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          Most cited references32

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          Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

          Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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            limma powers differential expression analyses for RNA-sequencing and microarray studies

            limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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              HTSeq—a Python framework to work with high-throughput sequencing data

              Motivation: A large choice of tools exists for many standard tasks in the analysis of high-throughput sequencing (HTS) data. However, once a project deviates from standard workflows, custom scripts are needed. Results: We present HTSeq, a Python library to facilitate the rapid development of such scripts. HTSeq offers parsers for many common data formats in HTS projects, as well as classes to represent data, such as genomic coordinates, sequences, sequencing reads, alignments, gene model information and variant calls, and provides data structures that allow for querying via genomic coordinates. We also present htseq-count, a tool developed with HTSeq that preprocesses RNA-Seq data for differential expression analysis by counting the overlap of reads with genes. Availability and implementation: HTSeq is released as an open-source software under the GNU General Public Licence and available from http://www-huber.embl.de/HTSeq or from the Python Package Index at https://pypi.python.org/pypi/HTSeq. Contact: sanders@fs.tum.de
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                Author and article information

                Contributors
                Journal
                iScience
                iScience
                iScience
                Elsevier
                2589-0042
                29 May 2021
                25 June 2021
                29 May 2021
                : 24
                : 6
                : 102676
                Affiliations
                [1 ]Barts Cancer Institute, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ, UK
                [2 ]School of Engineering and Materials Science, Queen Mary University of London, Mile End, London E1 4NS, UK
                Author notes
                []Corresponding author o.pearce@ 123456qmul.ac.uk
                [3]

                These authors contributed equally

                [4]

                Senior author

                [5]

                Lead contact

                Article
                S2589-0042(21)00644-1 102676
                10.1016/j.isci.2021.102676
                8215303
                34189439
                29ab1054-510e-432d-bcd3-9afdd5a9ad0e
                © 2021 The Authors.

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 21 December 2020
                : 8 March 2021
                : 27 May 2021
                Categories
                Article

                biological sciences,cancer systems biology,cell biology,methodology in biological sciences,molecular biology

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