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      EndoDB: a database of endothelial cell transcriptomics data

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          Abstract

          Endothelial cells (ECs) line blood vessels, regulate homeostatic processes (blood flow, immune cell trafficking), but are also involved in many prevalent diseases. The increasing use of high-throughput technologies such as gene expression microarrays and (single cell) RNA sequencing generated a wealth of data on the molecular basis of EC (dys-)function. Extracting biological insight from these datasets is challenging for scientists who are not proficient in bioinformatics. To facilitate the re-use of publicly available EC transcriptomics data, we developed the endothelial database EndoDB, a web-accessible collection of expert curated, quality assured and pre-analyzed data collected from 360 datasets comprising a total of 4741 bulk and 5847 single cell endothelial transcriptomes from six different organisms. Unlike other added-value databases, EndoDB allows to easily retrieve and explore data of specific studies, determine under which conditions genes and pathways of interest are deregulated and assess reprogramming of metabolism via principal component analysis, differential gene expression analysis, gene set enrichment analysis, heatmaps and metabolic and transcription factor analysis, while single cell data are visualized as gene expression color-coded t-SNE plots. Plots and tables in EndoDB are customizable, downloadable and interactive. EndoDB is freely available at https://vibcancer.be/software-tools/endodb, and will be updated to include new studies.

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          Genes that mediate breast cancer metastasis to the brain.

          The molecular basis for breast cancer metastasis to the brain is largely unknown. Brain relapse typically occurs years after the removal of a breast tumour, suggesting that disseminated cancer cells must acquire specialized functions to take over this organ. Here we show that breast cancer metastasis to the brain involves mediators of extravasation through non-fenestrated capillaries, complemented by specific enhancers of blood-brain barrier crossing and brain colonization. We isolated cells that preferentially infiltrate the brain from patients with advanced disease. Gene expression analysis of these cells and of clinical samples, coupled with functional analysis, identified the cyclooxygenase COX2 (also known as PTGS2), the epidermal growth factor receptor (EGFR) ligand HBEGF, and the alpha2,6-sialyltransferase ST6GALNAC5 as mediators of cancer cell passage through the blood-brain barrier. EGFR ligands and COX2 were previously linked to breast cancer infiltration of the lungs, but not the bones or liver, suggesting a sharing of these mediators in cerebral and pulmonary metastases. In contrast, ST6GALNAC5 specifically mediates brain metastasis. Normally restricted to the brain, the expression of ST6GALNAC5 in breast cancer cells enhances their adhesion to brain endothelial cells and their passage through the blood-brain barrier. This co-option of a brain sialyltransferase highlights the role of cell-surface glycosylation in organ-specific metastatic interactions.
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            A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor

            Single-cell RNA sequencing (scRNA-seq) is widely used to profile the transcriptome of individual cells. This provides biological resolution that cannot be matched by bulk RNA sequencing, at the cost of increased technical noise and data complexity. The differences between scRNA-seq and bulk RNA-seq data mean that the analysis of the former cannot be performed by recycling bioinformatics pipelines for the latter. Rather, dedicated single-cell methods are required at various steps to exploit the cellular resolution while accounting for technical noise. This article describes a computational workflow for low-level analyses of scRNA-seq data, based primarily on software packages from the open-source Bioconductor project. It covers basic steps including quality control, data exploration and normalization, as well as more complex procedures such as cell cycle phase assignment, identification of highly variable and correlated genes, clustering into subpopulations and marker gene detection. Analyses were demonstrated on gene-level count data from several publicly available datasets involving haematopoietic stem cells, brain-derived cells, T-helper cells and mouse embryonic stem cells. This will provide a range of usage scenarios from which readers can construct their own analysis pipelines.
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              Comparative Analysis of Single-Cell RNA Sequencing Methods.

              Single-cell RNA sequencing (scRNA-seq) offers new possibilities to address biological and medical questions. However, systematic comparisons of the performance of diverse scRNA-seq protocols are lacking. We generated data from 583 mouse embryonic stem cells to evaluate six prominent scRNA-seq methods: CEL-seq2, Drop-seq, MARS-seq, SCRB-seq, Smart-seq, and Smart-seq2. While Smart-seq2 detected the most genes per cell and across cells, CEL-seq2, Drop-seq, MARS-seq, and SCRB-seq quantified mRNA levels with less amplification noise due to the use of unique molecular identifiers (UMIs). Power simulations at different sequencing depths showed that Drop-seq is more cost-efficient for transcriptome quantification of large numbers of cells, while MARS-seq, SCRB-seq, and Smart-seq2 are more efficient when analyzing fewer cells. Our quantitative comparison offers the basis for an informed choice among six prominent scRNA-seq methods, and it provides a framework for benchmarking further improvements of scRNA-seq protocols.
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                Author and article information

                Journal
                Nucleic Acids Res
                Nucleic Acids Res
                nar
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                08 January 2019
                24 October 2018
                24 October 2018
                : 47
                : Database issue , Database issue
                : D736-D744
                Affiliations
                [1 ]Department of Oncology and Leuven Cancer Institute (LKI), Laboratory of Angiogenesis and Vascular Metabolism, KU Leuven, 3000 Leuven, Belgium
                [2 ]State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangzhou 510060, Guangdong, P.R. China
                [3 ]Laboratory of Angiogenesis and Vascular Metabolism, Center for Cancer Biology, VIB, 3000 Leuven, Belgium
                Author notes
                To whom correspondence should be addressed. Tel: +32 16 373 204; Fax: +32 16 372 585; Email: peter.carmeliet@ 123456kuleuven.vib.be . Correspondence may also be addressed to Jermaine Goveia. Tel: +32 16 373 204; Fax: +32 16 372 585; Email: jermaine.goveia@ 123456kuleuven.vib.be . Correspondence may also be addressed to Xuri Li. Tel: +86 20 8733 1815; Fax: +86 20 8733 1815; Email: lixr6@ 123456mail.sysu.edu.cn

                The authors wish it to be known that, in their opinion, the first three authors should be regarded as Joint First Authors.

                Present address: Andreas Pircher, Department of Hematology and Oncology, Internal Medicine V, Medical University Innsbruck, Innsbruck, Austria.

                Present address: Lena-Christin Conradi, Clinic of General, Visceral and Pediatric Surgery, University Medical Center Göttingen, Göttingen, Germany.

                Author information
                http://orcid.org/0000-0003-4887-7672
                Article
                gky997
                10.1093/nar/gky997
                6324065
                30357379
                be0f845a-83c6-4d02-b894-9ae06e08f32a
                © The Author(s) 2018. Published by Oxford University Press on behalf of Nucleic Acids Research.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 09 October 2018
                : 20 September 2018
                : 14 August 2018
                Page count
                Pages: 9
                Funding
                Funded by: Austrian Science Fund 10.13039/501100002428
                Award ID: J3730-B26
                Funded by: Fritz Thyssen Stiftung 10.13039/501100003390
                Award ID: 10.16.2.017MN
                Funded by: National Natural Science Foundation of China 10.13039/501100001809
                Award ID: 81330021
                Award ID: 81670855
                Funded by: Foundation against Cancer 10.13039/501100005026
                Award ID: 2012–175
                Award ID: 2016–078
                Funded by: ERC Advanced Research
                Award ID: EU-ERC743074
                Categories
                Database Issue

                Genetics
                Genetics

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