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      Differential microglia and macrophage profiles in human IDH-mutant and -wild type glioblastoma

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          Abstract

          Microglia and macrophages are the largest component of the inflammatory infiltrate in glioblastoma (GBM). However, whether there are differences in their representation and activity in the prognostically-favorable isocitrate dehydrogenase (IDH)-mutated compared to -wild type GBMs is unknown. Studies on human specimens of untreated IDH-mutant GBMs are rare given they comprise 10% of all GBMs and often present at lower grades, receiving treatments prior to dedifferentiation that can drastically alter microglia and macrophage phenotypes. We were able to obtain large samples of four previously untreated IDH-mutant GBM. Using flow cytometry, immunofluorescence techniques with automated segmentation protocols that quantify at the individual-cell level, and comparison between single-cell RNA-sequencing (scRNA-seq) databases of human GBM, we discerned dissimilarities between GBM-associated microglia and macrophages (GAMMs) in IDH-mutant and -wild type GBMs. We found there are significantly fewer GAMM in IDH-mutant GBMs, but they are more pro-inflammatory, suggesting this contributes to the better prognosis of these tumors. Our pro-inflammatory score which combines the expression of inflammatory markers (CD68/HLA-A, -B, -C/TNF/CD163/IL10/TGFB2), Iba1 intensity, and GAMM surface area also indicates that more pro-inflammatory GAMMs are associated with longer overall survival independent of IDH status. Interrogation of scRNA-seq databases demonstrates microglia in IDH-mutants are mainly pro-inflammatory, while anti-inflammatory macrophages that upregulate genes such as FCER1G and TYROBP predominate in IDH-wild type GBM. Taken together, these observations are the first head-to-head comparison of GAMMs in treatment-naïve IDH-mutant versus -wild type GBMs. Our findings highlight biological disparities in the innate immune microenvironment related to IDH prognosis that can be exploited for therapeutic purposes.

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          Most cited references33

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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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            Diverse Requirements for Microglial Survival, Specification, and Function Revealed by Defined-Medium Cultures.

            Microglia, the resident macrophages of the CNS, engage in various CNS-specific functions that are critical for development and health. To better study microglia and the properties that distinguish them from other tissue macrophage populations, we have optimized serum-free culture conditions to permit robust survival of highly ramified adult microglia under defined-medium conditions. We find that astrocyte-derived factors prevent microglial death ex vivo and that this activity results from three primary components, CSF-1/IL-34, TGF-β2, and cholesterol. Using microglial cultures that have never been exposed to serum, we demonstrate a dramatic and lasting change in phagocytic capacity after serum exposure. Finally, we find that mature microglia rapidly lose signature gene expression after isolation, and that this loss can be reversed by engrafting cells back into an intact CNS environment. These data indicate that the specialized gene expression profile of mature microglia requires continuous instructive signaling from the intact CNS.
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              Computational assignment of cell-cycle stage from single-cell transcriptome data.

              The transcriptome of single cells can reveal important information about cellular states and heterogeneity within populations of cells. Recently, single-cell RNA-sequencing has facilitated expression profiling of large numbers of single cells in parallel. To fully exploit these data, it is critical that suitable computational approaches are developed. One key challenge, especially pertinent when considering dividing populations of cells, is to understand the cell-cycle stage of each captured cell. Here we describe and compare five established supervised machine learning methods and a custom-built predictor for allocating cells to their cell-cycle stage on the basis of their transcriptome. In particular, we assess the impact of different normalisation strategies and the usage of prior knowledge on the predictive power of the classifiers. We tested the methods on previously published datasets and found that a PCA-based approach and the custom predictor performed best. Moreover, our analysis shows that the performance depends strongly on normalisation and the usage of prior knowledge. Only by leveraging prior knowledge in form of cell-cycle annotated genes and by preprocessing the data using a rank-based normalisation, is it possible to robustly capture the transcriptional cell-cycle signature across different cell types, organisms and experimental protocols.
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                Author and article information

                Journal
                Oncotarget
                Oncotarget
                Oncotarget
                ImpactJ
                Oncotarget
                Impact Journals LLC
                1949-2553
                3 May 2019
                3 May 2019
                : 10
                : 33
                : 3129-3143
                Affiliations
                1 Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada
                2 Centre for Health Genomics and Informatics, University of Calgary, Calgary, AB, Canada
                3 Department of Pathology and Laboratory Medicine, University of Calgary, Calgary, AB, Canada
                Author notes
                Correspondence to: John J.P. Kelly, jjkelly@ 123456ucalgary.ca
                [*]

                These authors contributed equal supervision to this work

                Article
                26863
                10.18632/oncotarget.26863
                6517100
                31139325
                d5764c47-d3c4-46e5-945a-cf34cd4cb175
                Copyright: © 2019 Poon et al.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License 3.0 (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 11 January 2019
                : 4 March 2019
                Categories
                Research Paper

                Oncology & Radiotherapy
                glioblastoma,microglia,macrophages,isocitrate dehydrogenase,single-cell rna sequencing

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