Single-cell RNA sequencing reveals functional heterogeneity of glioma-associated brain macrophages

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Abstract

Microglia are resident myeloid cells in the central nervous system (CNS) that control homeostasis and protect CNS from damage and infections. Microglia and peripheral myeloid cells accumulate and adapt tumor supporting roles in human glioblastomas that show prevalence in men. Cell heterogeneity and functional phenotypes of myeloid subpopulations in gliomas remain elusive. Here we show single-cell RNA sequencing (scRNA-seq) of CD11b ⁺ myeloid cells in naïve and GL261 glioma-bearing mice that reveal distinct profiles of microglia, infiltrating monocytes/macrophages and CNS border-associated macrophages. We demonstrate an unforeseen molecular heterogeneity among myeloid cells in naïve and glioma-bearing brains, validate selected marker proteins and show distinct spatial distribution of identified subsets in experimental gliomas. We find higher expression of MHCII encoding genes in glioma-activated male microglia, which was corroborated in bulk and scRNA-seq data from human diffuse gliomas. Our data suggest that sex-specific gene expression in glioma-activated microglia may be relevant to the incidence and outcomes of glioma patients.

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clusterProfiler: an R package for comparing biological themes among gene clusters.

Guangchuang Yu, Li-Gen Wang, Yanyan Han … (2012)

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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Comprehensive Integration of Single-Cell Data

Tim Stuart, Andrew Butler, Paul Hoffman … (2019)

Single-cell transcriptomics has transformed our ability to characterize cell states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, a key analytical challenge is to integrate these datasets to better understand cellular identity and function. Here, we develop a strategy to "anchor" diverse datasets together, enabling us to integrate single-cell measurements not only across scRNA-seq technologies, but also across different modalities. After demonstrating improvement over existing methods for integrating scRNA-seq data, we anchor scRNA-seq experiments with scATAC-seq to explore chromatin differences in closely related interneuron subsets and project protein expression measurements onto a bone marrow atlas to characterize lymphocyte populations. Lastly, we harmonize in situ gene expression and scRNA-seq datasets, allowing transcriptome-wide imputation of spatial gene expression patterns. Our work presents a strategy for the assembly of harmonized references and transfer of information across datasets.

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Integrating single-cell transcriptomic data across different conditions, technologies, and species

Rahul Satija, Efthymia Papalexi, Peter Smibert … (2018)

Computational single-cell RNA-seq (scRNA-seq) methods have been successfully applied to experiments representing a single condition, technology, or species to discover and define cellular phenotypes. However, identifying subpopulations of cells that are present across multiple data sets remains challenging. Here, we introduce an analytical strategy for integrating scRNA-seq data sets based on common sources of variation, enabling the identification of shared populations across data sets and downstream comparative analysis. We apply this approach, implemented in our R toolkit Seurat (http://satijalab.org/seurat/), to align scRNA-seq data sets of peripheral blood mononuclear cells under resting and stimulated conditions, hematopoietic progenitors sequenced using two profiling technologies, and pancreatic cell 'atlases' generated from human and mouse islets. In each case, we learn distinct or transitional cell states jointly across data sets, while boosting statistical power through integrated analysis. Our approach facilitates general comparisons of scRNA-seq data sets, potentially deepening our understanding of how distinct cell states respond to perturbation, disease, and evolution.