Searching the Resource Information Network

Our searching services are busy right now. Please try again later

  • Register
X
Forgot Password

If you have forgotten your password you can enter your email here and get a temporary password sent to your email.

X

Leaving Community

Are you sure you want to leave this community? Leaving the community will revoke any permissions you have been granted in this community.

No
Yes

Comprehensive gene expression meta-analysis identifies signature genes that distinguish microglia from peripheral monocytes/macrophages in health and glioma.

Verena Haage | Marcus Semtner | Ramon Oliveira Vidal | Daniel Perez Hernandez | Winnie W Pong | Zhihong Chen | Dolores Hambardzumyan | Vincent Magrini | Amy Ly | Jason Walker | Elaine Mardis | Philipp Mertins | Sascha Sauer | Helmut Kettenmann | David H Gutmann
Acta neuropathologica communications | 2019

Monocytes/macrophages have begun to emerge as key cellular modulators of brain homeostasis and central nervous system (CNS) disease. In the healthy brain, resident microglia are the predominant macrophage cell population; however, under conditions of blood-brain barrier leakage, peripheral monocytes/macrophages can infiltrate the brain and participate in CNS disease pathogenesis. Distinguishing these two populations is often challenging, owing to a paucity of universally accepted and reliable markers. To identify discriminatory marker sets for microglia and peripheral monocytes/macrophages, we employed a large meta-analytic approach using five published murine transcriptional datasets. Following hierarchical clustering, we filtered the top differentially expressed genes (DEGs) through a brain cell type-specific sequencing database, which led to the identification of eight microglia and eight peripheral monocyte/macrophage markers. We then validated their differential expression, leveraging a published single cell RNA sequencing dataset and quantitative RT-PCR using freshly isolated microglia and peripheral monocytes/macrophages from two different mouse strains. We further verified the translation of these DEGs at the protein level. As top microglia DEGs, we identified P2ry12, Tmem119, Slc2a5 and Fcrls, whereas Emilin2, Gda, Hp and Sell emerged as the best DEGs for identifying peripheral monocytes/macrophages. Lastly, we evaluated their utility in discriminating monocyte/macrophage populations in the setting of brain pathology (glioma), and found that these DEG sets distinguished glioma-associated microglia from macrophages in both RCAS and GL261 mouse models of glioblastoma. Taken together, this unbiased bioinformatic approach facilitated the discovery of a robust set of microglia and peripheral monocyte/macrophage expression markers to discriminate these monocyte populations in both health and disease.

Pubmed ID: 30764877

Publication data is provided by the National Library of Medicine ® and PubMed ®. Data is retrieved from PubMed ® on a weekly schedule. For terms and conditions see the National Library of Medicine Terms and Conditions.

This is a list of tools and resources that we have found mentioned in this publication.


UniProt (tool)

RRID:SCR_002380

Collection of data of protein sequence and functional information. Resource for protein sequence and annotation data. Consortium for preservation of the UniProt databases: UniProt Knowledgebase (UniProtKB), UniProt Reference Clusters (UniRef), and UniProt Archive (UniParc), UniProt Proteomes. Collaboration between European Bioinformatics Institute (EMBL-EBI), SIB Swiss Institute of Bioinformatics and Protein Information Resource. Swiss-Prot is a curated subset of UniProtKB.

View all literature mentions

Thermo Fisher Scientific (tool)

RRID:SCR_008452

Commercial vendor and service provider of laboratory reagents and antibodies. Supplier of scientific instrumentation, reagents and consumables, and software services.

View all literature mentions

LIMMA (tool)

RRID:SCR_010943

Software package for the analysis of gene expression microarray data, especially the use of linear models for analyzing designed experiments and the assessment of differential expression.

View all literature mentions

TopHat (tool)

RRID:SCR_013035

Software tool for fast and high throughput alignment of shotgun cDNA sequencing reads generated by transcriptomics technologies. Fast splice junction mapper for RNA-Seq reads. Aligns RNA-Seq reads to mammalian-sized genomes using ultra high-throughput short read aligner Bowtie, and then analyzes mapping results to identify splice junctions between exons.TopHat2 is accurate alignment of transcriptomes in presence of insertions, deletions and gene fusions.

View all literature mentions

BD Biosciences (tool)

RRID:SCR_013311

An Antibody supplier

View all literature mentions

MaxQuant (tool)

RRID:SCR_014485

A quantitative proteomics software package for analyzing large-scale mass-spectrometric data sets. It is a set of algorithms that include peak detection and scoring of peptides, mass calibration, database searches for protein identification, protein quantification, and provides summary statistics.

View all literature mentions

Cufflinks (tool)

RRID:SCR_014597

Software tool for transcriptome assembly and differential expression analysis for RNA-Seq. Includes script called cuffmerge that can be used to merge together several Cufflinks assemblies. It also handles running Cuffcompare as well as automatically filtering a number of transfrags that are likely to be artifacts. If the researcher has a reference GTF file, the researcher can provide it to the script to more effectively merge novel isoforms and maximize overall assembly quality.

View all literature mentions

DESeq2 (tool)

RRID:SCR_015687

Software package for differential gene expression analysis based on the negative binomial distribution. Used for analyzing RNA-seq data for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates.

View all literature mentions

Perseus (tool)

RRID:SCR_015753

Software that supports biological and biomedical researchers in interpreting protein quantification, interaction and post-translational modification data. Perseus contains a comprehensive portfolio of statistical tools for high-dimensional omics data analysis covering normalization, pattern recognition, time-series analysis, cross-omics comparisons and multiplehypothesis testing.

View all literature mentions

Seurat (tool)

RRID:SCR_016341

Software as R package designed for QC, analysis, and exploration of single cell RNA-seq data. Enable users to identify and interpret sources of heterogeneity from single cell transcriptomic measurements, and to integrate diverse types of single cell data.

View all literature mentions

GeneOverlap (tool)

RRID:SCR_018419

Software package to test and visualize gene overlaps. Given two gene lists, tests significance of their overlap in comparison with genomic background.

View all literature mentions

C57BL/6J (tool)

RRID:IMSR_JAX:000664

Mus musculus with name C57BL/6J from IMSR.

View all literature mentions

C57BL/6J (tool)

RRID:IMSR_JAX:000664

Mus musculus with name C57BL/6J from IMSR.

View all literature mentions