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Microglia are cells that defend the central nervous system. However, because they migrate into the brain during development, the changes that they undergo, including those that affect gene expression, have been difficult to document. Matcovitch-Natan et al. transcriptionally profiled gene expression and analyzed epigenetic signatures of microglia at the single-cell level in the early postnatal life of mice. They identified three stages of microglia development, which are characterized by gene expression and linked with chromatin changes, occurring in sync with the developing brain. Furthermore, they showed that the proper development of microglia is affected by the microbiome. Science , this issue p. 789 The microbiota help regulate the development of active immune defense in the central nervous system of mice. Microglia, the resident myeloid cells of the central nervous system, play important roles in life-long brain maintenance and in pathology. Despite their importance, their regulatory dynamics during brain development have not been fully elucidated. Using genome-wide chromatin and expression profiling coupled with single-cell transcriptomic analysis throughout development, we found that microglia undergo three temporal stages of development in synchrony with the brain—early, pre-, and adult microglia—which are under distinct regulatory circuits. Knockout of the gene encoding the adult microglia transcription factor MAFB and environmental perturbations, such as those affecting the microbiome or prenatal immune activation, led to disruption of developmental genes and immune response pathways. Together, our work identifies a stepwise microglia developmental program integrating immune response pathways that may be associated with several neurodevelopmental disorders.

Since the first publications coining the term RNA-seq (RNA sequencing) appeared in 2008, the number of publications containing RNA-seq data has grown exponentially, hitting an all-time high of 2,808 publications in 2016 (PubMed). With this wealth of RNA-seq data being generated, it is a challenge to extract maximal meaning from these datasets, and without the appropriate skills and background, there is risk of misinterpretation of these data. However, a general understanding of the principles underlying each step of RNA-seq data analysis allows investigators without a background in programming and bioinformatics to critically analyze their own datasets as well as published data. Our goals in the present review are to break down the steps of a typical RNA-seq analysis and to highlight the pitfalls and checkpoints along the way that are vital for bench scientists and biomedical researchers performing experiments that use RNA-seq.

Background With the rise of RNA-seq as an essential and ubiquitous tool for biomedical research, the need for guidelines on quality control (QC) is pressing. Specifically, there remains limited data as to which technical metrics are most informative in identifying low-quality samples. Results Here, we addressed this issue by developing the Quality Control Diagnostic Renderer (QC-DR), software designed to simultaneously visualize a comprehensive panel of QC metrics generated by an RNA-seq pipeline and flag samples with aberrant values when compared to a reference dataset. As an example, we applied QC-DR to the Successful Clinical Response in Pneumonia Therapy (SCRIPT) dataset, a large clinical RNA-seq dataset of sequenced alveolar macrophages (n = 252). Next, we used this dataset to assess relationships between a variety of QC metrics and sample quality. Among the most highly correlated pipeline QC metrics were % and # Uniquely Aligned Reads , % rRNA reads , # Detected Genes , and our newly developed metric of Area Under the Gene Body Coverage Curve (AUC-GBC ), while experimental QC metrics derived from the lab were not significantly correlated. We then trained a set of machine learning models on the SCRIPT dataset to evaluate the relative contribution of QC metrics to sample quality prediction. Our model performs well when tested on an independent dataset despite differences in the distribution of QC metrics. Conclusions Our results support the conclusion that any individual QC metric is limited in its predictive value and suggests approaches based on the integration of multiple metrics with QC thresholds. In summary, our work provides new insights, practical guidance, and novel QC software which can be used to improve the methodological rigor of RNA-seq studies.

During ageing, microglia acquire a phenotype that may negatively affect brain function. Here we show that ageing microglial phenotype is largely imposed by interferon type I (IFN-I) chronically present in aged brain milieu. Overexpression of IFN-β in the CNS of adult wild-type mice, but not of mice lacking IFN-I receptor on their microglia, induces an ageing-like transcriptional microglial signature, and impairs cognitive performance. Furthermore, we demonstrate that age-related IFN-I milieu downregulates microglial myocyte-specific enhancer factor 2C (Mef2C). Immune challenge in mice lacking Mef2C in microglia results in an exaggerated microglial response and has an adverse effect on mice behaviour. Overall, our data indicate that the chronic presence of IFN-I in the brain microenvironment, which negatively affects cognitive function, is mediated via modulation of microglial activity. These findings may shed new light on other neurological conditions characterized by elevated IFN-I signalling in the brain. Microglia cells in the brain regulate immune responses, but in ageing can negatively affect brain function. Here the authors show that the chronic presence of type I interferon in aged mouse brain impedes cognitive ability by altering microglia transcriptome and limiting Mef2C, a microglia ‘off’ signal.

Characterization of gene lists obtained from high-throughput genomic experiments is an essential task to uncover the underlying biological insights. A common strategy is to perform enrichment analyses that utilize standardized biological annotations, such as GO and KEGG pathways, which attempt to encompass all domains of biology. However, this approach provides generalized, static results that may fail to capture subtleties associated with research questions within a specific domain. Thus, there is a need for an application that can provide precise, relevant results by leveraging the latest research. We have therefore developed an interactive web application, Macrophage Annotation of Gene Network Enrichment Tool (MAGNET), for performing enrichment analyses on gene sets that are specifically relevant to macrophages. Using the hypergeometric distribution, MAGNET assesses the significance of overlapping genes with annotations that were curated from published manuscripts and data repositories. We implemented numerous features that enhance utility and user-friendliness, such as the simultaneous testing of multiple gene sets, different visualization options, option to upload custom datasets, and downloadable outputs. Here, we use three example studies compared against our current database of ten publications on mouse macrophages to demonstrate that MAGNET provides relevant and unique results that complement conventional enrichment analysis tools. Although specific to macrophage datasets, we envision MAGNET will catalyze developments of similar applications in other domains of interest. MAGNET can be freely accessed at the URL https://magnet-winterlab.herokuapp.com . Website implemented in Python and PostgreSQL, with all major browsers supported. The source code is available at https://github.com/sychen9584/MAGNET .

Limited understanding of the role for specific macrophage subsets in the pathogenesis of cholestatic liver injury is a barrier to advancing medical therapy. Macrophages have previously been implicated in both the mal-adaptive and protective responses in obstructive cholestasis. Recently two macrophage subsets were identified in non-diseased human liver; however, no studies to date fully define the heterogeneous macrophage subsets during the pathogenesis of cholestasis. Here, we aim to further characterize the transcriptional profile of macrophages in pediatric cholestatic liver disease. We isolated live hepatic immune cells from patients with biliary atresia (BA), Alagille syndrome (ALGS), and non-cholestatic pediatric liver by fluorescence activated cell sorting. Through single-cell RNA sequencing analysis and immunofluorescence, we characterized cholestatic macrophages. We next compared the transcriptional profile of pediatric cholestatic and non-cholestatic macrophage populations to previously published data on normal adult hepatic macrophages. We identified 3 distinct macrophage populations across cholestatic liver samples and annotated them as lipid-associated macrophages, monocyte-like macrophages, and adaptive macrophages based on their transcriptional profile. Immunofluorescence of liver tissue using markers for each subset confirmed their presence across BA (n = 6) and ALGS (n = 6) patients. Cholestatic macrophages demonstrated reduced expression of immune regulatory genes as compared to normal hepatic macrophages and were distinct from macrophage populations defined in either healthy adult or pediatric non-cholestatic liver. We are the first to perform single-cell RNA sequencing on human pediatric cholestatic liver and identified three macrophage subsets with distinct transcriptional signatures from healthy liver macrophages. Further analyses will identify similarities and differences in these macrophage sub-populations across etiologies of cholestatic liver disease. Taken together, these findings may allow for future development of targeted therapeutic strategies to reprogram macrophages to an immune regulatory phenotype and reduce cholestatic liver injury.

High-throughput sequencing assays have become an increasingly common part of biological research across multiple fields. Even as the resulting sequences pile up in public databases, it is not always obvious how to make use of these data sets. Functional genomics offers approaches to integrate these \"big\" data into our understanding of rheumatic diseases. This review aims to provide a primer on thinking about big data from functional genomics in the context of rheumatology, using examples from the field’s literature as well as the author’s own work to illustrate the execution of functional genomics research. Study design is crucial to ensure the right samples are used to address the question of interest. In addition, sequencing assays produce a variety of data types, from gene expression to 3D chromatin structure and single-cell technologies, that can be integrated into a model of the underlying gene regulatory networks. The best approach for this analysis uses the scientific process: bioinformatic methods should be used in an iterative, hypothesis-driven manner to uncover the disease mechanism. Finally, the future of functional genomics will see big data fully integrated into rheumatology, leading to computationally trained researchers and interactive databases. The goal of this review is not to provide a manual, but to enhance the familiarity of readers with functional genomic approaches and provide a better sense of the challenges and possibilities.

In addition to their role in systemic innate immunity, macrophages have important tissue-specific roles. In this Review, Jung and colleagues discuss how differentiation and tissue-specific activation of macrophages are regulated. Macrophages provide a critical systemic network cells of the innate immune system. Emerging data suggest that in addition, they have important tissue-specific functions that range from clearance of surfactant from the lungs to neuronal pruning and establishment of gut homeostasis. The differentiation and tissue-specific activation of macrophages require precise regulation of gene expression, a process governed by epigenetic mechanisms such as DNA methylation, histone modification and chromatin structure. We argue that epigenetic regulation of macrophages is determined by lineage- and tissue-specific transcription factors controlled by the built-in programming of myeloid development in combination with signaling from the tissue environment. Perturbation of epigenetic mechanisms of tissue macrophage identity can affect normal macrophage tissue function and contribute to pathologies ranging from obesity and autoimmunity to neurodegenerative diseases.

Chronic pelvic pain conditions such as interstitial cystitis/bladder pain syndrome (IC/BPS) remain clinical and mechanistic enigmas. Microglia are resident immune cells of the central nervous system (CNS) that respond to changes in the gut microbiome, and studies have linked microglial activation to acute and chronic pain in a variety of models, including pelvic pain. We have previously reported that mice deficient for the lipase acyloxyacyl hydrolase (AOAH) develop pelvic allodynia and exhibit symptoms, comorbidities, and gut dysbiosis mimicking IC/BPS. Here, we assessed the role of AOAH in microglial activation and pelvic pain. RNAseq analyses using the ARCHS4 database and confocal microscopy revealed that AOAH is highly expressed in wild type microglia but at low levels in astrocytes, suggesting a functional role for AOAH in microglia. Pharmacologic ablation of CNS microglia with PLX5622 resulted in decreased pelvic allodynia in AOAH-deficient mice and resurgence of pelvic pain upon drug washout. Skeletal analyses revealed that AOAH-deficient mice have an activated microglia morphology in the medial prefrontal cortex and paraventricular nucleus, brain regions associated with pain modulation. Because microglia express Toll-like receptors and respond to microbial components, we also examine the potential role of dysbiosis in microglial activation. Consistent with our hypothesis of microglia activation by leakage of gut microbes, we observed increased serum endotoxins in AOAH-deficient mice and increased activation of cultured BV2 microglial cells by stool of AOAH-deficient mice. Together, these findings demonstrate a role for AOAH in microglial modulation of pelvic pain and thus identify a novel therapeutic target for IC/BPS.

The application of deep sequencing to map 5' capped transcripts has confirmed the existence of at least two distinct promoter classes in metazoans: \"focused\" promoters with transcription start sites (TSSs) that occur in a narrowly defined genomic span and \"dispersed\" promoters with TSSs that are spread over a larger window. Previous studies have explored the presence of genomic features, such as CpG islands and sequence motifs, in these promoter classes, but virtually no studies have directly investigated the relationship with chromatin features. Here, we show that promoter classes are significantly differentiated by nucleosome organization and chromatin structure. Dispersed promoters display higher associations with well-positioned nucleosomes downstream of the TSS and a more clearly defined nucleosome free region upstream, while focused promoters have a less organized nucleosome structure, yet higher presence of RNA polymerase II. These differences extend to histone variants (H2A.Z) and marks (H3K4 methylation), as well as insulator binding (such as CTCF), independent of the expression levels of affected genes. Notably, differences are conserved across mammals and flies, and they provide for a clearer separation of promoter architectures than the presence and absence of CpG islands or the occurrence of stalled RNA polymerase. Computational models support the stronger contribution of chromatin features to the definition of dispersed promoters compared to focused start sites. Our results show that promoter classes defined from 5' capped transcripts not only reflect differences in the initiation process at the core promoter but also are indicative of divergent transcriptional programs established within gene-proximal nucleosome organization.