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Inducible cell-specific mouse models for paired epigenetic and transcriptomic studies of microglia and astroglia: LPS Experiment RNA-Seq Data

ABSTRACT: Studies of neuroepigenetic mechanisms in health and disease are hindered by a lack of approaches to analyze both the transcriptome and epigenome of specific cell types isolated from the complex milieu of the CNS. Cell isolation by cell surface markers is complicated by preparation artifacts, changes in markers with experimental conditions, and lack of specific markers. This study validates a Nuclear Tagging and Translating Ribosome Affinity Purification (NuTRAP) approach with tamoxifen (Tam) inducible cell-type specific cre recombination to allow the parallel interrogation of the epigenome and the transcriptome in astrocytes (Aldh1l1-creERT2) or microglia (Cx3cr1-creERT2). The recombined NuTRAP construct labels, in a cell-type specific manner, nuclei (RanGAP1) with biotin and mCherry, and the ribosomes (L10a) with EGFP, enabling INTACT isolation of DNA and TRAP isolation of RNA. Validation experiments by flow cytometry and imaging demonstrate cell-type specific induction of the NuTRAP construct. Transcriptomic studies demonstrate isolation of highly enriched RNA by TRAP and oxidative bisulfite studies of INTACT-isolated DNA demonstrate differential DNA modification patterns in microglia and astrocytes. LPS administration in Cx3cr1 NuTRAP mice demonstrates that microglia-specific transcriptome and epigenome changes are revealed that cannot be detected with tissue-level samples. These experiments demonstrate that the NuTRAP approach can be applied to CNS cell populations and that INTACT approaches can be used to study DNA modifications. These results also provide an approach for generation and validation of NuTRAP neuroscience models crossed to any relevant cell-type specific cre line.

ORGANISM(S): Mus musculus

PROVIDER: GSE140974 | GEO | 2020/01/01

REPOSITORIES: GEO

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Project description:Epigenetic regulation of the genome through DNA modifications, mainly methylcytosine (mC) and hydroxymethylcytosine (hmC), alters DNA accessibility, genomic organization and gene expression and is thought to be altered during neurodegenerative diseases, such as age-related macular degeneration (AMD). Analysis of retina epigenetic and transcriptomic signatures at the cell-type specific level is crucial to understanding the pathophysiology of retinal degeneration. We have discovered that Aldh1l1 is specifically expressed in the major macroglia of the retina, the Müller glia, and, unlike the brain, is not expressed in retinal astrocytes. This allows a novel model to study paired epigenetic and transcriptomic signatures in Müller glia using Nuclear Tagging and Translating Ribosome Affinity Purification (NuTRAP) for temporally controlled labeling and isolation of Müller glial DNA and RNA. As validated through a variety of approaches, the Aldh1l1cre/ERT2-NuTRAP model provides Müller glia specific translatome and epigenome profiles. Application of this approach to models of acute injury (optic nerve crush) and chronic stress (aging) uncovered common Müller glia-specific transcriptome changes in inflammatory pathways, as well as differential signatures for each stimulus. The expression of components of the IL1b signalling axis, complement system, and markers of gliosis was enhanced in Müller glia in response to optic nerve crush but not in response to aging. The expression of components of the purinergic receptor and focal adhesion signalling pathways changed uniquely in response to aging but not with optic nerve crush. The Aldh1l1cre/ERT2-NuTRAP model allows focusing molecular analyses to a single, minority cell type within the retina, providing more substantial effect sizes than whole tissue analyses. The NuTRAP model, nucleic acid isolation, and validation approaches presented here can be applied to any retina cell type for which a cell type-specific cre is available.

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Inducible cell-specific mouse models for paired epigenetic and transcriptomic studies of microglia and astroglia: LPS Experiment RNA-Seq Data

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