Dataset Information

Hipposeq: a comprehensive RNA-seq database of gene expression in hippocampal principal neurons.

ABSTRACT: Clarifying gene expression in narrowly defined neuronal populations can provide insight into cellular identity, computation, and functionality. Here, we used next-generation RNA sequencing (RNA-seq) to produce a quantitative, whole genome characterization of gene expression for the major excitatory neuronal classes of the hippocampus; namely, granule cells and mossy cells of the dentate gyrus, and pyramidal cells of areas CA3, CA2, and CA1. Moreover, for the canonical cell classes of the trisynaptic loop, we profiled transcriptomes at both dorsal and ventral poles, producing a cell-class- and region-specific transcriptional description for these populations. This dataset clarifies the transcriptional properties and identities of lesser-known cell classes, and moreover reveals unexpected variation in the trisynaptic loop across the dorsal-ventral axis. We have created a public resource, Hipposeq (http://hipposeq.janelia.org), which provides analysis and visualization of these data and will act as a roadmap relating molecules to cells, circuits, and computation in the hippocampus.

SUBMITTER: Cembrowski MS

PROVIDER: S-EPMC4846374 | biostudies-literature | 2016 Apr

REPOSITORIES: biostudies-literature

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Hipposeq: a comprehensive RNA-seq database of gene expression in hippocampal principal neurons.

Cembrowski Mark S MS Wang Lihua L Sugino Ken K Shields Brenda C BC Spruston Nelson N

eLife 20160426

Clarifying gene expression in narrowly defined neuronal populations can provide insight into cellular identity, computation, and functionality. Here, we used next-generation RNA sequencing (RNA-seq) to produce a quantitative, whole genome characterization of gene expression for the major excitatory neuronal classes of the hippocampus; namely, granule cells and mossy cells of the dentate gyrus, and pyramidal cells of areas CA3, CA2, and CA1. Moreover, for the canonical cell classes of the trisyna ...[more]

PMID: 27113915

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Project description:BackgroundInvasive urothelial carcinoma (iUC) is a major cause of death in humans, and approximately 165,000 individuals succumb to this cancer annually worldwide. Comparative oncology using relevant animal models is necessary to improve our understanding of progression, diagnosis, and treatment of iUC. Companion canines are a preferred animal model of iUC due to spontaneous tumor development and similarity to human disease in terms of histopathology, metastatic behavior, and treatment response. However, the comprehensive molecular characterization of canine iUC is not well documented. In this study, we performed transcriptome analysis of tissue samples from canine iUC and normal bladders using an RNA sequencing (RNA-Seq) approach to identify key molecular pathways in canine iUC.MethodsTotal RNA was extracted from bladder tissues of 11 dogs with iUC and five healthy dogs, and RNA-Seq was conducted. Ingenuity Pathway Analysis (IPA) was used to assign differentially expressed genes to known upstream regulators and functional networks.ResultsDifferential gene expression analysis of the RNA-Seq data revealed 2531 differentially expressed genes, comprising 1007 upregulated and 1524 downregulated genes, in canine iUC. IPA revealed that the most activated upstream regulator was PTGER2 (encoding the prostaglandin E2 receptor EP2), which is consistent with the therapeutic efficiency of cyclooxygenase inhibitors in canine iUC. Similar to human iUC, canine iUC exhibited upregulated ERBB2 and downregulated TP53 pathways. Biological functions associated with cancer, cell proliferation, and leukocyte migration were predicted to be activated, while muscle functions were predicted to be inhibited, indicating muscle-invasive tumor property.ConclusionsOur data confirmed similarities in gene expression patterns between canine and human iUC and identified potential therapeutic targets (PTGER2, ERBB2, CCND1, Vegf, and EGFR), suggesting the value of naturally occurring canine iUC as a relevant animal model for human iUC.

Project description:For functional genomics studies, it is important to understand the dynamic expression profiles of transcribed genes in different tissues, stages of development and in response to environmental stimuli. The proliferation in the use of next-generation sequencing technologies by the plant research community has led to the accumulation of large volumes of expression data. However, analysis of these datasets is complicated by the frequent occurrence of polyploidy among economically-important crop species. In addition, processing and analyzing such large volumes of sequence data is a technical and time-consuming task, limiting their application in functional genomics studies, particularly for smaller laboratories which lack access to high-powered computing infrastructure. Wheat is a good example of a young polyploid species with three similar genomes (97 % identical among homoeologous genes), rapidly accumulating RNA-seq datasets and a large research community.We present WheatExp, an expression database and visualization tool to analyze and compare homoeologue-specific transcript profiles across a broad range of tissues from different developmental stages in polyploid wheat. Beginning with publicly-available RNA-seq datasets, we developed a pipeline to distinguish between homoeologous transcripts from annotated genes in tetraploid and hexaploid wheat. Data from multiple studies is processed and compiled into a database which can be queried either by BLAST or by searching for a known gene of interest by name or functional domain. Expression data of multiple genes can be displayed side-by-side across all expression datasets providing immediate access to a comprehensive panel of expression data for specific subsets of wheat genes.The development of a publicly accessible expression database hosted on the GrainGenes website - http://wheat.pw.usda.gov/WheatExp/ - coupled with a simple and readily-comparable visualization tool will empower the wheat research community to use RNA-seq data and to perform functional analyses of target genes. The presented expression data is homoeologue-specific allowing for the analysis of relative contributions from each genome to the overall expression of a gene, a critical consideration for breeding applications. Our approach can be expanded to other polyploid species by adjusting sequence mapping parameters according to the specific divergence of their genomes.

Dataset Information

Hipposeq: a comprehensive RNA-seq database of gene expression in hippocampal principal neurons.

Publications

Hipposeq: a comprehensive RNA-seq database of gene expression in hippocampal principal neurons.

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