Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Extra-coding RNAs regulate neuronal DNA methylation and long-term memory formation [RNA-seq]

ABSTRACT: This dataset contains whole-genome RNA sequencing results from cortical neuronal cultures and serves as the basis for characterization of extra-coding RNA species from neuronal systems. This experiment contains six biological samples, each of which underwent PolyA+ and PolyA- RNA-seq. Samples were either unstimulated (i.e., treated with media alone; samples V1 and V2), stimulated with 25mM potassium chloride for 1hr (K1, K2) or inactivated with tetrodotoxin for 1hr (T1, T2). Datasets were obtained using RNA-seq from PolyA+ fractions or PolyA- fractions of RNA. PolyA- fractions are denoted "ec". Thus, 12 samples are listed here due to the difference in RNA library preparation.

ORGANISM(S): Rattus norvegicus

SUBMITTER: Jeremy Day

PROVIDER: E-GEOD-64902 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

ACCESS DATA

Similar Datasets

Project description:Purpose: The study focused on the RNA content of supportive (AFT024) and non supportive (BFC012) stromal lines and their respective exosomes to analyze the molecular complexity of the Hematopoietic Stem and Progenitor Cell niche Methods: All the samples from small RNA (SR) and polyA-RNA libraries were sequenced using 1Ã50 bp single reads high-throughput sequencing (RNA-Seq) in single lane on Illumina HiSeq 2500. The sequence reads that passed quality filters were analyzed at the transcript isoform level with TopHat followed by Cufflinks and by Fisher's test for polyA-RNA, or with bowtie and parse tools for smallRNA (galaxy server https://lbcd41.snv.jussieu.fr/). Results: Using an optimized data analysis workflow, we mapped about 30 million sequence reads per sample to the mouse genome (build mm10). We focused on differentially expressed transcripts between i) exosomes from supportive and non supportive stromal cells and ii) exosomes and their respective cells.Using a subtractive strategy, we identified 324 mRNAs and 23 miRNAs specific to the exosomes of the supportive stromal line AFT024. We used gene ontology annotation to study the biological functions associated to these specific gene sets. Transcripts involved in negative regulation of apoptosis were found enriched in AFT exosomes. Conclusions: Our RNA seq analysis combined with biological assays reveal that exosomes serve as an important and novel mediator for the HSPC supporting capacity of stromal cells. This unprecedented effort to resolve the molecular complexity of the HSPC-targeted exosomes, provide new candidat genes of the HSPC support and may help designing innovative stromal-free culture conditions to deliver specific molecules to HSPCs. Fetal liver derived stromal lines and their corresponding secreted exosomes were sequenced using Illumina HiSeq2500 technology (Fasteris, CH). Due to low amount of exosomal RNAs, several RNA extraction were pooled and split to perform 2 technical sequencing replicates that were merged during bionformatic analyses.

Project description:Recent advances in transcriptome sequencing have enabled the discovery of thousands of long non-coding RNAs (lncRNAs) across many species. Though several lncRNAs have been shown to play important roles in diverse biological processes, the functions and mechanisms of most lncRNAs remain unknown. Two significant obstacles lie between transcriptome sequencing and functional characterization of lncRNAs: identifying truly non-coding genes from de novo reconstructed transcriptomes, and prioritizing the hundreds of resulting putative lncRNAs for downstream experimental interrogation. We present slncky, a computational lncRNA discovery tool that produces a high-quality set of lncRNAs from RNA-sequencing data and further uses evolutionary constraint to prioritize lncRNAs that are likely to be functionally important. Our automated filtering pipeline is comparable to manual curation efforts and more sensitive than previously published computational approaches. Furthermore, we develop a sensitive alignment pipeline for aligning lncRNA loci and propose new evolutionary metrics relevant for analyzing sequence and transcript evolution. Our analysis reveals that evolutionary selection acts in several distinct patterns, and uncovers two notable classes of intergenic lncRNAs: one showing strong purifying selection on RNA sequence and another where constraint is restricted to the regulation but not the sequence of the transcript. To study a comprehensive and comparable set of lncRNAs expressed in the pluripotent state, we analyzed RNA-Seq data from pluripotent cells derived from several strains and species, and grown in two physiological conditions. First we derived ânaÃ¯veâ ES cells (ESCs) from each of three different mice strains: 129SvEv, NOD, and Mus musculus castaneus (cast) mouse, a wild mouse subspecies originally from Thailand, as well as naÃ¯ve induced pluripotent stem (iPS) cells from rat and human. Next, to facilitate comparisons across pluripotent cells from different species, we also cultured mouse and rat cells in âprimedâ epiblast stem cell (EpiSC) culture conditions, since human iPS cells in culture are much more similar molecularly and functionally to mouse primed EpiSCs rather than to a ground state naÃ¯ve ESCs. We polyA selected RNA from each cell type and deeply sequenced on HiSeq2500

Dataset Information

Extra-coding RNAs regulate neuronal DNA methylation and long-term memory formation [RNA-seq]

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure