Synaptic small and total RNAome from hippocampal Synaptosomes and the synaptic compartments of microfluidic chambers
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA668132 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR12795974.fastq.gz | Fastqsanger.gz | |||
| SRR12795975.fastq.gz | Fastqsanger.gz | |||
| SRR12795976.fastq.gz | Fastqsanger.gz | |||
| SRR12795977.fastq.gz | Fastqsanger.gz | |||
| SRR12795978.fastq.gz | Fastqsanger.gz |
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Project description:Here we isolate high quality synaptosomes from the hippocampus of young wild type mice and provide, at an unprecedented level, the coding and small non-coding synaptic RNAome. These data are complimented by a novel approach to analyze the synaptic RNAome from primary hippocampal neurons grown in microfluidic chambers. Our data show that synaptic microRNAs control almost the entire synaptic mRNAome and we identified several hub microRNAs. By combining the in vivo synaptosomal data with our novel microfluid chamber system, we also provide evidence to support the hypothesis that part of the synaptic microRNAome may be supplied to neurons via astrocytes. Moreover, the microfluidic system is suitable to study the dynamics of the synaptic RNAome in response to stimulation as shown with KCL treatment.
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Project description:Neurons are highly compartmentalized cells that depend on local protein synthesis. Messenger RNAs (mRNAs) have thus been detected in neuronal dendrites, and more recently in the pre- and postsynaptic compartments as well. Other RNA species such as microRNAs have also been described at synapses where they are believed to control mRNA availability for local translation. A combined dataset analyzing the synaptic coding and non-coding RNAome via next-generation sequencing approaches is, however, still lacking. Here, we isolate synaptosomes from the hippocampus of young wild-type mice and provide the coding and non-coding synaptic RNAome. These data are complemented by a novel approach for analyzing the synaptic RNAome from primary hippocampal neurons grown in microfluidic chambers. Our data show that synaptic microRNAs control almost the entire synaptic mRNAome, and we identified several hub microRNAs. By combining the in vivo synaptosomal data with our novel microfluidic chamber system, our findings also support the hypothesis that part of the synaptic microRNAome may be supplied to neurons via astrocytes. Moreover, the microfluidic system is suitable for studying the dynamics of the synaptic RNAome in response to stimulation. In conclusion, our data provide a valuable resource and point to several important targets for further research.
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Project description:Current RNA expression profiling methods rely on enrichment steps for specific RNA classes, thereby not detecting all RNA species in an unperturbed manner. We report strand-specific RNAome sequencing that determines expression of small and large RNAs from ribosomal RNA-depleted total RNA in a single sequence run. Since current analysis pipelines cannot reliably analyze small and large RNAs simultaneously, we developed TRAP, Total Rna Analysis Pipeline, a robust interface that is also compatible with existing RNA sequencing protocols. RNAome sequencing quantitatively preserved all RNA classes, allowing cross-class comparisons that facilitates the identification of relationships between different RNA classes. We demonstrate the strength of RNAome sequencing in mouse embryonic stem cells treated with cisplatin. MicroRNA and mRNA expression in RNAome sequencing significantly correlated between replicates and was in concordance with both existing RNA sequencing methods and gene expression arrays generated from the same samples. Moreover, RNAome sequencing also detected additional RNA classes such as enhancer RNAs, anti-sense RNAs, novel RNA species and numerous differentially expressed RNAs undetectable by other methods. At the level of complete RNA classes, RNAome sequencing also identified a specific global repression of the microRNA and microRNA isoform classes after cisplatin treatment whereas all other classes such as mRNAs were unchanged. These characteristics of RNAome sequencing will significantly improve expression analysis as well as studies on RNA biology not covered by existing methods. 8 mouse embryonic stem cell samples, control and cisplatin treated, 4 replicates per group
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Project description:Current RNA expression profiling methods rely on enrichment steps for specific RNA classes, thereby not detecting all RNA species in an unperturbed manner. We report strand-specific RNAome sequencing that determines expression of small and large RNAs from ribosomal RNA-depleted total RNA in a single sequence run. Since current analysis pipelines cannot reliably analyze small and large RNAs simultaneously, we developed TRAP, Total Rna Analysis Pipeline, a robust interface that is also compatible with existing RNA sequencing protocols. RNAome sequencing quantitatively preserved all RNA classes, allowing cross-class comparisons that facilitates the identification of relationships between different RNA classes. We demonstrate the strength of RNAome sequencing in mouse embryonic stem cells treated with cisplatin. MicroRNA and mRNA expression in RNAome sequencing significantly correlated between replicates and was in concordance with both existing RNA sequencing methods and gene expression arrays generated from the same samples. Moreover, RNAome sequencing also detected additional RNA classes such as enhancer RNAs, anti-sense RNAs, novel RNA species and numerous differentially expressed RNAs undetectable by other methods. At the level of complete RNA classes, RNAome sequencing also identified a specific global repression of the microRNA and microRNA isoform classes after cisplatin treatment whereas all other classes such as mRNAs were unchanged. These characteristics of RNAome sequencing will significantly improve expression analysis as well as studies on RNA biology not covered by existing methods.
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