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Identification of the stress granule transcriptome via RNA-editing in bulk S2 cells, single S2 cells and in Drosophila neurons

ABSTRACT: Identification of the stress granule transcriptome via RNA-editing in bulk S2 cells, single S2 cells and in Drosophila neurons

PROVIDER: PRJNA733657 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR14684900_1.fastq.gz	Fastqsanger.gz
	SRR14684900_2.fastq.gz	Fastqsanger.gz
	SRR14684901_1.fastq.gz	Fastqsanger.gz
	SRR14684901_2.fastq.gz	Fastqsanger.gz
	SRR14684902_1.fastq.gz	Fastqsanger.gz

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Identification of the stress granule transcriptome via RNA-editing in bulk S2 cells, single S2 cells and in Drosophila neurons

Project description:Stress granules are phase separated assemblies formed around mRNAs that have now been identified after stress granule purification from cell culture. Here, we present a purification free method to detect stress granules RNAs in single cells and in tissues, including those displaying cell heterogeneity. We adapted TRIBE (Target of RNA-binding proteins Identified by Editing) to detect stress-granule RNAs by fusing a stress-granule RNA-binding protein (FMR1) to the catalytic domain of an RNA-editing enzyme (ADAR). RNAs colocalized with this fusion are edited, producing mutations that are detectable by sequencing. We then show that this “in situ" method can reliably identify stress granule RNAs in single S2 cells and in Drosophila neurons, and that they encode cell cycle, transcription and splicing factors. The identification of stress granule RNAs without perturbation opens the possibility to examine cell-to-cell variability and identify the RNA content not only in stress granules, but also in other RNA based assemblies in single cells derived from tissues.

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The Transcriptional Response to Oxidative Stress is Independent of Stress-Granule Formation

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Project description:RNA editing is a mutational mechanism that specifically alters the nucleotide content in sets of transcripts while leaving their cognate genomic blueprint intact. Editing has been detected from bulk RNA-seq data in thousands of distinct transcripts, but apparent editing rates can vary widely (from under 1% to almost 100%). These observed editing rates could result from approximately equal rates of editing within each individual cell in the bulk sample, or alternatively, editing estimates from a population of cells could reflect an average of distinct, biologically significant editing signatures that vary substantially between individual cells in the population. To distinguish between these two possibilities we have constructed a hierarchical Bayesian model which quantifies the variance of editing rates at specific sites using RNA-seq data from both single cells and a cognate bulk sample consisting of ∼ 106 cells. The model was applied to data from murine bone-marrow derived macrophages and dendritic cells, and predicted high variance for specific edited sites in both cell types tested. We then 1 validated these predictions using targeted amplification of specific editable transcripts from individual macrophages. Our data demonstrate substantial variance in editing signatures between single cells, supporting the notion that RNA editing generates diversity within cellular populations. Such editing-mediated RNA-level sequence diversity could contribute to the functional heterogeneity apparent in cells of the innate immune system.

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