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SHIFTR enables the unbiased identification of proteins bound to specific RNA regions in live cells

ABSTRACT: SHIFTR enables the unbiased identification of proteins bound to specific RNA regions in live cells

PROVIDER: PRJNA1046161 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR26978103_1.fastq.gz	Fastqsanger.gz
	SRR26978103_2.fastq.gz	Fastqsanger.gz
	SRR26978104_1.fastq.gz	Fastqsanger.gz
	SRR26978104_2.fastq.gz	Fastqsanger.gz
	SRR26978105_1.fastq.gz	Fastqsanger.gz

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SHIFTR enables the unbiased identification of proteins bound to specific RNA regions in live cells

Project description:RNA-protein interactions determine the cellular fate of RNA and are central to regulating gene expression outcomes in health and disease. To date, no method exists that is able to identify proteins that interact with specific regions within endogenous RNAs in live cells. Here, we develop SHIFTR (Selective RNase H-mediated interactome framing for target RNA regions), an efficient and scalable approach to identify proteins bound to selected regions within endogenous RNAs using mass spectrometry. Compared to state-of-the-art techniques, SHIFTR is superior in accuracy, captures minimal background interactions and requires orders of magnitude lower input material. We establish SHIFTR workflows for targeting RNA classes of different length and abundance, including short and long non-coding RNAs, as well as mRNAs and demonstrate that SHIFTR is compatible with sequentially mapping interactomes for multiple target RNAs in a single experiment. Using SHIFTR, we comprehensively identify interactions of cis-regulatory elements located at the 5ʹ and 3ʹ-terminal regions of the authentic SARS-CoV-2 RNA genome in infected cells and accurately recover known and novel interactions linked to the function of these viral RNA elements. SHIFTR enables the systematic mapping of region-resolved RNA interactomes for any RNA in any cell type and has the potential to revolutionize our understanding of transcriptomes and their regulation.

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SHIFTR enables the unbiased identification of proteins bound to specific RNA regions in live cells.

Project description:RNA-protein interactions determine the cellular fate of RNA and are central to regulating gene expression outcomes in health and disease. To date, no method exists that is able to identify proteins that interact with specific regions within endogenous RNAs in live cells. Here, we develop SHIFTR (Selective RNase H-mediated interactome framing for target RNA regions), an efficient and scalable approach to identify proteins bound to selected regions within endogenous RNAs using mass spectrometry. Compared to state-of-the-art techniques, SHIFTR is superior in accuracy, captures minimal background interactions and requires orders of magnitude lower input material. We establish SHIFTR workflows for targeting RNA classes of different length and abundance, including short and long non-coding RNAs, as well as mRNAs and demonstrate that SHIFTR is compatible with sequentially mapping interactomes for multiple target RNAs in a single experiment. Using SHIFTR, we comprehensively identify interactions of cis-regulatory elements located at the 5' and 3'-terminal regions of authentic SARS-CoV-2 RNAs in infected cells and accurately recover known and novel interactions linked to the function of these viral RNA elements. SHIFTR enables the systematic mapping of region-resolved RNA interactomes for any RNA in any cell type and has the potential to revolutionize our understanding of transcriptomes and their regulation.

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SHIFTR enables the unbiased identification of proteins bound to specific RNA regions in transcriptomes of virus and host

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