Dataset Information

Deciphering Transcriptional Dynamics In Vivo by Counting Nascent RNA Molecules.

ABSTRACT: Deciphering how the regulatory DNA sequence of a gene dictates its expression in response to intra and extracellular cues is one of the leading challenges in modern genomics. The development of novel single-cell sequencing and imaging techniques, as well as a better exploitation of currently available single-molecule imaging techniques, provides an avenue to interrogate the process of transcription and its dynamics in cells by quantifying the number of RNA polymerases engaged in the transcription of a gene (or equivalently the number of nascent RNAs) at a given moment in time. In this paper, we propose that measurements of the cell-to-cell variability in the number of nascent RNAs provide a mostly unexplored method for deciphering mechanisms of transcription initiation in cells. We propose a simple kinetic model of transcription initiation and elongation from which we calculate nascent RNA copy-number fluctuations. To demonstrate the usefulness of this approach, we test our theory against published nascent RNA data for twelve constitutively expressed yeast genes. Rather than transcription being initiated through a single rate limiting step, as it had been previously proposed, our single-cell analysis reveals the presence of at least two rate limiting steps. Surprisingly, half of the genes analyzed have nearly identical rates of transcription initiation, suggesting a common mechanism. Our analytical framework can be used to extract quantitative information about dynamics of transcription from single-cell sequencing data, as well as from single-molecule imaging and electron micrographs of fixed cells, and provides the mathematical means to exploit the quantitative power of these technologies.

SUBMITTER: Choubey S

PROVIDER: S-EPMC4636183 | biostudies-other | 2015 Nov

REPOSITORIES: biostudies-other

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Deciphering Transcriptional Dynamics In Vivo by Counting Nascent RNA Molecules.

Choubey Sandeep S Kondev Jane J Sanchez Alvaro A

PLoS computational biology 20151106 11

Deciphering how the regulatory DNA sequence of a gene dictates its expression in response to intra and extracellular cues is one of the leading challenges in modern genomics. The development of novel single-cell sequencing and imaging techniques, as well as a better exploitation of currently available single-molecule imaging techniques, provides an avenue to interrogate the process of transcription and its dynamics in cells by quantifying the number of RNA polymerases engaged in the transcriptio ...[more]

PMID: 26544860

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Project description:Circular RNAs (circRNAs) are covalently closed endogenous molecules with tissue- and disease-specific expression patterns, which have potential as diagnostic and prognostic biomarkers in cancer. The molecules are formed by a backsplicing event linking the 3'-end of an exon to the 5'-end of the same or an upstream exon, and they exert diverse regulatory functions important in carcinogenesis. The landscape of circRNA expression has not been characterized in B-cell malignancies, and current methods for circRNA quantification have several limitations that prevent development of clinically applicable assays. Here, we demonstrate that circRNAs can be accurately quantified without enzymatic reactions or bias using color-coded probes (NanoString technology). First, we performed high-throughput RNA sequencing (RNA-seq) of several mantle cell lymphoma and multiple myeloma cell lines to profile the genome-wide landscape of circRNA expression. We detected several circRNAs known to be deregulated in other cancers and identified a novel circRNA from the IKZF3 gene. Based on these data, we selected 52 unique circRNAs for which we designed color-coded probes spanning their specific backsplicing junctions. These circRNAs were quantified in cell lines and patient samples from several different B-cell malignancies (mantle cell lymphoma, multiple myeloma, follicular lymphoma, diffuse large B-cell lymphoma, Burkitt lymphoma and chronic lymphocytic leukemia) simultaneously using the NanoString technology. The circRNA expression profiles obtained could distinguish different B-cell malignancies, and confirmed the presence of the novel circRNA derived from IKZF3. The NanoString assays were specific for circRNA detection and data were more reproducible and quantitatively more accurate than RNA-seq data. In addition, we obtained high-quality data on severely degraded RNA samples from formalin-fixed, paraffin-embedded (FFPE) tissues. Together, we provide a map of circRNA expression in B-cell malignancies and present an enzyme-free digital counting methodology, which has the potential to become a new gold standard for circRNA quantification.

Dataset Information

Deciphering Transcriptional Dynamics In Vivo by Counting Nascent RNA Molecules.

Publications

Deciphering Transcriptional Dynamics In Vivo by Counting Nascent RNA Molecules.

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