Evidence map›Paper›PMID 23979999›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2013

Measuring transcription dynamics in living cells using fluctuation analysis.

Matthew L Ferguson, Daniel R Larson

Open access · greenAbstract read
In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
3.9field-weighted citation impact, top 9% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed, 37 citations in OpenAlex.

  1. Article
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  3. Unified bursting strategies in ectopic and endogenousbioRxiv : the preprint server for biology · 2024
    Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors at 2 institutions in 1 country.

Matthew L FergusonCenter for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
Daniel R Larson
Center for Cancer Research · USNational Cancer Institute · US

Funding

Transcription Dynamics in Single CellsZIABC011383 · NCI · DIVISION OF BASIC SCIENCES - NCI · PI LARSON, DANIEL · 2011 to 2025
$23.8M
Intramural NIH HHS Z99 CA999999
6 · The paper itself

Abstract

Single-cell studies of gene regulation suggest that transcription dynamics play a fundamental role in determining expression heterogeneity within a population. In addition, the three-dimensional organization of the nucleus seems to both reflect and influence expression patterns in the cell. Therefore, to gain a holistic understanding of transcriptional regulation, it is necessary to develop methods for studying transcription of single genes in living cells with high spatial and temporal resolution. In this chapter, we describe a recently developed approach for visualizing and quantifying pre-mRNA synthesis at a single active gene in the nucleus. The approach is based on the high-affinity interaction between MS2/PP7 bacteriophage coat proteins and RNA hairpins which are transcribed by the gene of interest. The MS2/PP7 coat protein is fused to a fluorescent protein and binds the nascent mRNA, allowing for detection of single transcription events in the fluorescence microscope. By time-lapse fluorescence imaging and quantitative image analysis, one can generate a time trace of fluorescence intensity at the site of transcription. By temporal autocorrelation analysis, one can determine enzymatic activities of RNAP such as initiation rate and elongation rate. In this protocol, we summarize the experimental concept, design, and execution for real-time observation of transcription in living cells.

Indexed as

BacteriophagesCapsid ProteinsGene Expression RegulationInverted Repeat SequencesMicroscopy, FluorescenceSingle-Cell AnalysisTranscription, GeneticCapsid Proteins

Identifiers

PMID23979999
PMCPMC6310616
OpenAlexW2299130105

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.