Evidence map›Paper›PMID 29970232›Full record

ReviewBiophysical journal2018

Single-Cell Imaging Approaches for Studying Small-RNA-Induced Gene Regulation.

Hye Ran Koh, Sua Myong

Open access · bronzeAbstract readReview
In one paragraph

Review in Biophysical journal, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.1field-weighted citation impact, top 53% 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

1 citing paper in PubMed, 3 citations in OpenAlex.

  1. Article
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 1 institution in 2 countries.

Hye Ran KohT.C. Jenkins Department of Biophysics, Johns Hopkins University, Baltimore, Maryland; Department of Chemistry, Chung-Ang University, Seoul, Korea.
Sua MyongT.C. Jenkins Department of Biophysics, Johns Hopkins University, Baltimore, Maryland; Department of Physics, Center for the Physics of Living Cells and Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois. Electronic address: smyong@jhu.edu.
Johns Hopkins University · US

Funding

Quantitative Stepwise Analysis of RNA InterferenceDP2GM105453 · NIGMS · UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN · PI MYONG, SUA · 2012 to 2012
$2.4M
Quantitative Analysis of G-Quadruplex Folding and its Role in Gene ExpressionR01GM115631 · NIGMS · JOHNS HOPKINS UNIVERSITY · PI MYONG, SUA · 2016 to 2019
$1.2M
NIGMS NIH HHS DP2 GM105453NIGMS NIH HHS R01 GM115631
6 · The paper itself

Abstract

RNA interference (RNAi) is a process by which gene expression is downregulated by small interfering RNAs or microRNAs. The quantification of the RNAi efficiency can be performed at both the messenger RNA (mRNA) and the protein level, which is required to assess the potency of small interfering RNAs or microRNAs. Recently, we employed a single-cell mRNA imaging method to study RNAi in which we visualized individual mRNA targets with high precision while resolving the cellular localization and cell-to-cell heterogeneity in addition to RNAi efficiency. In this Biophysical Perspective, we highlight our recent work on quantitative analysis of the RNAi pathway and point out some important future directions. Alongside, we discuss about several single-cell imaging techniques that can be applied to study RNAi. The single-cell imaging techniques discussed here are widely applicable to other gene regulation processes such as the CRISPR-CAS system.

Indexed as

RNA InterferenceHumansMolecular ImagingNeuronsRNA, MessengerRNA, Small InterferingSingle-Cell AnalysisRNA, MessengerRNA, Small Interfering

Identifiers

PMID29970232
PMCPMC6051022
OpenAlexW2809847179

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.