Evidence map›Paper›PMID 27780260›Full record

ArticlePloS one2016

Two New FRET Imaging Measures: Linearly Proportional to and Highly Contrasting the Fraction of Active Molecules.

Masataka Yamao, Kazuhiro Aoki, Naoto Yukinawa, Shin Ishii, Michiyuki Matsuda, Honda Naoki

Abstract read
In one paragraph

Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
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

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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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Optimal inference of molecular interaction dynamics in FRET microscopy.Proceedings of the National Academy of Sciences of the United States of America · 2023
    Article
  4. Article
  5. 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

6 authors.

Masataka YamaoGraduate School of Information Science, Nara Institute of Science and Technology, Ikoma, Nara, Japan.
Kazuhiro AokiNational Institute for Basic Biology, Okazaki, Aichi, Japan.
Naoto YukinawaOkinawa Institute of Science and Technology Graduate University, Kunigami, Okinawa, Japan.
Shin IshiiImaging Platform for Spatio-temporal Information, Kyoto University, Sakyo, Kyoto, Japan.
Michiyuki MatsudaImaging Platform for Spatio-temporal Information, Kyoto University, Sakyo, Kyoto, Japan.
Honda NaokiImaging Platform for Spatio-temporal Information, Kyoto University, Sakyo, Kyoto, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We developed two new FRET imaging measures for intramolecular FRET biosensors, called linearly proportional (LP) and highly contrasting (HC) measures, which can be easily calculated by the fluorescence intensities of donor and acceptor as a ratio between their weighted sums. As an alternative to the conventional ratiometric measure, which non-linearly depends on the fraction of active molecule, we first developed the LP measure, which is linearly proportional to the fraction of active molecules. The LP measure inherently unmixes bleed-through signals and is robust against fluorescence noise. By extending the LP measure, we furthermore designed the HC measure, which provides highly contrasting images of the molecular activity, more than the ratiometric measure. In addition to their advantages, these measures are insensitive to the biosensor expression level, which is a fundamental property of the ratiometric measure. Using artificial data and FRET imaging data, we showed that the LP measure effectively represents the fraction of active molecules and that the HC measure improves visual interpretability by providing high contrast images of molecular activity. Therefore, the LP and HC measures allow us to gain more quantitative and qualitative insights from FRET imaging than the ratiometric measure.

Indexed as

AlgorithmsBiosensing TechniquesComputational BiologyFluorescence Resonance Energy Transfer

Identifiers

PMID27780260
PMCPMC5079603

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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.