Evidence map›Paper›PMID 26424801›Full record

ArticleMolecular biology of the cell2015

Probability-based particle detection that enables threshold-free and robust in vivo single-molecule tracking.

Carlas S Smith, Sjoerd Stallinga, Keith A Lidke, Bernd Rieger, David Grunwald

Abstract read
In one paragraph

Article in Molecular biology of the cell, 2015. 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
–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

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.

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  10. Temporal and spatial regulation of mRNA export: Single particle RNA-imaging provides new tools and insights.BioEssays : news and reviews in molecular, cellular and developmental biology · 2017
    Review
  11. 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

5 authors.

Carlas S SmithRNA Therapeutics Institute, University of Massachusetts Medical School, Worcester, MA 01605.
Sjoerd StallingaQuantitative Imaging Group, Department of Imaging Science and Technology, Faculty of Applied Sciences, Delft University of Technology, 2628 CJ Delft, Netherlands.
Keith A LidkeDepartment of Physics and Astronomy, University of New Mexico, Albuquerque, NM 87131.
Bernd RiegerQuantitative Imaging Group, Department of Imaging Science and Technology, Faculty of Applied Sciences, Delft University of Technology, 2628 CJ Delft, Netherlands.
David GrunwaldRNA Therapeutics Institute, University of Massachusetts Medical School, Worcester, MA 01605 David.Grunwald@umassmed.edu.

Funding

X-ray Cryst. CoreP50GM082545 · NIGMS · UNIVERSITY OF UTAH · PI SUNDQUIST, WESLEY I. · 2007 to 2018
$51.1M
NIGMS NIH HHS P50 GM082545
6 · The paper itself

Abstract

Single-molecule detection in fluorescence nanoscopy has become a powerful tool in cell biology but can present vexing issues in image analysis, such as limited signal, unspecific background, empirically set thresholds, image filtering, and false-positive detection limiting overall detection efficiency. Here we present a framework in which expert knowledge and parameter tweaking are replaced with a probability-based hypothesis test. Our method delivers robust and threshold-free signal detection with a defined error estimate and improved detection of weaker signals. The probability value has consequences for downstream data analysis, such as weighing a series of detections and corresponding probabilities, Bayesian propagation of probability, or defining metrics in tracking applications. We show that the method outperforms all current approaches, yielding a detection efficiency of >70% and a false-positive detection rate of <5% under conditions down to 17 photons/pixel background and 180 photons/molecule signal, which is beneficial for any kind of photon-limited application. Examples include limited brightness and photostability, phototoxicity in live-cell single-molecule imaging, and use of new labels for nanoscopy. We present simulations, experimental data, and tracking of low-signal mRNAs in yeast cells.

Indexed as

Bayes TheoremComputer SimulationMicroscopy, FluorescenceMolecular ImagingPhotonsProbabilityRNA, MessengerSaccharomyces cerevisiaeRNA, Messenger

Identifiers

PMID26424801
PMCPMC4710236

What OpenQuestion holds

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LicenceCC BY-NC-SA
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.