Evidence map›Paper›PMID 28494949›Full record

ArticleBiophysical journal2017

Molecular Counting with Localization Microscopy: A Bayesian Estimate Based on Fluorophore Statistics.

Daniel Nino, Nafiseh Rafiei, Yong Wang, Anton Zilman, Joshua N Milstein

Abstract read
In one paragraph

Article in Biophysical journal, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. The Decade of Super-Resolution Microscopy of the Presynapse.Frontiers in synaptic neuroscience · 2020
    Review
  9. Article
  10. Single-molecule localization microscopy as nonlinear inverse problem.Proceedings of the National Academy of Sciences of the United States of America · 2019
    Article
  11. 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

5 authors.

Daniel NinoDepartment of Physics, University of Toronto, Toronto, Ontario, Canada; Department of Chemical and Physical Sciences, University of Toronto Mississauga, Mississauga, Ontario, Canada.
Nafiseh RafieiDepartment of Chemical and Physical Sciences, University of Toronto Mississauga, Mississauga, Ontario, Canada; Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada.
Yong WangDepartment of Physics, University of Arkansas, Fayetteville, Arkansas.
Anton ZilmanDepartment of Physics, University of Toronto, Toronto, Ontario, Canada; Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada.
Joshua N MilsteinDepartment of Physics, University of Toronto, Toronto, Ontario, Canada; Department of Chemical and Physical Sciences, University of Toronto Mississauga, Mississauga, Ontario, Canada; Institute of Biomaterials and Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada. Electronic address: josh.milstein@utoronto.ca.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Superresolved localization microscopy has the potential to serve as an accurate, single-cell technique for counting the abundance of intracellular molecules. However, the stochastic blinking of single fluorophores can introduce large uncertainties into the final count. Here we provide a theoretical foundation for applying superresolved localization microscopy to the problem of molecular counting based on the distribution of blinking events from a single fluorophore. We also show that by redundantly tagging single molecules with multiple, blinking fluorophores, the accuracy of the technique can be enhanced by harnessing the central limit theorem. The coefficient of variation then, for the number of molecules M estimated from a given number of blinks B, scales like ∼1/N

Indexed as

BacteriaBayes TheoremMicroscopyModels, TheoreticalMolecular ImagingPlasmidsStochastic Processes

Identifiers

PMID28494949
PMCPMC5425379

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.