Evidence map›Paper›PMID 27852898›Full record

ArticleMolecular biology of the cell2017

Extracting microtubule networks from superresolution single-molecule localization microscopy data.

Zhen Zhang, Yukako Nishimura, Pakorn Kanchanawong

Abstract read
In one paragraph

Article in Molecular biology of the cell, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.

0numbers the graph read from it
0cells of the map it votes in
32citing 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

32 citing papers in PubMed.

  1. Article
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  5. Article
  6. Article
  7. Review
  8. Therapeutic Potential of Graphene Quantum Dots on Pterygium Cells.Investigative ophthalmology & visual science · 2025
    Article
  9. Automatic extraction of actin networks in plants.PLoS computational biology · 2023
    Article
  10. Article
  11. Article
  12. Tracing and tracking filamentous structures across scales: A systematic review.Computational and structural biotechnology journal · 2023
    Review
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Single-molecule localization microscopy.Nature reviews. Methods primers · 2021
    Article
  19. Super-Resolution Imaging Approaches for Quantifying F-Actin in Immune Cells.Frontiers in cell and developmental biology · 2021
    Review
  20. Review
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

3 authors.

Zhen ZhangMechanobiology Institute, National University of Singapore, 117411 Singapore.
Yukako NishimuraMechanobiology Institute, National University of Singapore, 117411 Singapore.
Pakorn KanchanawongMechanobiology Institute, National University of Singapore, 117411 Singapore biekp@nus.edu.sg.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Microtubule filaments form ubiquitous networks that specify spatial organization in cells. However, quantitative analysis of microtubule networks is hampered by their complex architecture, limiting insights into the interplay between their organization and cellular functions. Although superresolution microscopy has greatly facilitated high-resolution imaging of microtubule filaments, extraction of complete filament networks from such data sets is challenging. Here we describe a computational tool for automated retrieval of microtubule filaments from single-molecule-localization-based superresolution microscopy images. We present a user-friendly, graphically interfaced implementation and a quantitative analysis of microtubule network architecture phenotypes in fibroblasts.

Indexed as

CytoskeletonImage Processing, Computer-AssistedMicroscopyMicroscopy, FluorescenceMicrotubulesSingle Molecule ImagingSoftware

Identifiers

PMID27852898
PMCPMC5231901

What OpenQuestion holds

Textmetadata
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.