Evidence map›Paper›PMID 30846705›Full record

ArticleScientific reports2019

MTrack: Automated Detection, Tracking, and Analysis of Dynamic Microtubules.

Varun Kapoor, William G Hirst, Christoph Hentschel, Stephan Preibisch, Simone Reber

Abstract read
In one paragraph

Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing 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

22 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
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  8. Article
  9. Quantifying Protein Dynamics by Kymograph Analysis.Methods in molecular biology (Clifton, N.J.) · 2024
    Article
  10. Automatic extraction of actin networks in plants.PLoS computational biology · 2023
    Article
  11. Article
  12. Article
  13. Tracing and tracking filamentous structures across scales: A systematic review.Computational and structural biotechnology journal · 2023
    Review
  14. Article
  15. Article
  16. Review
  17. STAR protocols · 2020
    Article
  18. Article
  19. Article
  20. CytoSeg 2.0: automated extraction of actin filaments.Bioinformatics (Oxford, England) · 2020
    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.

Varun KapoorBerlin Institute for Medical Systems Biology (BIMSB), Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), 13125, Berlin, Germany.
William G HirstQuantitative Biology Lab, IRI Life Sciences, Humboldt-Universität zu Berlin, 10115, Berlin, Germany.
Christoph HentschelQuantitative Biology Lab, IRI Life Sciences, Humboldt-Universität zu Berlin, 10115, Berlin, Germany.
Stephan PreibischBerlin Institute for Medical Systems Biology (BIMSB), Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), 13125, Berlin, Germany. stephan.preibisch@mdc-berlin.de.ORCID 0000-0002-0276-494X
Simone ReberQuantitative Biology Lab, IRI Life Sciences, Humboldt-Universität zu Berlin, 10115, Berlin, Germany. simone.reber@iri-lifesciences.de.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Microtubules are polar, dynamic filaments fundamental to many cellular processes. In vitro reconstitution approaches with purified tubulin are essential to elucidate different aspects of microtubule behavior. To date, deriving data from fluorescence microscopy images by manually creating and analyzing kymographs is still commonplace. Here, we present MTrack, implemented as a plug-in for the open-source platform Fiji, which automatically identifies and tracks dynamic microtubules with sub-pixel resolution using advanced objection recognition. MTrack provides automatic data interpretation yielding relevant parameters of microtubule dynamic instability together with population statistics. The application of our software produces unbiased and comparable quantitative datasets in a fully automated fashion. This helps the experimentalist to achieve higher reproducibility at higher throughput on a user-friendly platform. We use simulated data and real data to benchmark our algorithm and show that it reliably detects, tracks, and analyzes dynamic microtubules and achieves sub-pixel precision even at low signal-to-noise ratios.

Indexed as

SoftwareAlgorithmsImage Processing, Computer-AssistedMicroscopy, FluorescenceMicrotubulesReproducibility of ResultsSignal-To-Noise Ratio

Identifiers

PMID30846705
PMCPMC6405942

What OpenQuestion holds

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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.