Evidence map›Paper›PMID 33191949›Full record

ReviewJournal of physics D: Applied physics2019

NanoJ: a high-performance open-source super-resolution microscopy toolbox.

Romain F Laine, Kalina L Tosheva, Nils Gustafsson, Robert D M Gray, Pedro Almada, David Albrecht, Gabriel T Risa, Fredrik Hurtig, Ann-Christin Lindås, Buzz Baum and 5 more

Abstract readReview
In one paragraph

Review in Journal of physics D: Applied physics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 98 papers.

0numbers the graph read from it
0cells of the map it votes in
98citing 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

98 citing papers in PubMed.

  1. Article
  2. Review
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  4. Label-free viability assessment ofBiomedical optics express · 2026
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  18. Review
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38 more citing papers are in PubMed but not listed here.

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

15 authors.

Romain F LaineMRC-Laboratory for Molecular Cell Biology, University College London, London, United Kingdom.ORCID 0000-0002-2151-4487
Kalina L ToshevaMRC-Laboratory for Molecular Cell Biology, University College London, London, United Kingdom.
Nils GustafssonMRC-Laboratory for Molecular Cell Biology, University College London, London, United Kingdom.
Robert D M GrayMRC-Laboratory for Molecular Cell Biology, University College London, London, United Kingdom.
Pedro AlmadaMRC-Laboratory for Molecular Cell Biology, University College London, London, United Kingdom.
David AlbrechtMRC-Laboratory for Molecular Cell Biology, University College London, London, United Kingdom.ORCID 0000-0001-9455-1134
Gabriel T RisaMRC-Laboratory for Molecular Cell Biology, University College London, London, United Kingdom.
Fredrik HurtigDepartment of Molecular Biosciences, The Wenner-Gren Institute, Stockholm University, Stockholm, Sweden.
Ann-Christin LindåsDepartment of Molecular Biosciences, The Wenner-Gren Institute, Stockholm University, Stockholm, Sweden.ORCID 0000-0002-0368-3295
Buzz BaumMRC-Laboratory for Molecular Cell Biology, University College London, London, United Kingdom.ORCID 0000-0002-9201-6186
Jason MercerMRC-Laboratory for Molecular Cell Biology, University College London, London, United Kingdom.ORCID 0000-0003-1466-9541
Christophe LeterrierCNRS, INP, Institute of Neurophysiopathology, NeuroCyto, Aix-Marseille University, Marseille, France.ORCID 0000-0002-2957-2032
Pedro M PereiraMRC-Laboratory for Molecular Cell Biology, University College London, London, United Kingdom.ORCID 0000-0002-1426-9540
Siân CulleyMRC-Laboratory for Molecular Cell Biology, University College London, London, United Kingdom.ORCID 0000-0003-2112-0143
Ricardo HenriquesMRC-Laboratory for Molecular Cell Biology, University College London, London, United Kingdom.ORCID 0000-0002-2043-5234

Funding

Biotechnology and Biological Sciences Research Council BB/M022374/1Biotechnology and Biological Sciences Research Council BB/P001440/1Medical Research Council MR/K015826/1Wellcome Trust
6 · The paper itself

Abstract

Super-resolution microscopy (SRM) has become essential for the study of nanoscale biological processes. This type of imaging often requires the use of specialised image analysis tools to process a large volume of recorded data and extract quantitative information. In recent years, our team has built an open-source image analysis framework for SRM designed to combine high performance and ease of use. We named it NanoJ-a reference to the popular ImageJ software it was developed for. In this paper, we highlight the current capabilities of NanoJ for several essential processing steps: spatio-temporal alignment of raw data (NanoJ-Core), super-resolution image reconstruction (NanoJ-SRRF), image quality assessment (NanoJ-SQUIRREL), structural modelling (NanoJ-VirusMapper) and control of the sample environment (NanoJ-Fluidics). We expect to expand NanoJ in the future through the development of new tools designed to improve quantitative data analysis and measure the reliability of fluorescent microscopy studies.

Indexed as

Fijifluidicsimage analysisImageJimage quality assessmentsingle-particle analysissuper-resolution microscopy

Identifiers

PMID33191949
PMCPMC7655149

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.