Evidence map›Paper›PMID 35663461›Full record

ArticleNature reviews. Methods primers2021

Single-molecule localization microscopy.

Mickaël Lelek, Melina T Gyparaki, Gerti Beliu, Florian Schueder, Juliette Griffié, Suliana Manley, Ralf Jungmann, Markus Sauer, Melike Lakadamyali, Christophe Zimmer

Abstract read
In one paragraph

Article in Nature reviews. Methods primers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 408 papers.

0numbers the graph read from it
0cells of the map it votes in
408citing 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

408 citing papers in PubMed.

  1. Review
  2. Article
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  7. Article
  8. Multiscale modeling primer: Focus on chromatin and epigenetics.The Journal of biological chemistry · 2026
    Review
  9. Article
  10. Article
  11. Article
  12. Review
  13. Article
  14. Review
  15. Review
  16. Review
  17. Article
  18. Article
  19. Deep Learning for Localization Microscopy in 2D and 3D.Accounts of chemical research · 2026
    Article
  20. Article

348 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

10 authors.

Mickaël LelekImaging and Modeling Unit, Department of Computational Biology, Institut Pasteur, Paris, France.
Melina T GyparakiDepartment of Biology, University of Pennsylvania, Philadelphia, PA, USA.
Gerti BeliuDepartment of Biotechnology and Biophysics Biocenter, University of Würzburg, Würzburg, Germany.
Florian SchuederFaculty of Physics and Center for Nanoscience, Ludwig Maximilian University, Munich, Germany.ORCID 0000-0003-3412-5066
Juliette GriffiéLaboratory of Experimental Biophysics, Institute of Physics, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
Suliana ManleyLaboratory of Experimental Biophysics, Institute of Physics, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.ORCID 0000-0002-4755-4778
Ralf JungmannFaculty of Physics and Center for Nanoscience, Ludwig Maximilian University, Munich, Germany.ORCID 0000-0003-4607-3312
Markus SauerDepartment of Biotechnology and Biophysics Biocenter, University of Würzburg, Würzburg, Germany.ORCID 0000-0002-1692-3219
Melike LakadamyaliDepartment of Physiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Christophe ZimmerImaging and Modeling Unit, Department of Computational Biology, Institut Pasteur, Paris, France.ORCID 0000-0001-9910-1589

Funding

Calibration nanotemplates as universal standards for determining protein copy number in super-resolution microscopyR01GM133842 · NIGMS · UNIVERSITY OF PENNSYLVANIA · PI LAKADAMYALI, MELIKE · 2019 to 2022
$1.7M
NIGMS NIH HHS R01 GM133842
6 · The paper itself

Abstract

Single-molecule localization microscopy (SMLM) describes a family of powerful imaging techniques that dramatically improve spatial resolution over standard, diffraction-limited microscopy techniques and can image biological structures at the molecular scale. In SMLM, individual fluorescent molecules are computationally localized from diffraction-limited image sequences and the localizations are used to generate a super-resolution image or a time course of super-resolution images, or to define molecular trajectories. In this Primer, we introduce the basic principles of SMLM techniques before describing the main experimental considerations when performing SMLM, including fluorescent labelling, sample preparation, hardware requirements and image acquisition in fixed and live cells. We then explain how low-resolution image sequences are computationally processed to reconstruct super-resolution images and/or extract quantitative information, and highlight a selection of biological discoveries enabled by SMLM and closely related methods. We discuss some of the main limitations and potential artefacts of SMLM, as well as ways to alleviate them. Finally, we present an outlook on advanced techniques and promising new developments in the fast-evolving field of SMLM. We hope that this Primer will be a useful reference for both newcomers and practitioners of SMLM.

Identifiers

PMID35663461
PMCPMC9160414

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Registered trials

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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.