Skip to main content
SpringerLink
Log in

LocMoFit quantifies cellular structures in super-resolution data

  • Research Briefing
  • Published:

From Nature Methods

View current issue Submit your manuscript

Localization Model Fit (LocMoFit) is a tool that enables fitting of super-resolution microscopy data to an arbitrary geometric model. The fit extracts quantitative parameters of individual cellular structures, which can be used to investigate dynamic and heterogenous protein assemblies and to create average protein distribution maps.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1: The Localization Model Fit (LocMoFit) principle and applications.

References

  1. Liu, S., Hoess, P. & Ries, J. Super-resolution microscopy for structural cell biology. Annu. Rev. Biophys. 51, 301–326 (2022). A comprehensive review that summarizes the applications of super-resolution microscopy in structural cell biology.

    Article  Google Scholar 

  2. Wu, Y.-L., Tschanz, A., Krupnik, L. & Ries, J. Quantitative data analysis in single-molecule localization microscopy. Trends Cell Biol. 30, 837–851 (2020). A review article that summarizes current quantitative data analysis approaches in single-molecule localization microscopy.

    Article  CAS  Google Scholar 

  3. Ries, J. SMAP: a modular super-resolution microscopy analysis platform for SMLM data. Nat. Methods 17, 870–872 (2020). This is an overview of our super-resolution microscopy analysis platform, which provides easy access to LocMoFit.

    Article  CAS  Google Scholar 

  4. Mund, M. et al. Super-resolution microscopy reveals partial preassembly and subsequent bending of the clathrin coat during endocytosis. Preprint at bioRxiv https://doi.org/10.1101/2021.10.12.463947 (2021). This preprint paper reports a real-life application of LocMoFit to answer biological questions.

Download references

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This is a summary of: Wu, Y.-L. et al. Maximum-likelihood model fitting for quantitative analysis of SMLM data. Nat. Methods https://doi.org/10.1038/s41592-022-01676-z (2022).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

LocMoFit quantifies cellular structures in super-resolution data. Nat Methods 20, 44–45 (2023). https://doi.org/10.1038/s41592-022-01696-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41592-022-01696-9

  • Springer Nature America, Inc.

Search

Navigation