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Modelling 3D supramolecular structure from sparse single-molecule localisation microscopy data.

Alistair Curd1, Alexa Cleasby1, Michelle Baird2

  • 1Faculty of Biological Sciences, Astbury Centre for Structural Molecular Biology, School of Molecular and Cellular Biology, University of Leeds, Leeds, UK.

Journal of Microscopy
|October 25, 2023
PubMed
Summary
This summary is machine-generated.

We developed PERPL software to analyze sparse single-molecule localisation microscopy data, revealing molecular organization in cellular structures. This method uncovers patterns even with less than 1% detection efficiency.

Keywords:
Z‐diskrelative positionssingle‐molecule localisation microscopysparse data analysisstructural modellingsuperresolution microscopysymmetry

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Area of Science:

  • Biophysics
  • Cell Biology
  • Microscopy

Background:

  • Conventional microscopy lacks resolution for molecular organization.
  • Single-molecule localisation microscopy (SMLM) offers higher resolution but faces challenges with low detection efficiency ( < 1%) in dense samples.
  • Segmenting individual complexes in SMLM data is difficult.

Purpose of the Study:

  • To develop a computational tool to overcome limitations in SMLM data analysis.
  • To enable the study of molecular organization and conformation within supramolecular complexes.
  • To demonstrate the utility of the developed software on biological samples.

Main Methods:

  • Development of PERPL (Pattern Extraction from Relative Positions of Localisations) software.
  • PERPL assesses model likelihoods based on pairwise localization positions in incomplete SMLM data.
  • Application of PERPL to analyze the 3D lattice of Z-disk proteins in mammalian cardiomyocytes using mEos fluorescent protein.

Main Results:

  • PERPL successfully analyzes SMLM data with very low detection efficiency (< 1%).
  • The software identified known and novel structural features of Z-disk proteins at approximately 20 nm resolution.
  • Demonstrated the capability to resolve fine structural details in challenging biological samples.

Conclusions:

  • PERPL is an effective computational approach for analyzing sparse SMLM data.
  • The software enhances the understanding of molecular organization in complex cellular structures.
  • This method advances the application of SMLM in cell biology and biophysics.