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Detecting continuous structural heterogeneity in single molecule localization microscopy data with a point cloud

Sobhan Haghparast1, Yi Zhang1, Qian Tao1

  • 1Department of Imaging Physics, Delft University of Technology, Delft, 2628, The Netherlands.

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|December 3, 2025
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Summary
This summary is machine-generated.

This study introduces a novel Point Cloud Variational Auto-Encoder to analyze single molecule localization microscopy data. The method efficiently detects nanoscale variations in macromolecular complexes, improving image quality and structural analysis.

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Area of Science:

  • Biophysics
  • Structural Biology
  • Computational Biology

Background:

  • Single molecule localization microscopy (SMLM) struggles with low signal-to-noise ratios for macromolecular complexes due to limited labeling and photon counts.
  • Particle fusion enhances SMLM image quality but assumes structural homogeneity, which is often not the case.
  • Heterogeneity in SMLM data can stem from geometric variations or different conformational states of the structures.

Purpose of the Study:

  • To develop a computational method for detecting multiple modes of variation in SMLM datasets.
  • To analyze 2D and 3D localization data directly, bypassing the need for pixelated images.
  • To quantify nanoscale structural heterogeneity in macromolecular complexes.

Main Methods:

  • Introduction of a Point Cloud Variational Auto-Encoder (PC-VAE) designed for 2D and 3D localization data.
  • The PC-VAE operates directly on lists of localizations, offering computational efficiency.
  • Linear scaling with dataset size and rapid network training (four epochs) enable analysis in minutes.

Main Results:

  • The PC-VAE successfully detected nanoscale radius variations in 2D Nuclear Pore Complex data.
  • Height variations were identified in 3D DNA origami tetrahedron datasets.
  • Both radius and height variations, on the few-nanometer scale, were detected in 3D Nuclear Pore Complex data.

Conclusions:

  • The developed PC-VAE method effectively detects subtle, nanoscale structural heterogeneity in macromolecular complexes using SMLM data.
  • The computational approach provides a rapid and scalable solution for analyzing complex biological structures.
  • This method advances the capability to characterize conformational flexibility and geometric variations in biological assemblies.