Particle fusion in localization microscopy

Research output: ThesisDissertation (TU Delft)

66 Downloads (Pure)

Abstract

Single molecule localization microscopy (SMLM) shows promise for quantitative structural analysis of subcellular complexes and organelles with a resolution well below the diffraction limit. This superresolution microscopy technique relies on the blinking events of fluorescent molecules that labeled the structure of interest and are spatiotemporally spread over the entire field of view and time. Once hundred thousands frames of these sparse events are recorded, single molecule positions are localized with nanometer precision to form a 2D/3D point set of coordinates. Therefore, SMLM images are not conventional pixelated images but rather spatial point patterns. Photon scarcity and incomplete labeling of the imaged structure, however, limit the resolution that can possibly be achieved by means of SMLM. Moreover, due to experimental limitations the axial resolution is typically ~2-3 times worse than the lateral resolution in conventional setups. Inspired by single particle analysis (SPA) in cryo-electron microscopy (cryo-EM), proper alignment of repeated structures ("particle fusion") in a 2D/3D SMLM measurement can overcome these limiting factors and so push for isotropic resolution. The existing approaches for particle fusion in SMLM can be classified into customized routines that are borrowed from SPA in EM or methods that use strong prior knowledge about the structure to be reconstructed. While the first approaches are completely ignoring the differences in image formation model between EM and SMLM, the second ones are highly prone to the template-bias problem. In this thesis, a dedicated particle fusion pipeline for 2D/3D SMLM data is proposed. The approach properly considers the pointillistic nature of the SMLM modality and takes into account the localization uncertainties. Furthermore, while it does not require any prior knowledge about the underlying structure of the particles, it can incorporate certain features such as symmetry into the fusion process. Owing to the novel all-to-all registration scheme, the application of the devised pipeline on experimental data with very poor labeling density has been successfully demonstrated. The requirements for successful particle fusion for different SMLM modalities, namely PAINT and STORM, have been characterized through extensive study on 2D and 3D experimental and simulation data. In 2D, an FRC resolution of 3.3 nm on DNA-origami nanostructures has been achieved, and, in 3D, it was demonstrated how the combination of SMLM as a light microscopy technique and a computational approach enables structural analysis of the Nuclear Pore Complex. Future advances of SMLM rely highly on computational routines after data acquisition. Advanced data analysis techniques such as particle fusion can help pushing the boundaries of structural biology using light microscopy.
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • Delft University of Technology
Supervisors/Advisors
  • Rieger, B., Supervisor
  • Stallinga, S., Supervisor
Award date17 Mar 2020
Place of PublicationDelft
DOIs
Publication statusIn preparation - 17 Mar 2020

Keywords

  • single molecule localization microscopy
  • particle averaging
  • particle fusion
  • SMLM

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  • Research Output

    Three dimensional particle averaging for structural imaging of macromolecular complexes by localization microscopy

    Heydarian, H., Przybylski, A., Schreuder, F., Jungmann, R., Van Werkhoven, B., Keller-Findeisen, J., Ries, J., Stallinga, S., Bates, M. & Rieger, B., 2020, In : bioRxiv.

    Research output: Contribution to journalArticleScientific

  • Template-free 2D particle fusion in localization microscopy

    Heydarian, H., Schueder, F., Strauss, M. T., van Werkhoven, B., Fazel, M., Lidke, K. A., Jungmann, R., Stallinga, S. & Rieger, B., 2018, In : Nature Methods. 15, p. 781-784

    Research output: Contribution to journalArticleScientificpeer-review

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    69 Downloads (Pure)

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