Publications

Detecting structural heterogeneity in single-molecule localization microscopy data

Published in Nature Communications, 2021

Particle fusion for single molecule localization microscopy improves signal-to-noise ratio and overcomes underlabeling, but ignores structural heterogeneity or conformational variability. We present a-priori knowledge-free unsupervised classification of structurally different particles employing the Bhattacharya cost function as dissimilarity metric. We achieve 96% classification accuracy on mixtures of up to four different DNA-origami structures, detect rare classes of origami occuring at 2% rate, and capture variation in ellipticity of nuclear pore complexes.

Recommended citation: Huijben, T.A., Heydarian, H., Auer, A. et al. Detecting structural heterogeneity in single-molecule localization microscopy data. Nat Commun 12, 3791 (2021). https://www.nature.com/articles/s41467-021-24106-8

3D particle averaging and detection of macromolecular symmetry in localization microscopy

Published in Nature Communications, 2021

Single molecule localization microscopy offers in principle resolution down to the molecular level, but in practice this is limited primarily by incomplete fluorescent labeling of the structure. This missing information can be completed by merging information from many structurally identical particles. In this work, we present an approach for 3D single particle analysis in localization microscopy which hugely increases signal-to-noise ratio and resolution and enables determining the symmetry groups of macromolecular complexes. Our method does not require a structural template, and handles anisotropic localization uncertainties. We demonstrate 3D reconstructions of DNA-origami tetrahedrons, Nup96 and Nup107 subcomplexes of the nuclear pore complex acquired using multiple single molecule localization microscopy techniques, with their structural symmetry deducted from the data.

Recommended citation: Hamidreza, Heydarian et al. (2021). "3D particle averaging and detection of macromolecular symmetry in localization microscopy." Nature Communications. https://www.nature.com/articles/s41467-021-22006-5

DATA FUSION IN LOCALIZATION MICROSCOPY

Published in Computational Modeling: From Chemistry to Materials to Biology, 2021

The following sections are included:

Recommended citation: HAMIDREZA HEYDARIAN, MARK BATES, FLORIAN SCHUEDER, RALF JUNGMANN, SJOERD STALLINGA, and BERND RIEGER, Computational Modeling: From Chemistry to Materials to Biology. February 2021, 201-204 https://doi.org/10.1142/9789811228216_0024

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

Published in biorxiv, 2019

We present an approach for 3D particle fusion in localization microscopy which dramatically increases signal-to-noise ratio and resolution in single particle analysis. Our method does not require a structural template, and properly handles anisotropic localization uncertainties. We demonstrate 3D particle reconstructions of the Nup107 subcomplex of the nuclear pore complex (NPC), cross-validated using multiple localization microscopy techniques, as well as two-color 3D reconstructions of the NPC, and reconstructions of DNA-origami tetrahedrons.

Recommended citation: Hamidreza, Heydarian et al. (2019). "Three dimensional particle averaging for structural imaging of macromolecular complexes by localization microscopy." biorxiv. https://www.biorxiv.org/content/10.1101/837575v1

Template-free 2D particle fusion in localization microscopy

Published in Nature Methods, 2018

Methods that fuse multiple localization microscopy images of a single structure can improve signal-to-noise ratio and resolution, but they generally suffer from template bias or sensitivity to registration errors. We present a template-free particle-fusion approach based on an all-to-all registration that provides robustness against individual misregistrations and underlabeling. We achieved 3.3-nm Fourier ring correlation (FRC) image resolution by fusing 383 DNA origami nanostructures with 80% labeling density, and 5.0-nm resolution for structures with 30% labeling density.

Recommended citation: Heydarian, Hamidreza et al. (2018). "Template-free 2D particle fusion in localization microscopy." Nature Methods. 15. https://www.nature.com/articles/s41592-018-0136-6