Three dimensional particle averaging for structural imaging of macromolecular complexes by localization microscopy
Rieger, B.; Stallinga, S.; Heydarian, H.; Schueder, F.; Jungmann, R.; Ries, J.; Przybylski, A.; Bates, M.; Keller-Findeisen, J.; van Werkhoven, B.
Show abstract
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.
Matching journals
The top 1 journal accounts for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- DeepETPicker: Fast and accurate 3D particle picking for cryo-electron tomography using weakly supervised deep learning 97%
- Overcoming Resolution Attenuation During Tilted Cryo-EM Data Collection 96%
- Photon-free (s)CMOS camera characterization for artifact reduction in high- and super-resolution microscopy 96%
Similar papers in this journal
- In situ structure of bacterial 50S ribosomes at 3.0 A resolution from vitreous sections. 96%
- 3D-Aligner: An advanced computational tool designed to correct image distortion in expansion microscopy for precise 3D reconstitution and quantitative analysis 96%
- cryoTIGER: Deep-Learning Based Tilt Interpolation Generator for Enhanced Reconstruction in Cryo Electron Tomography 95%
Similar papers in this journal
- Three-dimensional structured illumination microscopy with enhanced axial resolution 96%
- Reflective multi-immersion microscope objectives inspired by the Schmidt telescope 94%
- Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.