Research Article: Deformed alignment of super-resolution images for semi-flexible structures

Date Published: March 13, 2019

Publisher: Public Library of Science

Author(s): Xiaoyu Shi, Galo Garcia, Yina Wang, Jeremy F. Reiter, Bo Huang, Thomas Abraham.

http://doi.org/10.1371/journal.pone.0212735

Abstract

Due to low labeling efficiency and structural heterogeneity in fluorescence-based single-molecule localization microscopy (SMLM), image alignment and quantitative analysis is often required to make accurate conclusions on the spatial relationships between proteins. Cryo-electron microscopy (EM) image alignment procedures have been applied to average structures taken with super-resolution microscopy. However, unlike cryo-EM, the much larger cellular structures analyzed by super-resolution microscopy are often heterogeneous, resulting in misalignment. And the light-microscopy image library is much smaller, which makes classification challenging. To overcome these two challenges, we developed a method to deform semi-flexible ring-shaped structures and then align the 3D structures without classification. These algorithms can register semi-flexible structures with an accuracy of several nanometers in short computation time and with greatly reduced memory requirements. We demonstrated our methods by aligning experimental Stochastic Optical Reconstruction Microscopy (STORM) images of ciliary distal appendages and simulated structures. Symmetries, dimensions, and locations of protein complexes in 3D are revealed by the alignment and averaging for heterogeneous, tilted, and under-labeled structures.

Partial Text

In the past decade, the development of localization-based super-resolution microscopy has brought the light microscopy to nanometer scales. Imaging beyond the diffraction limit addressed structural and functional biomedical questions of subcellular organelles that could not be resolved by conventional light microcopy. For example, the in situ dissection of macromolecular protein complexes includes ciliary transition zone [1–3], neuronal synapses [4], nuclear pore complex [5, 6], focal adhesion complex [7], clathrin-coated pits [8], centrosome [9, 10], and the escort complex at viral budding sites [11].

We have demonstrated a deformed 2D alignment algorithm that can accurately align semi-flexible ring structures from both experimental STORM images of ciliary distal appendages and simulated images with high noise and complexity. Information on symmetry and common structural features were efficiently extracted from a few tens of heterogeneous structures. The cross-correlation based alignment algorithm is largely accelerated by DFT and registration in the frequency domain. We also demonstrated that our 3D alignment algorithm can accurately align images of structures tilted in space, using 3D STORM images of ciliary distal appendages, and simulated structures randomly rotated around x axis. For two-color alignment, we simply applied the alignment parameters for the reference channel to the other channel, and achieved sub-pixel alignment of two-color super-resolution images. The two-color algorithm can be easily expanded to multiple color alignment. Use of these algorithms makes accurate registration of super-resolution images within a hundredth of a pixel. Information on the general geometric feature of heterogeneous structures can be extracted with tens of images. All the algorithms including deformed 2D, 3D, and two-color alignments are computationally manageable on a regular desktop computer. The deformed 2D algorithm can be applied to any semi-flexible ring-shaped structures. The 3D and multicolor algorithms will provide substantial advantage for any applications that require aligning and averaging super-resolution images in 3D.

 

Source:

http://doi.org/10.1371/journal.pone.0212735

 

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