Nuclear pore complex at super resolution

It is very difficult to decipher the spatial arrangement of proteins in a given complex. The most common methods are X-ray crystallography (which is very difficult to achieve for single proteins, let alone complexes), cryo-electron microscopy  and electron tomography (ditto) or  biochemical approaches (e.g. which protein domains interact with each other).  A paper published in today’s Science, shows a different approach, using super-resolution microscopy.

The Nuclear Pore Complex (NPC) is a large complex that enables movement of large particles in and out of the nucleus. Though electron tomography previously showed the general structure of NPC, the spatial order of individual proteins was unclear. Here, the researchers used stochastic optical reconstruction microscopy (STORM), a super resolution microscopy method, of either antibody or nanobody labeled samples. The idea of STORM is to get to the point where fluorophores begin to photobleach, and then take a thousands of images very fast (here they used 100 frames/second). the idea is that only a few fluorophores will lit up in each image, thus reducing background.

NPC viewed by regular confocal microscopy (A) or STORM (B). Scale bars indicate 3 μm and 300 nm, respectively. (C)- images after quality control. (D) - average "ring". (E) Normalized mean radius. (F) Precision of determining the radial position was estimated by cross-validation, performed by averaging 17 sets of 500 pores each (red line marks the median). The standard deviation of the distribution is 0.1 nm. The whiskers on the box plot encompass 99.3% of the distribution.  Source: Szymborska et al. (2013) Science: Vol. 341  no. 6146  pp. 655-658

NPCs viewed by regular confocal microscopy (A) or STORM (B). Scale bars indicate 3 μm and 300 nm, respectively. (C)- images after quality control. (D) – average “ring”. (E) Normalized mean radius. (F) Precision of determining the radial position was estimated by cross-validation, performed by averaging 17 sets of 500 pores each (red line marks the median). The standard deviation of the distribution is 0.1 nm. The whiskers on the box plot encompass 99.3% of the distribution. Source: Szymborska et al. (2013) Science: Vol. 341 no. 6146 pp. 655-658

Furthermore, for each protein, they were able to average thousands of NPCs from the same cell, thus improving statistical analysis compared to cryo-EM (which usually compares only a few good images). They claim to had a standard deviation of ~0.1nm(!).

Since the NPC is ring shaped, they calculated the radius of the ring for each protein they tested. Thus they were able to spatially align the proteins based on their distance from the center of the ring. systematic labeling of different components allowed them to create a model of the NPC scaffold structure.

The potential for their method is big, particularly for structures that are abundant in cells (ribosomes, transcription pre-initiation complex, gated channels, photosynthetic centers – choose your favorite complex) to allow for good statistical analysis.

Though in this paper they used fixed cells, it would be most interesting to implement similar methods to live cells, and view structural changes in real time.

ResearchBlogging.orgSzymborska A, de Marco A, Daigle N, Cordes VC, Briggs JA, & Ellenberg J (2013). Nuclear Pore Scaffold Structure Analyzed by Super-Resolution Microscopy and Particle Averaging. Science (New York, N.Y.), 341 (6146), 655-658 PMID: 23845946

Ries J, Kaplan C, Platonova E, Eghlidi H, & Ewers H (2012). A simple, versatile method for GFP-based super-resolution microscopy via nanobodies. Nature methods, 9 (6), 582-4 PMID: 22543348

Schermelleh L, Heintzmann R, & Leonhardt H (2010). A guide to super-resolution fluorescence microscopy. The Journal of cell biology, 190 (2), 165-75 PMID: 20643879

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