Simulating structurally variable nuclear pore complexes for microscopy

Author:

Theiss Maria1ORCID,Hériché Jean-Karim2ORCID,Russell Craig1,Helekal David3,Soppitt Alisdair4,Ries Jonas25,Ellenberg Jan2,Brazma Alvis1,Uhlmann Virginie1ORCID

Affiliation:

1. European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI) , Wellcome Genome Campus , Hinxton, CB10 1SD, United Kingdom

2. Cell Biology and Biophysics Unit, European Molecular Biology Laboratory (EMBL) , Heidelberg 69117, Germany

3. Centre for Doctoral Training in Mathematics for Real-World Systems, University of Warwick , Coventry CV4 7AL, United Kingdom

4. EPSRC Centre for Doctoral Training in Modelling of Heterogeneous Systems, University of Warwick , Coventry CV4 7AL, United Kingdom

5. Max Perutz Labs, University of Vienna, Department of Structural and Computational Biology, Dr.-Bohr-Gasse 9, Vienna 1030, Austria

Abstract

Abstract Motivation The nuclear pore complex (NPC) is the only passageway for macromolecules between nucleus and cytoplasm, and an important reference standard in microscopy: it is massive and stereotypically arranged. The average architecture of NPC proteins has been resolved with pseudoatomic precision, however observed NPC heterogeneities evidence a high degree of divergence from this average. Single-molecule localization microscopy (SMLM) images NPCs at protein-level resolution, whereupon image analysis software studies NPC variability. However, the true picture of this variability is unknown. In quantitative image analysis experiments, it is thus difficult to distinguish intrinsically high SMLM noise from variability of the underlying structure. Results We introduce CIR4MICS (‘ceramics’, Configurable, Irregular Rings FOR MICroscopy Simulations), a pipeline that synthesizes ground truth datasets of structurally variable NPCs based on architectural models of the true NPC. Users can select one or more N- or C-terminally tagged NPC proteins, and simulate a wide range of geometric variations. We also represent the NPC as a spring-model such that arbitrary deforming forces, of user-defined magnitudes, simulate irregularly shaped variations. Further, we provide annotated reference datasets of simulated human NPCs, which facilitate a side-by-side comparison with real data. To demonstrate, we synthetically replicate a geometric analysis of real NPC radii and reveal that a range of simulated variability parameters can lead to observed results. Our simulator is therefore valuable to test the capabilities of image analysis methods, as well as to inform experimentalists about the requirements of hypothesis-driven imaging studies. Availability and implementation Code: https://github.com/uhlmanngroup/cir4mics. Simulated data: BioStudies S-BSST1058.

Funder

Wellcome Trust

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

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