Inferring the organization of fluorescently labeled nanosized structures from single molecule localization microscopy (SMLM) data, typically obscured by stochastic noise and background, remains challenging. To overcome this, we developed a method to extract high-resolution ordered features from SMLM data that requires only a low fraction of targets to be localized with high precision. First, experimentally measured localizations are analyzed to produce relative position distributions (RPDs). Next, model RPDs are constructed using hypotheses of how the molecule is organized. Finally, a statistical comparison is used to select the most likely model. This approach allows pattern recognition at sub-1% detection efficiencies for target molecules, in large and heterogeneous samples and in 2D and 3D data sets. As a proof-of-concept, we infer ultrastructure of Nup107 within the nuclear pore, DNA origami structures, and α-actinin-2 within the cardiomyocyte Z-disc and assess the quality of images of centrioles to improve the averaged single-particle reconstruction.

SUMMARYAtrial fibrillation (AF) is a common cardiac disease of genuine clinical concern with high rates of morbidity, leading to major personal and National Health Service costs. Computer modelling of AF using biophysically detailed cellular models with realistic 3D anatomical geometry allows investigation of the underlying ionic mechanisms in far more detail than with experimental physiology. We have developed a 3D virtual human atrium that combines detailed cellular electrophysiology including ion channel kinetics and homeostasis of ionic concentrations with anatomical details. The segmented anatomical structure and the multivariable nature of the system make the 3D simulations of AF computationally large and intensive. * Computational demands are such that a full problem-solving environment requires access to resources of high-performance computing (HPC), high-performance visualization (HPV), remote data repositories and backend infrastructure. This is a classic example of eScience and Grid-enabled computing. This study was carried out using multiple processor shared memory systems and massively parallel distributed memory systems. With the envisaged increase in anatomical and molecular detail in our cardiac models the requirement for HPC resources is predicted to increase many fold (∼ 1-10 teraflops). Distributed computing is essential, both through massively parallel systems (a single supercomputer) and multiple parallel systems made accessible through the Grid. Analysis and interpretation of results are enhanced by HPV, which in itself is a large data computing aspect of cardiac modelling.

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We present a method for extracting high-resolution ordered features from localisation microscopy data by analysis of relative molecular positions in 2D or 3D. This approach allows pattern recognition at sub-1% protein detection efficiencies, in large and heterogeneous samples, and in 2D and 3D datasets. We used this method to infer ultrastructure of the nuclear pore, the cardiomyocyte Z-disk, DNA origami structures and the centriole. of Biomedical Imaging and Bioengineering, US National Institutes of Health. The dSTORM system was funded by alumnus M. Beverley, in support of the University of Leeds 'making a world of difference' campaign. R.J. acknowledges support by the DFG through the Emmy Noether Program (DFG JU 2957/1-1), the ERC through an ERC Starting Grant (MolMap, Grant agreement number 680241), the Max Planck Society, the Max Planck Foundation and the Center for Nanoscience (CeNS). T.S. acknowledges support from the DFG through the Graduate School of Quantitative Biosciences Munich (QBM). Isolated cardiomyocytes were a kind gift from the Steele Group, University of Leeds. We thank Ulf Matti and Philipp Hoess for sample preparation and imaging of the Nup107 cells, and Niccolò Banterle for the centriole sample preparation. We would also like to acknowledge Michael W. Davidson for his contributions to the development of the mEos3.2 constructs. Author contributionsA.Curd conceived and implemented the analysis approach, and developed software. J.L. developed software. R.H. and A.Cleasby imaged Z-disk proteins in 3D PALM. B.R. imaged ACTN2 Affimer in 3D dSTORM. C.H and B.R. crystallised the Affimer-CH domain construct and solved its structure. M.B. and M.P. developed fluorescent protein constructs. H.T., H.S. and M.P. developed 3D PALM labelling and imaging techniques. C.S and S.M. provided the Cep152 localisation data and conceived the particle quality assessment concept. J.R. provided the 3D Nup107 localisation data. M.P. conceived the Z-disk protein experiment and acquired confocal data on Z-disk protein labelling. A.Curd and M.P. wrote the manuscript with input from all other authors.

Author summary Improvements in technology often drive scientific discovery. Therefore, research requires sustained investment in the latest equipment and training for the researchers who are going to use it. Prioritising and administering infrastructure investment is challenging because future needs are difficult to predict. In the past, highly computationally demanding research was associated primarily with particle physics and astronomy experiments. However, as biology becomes more quantitative and bioscientists generate more and more data, their computational requirements may ultimately exceed those of physical scientists. Computation has always been central to bioinformatics, but now imaging experiments have rapidly growing data processing and storage requirements. There is also an urgent need for new modelling and simulation tools to provide insight and understanding of these biophysical experiments. Bioscience communities must work together to provide the software and skills training needed in their areas. Research-active institutions need to recognise that computation is now vital in many more areas of discovery and create an environment where it can be embraced. The public must also become aware of both the power and limitations of computing, particularly with respect to their health and personal data.

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