Accelerating single molecule localization microscopy through parallel processing on a high‐performance computing cluster

• Published in: Journal of microscopy (Oxford) 2019-02, Vol.273 (2), p.148-160
• Main Authors: MUNRO, I., GARCÍA, E., YAN, M., GULDBRAND, S., KUMAR, S., KWAKWA, K., DUNSBY, C., NEIL, M.A.A., FRENCH, P.M.W.
• Format: Article
• Language: English
• Subjects: Algorithms; Automated image analysis; Bright spots; Clusters; Computation; Computer programs; Computers; Confocal microscopy; Data analysis; Data processing; Diffraction; Drug discovery; Emitters; Experiments; Field of view; Fluorescence; high‐performance computing; Hot Topic—fast‐tracked short communication; Image Processing, Computer-Assisted - methods; Iterative algorithms; Iterative methods; Labeling; Localization; Mapping; Microscopy; Optical microscopy; Parallel processing; Personal computers; Scanning microscopy; Single Molecule Imaging - methods; Software; Software development tools; super‐resolved microscopy; Workflow; Workstations
• ISSN: 0022-2720; 1365-2818; 1365-2818
• DOI: 10.1111/jmi.12772

Summary Super‐resolved microscopy techniques have revolutionized the ability to study biological structures below the diffraction limit. Single molecule localization microscopy (SMLM) techniques are widely used because they are relatively straightforward to implement and can be realized at relatively low cost, e.g. compared to laser scanning microscopy techniques. However, while the data analysis can be readily undertaken using open source or other software tools, large SMLM data volumes and the complexity of the algorithms used often lead to long image data processing times that can hinder the iterative optimization of experiments. There is increasing interest in high throughput SMLM, but its further development and application is inhibited by the data processing challenges. We present here a widely applicable approach to accelerating SMLM data processing via a parallelized implementation of ThunderSTORM on a high‐performance computing (HPC) cluster and quantify the speed advantage for a four‐node cluster (with 24 cores and 128 GB RAM per node) compared to a high specification (28 cores, 128 GB RAM, SSD‐enabled) desktop workstation. This data processing speed can be readily scaled by accessing more HPC resources. Our approach is not specific to ThunderSTORM and can be adapted for a wide range of SMLM software. Lay Description Optical microscopy is now able to provide images with a resolution far beyond the diffraction limit thanks to relatively new super‐resolved microscopy (SRM) techniques, which have revolutionized the ability to study biological structures. One approach to SRM is to randomly switch on and off the emission of fluorescent molecules in an otherwise conventional fluorescence microscope. If only a sparse subset of the fluorescent molecules labelling a sample can be switched on at a time, then each emitter will be, on average, spaced further apart than the diffraction‐limited resolution of the conventional microscope and the separate bright spots in the image corresponding to each emitter can be localised to high precision by finding the centre of each feature using a computer program. Thus, a precise map of the emitter positions can be recorded by sequentially mapping the localisation of different subsets of emitters as they are switched on and others switched off. Typically, this approach, described as single molecule localisation microscopy (SMLM), results in large image data sets that can take many minutes to hours to process, depending