Accelerating single molecule localisation microscopy through parallel processing on a high-performance computing cluster
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
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.
Date Issued
2019-02-01
Date Acceptance
2018-11-18
Citation
Journal of Microscopy, 2019, 273 (2), pp.148-160
ISSN
1365-2818
Publisher
Wiley
Start Page
148
End Page
160
Journal / Book Title
Journal of Microscopy
Volume
273
Issue
2
Copyright Statement
© 2018 The Authors. Journal of Microscopy published by JohnWiley & Sons Ltd on behalf of Royal Microscopical Society.
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Sponsor
Medical Research Council
Grant Number
MR/K015834/1
Subjects
Science & Technology
Technology
Microscopy
Automated image analysis
high-performance computing
super-resolved microscopy
SUPERRESOLUTION MICROSCOPY
FLUORESCENCE MICROSCOPY
RESOLUTION LIMIT
ALGORITHM
SOFTWARE
BREAKING
ACCURATE
PLATFORM
BARRIER
0204 Condensed Matter Physics
0912 Materials Engineering
0601 Biochemistry And Cell Biology
Publication Status
Published
Date Publish Online
2018-12-03