Data from: Automated single particle detection and tracking for large microscopy datasets

  • Rhodri S Wilson (Creator)
  • Lei Yang (Creator)
  • Alison R. Dun (Creator)
  • Annya M. Smyth (Creator)
  • Rory R. Duncan (Creator)
  • Colin Rickman (Creator)
  • Weiping Lu (Creator)



Recent advances in optical microscopy have enabled the acquisition of very large datasets from living cells with unprecedented spatial and temporal resolutions. Our ability to process these datasets now plays an essential role in order to understand many biological processes. In this paper, we present an automated particle detection algorithm capable of operating in low signal-to-noise fluorescence microscopy environments and handling large datasets. When combined with our particle linking framework, it can provide hitherto intractable quantitative measurements describing the dynamics of large cohorts of cellular components from organelles to single molecules. We begin with validating the performance of our method on synthetic image data, and then extend the validation to include experiment images with ground truth. Finally, we apply the algorithm to two single-particle-tracking photo-activated localization microscopy biological datasets, acquired from living primary cells with very high temporal rates. Our analysis of the dynamics of very large cohorts of 10 000 s of membrane-associated protein molecules show that they behave as if caged in nanodomains. We show that the robustness and efficiency of our method provides a tool for the examination of single-molecule behaviour with unprecedented spatial detail and high acquisition rates.

Data Citation

Wilson, R.S. (2016), Data from: Automated single particle detection and tracking for large microscopy datasets, Dryad, Dataset, 10.5061/dryad.6kg29
Date made available22 Apr 2016

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