Cited by 51Cell Image Analysis TechniquesAdvanced Fluorescence Microscopy TechniquesCellular transport and secretion

Deep learning-assisted analysis of single-particle tracking for automated correlation between diffusion and function

Open access
Kæstel-Hansen, Jacob, de Sautu, Marilina, Saminathan, Anand, Scanavachi, Gustavo, Bango Da Cunha Correia, Ricardo F., Juma Nielsen, Annette, Bleshøy, Sara Vogt, Tsolakidis, Konstantinos +3 more·2025·Nature Methods

Published version of a preprint

Abstract

Abstract Sub-cellular diffusion in living systems reflects cellular processes and interactions. Recent advances in optical microscopy allow the tracking of this nanoscale diffusion of individual objects with an unprecedented level of precision. However, the agnostic and automated extraction of functional information from the diffusion of molecules and organelles within the sub-cellular environment, is labor-intensive and poses a significant challenge. Here we introduce DeepSPT, a deep learning framework to interpret the diffusional 2D or 3D temporal behavior of objects in a rapid and efficient manner, agnostically. Demonstrating its versatility, we have applied DeepSPT to automated mapping of the early events of viral infections, identifying distinct types of endosomal organelles, and clathrin-coated pits and vesicles with up to 95% accuracy and within seconds instead of weeks. The fact that DeepSPT effectively extracts biological information from diffusion alone illustrates that besides structure, motion encodes function at the molecular and subcellular level.

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Authors
Kæstel-Hansen, J., de Sautu, M., Saminathan, A., Scanavachi, G., Bango Da Cunha Correia, R. F., Juma Nielsen, A., Bleshøy, S. V., Tsolakidis, K., Boomsma, W., Kirchhausen, T., & Hatzakis, N. S.
Affiliations
  • Boston Children's Hospital (US)
  • University of Copenhagen (DK)
Corresponding author
  • Kirchhausen, Tomas
  • Hatzakis, Nikos S.
Venue
Nature Methods
Published
2025-05
Volume
22
Issue
5
Pages
1091-1100
ISSN
  • 1548-7091 (ISSN-L)
  • 1548-7105
OpenAlex
W4410251380
Cited by
51
References
92
Citations by year
2026: 34 · 2025: 13 · 2024: 4
FWCI
17.72
Type
journal article
Language
en
Open access
Locations
  • Nature Methods (publishedVersion, cc-by-nc-nd)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • Research Square (acceptedVersion, cc-by)
  • PubMed (publishedVersion)
  • Research at the University of Copenhagen (University of Copenhagen) (publishedVersion, other-oa)
  • PubMed Central (acceptedVersion)
  • PubMed Central (submittedVersion, cc-by-nc-nd)
  • PubMed Central (submittedVersion, cc-by)
Funders
  • U.S. Department of Health and Human Services (CA13202)
  • Velux Fonden (18333, 40516)
  • Villum Fonden (18333, 40516)
  • Ionis Pharmaceuticals
  • Novo Nordisk (NNF14CC0001)
  • Novo Nordisk Fonden (NNF 14CC0001, NNF22OC0075851, NNF23OC0081287, NNF20OC0062606)
  • National Institutes of Health (CA-13202)
  • National Cancer Institute (CA-13202, R01CA013202, R37CA013202)
  • National Institute of General Medical Sciences (R35GM130386)
  • National Institute of Allergy and Infectious Diseases (R01AI163019)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Advanced Fluorescence Microscopy Techniques
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cell Biology › Cellular transport and secretion
Keywords
single-particle tracking, clathrin-coated pits, 3D tracking, organelle tracking

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