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

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

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Kæstel-Hansen, Jacob, de Sautu, Marilina, Saminathan, Anand, Scanavachi, Gustavo, Da Cunha Correia, Ricardo F. Bango, Nielsen, Annette Juma, Bleshøy, Sara Vogt, Boomsma, Wouter, Kirchhausen, Tom, Hatzakis, Nikos S.·2023·Nature Methods
DOI 10.1101/2023.11.16.567393PMC PMC12272723

Preprint — published as 10.1038/s41592-025-02665-8 · Paper page

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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 indicates that besides structure, motion encodes function at the molecular and subcellular level.

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Research works printed but not linked: 59

  1. 10.1016/j.cell.2012.05.047 · DOI · Paper page
  2. 10.1038/nature25146 · DOI
  3. 10.1038/nature24264 · DOI
  4. 2022, Capturing the start point of the virus-cell interaction with high-speed 3D singlevirus tracking, Nat. Methods, 19, 1642, 10.1038/s41592-022-01672-3 · DOI
  5. 10.1038/s41467-019-13284-1 · DOI
  6. 10.1091/mbc.E16-03-0164 · DOI
  7. 2021, Single-Molecule Study of Thermomyces lanuginosus Lipase in a Detergency Application System Reveals Diffusion Pattern Remodeling by Surfactants and Calcium, ACS Appl. Mater. Interfaces, 13, 33704, 10.1021/acsami.1c08809 · DOI
  8. 2021, Biased cytochrome P450-mediated metabolism via small-molecule ligands binding P450 oxidoreductase, Nat. Commun, 12, 2260, 10.1038/s41467-021-22562-w · DOI · Paper page
  9. 10.1126/science.abn6583 · DOI · Paper page
  10. 10.1038/nmeth.2808 · DOI
  11. 10.1038/nmeth.1237 · DOI
  12. 2020, Ultrasmall TPGS-PLGA Hybrid Nanoparticles for Site-Specific Delivery of Antibiotics into Pseudomonas aeruginosa Biofilms in Lungs, ACS Appl. Mater. Interfaces, 12, 380, 10.1021/acsami.9b19644 · DOI
  13. 10.1126/science.1088525 · DOI
  14. 2013, Particle tracking in living cells: a review of the mean square displacement method and beyond, Rheol. Acta, 52, 425, 10.1007/s00397-013-0694-6 · DOI
  15. 10.1103/PhysRevLett.101.248103 · DOI
  16. 10.1103/PhysRevE.82.041914 · DOI
  17. 10.1016/j.bpj.2019.06.015 · DOI
  18. 1993, Classification of trajectories—Extracting invariants with a neural network, Neural Netw, 6, 1011, 10.1016/S0893-6080(09)80010-5 · DOI
  19. 2019, Classification of diffusion modes in single-particle tracking data: Feature-based versus deep-learning approach, Phys. Rev. E, 100, 032410, 10.1103/PhysRevE.100.032410 · DOI
  20. Pinholt, H. D. , Bohr, S. S.-R. , Iversen, J. F. , Boomsma, W. & Hatzakis, N. S. Single-particle diffusional fingerprinting: A machine-learning framework for quantitative analysis of heterogeneous diffusion. Proc Natl Acad Sci USA 118, (2021). · DOI
  21. 2023, Dimensional Reduction for Single-Molecule Imaging of DNA and Nucleosome Condensation by Polyamines, HP1α and Ki-67, J. Phys. Chem. B, 127, 1922, 10.1021/acs.jpcb.2c07011 · DOI
  22. 10.1016/j.bpj.2018.01.012 · DOI
  23. 10.1038/nmeth.3483 · DOI
  24. 10.1038/nmeth.2367 · DOI
  25. 2015, Detection of Diffusion Heterogeneity in Single Particle Tracking Trajectories Using a Hidden Markov Model with Measurement Noise Propagation, PLoS ONE, 10, e0140759, 10.1371/journal.pone.0140759 · DOI
  26. Chen, Z. , Geffroy, L. & Biteen, J. S. NOBIAS: Analyzing anomalous diffusion in single-molecule tracks with nonparametric Bayesian inference. Front. Bioinform. 1, (2021). · DOI
  27. 2019, Particle mobility analysis using deep learning and the moment scaling spectrum, Sci. Rep, 9, 17160, 10.1038/s41598-019-53663-8 · DOI
  28. 10.1186/s12859-016-1064-z · DOI
  29. 10.1039/C8CP02566E · DOI
  30. 10.1109/IJCNN52387.2021.9533629 · DOI
  31. 10.1371/journal.pone.0170165 · DOI
  32. 10.1016/j.jsb.2007.04.003 · DOI
  33. 2021, Objective comparison of methods to decode anomalous diffusion, Nat. Commun, 12, 6253, 10.1038/s41467-021-26320-w · DOI
  34. Thomsen, J. et al. DeepFRET, a software for rapid and automated single-molecule FRET data classification using deep learning. eLife 9, (2020). · DOI
  35. 2022, A density-based enrichment measure for assessing colocalization in single-molecule localization microscopy data, Nat. Commun, 13, 4388, 10.1038/s41467-022-32064-y · DOI
  36. 10.1152/ajpcell.00462.2010 · DOI
  37. 2022, Single-particle combinatorial multiplexed liposome fusion mediated by DNA, Nat. Chem, 14, 558, 10.1038/s41557-022-00912-5 · DOI
  38. 2023, Mind your tag in single-molecule measurements, Cell Rep. Methods, 3, 100623, 10.1016/j.crmeth.2023.100623 · DOI
  39. 10.1016/j.bpj.2012.06.029 · DOI
  40. 10.1146/annurev.biophys.26.1.373 · DOI
  41. 10.1038/mt.2011.102 · DOI
  42. 10.48550/arxiv.1706.04599 · DOI · Paper page
  43. 10.1371/journal.ppat.1004355 · DOI
  44. Salgado, E. N. , Garcia Rodriguez, B. , Narayanaswamy, N. , Krishnan, Y. & Harrison, S. C. Visualization of Calcium Ion Loss from Rotavirus during Cell Entry. J. Virol. 92, (2018). · DOI
  45. 10.1126/science.1257998 · DOI
  46. 10.1128/JVI.00234-11 · DOI
  47. 10.1038/nature08779 · DOI
  48. 10.1016/j.cell.2005.06.043 · DOI
  49. 10.1038/nrm1959 · DOI
  50. 10.1146/annurev.cellbio.23.090506.123319 · DOI · Paper page
  51. 10.1091/mbc.E14-07-1240 · DOI
  52. 10.1126/science.abj8754 · DOI · Paper page
  53. 2023, Regulation of membrane protein structure and function by their lipid nano-environment, Nat. Rev. Mol. Cell Biol, 24, 107, 10.1038/s41580-022-00524-4 · DOI
  54. 10.1038/s41592-019-0686-2 · DOI · Paper page
  55. 2015, Medical Image Computing and Computer-Assisted Intervention (MICCAI), 9351, 234
  56. 10.1145/3292500.3330701 · DOI
  57. 10.1073/pnas.2007837117 · DOI
  58. 2023, Enhanced hexamerization of insulin via assembly pathway rerouting revealed by single particle studies, Commun. Biol, 6, 178, 10.1038/s42003-022-04386-6 · DOI
  59. 2020, The ABC of insulin: the organic chemistry of a small protein, Chem. Eur. J, 26, 8341, 10.1002/chem.202000337 · DOI

Metadata

Authors
Kæstel-Hansen, J., de Sautu, M., Saminathan, A., Scanavachi, G., Da Cunha Correia, R. F. B., Nielsen, A. J., Bleshøy, S. V., Boomsma, W., Kirchhausen, T., & Hatzakis, N. S.
Affiliations
  • Boston Children's Hospital (US)
  • University of Copenhagen (DK)
Corresponding author
Hatzakis, Nikos S.
ORCID
Venue
Nature Methods
Publisher
openRxiv
Published
2023-11-17
Volume
22
Issue
5
Pages
1091-1100
ISSN
1548-7091 (ISSN-L)
DOI
10.1101/2023.11.16.567393
PMC
PMC12272723
OpenAlex
W4410251380
Cited by
51
References
1
Citations by year
2026: 34 · 2025: 13 · 2024: 4
FWCI
17.58
Type
journal article
Language
en
Open access
Licence
cc-by-nc-nd
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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