open access publication

Preprint, 2024

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

Research Square, Volume 5, 02-13, Pages rs.3.rs-3716053, 10.21203/rs.3.rs-3716053/v1

Contributors

Hatzakis, Nikos S 0000-0003-4202-0328 [1] Kæstel-Hansen, Jacob 0000-0001-7365-9664 [1] De Sautu, Marilina 0000-0001-5725-160X [2] Saminathan, Anand [2] Scanavachi, Gustavo 0000-0002-4001-3389 [2] Correia, Ricardo [2] Nielsen, Annette Juma 0000-0001-5746-0954 [3] Bleshoey, Sara [3] Boomsma, Wouter Krogh 0000-0002-8257-3827 [1] Kirchhausen, Tomas L [2]

Affiliations

  1. [1] University of Copenhagen
    2. [NORA names: KU University of Copenhagen; University; Denmark; Europe, EU; Nordic; OECD];
  2. [2] Harvard University
    3. [NORA names: United States; America, North; OECD];
  3. [3] Department of Biology
    4. [NORA names: Miscellaneous]

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.

Keywords

Single particle tracking, accuracy, automated correlation, automated extraction, automated mapping, behavior, biological information, cellular processes, clathrin-coated pits, correlation, deep learning framework, diffusion, diffusion of molecules, early event, early events of viral infection, efficient manner, endosomal organelles, environment, events of viral infection, extract biological information, extraction of functional information, framework, function, functional information, individual objects, infection, information, interaction, labor-intensive, learning framework, level of precision, levels, living systems, manner, maps, microscopy, molecules, motion, nanoscale diffusion, objective, optical microscopy, organelles, particle tracking, pits, precision, process, structure, sub-cellular environment, subcellular level, system, temporal behavior, tracking, versatility, vesicles, viral infection, weeks

Funders

  • National Institute of Allergy and Infectious Diseases
  • National Institute of General Medical Sciences

Data Provider: Digital Science

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