Single-Molecule Tracking for Live Cells

A special issue of Cells (ISSN 2073-4409).

Deadline for manuscript submissions: 20 September 2026 | Viewed by 1061

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Department of Geriatrics, Asklepios Clinic, D-88131 Lindau, Germany
Interests: single molecules; single-molecule biophysics & biochemistry based on the stochastic nature of diffusion
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Dear Colleagues,

We have measured the anomalous diffusion in human prostate cancer cells that were transfected with an Alexa633 fluorescent RNA probe and co-transfected with enhanced green fluorescent protein-labeled argonaute2 protein via laser scanning microscopy. The image analysis arose from diffusion based on a “two-level system”. The trap was an interaction site where the diffusive motion was slowed down. Anomalous subdiffusive spreading occurred at cellular traps. The cellular traps were not immobile. We demonstrated how the novel analysis method of imaging data resulted in new information about the number of traps in the crowded and heterogeneous environment of a single human prostate cancer cell. The imaging data were consistent with and explained by modern ideas of the anomalous diffusion of mixed origins in live cells.

This Special Edition is about the single-molecule level versus the many-molecule level in single live cells and diluted liquids (solutions). We would appreciate if you would consider submitting a paper to this Special Edition of the journal Cells.

You may choose our Joint Special Issue in Biophysica.

Prof. Dr. Zeno Földes-Papp
Guest Editor

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Keywords

  • single-molecule level
  • many-molecule level
  • biological cell
  • diluted liquids
  • single-molecule localization microscopy
  • nanoscopy
  • modeling
  • diffusion
  • theory

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Published Papers (1 paper)

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Review

26 pages, 1625 KB  
Review
Machine Learning in Single-Molecule Tracking Analysis of Superresolution Optical Microscopy Data
by Lucas A. Saavedra and Francisco J. Barrantes
Cells 2026, 15(8), 686; https://doi.org/10.3390/cells15080686 - 13 Apr 2026
Viewed by 424
Abstract
Machine learning (ML) is transforming the analysis of biomolecular data, holding significant promise for improving the efficiency and accuracy of microscopy image analysis and for studying the dynamics of molecules in live cells. As data-driven approaches continue to evolve, they may eventually replace [...] Read more.
Machine learning (ML) is transforming the analysis of biomolecular data, holding significant promise for improving the efficiency and accuracy of microscopy image analysis and for studying the dynamics of molecules in live cells. As data-driven approaches continue to evolve, they may eventually replace traditional statistical methods that rely on conventional analytical methods. This review examines and critically analyses the state of the art of ML techniques as applied to various levels of data supervision in the analysis of dynamic single-molecule datasets obtained using superresolution optical microscopy. Collectively encompassed under the umbrella of “nanoscopy”, these methods currently comprise targeted techniques such as stimulated emission depletion (STED) microscopy and stochastic techniques like single-molecule localization microscopies (SMLMs), comprising photoactivated localization microscopy (PALM), DNA points accumulation for imaging in nanoscale topography (DNA-PAINT) microscopy, and minimal fluorescence photon flux (MINFLUX) microscopy. These techniques all enable the imaging of subcellular components and molecules beyond the diffraction limit, and some are additionally capable of studying their dynamics in real time, as reviewed here, using several ML techniques that facilitate motion analysis in two or three dimensions with qualitative and quantitative characterisation in the live cell. It is expected that the growing use of learning-based approaches in biological microscopy data processing will dramatically increase throughput and accelerate progress in this rapidly developing field. Full article
(This article belongs to the Special Issue Single-Molecule Tracking for Live Cells)
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