ReviewBiophysical journal2015
Analysis and Interpretation of Superresolution Single-Particle Trajectories.
Review in Biophysical journal, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- Physics-informed deep learning for stochastic particle dynamics estimation.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Synapsin 2a tetramerisation selectively controls the presynaptic nanoscale organisation of reserve synaptic vesicles.Nature communications · 2024Article
- High-throughput super-resolution single-particle trajectory analysis reconstructs organelle dynamics and membrane reorganization.Cell reports methods · 2022Article
- A guide for single-particle chromatin tracking in live cell nuclei.Cell biology international · 2022Review
- Studying the Dynamics of Chromatin-Binding Proteins in Mammalian Cells Using Single-Molecule Localization Microscopy.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Monitoring the spatio-temporal organization and dynamics of the genome.Nucleic acids research · 2020Review
- The Decade of Super-Resolution Microscopy of the Presynapse.Frontiers in synaptic neuroscience · 2020Review
- Biophysics of high density nanometer regions extracted from super-resolution single particle trajectories: application to voltage-gated calcium channels and phospholipids.Scientific reports · 2019Article
- Particle Mobility Analysis Using Deep Learning and the Moment Scaling Spectrum.Scientific reports · 2019Article
- Revealing Compartmentalized Diffusion in Living Cells with Interferometric Scattering Microscopy.Biophysical journal · 2018Article
- Unraveling the Thousand Word Picture: An Introduction to Super-Resolution Data Analysis.Chemical reviews · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A large number (tens of thousands) of single molecular trajectories on a cell membrane can now be collected by superresolution methods. The data contains information about the diffusive motion of molecule, proteins, or receptors and here we review methods for its recovery by statistical analysis of the data. The information includes the forces, organization of the membrane, the diffusion tensor, the long-time behavior of the trajectories, and more. To recover the long-time behavior and statistics of long trajectories, a stochastic model of their nonequilibrium motion is required. Modeling and data analysis serve extracting novel biophysical features at an unprecedented spatiotemporal resolution. The review presents data analysis, modeling, and stochastic simulations applied in particular on surface receptors evolving in neuronal cells.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.