ArticleBiophysical journal2018
Multistep Track Segmentation and Motion Classification for Transient Mobility Analysis.
Article in Biophysical journal, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers.
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Who cites it
49 citing papers in PubMed.
- Integrins and tetraspanins mediate CD36-actin coupling and regulate CD36 organization and signaling.The Journal of cell biology · 2026Article
- G protein diffusion states reveal receptor binding dynamics at single-molecule scale.Cell chemical biology · 2026Article
- Characterizing cellular receptor dynamics in live fluorescence imaging using stochastic modeling and machine learning.iScience · 2026Article
- Multivalency influences virus dynamics at the glycocalyx: A study of glycosaminoglycan binding in HPV16 entry.Biophysical journal · 2026Article
- CD33 and clusterin interact biophysically and genetically to modulate Alzheimer risk.Nature communications · 2026Article
- Graph neural network modeling of receptor interaction kinetics from single-molecule imaging data.bioRxiv : the preprint server for biology · 2026Article
- Review of Machine Learning for Single-Particle Tracking: Methods, Challenges, and Biophysical Insights.Chemical & biomedical imaging · 2026Review
- Local GPCR density tips the balance of μ-opioid receptor trafficking.bioRxiv : the preprint server for biology · 2026Article
- Feature Selection and Hyperparameter Optimization for Machine Learned Classification of 3D Single-Particle Tracking.Chemical & biomedical imaging · 2026Article
- Heterotrimeric G proteins containing Gβ1γ2 subunits exhibit subtype-specific mobility differences in live cells.PloS one · 2026Article
- A Pseudomonas aeruginosa quorum-sensing inducer controls lung permeability in establishing chronic infection via EGFR.Nature communications · 2025Article
- Pointwise prediction of protein diffusive properties using machine learning.JPhys photonics · 2025Article
- Deep learning-assisted analysis of single-particle tracking for automated correlation between diffusion and function.Nature methods · 2025Article
- Physics-informed deep learning for stochastic particle dynamics estimation.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Trajectory Analysis in Single-Particle Tracking: From Mean Squared Displacement to Machine Learning Approaches.International journal of molecular sciences · 2024Review
- Single-Molecule Imaging Reveals Differential AT1R Stoichiometry Change in Biased Signaling.International journal of molecular sciences · 2023Article
- Extract latent features of single-particle trajectories with historical experience learning.Biophysical journal · 2023Article
- Inferring pointwise diffusion properties of single trajectories with deep learning.Biophysical journal · 2023Article
- Multiscale imaging and quantitative analysis of plasma membrane protein-cortical actin interplay.Biophysical journal · 2023Article
- Single-molecule tracking to determine the abundances and stoichiometries of freely-diffusing protein complexes in living cells: Past applications and future prospects.The Journal of chemical physics · 2023Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Molecular interactions are often transient and might change within the window of observation, leading to changes in molecule movement. Therefore, accurate motion analysis often requires transient motion classification. Here we present an accurate and computationally efficient transient mobility analysis framework, termed "divide-and-conquer moment scaling spectrum" (DC-MSS). DC-MSS works in a multistep fashion: 1) it utilizes a local movement descriptor throughout a track to divide it into initial segments of putatively different motion classes; 2) it classifies these segments via moment scaling spectrum (MSS) analysis of molecule displacements; and 3) it uses the MSS analysis results to refine the track segmentation. This strategy uncouples the initial identification of motion switches from motion classification, allowing DC-MSS to circumvent the sensitivity-accuracy tradeoff of classic rolling window approaches for transient motion analysis, while at the same time harnessing the classification power of MSS analysis. Testing of DC-MSS demonstrates that it detects switches among free diffusion, confined diffusion, directed diffusion, and immobility with great sensitivity. To illustrate the utility of DC-MSS, we have applied it to single-particle tracks of the transmembrane protein CD44 on the surface of macrophages, revealing actin cortex-dependent transient mobility changes.
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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.