ArticlePLoS computational biology2015
Extracting Diffusive States of Rho GTPase in Live Cells: Towards In Vivo Biochemistry.
Article in PLoS computational biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 29 citations in OpenAlex.
- Trajectory Analysis in Single-Particle Tracking: From Mean Squared Displacement to Machine Learning Approaches.International journal of molecular sciences · 2024Review
- Robust Quantification of Live-Cell Single-Molecule Tracking Data for Fluorophores with Different Photophysical Properties.The journal of physical chemistry. B · 2024Article
- Rho of Plants patterning: linking mathematical models and molecular diversity.Journal of experimental botany · 2024Review
- Dynamic switching of transcriptional regulators between two distinct low-mobility chromatin states.Science advances · 2023Article
- Uncovering diffusive states of the yeast membrane protein, Pma1, and how labeling method can change diffusive behavior.The European physical journal. E, Soft matter · 2023Article
- Sensing the shape of a cell with reaction diffusion and energy minimization.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- The glucocorticoid receptor associates with the cohesin loader NIPBL to promote long-range gene regulation.Science advances · 2022Article
- Exploring the influence of cytosolic and membrane FAK activation on YAP/TAZ nuclear translocation.Biophysical journal · 2021Article
- SMAUG: Analyzing single-molecule tracks with nonparametric Bayesian statistics.Methods (San Diego, Calif.) · 2021Article
- An intrinsically disordered region-mediated confinement state contributes to the dynamics and function of transcription factors.Molecular cell · 2021Article
- Covariance distributions in single particle tracking.Physical review. E · 2021Article
- WASP family proteins regulate the mobility of the B cell receptor during signaling activation.Nature communications · 2020Article
- A Hidden Markov Model for Detecting Confinement in Single-Particle Tracking Trajectories.Biophysical journal · 2018Article
- Pointwise error estimates in localization microscopy.Nature communications · 2017Article
- Arg188 drives RhoC membrane binding.Small GTPases · 2017Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
Resolving distinct biochemical interaction states when analyzing the trajectories of diffusing proteins in live cells on an individual basis remains challenging because of the limited statistics provided by the relatively short trajectories available experimentally. Here, we introduce a novel, machine-learning based classification methodology, which we call perturbation expectation-maximization (pEM), that simultaneously analyzes a population of protein trajectories to uncover the system of diffusive behaviors which collectively result from distinct biochemical interactions. We validate the performance of pEM in silico and demonstrate that pEM is capable of uncovering the proper number of underlying diffusive states with an accurate characterization of their diffusion properties. We then apply pEM to experimental protein trajectories of Rho GTPases, an integral regulator of cytoskeletal dynamics and cellular homeostasis, in vivo via single particle tracking photo-activated localization microscopy. Remarkably, pEM uncovers 6 distinct diffusive states conserved across various Rho GTPase family members. The variability across family members in the propensities for each diffusive state reveals non-redundant roles in the activation states of RhoA and RhoC. In a resting cell, our results support a model where RhoA is constantly cycling between activation states, with an imbalance of rates favoring an inactive state. RhoC, on the other hand, remains predominantly inactive.
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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.