scholarly journals NOBIAS: Analyzing Anomalous Diffusion in Single-Molecule Tracks With Nonparametric Bayesian Inference

2021 ◽  
Vol 1 ◽  
Author(s):  
Ziyuan Chen ◽  
Laurent Geffroy ◽  
Julie S. Biteen

Single particle tracking (SPT) enables the investigation of biomolecular dynamics at a high temporal and spatial resolution in living cells, and the analysis of these SPT datasets can reveal biochemical interactions and mechanisms. Still, how to make the best use of these tracking data for a broad set of experimental conditions remains an analysis challenge in the field. Here, we develop a new SPT analysis framework: NOBIAS (NOnparametric Bayesian Inference for Anomalous Diffusion in Single-Molecule Tracking), which applies nonparametric Bayesian statistics and deep learning approaches to thoroughly analyze SPT datasets. In particular, NOBIAS handles complicated live-cell SPT data for which: the number of diffusive states is unknown, mixtures of different diffusive populations may exist within single trajectories, symmetry cannot be assumed between the x and y directions, and anomalous diffusion is possible. NOBIAS provides the number of diffusive states without manual supervision, it quantifies the dynamics and relative populations of each diffusive state, it provides the transition probabilities between states, and it assesses the anomalous diffusion behavior for each state. We validate the performance of NOBIAS with simulated datasets and apply it to the diffusion of single outer-membrane proteins in Bacteroides thetaiotaomicron. Furthermore, we compare NOBIAS with other SPT analysis methods and find that, in addition to these advantages, NOBIAS is robust and has high computational efficiency and is particularly advantageous due to its ability to treat experimental trajectories with asymmetry and anomalous diffusion.

2021 ◽  
Author(s):  
Ziyuan Chen ◽  
Laurent Geffroy ◽  
Julie Suzanne Biteen

Single particle tracking (SPT) enables the investigation of biomolecular dynamics at a high temporal and spatial resolution in living cells, and the analysis of these SPT datasets can reveal biochemical interactions and mechanisms. Still, how to make the best use of these tracking data for a broad set of experimental conditions remains an analysis challenge in the field. Here, we develop a new SPT analysis framework: NOBIAS (Nonparametric Bayesian Inference for Anomalous Diffusion in Single-Molecule Tracking), which applies nonparametric Bayesian statistics and deep learning approaches to thoroughly analyze SPT datasets. In particular, NOBIAS handles complicated live-cell SPT data for which: the number of diffusive states is unknown, mixtures of different diffusive populations may exist within single trajectories, symmetry cannot be assumed between the x and y directions, and anomalous diffusion is possible. NOBIAS provides the number of diffusive states without manual supervision, it quantifies the dynamics and relative populations of each diffusive state, it provides the transition probabilities between states, and it assesses the anomalous diffusion behavior for each state. We validate the performance of NOBIAS with simulated datasets and apply it to the diffusion of single outer-membrane proteins in Bacteroides thetaiotaomicron. Furthermore, we compare NOBIAS with other SPT analysis methods and find that, in addition to these advantages, NOBIAS is robust and has high computational efficiency and is particularly advantageous due to its ability to treat experimental trajectories with asymmetry and anomalous diffusion.


2019 ◽  
Vol 70 (1) ◽  
pp. 301-322 ◽  
Author(s):  
Gavin Young ◽  
Philipp Kukura

Interferometric scattering microscopy (iSCAT) is an extremely sensitive imaging method based on the efficient detection of light scattered by nanoscopic objects. The ability to, at least in principle, maintain high imaging contrast independent of the exposure time or the scattering cross section of the object allows for unique applications in single-particle tracking, label-free imaging of nanoscopic (dis)assembly, and quantitative single-molecule characterization. We illustrate these capabilities in areas as diverse as mechanistic studies of motor protein function, viral capsid assembly, and single-molecule mass measurement in solution. We anticipate that iSCAT will become a widely used approach to unravel previously hidden details of biomolecular dynamics and interactions.


2015 ◽  
Vol 108 (3) ◽  
pp. 540-556 ◽  
Author(s):  
Keegan E. Hines ◽  
John R. Bankston ◽  
Richard W. Aldrich

2010 ◽  
Vol 98 (1) ◽  
pp. 164-173 ◽  
Author(s):  
J. Nick Taylor ◽  
Dmitrii E. Makarov ◽  
Christy F. Landes

2015 ◽  
Vol 12 (7) ◽  
pp. 594-595 ◽  
Author(s):  
Mohamed El Beheiry ◽  
Maxime Dahan ◽  
Jean-Baptiste Masson

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