Recovering a stochastic process from super-resolution noisy ensembles of single-particle trajectories.

Recovering a stochastic process from noisy ensembles of single-particle trajectories is resolved here using the coarse-grained Langevin equation as a model. The massive redundancy contained in single-particle tracking data allows recovering local parameters of the underlying physical model. We use several parametric and nonparametric estimators to compute the first and second moments of the process, to recover the local drift, its derivative, and the diffusion tensor, and to deconvolve the instrumental from the physical noise. We use numerical simulations to also explore the range of validity for these estimators. The present analysis allows defining what can exactly be recovered from statistics of super-resolution microscopy trajectories used for characterizing molecular trafficking underlying cellular functions.

[1]  David Holcman,et al.  Residence times of receptors in dendritic spines analyzed by stochastic simulations in empirical domains. , 2014, Biophysical journal.

[2]  Christian L. Vestergaard,et al.  Optimal estimation of diffusion coefficients from single-particle trajectories. , 2013, Physical review. E, Statistical, nonlinear, and soft matter physics.

[3]  S. Manley,et al.  Heterogeneity of AMPA receptor trafficking and molecular interactions revealed by superresolution analysis of live cell imaging , 2012, Proceedings of the National Academy of Sciences.

[4]  K. Jacobson,et al.  Single-particle tracking: applications to membrane dynamics. , 1997, Annual review of biophysics and biomolecular structure.

[5]  J. Lippincott-Schwartz,et al.  High-density mapping of single-molecule trajectories with photoactivated localization microscopy , 2008, Nature Methods.

[6]  A. Kusumi,et al.  Confined lateral diffusion of membrane receptors as studied by single particle tracking (nanovid microscopy). Effects of calcium-induced differentiation in cultured epithelial cells. , 1993, Biophysical journal.

[7]  Paul C. Blainey,et al.  Optimal estimation of diffusion coefficients from single-particle trajectories. , 2013 .

[8]  Zeev Schuss,et al.  Nonlinear Filtering and Optimal Phase Tracking , 2011 .

[9]  J. Rädler,et al.  Chemotactic cell trapping in controlled alternating gradient fields , 2011, Proceedings of the National Academy of Sciences.

[10]  A. Berglund.  Statistics of camera-based single-particle tracking. , 2010, Physical review. E, Statistical, nonlinear, and soft matter physics.

[11]  Michael J Saxton,et al.  Single-particle tracking: connecting the dots , 2008, Nature Methods.

[12]  Joachim O Rädler,et al.  Temporal analysis of active and passive transport in living cells. , 2008, Physical review letters.

[13]  S. Karlin,et al.  A second course in stochastic processes , 1981 .

[14]  B. Lindner,et al.  Distributions of diffusion measures from a local mean-square displacement analysis. , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

[15]  David Holcman,et al.  Unraveling novel features hidden in superresolution microscopy data , 2013, Communicative & integrative biology.

[16]  W. Webb,et al.  Precise nanometer localization analysis for individual fluorescent probes. , 2002, Biophysical journal.

[17]  M. Emmert,et al.  Amoeboid migration mode adaption in quasi-3D spatial density gradients of varying lattice geometry , 2014 .

[18]  C. Leonhardt,et al.  Contact-controlled amoeboid motility induces dynamic cell trapping in 3D-microstructured surfaces , 2012 .

[19]  Z. Schuss,et al.  Narrow escape through a funnel and effective diffusion on a crowded membrane. , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.