Investigating AMPA Receptor Diffusion and Nanoscale Organization at Synapses with High-Density Single-Molecule Tracking Methods

Imaging Live-Cell Dynamics and Structure at the Single-Molecule Level

“…The high density of trajectories so obtained supplies enough statistical information to reconstruct high-resolution diffusion maps and infer distinct modes of diffusion. Subsequently, this technique has been successfully applied to a wide range of biological systems, including the characterization of dynamics of neurotransmitter receptors in axons, integrins inside focal adhesions, and transcription factors (Constals et al, 2014;Frost et al, 2010;Heidbreder et al, 2012;Hoze et al, 2012;Izeddin et al, 2014;Nair et al, 2013;Rossier et al, 2012;Shrivastava et al, 2013;Yang et al, 2012).…”

An algorithm-centric Monte Carlo method to empirically quantify motion type estimation uncertainty in single-particle tracking

“…Here we describe a novel, algorithmic-centric, Monte Carlo method to assess the effect of experimental parameters such as signal to noise ratio (SNR), particle detection error, trajectory length, and the diffusivity characteristics of the moving particle on the uncertainty associated with motion type classification The method is easily extensible to a wide variety of SPT algorithms, is made widely available via its implementation in our Open Microscopy Environment inteGrated Analysis (OMEGA) software tool for the management and analysis of tracking data 7 , and forms an integral part of our Minimum Information About Particle Tracking Experiments (MIAPTE) data model 8 . 3 Background Time series of optical images acquired from living cells are the starting point for the analysis of the intracellular dynamics and interactions of myriads of heterogeneous cellular features [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26] . Single-particle Tracking (SPT) experiments entail three distinct steps ( Figure 1): particle detection and tracking, trajectory analysis 5,11,[27][28][29][30][31][32][33][34][35][36][37][38] , and physical interpretation through modeling 17,[39][40][41] .…”