Single particle trajectories reveal active endoplasmic reticulum luminal flow

The endoplasmic reticulum (ER), a network of membranous sheets and pipes, supports functions encompassing biogenesis of secretory proteins and delivery of functional solutes throughout the cell[1, 2]. Molecular mobility through the ER network enables these functionalities, but diffusion alone is not sufficient to explain luminal transport across supramicrometre distances. Understanding the ER structure–function relationship is critical in light of mutations in ER morphology-regulating proteins that give rise to neurodegenerative disorders[3, 4]. Here, super-resolution microscopy and analysis of single particle trajectories of ER luminal proteins revealed that the topological organization of the ER correlates with distinct trafficking modes of its luminal content: with a dominant diffusive component in tubular junctions and a fast flow component in tubules. Particle trajectory orientations resolved over time revealed an alternating current of the ER contents, while fast ER super-resolution identified energy-dependent tubule contraction events at specific points as a plausible mechanism for generating active ER luminal flow. The discovery of active flow in the ER has implications for timely ER content distribution throughout the cell, particularly important for cells with extensive ER-containing projections such as neurons.Wellcome Trust - 3-3249/Z/16/Z and 089703/Z/09/Z [Kaminski] UK Demential Research Institute [Avezov] Wellcome Trust - 200848/Z/16/Z, WT: UNS18966 [Ron] FRM Team Research Grant [Holcman] Engineering and Physical Sciences Research Council (EPSRC) - EP/L015889/1 and EP/H018301/1 [Kaminski] Medical Research Council (MRC) - MR/K015850/1 and MR/K02292X/1 [Kaminski

Objective comparison of particle tracking methods

Particle tracking is of key importance for quantitative analysis of intracellular dynamic processes from time-lapse microscopy image data. Because manually detecting and following large numbers of individual particles is not feasible, automated computational methods have been developed for these tasks by many groups. Aiming to perform an objective comparison of methods, we gathered the community and organized an open competition in which participating teams applied their own methods independently to a commonly defined data set including diverse scenarios. Performance was assessed using commonly defined measures. Although no single method performed best across all scenarios, the results revealed clear differences between the various approaches, leading to notable practical conclusions for users and developers

Multiple Hypothesis Tracking for Cluttered Biological Image Sequences

International audienceIn this paper, we present a method for simultaneously tracking thousands of targets in biological image sequences, whichis of major importance in modern biology. The complexity and inherent randomness of the problem lead us to propose a unifiedprobabilistic framework for tracking biological particles in microscope images. The framework includes realistic models of particlemotion and existence, and of fluorescence image features. For the track extraction process per se, the very cluttered conditionsmotivate the adoption of a multiframe approach which enforces tracking decision robustness to poor imaging conditions and to randomtarget movements. We tackle the large-scale nature of the problem by adapting the Multiple Hypothesis Tracking algorithm to theproposed framework, resulting in a method with a favorable trade-off between the model complexity and the computational cost of thetracking procedure. When compared to the state-of-the-art tracking techniques for bioimaging, the proposed algorithm is shown to bethe only method providing high quality results despite the critically poor imaging conditions and the dense target presence. We thusdemonstrate the benefits of advanced Bayesian tracking techniques for the accurate computational modeling of dynamical biologicalprocesses, which is promising for further developments in this domain

Multiple Hypothesis Tracking for Cluttered Biological Image Sequences

International audienceIn this paper, we present a method for simultaneously tracking thousands of targets in biological image sequences, whichis of major importance in modern biology. The complexity and inherent randomness of the problem lead us to propose a unifiedprobabilistic framework for tracking biological particles in microscope images. The framework includes realistic models of particlemotion and existence, and of fluorescence image features. For the track extraction process per se, the very cluttered conditionsmotivate the adoption of a multiframe approach which enforces tracking decision robustness to poor imaging conditions and to randomtarget movements. We tackle the large-scale nature of the problem by adapting the Multiple Hypothesis Tracking algorithm to theproposed framework, resulting in a method with a favorable trade-off between the model complexity and the computational cost of thetracking procedure. When compared to the state-of-the-art tracking techniques for bioimaging, the proposed algorithm is shown to bethe only method providing high quality results despite the critically poor imaging conditions and the dense target presence. We thusdemonstrate the benefits of advanced Bayesian tracking techniques for the accurate computational modeling of dynamical biologicalprocesses, which is promising for further developments in this domain

Multiple Hypothesis Tracking for Cluttered Biological Image Sequences

International audience<p>In this paper, we present a method for simultaneously tracking thousands of targets in biological image sequences, whichis of major importance in modern biology. The complexity and inherent randomness of the problem lead us to propose a unifiedprobabilistic framework for tracking biological particles in microscope images. The framework includes realistic models of particlemotion and existence, and of fluorescence image features. For the track extraction process per se, the very cluttered conditionsmotivate the adoption of a multiframe approach which enforces tracking decision robustness to poor imaging conditions and to randomtarget movements. We tackle the large-scale nature of the problem by adapting the Multiple Hypothesis Tracking algorithm to theproposed framework, resulting in a method with a favorable trade-off between the model complexity and the computational cost of thetracking procedure. When compared to the state-of-the-art tracking techniques for bioimaging, the proposed algorithm is shown to bethe only method providing high quality results despite the critically poor imaging conditions and the dense target presence. We thusdemonstrate the benefits of advanced Bayesian tracking techniques for the accurate computational modeling of dynamical biologicalprocesses, which is promising for further developments in this domain.</p