A statistical analysis of particle trajectories in living cells

Recent advances in molecular biology and fluorescence microscopy imaging have made possible the inference of the dynamics of single molecules in living cells. Such inference allows to determine the organization and function of the cell. The trajectories of particles in the cells, computed with tracking algorithms, can be modelled with diffusion processes. Three types of diffusion are considered : (i) free diffusion; (ii) subdiffusion or (iii) superdiffusion. The Mean Square Displacement (MSD) is generally used to determine the different types of dynamics of the particles in living cells (Qian, Sheetz and Elson 1991). We propose here a non-parametric three-decision test as an alternative to the MSD method. The rejection of the null hypothesis -- free diffusion -- is accompanied by claims of the direction of the alternative (subdiffusion or a superdiffusion). We study the asymptotic behaviour of the test statistic under the null hypothesis, and under parametric alternatives which are currently considered in the biophysics literature, (Monnier et al,2012) for example. In addition, we adapt the procedure of Benjamini and Hochberg (2000) to fit with the three-decision test setting, in order to apply the test procedure to a collection of independent trajectories. The performance of our procedure is much better than the MSD method as confirmed by Monte Carlo experiments. The method is demonstrated on real data sets corresponding to protein dynamics observed in fluorescence microscopy.Comment: Revised introduction. A clearer and shorter description of the model (section 2

PEWA: Patch-Based Exponentially Weighted Aggregation for Image Denoising

International audienceWe present a statistical aggregation method, which combines image patches denoised with conventional algorithms. We evaluate the SURE estimator of each denoised candidate image patch to compute the exponential weighted aggregation (EWA) estimator. The PEWA algorithm has an interpretation with Gibbs distribution, is based on a MCMC sampling and is able to produce results that are comparable to the current state-of-the-art

A GPU-accelerated real-time NLMeans algorithm for denoising color video sequences

Abstract. The NLMeans filter, originally proposed by Buades et al., is a very popular filter for the removal of white Gaussian noise, due to its simplicity and excellent performance. The strength of this filter lies in exploiting the repetitive character of structures in images. However, to fully take advantage of the repetitivity a computationally extensive search for similar candidate blocks is indispensable. In previous work, we presented a number of algorithmic acceleration techniques for the NLMeans filter for still grayscale images. In this paper, we go one step further and incorporate both temporal information and color information into the NLMeans algorithm, in order to restore video sequences. Starting from our algorithmic acceleration techniques, we investigate how the NLMeans algorithm can be easily mapped onto recent parallel computing architectures. In particular, we consider the graphical processing unit (GPU), which is available on most recent computers. Our developments lead to a high-quality denoising filter that can process DVD-resolution video sequences in real-time on a mid-range GPU

Biomolecule Trafficking and Network Tomography-based Simulations

International audienceDuring the past two decades many groundbreaking technologies, including Green Fluorescent Protein (GFP)-tagging and super-resolution microscopy, emerged and allowed the visualization of protein dynamics and molecular interactions at different levels of spatial and temporal resolution. In the meantime, the automated quantification of microscopy images depicting moving biomolecules has become of major importance in cell biology since it offers a better understanding of fundamental mechanisms including membrane transport, cell signaling, cell division and motility. Consequently, dedicated image analysis methods have been developed to process challenging temporal series of 2D-3D images and to estimate individual trajectories of biomolecules. Nevertheless, the current tracking methods cannot provide global information about biomolecule trafficking. This motivated the development of simulation techniques able to generate realistic fluorescence microscopy image sequences depicting trafficking of small moving particles in interaction, with variable velocities within the cell. In this chapter, we describe a simulation approach based on the concept of Network Tomography (NT) which is generally used in network communications and transport to infer the main routes of communication between origins and destinations. The trafficking model, scaled down for microscopy, is combined with real 2D-3D image sequences to generate artificial videos depicting fluorescently tagged moving proteins within cells. Simulation in bioimaging is timely since it has become essential to build ground truth datasets for image processing algorithm evaluation such as biomolecule detectors and trackers, as well as to generate training datasets for deep learning algorithms

Statistical and computational methods for intracellular trajectory analysis in fluorescence microscopy

International audienceThe characterization of molecule dynamics in living cells is essential to decipher biological mechanisms and processes. This topic is usually addressed in fluorescent video-microscopy from particle trajectories computed by object tracking algorithms. However, classifying individual trajectories into predefined diffusion classes (e.g. sub-diffusion, free diffusion (or Brownian motion), super-diffusion), estimating diffusion model parameters, or detecting diffusion regime changes, is a difficult task in most cases. To address this challenging issue, we propose a computational framework based on statistical tests (with the Brownian motion as the null hypothesis) to analyze short and long trajectories, and derive spatial diffusion maps. The methodological approach is well-grounded in statistics and is more robust than previous techniques, including the Mean Square Displacement (MSD) method and variants. In this talk, I will present the concepts and methods and focus on dynamics of biomolecules involved in exocytosis and endocytosis mechanisms, observed in total internal reflection fluorescence (TIRF) and lattice light sheet microscopy. The algorithms, dedicated to short or long trajectories, are flexible in most cases, with a minimal number of control parameters to be tuned (p-values). They can be applied to a large range of problems in cell imaging and can be integrated in generic image-based workflows, including for high content screeningapplications