Patch-Based Markov Models for Event Detection in Fluorescence Bioimaging

International audienceThe study of protein dynamics is essential for understanding the multi-molecular complexes at subcellular levels. Fluorescent Protein (XFP)-tagging and time-lapse fluorescence microscopy enable to observe molecular dynamics and interactions in live cells, unraveling the live states of the matter. Original image analysis methods are then required to process challenging 2D or 3D image sequences. Recently, tracking methods that estimate the whole trajectories of moving objects have been successfully developed. In this paper, we address rather the detection of meaningful events in spatio-temporal fluorescence image sequences, such as apparent stable "stocking areas" involved in membrane transport. We propose an original patch-based Markov modeling to detect spatial irregularities in fluorescence images with low false alarm rates. This approach has been developed for real image sequences of cells expressing XFP-tagged Rab proteins, known to regulate membrane trafficking

Motion Analysis of Live Objects by Super-Resolution Fluorescence Microscopy

Motion analysis plays an important role in studing activities or behaviors of live objects in medicine, biotechnology, chemistry, physics, spectroscopy, nanotechnology, enzymology, and biological engineering. This paper briefly reviews the developments in this area mostly in the recent three years, especially for cellular analysis in fluorescence microscopy. The topic has received much attention with the increasing demands in biomedical applications. The tasks of motion analysis include detection and tracking of objects, as well as analysis of motion behavior, living activity, events, motion statistics, and so forth. In the last decades, hundreds of papers have been published in this research topic. They cover a wide area, such as investigation of cell, cancer, virus, sperm, microbe, karyogram, and so forth. These contributions are summarized in this review. Developed methods and practical examples are also introduced. The review is useful to people in the related field for easy referral of the state of the art

A Patch-Based Method for Repetitive and Transient Event Detection in Fluorescence Imaging

Automatic detection and characterization of molecular behavior in large data sets obtained by fast imaging in advanced light microscopy become key issues to decipher the dynamic architectures and their coordination in the living cell. Automatic quantification of the number of sudden and transient events observed in fluorescence microscopy is discussed in this paper. We propose a calibrated method based on the comparison of image patches expected to distinguish sudden appearing/vanishing fluorescent spots from other motion behaviors such as lateral movements. We analyze the performances of two statistical control procedures and compare the proposed approach to a frame difference approach using the same controls on a benchmark of synthetic image sequences. We have then selected a molecular model related to membrane trafficking and considered real image sequences obtained in cells stably expressing an endocytic-recycling trans-membrane protein, the Langerin-YFP, for validation. With this model, we targeted the efficient detection of fast and transient local fluorescence concentration arising in image sequences from a data base provided by two different microscopy modalities, wide field (WF) video microscopy using maximum intensity projection along the axial direction and total internal reflection fluorescence microscopy. Finally, the proposed detection method is briefly used to statistically explore the effect of several perturbations on the rate of transient events detected on the pilot biological model

Particle Filtering Methods for Subcellular Motion Analysis

Advances in fluorescent probing and microscopic imaging technology have revolutionized biology in the past decade and have opened the door for studying subcellular dynamical processes. However, accurate and reproducible methods for processing and analyzing the images acquired for such studies are still lacking. Since manual image analysis is time consuming, potentially inaccurate, and poorly reproducible, many biologically highly relevant questions are either left unaddressed, or are answered with great uncertainty. The subject of this thesis is particle filtering methods and their application for multiple object tracking in different biological imaging applications. Particle filtering is a technique for implementing recursive Bayesian filtering by Monte Carlo sampling. A fundamental concept behind the Bayesian approach for performing inference is the possibility to encode the information about the imaging system, possible noise sources, and the system dynamics in terms of probability density functions. In this thesis, a set of novel PF based metho

Robust density modelling using the student's t-distribution for human action recognition

The extraction of human features from videos is often inaccurate and prone to outliers. Such outliers can severely affect density modelling when the Gaussian distribution is used as the model since it is highly sensitive to outliers. The Gaussian distribution is also often used as base component of graphical models for recognising human actions in the videos (hidden Markov model and others) and the presence of outliers can significantly affect the recognition accuracy. In contrast, the Student's t-distribution is more robust to outliers and can be exploited to improve the recognition rate in the presence of abnormal data. In this paper, we present an HMM which uses mixtures of t-distributions as observation probabilities and show how experiments over two well-known datasets (Weizmann, MuHAVi) reported a remarkable improvement in classification accuracy. © 2011 IEEE

Unraveling the Thousand Word Picture: An Introduction to Super-Resolution Data Analysis

Super-resolution microscopy provides direct insight into fundamental biological processes occurring at length scales smaller than light’s diffraction limit. The analysis of data at such scales has brought statistical and machine learning methods into the mainstream. Here we provide a survey of data analysis methods starting from an overview of basic statistical techniques underlying the analysis of super-resolution and, more broadly, imaging data. We subsequently break down the analysis of super-resolution data into four problems: the localization problem, the counting problem, the linking problem, and what we’ve termed the interpretation problem

Single-molecule FRET studies of protein function and conformational dynamics

All fundamental cellular processes are governed by dynamic interactions of nano-sized biomolecules such as proteins and DNA. A technique well suited to resolve inter- and intramolecular distances is Förster Resonance Energy Transfer (FRET). By combining FRET with single-molecule spectroscopy, heterogeneities in the system can be detected and dynamic information becomes accessible. In this thesis, single-molecule FRET (smFRET) experiments were performed using either the small observation volume of a confocal microscope or the evanescent field of a total internal reflection (TIRF) microscope to get insights into the enzymatic activity of three proteins. To study the end-joining of DNA double strands by the T4 DNA ligase, a DNA origami platform was designed to control the stoichiometry and spatial arrangement of the interaction partners, thereby increasing their local concentration. The ligation process could be followed in real time and the same pair of DNA strands repeatedly ligated and cut, which highlights the applicability of the DNA origami platform to a wide range of multimolecular interaction studies on the single-molecule level. In a second project, the underlying catalytic mechanism of NSP2, a rotavirus protein required for genome replication and virus assembly, was investigated by comparing the RNA unwinding activity of full-length and C-terminally truncated mutants. Although the C-terminal region was less efficient in destabilizing the secondary structure of RNA, it proved to be essential for RNA release via charge repulsion and is thus a prerequisite for a full cycle of chaperone activity. In the last study, the conformational states of bacterial adhesion protein SdrG were studied. This protein initiates nosocomial infections through its stable attachment to human fibrinogen. By combining FRET-derived distances and molecular dynamics (MD) simulations, the structure of the dynamic apoprotein could be modeled, providing information inaccessible to other methods. The diverse topics researched in this thesis - ranging from DNA nanotechnology to viral and bacterial infections - emphasize the multifaceted capabilities of smFRET