Non-rigid Contour-Based Registration of Cell Nuclei in 2D Live Cell Microscopy Images Using a Dynamic Elasticity Model

International audienceThe analysis of the pure motion of subnuclear structures without influence of the cell nucleus motion and deformation is essential in live cell imaging. In this work, we propose a 2D contour-based image registration approach for compensation of nucleus motion and deformation in fluorescence microscopy time-lapse sequences. The proposed approach extends our previous approach which uses a static elasticity model to register cell images. Compared to that scheme, the new approach employs a dynamic elasticity model for forward simulation of nucleus motion and deformation based on the motion of its contours. The contour matching process is embedded as a constraint into the system of equations describing the elastic behavior of the nucleus. This results in better performance in terms of the registration accuracy. Our approach was successfully applied to real live cell microscopy image sequences of different types of cells including image data that was specifically designed and acquired for evaluation of cell image registration methods. An experimental comparison with existing contour-based registration methods and an intensity-based registration method has been performed. We also studied the dependence of the results on the choice of method parameters

Filter-Based Probabilistic Markov Random Field Image Priors: Learning, Evaluation, and Image Analysis

Markov random fields (MRF) based on linear filter responses are one of the most popular forms for modeling image priors due to their rigorous probabilistic interpretations and versatility in various applications. In this dissertation, we propose an application-independent method to quantitatively evaluate MRF image priors using model samples. To this end, we developed an efficient auxiliary-variable Gibbs samplers for a general class of MRFs with flexible potentials. We found that the popular pairwise and high-order MRF priors capture image statistics quite roughly and exhibit poor generative properties. We further developed new learning strategies and obtained high-order MRFs that well capture the statistics of the inbuilt features, thus being real maximum-entropy models, and other important statistical properties of natural images, outlining the capabilities of MRFs. We suggest a multi-modal extension of MRF potentials which not only allows to train more expressive priors, but also helps to reveal more insights of MRF variants, based on which we are able to train compact, fully-convolutional restricted Boltzmann machines (RBM) that can model visual repetitive textures even better than more complex and deep models. The learned high-order MRFs allow us to develop new methods for various real-world image analysis problems. For denoising of natural images and deconvolution of microscopy images, the MRF priors are employed in a pure generative setting. We propose efficient sampling-based methods to infer Bayesian minimum mean squared error (MMSE) estimates, which substantially outperform maximum a-posteriori (MAP) estimates and can compete with state-of-the-art discriminative methods. For non-rigid registration of live cell nuclei in time-lapse microscopy images, we propose a global optical flow-based method. The statistics of noise in fluorescence microscopy images are studied to derive an adaptive weighting scheme for increasing model robustness. High-order MRFs are also employed to train image filters for extracting important features of cell nuclei and the deformation of nuclei are then estimated in the learned feature spaces. The developed method outperforms previous approaches in terms of both registration accuracy and computational efficiency

Non-rigid multi-frame registration of cell nuclei in live cell microscopy image data

To gain a better understanding of cellular and molecular processes it is important to quantitatively analyze the motion of subcellular particles in live cell microscopy image sequences. For accurate quantification of the subcellular particle motion, compensation of the motion and deformation of the cell nucleus is required. This thesis deals with non-rigid registration of cell nuclei in 2D and 3D live cell fluorescence microscopy images. We developed two multi-frame non-rigid registration approaches which simultaneously exploit information from multiple consecutive frames of an image sequence to improve the registration accuracy. The multi-frame registration approaches are based on local optic flow estimation, use information from multiple consecutive images, and take into account computed transformations from previous time steps. The first approach comprises three intensity-based variants and two different temporal weighting schemes. The second approach determines diffeomorphic transformations in the log-domain which allows efficient computation of the inverse transformations. We use a temporally weighted mean image which is constructed based on inverse transformations and multiple consecutive frames. In addition, we employ a flow boundary preserving method for regularization of computed deformation vector fields. Both multi-frame registration approaches have been successfully applied to 2D and 3D synthetic as well as real live cell microscopy image sequences. We have performed an extensive quantitative evaluation of our approaches and compared their performance with previous non-rigid pairwise, multi-frame, and temporal groupwise registration approaches

Accurate Estimation of Particle Dynamics Bypassing Substrate Drift Bias: Application to Cell Nucleus Motion

In microscopic imaging, the movement of a living substrate can be caused by its own displacement (e.g., cell motion/migration) or other technical factors such as microscope stage drift. This drifting motion is one of the main biases resulting in poor estimation of particle dynamics since it seriously affects the estimation of the biophysical parameters (the diffusion constant D and anomalous exponent α), especially when performed on the basis of mean squared displacement (MSD) analysis. In this paper, we compare a few substrate drift correction/registration methods based on the use of additional fluorescent spots (landmarks). In the particular case of cell nucleus motion, we labeled telomeres spreading throughout the cell nucleus. We show that compared to the MSD analysis, the use of Gaussian processes is an effective and more accurate way to estimate the substrate drift, and major biophysical parameters of particle dynamics

PREPROCESSING AND REGISTRATION OF MINISCOPE-BASED CALCIUM IMAGING OF THE RODENT BRAIN

Microscopic imaging is central to the brain and cognition studies in animals and often requires advanced image processing. In vivo recordings on awake behaving animals require stabilization of the images as the tissue in the images undergoes non-rigid deformations due to animal movement, pulse beat and breathing of the animal. Here we propose an approach to compensation for the tissue motion in calcium imaging data acquired with miniaturized wearable microscopes (miniscopes) from live rodent brains. Our approach includes preprocessing of the images in which we compensate for non-uniform illumination, remove calcium transients and instrument-specific noise. For image registration we use the multiscale mutual information based non-rigid algorithm with B-spline transformation model. We present the preliminary results of such motion compensation approach applied to the real miniscope image stacks

Biological image analysis

In biological research images are extensively used to monitor growth, dynamics and changes in biological specimen, such as cells or plants. Many of these images are used solely for observation or are manually annotated by an expert. In this dissertation we discuss several methods to automate the annotating and analysis of bio-images. Two large clusters of methods have been investigated and developed. A first set of methods focuses on the automatic delineation of relevant objects in bio-images, such as individual cells in microscopic images. Since these methods should be useful for many different applications, e.g. to detect and delineate different objects (cells, plants, leafs, ...) in different types of images (different types of microscopes, regular colour photographs, ...), the methods should be easy to adjust. Therefore we developed a methodology relying on probability theory, where all required parameters can easily be estimated by a biologist, without requiring any knowledge on the techniques used in the actual software. A second cluster of investigated techniques focuses on the analysis of shapes. By defining new features that describe shapes, we are able to automatically classify shapes, retrieve similar shapes from a database and even analyse how an object deforms through time

Dynamics of chromatin structure and nuclear multiprotein complexes investigated by quantitative fluorescence live cell microscopy and computational modeling

Biology has rapidly been transformed into a mainly data-driven, quantitative science. Demands on biological imaging are moving towards quantitative annotations of genes in vivo. In this work I have studied in detail the spatio-temporal distribution and the molecular interaction of protein ensembles as well as of multiprotein aggregates. I have provided the methodology to estimate biophysical parameters such as diffusion coefficients, anomalous diffusion and the free fraction in the binding equilibrium of protein ensembles using fluorescence photobleaching analysis and numcerical modeling and parameter estimation. On the side of protein complexes I have extended existing single particle tracking approaches to allow to automatically detect the exact timing of mobility changes of single particles in live cells. Here, I was able to provide quantitative parameters also on the diffusion coefficient, anomalous diffusion, velocity and chromatin interaction. The nuclear protein ensemble I studied was murine linker histone H1° fused to GFP. I was able to show that diffusion and binding of H1°-GFP to chromatin can be addressed using photobleaching analysis and numcerical modeling. I have thus obtained diffusion coefficients for wild-type H1° and seven point mutants with differential binding affinity ranging from D = 0.01 mm²/s (strongest binder) to D = 0.1 mm²/s (weakest binder). Likewise, I was able to estimate the free fraction to range from = 400 ppm to = 3000 ppm. Exemplary of large multiprotein complexes I chose PML nuclear bodies (PML NBs), named after their constituent promyelotic leukemia protein. I studied in detail their dynamic mobility during early mitosis, ranging from prophase to prometaphase. A dramatic global increase in PML NB mobility was found during this period with the diffusion coefficient increasing from D = 0.001 mm²/s at interphase to D = 0.005 mm²/s at prophase. Similarly, velocities increased from v = 0.7 mm/min to v = 1.4mm/min and concomittant with a loss in subdiffusive motion. I was able to establish loss of tethering to chromatin as the most likely reason behind this increase as opposed to material flow or chromatin condensation. Lastly, I was also able to relate the timing of the mobility increase to other important cellular events. The increase of PML NB mobility predominantly occured after nuclear entry of cyclin B1, which irreversibly commits the cell to mitosis, and before nuclear envelope breakdown (NEBD)

Dynamique de la paroi cellulaire dans la régulation de la morphogenèse et de la croissance cellulaire

Cells in nature develop in a wide range of forms, following diverse growth patterns. Despite the importance of these fundamental processes, how cells regulate their growth and morphogenesis is still poorly understood. In this thesis, I explored these processes, focusing my investigations on tip growing walled cells and in particular, by exploiting the fission yeast Schyzosaccharomyces pombe, adopting a mainly biomechanical approach. To this aim, I first developed novel methods to measure key cell wall mechanical parameters in vivo and in large scale, which allowed the very first observations of cell wall dynamics. This revealed that the cell wall is softer and highly variable at growing poles, and almost stable and stiffer at non-growing sites. During elongation, there is an interplay between wall mechanics and cell growth, whose active control allows cell expansion while preserving cell integrity. In addition, I observed that there is a strong correlation between cell wall mechanics and cell morphology, and ectopic perturbations of wall properties directly affect shape establishment and maintenance. Together my results show that the regulation of wall mechanics is fundamental in the determination of cell dynamics in tip growing walled cells. Moreover, this suggests that dynamic observation of cell surface mechanics is crucial for a complete understanding of multifactorial and complex processes as growth and morphogenesis.Les cellules dans la nature se développent dans un large éventail de formes, suivant divers modèles de croissance. Malgré l'importance de ces processus fondamentaux, la façon dont les cellules régulent leur croissance et leur morphogenèse est encore mal comprise. Dans cette thèse, j'ai exploré ces aspects, avec une approche principalement biomécanique, en concentrant mes investigations sur des cellules à paroi à croissance de pointe et en exploitant en particulier la levure fissipare Schyzosaccharomyces pombe. J'ai d'abord développé de nouvelles méthodes pour mesurer les paramètres mécaniques clés de la paroi cellulaire in vivo et à grande échelle, ce qui a permis les premières observations de la dynamique des parois cellulaires. Ceci a révélé que la paroi cellulaire est plus souple et très variable au niveau des pôles de croissance, et presque stable et plus rigide dans les sites non cultivés. Au cours de l'allongement, il existe une interaction entre la mécanique des parois et la croissance cellulaire, dont le contrôle actif permet l'expansion cellulaire tout en préservant l'intégrité des cellules. De plus, j'ai observé qu'il existe une forte corrélation entre la mécanique des parois cellulaires et la morphologie cellulaire, et des perturbations des propriétés de la paroi affectent directement l'établissement et la maintenance de la forme. Ensemble, mes résultats montrent que la régulation de la paroi est fondamentale dans la détermination de la dynamique cellulaire dans les cellules à parois épaissies. Globalement, cela suggère que l'observation dynamique de la mécanique de surface cellulaire est essentielle pour une compréhension complète des processus multifactoriels et complexes comme la croissance et la morphogenèse

Mechanical Development and Functional Mechanosensitivity During Early Cardiogenesis

This thesis addresses the questions of when and how mechanical stiffness arises during embryonic heart development and how mechanics affects early cardiomyocyte and myocardium contractile function and cytoskeletal organization. Previous studies addressing how mechanics influence the contractile and electrochemical capacity of mature cardiomyocytes on compliant substrates are reviewed in light of theory explaining how contractile striated fibers might optimally align on intermediate substrates. Embryonic heart and brain tissue stiffness through early development are measured by micropipette aspiration, and the earliest functional heart is found to be three-fold stiffer than early embryonic tissue while brain remains soft. Contraction strain in intact embryonic day 4 (E4) heart tubes shows an optimum relative to hearts with softened or stiffened extracellular matrices. Contraction wave velocity, however, goes linearly with softening or stiffening of tissue, consistent with a theory. Isolated E4 cardiomyocytes cultured on collagen-coated substrates of various stiffnesses show optimal contraction on substrates that match the stiffness of E4 tissue. Sarcomere organization shows optimal organization in intact tissue relative to soft and on intermediate substrates relative to soft or very stiff. The feedback between matrix stiffness and contractile capacity of cardiomyocytes in developing heart tissue is modeled and extended to include interactions with nuclear structural proteins, Lamins. A method for perturbing and imaging nuclear lamina in vivo is discussed and preliminary measurements indicate that the nucleus could act as a measure for intracellular stresses