Time-lapse Fluorescence Microscopy Images Research Articles

Spatio-Temporal Modelling of the p53–mdm2 Oscillatory System

In this paper we investigate the role of spatial effects in determining the dynamics of a subclass of signalling pathways characterised by their ability to demonstrate oscillatory behaviour. To this end, we formulate a simple spatial model of the p53 network that accounts for both a neg- ative feedback and a transcriptional delay. We show that the formation of protein density patterns can depend on the shape of the cell, position of the nucleus, and the protein diffusion rates. The temporal changes in the total amounts of protein are also subject to spatial influences. The level of DNA damage required to induce sustained oscillations, for instance, depends on the morphology of the cell. The model also provides a new interpretation of experimentally observed undamped oscillations in p53 levels in single cells. Our simulations reveal that alternate sequences of high- and low-amplitude oscillations can occur. We propose that the digital pulses may correspond to snap-shots of our high-amplitude sequences. Shorter waiting-times between subsequent time-lapse fluorescence microscopy images in combination with lower detection thresholds may reveal the ir- regular high-frequency oscillations suggested by our spatial model.

Size-selectivity and anomalous subdiffusion of nanoparticles through carbon nanofiber-basedmembranes

A simulation is presented here that serves the dual functions of generatinga nanoporous membrane replica and executing the Brownian motion ofnanoparticles through the virtual membrane. Specifically, the concentrationprofile of a dilute solution of fluorescent particles in a stochastic andSiO2-coated carbon nanofiber (oxCNF), nanoporous membrane was simulated. The quality ofthe simulated profile was determined by comparing the results with experimentalconcentration profiles. The experimental concentration profiles were collected adjacent tothe oxCNF membrane surface from time-lapse fluorescence microscopy images. Thesimulation proved ideal as an accurate predictor of particle diffusion—the simulatedconcentration profile merged with the experimental profiles at the inlet/exit surfaces of theoxCNF membrane. In particular, the oxCNF barrier was found to hinder the transport of50 and 100 nm particles and transmembrane trajectories were indicative of anomaloussubdiffusion; the diffusion coefficient was found to be a function of time and space.

Multiple object tracking in molecular bioimaging by Rao-Blackwellized marginal particle filtering

Time-lapse fluorescence microscopy imaging has rapidly evolved in the past decade and has opened new avenues for studying intracellular processes in vivo. Such studies generate vast amounts of noisy image data that cannot be analyzed efficiently and reliably by means of manual processing. Many popular tracking techniques exist but often fail to yield satisfactory results in the case of high object densities, high noise levels, and complex motion patterns. Probabilistic tracking algorithms, based on Bayesian estimation, have recently been shown to offer several improvements over classical approaches, by better integration of spatial and temporal information, and the possibility to more effectively incorporate prior knowledge about object dynamics and image formation. In this paper, we extend our previous work in this area and propose an improved, fully automated particle filtering algorithm for the tracking of many subresolution objects in fluorescence microscopy image sequences. It involves a new track management procedure and allows the use of multiple dynamics models. The accuracy and reliability of the algorithm are further improved by applying marginalization concepts. Experiments on synthetic as well as real image data from three different biological applications clearly demonstrate the superiority of the algorithm compared to previous particle filtering solutions.

Redistribution of accumulated cell iron: a modality of chelation with therapeutic implications

AbstractVarious pathologies are characterized by the accumulation of toxic iron in cell compartments. In anemia of chronic disease, iron is withheld by macrophages, leaving extracellular fluids iron-depleted. In Friedreich ataxia, iron levels rise in the mitochondria of excitable cells but decrease in the cytosol. We explored the possibility of using deferiprone, a membrane-permeant iron chelator in clinical use, to capture labile iron accumulated in specific organelles of cardiomyocytes and macrophages and convey it to other locations for physiologic reuse. Deferiprone's capacity for shuttling iron between cellular organelles was assessed with organelle-targeted fluorescent iron sensors in conjunction with time-lapse fluorescence microscopy imaging. Deferiprone facilitated transfer of iron from extracellular media into nuclei and mitochondria, from nuclei to mitochondria, from endosomes to nuclei, and from intracellular compartments to extracellular apotransferrin. Furthermore, it mobilized iron from iron-loaded cells and donated it to preerythroid cells for hemoglobin synthesis, both in the presence and in the absence of transferrin. These unique properties of deferiprone underlie mechanistically its capacity to alleviate iron accumulation in dentate nuclei of Friedreich ataxia patients and to donate tissue-chelated iron to plasma transferrin in thalassemia intermedia patients. Deferiprone's shuttling properties could be exploited clinically for treating diseases involving regional iron accumulation.

Context based mixture model for cell phase identification in automated fluorescence microscopy.

BackgroundAutomated identification of cell cycle phases of individual live cells in a large population captured via automated fluorescence microscopy technique is important for cancer drug discovery and cell cycle studies. Time-lapse fluorescence microscopy images provide an important method to study the cell cycle process under different conditions of perturbation. Existing methods are limited in dealing with such time-lapse data sets while manual analysis is not feasible. This paper presents statistical data analysis and statistical pattern recognition to perform this task.ResultsThe data is generated from Hela H2B GFP cells imaged during a 2-day period with images acquired 15 minutes apart using an automated time-lapse fluorescence microscopy. The patterns are described with four kinds of features, including twelve general features, Haralick texture features, Zernike moment features, and wavelet features. To generate a new set of features with more discriminate power, the commonly used feature reduction techniques are used, which include Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA), Maximum Margin Criterion (MMC), Stepwise Discriminate Analysis based Feature Selection (SDAFS), and Genetic Algorithm based Feature Selection (GAFS). Then, we propose a Context Based Mixture Model (CBMM) for dealing with the time-series cell sequence information and compare it to other traditional classifiers: Support Vector Machine (SVM), Neural Network (NN), and K-Nearest Neighbor (KNN). Being a standard practice in machine learning, we systematically compare the performance of a number of common feature reduction techniques and classifiers to select an optimal combination of a feature reduction technique and a classifier. A cellular database containing 100 manually labelled subsequence is built for evaluating the performance of the classifiers. The generalization error is estimated using the cross validation technique. The experimental results show that CBMM outperforms all other classifies in identifying prophase and has the best overall performance.ConclusionThe application of feature reduction techniques can improve the prediction accuracy significantly. CBMM can effectively utilize the contextual information and has the best overall performance when combined with any of the previously mentioned feature reduction techniques.

Automated Segmentation, Classification, and Tracking of Cancer Cell Nuclei in Time-Lapse Microscopy

Quantitative measurement of cell cycle progression in individual cells over time is important in understanding drug treatment effects on cancer cells. Recent advances in time-lapse fluorescence microscopy imaging have provided an important tool to study the cell cycle process under different conditions of perturbation. However, existing computational imaging methods are rather limited in analyzing and tracking such time-lapse datasets, and manual analysis is unreasonably time-consuming and subject to observer variances. This paper presents an automated system that integrates a series of advanced analysis methods to fill this gap. The cellular image analysis methods can be used to segment, classify, and track individual cells in a living cell population over a few days. Experimental results show that the proposed method is efficient and effective in cell tracking and phase identification.