Time-lapse Fluorescence Microscopy Images Research Articles

Quantification of collective signalling in time-lapse microscopy images.

Live-cell imaging of fluorescent biosensors has demonstrated that space-time correlations in signalling of cell collectives play an important organisational role in morphogenesis, wound healing, regeneration, and maintaining epithelial homeostasis. Here, we demonstrate how to quantify one such phenomenon, namely apoptosis-induced ERK activity waves in the MCF10A epithelium. We present a protocol that starts from raw time-lapse fluorescence microscopy images and, through a sequence of image manipulations, ends with ARCOS, our computational method to detect and quantify collective signalling. We also describe the same workflow in the interactive napari image viewer to quantify collective phenomena for users without prior programming experience. Our approach can be applied to space-time correlations in cells, cell collectives, or communities of multicellular organisms, in 2D and 3D geometries.

CyberSco.Py an open-source software for event-based, conditional microscopy

Timelapse fluorescence microscopy imaging is routinely used in quantitative cell biology. However, microscopes could become much more powerful investigation systems if they were endowed with simple unsupervised decision-making algorithms to transform them into fully responsive and automated measurement devices. Here, we report CyberSco.Py, Python software for advanced automated timelapse experiments. We provide proof-of-principle of a user-friendly framework that increases the tunability and flexibility when setting up and running fluorescence timelapse microscopy experiments. Importantly, CyberSco.Py combines real-time image analysis with automation capability, which allows users to create conditional, event-based experiments in which the imaging acquisition parameters and the status of various devices can be changed automatically based on the image analysis. We exemplify the relevance of CyberSco.Py to cell biology using several use case experiments with budding yeast. We anticipate that CyberSco.Py could be used to address the growing need for smart microscopy systems to implement more informative quantitative cell biology experiments.

RECURRENT NEURAL NETWORK FOR MONITORING MOUSE EMBRYONIC STEM CELL COLONY IN VITRO USING TIME-LAPSE FLUORESCENCE MICROSCOPY IMAGES

Embryonic stem cells represent a cellular resource for basic biological studies and used as medically relevant cells in vitro. Time-lapse fluorescence microscopy images taken during cell culture are frequently used to monitor time-series morphology changes and status transitions of embryonic stem cell (ESC) colonies, and to select culture medium for maintaining ESCs in undifferentiated or early differentiated stages. Recurrent Neural Network (RNN)-based prediction of time-lapse images generated in future culture can be an effective method for monitoring. Because status transitions of ESC colonies are usually complete in a few consecutive frames of time-lapse images, this study proposes a Long Short-Term Memory (LSTM) structure that incorporates a few new images taken from incubators to learn the newest colony morphology and status for a new RNN training and thus generate predicted time-lapse images. Image processing procedures of entropy, bi-lateral filter and convex-hull calculations are implemented to extract colony regions on the images. Morphology changes and status transitions are then calculated using the extracted ESC colonies. The experiment results show predicted time-lapse images in 5h future and the colony morphology and status transition calculation give accurate visualization and quantitative analyses to monitor the mouse ESCs in vitro at the undifferentiated or early differentiated stages.

Deep probabilistic tracking of particles in fluorescence microscopy images.

Tracking of particles in temporal fluorescence microscopy image sequences is of fundamental importance to quantify dynamic processes of intracellular structures as well as virus structures. We introduce a probabilistic deep learning approach for fluorescent particle tracking, which is based on a recurrent neural network that mimics classical Bayesian filtering. Compared to previous deep learning methods for particle tracking, our approach takes into account uncertainty, both aleatoric and epistemic uncertainty. Thus, information about the reliability of the computed trajectories is determined. Manual tuning of tracking parameters is not necessary and prior knowledge about the noise statistics is not required. Short and long-term temporal dependencies of individual object dynamics are exploited for state prediction, and assigned detections are used to update the predicted states. For correspondence finding, we introduce a neural network which computes assignment probabilities jointly across multiple detections as well as determines the probabilities of missing detections. Training requires only simulated data and therefore tedious manual annotation of ground truth is not needed. We performed a quantitative performance evaluation based on synthetic and real 2D as well as 3D fluorescence microscopy images. We used image data of the Particle Tracking Challenge as well as real time-lapse fluorescence microscopy images displaying virus structures and chromatin structures. It turned out that our approach yields state-of-the-art results or improves the tracking results compared to previous methods.

Engineered lipid bicelle nanostructures for membrane-disruptive antibacterial applications

Lipid bicelles are cell membrane-mimicking nanostructures that self-assemble from mixtures of long- and short-chain lipidic components and are widely used in various applications related to structural biology, drug delivery, and interfacial science. To date, most research efforts have focused on bicelles as passive structural carriers to host membrane proteins or hydrophobic drugs, while there remains untapped potential to engineer functionally active lipid bicelles that contain biologically important lipidic components for targeted applications. Herein, we developed antibiotic-free, antibacterial bicellar nanostructures composed of a long-chain phospholipid and glycerol monolaurate, which is a monoglyceride that exhibits membrane-disruptive inhibitory activity against various bacteria and membrane-enveloped viruses. Quartz crystal microbalance-dissipation and time-lapse fluorescence microscopy imaging experiments were conducted to identify fusogenic bicellar compositions with optimal levels of pore-like, membrane-disruptive activity that was distinct from the activity of the monoglyceride alone. Cryogenic transmission electron microscopy was performed to characterize the lamellar-phase nanostructure properties of the lead bicelle composition along with in vitro antibacterial assays, which identified that the bicelles inhibited Staphylococcus aureus bacteria via a killing mechanism. Collectively, these findings demonstrate the potential of applying molecular-level engineering strategies to fabricate lipid bicelles with membrane-disruptive properties relevant to anti-infective applications.

Automatic three-dimensional segmentation of mouse embryonic stem cell nuclei by utilising multiple channels of confocal fluorescence images.

Time-lapse confocal fluorescence microscopy images from mouse embryonic stem cells (ESCs) carrying reporter genes, histone H2B-mCherry and Mvh-Venus, have been used to monitor dynamic changes in cellular/differentiation characteristics of live ESCs. Accurate cell nucleus segmentation is required to analyse the ESC dynamics and differentiation at a single cell resolution. Several methods used concavities on nucleus contours to segment overlapping cell nuclei. Our proposed method evaluates not only the concavities but also the size and shape of every 2D nucleus region to determine if any of the strait, extrusion, convexity and large diameter criteria is satisfied to segment overlapping nuclei inside the region. We then use a 3D segmentation method to reconstruct simple, convex, and reasonably sized 3D nuclei along the image stacking direction using the radius and centre of every segmented region in respective microscopy images.To avoid false concavities on nucleus boundaries, fluorescence images of the H2B-mCherry reporter are used for localisation of cell nuclei and Venus fluorescence images are used for determining the cell colony ranges. We use a series of image preprocessing procedures to remove noise outside and inside cell colonies, and in respective nuclei, and to smooth nucleus boundaries based on the colony ranges. We propose dynamic data structures to record every segmented nucleus region and solid in sets (volumes) of 3D confocal images. The experimental results show that the proposed image preprocessing method preserves the areas of mouse ESC nuclei on microscopy images and that the segmentation method effectively segment out every nucleus with a reasonable size and shape. All 3D nuclei in a set (volume) of confocal microscopy images can be accessed by the dynamic data structures for 3D reconstruction. The 3D nuclei in time-lapse confocal microscopy images can be tracked to calculate cell movement and proliferation in consecutive volumes for understanding the dynamics of the differentiation characteristics about ESCs. LAY DESCRIPTION: Embryonic stem cells (ESCs) are considered as an ideal source for basic cell biology study and producing medically useful cells in vitro.This study uses time-lapse confocal fluorescence microscopy images from mouse ESCs carrying reporter gene to monitor dynamic changes in cellular/differentiation characteristics of live ESCs. To automate analyses of ESC differentiation behaviours, accurate cell nucleus segmentation to distinguish respective cells are required. A series of image preprocessing procedures are implemented to remove noise in live-cell fluorescence images but yield overlapping cell nuclei. A segmentation method that evaluates boundary concavities and the size and shape of every nucleus is then used to determine if any of the strait, extrusion, convexity, large and local minimum diameter criteria satisfied to segment overlapping nuclei. We propose a dynamic data structure to record every newly segmented nucleus. The experimental results show that the proposed image preprocessing method preserves the areas of mouse ESC nuclei and that the segmentation method effectively detects overlapping nuclei. All segmented nuclei in confocal images can be accessed using the dynamic data structures to be visualised and manipulated for quantitative analyses of the ESC differentiation behaviours. The manipulation can be tracking of segmented 3D cell nuclei in time-lapse images to calculate their dynamics of differentiation characteristics.

Supported Lipid Bilayer Formation from Phospholipid-Fatty Acid Bicellar Mixtures.

Supported lipid bilayers (SLBs) are versatile cell membrane-mimicking biointerfaces for various applications such as biosensors and drug delivery systems, and there is broad interest in developing simple, cost-effective methods to achieve SLB fabrication. One promising approach involves the deposition of quasi-two-dimensional bicelle nanostructures that are composed of long-chain phospholipids and either short-chain phospholipids or detergent molecules. While a variety of long-chain phospholipids have been used to prepare bicelles for SLB fabrication applications, only two short-chain phospholipids, 1,2-dihexanoyl-sn-glycero-3-phosphocholine and 1,2-diheptanoyl-sn-glycero-3-phosphocholine (collectively referred to as DHPC), have been investigated. There remains an outstanding need to identify natural alternatives to DHPC, especially ones that are more affordable, to improve fabrication prospects and application opportunities. Herein, we explored the potential to fabricate SLBs from bicellar mixtures composed of long-chain phospholipids and lauric acid (LA), which is a low-cost, naturally abundant fatty acid that is widely used in soapmaking and various industrial applications. Quartz crystal microbalance-dissipation (QCM-D) experiments were conducted to track bicelle adsorption onto silica surfaces as a function of bicelle composition and lipid concentration, along with time-lapse fluorescence microscopy imaging and fluorescence recovery after photobleaching (FRAP) experiments to further characterize lipid adlayer properties. The results identified optimal conditions where it is possible to efficiently form SLBs from LA-containing bicelles at low lipid concentrations while also unraveling mechanistic insights into the bicelle-mediated SLB formation process and verifying that LA-containing bicelles are biocompatible with human cells for surface coating applications.

Lipid Bicelle Micropatterning Using Chemical Lift-Off Lithography.

Supported lipid membranes are versatile biomimetic coatings for the chemical functionalization of inorganic surfaces. Developing simple and effective fabrication strategies to form supported lipid membranes with micropatterned geometries is a long-standing challenge. Herein, we demonstrate how the combination of chemical lift-off lithography (CLL) and easily prepared lipid bicelle nanostructures can yield micropatterned, supported lipid membranes on gold surfaces with high pattern resolution, conformal character, and biofunctionality. Using CLL, we functionalized gold surfaces with patterned arrays of hydrophilic and hydrophobic self-assembled monolayers (SAMs). Time-lapse fluorescence microscopy imaging revealed that lipid bicelles adsorbed preferentially onto the hydrophilic SAM regions, while there was negligible lipid adsorption onto the hydrophobic SAM regions. Functional receptors could be embedded within the lipid bicelles, which facilitated selective detection of receptor-ligand binding interactions in a model streptavidin-biotin system. Quartz crystal microbalance-dissipation measurements further identified that lipid bicelles adsorb irreversibly and remain intact on top of the hydrophilic SAM regions. Taken together, our findings indicate that lipid bicelles are useful lipid nanostructures for reproducibly assembling micropatterned, supported lipid membranes with precise pattern fidelity.

Abstract P1-06-05: Image driven biophysical mathematical modeling of multicellular breast tumor spheroids

Abstract Introduction: Conventional 2D monolayer cell culture methods have distinct weaknesses in mimicking in vivo tumors as they ignore major phenotypic physical tumor microenvironment (TME) factors. 3D multicellular tumor spheroid (MTS) systems have been developed to study cancer cell growth in the presence of such TME factors, including cell-cell and cell-extracellular matrix (ECM) mechanical interactions. However, the development of analysis methodologies for these complex culture systems has considerably lagged. We hypothesize that a coupled experimental-computational framework using a microscopy image-driven biophysical mathematical model of MTS systems can accurately estimate phenotypic growth and biophysical TME properties, revealing significantly more mechanistic information than traditional morphometric analysis methods. Materials and Methods: MDA-MB-231 triple-negative breast cancer cells were used to generate spheroids. A single 3D MTS embedded in Collagen I ECM was used as a 3D culture tumor model system as they exhibit cell-cell and cell-ECM interactions that mimic in vivo tumors. Fluorescent fiducial microspheres were placed in the ECM to track displacement induced by the MTS. Time-lapse fluorescence microscopy images were taken of the MTS and microspheres for 72 hours on an automated fluorescent microscope every 12-hours. Images were compiled using a customized fully-automated tiling/stitching/image processing software in MATLAB. Time-lapse images of the MTS and microspheres are used in conjunction with subsequent mathematical modeling analysis for biophysical parameter estimation. We assess MTS using a mathematical model based on two coupled partial differential equations describing growth/motility and mechanical equilibrium in response to cellular-induced forces. Parameters describing cell proliferation rate (k), cell diffusion (D), and cellular mechanical forces (λ) are estimated based on fitting acquired microscopy images to the model via an iterative least-squares parameter estimation. Experiments and model-based analyses were performed in triplicate to define the reproducibility of biophysical parameter estimation. Results and Discussion: In this preliminary study, our modeling framework is able to estimate biophysical parameters of cell diffusion, proliferation rate, and cellular mechanical force in a 3D breast cancer cell culture system of invading MTS embedded in ECM with coefficients of variation across all parameters less than 15% on average. The model predictions accurately represent changes in cellular density and ECM deformation induced by cellular traction forces throughout the observed time series, demonstrating that this model is capable of representing MTS/ECM growth behavior and parameterizing the driving biophysical properties. Conclusions: These results indicate that our imaging-driven mathematical modeling framework for estimating biophysical model parameters has potential to reliably characterize 3D MTS growth and invasion. With reproducibility established, we plan to extend the method to investigate these phenotypic biophysical TME properties in a range of culture and treatment condition interventions. These biophysical properties could be utilized in developing anti-neoplastic drug sensitivity assays to improve cancer therapy by enabling patient-specific estimation/prediction of response to agents and dosing conditions. Acknowledgements: NIH-NCI K25CA204599 and P30CA012197 Citation Format: Haley Brooke Johnson, Emily E. Fannin, Alexandra Thomas, Jared A Weis. Image driven biophysical mathematical modeling of multicellular breast tumor spheroids [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P1-06-05.

A Recurrent Neural Network for Particle Tracking in Microscopy Images Using Future Information, Track Hypotheses, and Multiple Detections.

Automatic tracking of particles in time-lapse fluorescence microscopy images is essential for quantifying the dynamic behavior of subcellular structures and virus structures. We introduce a novel particle tracking approach based on a deep recurrent neural network architecture that exploits past and future information in both forward and backward direction. Assignment probabilities are determined jointly across multiple detections, and the probability of missing detections is computed. In addition, existence probabilities are determined by the network to handle track initiation and termination. For correspondence finding, track hypotheses are propagated to future time points so that information at later time points can be used to resolve ambiguities. A handcrafted similarity measure and handcrafted motion features are not necessary. Manually labeled data is not required for network training. We evaluated the performance of our approach using image data of the Particle Tracking Challenge as well as real fluorescence microscopy image sequences of virus structures. It turned out that the proposed approach outperforms previous methods.

PiQSARS: A pipeline for quantitative and statistical analyses of ratiometric fluorescent biosensors

Genetically encoded ratiometric fluorescent probes are cutting-edge tools in biology. They allow precise and dynamic measurement of various physiological parameters within cell compartments. Because data extraction and analysis are time consuming and may lead to inconsistencies between results, we describe here a standardized pipeline for•Semi-automated treatment of time-lapse fluorescence microscopy images.•Quantification of individual cell signal.•Statistical analysis of the data.First, a dedicated macro was developed using the FIJI software to reproducibly quantify the fluorescence ratio as a function of time. Raw data are then exported and analyzed using R and MATLAB softwares. Calculation and statistical analysis of selected graphic parameters are performed. In addition, a functional principal component analysis allows summarizing the dataset. Finally, a principal component analysis is performed to check consistency and final analysis is presented as a visual diagram. The method is adapted to any ratiometric fluorescent probe. As an example, the analysis of the cytoplasmic HyPer probe in response to an acute cell treatment with increasing amounts of hydrogen peroxide is shown. In conclusion, the pipeline allows to save time and analyze a larger amount of samples while reducing manual interventions and consequently increasing the robustness of the analysis.

A novel generic dictionary-based denoising method for improving noisy and densely packed nuclei segmentation in 3D time-lapse fluorescence microscopy images

Time-lapse fluorescence microscopy is an essential technique for quantifying various characteristics of cellular processes, i.e. cell survival, migration, and differentiation. To perform high-throughput quantification of cellular processes, nuclei segmentation and tracking should be performed in an automated manner. Nevertheless, nuclei segmentation and tracking are challenging tasks due to embedded noise, intensity inhomogeneity, shape variation as well as a weak boundary of nuclei. Although several nuclei segmentation approaches have been reported in the literature, dealing with embedded noise remains the most challenging part of any segmentation algorithm. We propose a novel denoising algorithm, based on sparse coding, that can both enhance very faint and noisy nuclei signal but simultaneously detect nuclei position accurately. Furthermore our method is based on a limited number of parameters, with only one being critical, which is the approximate size of the objects of interest. We also show that our denoising method coupled with classical segmentation method works properly in the context of the most challenging cases. To evaluate the performance of the proposed method, we tested our method on two datasets from the cell tracking challenge. Across all datasets, the proposed method achieved satisfactory results with 96:96% recall for the C. elegans dataset. Besides, in the Drosophila dataset, our method achieved very high recall (99:3%).

Collective Cell Behavior in Mechanosensing of Substrate Thickness

Extracellular matrix stiffness has a profound effect on the behavior of many cell types. Adherent cells apply contractile forces to the material on which they adhere and sense the resistance of the material to deformation—its stiffness. This is dependent on both the elastic modulus and the thickness of the material, with the corollary that single cells are able to sense underlying stiff materials through soft hydrogel materials at low (<10 μm) thicknesses. Here, we hypothesized that cohesive colonies of cells exert more force and create more hydrogel deformation than single cells, therefore enabling them to mechanosense more deeply into underlying materials than single cells. To test this, we modulated the thickness of soft (1 kPa) elastic extracellular-matrix-functionalized polyacrylamide hydrogels adhered to glass substrates and allowed colonies of MG63 cells to form on their surfaces. Cell morphology and deformations of fluorescent fiducial-marker-labeled hydrogels were quantified by time-lapse fluorescence microscopy imaging. Single-cell spreading increased with respect to decreasing hydrogel thickness, with data fitting to an exponential model with half-maximal response at a thickness of 3.2 μm. By quantifying cell area within colonies of defined area, we similarly found that colony-cell spreading increased with decreasing hydrogel thickness but with a greater half-maximal response at 54 μm. Depth-sensing was dependent on Rho-associated protein kinase-mediated cellular contractility. Surface hydrogel deformations were significantly greater on thick hydrogels compared to thin hydrogels. In addition, deformations extended greater distances from the periphery of colonies on thick hydrogels compared to thin hydrogels. Our data suggest that by acting collectively, cells mechanosense rigid materials beneath elastic hydrogels at greater depths than individual cells. This raises the possibility that the collective action of cells in colonies or sheets may allow cells to sense structures of differing material properties at comparatively large distances.

On the Complex and Reversible Pathways of CdSe Quantum Dots Driven by Pyroelectric-Dielectrophoresis.

Electrophoresis (EP) and dielectrophoresis (DEP) are the two well-established methodologies to manipulate nanoparticles (NPs). Recently, DEP by a virtual electrode platform was demonstrated on ferroelectric substrates, where the driving force is due to the strong electric field generated by the pyroelectric effect, thus opening new scenarios for manipulating the matter. Such an innovative approach named pyroelectric-DEP has several advantages over traditional EP and DEP. However, a detailed study on this novel approach is required for understanding the complex pathways traced by NPs under the action of the pyroelectric-driven forces and thus for explaining the final patterns. Here, we investigate experimentally the dynamic behavior of CdSe NPs through time-lapse fluorescence microscopy imaging. Complete visualization and measurement of the directed-assembling process of NPs immersed in polydimethylsiloxane fluid is reported, which shows some unpredicted results with respect to the previous works, thus opening the route for designing in principle a reversible and switchable device allowing two different and reversible final NP-patterned states. The observed phenomena are fully analyzed by experimental and simulated analysis, and the movements of NPs is performed to elucidate in depth the involved processes. The investigation furnishes an interesting result that the complex behavior of the NPs can be fully comprehended and explained by considering the superposition of both EP and DEP forces.

Revealing the sequence of interactions of PuroA peptide with Candida albicans cells by live-cell imaging

To determine the mechanism(s) of action of antimicrobial peptides (AMPs) it is desirable to provide details of their interaction kinetics with cellular, sub-cellular and molecular targets. The synthetic peptide, PuroA, displays potent antimicrobial activities which have been attributed to peptide-induced membrane destabilization, or intracellular mechanisms of action (DNA-binding) or both. We used time-lapse fluorescence microscopy and fluorescence lifetime imaging microscopy (FLIM) to directly monitor the localization and interaction kinetics of a FITC- PuroA peptide on single Candida albicans cells in real time. Our results reveal the sequence of events leading to cell death. Within 1 minute, FITC-PuroA was observed to interact with SYTO-labelled nucleic acids, resulting in a noticeable quenching in the fluorescence lifetime of the peptide label at the nucleus of yeast cells, and cell-cycle arrest. A propidium iodide (PI) influx assay confirmed that peptide translocation itself did not disrupt the cell membrane integrity; however, PI entry occurred 25–45 minutes later, which correlated with an increase in fractional fluorescence of pores and an overall loss of cell size. Our results clarify that membrane disruption appears to be the mechanism by which the C. albicans cells are killed and this occurs after FITC-PuroA translocation and binding to intracellular targets.

Automated Detection and Tracking of Cell Clusters in Time-Lapse Fluorescence Microscopy Images

Fluorescence microscopy imaging of live cells has drawn the attention of many researchers in the past decade. Because of the large amount of image data produced by a fluorescence microscope, automated cell detection and tracking algorithms have become an emerging need to help cell biologists visualize and analyze cell kinematics such as changes in cell population caused by proliferation, attachment, and differentiation. This study uses the adaptive background subtraction technique to detect cell clusters in time-lapse fluorescence microscopy images and the discrete Kalman filter to track the position, motion, merging and splitting of these clusters. A set of two-dimensional (2D) time-series images from mouse embryonic stem cells obtained using wide-field microscopy and a set of three-dimensional (3D) time-series from HeLa cells obtained using confocal microscopy are used to demonstrate that biologists can visualize various quantitative measures such as motion, merging and splitting, centroids, areas, and growth rates of cell clusters to acquire useful information through 2D and 3D interfaces.

Detection and localization of mouse induced pluripotent stem cell formation using time-lapse fluorescence microscopy images.

We present an automated method for detection and localization of mouse Induced pluripotent stem (iPS) cells formation by using the fluorescence microscopy images. The differentiated cells that possibly undergo reprogramming to iPS cells can be detected by this method for screening reagents or culture conditions in iPS induction. Our method includes image preprocessing to enhance the fluorescence microscopy images, followed by cell detection to detect isolated fluorescent (reprogramming or reprogrammed iPS) cells. Finally, a clustering technique is used to detect and localize the fluorescent cell clusters. A cluster formation indicates that the reprogramming iPS cells may become reprogrammed. The implementation results show that the automatic method can successfully detect and localize the mouse iPS cell formation, thereby yield a potential tool for helping iPS cell culture.

Single-Cell Analysis of the Dps Response to Oxidative Stress.

Microorganisms have developed an elaborate spectrum of mechanisms to respond and adapt to environmental stress conditions. Among these is the expression of dps, coding for the DNA-binding protein from starved cells. Dps becomes the dominant nucleoid-organizing protein in stationary-phase Escherichia coli cells and is required for robust survival under stress conditions, including carbon or nitrogen starvation, oxidative stress, metal exposure, and irradiation. To study the complex regulation of Dps in E. coli, we utilized time-lapse fluorescence microscopy imaging to examine the kinetics, input encoding, and variability of the Dps response in single cells. In the presence of an oxidative stressor, we observed a single pulse of activation of Dps production. Increased concentrations of H2O2 led to increased intensity and duration of the pulse. While lower concentrations of H2O2 robustly activated the Dps response with little effect on the growth rate, higher concentrations of H2O2 resulted in dramatically lower and highly varied growth rates. A comparison of cells exposed to the same concentration of H2O2 revealed that increased levels of Dps expression did not confer a growth advantage, indicating that recovery from stress may rely primarily upon variation in the amount of damage caused to individual cells. We show for the first time the response of the DNA-binding protein from starved cells (Dps) to oxidative stress in single cells of E. coli Through time-lapse fluorescence microscopy, a single pulse of Dps production is observed in cells exposed to H2O2, with a duration and intensity of induction proportional to the concentration of the applied stress. More intense Dps expression did not provide a growth benefit to the bacteria, suggesting that healing from oxidative stress may largely depend upon the amount of damage in each individual cell.

Automated analysis of time-lapse fluorescence microscopy images: from live cell images to intracellular foci

Complete, accurate and reproducible analysis of intracellular foci from fluorescence microscopy image sequences of live cells requires full automation of all processing steps involved: cell segmentation and tracking followed by foci segmentation and pattern analysis. Integrated systems for this purpose are lacking. Extending our previous work in cell segmentation and tracking, we developed a new system for performing fully automated analysis of fluorescent foci in single cells. The system was validated by applying it to two common tasks: intracellular foci counting (in DNA damage repair experiments) and cell-phase identification based on foci pattern analysis (in DNA replication experiments). Experimental results show that the system performs comparably to expert human observers. Thus, it may replace tedious manual analyses for the considered tasks, and enables high-content screening. The described system was implemented in MATLAB (The MathWorks, Inc., USA) and compiled to run within the MATLAB environment. The routines together with four sample datasets are available at http://celmia.bigr.nl/. The software is planned for public release, free of charge for non-commercial use, after publication of this article.

Advanced Level-Set-Based Cell Tracking in Time-Lapse Fluorescence Microscopy

Cell segmentation and tracking in time-lapse fluorescence microscopy images is a task of fundamental importance in many biological studies on cell migration and proliferation. In recent years, level sets have been shown to provide a very appropriate framework for this purpose, as they are well suited to capture topological changes occurring during mitosis, and they easily extend to higher dimensional image data. This model evolution approach has also been extended to deal with many cells concurrently. Notwithstanding its high potential, the multiple-level-set method suffers from a number of shortcomings, which limit its applicability to a larger variety of cell biological imaging studies. In this paper, we propose several modifications and extensions to the coupled-active-surfaces algorithm, which considerably improve its robustness and applicability. Our algorithm was validated by comparing it to the original algorithm and two other cell segmentation algorithms. For the evaluation, four real fluorescence microscopy image datasets were used, involving different cell types and labelings that are representative of a large range of biological experiments. Improved tracking performance in terms of precision (up to 11%), recall (up to 8%), ability to correctly capture all cell division events, and computation time (up to nine times reduction) is achieved.