Computational imaging in cell biology

Characterizing morphological alterations in blood related disorders through Atomic Force Microscopy

Atomic Force Microscopy (AFM) is a very flexible method that can create topographical images from a range of materials and image surfaces. Significantly, AFM has emerged as an invaluable tool for dissecting the morphology and biochemical aspects of body cells and tissues. The high-resolution imaging capabilities of AFM enable researchers to discern alterations in cell morphology and understand the underlying mechanisms of diseases. It contributes to understanding disease etiology and progression. In the context of this review, our focus will be directed towards elucidating the pivotal role of AFM in analysis of blood related disorders. Through detailed comparisons with normal cells, we delve into the alterations in size, shape, and surface characteristics induced by conditions such as cancer, diabetes, anaemia, and infections caused by pathogens. In essence, various work described in this article highlights to bridge the gap between traditional microscopy and in-depth analysis of blood-related pathologies, which in turn offers valuable perspectives for both research and clinical applications in the field.

Fourier Ptychographic Microscopy 10 Years on: A Review

Fourier ptychographic microscopy (FPM) emerged as a prominent imaging technique in 2013, attracting significant interest due to its remarkable features such as precise phase retrieval, expansive field of view (FOV), and superior resolution. Over the past decade, FPM has become an essential tool in microscopy, with applications in metrology, scientific research, biomedicine, and inspection. This achievement arises from its ability to effectively address the persistent challenge of achieving a trade-off between FOV and resolution in imaging systems. It has a wide range of applications, including label-free imaging, drug screening, and digital pathology. In this comprehensive review, we present a concise overview of the fundamental principles of FPM and compare it with similar imaging techniques. In addition, we present a study on achieving colorization of restored photographs and enhancing the speed of FPM. Subsequently, we showcase several FPM applications utilizing the previously described technologies, with a specific focus on digital pathology, drug screening, and three-dimensional imaging. We thoroughly examine the benefits and challenges associated with integrating deep learning and FPM. To summarize, we express our own viewpoints on the technological progress of FPM and explore prospective avenues for its future developments.

Engineering cell-substrate interactions on porous membranes for microphysiological systems

Microphysiological systems are now widely used to recapitulate physiological and pathological microenvironments in order to study and understand a variety of cellular processes as well as drug delivery and stem cell differentiation. Central to many of these systems are porous membranes that enable tissue barrier formation as well as compartmentalization while still facilitating small molecule diffusion, cellular transmigration and cell-cell communication. The role or impact of porous membranes on the cells cultured upon them has not been widely studied or reviewed. Although many chemical and physical substrate characteristics have been shown to be effective in controlling and directing cellular behavior, the influence of pore characteristics and the ability to engineer porous membranes to influence these responses is not fully understood. In this mini-review, we show that many studies point to a multiphasic cell-substrate response, where increasing pore sizes and pore-pore spacing generally leads to improved cell-substrate interactions. However, the smallest pores in the nano-scale sometimes promote the strongest cell-substrate interactions, while the very largest micron-scale pores hinder cell-substrate interactions. This synopsis provides an insight into the importance of membrane pores in controlling cellular responses, and may help with the design and utilization of porous membranes for induction of desired cell processes in the development of biomimetic platforms.

Automated Cell Tracking Using Motion Prediction-Based Matching and Event Handling

Automated cell segmentation and tracking enables the quantification of static and dynamic cell characteristics and is significant for disease diagnosis, treatment, drug development, and other biomedical applications. This paper introduces a method for fully automated cell tracking, lineage construction, and quantification. Cell detection is performed in the joint spatio-temporal domain by a motion diffusion-based Partial Differential Equation (PDE) combined with energy minimizing active contours. In the tracking stage, we adopt a variational joint local-global optical flow technique to determine the motion vector field. We utilize the predicted cell motion jointly with spatial cell features to define a maximum likelihood criterion to find inter-frame cell correspondences assuming Markov dependency. We formulate cell tracking and cell event detection as a graph partitioning problem. We propose a solution obtained by minimization of a global cost function defined over the set of all cell tracks. We construct a cell lineage tree that represents the cell tracks and cell events. Finally, we compute morphological, motility, and diffusivity measures and validate cell tracking against manually generated reference standards. The automated tracking method applied to reference segmentation maps produces an average tracking accuracy score ( TRA) of 99 percent, and the fully automated segmentation and tracking system produces an average TRA of 89 percent.

Live Cell Imaging of Bone Cell and Organ Cultures

Over the past two decades there have been unprecedented advances in the capabilities for live cell imaging using light and confocal microscopy. Together with the discovery of green fluorescent protein and its derivatives and the development of a vast array of fluorescent imaging probes and conjugates, it is now possible to image virtually any intracellular or extracellular protein or structure. Traditional static imaging of fixed bone cells and tissues takes a snapshot view of events at a specific time point, but can often miss the dynamic aspects of the events being investigated. This chapter provides an overview of the application of live cell imaging approaches for the study of bone cells and bone organ cultures. Rather than emphasizing technical aspects of the imaging equipment, which may vary in different laboratories, we focus on what we consider to be the important principles that are of most practical use for an investigator setting up these techniques in their own laboratory. We also provide detailed protocols that our laboratory has used for live imaging of bone cell and organ cultures.

Real-Time and 3D Quantification of Cancer Cell Dynamics: Exploiting a Bioengineered Human Bone Metastatic Microtissue

The study of dynamic processes in the bone metastatic compartment has been challenged by the restrictive access and limited live imaging capabilities that in vivo bone models provide. In this protocol, we show the use of a human bone metastatic bioengineered microtissue for the quantitative investigation of cancer cells in an in vitro bone-like microenvironment. Using live cell epifluorescence microscopy, traditional- and spinning disc-confocal laser scanning microscopy, we demonstrate how to obtain multidimensional real-time data of fluorescently labeled cancer cells in the metastatic microenvironment. Using 4D imaging data processing software such as ImageJ and Imaris, we show how to transform qualitative images and videos into quantitative data of cancer cell attachment, morphology, proliferation, and migration in vitro in the human bone metastatic microtissue.

Impedance biosensor for real-time monitoring and prediction of thrombotic individual profile in flowing blood

A new biosensor for the real-time analysis of thrombus formation is reported. The fast and accurate monitoring of the individual thrombotic risk represents a challenge in cardiovascular diagnostics and in treatment of hemostatic diseases. Thrombus volume, as representative index of the related thrombotic status, is usually estimated with confocal microscope at the end of each in vitro experiment, without providing a useful behavioral information of the biological sample such as platelets adhesion and aggregation in flowing blood. Our device has been developed to work either independently or integrated with the microscopy system; thus, images of the fluorescently labeled platelets are acquired in real-time during the whole blood perfusion, while the global electrical impedance of the blood sample is simultaneously monitored between a pair of specifically designed gold microelectrodes. Fusing optical and electrical data with a novel technique, the dynamic of thrombus formation events in flowing blood can be reconstructed in real-time, allowing an accurate extrapolation of the three-dimensional shape and the spatial distribution of platelet thrombi forming and growing within artificial capillaries. This biosensor is accurate and it has been used to discriminate different hemostatic conditions and to identify weakening and detaching platelet aggregates. The results obtained appear compatible with those quantified with the traditional optical method. With advantages in terms of small size, user-friendliness and promptness of response, it is a promising device for the fast and automatic individual health monitoring at the Point of Care (POC).

Modified cuckoo search algorithm in microscopic image segmentation of hippocampus

Microscopic image analysis is one of the challenging tasks due to the presence of weak correlation and different segments of interest that may lead to ambiguity. It is also valuable in foremost meadows of technology and medicine. Identification and counting of cells play a vital role in features extraction to diagnose particular diseases precisely. Different segments should be identified accurately in order to identify and to count cells in a microscope image. Consequently, in the current work, a novel method for cell segmentation and identification has been proposed that incorporated marking cells. Thus, a novel method based on cuckoo search after pre-processing step is employed. The method is developed and evaluated on light microscope images of rats' hippocampus which used as a sample for the brain cells. The proposed method can be applied on the color images directly. The proposed approach incorporates the McCulloch's method for lévy flight production in cuckoo search (CS) algorithm. Several objective functions, namely Otsu's method, Kapur entropy and Tsallis entropy are used for segmentation. In the cuckoo search process, the Otsu's between class variance, Kapur's entropy and Tsallis entropy are employed as the objective functions to be optimized. Experimental results are validated by different metrics, namely the peak signal to noise ratio (PSNR), mean square error, feature similarity index and CPU running time for all the test cases. The experimental results established that the Kapur's entropy segmentation method based on the modified CS required the least computational time compared to Otsu's between-class variance segmentation method and the Tsallis entropy segmentation method. Nevertheless, Tsallis entropy method with optimized multi-threshold levels achieved superior performance compared to the other two segmentation methods in terms of the PSNR.

MitoGen: A Framework for Generating 3D Synthetic Time-Lapse Sequences of Cell Populations in Fluorescence Microscopy

The proper analysis of biological microscopy images is an important and complex task. Therefore, it requires verification of all steps involved in the process, including image segmentation and tracking algorithms. It is generally better to verify algorithms with computer-generated ground truth datasets, which, compared to manually annotated data, nowadays have reached high quality and can be produced in large quantities even for 3D time-lapse image sequences. Here, we propose a novel framework, called MitoGen, which is capable of generating ground truth datasets with fully 3D time-lapse sequences of synthetic fluorescence-stained cell populations. MitoGen shows biologically justified cell motility, shape and texture changes as well as cell divisions. Standard fluorescence microscopy phenomena such as photobleaching, blur with real point spread function (PSF), and several types of noise, are simulated to obtain realistic images. The MitoGen framework is scalable in both space and time. MitoGen generates visually plausible data that shows good agreement with real data in terms of image descriptors and mean square displacement (MSD) trajectory analysis. Additionally, it is also shown in this paper that four publicly available segmentation and tracking algorithms exhibit similar performance on both real and MitoGen-generated data. The implementation of MitoGen is freely available.

Joint level-set and spatio-temporal motion detection for cell segmentation

BACKGROUND

Cell segmentation is a critical step for quantification and monitoring of cell cycle progression, cell migration, and growth control to investigate cellular immune response, embryonic development, tumorigenesis, and drug effects on live cells in time-lapse microscopy images.

METHODS

In this study, we propose a joint spatio-temporal diffusion and region-based level-set optimization approach for moving cell segmentation. Moving regions are initially detected in each set of three consecutive sequence images by numerically solving a system of coupled spatio-temporal partial differential equations. In order to standardize intensities of each frame, we apply a histogram transformation approach to match the pixel intensities of each processed frame with an intensity distribution model learned from all frames of the sequence during the training stage. After the spatio-temporal diffusion stage is completed, we compute the edge map by nonparametric density estimation using Parzen kernels. This process is followed by watershed-based segmentation and moving cell detection. We use this result as an initial level-set function to evolve the cell boundaries, refine the delineation, and optimize the final segmentation result.

RESULTS

We applied this method to several datasets of fluorescence microscopy images with varying levels of difficulty with respect to cell density, resolution, contrast, and signal-to-noise ratio. We compared the results with those produced by Chan and Vese segmentation, a temporally linked level-set technique, and nonlinear diffusion-based segmentation. We validated all segmentation techniques against reference masks provided by the international Cell Tracking Challenge consortium. The proposed approach delineated cells with an average Dice similarity coefficient of 89 % over a variety of simulated and real fluorescent image sequences. It yielded average improvements of 11 % in segmentation accuracy compared to both strictly spatial and temporally linked Chan-Vese techniques, and 4 % compared to the nonlinear spatio-temporal diffusion method.

CONCLUSIONS

Despite the wide variation in cell shape, density, mitotic events, and image quality among the datasets, our proposed method produced promising segmentation results. These results indicate the efficiency and robustness of this method especially for mitotic events and low SNR imaging, enabling the application of subsequent quantification tasks.

Object Segmentation and Ground Truth in 3D Embryonic Imaging

Many questions in developmental biology depend on measuring the position and movement of individual cells within developing embryos. Yet, tools that provide this data are often challenged by high cell density and their accuracy is difficult to measure. Here, we present a three-step procedure to address this problem. Step one is a novel segmentation algorithm based on image derivatives that, in combination with selective post-processing, reliably and automatically segments cell nuclei from images of densely packed tissue. Step two is a quantitative validation using synthetic images to ascertain the efficiency of the algorithm with respect to signal-to-noise ratio and object density. Finally, we propose an original method to generate reliable and experimentally faithful ground truth datasets: Sparse-dense dual-labeled embryo chimeras are used to unambiguously measure segmentation errors within experimental data. Together, the three steps outlined here establish a robust, iterative procedure to fine-tune image analysis algorithms and microscopy settings associated with embryonic 3D image data sets.

Bioinformatics approaches to single-cell analysis in developmental biology

Individual cells within the same population show various degrees of heterogeneity, which may be better handled with single-cell analysis to address biological and clinical questions. Single-cell analysis is especially important in developmental biology as subtle spatial and temporal differences in cells have significant associations with cell fate decisions during differentiation and with the description of a particular state of a cell exhibiting an aberrant phenotype. Biotechnological advances, especially in the area of microfluidics, have led to a robust, massively parallel and multi-dimensional capturing, sorting, and lysis of single-cells and amplification of related macromolecules, which have enabled the use of imaging and omics techniques on single cells. There have been improvements in computational single-cell image analysis in developmental biology regarding feature extraction, segmentation, image enhancement and machine learning, handling limitations of optical resolution to gain new perspectives from the raw microscopy images. Omics approaches, such as transcriptomics, genomics and epigenomics, targeting gene and small RNA expression, single nucleotide and structural variations and methylation and histone modifications, rely heavily on high-throughput sequencing technologies. Although there are well-established bioinformatics methods for analysis of sequence data, there are limited bioinformatics approaches which address experimental design, sample size considerations, amplification bias, normalization, differential expression, coverage, clustering and classification issues, specifically applied at the single-cell level. In this review, we summarize biological and technological advancements, discuss challenges faced in the aforementioned data acquisition and analysis issues and present future prospects for application of single-cell analyses to developmental biology.

Segmentation and tracking of stem cells in time lapse microscopy to quantify dynamic behavioral changes during spheroid formation

Dynamic behavior of stem cells during in vitro development is diverse. Previous cell tracking studies have focused more on cell proliferation than on cell aggregation. However, the enhancement of cell proliferation in association with cell aggregation has been reported. In a previous study, we also demonstrated that the aggregation of adult human mesenchymal stem cells to form three-dimensional (3D) cellular spheroids helped maintain the expression of stemness marker genes in the cells. However, the dynamic behavioral changes triggered by spheroid formation remain to be investigated. A scheme of image processing techniques is proposed to meet this need. A hybrid-thresholding technique was first developed for efficient segmentation of cell clusters, after which a cell tracking method based on pair-matching with topological constraints was designed. Two morphological indices were derived to track the timing of 3D spheroid formation during the cellular aggregation process. Five cell motility indices measured from single cells and 3D spheroids were then compared. After confirmation of more than 90% correspondence between the results obtained by manual tracking and the proposed methods, an analysis of cellular behavior reveals a significant increase in motility in association with spheroid formation, consistent with a previous report that used a gene expression approach. This study proposed a systematic image analysis method to quantify the dynamic behavior of stem cells for stemness evaluation during cell culturing in vitro. Results demonstrated the validity of the developed platform in investigation of the dynamic behavior of cell aggregation in stem cell cultures in vitro.

Characterization of macrolesions induced by myocardial cavitation-enabled therapy

Intermittent high intensity ultrasound pulses with circulating contrast agent microbubbles can induce scattered cavitation caused myocardial microlesions of potential value for tissue reduction therapy. Here, computer-aided histological evaluation of the effective treated volume was implemented to optimize ultrasound pulse parameters, exposure duration, and contrast agent dose. Rats were treated with 1.5 MHz focused ultrasound bursts and Evans blue staining indicates lethal cardiomyocytic injury. Each heart was sectioned to provide samples covering the entire exposed myocardial volume. Both brightfield and fluorescence images were taken for up to 40 tissue sections. Tissue identification and microlesion detection were first done based on 2-D images to form microlesion masks containing the outline of the heart and the stained cell regions. Image registration was then performed on the microlesion masks to reconstruct a volume-based model according to the morphology of the heart. The therapeutic beam path was estimated from the 3-D stacked microlesions, and finally the total microlesion volume, here termed macrolesion, was characterized along the therapeutic beam axis. Radially symmetric fractional macrolesions were characterized via stepping disks of variable radius determined by the local distribution of microlesions. Treated groups showed significant macrolesions of a median volume of 87.3 μL, 2.7 mm radius, 4.8 mm length, and 14.0% lesion density compared to zero radius, length, and lesion density for sham. The proposed radially symmetric lesion model is a robust evaluation for myocardial cavitation-enabled therapy. Future work will include validating the proposed method with varying acoustic exposures and optimizing involved parameters to provide macrolesion characterization.

Analysis of cellular events using CellCard™ System in cell-based high-content multiplexed assays

High-content screening technologies utilize assays that monitor and quantify multiple cellular events. These assays are typically performed on a single cell type with automated microscopy and image analysis. However, in order to better understand the selectivity of a compound across multiple cell lines, these types of assay must be run serially, which is time consuming. The CellCard System developed by Vitra Bioscience enables multiple cell types to be assayed within a single microtiter well, thereby enabling the simultaneous determination of cellular responses across ten cell types. This multiplexed approach could address the demand for assay capacity, increase the quality of the biologic data, reduce timelines, and improve cost-effectiveness in hit identification and lead evaluation. The authors have carried out an in-depth evaluation of this technology platform using ten cancer cell lines and a library of compounds that affect cellular growth through different mechanisms. Multiple assays were used to investigate the compound effects on membrane integrity, cell cycle progression and apoptosis. In this technology review, the authors discuss personal experience with assay validation, data analysis, results such as cell type-specific compound effects, and the potential application of the CellCard System in drug discovery.

3D time series analysis of cell shape using Laplacian approaches

Background

Fundamental cellular processes such as cell movement, division or food uptake critically depend on cells being able to change shape. Fast acquisition of three-dimensional image time series has now become possible, but we lack efficient tools for analysing shape deformations in order to understand the real three-dimensional nature of shape changes.

Results

We present a framework for 3D+time cell shape analysis. The main contribution is three-fold: First, we develop a fast, automatic random walker method for cell segmentation. Second, a novel topology fixing method is proposed to fix segmented binary volumes without spherical topology. Third, we show that algorithms used for each individual step of the analysis pipeline (cell segmentation, topology fixing, spherical parameterization, and shape representation) are closely related to the Laplacian operator. The framework is applied to the shape analysis of neutrophil cells.

Conclusions

The method we propose for cell segmentation is faster than the traditional random walker method or the level set method, and performs better on 3D time-series of neutrophil cells, which are comparatively noisy as stacks have to be acquired fast enough to account for cell motion. Our method for topology fixing outperforms the tools provided by SPHARM-MAT and SPHARM-PDM in terms of their successful fixing rates. The different tasks in the presented pipeline for 3D+time shape analysis of cells can be solved using Laplacian approaches, opening the possibility of eventually combining individual steps in order to speed up computations.

Computational imaging, sensing and diagnostics for global health applications

In this review, we summarize some of the recent work in emerging computational imaging, sensing and diagnostics techniques, along with some of the complementary non-computational modalities that can potentially transform the delivery of health care globally. As computational resources are becoming more and more powerful, while also getting cheaper and more widely available, traditional imaging, sensing and diagnostic tools will continue to experience a revolution through simplification of their designs, making them compact, light-weight, cost-effective, and yet quite powerful in terms of their performance when compared to their bench-top counterparts.

Three-dimensional morphometric comparison of normal and apoptotic endothelial cells based on laser scanning confocal microscopy observation

Three-dimensional (3D) morphometric analysis of cellular and subcellular structures provides an effective method for spatial cell biology. Here, 3D cellular and nuclear morphologies are reconstructed to quantify and compare morphometric differences between normal and apoptotic endothelial cells. Human umbilical vein endothelial cells (HUVECs) are treated with 60 μM H2 O2 to get apoptotic cell model and then a series of sectional images are acquired from laser scanning confocal microscopy. The 3D cell model containing plasma membrane and cell nucleus is reconstructed and fused utilizing three sequential softwares or packages (Mimics, Geomagic, and VTK). The results reveal that H2 O2 can induce apoptosis effectively by regulating the activity of apoptosis-related biomolecules, including pro-apoptotic factors p53 and Bax, and anti-apoptotic factor Bcl-2. Compared with the normal HUVECs, the apoptotic cells exhibit significant 3D morphometric parameters (height, volume and nucleus-to-cytoplasm ratio) variation. The present research provides a new perspective on comparative quantitative analysis associated with cell apoptosis and points to the value of LSCM as an objective tool for 3D cell reconstruction.

Quantitative measurement of protein relocalization in live cells

Microscope cytometry provides a powerful means to study signaling in live cells. Here we present a quantitative method to measure protein relocalization over time, which reports the absolute fraction of a tagged protein in each compartment. Using this method, we studied an essential step in the early propagation of the pheromone signal in Saccharomyces cerevisiae: recruitment to the membrane of the scaffold Ste5 by activated Gβγ dimers. We found that the dose response of Ste5 recruitment is graded (EC50 = 0.44 ± 0.08 nM, Hill coefficient = 0.8 ± 0.1). Then, we determined the effective dissociation constant (K(de)) between Ste5 and membrane sites during the first few minutes when the negative feedback from the MAPK Fus3 is first activated. K(de) changed during the first minutes from a high affinity of < 0.65 nM to a steady-state value of 17 ± 9 nM. During the same period, the total number of binding sites decreased slightly, from 1940 ± 150 to 1400 ± 200. This work shows how careful quantification of a protein relocalization dynamic can give insight into the regulation mechanisms of a biological system.

A novel application of motion analysis for detecting stress responses in embryos at different stages of development

Background

Motion analysis is one of the tools available to biologists to extract biologically relevant information from image datasets and has been applied to a diverse range of organisms. The application of motion analysis during early development presents a challenge, as embryos often exhibit complex, subtle and diverse movement patterns. A method of motion analysis able to holistically quantify complex embryonic movements could be a powerful tool for fields such as toxicology and developmental biology to investigate whole organism stress responses. Here we assessed whether motion analysis could be used to distinguish the effects of stressors on three early developmental stages of each of three species: (i) the zebrafish Danio rerio (stages 19 h, 21.5 h and 33 h exposed to 1.5% ethanol and a salinity of 5); (ii) the African clawed toad Xenopus laevis (stages 24, 32 and 34 exposed to a salinity of 20); and iii) the pond snail Radix balthica (stages E3, E4, E6, E9 and E11 exposed to salinities of 5, 10 and 15). Image sequences were analysed using Sparse Optic Flow and the resultant frame-to-frame motion parameters were analysed using Discrete Fourier Transform to quantify the distribution of energy at different frequencies. This spectral frequency dataset was then used to construct a Bray-Curtis similarity matrix and differences in movement patterns between embryos in this matrix were tested for using ANOSIM.

Results

Spectral frequency analysis of these motion parameters was able to distinguish stage-specific effects of environmental stressors in most cases, including Xenopus laevis at stages 24, 32 and 34 exposed to a salinity of 20, Danio rerio at 33 hpf exposed to 1.5% ethanol, and Radix balthica at stages E4, E9 and E11 exposed to salinities of 5, 10 and 15. This technique was better able to distinguish embryos exposed to stressors than analysis of manual quantification of movement and within species distinguished most of the developmental stages studied in the control treatments.

Conclusion

This innovative use of motion analysis incorporates data quantifying embryonic movements at a range of frequencies and so provides an holistic analysis of an embryo’s movement patterns. This technique has potential applications for quantifying embryonic responses to environmental stressors such as exposure to pharmaceuticals or pollutants, and also as an automated tool for developmental staging of embryos.

Future scenarios for plant phenotyping

With increasing demand to support and accelerate progress in breeding for novel traits, the plant research community faces the need to accurately measure increasingly large numbers of plants and plant parameters. The goal is to provide quantitative analyses of plant structure and function relevant for traits that help plants better adapt to low-input agriculture and resource-limited environments. We provide an overview of the inherently multidisciplinary research in plant phenotyping, focusing on traits that will assist in selecting genotypes with increased resource use efficiency. We highlight opportunities and challenges for integrating noninvasive or minimally invasive technologies into screening protocols to characterize plant responses to environmental challenges for both controlled and field experimentation. Although technology evolves rapidly, parallel efforts are still required because large-scale phenotyping demands accurate reporting of at least a minimum set of information concerning experimental protocols, data management schemas, and integration with modeling. The journey toward systematic plant phenotyping has only just begun.

Using imaging methods to interrogate radiation-induced cell signaling

There is increasing emphasis on the use of systems biology approaches to define radiation-induced responses in cells and tissues. Such approaches frequently rely on global screening using various high throughput 'omics' platforms. Although these methods are ideal for obtaining an unbiased overview of cellular responses, they often cannot reflect the inherent heterogeneity of the system or provide detailed spatial information. Additionally, performing such studies with multiple sampling time points can be prohibitively expensive. Imaging provides a complementary method with high spatial and temporal resolution capable of following the dynamics of signaling processes. In this review, we utilize specific examples to illustrate how imaging approaches have furthered our understanding of radiation-induced cellular signaling. Particular emphasis is placed on protein colocalization, and oscillatory and transient signaling dynamics.

Live imaging of bone cell and organ cultures

Over the past two decades there have been unprecedented advances in the capabilities for live cell imaging using light and confocal microscopy. Together with the discovery of green fluorescent protein and its derivatives and the development of a vast array of fluorescent imaging probes and conjugates, it is now possible to image virtually any intracellular or extracellular protein or structure. Traditional static imaging of fixed bone cells and tissues takes a snapshot view of events at a specific time point, but can often miss the dynamic aspects of the events being investigated. This chapter provides an overview of the application of live cell imaging approaches for the study of bone cells and bone organ cultures. Rather than emphasizing technical aspects of the imaging equipment, we have focused on what we consider to be the important principles that are of most practical use for an investigator setting up these techniques in their own laboratory, together with detailed protocols that our laboratory has used for live imaging of bone cell and organ cultures.

INTERACTIVE REGISTRATION OF INTRACELLULAR VOLUMES WITH RADIAL BASIS FUNCTIONS

We propose a novel approach to 3D image registration of intracellular volumes. The approach extends a standard image registration framework to the curved cell geometry. An intracellular volume is mapped onto another intracellular domain by using two pairs of point set surfaces approximating their nuclear and plasma membranes. The mapping function consists of the affine transformation, tetrahedral barycentric interpolation, and least-squares formulation of radial basis functions for extracted cell geometry features. An interactive volume registration system is also developed based on our approach. We demonstrate that our approach is capable of creating cell models containing multiple organelles from observed data of living cells.

Three-dimensional distribution of vessels, passage cells and lateral roots along the root axis of winter wheat (Triticum aestivum)

BACKGROUND AND AIMS

The capacity of a plant to absorb and transport water and nutrients depends on anatomical structures within the roots and their co-ordination. However, most descriptions of root anatomical structure are limited to 2-D cross-sections, providing little information on 3-D spatial relationships and hardly anything on their temporal evolution. Three-dimensional reconstruction and visualization of root anatomical structures can illustrate spatial co-ordination among cells and tissues and provide new insights and understanding of the interrelation between structure and function.

METHODS

Classical paraffin serial-section methods, image processing, computer-aided 3-D reconstruction and 3-D visualization techniques were combined to analyse spatial relationships among metaxylem vessels, passage cells and lateral roots in nodal roots of winter wheat (Triticum aestivum).

KEY RESULTS

3-D reconstruction demonstrated that metaxylem vessels were neither parallel, nor did they run directly along the root axis from the root base to the root tip; rather they underwent substitution and transition. Most vessels were connected to pre-existent or newly formed vessels by pits on their lateral walls. The spatial distributions of both passage cells and lateral roots exhibited similar position-dependent patterns. In the transverse plane, the passage cells occurred opposite the poles of the protoxylem and the lateral roots opposite those of the protophloem. Along the axis of a young root segment, the passage cells were arranged in short and discontinuous longitudinal files, thus as the tissues mature, the sequence in which the passage cells lose their transport function is not basipetal. In older segments, passage cells decreased drastically in number and coexisted with lateral roots. The spatial distribution of lateral roots was similar to that of the passage cells, mirroring their similar functions as lateral pathways for water and nutrient transport to the stele.

CONCLUSIONS

With the 3-D reconstruction and visualization techniques developed here, the spatial relationships between vessels, passage cells and lateral roots and the temporal evolution of these relationships can be described. The technique helps to illustrate synchronization and spatial co-ordination among the root's radial and axial pathways for water and nutrient transport and the interdependence of structure and function in the root.

Live cell imaging and systems biology

Much of the experimental data used to construct mathematical models of molecular networks are derived from in vitro measurements. However, there is increasing evidence that in vitro measurements fail to capture both the complexity and the individuality found in single, living cells. These limitations can be overcome by live cell microscopy which is evolving to enable in vivo biochemistry. Here, we survey the current capabilities of live cell microscopy and illustrate how a number of different imaging approaches could be applied to analyze a specific molecular network. We argue that incorporation of such quantitative live-cell imaging methods is critical for the progress of systems biology.

Tracking and quantitative analysis of dynamic movements of cells and particles

Understanding complex cellular processes requires investigating the underlying mechanisms within a spatiotemporal context. Although cellular processes are dynamic in nature, most studies in molecular cell biology are based on fixed specimens, for example, using immunocytochemistry or fluorescence in situ hybridization (FISH). However, breakthroughs in fluorescence microscopy imaging techniques, in particular, the discovery of green fluorescent protein (GFP) and its spectral variants, have facilitated the study of a wide range of dynamic processes by allowing nondestructive labeling of target structures in living cells. In addition, the tremendous improvements in spatial and temporal resolution of light microscopes now allow cellular processes to be analyzed in unprecedented detail. These state-of-the-art imaging technologies, however, provide a huge amount of digital image data. To cope with the enormous amount of image data and to extract reproducible as well as quantitative information, computer-based image analysis is required. In this article, we describe methods for computer-based analysis of multidimensional live cell microscopy images and their application to study the dynamics of cells and particles. First, we sketch a general workflow for quantitative analysis of live cell images. Then, we detail computational methods for automatic image analysis comprising image preprocessing, segmentation, registration, tracking, and classification. We conclude with a discussion of quantitative analysis and systems biology.

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.

Quantitative comparison of spot detection methods in fluorescence microscopy

Quantitative analysis of biological image data generally involves the detection of many subresolution spots. Especially in live cell imaging, for which fluorescence microscopy is often used, the signal-to-noise ratio (SNR) can be extremely low, making automated spot detection a very challenging task. In the past, many methods have been proposed to perform this task, but a thorough quantitative evaluation and comparison of these methods is lacking in the literature. In this paper, we evaluate the performance of the most frequently used detection methods for this purpose. These include seven unsupervised and two supervised methods. We perform experiments on synthetic images of three different types, for which the ground truth was available, as well as on real image data sets acquired for two different biological studies, for which we obtained expert manual annotations to compare with. The results from both types of experiments suggest that for very low SNRs ( approximately 2), the supervised (machine learning) methods perform best overall. Of the unsupervised methods, the detectors based on the so-called h -dome transform from mathematical morphology or the multiscale variance-stabilizing transform perform comparably, and have the advantage that they do not require a cumbersome learning stage. At high SNRs ( > 5), the difference in performance of all considered detectors becomes negligible.

Tracking in cell and developmental biology

The past decade has seen an unprecedented data explosion in biology. It has become evident that in order to take full advantage of the potential wealth of information hidden in the data produced by even a single experiment, visual inspection and manual analysis are no longer adequate. To ensure efficiency, consistency, and completeness in data processing and analysis, computational tools are essential. Of particular importance to many modern live-cell imaging experiments is the ability to automatically track and analyze the motion of objects in time-lapse microscopy images. This article surveys the recent literature in this area. Covering all scales of microscopic observation, from cells, down to molecules, and up to entire organisms, it discusses the latest trends and successes in the development and application of computerized tracking methods in cell and developmental biology.

Automated matching of genomic structures in microscopic images of living cells using an information theoretic approach

Advances in microscopic imaging technology have revolutionized biology in recent years by enabling the study of dynamic processes inside living cells. Time-lapse microscopy produces large numbers of sequential images of living cells taken over time. In this paper we describe the novel approaches we have developed to automate and introduce high accuracy to the process of identifying genomic structures in living cells and matching them between consecutive time-points. We derive control points from landmarks within the structures and use the Kulback-Leibler divergence as an information-theoretic approach to correctly resolve potential close matches within the iterative closest point (ICP) algorithm. We also describe the steps needed to extend our techniques to analyze three dimensional voxel images. The approaches we describe are widely applicable in the analysis of timelapse microscopy data.

Time lapse imaging techniques for comparison of mineralization dynamics in primary murine osteoblasts and the late osteoblast/early osteocyte-like cell line MLO-A5

Mineralization of bone matrix and osteocyte differentiation occur simultaneously and appear interrelated both spatially and temporally. Although these are dynamic events, their study has been limited to using static imaging approaches, either alone or in combination with chemical and biochemical analysis and/or genetic manipulation. Here we describe the application of live cell imaging techniques to study mineralization dynamics in primary osteoblast cultures compared to a late osteoblast/early osteocyte-like cell line, MLO-A5. Mineral deposition was monitored using alizarin red as a vital stain for calcium. To monitor differentiation into an osteocyte-like phenotype, the calvarial cells were isolated from transgenic mice expressing green fluorescent protein (GFP) driven by an 8-kb dentin matrix protein-1 (Dmp1) promoter that gives osteocyte-selective expression. Time lapse imaging showed that there was a lag phase of 15-20 h after beta-glycerophosphate addition, followed by mineral deposition that was rapid in primary osteoblast cultures but more gradual in MLO-A5 cultures. In primary osteoblast cultures, mineral was deposited exclusively in association with clusters of cells expressing Dmp1-GFP, suggesting that they were already differentiating into osteocyte-like cells. In MLO-A5 cells, the first indication of mineralization was the appearance of punctate areas of alizarin red fluorescence of 4-7 mum in diameter, followed by mineral deposition throughout the culture in association with collagen fibrils. A high amount of cell motility was observed within mineralizing nodules and in mineralizing MLO-A5 cultures. These studies provide a novel approach for analyzing mineralization kinetics that will enable us to dissect in a time-specific manner the essential players in the mineralization process.

A review of imaging techniques for systems biology

This paper presents a review of imaging techniques and of their utility in system biology. During the last decade systems biology has matured into a distinct field and imaging has been increasingly used to enable the interplay of experimental and theoretical biology. In this review, we describe and compare the roles of microscopy, ultrasound, CT (Computed Tomography), MRI (Magnetic Resonance Imaging), PET (Positron Emission Tomography), and molecular probes such as quantum dots and nanoshells in systems biology. As a unified application area among these different imaging techniques, examples in cancer targeting are highlighted.

Membrane allocation profiling: a method to characterize three-dimensional cell shape and attachment based on surface reconstruction

Three-dimensional surface reconstructions from high resolution image stacks of biological specimens, observed by confocal microscopy, have changed the perspective of morphological understanding. In the field of cell-cell or cell-substrate interfaces, combining these two techniques leads to new insights yet also creates a tremendous amount of data. In this article, we present a technique to reduce large, multidimensional data sets from confocal microscopy into one single curve: a membrane allocation profile. Reconstructed cells are represented in a three-dimensional surface from image sections of individual cells. We virtually cut segments of the reconstructed cell membrane parallel to the substrate and calculate the surface areas of each segment. The obtained membrane allocation profiles lead to morphological insights and yield an in vivo ratio of attached and free membrane areas without cell fixation. As an example, glass substrates were modified with different proteins (fibronectin, laminin, concavalin A, extracellular matrix gel, and both isomers of poly-lysine) and presented to HEK293 cells to examine differences in cell morphology and adhesion. We proved that proteins on a substrate could increase the attached portion of a cell membrane, facing the modified substrate, from an average of 32% (glass) to 45% (poly-lysine) of the total membrane surface area.

Deformation-based nuclear morphometry: capturing nuclear shape variation in HeLa cells

The empirical characterization of nuclear shape distributions is an important unsolved problem with many applications in biology and medicine. Numerous genetic diseases and cancers have alterations in nuclear morphology, and methods for characterization of morphology could aid in both diagnoses and fundamental understanding of these disorders. Automated approaches have been used to measure features related to the size and shape of the cell nucleus, and statistical analysis of these features has often been performed assuming an underlying Euclidean (linear) vector space. We discuss the difficulties associated with the analysis of nuclear shape in light of the fact that shape spaces are nonlinear, and demonstrate methods for characterizing nuclear shapes and shape distributions based on spatial transformations that map one nucleus to another. By combining large deformation metric mapping with multidimensional scaling we offer a flexible approach for elucidating the intrinsic nonlinear degrees of freedom of a distribution of nuclear shapes. More specifically, we demonstrate approaches for nuclear shape interpolation and computation of mean nuclear shape. We also provide a method for estimating the number of free parameters that contribute to shape as well as an approach for visualizing most representative shape variations within a distribution of nuclei. The proposed methodology can be completely automated, is independent of the dimensionality of the images, and can handle complex shapes. Results obtained by analyzing two sets of images of HeLa cells are shown. In addition to identifying the modes of variation in normal HeLa nuclei, the effects of lamin A/C on nuclear morphology are quantitatively described.

Student learning of early embryonic development via the utilization of research resources from the nematode Caenorhabditis elegans

This study was undertaken to gain insights into undergraduate students' understanding of early embryonic development, specifically, how well they comprehend the concepts of volume constancy, cell lineages, body plan axes, and temporal and spatial dimensionality in development. To study student learning, a curriculum was developed incorporating resources from the Caenorhabditis elegans research community. Students engaged in a preactivity assessment, followed by instructional materials (IMs) emphasizing inquiry-based learning and a postinstruction assessment to gauge their learning. This study, conducted at two research sites with eight and nine students, respectively, shows that before instruction, most students confused embryonic cell cleavage, where total volume is constant, with regular cell division, in which total cell volume doubles. Despite their ability to construct a cell lineage tree, most of the study participants were not aware of its biological significance. All students correctly identified cells of anterior and posterior axis, but not cells of the dorsal and ventral axis. Although the students had no difficulty with the time dimensional aspect of development, most viewed an embryo as spatially two-dimensional rather than three-dimensional. Furthermore, this study indicates that combining authentic research resources with inquiry-based learning benefits student learning of key concepts in embryology.

Computational processing and analysis of dynamic fluorescence image data

With the many modes of live cell fluorescence imaging made possible by the rapid advances of fluorescent protein technology, researchers begin to face a new challenge: How to transform the vast amounts of unstructured image data into quantitative information for the discovery of new cell behaviors and the rigorous testing of mechanistic hypotheses? Although manual and semiautomatic computer-assisted image analysis are still used extensively, the demand for more reproducible and complete image measurements of complex cellular dynamics increases the need for fully automatic computational image processing approaches for both mechanistic studies and screening applications in cell biology. This chapter provides an overview of the issues that arise with the use of computational algorithms in live cell imaging studies, with particular emphasis on the close coordination of sample preparation, image acquisition, and computational image analysis. It also aims to introduce the terminology and central concepts of computer vision to facilitate the communication between cell biologists and computer scientists in collaborative imaging projects.

Fast 4D Microscopy

Many cellular processes involve fast movements of weakly labeled cellular structures in all directions, which should be recorded in 3D time-lapse microscopy (4D microscopy). This chapter introduces fast 4D imaging, which is used for sampling the cell's volume by collecting focal planes in time-lapse mode as rapidly as possible, without perturbing the sample by strong illumination. The final images should contain sufficient contrast allowing for the isolation of structures of interest by segmentation and the analysis of their intracellular movements by tracking. Because they are the most sensitive, systems using wide-field microscopy and deconvolution techniques are discussed in greater depth. We discuss important points to consider, including system components and multifunctionality, spatial resolution and sampling conditions, and mechanical and optical stability and how to test for it. We consider image formation using high numerical aperture optics and discuss the influence of optical blur and noise on image formation of living cells. Spherical aberrations, their consequences for axial image quality, and their impact on the success of deconvolution of low intensity image stacks are explained in detail. Simple protocols for acquiring and treating point spread functions (PSFs) and live cells are provided. A compromise for counteracting spherical aberration involving the use of a kit of immersion oils for PSF and cell acquisition is illustrated. Recommendations for evaluating acquisition conditions and deconvolution parameters are given. Finally, we discuss future developments based on the use of adaptive optics which will push back many of today's limits.

An automated tracking system to measure the dynamic properties of vesicles in living cells

Recent technological improvements have made it possible to examine the dynamics of individual vesicles at a very high temporal and spatial resolution. Quantification of the dynamic properties of secretory vesicles is labor-intensive and therefore it is crucial to develop software to automate the process of analyzing vesicle dynamics. Dual-threshold and binary image conversion were applied to enhance images and define the areas of objects of interest that were to be tracked. The movements, changes in fluorescence intensity, and changes in the area of each tracked object were measured using a new software system named the Protein Tracking system (PTrack). Simulations revealed that the system accurately recognized tracked objects and measured their dynamic properties. Comparison of the results from tracking real time-lapsed images manually with those automatically obtained using PTrack revealed similar patterns for changes in fluorescence intensity and a high accuracy (<89%). According to tracking results, PTrack can distinguish different vesicular organelles that are similar in shape, based on their unique dynamic properties. In conclusion, the novel tracking system, PTrack, should facilitate automated quantification of the dynamic properties of vesicles that are important when classifying vesicular protein locations.

Cytomics: A multiparametric, dynamic approach to cell research

Cytomics aims to determine the molecular phenotype of single cells. Within the context of the -omics, cytomics allows the investigation of multiple biochemical features of the heterogeneous cellular systems known as the cytomes. Cytomics can be considered as the science of single cell-based analyses that links genomics and proteomics with the dynamics of cell and tissue function, as modulated by external influences. Inherent to cytomics are the use of sensitive, scarcely invasive, fluorescence-based multiparametric methods and the event-integrating concept of individual cells to understand the complexity and behaviour of tissues and organisms. Among cytomic technologies, flow cytometry, confocal laser scanning microscopy and laser capture microdissection are of great relevance. Other recent technologies based on single cell bioimaging and bioinformatic tools become important in drug discovery and toxicity testing, because of both high-content and high-troughput. The multiparametric capacity of cytomics is very useful for the identification, characterization and isolation of stem cell populations. In our experience, flow cytometry is a powerful and versatile tool that allows quantitative analysis of single molecules, prokaryotic and eukaryotic cells for basic, biotechnological, environmental and clinical studies. The dynamic nature of cytomic assays leads to a real-time kinetic approach based on sequential examination of different single cells from a population undergoing a dynamic process, the in fluxo level. Finally, cytomic technologies may provide in vitro methods alternative to laboratory animals for toxicity assessment.