Automated characterization of cell shape changes during amoeboid motility by skeletonization

High-throughput super-resolution analysis of influenza virus pleomorphism reveals insights into viral spatial organization

Many viruses form highly pleomorphic particles. In influenza, virion structure is of interest not only in the context of virus assembly, but also because pleomorphic variations may correlate with infectivity and pathogenicity. We have used fluorescence super-resolution microscopy combined with a rapid automated analysis pipeline, a method well-suited to the study of large numbers of pleomorphic structures, to image many thousands of individual influenza virions; gaining information on their size, morphology and the distribution of membrane-embedded and internal proteins. We observed broad phenotypic variability in filament size, and Fourier transform analysis of super resolution images demonstrated no generalized common spatial frequency patterning of HA or NA on the virion surface, suggesting a model of virus particle assembly where the release of progeny filaments from cells occurs in a stochastic way. We also showed that viral RNP complexes are located preferentially within Archetti bodies when these were observed at filament ends, suggesting that these structures may play a role in virus transmission. Our approach therefore offers exciting new insights into influenza virus morphology and represents a powerful technique that is easily extendable to the study of pleomorphism in other pathogenic viruses.

Three-dimensional imaging of biological cells using surface plasmon coupled emission

SIGNIFICANCE

Biological cell imaging has become one of the most crucial research interests because of its applications in biomedical and microbiology studies. However, three-dimensional (3D) imaging of biological cells is critically challenging and often involves prohibitively expensive and complex equipment. Therefore, a low-cost imaging technique with a simpler optical arrangement is immensely needed.

AIM

The proposed approach will provide an accurate cell image at a low cost without needing any microscope or extensive processing of the collected data, often used in conventional imaging techniques.

APPROACH

We propose that patterns of surface plasmon coupled emission (SPCE) features from a fluorescently labeled biological cell can be used to image the cell. An imaging methodology has been developed and theoretically demonstrated to create 3D images of cells from the detected SPCE patterns. The 3D images created from the different SPCE properties at the far-field closely match the actual cell structures.

RESULTS

The developed technique has been applied to different regular and irregular cell shapes. In each case, the calculated root-mean-square error (RMSE) of the created images from the cell structures remains within a few percentages. Our work recreates the base of a circular-shaped cell with an RMSE of ≲1.4 % . In addition, the images of irregular-shaped cell bases have an RMSE of ≲2.8 % . Finally, we obtained a 3D image with an RMSE of ≲6.5 % for a random cellular structure.

CONCLUSIONS

Despite being in its initial stage of development, the proposed technique shows promising results considering its simplicity and the nominal cost it would require.

Orientation of Cell Polarity by Chemical Gradients

Accurate decoding of spatial chemical landscapes is critical for many cell functions. Eukaryotic cells decode local chemical gradients to orient growth or movement in productive directions. Recent work on yeast model systems, whose gradient sensing pathways display much less complexity than those in animal cells, has suggested new paradigms for how these very small cells successfully exploit information in noisy and dynamic pheromone gradients to identify their mates. Pheromone receptors regulate a polarity circuit centered on the conserved Rho-family GTPase, Cdc42. The polarity circuit contains both positive and negative feedback pathways, allowing spontaneous symmetry breaking and also polarity site disassembly and relocation. Cdc42 orients the actin cytoskeleton, leading to focused vesicle traffic that promotes movement of the polarity site and also reshapes the cortical distribution of receptors at the cell surface. In this article, we review the advances from work on yeasts and compare them with the excitable signaling pathways that have been revealed in chemotactic animal cells. Expected final online publication date for the Annual Review of Biophysics, Volume 51 is May 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Development of convolutional neural networks for recognition of tenogenic differentiation based on cellular morphology

BACKGROUND AND OBJECTIVE

The use of automated systems for image recognition is highly preferred for regenerative medicine applications to evaluate stem cell differentiation early in the culturing state with non-invasive methodologies instead of invasive counterparts. Bone marrow-derived mesenchymal stem cells (BMSCs) are able to differentiate into desired cell phenotypes, and thereby promise a proper cell source for tendon regeneration. The therapeutic success of stem cell therapy requires cellular characterization prior to the implantation of cells. The foremost problem is that traditional characterization techniques require cellular material which would be more useful for cell therapy, complex laboratory procedures, and human expertise. Convolutional neural networks (CNNs), a class of deep neural networks, have recently made great improvements in image-based classifications, recognition, and detection tasks. We, therefore, aim to develop a potential CNN model in order to recognize differentiated stem cells by learning features directly from image data of unlabelled cells.

METHODS

The differentiation of bone marrow mesenchymal stem cells (BMSCs) into tenocytes was induced with the treatment of bone morphogenetic protein-12 (BMP-12). Following the treatment and incubation step, the phase-contrast images of cells were obtained and immunofluorescence staining has been applied to characterize the differentiated state of BMSCs. CNN models were developed and trained with the phase-contrast cell images. The comparison of CNN models was performed with respect to prediction performance and training time. Moreover, we have evaluated the effect of image enhancement method, data augmentation, and fine-tuning training strategy to increase classification accuracy of CNN models. The best model was integrated into a mobile application.

RESULTS

All the CNN models can fit the biological data extracted from immunofluorescence characterization. CNN models enable the cell classification with satisfactory accuracies. The best result in terms of accuracy and training time is achieved by the model proposed based on Inception-ResNet V2 trained from scratch using image enhancement and data augmentation strategies (96.80%, 434.55 sec).

CONCLUSION

Our study reveals that the CNN models show good performance by identifying stem cell differentiation. Importantly this technique provides a faster and real-time tool in comparison to traditional methods enabling the adjustment of culture conditions during cultivation to improve the yield of therapeutic stem cells.

Analysis of protrusion dynamics in amoeboid cell motility by means of regularized contour flows

Amoeboid cell motility is essential for a wide range of biological processes including wound healing, embryonic morphogenesis, and cancer metastasis. It relies on complex dynamical patterns of cell shape changes that pose long-standing challenges to mathematical modeling and raise a need for automated and reproducible approaches to extract quantitative morphological features from image sequences. Here, we introduce a theoretical framework and a computational method for obtaining smooth representations of the spatiotemporal contour dynamics from stacks of segmented microscopy images. Based on a Gaussian process regression we propose a one-parameter family of regularized contour flows that allows us to continuously track reference points (virtual markers) between successive cell contours. We use this approach to define a coordinate system on the moving cell boundary and to represent different local geometric quantities in this frame of reference. In particular, we introduce the local marker dispersion as a measure to identify localized membrane expansions and provide a fully automated way to extract the properties of such expansions, including their area and growth time. The methods are available as an open-source software package called AmoePy, a Python-based toolbox for analyzing amoeboid cell motility (based on time-lapse microscopy data), including a graphical user interface and detailed documentation. Due to the mathematical rigor of our framework, we envision it to be of use for the development of novel cell motility models. We mainly use experimental data of the social amoeba Dictyostelium discoideum to illustrate and validate our approach.

Amoeboid motion is a crawling-like cell migration that plays an important key role in multiple biological processes such as wound healing and cancer metastasis. This type of cell motility results from expanding and simultaneously contracting parts of the cell membrane. From fluorescence images, we obtain a sequence of points, representing the cell membrane, for each time step. By using regression analysis on these sequences, we derive smooth representations, so-called contours, of the membrane. Since the number of measurements is discrete and often limited, the question is raised of how to link consecutive contours with each other. In this work, we present a novel mathematical framework in which these links are described by regularized flows allowing a certain degree of concentration or stretching of neighboring reference points on the same contour. This stretching rate, the so-called local dispersion, is used to identify expansions and contractions of the cell membrane providing a fully automated way of extracting properties of these cell shape changes. We applied our methods to time-lapse microscopy data of the social amoeba Dictyostelium discoideum.

Tools for computational analysis of moving boundary problems in cellular mechanobiology

A cell's ability to change shape is one of the most fundamental biological processes and is essential for maintaining healthy organisms. When the ability to control shape goes awry, it often results in a diseased system. As such, it is important to understand the mechanisms that allow a cell to sense and respond to its environment so as to maintain cellular shape homeostasis. Because of the inherent complexity of the system, computational models that are based on sound theoretical understanding of the biochemistry and biomechanics and that use experimentally measured parameters are an essential tool. These models involve an inherent feedback, whereby shape is determined by the action of regulatory signals whose spatial distribution depends on the shape. To carry out computational simulations of these moving boundary problems requires special computational techniques. A variety of alternative approaches, depending on the type and scale of question being asked, have been used to simulate various biological processes, including cell motility, division, mechanosensation, and cell engulfment. In general, these models consider the forces that act on the system (both internally generated, or externally imposed) and the mechanical properties of the cell that resist these forces. Moving forward, making these techniques more accessible to the non-expert will help improve interdisciplinary research thereby providing new insight into important biological processes that affect human health. This article is categorized under: Cancer > Cancer>Computational Models Cancer > Cancer>Molecular and Cellular Physiology.

Of Cell Shapes and Motion: The Physical Basis of Animal Cell Migration

Motile cells have developed a variety of migration modes relying on diverse traction-force-generation mechanisms. Before the behavior of intracellular components could be easily imaged, cell movements were mostly classified by different types of cellular shape dynamics. Indeed, even though some types of cells move without any significant change in shape, most cell propulsion mechanisms rely on global or local deformations of the cell surface. In this review, focusing mostly on metazoan cells, we discuss how different types of local and global shape changes underlie distinct migration modes. We then discuss mechanical differences between force-generation mechanisms and finish by speculating on how they may have evolved.

Shape-to-graph mapping method for efficient characterization and classification of complex geometries in biological images

With the ever-increasing quality and quantity of imaging data in biomedical research comes the demand for computational methodologies that enable efficient and reliable automated extraction of the quantitative information contained within these images. One of the challenges in providing such methodology is the need for tailoring algorithms to the specifics of the data, limiting their areas of application. Here we present a broadly applicable approach to quantification and classification of complex shapes and patterns in biological or other multi-component formations. This approach integrates the mapping of all shape boundaries within an image onto a global information-rich graph and machine learning on the multidimensional measures of the graph. We demonstrated the power of this method by (1) extracting subtle structural differences from visually indistinguishable images in our phenotype rescue experiments using the endothelial tube formations assay, (2) training the algorithm to identify biophysical parameters underlying the formation of different multicellular networks in our simulation model of collective cell behavior, and (3) analyzing the response of U2OS cell cultures to a broad array of small molecule perturbations.

In this paper, we present a methodology that is based on mapping an arbitrary set of outlines onto a complete, strictly defined structure, in which every point representing the shape becomes a terminal point of a global graph. Because this mapping preserves the whole complexity of the shape, it allows for extracting the full scope of geometric features of any scale. Importantly, an extensive set of graph-based metrics in each image makes integration with machine learning routines highly efficient even for a small data sets and provide an opportunity to backtrack the subtle morphological features responsible for the automated distinction into image classes. The resulting tool provides efficient, versatile, and robust quantification of complex shapes and patterns in experimental images.

Profiling cellular morphodynamics by spatiotemporal spectrum decomposition

Cellular morphology and associated morphodynamics are widely used for qualitative and quantitative assessments of cell state. Here we implement a framework to profile cellular morphodynamics based on an adaptive decomposition of local cell boundary motion into instantaneous frequency spectra defined by the Hilbert-Huang transform (HHT). Our approach revealed that spontaneously migrating cells with approximately homogeneous molecular makeup show remarkably consistent instantaneous frequency distributions, though they have markedly heterogeneous mobility. Distinctions in cell edge motion between these cells are captured predominantly by differences in the magnitude of the frequencies. We found that acute photo-inhibition of Vav2 guanine exchange factor, an activator of the Rho family of signaling proteins coordinating cell motility, produces significant shifts in the frequency distribution, but does not affect frequency magnitude. We therefore concluded that the frequency spectrum encodes the wiring of the molecular circuitry that regulates cell boundary movements, whereas the magnitude captures the activation level of the circuitry. We also used HHT spectra as multi-scale spatiotemporal features in statistical region merging to identify subcellular regions of distinct motion behavior. In line with our conclusion that different HHT spectra relate to different signaling regimes, we found that subcellular regions with different morphodynamics indeed exhibit distinct Rac1 activities. This algorithm thus can serve as an accurate and sensitive classifier of cellular morphodynamics to pinpoint spatial and temporal boundaries between signaling regimes.

Many studies in cell biology employ global shape descriptors to probe mechanisms of cell morphogenesis. Here, we implement a framework in this paper to profile cellular morphodynamics very locally. We employ the Hilbert-Huang transform (HHT) to extract along the entire cell edge spectra of instantaneous edge motion frequency and magnitude and use them to classify overall cell behavior as well as subcellular edge sectors of distinct dynamics. We find in fibroblast-like COS7 cells that the marked heterogeneity in mobility of an unstimulated population is fully captured by differences in the magnitude spectra, while the frequency spectra are conserved between cells. Using optogenetics to acutely inhibit morphogenetic signaling pathways we find that these molecular shifts are reflected by changes in the frequency spectra but not in the magnitude spectra. After clustering cell edge sectors with distinct morphodynamics we observe in cells expressing a Rac1 activity biosensor that the sectors with different frequency spectra associate with different signaling intensity and dynamics. Together, these observations let us conclude that the frequency spectrum encodes the wiring of the molecular circuitry that regulates edge movements, whereas the magnitude captures the activation level of the circuitry.

PaCeQuant: A Tool for High-Throughput Quantification of Pavement Cell Shape Characteristics

Pavement cells (PCs) are the most frequently occurring cell type in the leaf epidermis and play important roles in leaf growth and function. In many plant species, PCs form highly complex jigsaw-puzzle-shaped cells with interlocking lobes. Understanding of their development is of high interest for plant science research because of their importance for leaf growth and hence for plant fitness and crop yield. Studies of PC development, however, are limited, because robust methods are lacking that enable automatic segmentation and quantification of PC shape parameters suitable to reflect their cellular complexity. Here, we present our new ImageJ-based tool, PaCeQuant, which provides a fully automatic image analysis workflow for PC shape quantification. PaCeQuant automatically detects cell boundaries of PCs from confocal input images and enables manual correction of automatic segmentation results or direct import of manually segmented cells. PaCeQuant simultaneously extracts 27 shape features that include global, contour-based, skeleton-based, and PC-specific object descriptors. In addition, we included a method for classification and analysis of lobes at two-cell junctions and three-cell junctions, respectively. We provide an R script for graphical visualization and statistical analysis. We validated PaCeQuant by extensive comparative analysis to manual segmentation and existing quantification tools and demonstrated its usability to analyze PC shape characteristics during development and between different genotypes. PaCeQuant thus provides a platform for robust, efficient, and reproducible quantitative analysis of PC shape characteristics that can easily be applied to study PC development in large data sets.

A Graphical User Interface for Software-assisted Tracking of Protein Concentration in Dynamic Cellular Protrusions

Filopodia are dynamic, finger-like cellular protrusions associated with migration and cell-cell communication. In order to better understand the complex signaling mechanisms underlying filopodial initiation, elongation and subsequent stabilization or retraction, it is crucial to determine the spatio-temporal protein activity in these dynamic structures. To analyze protein function in filopodia, we recently developed a semi-automated tracking algorithm that adapts to filopodial shape-changes, thus allowing parallel analysis of protrusion dynamics and relative protein concentration along the whole filopodial length. Here, we present a detailed step-by-step protocol for optimized cell handling, image acquisition and software analysis. We further provide instructions for the use of optional features during image analysis and data representation, as well as troubleshooting guidelines for all critical steps along the way. Finally, we also include a comparison of the described image analysis software with other programs available for filopodia quantification. Together, the presented protocol provides a framework for accurate analysis of protein dynamics in filopodial protrusions using image analysis software.

Automated analysis of filopodial length and spatially resolved protein concentration via adaptive shape tracking

A novel approach based on the convex-hull algorithm is used for parallel analysis of growth dynamics and relative spatiotemporal protein concentration along flexible filopodial protrusions. Testing of filopodia formation in silico, in vitro, and in vivo validates the robustness and sensitivity of the proposed approach.

Filopodia are dynamic, actin-rich structures that transiently form on a variety of cell types. To understand the underlying control mechanisms requires precise monitoring of localization and concentration of individual regulatory and structural proteins as filopodia elongate and subsequently retract. Although several methods exist that analyze changes in filopodial shape, a software solution to reliably correlate growth dynamics with spatially resolved protein concentration along the filopodium independent of bending, lateral shift, or tilting is missing. Here we introduce a novel approach based on the convex-hull algorithm for parallel analysis of growth dynamics and relative spatiotemporal protein concentration along flexible filopodial protrusions. Detailed in silico tests using various geometries confirm that our technique accurately tracks growth dynamics and relative protein concentration along the filopodial length for a broad range of signal distributions. To validate our technique in living cells, we measure filopodial dynamics and quantify spatiotemporal localization of filopodia-associated proteins during the filopodial extension–retraction cycle in a variety of cell types in vitro and in vivo. Together these results show that the technique is suitable for simultaneous analysis of growth dynamics and spatiotemporal protein enrichment along filopodia. To allow readily application by other laboratories, we share source code and instructions for software handling.

Distinct cell shapes determine accurate chemotaxis

The behaviour of an organism often reflects a strategy for coping with its environment. Such behaviour in higher organisms can often be reduced to a few stereotyped modes of movement due to physiological limitations, but finding such modes in amoeboid cells is more difficult as they lack these constraints. Here, we examine cell shape and movement in starved Dictyostelium amoebae during migration toward a chemoattractant in a microfluidic chamber. We show that the incredible variety in amoeboid shape across a population can be reduced to a few modes of variation. Interestingly, cells use distinct modes depending on the applied chemical gradient, with specific cell shapes associated with shallow, difficult-to-sense gradients. Modelling and drug treatment reveals that these behaviours are intrinsically linked with accurate sensing at the physical limit. Since similar behaviours are observed in a diverse range of cell types, we propose that cell shape and behaviour are conserved traits.

High fidelity information processing in folic acid chemotaxis of Dictyostelium amoebae

Living cells depend upon the detection of chemical signals for their existence. Eukaryotic cells can sense a concentration difference as low as a few per cent across their bodies. This process was previously suggested to be limited by the receptor-ligand binding fluctuations. Here, we first determine the chemotaxis response of Dictyostelium cells to static folic acid gradients and show that they can significantly exceed this sensitivity, responding to gradients as shallow as 0.2% across the cell body. Second, using a previously developed information theory framework, we compare the total information gained about the gradient (based on the cell response) to its upper limit: the information gained at the receptor-ligand binding step. We find that the model originally applied to cAMP sensing fails as demonstrated by the violation of the data processing inequality, i.e. the total information exceeds the information at the receptor-ligand binding step. We propose an extended model with multiple known receptor types and with cells allowed to perform several independent measurements of receptor occupancy. This does not violate the data processing inequality and implies the receptor-ligand binding noise dominates both for low- and high-chemoattractant concentrations. We also speculate that the interplay between exploration and exploitation is used as a strategy for accurate sensing of otherwise unmeasurable levels of a chemoattractant.

Functional morphometric analysis in cellular behaviors: shape and size matter

Cellular morphogenesis in response to biophysical and topographical cues provides insights into cytoskeletal status, biointerface communications, and phenotypic adaptations in an incessant signaling feedback that governs cellular fate. Morphometric characterization is an important element in the study of the dynamic cellular behaviors, in their interactive response to environmental influence exerted by culture system. They collectively serve to reflect cellular proliferation, migration, and differentiation, which may serve as prognostic indices for clinical and pathological diagnosis. Various parameters are proposed to categorize morphological adaptations in relation to cellular function. In this review, the underlying principles, assumptions, and limitations of morphological characterizations are discussed. The significance, challenges, and implications of quantitative morphometric characterization of cell shapes and sizes in determining cellular functions are discussed.

Interaction of motility, directional sensing, and polarity modules recreates the behaviors of chemotaxing cells

Chemotaxis involves the coordinated action of separable but interrelated processes: motility, gradient sensing, and polarization. We have hypothesized that these are mediated by separate modules that account for these processes individually and that, when combined, recreate most of the behaviors of chemotactic cells. Here, we describe a mathematical model where the modules are implemented in terms of reaction-diffusion equations. Migration and the accompanying changes in cellular morphology are demonstrated in simulations using a mechanical model of the cell cortex implemented in the level set framework. The central module is an excitable network that accounts for random migration. The response to combinations of uniform stimuli and gradients is mediated by a local excitation, global inhibition module that biases the direction in which excitability is directed. A polarization module linked to the excitable network through the cytoskeleton allows unstimulated cells to move persistently and, for cells in gradients, to gradually acquire distinct sensitivity between front and back. Finally, by varying the strengths of various feedback loops in the model we obtain cellular behaviors that mirror those of genetically altered cell lines.

Chemotaxis is the movement of cells in response to spatial gradients of chemical cues. While single-celled organisms rely on sensing and responding to chemical gradients to search for nutrients, chemotaxis is also an essential component of the mammalian immune system. However, chemotaxis can also be deleterious, since chemotactic tumor cells can lead to metastasis. Due to its importance, understanding the process by which cells sense and respond to chemical gradients has attracted considerable interest. Moreover, because of the complexity of chemotactic signaling, which includes multiple feedback loops and redundant pathways, this has been a research area in which computational models have had a significant impact in understanding experimental findings. Here, we propose a modular description of the signaling network that regulates chemotaxis. The modules describe different processes that are observed in chemotactic cells. In addition to accounting for these behaviors individually, we show that the overall system recreates many features of the directed motion of migrating cells. The signaling described by our modules is implemented as a series of equations, whereas movement and the accompanying cellular deformations are simulated using a mechanical model of the cell and implemented using level set methods, a method that allows simulations of cells as they change morphology.

Excitable behavior in amoeboid chemotaxis

Chemotaxis, the directed motion of cells in response to chemical gradients, is a fundamental process. Eukaryotic cells detect spatial differences in chemoattractant receptor occupancy with high precision and use these differences to bias the location of actin-rich protrusions to guide their movement. Research into chemotaxis has benefitted greatly from a systems biology approach that combines novel experimental and computational tools to pose and test hypotheses. Recently, one such hypothesis has been postulated proposing that chemotaxis in eukaryotic cells is mediated by locally biasing the activity of an underlying excitable system. The excitable system hypothesis can account for a number of cellular behaviors related to chemotaxis, including the stochastic nature of the movement of unstimulated cells, the directional bias imposed by chemoattractant gradients, and the observed spatial and temporal distribution of signaling and cytoskeleton proteins.

A developmental framework for complex plasmodesmata formation revealed by large-scale imaging of the Arabidopsis leaf epidermis

Plasmodesmata (PD) form tubular connections that function as intercellular communication channels. They are essential for transporting nutrients and for coordinating development. During cytokinesis, simple PDs are inserted into the developing cell plate, while during wall extension, more complex (branched) forms of PD are laid down. We show that complex PDs are derived from existing simple PDs in a pattern that is accelerated when leaves undergo the sink-source transition. Complex PDs are inserted initially at the three-way junctions between epidermal cells but develop most rapidly in the anisocytic complexes around stomata. For a quantitative analysis of complex PD formation, we established a high-throughput imaging platform and constructed PDQUANT, a custom algorithm that detected cell boundaries and PD numbers in different wall faces. For anticlinal walls, the number of complex PDs increased with increasing cell size, while for periclinal walls, the number of PDs decreased. Complex PD insertion was accelerated by up to threefold in response to salicylic acid treatment and challenges with mannitol. In a single 30-min run, we could derive data for up to 11k PDs from 3k epidermal cells. This facile approach opens the door to a large-scale analysis of the endogenous and exogenous factors that influence PD formation.

Bringing the physical sciences into your cell biology research

Historically, much of biology was studied by physicists and mathematicians. With the advent of modern molecular biology, a wave of researchers became trained in a new scientific discipline filled with the language of genes, mutants, and the central dogma. These new molecular approaches have provided volumes of information on biomolecules and molecular pathways from the cellular to the organismal level. The challenge now is to determine how this seemingly endless list of components works together to promote the healthy function of complex living systems. This effort requires an interdisciplinary approach by investigators from both the biological and the physical sciences.

Understanding the cooperative interaction between myosin II and actin cross-linkers mediated by actin filaments during mechanosensation

Myosin II is a central mechanoenzyme in a wide range of cellular morphogenic processes. Its cellular localization is dependent not only on signal transduction pathways, but also on mechanical stress. We suggest that this stress-dependent distribution is the result of both the force-dependent binding to actin filaments and cooperative interactions between bound myosin heads. By assuming that the binding of myosin heads induces and/or stabilizes local conformational changes in the actin filaments that enhances myosin II binding locally, we successfully simulate the cooperative binding of myosin to actin observed experimentally. In addition, we can interpret the cooperative interactions between myosin and actin cross-linking proteins observed in cellular mechanosensation, provided that a similar mechanism operates among different proteins. Finally, we present a model that couples cooperative interactions to the assembly dynamics of myosin bipolar thick filaments and that accounts for the transient behaviors of the myosin II accumulation during mechanosensation. This mechanism is likely to be general for a range of myosin II-dependent cellular mechanosensory processes.

Cell shape dynamics: from waves to migration

We observe and quantify wave-like characteristics of amoeboid migration. Using the amoeba Dictyostelium discoideum, a model system for the study of chemotaxis, we demonstrate that cell shape changes in a wave-like manner. Cells have regions of high boundary curvature that propagate from the leading edge toward the back, usually along alternating sides of the cell. Curvature waves are easily seen in cells that do not adhere to a surface, such as cells that are electrostatically repelled from surfaces or cells that extend over the edge of micro-fabricated cliffs. Without surface contact, curvature waves travel from the leading edge to the back of a cell at -35 µm/min. Non-adherent myosin II null cells do not exhibit these curvature waves. At the leading edge of adherent cells, curvature waves are associated with protrusive activity. Like regions of high curvature, protrusive activity travels along the boundary in a wave-like manner. Upon contact with a surface, the protrusions stop moving relative to the surface, and the boundary shape thus reflects the history of protrusive motion. The wave-like character of protrusions provides a plausible mechanism for the zig-zagging of pseudopods and for the ability of cells both to swim in viscous fluids and to navigate complex three dimensional topography.

Recent advances in morphological cell image analysis

This paper summarizes the recent advances in image processing methods for morphological cell analysis. The topic of morphological analysis has received much attention with the increasing demands in both bioinformatics and biomedical applications. Among many factors that affect the diagnosis of a disease, morphological cell analysis and statistics have made great contributions to results and effects for a doctor. Morphological cell analysis finds the cellar shape, cellar regularity, classification, statistics, diagnosis, and so forth. In the last 20 years, about 1000 publications have reported the use of morphological cell analysis in biomedical research. Relevant solutions encompass a rather wide application area, such as cell clumps segmentation, morphological characteristics extraction, 3D reconstruction, abnormal cells identification, and statistical analysis. These reports are summarized in this paper to enable easy referral to suitable methods for practical solutions. Representative contributions and future research trends are also addressed.

Detailed interrogation of trypanosome cell biology via differential organelle staining and automated image analysis

BACKGROUND

Many trypanosomatid protozoa are important human or animal pathogens. The well defined morphology and precisely choreographed division of trypanosomatid cells makes morphological analysis a powerful tool for analyzing the effect of mutations, chemical insults and changes between lifecycle stages. High-throughput image analysis of micrographs has the potential to accelerate collection of quantitative morphological data. Trypanosomatid cells have two large DNA-containing organelles, the kinetoplast (mitochondrial DNA) and nucleus, which provide useful markers for morphometric analysis; however they need to be accurately identified and often lie in close proximity. This presents a technical challenge. Accurate identification and quantitation of the DNA content of these organelles is a central requirement of any automated analysis method.

RESULTS

We have developed a technique based on double staining of the DNA with a minor groove binding (4'', 6-diamidino-2-phenylindole (DAPI)) and a base pair intercalating (propidium iodide (PI) or SYBR green) fluorescent stain and color deconvolution. This allows the identification of kinetoplast and nuclear DNA in the micrograph based on whether the organelle has DNA with a more A-T or G-C rich composition. Following unambiguous identification of the kinetoplasts and nuclei the resulting images are amenable to quantitative automated analysis of kinetoplast and nucleus number and DNA content. On this foundation we have developed a demonstrative analysis tool capable of measuring kinetoplast and nucleus DNA content, size and position and cell body shape, length and width automatically.

CONCLUSIONS

Our approach to DNA staining and automated quantitative analysis of trypanosomatid morphology accelerated analysis of trypanosomatid protozoa. We have validated this approach using Leishmania mexicana, Crithidia fasciculata and wild-type and mutant Trypanosoma brucei. Automated analysis of T. brucei morphology was of comparable quality to manual analysis while being faster and less susceptible to experimentalist bias. The complete data set from each cell and all analysis parameters used can be recorded ensuring repeatability and allowing complete data archiving and reanalysis.

A stochastic model for chemotaxis based on the ordered extension of pseudopods

Many amoeboid cells move by extending pseudopods. Here I present a new stochastic model for chemotaxis that is based on pseudopod extensions by Dictyostelium cells. In the absence of external cues, pseudopod extension is highly ordered with two types of pseudopods: de novo formation of a pseudopod at the cell body in random directions, and alternating right/left splitting of an existing pseudopod that leads to a persistent zig-zag trajectory. We measured the directional probabilities of the extension of splitting and de novo pseudopods in chemoattractant gradients with different steepness. Very shallow cAMP gradients can bias the direction of splitting pseudopods, but the bias is not perfect. Orientation of de novo pseudopods require much steeper cAMP gradients and can be more precise. These measured probabilities of pseudopod directions were used to obtain an analytical model for chemotaxis of cell populations. Measured chemotaxis of wild-type cells and mutants with specific defects in these stochastic pseudopod properties are similar to predictions of the model. These results show that combining splitting and de novo pseudopods is a very effective way for cells to obtain very high sensitivity to stable gradient and still be responsive to changes in the direction of the gradient.

Tools for analyzing cell shape changes during chemotaxis

Chemotaxis refers to the ability of cells to sense the direction of external chemical gradients and respond by migrating towards the source. A thorough understanding of the chemotactic response of amoebae and neutrophils requires careful quantification of the cell shape changes observed during cell movement. The stochastic nature of this response calls for a statistical characterization of cellular morphology and this requires the processing of large data sets. For this reason, automatic image analysis algorithms are highly desirable and are becoming increasingly available. These usually include a combination of techniques from image segmentation, morphological transformations, as well as the incorporation of numerical algorithms and physical models. Here we review recent developments in the tracking and understanding of motile chemotaxing cells, with a particular emphasis on the description of pseudopodial activity in chemotactic Dictyostelium cells.

Cells navigate with a local-excitation, global-inhibition-biased excitable network

Cells have an internal compass that enables them to move along shallow chemical gradients. As amoeboid cells migrate, signaling events such as Ras and PI3K activation occur spontaneously on pseudopodia. Uniform stimuli trigger a symmetric response, whereupon cells stop and round up; then localized patches of activity appear as cells spread. Finally cells adapt and resume random migration. In contrast, chemotactic gradients continuously direct signaling events to the front of the cell. Local-excitation, global-inhibition (LEGI) and reaction-diffusion models have captured some of these features of chemotaxing cells, but no system has explained the complex response kinetics, sensitivity to shallow gradients, or the role of recently observed propagating waves within the actin cytoskeleton. We report here that Ras and PI3K activation move in phase with the cytoskeleton events and, drawing on all of these observations, propose the LEGI-biased excitable network hypothesis. We formulate a model that simulates most of the behaviors of chemotactic cells: In the absence of stimulation, there are spontaneous spots of activity. Stimulus increments trigger an initial burst of patches followed by localized secondary events. After a few minutes, the system adapts, again displaying random activity. In gradients, the activity patches are directed continuously and selectively toward the chemoattractant, providing an extraordinary degree of amplification. Importantly, by perturbing model parameters, we generate distinct behaviors consistent with known classes of mutants. Our study brings together heretofore diverse observations on spontaneous cytoskeletal activity, signaling responses to temporal stimuli, and spatial gradient sensing into a unified scheme.