The large-scale digital cell analysis system: an open system for nonperturbing live cell imaging

Automatic wavelet-based 3D nuclei segmentation and analysis for multicellular embryo quantification

Identification of individual cells in tissues, organs, and in various developing systems is a well-studied problem because it is an essential part of objectively analyzing quantitative images in numerous biological contexts. We developed a size-dependent wavelet-based segmentation method that provides robust segmentation without any preprocessing, filtering or fine-tuning steps, and is robust to the signal-to-noise ratio. The wavelet-based method achieves robust segmentation results with respect to True Positive rate, Precision, and segmentation accuracy compared with other commonly used methods. We applied the segmentation program to zebrafish embryonic development IN TOTO for nuclei segmentation, image registration, and nuclei shape analysis. These new approaches to segmentation provide a means to carry out quantitative patterning analysis with single-cell precision throughout three dimensional tissues and embryos and they have a high tolerance for non-uniform and noisy image data sets.

Radiation-Induced Mitotic Catastrophe Enhanced by Gold Nanoparticles: Assessment with a Specific Automated Image Processing Workflow

In recent years, the use of gold-based nanoparticles in radiotherapy has been extensively studied, and the associated radiosensitization mechanism has been evaluated in a variety of in vitro studies. Given that mitotic catastrophe is widely involved in radiation-induced cell death, we evaluated the effect of gold nanoparticles on this key event. Most of the methods currently used to visualize and quantify morphological changes and multinucleation are manual. To circumvent this time-consuming step, we developed and optimized an image processing workflow (based on freely accessible software and plugins) for the automated quantification of mitotic catastrophes. We validated this approach in three cell lines by comparing the number of radiation-induced mitotic catastrophes detected using the automated and manual methods in the presence and absence of nanoparticles. With the Bland-Altman analysis, the automated and manual counting methods were found to be fully interchangeable. The ultimate goal of this work was to determine whether mitotic catastrophe was critically involved in radiationinduced cell death after prior exposure to gold nanoparticles. In the radioresistant U87 cell line, exposure to gold nanoparticles was associated with a shorter time course for the events related to mitotic catastrophe, which peaked at 96 h postirradiation. Mitotic catastrophe was dose-dependent in both the presence and absence of gold nanoparticles. These results demonstrate that cell exposure to gold nanoparticles led to an increase in mitotic catastrophe events, and confirm the marked radiosensitizing effect observed in clonogenic assays.

Single-cell lineage tracking analysis reveals that an established cell line comprises putative cancer stem cells and their heterogeneous progeny

Mammalian cell culture has been used in many biological studies on the assumption that a cell line comprises putatively homogeneous clonal cells, thereby sharing similar phenotypic features. This fundamental assumption has not yet been fully tested; therefore, we developed a method for the chronological analysis of individual HeLa cells. The analysis was performed by live cell imaging, tracking of every single cell recorded on imaging videos, and determining the fates of individual cells. We found that cell fate varied significantly, indicating that, in contrast to the assumption, the HeLa cell line is composed of highly heterogeneous cells. Furthermore, our results reveal that only a limited number of cells are immortal and renew themselves, giving rise to the remaining cells. These cells have reduced reproductive ability, creating a functionally heterogeneous cell population. Hence, the HeLa cell line is maintained by the limited number of immortal cells, which could be putative cancer stem cells.

CellSegm - a MATLAB toolbox for high-throughput 3D cell segmentation

: The application of fluorescence microscopy in cell biology often generates a huge amount of imaging data. Automated whole cell segmentation of such data enables the detection and analysis of individual cells, where a manual delineation is often time consuming, or practically not feasible. Furthermore, compared to manual analysis, automation normally has a higher degree of reproducibility. CellSegm, the software presented in this work, is a Matlab based command line software toolbox providing an automated whole cell segmentation of images showing surface stained cells, acquired by fluorescence microscopy. It has options for both fully automated and semi-automated cell segmentation. Major algorithmic steps are: (i) smoothing, (ii) Hessian-based ridge enhancement, (iii) marker-controlled watershed segmentation, and (iv) feature-based classfication of cell candidates. Using a wide selection of image recordings and code snippets, we demonstrate that CellSegm has the ability to detect various types of surface stained cells in 3D. After detection and outlining of individual cells, the cell candidates can be subject to software based analysis, specified and programmed by the end-user, or they can be analyzed by other software tools. A segmentation of tissue samples with appropriate characteristics is also shown to be resolvable in CellSegm. The command-line interface of CellSegm facilitates scripting of the separate tools, all implemented in Matlab, offering a high degree of flexibility and tailored workflows for the end-user. The modularity and scripting capabilities of CellSegm enable automated workflows and quantitative analysis of microscopic data, suited for high-throughput image based screening.

Induction of mitotic cell death by overriding G2/M checkpoint in endometrial cancer cells with non-functional p53

OBJECTIVE

Endometrial tumors with non-functional p53, such as serous uterine endometrial carcinomas, are aggressive malignancies with a poor outcome, yet they have an Achilles' heel: due to loss of p53 function, these tumors may be sensitive to treatments which abrogate the G2/M checkpoint. Our objective was to exploit this weakness to induce mitotic cell death using two strategies: (1) EGFR inhibitor gefitinib combined with paclitaxel to arrest cells at mitosis, or (2) BI2536, an inhibitor of polo-like kinase 1 (PLK1), to block PLK1 activity.

METHODS

We examined the impact of combining gefitinib and paclitaxel or PLK1 inhibitor on expression of G2/M checkpoint controllers, cell viability, and cell cycle progression in endometrial cancer cells with mutant p53.

RESULTS

In cells lacking normal p53 activity, each treatment activated CDC25C and inactivated Wee1, which in turn activated cdc2 and sent cells rapidly through the G2/M checkpoint and into mitosis. Live cell imaging demonstrated irreversible mitotic arrest and eventual cell death. Combinatorial therapy with paclitaxel and gefitinib was highly synergistic and resulted in a 10-fold reduction in the IC50 for paclitaxel, from 14nM as a single agent to 1.3nM in the presence of gefitinib. However, BI2536 alone at low concentrations (5nM) was the most effective treatment and resulted in massive mitotic cell death. In a xenograft mouse model with p53-deficient cells, low dose BI2536 significantly inhibited tumor growth.

CONCLUSIONS

These findings reveal induction of mitotic cell death as a therapeutic strategy for endometrial tumors lacking functional p53.

A novel validation algorithm allows for automated cell tracking and the extraction of biologically meaningful parameters

Automated microscopy is currently the only method to non-invasively and label-free observe complex multi-cellular processes, such as cell migration, cell cycle, and cell differentiation. Extracting biological information from a time-series of micrographs requires each cell to be recognized and followed through sequential microscopic snapshots. Although recent attempts to automatize this process resulted in ever improving cell detection rates, manual identification of identical cells is still the most reliable technique. However, its tedious and subjective nature prevented tracking from becoming a standardized tool for the investigation of cell cultures. Here, we present a novel method to accomplish automated cell tracking with a reliability comparable to manual tracking. Previously, automated cell tracking could not rival the reliability of manual tracking because, in contrast to the human way of solving this task, none of the algorithms had an independent quality control mechanism; they missed validation. Thus, instead of trying to improve the cell detection or tracking rates, we proceeded from the idea to automatically inspect the tracking results and accept only those of high trustworthiness, while rejecting all other results. This validation algorithm works independently of the quality of cell detection and tracking through a systematic search for tracking errors. It is based only on very general assumptions about the spatiotemporal contiguity of cell paths. While traditional tracking often aims to yield genealogic information about single cells, the natural outcome of a validated cell tracking algorithm turns out to be a set of complete, but often unconnected cell paths, i.e. records of cells from mitosis to mitosis. This is a consequence of the fact that the validation algorithm takes complete paths as the unit of rejection/acceptance. The resulting set of complete paths can be used to automatically extract important biological parameters with high reliability and statistical significance. These include the distribution of life/cycle times and cell areas, as well as of the symmetry of cell divisions and motion analyses. The new algorithm thus allows for the quantification and parameterization of cell culture with unprecedented accuracy. To evaluate our validation algorithm, two large reference data sets were manually created. These data sets comprise more than 320,000 unstained adult pancreatic stem cells from rat, including 2592 mitotic events. The reference data sets specify every cell position and shape, and assign each cell to the correct branch of its genealogic tree. We provide these reference data sets for free use by others as a benchmark for the future improvement of automated tracking methods.

A time-series method for automated measurement of changes in mitotic and interphase duration from time-lapse movies

BACKGROUND

Automated time-lapse microscopy can visualize proliferation of large numbers of individual cells, enabling accurate measurement of the frequency of cell division and the duration of interphase and mitosis. However, extraction of quantitative information by manual inspection of time-lapse movies is too time-consuming to be useful for analysis of large experiments.

METHODOLOGY/PRINCIPAL FINDINGS

Here we present an automated time-series approach that can measure changes in the duration of mitosis and interphase in individual cells expressing fluorescent histone 2B. The approach requires analysis of only 2 features, nuclear area and average intensity. Compared to supervised learning approaches, this method reduces processing time and does not require generation of training data sets. We demonstrate that this method is as sensitive as manual analysis in identifying small changes in interphase or mitotic duration induced by drug or siRNA treatment.

CONCLUSIONS/SIGNIFICANCE

This approach should facilitate automated analysis of high-throughput time-lapse data sets to identify small molecules or gene products that influence timing of cell division.

Radiosensitivity of early and late M-phase HeLa cells isolated by a combination of fluorescent ubiquitination-based cell cycle indicator (Fucci) and mitotic shake-off

The mitotic shake-off method revealed the remarkable variation of radiosensitivity of HeLa cells during the cell cycle: M phase shows the greatest radiosensitivity and late S phase the greatest radioresistance. This method harvests all M-phase cells with a round shape, making it impossible to further subdivide M-phase cells. Recently, the fluorescent ubiquitination-based cell cycle indicator (Fucci) was developed; this system basically causes cells in G(1) to emit red fluorescence and other cells to emit green fluorescence. Because the green fluorescence rapidly disappears at late M phase, two-dimensional flow cytometry analysis can usually detect a green(high)/red(low) fraction including S-, G(2)- and early M-phase cells but not a transitional fraction between green(high)/red(low) and green(low)/red(low) including late M-phase cells. However, combining the shake-off method concentrated the transitional fraction, which enabled us to separate early and late M-phase cells without using any drugs. Here we demonstrate for the first time that cells in early M phase are more radiosensitive than those in late M phase, implying that early M phase is the most radiosensitive sub-phase during the cell cycle.

Oxygen tension changes the rate of migration of human skin keratinocytes in an age-related manner

Migration of keratinocytes to re-epithelialize wounds is a key step in dermal wound healing. In aged human skin, wound healing rates decrease and cellular damage by reactive oxygen species (ROS) accumulates. The relationship between age, ROS and human skin keratinocyte migration is not clearly understood. In this study, 4% and 21% oxygen tensions were used to modify levels of ROS produced by metabolism to model low and high oxidative stress conditions. When migration of keratinocytes from young and old primary skin was compared using an in vitro scratch assay, old keratinocytes migrated faster in high oxygen tension than did young keratinocytes, whereas young keratinocytes migrated faster in low oxygen tension. Although all young and old cells at the scratch margins showed intense increases in dihydroethidium oxidation immediately after scratching, the old keratinocytes grown at 21% oxygen demonstrated a greater decrease in the DHE oxidation following scratching and migrated the fastest. These results show that old and young keratinocytes respond to oxygen tension differently and support the hypothesis that keratinocyte migration is affected by the capacity to remove ROS.

Geranylgeranyl diphosphate depletion inhibits breast cancer cell migration

The objective of this study was to determine whether geranylgeranyl diphosphate synthase inhibition, and therefore geranylgeranyl diphosphate depletion, interferes with breast cancer cell migration. Digeranyl bisphosphonate is a specific geranylgeranyl diphosphate synthase inhibitor. We demonstrate that digeranyl bisphosphonate depleted geranylgeranyl diphosphate and inhibited protein geranylgeranylation in MDA-MB-231 cells. Similar to GGTI-286, a GGTase I inhibitor, digeranyl bisphosphate significantly inhibited migration of MDA-MB-231 cells as measured by transwell assay. Similarly, digeranyl bisphosphonate reduced motility of MDA-MB-231 cells in a time-dependent manner as measured by large scale digital cell analysis system microscopy. Digeranyl bisphosphonate was mildly toxic and did not induce apoptosis. Treatment of MDA-MB-231 cells with digeranyl bisphosphonate decreased membrane while it increased cytosolic RhoA localization. In addition, digeranyl bisphosphonate increased RhoA GTP binding in MDA-MB-231 cells. The specificity of geranylgeranyl diphosphonate synthase inhibition by digeranyl bisphosphonate was confirmed by exogenous addition of geranylgeranyl diphosphate. Geranylgeranyl diphosphate addition prevented the effects of digeranyl bisphosphonate on migration, RhoA localization, and GTP binding to RhoA in MDA-MB-231 cells. These studies suggest that geranylgeranyl diphosphate synthase inhibitors are a novel approach to interfere with cancer cell migration.

An open-source deconvolution software package for 3-D quantitative fluorescence microscopy imaging

Deconvolution techniques have been widely used for restoring the 3-D quantitative information of an unknown specimen observed using a wide-field fluorescence microscope. Deconv, an open-source deconvolution software package, was developed for 3-D quantitative fluorescence microscopy imaging and was released under the GNU Public License. Deconv provides numerical routines for simulation of a 3-D point spread function and deconvolution routines implemented three constrained iterative deconvolution algorithms: one based on a Poisson noise model and two others based on a Gaussian noise model. These algorithms are presented and evaluated using synthetic images and experimentally obtained microscope images, and the use of the library is explained. Deconv allows users to assess the utility of these deconvolution algorithms and to determine which are suited for a particular imaging application. The design of Deconv makes it easy for deconvolution capabilities to be incorporated into existing imaging applications.

Tracking cells in Life Cell Imaging videos using topological alignments

Background

With the increasing availability of live cell imaging technology, tracking cells and other moving objects in live cell videos has become a major challenge for bioimage informatics. An inherent problem for most cell tracking algorithms is over- or under-segmentation of cells – many algorithms tend to recognize one cell as several cells or vice versa.

Results

We propose to approach this problem through so-called topological alignments, which we apply to address the problem of linking segmentations of two consecutive frames in the video sequence. Starting from the output of a conventional segmentation procedure, we align pairs of consecutive frames through assigning sets of segments in one frame to sets of segments in the next frame. We achieve this through finding maximum weighted solutions to a generalized "bipartite matching" between two hierarchies of segments, where we derive weights from relative overlap scores of convex hulls of sets of segments. For solving the matching task, we rely on an integer linear program.

Conclusion

Practical experiments demonstrate that the matching task can be solved efficiently in practice, and that our method is both effective and useful for tracking cells in data sets derived from a so-called Large Scale Digital Cell Analysis System (LSDCAS).

Availability

The source code of the implementation is available for download from http://www.picb.ac.cn/patterns/Software/topaln.

DYNAMIK: a software environment for cell DYNAmics, Motility, and Information tracKing, with an application to Ras pathways

The emergence of new microscopy techniques in combination with the increasing resource of bioimaging data has given fresh impetus to utilizing image processing methods for studying biological processes. Cell tracking studies in particular, which are important for a wide range of biological processes such as embryonic development or the immune system, have recently become the focus of attention. These studies typically produce large volumes of data that are hard to investigate manually and therefore call for an automated approach. Due to the large variety of biological cells and the inhomogeneity of applications, however, there exists no widely accepted method or system for cell tracking until today. In this article, we present our publicly available DYNAMIK software environment that allows users to compute a suit of cell features and plot the trajectory of multiple cells over a sequence of frames. Using chemotaxis and Ras pathways as an example, we show how users can employ our software to compute statistics about cell motility and other cell information, and how to evaluate their test series based on the data computed. We see that DYNAMIK's segmentation and tracking compares favorably with the output produced by other software packages.

Multi-feature contour evolution for automatic live cell segmentation in time lapse imagery

Cell boundary segmentation in live cell image sequences is the first step towards quantitative analysis of cell motion and behavior. The time lapse microscopy imaging produces large volumes of image sequence collections which requires fast and robust automatic segmentation of cell boundaries to utilize further automated tools such as cell tracking to quantify and classify cell behavior. This paper presents a methodology that is based on utilizing the temporal context of the cell image sequences to accurately delineate the boundaries of non-homogeneous cells. A novel flux tensor-based detection of moving cells provides initial localization that is further refined by a multi-feature level set-based method using an efficient additive operator splitting scheme. The segmentation result is processed by a watershed-based algorithm to avoid merging boundaries of neighboring cells. By utilizing robust features, the level-set algorithm produces accurate segmentation for non-homogeneous cells with concave shapes and varying intensities.

Activation of meiosis-specific genes is associated with depolyploidization of human tumor cells following radiation-induced mitotic catastrophe

Cancer is frequently characterized histologically by the appearance of large cells that are either aneuploid or polyploid. Aneuploidy and polyploidy are hallmarks of radiation-induced mitotic catastrophe (MC), a common phenomenon occurring in tumor cells with impaired p53 function following exposure to various cytotoxic and genotoxic agents. MC is characterized by altered expression of mitotic regulators, untimely and abnormal cell division, delayed DNA damage, and changes in morphology. We report here that cells undergoing radiation-induced MC are more plastic with regards to ploidy and that this plasticity allows them to reorganize their genetic material through reduction division to produce smaller cells which are morphologically indistinguishable from control cells. Experiments conducted with the large-scale digital cell analysis system are discussed and show that a small fraction of polyploid cancer cells formed via radiation-induced MC can survive and start a process of depolyploidization that yields various outcomes. Although most multipolar divisions failed and cell fusion occurred, some of these divisions were successful and originated a variety of cell progeny characterized by different ploidy. Among these ploidy phenotypes, a progeny of small mononucleated cells, indistinguishable from the untreated control cells, is often seen. We report here evidence that meiosis-specific genes are expressed in the polyploid cells during depolyploidization. Tumor cells might take advantage of the temporary change from a promitotic to a promeiotic division regimen to facilitate depolyploidization and restore the proliferative state of the tumor cell population. These events might be mechanisms by which tumor progression and resistance to treatment occur in vivo.

CELL SEGMENTATION USING HESSIAN-BASED DETECTION AND CONTOUR EVOLUTION WITH DIRECTIONAL DERIVATIVES

The large amount of data produced by biological live cell imaging studies of cell behavior requires accurate automated cell segmentation algorithms for rapid, unbiased and reproducible scientific analysis. This paper presents a new approach to obtain precise boundaries of cells with complex shapes using ridge measures for initial detection and a modified geodesic active contour for curve evolution that exploits the halo effect present in phase-contrast microscopy. The level set contour evolution is controlled by a novel spatially adaptive stopping function based on the intensity profile perpendicular to the evolving front. The proposed approach is tested on human cancer cell images from LSDCAS and achieves high accuracy even in complex environments.

Endopolyploidy in irradiated p53-deficient tumour cell lines: persistence of cell division activity in giant cells expressing Aurora-B kinase

Recent findings including computerised live imaging suggest that polyploidy cells transiently emerging after severe genotoxic stress (and named 'endopolyploid cells') may have a role in tumour regrowth after anti-cancer treatment. Until now, mostly the factors enabling metaphase were studied in them. Here we investigate the mitotic activities and the role of Aurora-B, in view of potential depolyploidisation of these cells, because Aurora-B kinase is responsible for coordination and completion of mitosis. We observed that endopolyploid giant cells are formed via different means in irradiated p53 tumours, by: (1) division/fusion of daughter cells creating early multi-nucleated cells; (2) asynchronous division/fusion of sub-nuclei of these multi-nucleated cells; (3) a series of polyploidising mitoses reverting replicative interphase from aborted metaphase and forming giant cells with a single nucleus; (4) micronucleation of arrested metaphases enclosing genome fragments; or (5) incomplete division in the multi-polar mitoses forming late multi-nucleated giant cells. We also observed that these activities can release para-diploid cells, although infrequently. While apoptosis typically occurs after a substantial delay in these cells, we also found that approximately 2% of the endopolyploid cells evade apoptosis and senescence arrest and continue some form of mitotic activity. We describe here that catalytically active Aurora-B kinase is expressed in the nuclei of many endopolyploid cells in interphase, as well as being present at the centromeres, mitotic spindle and cleavage furrow during their attempted mitotes. The totally micronucleated giant cells (containing sub-genomic fragments in multiple micronuclei) represented only the minor fraction which failed to undergo mitosis, and Aurora-B was absent from it. These observations suggest that most endopolyploid tumour cells are not reproductively inert and that Aurora-B may contribute to the establishment of resistant tumours post-irradiation.