[show abstract][hide abstract] ABSTRACT: The function of protein phosphatase 1 nuclear-targeting subunit (PNUTS)--one of the most abundant nuclear-targeting subunits of protein phosphatase 1 (PP1c)--remains largely uncharacterized. We show that PNUTS depletion by small interfering RNA activates a G2 checkpoint in unperturbed cells and prolongs G2 checkpoint and Chk1 activation after ionizing-radiation-induced DNA damage. Overexpression of PNUTS-enhanced green fluorescent protein (EGFP)--which is rapidly and transiently recruited at DNA damage sites--inhibits G2 arrest. Finally, γH2AX, p53-binding protein 1, replication protein A and Rad51 foci are present for a prolonged period and clonogenic survival is decreased in PNUTS-depleted cells after ionizing radiation treatment. We identify the PP1c regulatory subunit PNUTS as a new and integral component of the DNA damage response involved in DNA repair.
[show abstract][hide abstract] ABSTRACT: Live-cell imaging allows detailed dynamic cellular phenotyping for cell biology and, in combination with small molecule or drug libraries, for high-content screening. Fully automated analysis of live cell movies has been hampered by the lack of computational approaches that allow tracking and recognition of individual cell fates over time in a precise manner. Here, we present a fully automated approach to analyze time-lapse movies of dividing cells. Our method dynamically categorizes cells into seven phases of the cell cycle and five aberrant morphological phenotypes over time. It reliably tracks cells and their progeny and can thus measure the length of mitotic phases and detect cause and effect if mitosis goes awry. We applied our computational scheme to annotate mitotic phenotypes induced by RNAi gene knockdown of CKAP5 (also known as ch-TOG) or by treatment with the drug nocodazole. Our approach can be readily applied to comparable assays aiming at uncovering the dynamic cause of cell division phenotypes.
Genome Research 10/2009; 19(11):2113-24. · 14.40 Impact Factor
[show abstract][hide abstract] ABSTRACT: Eukaryotic cells must first compact their chromosomes before faithfully segregating them during cell division. Failure to do so can lead to segregation defects with pathological consequences, such as aneuploidy and cancer. Duplicated interphase chromosomes are, therefore, reorganized into tight rods before being separated and directed to the newly forming daughter cells. This vital reorganization of chromatin remains poorly understood. To address the dynamics of mitotic condensation of single chromosomes in intact cells, we developed quantitative assays based on confocal time-lapse microscopy of live mammalian cells stably expressing fluorescently tagged core histones. Surprisingly, maximal compaction was not reached in metaphase, but in late anaphase, after sister chromatid segregation. We show that anaphase compaction proceeds by a mechanism of axial shortening of the chromatid arms from telomere to centromere. Chromatid axial shortening was not affected in condensin-depleted cells, but depended instead on dynamic microtubules and Aurora kinase. Acute perturbation of this compaction resulted in failure to rescue segregation defects and in multilobed daughter nuclei, suggesting functions in chromosome segregation and nuclear architecture.
[show abstract][hide abstract] ABSTRACT: Mitotic and meiotic chromosomes are the compact packages that faithfully transport the genetic and epigenetic information to the following cell generations. How chromatin dynamically cycles between the decompacted interphase state that supports transcription and replication and the compacted state required for chromosome segregation is not understood. To address this long-standing problem, the structure of chromatin should ideally be studied in the physiological context of intact cells and organisms. We discuss here, the contributions that live-cell imaging can and has made to the study of mitotic chromosome compaction and highlight the power and limitations of this approach. We review methodologies used and suggest that combinatorial approaches and developing new imaging technologies will be key to shedding light on this long-standing question in cell biology.
[show abstract][hide abstract] ABSTRACT: A bottleneck for high-throughput screening of live cells is the automated analysis of the generated image data. An important application in this context is the evaluation of the duration of cell cycle phases from confocal time-lapse microscopy image sequences, which typically involves a tracking step. The tracking step is an important part since it relates segmented cells from one time frame to the next. However, a main problem is that often the movement of single cells is superimposed with a global movement. The reason for the global movement lies in the high-throughput acquisition of the images and the repositioning of the microscope. If a tracking algorithm is applied to these images then only a superposition of the microscope movement and the cell movement is determined but not the real movement of the cells. In addition, since the displacements are generally larger, it is more difficult to determine the correspondences between cells. We have developed a phase-correlation based approach to compensate for the global movement of the microscope by registering each image of a sequence to a reference coordinate system. Our approach uses a windowing function in the spatial domain of the cross-power spectrum. This allows to determine the global movement by direct evaluation of the phase gradient, avoiding phase unwrapping. We present experimental results of applying our approach to synthetic and real image sequences. It turns out that the global movement can well be compensated and thus successfully decouples the global movement from the individual movement of the cells.
[show abstract][hide abstract] ABSTRACT: In high-throughput cell phenotype screens large amounts of image data are acquired. The evaluation of these microscopy images re- quires automated image analysis methods. Here we introduce a compu- tational scheme to process 3D multi-cell image sequences as they are produced in large-scale RNAi experiments. We describe an approach to automatically segment, track, and classify cell nuclei into seven difierent mitotic phases. In particular, we present an algorithm based on a flnite state machine to check the consistency of the resulting sequence of mi- totic phases and to correct classiflcation errors. Our approach enables automated determination of the duration of the single phases and thus the identiflcation of cell cultures with delayed mitotic progression.
Bildverarbeitung für die Medizin 2007, Algorithmen, Systeme, Anwendungen, Proceedings des Workshops vom 25.-27. März 2007 in München; 01/2007
[show abstract][hide abstract] ABSTRACT: Quantitative characterization of protein interactions under physiological conditions is vital for systems biology. Fluorescence photobleaching/activation experiments of GFP-tagged proteins are frequently used for this purpose, but robust analysis methods to extract physicochemical parameters from such data are lacking. Here, we implemented a reaction-diffusion model to determine the contributions of protein interaction and diffusion on fluorescence redistribution. The model was validated and applied to five chromatin-interacting proteins probed by photoactivation in living cells. We found that very transient interactions are common for chromatin proteins. Their observed mobility was limited by the amount of free protein available for diffusion but not by the short residence time of the bound proteins. Individual proteins thus locally scan chromatin for binding sites, rather than diffusing globally before rebinding at random nuclear positions. By taking the real cellular geometry and the inhomogeneous distribution of binding sites into account, our model provides a general framework to analyze the mobility of fluorescently tagged factors. Furthermore, it defines the experimental limitations of fluorescence perturbation experiments and highlights the need for complementary methods to measure transient biochemical interactions in living cells.
[show abstract][hide abstract] ABSTRACT: The evaluation of fluorescence microscopy images acquired in high-throughput cell phenotype screens constitutes a substantial bottleneck and motivates the development of automated image analysis methods. Here we introduce a computational scheme to process 3D multi-cell time-lapse images as they are produced in large-scale RNAi experiments. We describe an approach to automatically segment, track, and classify cell nuclei into different mitotic phases. This enables automated analysis of the duration of single phases of the cell life cycle and thus the identification of cell cultures that show an abnormal mitotic behavior. Our scheme proves a high accuracy, suggesting a promising future for automating the evaluation of high-throughput experiments.
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. 02/2006; 9(Pt 1):840-8.
[show abstract][hide abstract] ABSTRACT: In high-throughput RNAi knockdown screens large amounts of image data are acquired. The evaluation of these microscopy images constitutes a bottleneck and motivates the devel- opment of automated image analysis methods. This contribution is concerned with the au- tomated evaluation of RNAi knockdown experiments for studying delays in mitotic phases. To this end, 3D multi-cell image sequences of living cell nuclei are acquired. Based on these images, the duration of the mitotic phases has to be measured for the treated cells and compared with the normal cells from control experiments. To automatically determine the lengths of the cell cycle phases, we have developed a workflow that comprises segmentation, tracking of splitting nuclei, extraction of static and dynamic features, classification, and phase length determination. For fast and accurate segmentation we use a region adaptive thresholding technique on the maximum intensity projected images (Fig. 1a,b). We perform tracking of the splitting cell nuclei using a two step approach. First, correspondences are determined by exploit- ing the smoothness of potential trajectories. Second, mitosis events are detected based on morphological properties and the corresponding trajectories are merged (Fig. 1c). Based on the tracking result we automatically select the most informative slice for each nucleus from the 3D image, which is then used for feature extraction. Besides static image features, we additionally include dynamic image features which represent temporal changes of the cell morphology between ancestrally related cells. A support vector machine classifier is used to classify the nuclei into the following seven cell cycle phases: Interphase, Prophase, Prometaphase, Metaphase, Anaphase1, Anaphase2, and Telophase (Fig. 2). Finally, we have