Daniel Schmitter

Predicting Stem Cell Fate Changes by Differential Cell Cycle Progression Patterns

Stem cell self-renewal, commitment and reprogramming rely on a poorly understood coordination of cell cycle progression and execution of cell fate choices. Using existing experimental paradigms, it has not been possible to probe this relationship systematically in live stem cells in vitro or in vivo. Alterations in stem cell cycle kinetics probably occur long before changes in phenotypic markers are apparent and could be used as predictive parameters to reveal changes in stem cell fate. To explore this intriguing concept, we developed a single-cell tracking approach that enables automatic detection of cell cycle phases in live (stem) cells expressing fluorescent ubiquitylation-based cell-cycle indicator (FUCCI) probes. Using this tool, we have identified distinctive changes in lengths and fluorescence intensities of G1 (red fluorescence) and S/G2-M (green) that are associated with self-renewal and differentiation of single murine neural stem/progenitor cells (NSCs) and embryonic stem cells (ESCs). We further exploited these distinctive features using fluorescence-activated cell sorting to select for desired stem cell fates in two challenging cell culture settings. First, as G1 length was found to nearly double during NSC differentiation, resulting in progressively increasing red fluorescence intensity, we successfully purified stem cells from heterogeneous cell populations by their lower fluorescence. Second, as ESCs are almost exclusively marked by the green (S/G2-M) FUCCI probe due to their very short G1, we substantially augmented the proportion of reprogramming cells by sorting green cells early on during reprogramming from a NSC to an induced pluripotent stem cell state. Taken together, our studies begin to shed light on the crucial relationship between cell cycle progression and fate choice, and we are convinced that the presented approach can be exploited to predict and manipulate cell fate in a wealth of other mammalian cell systems.

Snakes on a Plane [A perfect snap for bioimage analysis]

In recent years, there has been an increasing interest in getting a proper quantitative understanding of cellular and molecular processes [1], [2]. One of the major challenges of current biomedical research is to characterize not only the spatial organization of these complex systems but also their spatiotemporal relationships [3], [4]. Microscopy has matured to the point that it enables sensitive time-lapse imaging of cells in vivo and even of single molecules [5], [6]. Making microscopy more quantitative brings important scientific benefits in the form of improved performance and reproducibility. This has been fostered by the development of technological achievements such as high-throughput microscopy. A direct consequence is that the size and complexity of image data are increasing. Time-lapse experiments commonly generate hundreds to thousands of images, each containing hundreds of objects to be analyzed [7]. These data often cannot be analyzed manually because the manpower required would be too extensive, which calls for automated methods for the analysis of biomedical images. Such computerized extraction of quantitative information out of the rapidly expanding amount of acquired data remains a major challenge. The development of the related algorithms is nontrivial and is one of the most active fronts in the new field of bioimage informatics [8]?[11]. Segmenting thousands of individual biological objects and tracking them over time is remarkably difficult. A typical algorithm will need to be tuned to the imaging modality and will have to cope with the fact that cells can be tightly packed and may appear in various configurations, making them difficult to segregate.

Circadian Clock Characteristics Are Altered in Human Thyroid Malignant Nodules

CONTEXT The circadian clock represents the bodys molecular time-keeping system. Recent findings revealed strong changes of clock gene expression in various types of human cancers. OBJECTIVE Due to emerging evidence on the connection between the circadian oscillator, cell cycle, and oncogenic transformation, we aimed to characterize the circadian clockwork in human benign and malignant thyroid nodules. DESIGN Clock transcript levels were assessed by quantitative RT-PCR in thyroid tissues. To provide molecular characteristics of human thyroid clockwork, primary thyrocytes established from normal or nodular thyroid tissue biopsies were subjected to in vitro synchronization with subsequent clock gene expression analysis by circadian bioluminescence reporter assay and by quantitative RT-PCR. RESULTS The expression levels of the Bmal1 were up-regulated in tissue samples of follicular thyroid carcinoma (FTC), and in papillary thyroid carcinoma (PTC), as compared with normal thyroid and benign nodules, whereas Cry2 was down-regulated in FTC and PTC. Human thyrocytes derived from normal thyroid tissue exhibited high-amplitude circadian oscillations of Bmal1-luciferase reporter expression and endogenous clock transcripts. Thyrocytes established from FTC and PTC exhibited clock transcript oscillations similar to those of normal thyroid tissue and benign nodules (except for Per2 altered in PTC), whereas cells derived from poorly differentiated thyroid carcinoma exhibited altered circadian oscillations. CONCLUSIONS This is the first study demonstrating a molecular makeup of the human thyroid circadian clock. Characterization of the thyroid clock machinery alterations upon thyroid nodule malignant transformation contributes to understanding the connections between circadian clocks and oncogenic transformation. Moreover, it might help in improving the thyroid nodule preoperative diagnostics.

A 2D/3D image analysis system to track fluorescently labeled structures in rod-shaped cells: application to measure spindle pole asymmetry during mitosis

BackgroundThe yeast Schizosaccharomyces pombe is frequently used as a model for studying the cell cycle. The cells are rod-shaped and divide by medial fission. The process of cell division, or cytokinesis, is controlled by a network of signaling proteins called the Septation Initiation Network (SIN); SIN proteins associate with the SPBs during nuclear division (mitosis). Some SIN proteins associate with both SPBs early in mitosis, and then display strongly asymmetric signal intensity at the SPBs in late mitosis, just before cytokinesis. This asymmetry is thought to be important for correct regulation of SIN signaling, and coordination of cytokinesis and mitosis. In order to study the dynamics of organelles or large protein complexes such as the spindle pole body (SPB), which have been labeled with a fluorescent protein tag in living cells, a number of the image analysis problems must be solved; the cell outline must be detected automatically, and the position and signal intensity associated with the structures of interest within the cell must be determined.ResultsWe present a new 2D and 3D image analysis system that permits versatile and robust analysis of motile, fluorescently labeled structures in rod-shaped cells. We have designed an image analysis system that we have implemented as a user-friendly software package allowing the fast and robust image-analysis of large numbers of rod-shaped cells. We have developed new robust algorithms, which we combined with existing methodologies to facilitate fast and accurate analysis. Our software permits the detection and segmentation of rod-shaped cells in either static or dynamic (i.e. time lapse) multi-channel images. It enables tracking of two structures (for example SPBs) in two different image channels. For 2D or 3D static images, the locations of the structures are identified, and then intensity values are extracted together with several quantitative parameters, such as length, width, cell orientation, background fluorescence and the distance between the structures of interest. Furthermore, two kinds of kymographs of the tracked structures can be established, one representing the migration with respect to their relative position, the other representing their individual trajectories inside the cell. This software package, called “RodCellJ”, allowed us to analyze a large number of S. pombe cells to understand the rules that govern SIN protein asymmetry. (Continued on next page)(Continued from previous page)Conclusions“RodCellJ” is freely available to the community as a package of several ImageJ plugins to simultaneously analyze the behavior of a large number of rod-shaped cells in an extensive manner. The integration of different image-processing techniques in a single package, as well as the development of novel algorithms does not only allow to speed up the analysis with respect to the usage of existing tools, but also accounts for higher accuracy. Its utility was demonstrated on both 2D and 3D static and dynamic images to study the septation initiation network of the yeast Schizosaccharomyces pombe. More generally, it can be used in any kind of biological context where fluorescent-protein labeled structures need to be analyzed in rod-shaped cells.AvailabilityRodCellJ is freely available under http://bigwww.epfl.ch/algorithms.html.

New Parametric 3D Snake for Medical Segmentation of Structures with Cylindrical Topology

We propose a new parametric 3D snake with cylindrical topology. Its construction is based on interpolatory basis functions which facilitates user-interaction because the control points of the snake directly lie on the surface of the deformable cylinder. We prove that the basis functions exactly reproduce a cylinder and propose a new parametrization as a tensor-product spline surface. We provide explicit formulas for the energy function based on Greens theorem that speed up the computation of the optimization algorithm. We have implemented the proposed framework as a freely available open-source plugin for the bioimaging platform Icy. Its utility has been tested on phantom data as well as on real 3D data to segment the spinal cord and the descending aorta.

Efficient Shape Priors for Spline-Based Snakes

Parametric active contours are an attractive approach for image segmentation, thanks to their computational efficiency. They are driven by application-dependent energies that reflect the prior knowledge on the object to be segmented. We propose an energy involving shape priors acting in a regularization-like manner. Thereby, the shape of the snake is orthogonally projected onto the space that spans the affine transformations of a given shape prior. The formulation of the curves is continuous, which provides computational benefits when compared with landmark-based (discrete) methods. We show that this approach improves the robustness and quality of spline-based segmentation algorithms, while its computational overhead is negligible. An interactive and ready-to-use implementation of the proposed algorithm is available and was successfully tested on real data in order to segment Drosophila flies and yeast cells in microscopic images.

ATLAS-FREE BRAIN SEGMENTATION IN 3D PROTON-DENSITY-LIKE MRI IMAGES

We present a new method for the atlas-free brain segmentation of proton-density-like 3D MRI images. We show how steerable filters can be efficiently combined with parametric spline surfaces to produce a fast and robust 3D brain segmentation algorithm. The novelty lies in the computation of brain edge maps through optimal steerable surface detectors which provide efficient energies for the rapid optimization of snakes. Our experimental results show the promising potential of the method for fast and accurate brain extraction.

Analysis of S. pombe SIN protein association to the SPB reveals two genetically separable states of the SIN.

ABSTRACT The Schizosaccharomyces pombe septation initiation network (SIN) regulates cytokinesis, and asymmetric association of SIN proteins with the mitotic spindle pole bodies (SPBs) is important for its regulation. Here, we have used semi-automated image analysis to study SIN proteins in large numbers of wild-type and mutant cells. Our principal conclusions are: first, that the association of Cdc7p with the SPBs in early mitosis is frequently asymmetric, with a bias in favour of the new SPB; second, that the early association of Cdc7p–GFP to the SPB depends on Plo1p but not Spg1p, and is unaffected by mutations that influence its asymmetry in anaphase; third, that Cdc7p asymmetry in anaphase B is delayed by Pom1p and by activation of the spindle assembly checkpoint, and is promoted by Rad24p; and fourth, that the length of the spindle, expressed as a fraction of the length of the cell, at which Cdc7p becomes asymmetric is similar in cells dividing at different sizes. These data reveal that multiple regulatory mechanisms control the SIN in mitosis and lead us to propose a two-state model to describe the SIN.

A family of smooth and interpolatory basis functions for parametric curve and surface representation

Interpolatory basis functions are helpful to specify parametric curves or surfaces that can be modified by simple user-interaction. Their main advantage is a characterization of the object by a set of control points that lie on the shape itself (i.e., curve or surface). In this paper, we characterize a new family of compactly supported piecewise-exponential basis functions that are smooth and satisfy the interpolation property. They can be seen as a generalization and extension of the Keys interpolation kernel using cardinal exponential B-splines. The proposed interpolators can be designed to reproduce trigonometric, hyperbolic, and polynomial functions or combinations of them. We illustrate the construction and give concrete examples on how to use such functions to construct parametric curves and surfaces.

Trigonometric Interpolation Kernel to Construct Deformable Shapes for User-Interactive Applications

We present a new trigonometric basis function that is capable of perfectly reproducing circles, spheres and ellipsoids while at the same time being interpolatory. Such basis functions have the advantage that they allow to construct shapes through a sequence of control points that lie on their contour (2-D) or surface (3-D) which facilitates user-interaction, especially in 3-D. Our piecewise exponential basis function has finite support, which enables local control for shape modification. We derive and prove all the necessary properties of the kernel to represent shapes that can be smoothly deformed and show how idealized shapes such as ellipses and spheres can be constructed.