Journal papers
- I. Smal, N. Carranza-Herrezuelo, S. Klein, P. Wielopolski, A. Moelker, T. Springeling, M. Bernsen, W. Niessen, E. Meijering. "Reversible Jump MCMC Methods for Fully Automatic Motion Analysis in Tagged MRI", Medical Image Analysis, in press (Abstract, pdf)
- R. M. Buey, R. Mohan, K. Leslie, T. Walzthoeni, J. H. Missimer, A. Menzel, S. Bjelic, K. Bargsten, I. Grigoriev, I. Smal, E. Meijering, R. Aebersold, A. Akhmanova, M. O. Steinmetz. "Insights into EB Structure and the Role of its C-terminal Domain in Discriminating Microtubule Tips from Lattice.", Molecular Biology of the Cell, 22(16):2912-2923, July 2011 (Abstract, pdf)
- I. Grigoriev, K. L. Yu, E. Martinez-Sanchez, A. Serra-Marques, I. Smal, E. Meijering, J. Demmers, J. Peränen, R. J. Pasterkamp, P. van der Sluijs, C. C. Hoogenraad, A. Akhmanova. "Rab6, Rab8 and MICAL3 Cooperate in Controlling Docking and Fusion of Exocytotic Carriers", Current Biology, 21(11):967-974, May 2011 (Abstract, pdf)
- S. Montenegro Gouveia, K. Leslie, L. C. Kapitein, R. M. Buey, I. Grigoriev, M. Wagenbach, I. Smal, E. Meijering, C. C. Hoogenraad, L. Wordeman, M. O. Steinmetz and A. Akhmanova. "In Vitro Reconstitution of the Functional Interplay between MCAK and EB3 at Microtubule Plus Ends", Current Biology, 20(19):1717-1722, October 2010 (Abstract, pdf)
- T. Stepanova, I. Smal, J. van Haren, U. Akinci, Z. Liu, M. Miedema, R. Limpens, M. van Ham, M. van der Reijden, R. Poot, F. Grosveld, M. Mommaas, E. Meijering, N. Galjart. "History-Dependent Catastrophes Regulate Axonal Microtubule Behaviour", Current Biology, 20(11):1023-1028, May 2010 (Abstract, pdf)
- E. Meijering, O. Dzyubachyk, I. Smal, W. A. van Cappellen. "Tracking in Cell and Developmental Biology", Seminars in Cell and Developmental Biology, 20(8):894-902, October 2009 (Abstract, pdf)
- I. Smal, I. Grigoriev, A. Akhmanova, W. J. Niessen, E. Meijering. "Microtubule Dynamics Analysis Using Kymographs and Variable-Rate Particle Filters", IEEE Transactions on Image Processing, 19(7):1861-1876, July 2010 (Abstract, pdf)
- I. Smal, M. Loog, W. Niessen, E. Meijering. "Quantitative Comparison of Spot Detection Methods in Fluorescence Microscopy", IEEE Transactions on Medical Imaging, 29(2):282-301, February 2010 (Abstract, pdf)
- I. Smal, E. Meijering, K. Draegestein, N. Galjart, I. Grigoriev, A. Akhmanova, M. E. van Royen, A. B. Houtsmuller, W. Niessen. "Multiple Object Tracking in Molecular Bioimaging by Rao-Blackwellized Marginal Particle Filtering", Medical Image Analysis, 12(6):764-777, December 2008 (Abstract, pdf)
- I. Smal, K. Draegestein, N. Galjart, W. Niessen, E. Meijering, "Particle Filtering for Multiple Object Tracking in Dynamic Fluorescence Microscopy Images: Application to Microtubule Growth Analysis", IEEE Transactions on Medical Imaging, 27(6):789-804, June 2008 (Abstract, pdf)
- E. Meijering, I. Smal, G. Danuser, "Tracking in Molecular Bioimaging", IEEE Signal Processing Magazine, vol. 23, no. 3, May 2006, pp. 46-53 (Abstract, pdf)
- I. Smal, "Investigation of Subharmonic Oscillations in Ferroresonance Circuits", Technical Electrodynamics, no. 4, Kiev, Ukraine, 2001, pp.14-18 (Abstract, pdf)
- L. A. Sinitsky, I. Smal, "Influence of the Approximation of the Magnetizing Curve on the Results of Calculations of the Ferroresonance in the RLC-circuit", Digest of National Lviv University "Lvivska Politehnika", no.418, Lviv, Ukraine, 2001, pp.153-159
- L. A. Sinitsky, I. Smal, "Synthesis of Oscillators Reproducing one of the Solutions of Hamiltonian System.", Journal of Physical Studies, no.1, vol.4, Lviv, Ukraine, 2000, pp. 1-5. (Abstract, pdf)
- L. A. Sinitsky, I. Smal, "Synthesis of Oscillators of Relaxation Oscillations with prescribed Form of Impulses", Electronics and Communication, no.7, Kiev, Ukraine, 1999, pp. 26-32 (Abstract, pdf)
- L. A. Sinitsky, I. Smal, "On the Simulation of Oscillations of Given Form", Engineering Simulation, Vol.17, USA, 1999, pp.23-32. (and reprinted in Electronic Modeling, no.1, vol.21, Kiev, Ukraine, 1999, pp. 19-27) (Abstract, pdf)
Copyright (c) 2011 by the authors. Published version (c) 2011 by Elsevier. Personal use of this material is permitted. However, permission to reprint or republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works, must be obtained from the copyright holder.
Tagged magnetic resonance imaging (tMRI) is a well-known noninvasive method for studying regional heart dynamics. It offers great potential for quantitative analysis of a variety of kine(ma)tic parameters, but its clinical use has so far been limited, in part due to the lack of robustness and accuracy of existing tag tracking algorithms in dealing with low (and intrinsically time-varying) image quality. In this paper, we evaluate the performance of four frequently used concepts found in the literature (optical flow, harmonic phase (HARP) magnetic resonance imaging, active contour fitting, and non-rigid image registration) for cardiac motion analysis in 2D tMRI image sequences, using both synthetic image data (with ground truth) and real data from preclinical (small animal) and clinical (human) studies. In addition we propose a new probabilistic method for tag tracking that serves as a complementary step to existing methods. The new method is based on a Bayesian estimation framework, implemented by means of reversible jump Markov chain Monte Carlo (MCMC) methods, and combines information about the heart dynamics, the imaging process, and tag appearance. The experimental results demonstrate that the new method improves the performance of even the best of the four previous methods. Yielding higher consistency, accuracy, and intrinsic tag reliability assessment, the proposed method allows for improved analysis of cardiac motion.
End binding proteins (EB) comprise a conserved family of microtubule plus-end tracking proteins. The concerted action of calponin homology (CH), linker and C-terminal domains of EBs is important for their autonomous microtubule tip tracking, regulation of microtubule dynamics and recruitment of numerous partners to microtubule ends. Here we report the detailed structural and biochemical analysis of mammalian EBs. Small-angle X-ray scattering, electron microscopy and chemical cross-linking in combination with mass spectrometry indicate that EB is an elongated molecule with two interacting CH domains, an arrangement reminiscent of the one seen in other microtubule- and actin-binding proteins. Removal of the negatively charged C-terminal tail did not affect the overall conformation of EB; however, it increased the dwell times of EBs on the microtubule lattice in microtubule tip tracking reconstitution experiments. An even more stable association with the microtubule lattice was observed when the entire negatively charged C-terminal domain of EB was replaced by a neutral coiled-coil motif. In contrast, the interaction of EB with growing microtubule tips was not significantly affected by these C-terminal domain mutations. Our data indicate that long range electrostatic repulsive interactions between the C-terminus and the microtubule lattice drive the specificity of EBs for growing microtubule ends.
Copyright (c) 2010 by the authors. Published version (c) 2010 by Elsevier. Personal use of this material is permitted. However, permission to reprint or republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works, must be obtained from the copyright holder.
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Rab6 is a conserved small GTPase that localizes to the Golgi apparatus and cytoplasmic vesicles and controls transport and fusion of secretory carriers. Another Rab implicated in trafficking from the trans-Golgi to the plasma membrane is Rab8. Here we show that Rab8A stably associates with exocytotic vesicles in a Rab6-dependent manner. Rab8A function is not needed for budding or motility of exocytotic carriers but is required for their docking and fusion. These processes also depend on the Rab6-interacting cortical factor ELKS, suggesting that Rab8A and ELKS act in the same pathway. We show that Rab8A and ELKS can be linked by MICAL3, a member of MICAL family of flavoprotein monooxygenases. Expression of a MICAL3 mutant with an inactive monooxygenase domain resulted in a strong accumulation of secretory vesicles that were docked at the cell cortex but failed to fuse with the plasma membrane, an effect that correlated with the strongly reduced mobility of MICAL3. We propose that the monooxygenase activity of MICAL3 is required to regulate its own turnover and the concomitant remodelling of vesicle-docking protein complexes in which it is engaged. Taken together, our study illustrates cooperation of two Rab proteins in constitutive exocytosis and implicates a redox enzyme in this process.
Copyright (c) 2010 by the authors. Published version (c) 2010 by Elsevier. Personal use of this material is permitted. However, permission to reprint or republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works, must be obtained from the copyright holder.
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The kinesin-13 family member MCAK is a potent microtubule depolymerase.
Paradoxically, in cells it accumulates at the growing, rather than the shortening microtubule plus-ends.
This plus-end tracking behavior requires the interaction between MCAK and members of the EB family, but the effect of EBs on the microtubule destabilizing activity of MCAK and the functional
significance of MCAK accumulation at the growing microtubule tips have so far remained elusive. Here,
we dissect the functional interplay between MCAK and EB3 by reconstituting EB3-dependent MCAK
activity on dynamic microtubules in vitro. While MCAK alone efficiently blocks microtubule assembly,
the addition of EB3 restores robust microtubule growth, an effect that is not dependent on the binding
of MCAK to EB3. At the same time, EB3 targets MCAK to growing microtubule ends by increasing its
association rate with microtubule tips, a process that requires direct interaction between the two
proteins. This EB3-dependent microtubule plus-end accumulation does not affect the velocity of
microtubule growth or shortening but enhances the capacity of MCAK to induce catastrophes. The
combination of MCAK and EB3 thus promotes rapid switching between microtubule growth and
shortening, which can be important for remodeling of the microtubule cytoskeleton.
Copyright (c) 2010 by the authors. Published version (c) 2010 by Elsevier. Personal use of this material is permitted. However, permission to reprint or republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works, must be obtained from the copyright holder.
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In CHO cells microtubules (MTs) originate at the MT organizing centre (MTOC) and grow persistently towards the edge of the cell, where they undergo catastrophe. In axons and dendrites MT dynamics must be regulated differently, as MTs mainly grow parallel to the plasma membrane and there is no internal MTOC. GFP-tagged MT plus-end tracking proteins (+TIPs) mark growing MTs in neurons and their fluorescent "comet-like" pattern on MT ends reflects the turnover of +TIP binding sites. Using different GFP-tagged +TIPs, fluorescence-based segmentation and tracking tools, and protein depletion approaches, we provide a quantitative view on growing MTs inside neurites. In wild type neurons MTs grow with a constant average velocity, whereas in neurons lacking the +TIPs CLIP-115 or CLIP-170 MT growth rates are higher, as are the average MT growth distances. By contrast, the average duration of a MT growth event is similar in all types of neurons, suggesting that axonal MTs undergo catastrophes at random positions yet in a programmed fashion. Using N1E-115 neuroblastoma cells, we find that EB1, the core +TIP, regulates MT growth rate, track length, and track duration, consistent with in vitro data. Moreover, the turnover of +TIP binding sites at neurite MT ends depends on EB1 but not on CLIPs. We propose that in axons EB1 stimulates MT growth as well as structural transitions at MT ends, thereby regulating MT catastrophes and the turnover of +TIP binding sites.
Copyright (c) 2010 by the authors. Published version (c) 2010 by Elsevier. Personal use of this material is permitted. However, permission to reprint or republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works, must be obtained from the copyright holder.
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The past decade has seen an unprecedented data explosion in biology. It has become evident that in order to take full advantage of the potential wealth of information hidden in the data produced by even a single experiment, visual inspection and manual analysis are no longer adequate. To ensure efficiency, consistency, and completeness in data processing and analysis, computational tools are essential. Of particular importance to many modern live-cell imaging experiments is the ability to automatically track and analyze the motion of objects in time-lapse microscopy images. This article surveys the recent literature in this area. Covering all scales of microscopic observation, from cells, down to molecules, and up to entire organisms, it discusses the latest trends and successes in the development and application of computerized tracking methods in cell and developmental biology.
Copyright (c) 2010 by the authors. Published version (c) 2010 by IEEE.
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Studying intracellular dynamics is of fundamental importance for understanding healthy life at the molecular level and for developing drugs to target disease processes. One of the key technologies to enable this research is the automated tracking and motion analysis of these objects in microscopy image sequences. To make better use of the spatiotemporal information than common frame-by-frame tracking methods, two alternative approaches have recently been proposed, based on either Bayesian estimation or space-time segmentation. In this paper, we propose to combine the power of both approaches, and develop a new probabilistic method to segment the traces of the moving objects in kymograph representations of the image data. It is based on variable-rate particle filtering and uses multiscale trend analysis of the extracted traces to estimate the relevant kinematic parameters. Experiments on realistic synthetically generated images as well as on real biological image data demonstrate the improved potential of the new method for the analysis of microtubule dynamics in vitro.
Copyright (c) 2009 by the authors. Published version (c) 2009 by IEEE.
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Quantitative analysis of biological image data generally involves the detection of many subresolution spots.
Especially in live cell imaging, for which fluorescence microscopy is often used, the signal-to-noise ratio (SNR)
can be extremely low, making automated spot detection a very challenging task.
In the past, many methods have been proposed to perform this task, but a thorough quantitative evaluation and
comparison of these methods is lacking in the literature. In this paper, we evaluate the performance of the most
frequently used detection methods for this purpose. These include seven unsupervised and two supervised methods.
We perform experiments on synthetic images of three different types, for which the ground truth was available,
as well as on real image data sets acquired for two different biological studies, for which we obtained expert manual
annotations to compare with. The results from both types of experiments suggest that for very low SNRs (~ 2),
the supervised (machine learning) methods perform best overall. Of the unsupervised methods,
the detectors based on the so-called h-dome transform from mathematical morphology or the multiscale
variance-stabilizing transform perform comparably, and have the advantage that they do not require a
cumbersome learning stage. At high SNRs (> 5), the difference in performance of all considered detectors becomes negligible.
Copyright (c) 2008 by the authors. Published version (c) 2008 by Elsevier. Personal use of this material is permitted. However, permission to reprint or republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works, must be obtained from the copyright holder.
Time-lapse fluorescence microscopy imaging has rapidly evolved in the past decade and has opened new avenues for studying intracellular processes in vivo. Such studies generate vast amounts of noisy image data that cannot be analyzed efficiently and reliably by means of manual processing. Many popular tracking techniques exist but often fail to yield satisfactory results in the case of high object densities, high noise levels, and complex motion patterns. Probabilistic tracking algorithms, based on Bayesian estimation, have recently been shown to offer several improvements over classical approaches, by better integration of spatial and temporal information, and the possibility to more effectively incorporate prior knowledge about object dynamics and image formation. In this paper, we extend our previous work in this area and propose an improved, fully automated particle filtering algorithm for the tracking of many subresolution objects in fluorescence microscopy image sequences. It involves a new track management procedure and allows the use of multiple dynamics models. The accuracy and reliability of the algorithm are further improved by applying marginalization concepts. Experiments on synthetic as well as real image data from three different biological applications clearly demonstrate the superiority of the algorithm compared to previous particle filtering solutions.
Copyright (c) 2006 by the authors. Published version (c) 2006 by IEEE. Personal use of this material is permitted. However, permission to reprint or republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works, must be obtained from the copyright holder.
Quantitative analysis of dynamic processes in living cells by means of fluorescence microscopy imaging requires tracking of hundreds of bright spots in noisy image sequences. Deterministic approaches, which use object detection prior to tracking, perform poorly in the case of noisy image data. We propose an improved, completely automatic tracker, built within a Bayesian probabilistic framework. It better exploits spatiotemporal information and prior knowledge than common approaches, yielding more robust tracking also in cases of photobleaching and object interaction. The tracking method was evaluated using simulated but realistic image sequences, for which ground truth was available. The results of these experiments show that the method is more accurate and robust than popular tracking methods. In addition, validation experiments were conducted with real fluorescence microscopy image data acquired for microtubule growth analysis. These demonstrate that the method yields results that are in good agreement with manual tracking performed by expert cell biologists. Our findings suggest that the method may replace laborious manual procedures.
Copyright (c) 2006 by the authors. Published version (c) 2006 by IEEE. Personal use of this material is permitted. However, permission to reprint or republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works, must be obtained from the copyright holder.
Automated tracking and analysis of moving objects in image sequences has been and continues to be one of the major themes in digital image analysis research. This is not surprising in view of its many applications in video surveillance, multimedia services, automated vehicle guidance and driver assistance, remote sensing and meteorology, and medical imaging. It is also a recurring theme in molecular biology. By their very nature, biomolecular systems are dynamic, and it is one of the major challenges of biomedical research and pharmaceutical industries in the postgenomic era to unravel the spatial and temporal relationships of these complex systems and to devise strategies to manipulate them. Results in this area can be expected to have profound social and economic impact in the near future, as they can be harnessed to improve human health and well-being. Studies into biomolecular dynamics generate ever increasing amounts of image data. To be able to handle these data and to fully exploit them for describing biological processes on a quantitative level and building accurate mathematical models of dynamic structures, computerized motion analysis is rapidly becoming a requisite.
Over the past decades, a number of image analysis techniques have been developed in support of such studies, the details of which were often buried in the small prints of the methods sections of papers published in the biology and biophysics literature. The majority of these techniques were based on rather rudimentary principles, however. The purpose of this article is to stimulate the application of more advanced computer vision techniques to tracking in biological molecular imaging, by surveying the literature and sketching the current state of affairs in the field for a signal and image processing audience. After describing the basic principles of visualizing molecular dynamics in living cells and giving some examples of biological molecular dynamics studies, we summarize the problems and limitations intrinsic to imaging at this scale. Then we discuss the main ingredients of the commonly used tracking paradigm and subsequently reconsider its competence by comparing it to certain aspects of visual motion perception in human beings, keeping in mind the complexity and variability of biological image data. We conclude by summarizing the main points of attention for future research and the challenges that lie ahead.
Over the past decades, a number of image analysis techniques have been developed in support of such studies, the details of which were often buried in the small prints of the methods sections of papers published in the biology and biophysics literature. The majority of these techniques were based on rather rudimentary principles, however. The purpose of this article is to stimulate the application of more advanced computer vision techniques to tracking in biological molecular imaging, by surveying the literature and sketching the current state of affairs in the field for a signal and image processing audience. After describing the basic principles of visualizing molecular dynamics in living cells and giving some examples of biological molecular dynamics studies, we summarize the problems and limitations intrinsic to imaging at this scale. Then we discuss the main ingredients of the commonly used tracking paradigm and subsequently reconsider its competence by comparing it to certain aspects of visual motion perception in human beings, keeping in mind the complexity and variability of biological image data. We conclude by summarizing the main points of attention for future research and the challenges that lie ahead.
Copyright (c) 2001 by the authors. Personal use of this material is permitted. However, permission to reprint or republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works, must be obtained from the copyright holder.
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Copyright (c) 2000 by the authors. Personal use of this material is permitted. However, permission to reprint or republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works, must be obtained from the copyright holder.
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Nonlinear periodic system is synthesised which reproduces one of the solution of Hamiltonian system. Arbitrarily prescribed periodic waveform can be synthesised due to appropriate chosen potential of Hamiltonian equations. Proposed method of the synthesis can be extended on Hamiltonian equations with n-degrees of freedom. In this case synthesis of quasiperiodic waveforms which have n-basic frequencies is possible. As an example autooscillator for generation of the waveform similar to the cardiogram was synthesized.
Copyright (c) 1999 by the authors. Personal use of this material is permitted. However, permission to reprint or republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works, must be obtained from the copyright holder.
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Copyright (c) 1999 by the authors. Personal use of this material is permitted. However, permission to reprint or republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works, must be obtained from the copyright holder.
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