Ihor Smal

Methods for cell and particle tracking.

Achieving complete understanding of any living thing inevitably requires thorough analysis of both its anatomic and dynamic properties. Live-cell imaging experiments carried out to this end often produce massive amounts of time-lapse image data containing far more information than can be digested by a human observer. Computerized image analysis offers the potential to take full advantage of available data in an efficient and reproducible manner. A recurring task in many experiments is the tracking of large numbers of cells or particles and the analysis of their (morpho)dynamic behavior. In the past decade, many methods have been developed for this purpose, and software tools based on these are increasingly becoming available. Here, we survey the latest developments in this area and discuss the various computational approaches, software tools, and quantitative measures for tracking and motion analysis of cells and particles in time-lapse microscopy images.

Objective comparison of particle tracking methods

Particle tracking is of key importance for quantitative analysis of intracellular dynamic processes from time-lapse microscopy image data. Because manually detecting and following large numbers of individual particles is not feasible, automated computational methods have been developed for these tasks by many groups. Aiming to perform an objective comparison of methods, we gathered the community and organized an open competition in which participating teams applied their own methods independently to a commonly defined data set including diverse scenarios. Performance was assessed using commonly defined measures. Although no single method performed best across all scenarios, the results revealed clear differences between the various approaches, leading to notable practical conclusions for users and developers.

Tracking in cell and developmental biology.

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.

Quantitative Comparison of Spot Detection Methods in Fluorescence Microscopy

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.

Particle Filtering for Multiple Object Tracking in Dynamic Fluorescence Microscopy Images: Application to Microtubule Growth Analysis

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.

In Vitro Reconstitution of the Functional Interplay between MCAK and EB3 at Microtubule Plus Ends

The kinesin-13 family member mitotic centromere-associated kinesin (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 end-binding protein (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. Whereas 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.

Rab6, Rab8, and MICAL3 cooperate in controlling docking and fusion of exocytotic carriers.

Rab6 is a conserved small GTPase that localizes to the Golgi apparatus and cytoplasmic vesicles and controls transport and fusion of secretory carriers [1]. Another Rab implicated in trafficking from the trans-Golgi to the plasma membrane is Rab8 [2-5]. 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 [1], suggesting that Rab8A and ELKS act in the same pathway. We show that Rab8A and ELKS can be linked by MICAL3, a member of the MICAL family of flavoprotein monooxygenases [6]. 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 remodeling of vesicle-docking protein complexes in which it is engaged. Taken together, the results of our study illustrate cooperation of two Rab proteins in constitutive exocytosis and implicates a redox enzyme in this process.

Multiple object tracking in molecular bioimaging by Rao-Blackwellized marginal particle filtering.

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.

Insights into EB1 structure and the role of its C-terminal domain for discriminating microtubule tips from the lattice

EBs, key microtubule (MT) tip–tracking proteins, are elongated molecules with two interacting calponin homology (CH) domains, an arrangement reminiscent of MT- and actin-binding CH proteins. In addition, electrostatic interactions between the C-terminus of EBs and MTs drive the specificity of EBs for growing MT ends.

End-binding proteins sensitize microtubules to the action of microtubule-targeting agents

Microtubule-targeting agents (MTAs) are widely used for treatment of cancer and other diseases, and a detailed understanding of the mechanism of their action is important for the development of improved microtubule-directed therapies. Although there is a large body of data on the interactions of different MTAs with purified tubulin and microtubules, much less is known about how the effects of MTAs are modulated by microtubule-associated proteins. Among the regulatory factors with a potential to have a strong impact on MTA activity are the microtubule plus end-tracking proteins, which control multiple aspects of microtubule dynamic instability. Here, we reconstituted microtubule dynamics in vitro to investigate the influence of end-binding proteins (EBs), the core components of the microtubule plus end-tracking protein machinery, on the effects that MTAs exert on microtubule plus-end growth. We found that EBs promote microtubule catastrophe induction in the presence of all MTAs tested. Analysis of microtubule growth times supported the view that catastrophes are microtubule age dependent. This analysis indicated that MTAs affect microtubule aging in multiple ways: destabilizing MTAs, such as colchicine and vinblastine, accelerate aging in an EB-dependent manner, whereas stabilizing MTAs, such as paclitaxel and peloruside A, induce not only catastrophes but also rescues and can reverse the aging process.