Time-resolved analysis and visualization of dynamic processes in living cells

Abstract

Recent development of in vivo microscopy techniques, including green fluorescent proteins, has allowed the visualization of a wide range of dynamic processes in living cells. For quantitative and visual interpretation of such processes, new concepts for time-resolved image analysis and continuous timespace visualization are required. Here, we describe a versatile and fully automated approach consisting of four techniques, namely highly sensitive object detection, fuzzy logic-based dynamic object tracking, computer graphical visualization, and measurement in time-space. Systematic model simulations were performed to evaluate the reliability of the automated object detection and tracking method. To demonstrate potential applications, the method was applied to the analysis of secretory membrane traffic and the functional dynamics of nuclear compartments enriched in pre-mRNA splicing factors.The development of in vivo microscopy techniques and fluorescent reagents has stimulated interest in studying the dynamics of cellular processes (for review, see refs. 1 and 2). These types of experiments generate large and complex data sets and require tools for visual and quantitative analysis of the observed dynamic processes in space and time.Imaging fast moving vesicles in living cells at high speed and high spatial resolution generally implies a low signal-to-noise ratio, hampering accurate object detection. As a consequence of the optical aperture problem, tracking of small objects based on visual similarity criteria is difficult because many objects appear very similar (3). Highly sensitive object detection and tracking has been recognized as crucial for an accurate evaluation of such data. However, a quantitative interpretation of trafficking vesicles has been generally based on manual evaluation of a user-biased selection of objects with apparently highest motility. Such an evaluation is very time-consuming and also is limited by the perception of the manual inspector.Processes in the cell nucleus are much slower and need to be observed over a longer period of time. To avoid disruptions of nuclear processes, the total light exposure during in vivo observation must be minimized. Thus, the signal-to-noise ratio and, more importantly, the number of time series taken in a particular experiment is considerably reduced, leading to a loss in spatiotemporal resolution. Displaying time series as movies is a widely used method for visual interpretation. However, this approach does not improve temporal resolution; that is, additional information about the continuous development of the observed processes between the imaged time steps (subpixel resolution in time) is not obtained. Furthermore, quantitative information is not revealed by such a visual approach. In a first approach to quantitatively describe nuclear dynamics in vivo, single particle tracking (4) has been used to estimate the diffusion of chromatin in living cells of different species (5).Here, we describe a novel technique for fully automated analysis and tim...

Quantitative Imaging of Pre-mRNA Splicing Factors in Living Cells

“…The method can easily be applied to biological analysis of completely different dynamic cellular events. We have successfully applied this system to a wide variety of applications, including the analysis of membrane traffic (Tvaruskó et al 1999) and GFP-tagged centromeres (Sullivan and Eils, unpublished data), as well as root growth in botanical samples by tracking fluorescently labeled beeds attached to root tips (Schurr and Eils, unpublished data). With this method at hand, it is now possible to study the functional dynamics of living cells at high resolution in time and space.…”

“…For image sequence analysis, we used a highly sensitive image analysis system for time-resolved analysis of dynamic processes (Tvaruskó et al, 1999). This system comprises three modules, namely object detection, object tracking over time, and time-space visualization of tracked objects.…”

“…In live cell studies with typically low signal-to-noise-ratio an approach based on gray value maxima only often fails. We recently developed a fully automated system for time-resolved analysis of dynamic processes in living cells (Tvaruskó et al, 1999), which is based on the assumption that structures of interest can be characterized by regions of locally homogeneous gray value distribution rather than by absolutely maximal intensity. By image reconstruction in time and space, it has been shown that it is possible to partially regain both temporal and spatial resolution.…”

Detection and tracking of clathrin-coated pits in biological images

“…Recently, several algorithms have been developed for tracking the motion of particles and cells [8][9][10][11][12]. One method is based on appearance features [11], which assume that the shape of a certain cell or particle changes only slightly between consecutive frames.…”

“…One method is based on appearance features [11], which assume that the shape of a certain cell or particle changes only slightly between consecutive frames. Another method is to maximize the smoothness of the particle trajectory and the velocity [10,12] for the trajectory direction and the velocity of a moving particle should only change slightly between consecutive frames. However, these methods do not work well with our data set as only partial information of the particles' movement is considered.…”