Expression plasmids and construction
The cloning of the Xenopus laevi GFP-NLS-vimentin expression plasmid has been described previously . For the generation of the N-terminally tagged GFP-vimentin construct the vimentin cDNA was modified at the 5' – end to contain a BspE I – site followed by a Nde I – site containing the start methionine in frame subcloned into pBlueScript (Stratagene). The BspE I / BspE I fragment was then subcloned into pEGFP – C1 and the orientation verified by DNA sequencing.
Cell culture and transfection
SW13 lacking endogenous vimentin  were usually grown in DMEM (Invitrogen, Karlsruhe, Germany) supplemented with 10% fetal calf serum (Seromed, Berlin, Germany), 20 mM glutamine and 100 μg/ml penicillin/streptomycin (Invitrogen) at 37°C and 5% CO2. For live cell imaging purposes the cells were resuspended in complete DMEM without Phenol Red (Invitrogen) and grown in 2- or 4-well Lab-Tek® II chambers (Nalge Nunc International, Rochester, USA). Transient transfections were carried out using the FuGene 6 transfection reagent according to the manufacture's protocol (Roche, Mannheim, Germany).
Immediately before imaging, chromatin counter stain was obtained by incubating the cells with 1 μg/ml Hoechst 33342 in complete DMEM without Phenol Red for 20 min followed by three times washing with DMEM without Phenol Red. Cells were then kept in complete DMEM supplemented with 20 mM Hepes without Phenol Red. For tracking of nuclear particles we stably transfected SW13 cells with the expression plasmid encoding Xenopus laevis GFP-NLS-vimentin. To track vimentin particles in the cytoplasm, SW13 cells were transiently transfected with the Xenopus laevis GFP-vimentin cDNA construct described above.
Mircroinjection of fluorescent polystyrene microspheres
SW13 cells were cultured in P35G-1.5-7-C-Grid cell locate culture dishes (MatTek Corporation, Ashland, MA) in complete DMEM medium for 1 day after plating. Carboxylate-modified 0.1 μm microspheres (FluoSpheres, #F8800, Molecular Probes, Leiden, NL) were obtained as 2 % solids in solution and further diluted to 0.04 % solution in 1 M BSA in PBS. Before microinjection, the microspheres were sonificated for 30 seconds to avoid aggregation. The AIS 2 system (Cell Biology Trading, Hamburg, Germany) was used for microinjection. Injection needles were drawn from borosilicate glass capillaries GC120TF-10 (Harvard Apparatus, Edenbridge, UK) using a Flaming Brown micropipette puller P-97 (Sutter Instruments, Novato, CA). The injection pressure was adjusted to 20–250 kPa in the different experiments. For evaluation of cell viability microinjected cells were grown at 37°C with 5% CO2 overnight and examined the next day.
Live cell imaging was carried out on a confocal laser scanning microscope TCS SP2 AOBS (Leica Microsystems, Wetzlar, Germany) using a 63x oil immersion objective with 1.4 optical apperture (HCX PL APO lbd.BL 63x / 1.4, #506192, Leica Microsystems). The microscope was further equipped with a 29 mm objective heater (#0280.010, Leica Microsystems) with temperature – controlled device (#0504.000, Leica Microsystems) and a temperature – controlled fan (ASI 400E, Nevtek, Burnsville, VA, USA). A diode laser (λ = 405 nm) was used for excitation of Hoechst 33342. An argon (λ = 488 nm) and a helium/neon laser (λ = 543 nm) was used for EGFP and fluorescent microsphere excitation, respectively. 3-D image stacks, each consisting of 17 2-D-images, of GFP-vimentin particles and Hoechst 33342 stained chromatin were acquired in parallel at the maximum scanning speed of 1400 Hz (i.e. scan lines per second) with a constant Δt = 10 seconds. Imaging format was set to 256 × 256 pixel, voxel sizes were generally between 0,093 μm × 0,093 μm × 0,325 μm and 0,093 μm × 0,093 μm × 0,450 μm. The laser intensity was adjusted to a minimum to avoid photo damage during imaging. For this purpose the acousto-optical beam splitter (AOBS) were set between 2 – 5% for both lasers with photo multiplier (PMT) settings of 715.7 Volt for the diode laser and 717.4 Volt for the argon and helium/neon lasers. All the image processing steps were carried out in TIKAL (see below).
Before drug treatment, we acquired 3-D time series of cells with a time-lapse of Δt = 10 seconds for 10 minutes. Immediately afterwards the medium was exchanged for one of the following solutions: i) 600 mM sorbitol; ii) 20 mM azide / 50 mM deoxyglucose; iii) 0.04 μg/ml nocodazole; and iv) 1 μg/ml cytochalasin D – all in complete DMEM without Phenol Red except for ii), which was applied in PBS . After change of medium, we immediately acquired further 3-D time series images for another 10 min. Thereafter, the drug containing medium was replaced by complete DMEM without Phenol Red. In order to document the recovery and viability of cells, images were acquired for another 10 minutes with the same microscope settings.
4-D image registration [40, 41] of consecutively captured three-dimensional images of cell nuclei counterstained with Hoechst 33342 was performed for correcting for global movement of cell nuclei. In order to reduce alignment artifacts due to acquisition noise all three-dimensional image stacks were preprocessed using a 3-D median filter and a 3-D automatic gamma correction for maximum gray value range. Image registration was then performed using an implementation of an automated image registration algorithm  running on a high-performance computing cluster. In this study, we only applied rigid and affine transformation since we did not observe drastic local deformations in cellular shape which would require correction by our non-rigid transformation method . Rigid and affine transformation matrices were computed in a two-step process for optimal 4-D object alignment. First, objects at time point t + 1 were consecutively aligned with 3-D objects at time point t providing a pre-registered image stack at each time point. Secondly, each pre-registered image stack was aligned with respect to the initial image stack at time point t = 0. Transformation matrices calculated for the chromatin stained images were applied to all corresponding image stacks in the other color channels.
Tracking beads and vimentin particles
Image processing was carried out using our in-house developed image analysis platform. The analysis chain consisted of three major modules: image preprocessing and segmentation, 4-D tracking and quantification of dynamics, 4-D visualization and user interaction with 4-D data sets. To reduce noise in the vimentin channel, images were subjected to 3-D diffusion filtering  followed by segmentation with a pyramid linking algorithm . Particle tracking was performed by extending our already implemented single particle tracking algorithm from 2-D + time to 3-D + time [43, 44]. The tracking algorithm uses parameters such as the individual center of masses, volumes, total grey value intensities, velocities and accelerations. To control possible tracking and segmentation artifacts we visualized 4-D tracks of beads and vimentin particles, respectively, together with their isosurface reconstruction at each time point.
Correlation analysis of chromatin density and tracked nuclear particle
Chromatin images were preprocessed by a 2-D Gaussian smoothing filter to reduce noise. Additionally a gamma filter was applied to use the full grey value range of 8 bits. For additional noise reduction the 256 different grey values were divided into eight grey value classes ranging from 0 – 32, 33 – 65, etc. The centre of mass for each individual polystyrene bead or nuclear vimentin body was calculated and tracked in 3-D over time. For each time point the corresponding binned mean grey value intensities of a 9 × 9 pixel area around the centre of mass was determined.
Calculation of mean squared displacements and anomalous diffusion coefficients
The mean square displacement (MSD) was calculated for each particle and time point of the data set according to <Δd2> = < [d(t) - d(t + Δt)] 2> and plotted as <d2> (μm2) versus Δt (s) using Matlab (The MathWorks, Inc., Novi, MI). Further evaluation of anomalous diffusion (α) [25, 26] was determined by using the regression curve fitting functions implemented in Matlab.
TIKAL image processing platform
TIKAL is an in-house developed platform for multi – purpose image processing (executable code can be requested at: http://www.dkfz.de/ibios/repository/tikal/). The program consists of a graphical user interface for easy access of the underlying algorithms. The software is written in C/C++ and can be deployed both on Linux and Windows systems. The main components of the program are separated into several main modules, namely a filter, registration, tracking, visualization and data handling module. The filter module contains 2-D and 3-D image processing algorithms such as gamma correction, median, gaussian, anisotropic diffusion filters . Segmentation filters range from simple threshold operations to complex pyramid linking segmentation . The registration module is capable of handling single 3-D image stacks as well as 4-D time-lapse series of image stacks. Image stacks can be corrected by a combination of rigid and affine transformations as well as non-linear correction of local deformations by registration based on a thin plate spline model [22, 23]. The tracking  and visualization part can be combined to allow the user a visual inspection of the tracking result and enable a correction of falsely assigned trajectories. The tracking and visualization module contains functions for calculation of quantitative parameters such as distances, velocities and mean squared displacement (MSD).
The software is capable to import both the proprietary Leica and the commonly used Tiff file format. Currently we are working on an implementation to integrate and combine our software with the Open Microscopy Environment, OME . This is of particular importance when working with huge data sets, when either computer memory or disk space become limiting factors in the analysis stream.