Mini-Review |
Address correspondence to Roland Eils, Intelligent Bioinformatics Systems Division, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany. Tel.: 49-6221-423600. Fax: 49-6221-423610. E-mail: r.eils{at}dkfz.de
![]() |
Abstract |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
Dynamic processes are at the very basis of cellular function. In an attempt to understand these processes, cellular structures have been studied in fixed and living specimens by various microscopic techniques including phase contrast, differential interference contrast, and confocal microscopy. Fluorescent dyes such as fluorescein and rhodamine, together with recombinant fluorescent protein technology (Chalfie et al., 1994) and voltage- (Loew, 1992) and pH-sensitive dyes (Adie et al., 2002) allow virtually any cellular structure to be tagged. In combination with techniques in live cells like FRAP (Axelrod et al., 1976) and fluorescence resonance energy transfer (Sekar and Periasamy, 2003), it is now possible to obtain spatio-temporal, biochemical, and biophysical information about the cell in a manner not imaginable before.
![]() |
Evolution of quantitative live-cell microscopy |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
|
![]() |
Single particle tracking |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
![]() |
Image registration |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
A parametric image registration algorithm specifies the parameters of a transformation in a way that physically corresponding points at two consecutive time steps are brought together as close as possible. Such algorithms have been broadly studied in medical imaging and cell biology (Maintz and Viergever, 1998; Bornfleth et al., 1999). Although one class of algorithms operates on previously extracted surface points (Lavallee and Szeliski, 1995), other algorithms register the images directly based on the gray-value changes. Nonrigid deformations, i.e., transformations others than rotation and translation, present an active body of research in computer vision. Nonrigid approaches differ with respect to the underlying motion model (Terzopoulos et al., 1991; Szeliski, 1996). Most commonly, a cost or error function is defined and an optimization method is chosen that iteratively adjusts the parameters until an optimum has been achieved. Other approaches extract specific features (e.g., correspondence between points) that serve as a basis for directly calculating the model parameters (Arun et al., 1987; Rohr, 1997).
![]() |
Computer vision |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
![]() |
Visualization |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
Typically, 3-D images have been represented as stereoscopic pairs or as anaglyphs by pixel shift method (White, 1995). Displaying time series as movies is still a widely used method for visual interpretation. For fast-moving objects such as trafficking vesicles imaged with high time resolution, time-lapse movies are very informative. However, for much slower nuclear processes or for processes with mixed kinetics that need to be observed over a longer period of time, the total number of time points for imaging are limited due to the photo toxicity of the light exposure during in vivo observation (Konig et al., 1996). Therefore, an interpolation between consecutive time steps is required to reconstruct intermediate time steps. As a "side effect," additional information about the continuous development of the observed processes between the imaged time steps (subpixel resolution in time) is achieved, and quantitative information can be derived (see next section).
Although early studies explored 4-D data sets by simply browsing through an image gallery and highlighting interactively selected structures (Thomas et al., 1996), better 4-D imaging data are achieved by computer graphics. Two commonly used rendering algorithms for displaying 3-D structures are volume rendering and surface rendering (Chen et al., 1995; Fig. 1). Volume rendering is a technique for visualizing complex 3-D data sets without explicit definition of surface geometry. Volume visualization is achieved in three steps: classification, shading, and projection. The classification step assigns a level of opacity, contrast, and color to each voxel in the 3-D volume (e.g., Wright et al., 1993). Then, shading techniques are used to simulate both the object surface characteristics and the position and orientation of surfaces with respect to light sources and the observer. The colored, semitransparent volume is then projected onto a plane perpendicular to the observer's direction. A ray is cast into the volume through each grid point on the projection plane. As the ray progresses through the volume, it computes the color and opacity at evenly spaced sample locations, and finally yields a single pixel color. Although volume rendering techniques provide a satisfactory display of biological structures, this method is limited to pure visualization and does not deliver quantitative information. In addition, the high anisotropy typical for live-cell imaging with low z-resolution limits the quality of this visualization technique.
These limitations are overcome by surface-rendering techniques, where the object surface is represented by polygons. The polygonal surface is displayed by projecting all the polygons onto a plane that is perpendicular to a selected viewing direction. The user can examine the displayed structure by changing the viewing direction interactively. The most commonly used method to triangulate the 3-D surface is the Marching Cube algorithm (Cline et al., 1988). The 3-D structure is defined by a threshold value throughout the data set, constructing an isosurface. The drawback of this method is that the surface of many biological structures cannot be defined using a single intensity value, resulting in loss of relevant information.
![]() |
Quantitative image analysis |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
Measuring concentration changes by FRAP and fluorescence loss in photobleaching have become standard methods to evaluate diffusion, binding, and trafficking in live cells (for review see Phair and Misteli, 2001). These methods give direct access to kinetic parameters such as the diffusion coefficient of molecules (Axelrod et al., 1976; Edidin et al., 1976; Siggia et al., 2000) or exchange rates of molecules between different compartments (Hirschberg et al., 1998; Phair and Misteli, 2001). In combination with motion estimation techniques, parameters such as the velocity of the mass center for individual objects or for each point on the object surface can be readily accessed. Further, local parameters such as acceleration, tension, or bending (Bookstein, 1989) can be estimated. During motion estimation, global quantities are estimated such as the parameters of rotation and translation (Germain et al., 1999). The evolution of these eigenvalues can be used to characterize and analyze the observed motion.
Statistical analysis of velocity histograms can be applied to compute peak velocities corresponding to the most frequently occurring velocity values (Uttenweiler et al., 2000). Additionally, the dynamics of different objects can be compared by their velocity histograms. An alternative technique for statistical analysis is the confinement tree analysis of the intensity image (Mattes et al., 2001). For different threshold levels, objects (confiners) are segmented in the image. Calculated for different levels, the confiners define a confinement tree. Besides the estimation of global quantitative values (e.g., the global homogeneity of the motion), this approach allows the analysis and comparison of movements.
A challenge for future work is to better understand the biomechanical behavior of cellular structures, e.g., cellular membranes, by fitting a biophysical model to the dataan approach already successfully implemented in various fields of medical image analysis (Ferrant et al., 2001).
![]() |
Applications |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
Studies on nuclear architecture have revealed that chromatin (Marshall et al., 1997) undergoes slow diffusional motion, and that this movement is confined to relatively small regions in the nucleus. Importantly, the constraint on diffusional motion is regulated throughout the cell cycle (Heun et al., 2001; Vazquez et al., 2001). A long-standing question has been whether nuclear compartments can also undergo directed, energy-dependent movements, thereby providing a potential mechanism of regulated gene expression. Computational imaging revealed that several nuclear subcompartments do undergo directional transport dependent on metabolic energy (Calapez et al., 2002; Muratani et al., 2002; Platani et al., 2002).
The role of dynamic tension in actin polymerization in motile cells was investigated by analyzing polarized light images of the flow of the actin network and the motion of actin bundles and filopodia in crawling neurons (Oldenbourg et al., 2000). In a study on nuclear envelope breakdown, quantification and visualization of four-channel images with labeled chromatin, lamin-B receptor, nucleoporin, and tubulin (Beaudouin et al., 2002) revealed that piercing of the nuclear envelope by spindle microtubules was the mechanism responsible for forming the initial hole during nuclear envelope breakdown. To further investigate stresses on the nuclear envelope during breakdown, a crosswise grid pattern was bleached onto the nuclear envelope before breakdown. Stresses detected during hole formation were compared for the different grid vertices with respect to the positions of the hole, thus providing information about localized stresses during the tearing process (Mattes et al., 2001). Conversely, the effect of stress on the morphology of cells has been measured using an experimental approach of imposing known stresses on cells in solid-state culture. Changes in height, width, volume, and surface area of the cell are measured from 3-D confocal microscopy images, helping to understand the mechano-transduction response (Guilak, 1995).
The positioning of chromosomes during the cell cycle was investigated in live mammalian cells with a combined experimental and computational approach. In contrast to the random behavior predicted by a computer model of chromosome dynamics, a striking order of chromosomes was observed throughout mitosis (Gerlich et al., 2003). Further, strong similarities between daughter and mother cells were found for mitotic single chromosome positioning. These results support the existence of an active mechanism that transmits chromosomal positions from one cell generation to the next.
![]() |
Summary |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
Submitted: 18 February 2003
Revised: 1 April 2003
Accepted: 11 April 2003
![]() |
References |
---|
![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
Adie, E.J., S. Kalinka, L. Smith, M.J. Francis, A. Marenghi, M.E. Cooper, M. Briggs, N.P. Michael, G. Milligan, and S. Game. 2002. A pH-sensitive fluor, CypHer 5, used to monitor agonist-induced G protein-coupled receptor internalization in live cells. Biotechniques. 33:11521154, 11561157.
Allen, R.D., and N.S. Allen. 1983. Video-enhanced microscopy with a computer frame memory. J. Microsc. 129:317.[Medline]
Anandan, P. 1989. A computational framework and an algorithm for the measurement of visual motion. Int. J. Comp. Vis. 2:283310.
Arun, K.S., T.S. Huang, and S.D. Blostein. 1987. Least square fitting of two 3D point sets. Transactions of Pattern Analysis and Machine Intelligence. 9:698700.
Axelrod, D., D.E. Koppel, J. Schlessinger, E. Elson, and W.W. Webb. 1976. Mobility measurement by analysis of fluorescence photobleaching recovery kinetics. Biophys. J. 16:10551069.[Abstract]
Beaudouin, J., D. Gerlich, N. Daigle, R. Eils, and J. Ellenberg. 2002. Nuclear envelope breakdown proceeds by microtubule-induced tearing of the lamina. Cell. 108:8396.[Medline]
Berg, H.C., and D.A. Brown. 1972. Chemotaxis in Escherichia coli analysed by three-dimensional tracking. Nature. 239:500504.[Medline]
Berns, G.S., and M.W. Berns. 1982. Computer-based tracking of living cells. Exp. Cell Res. 142:103109.[Medline]
Bookstein, F.L. 1989. Principal warps: thin-plate splines and the decomposition of deformations. Transactions of Pattern Analysis and Machine Intelligence. 11:567585.[CrossRef]
Bornfleth, H., P. Edelmann, D. Zink, T. Cremer, and C. Cremer. 1999. Quantitative motion analysis of subchromosomal foci in living cells using four-dimensional microscopy. Biophys. J. 77:28712886.
Calapez, A., H.M. Pereira, A. Calado, J. Braga, J. Rino, C. Carvalho, J.P. Tavanez, E. Wahle, A.C. Rosa, and M. Carmo-Fonseca. 2002. The intranuclear mobility of messenger RNA binding proteins is ATP dependent and temperature sensitive. J. Cell Biol. 159:795805.
Chalfie, M., Y. Tu, G. Euskirchen, W.W. Ward, and D.C. Prasher. 1994. Green fluorescent protein as a marker for gene expression. Science. 263:802805.[Medline]
Chen, H., J.R. Swedlow, M. Grote, J.W. Sedat, and D.A. Agard. 1995. Handbook of biological confocal microscopy. Handbook of Biological Confocal Microscopy. J.B. Pawley, editor. Plenum Press, New York. 197209.
Cline, H.E., W.E. Lorensen, S. Ludke, C.R. Crawford, and B.C. Teeter. 1988. Two algorithms for the three-dimensional reconstruction of tomograms. Med. Phys. 15:320327.[Medline]
Danuser, G. 2001. Super-resolution microscopy using normal flow decoding and geometric constraints. J. Microsc. 204:136149.[CrossRef][Medline]
Danuser, G., P.T. Tran, and E.D. Salmon. 2000. Tracking differential interference contrast diffraction line images with nanometre sensitivity. J. Microsc. 198:3453.[CrossRef][Medline]
Edidin, M., Y. Zagyansky, and T.J. Lardner. 1976. Measurement of membrane protein lateral diffusion in single cells. Science. 191:466468.[Medline]
Eils, R., S. Dietzel, E. Bertin, E. Schrock, M.R. Speicher, T. Ried, M. Robert-Nicoud, C. Cremer, and T. Cremer. 1996. Three-dimensional reconstruction of painted human interphase chromosomes: active and inactive X chromosome territories have similar volumes but differ in shape and surface structure. J. Cell Biol. 135:14271440.[Abstract]
Eils, R., D. Gerlich, W. Tvarusko, D.L. Spector, and T. Misteli. 2000. Quantitative imaging of pre-mRNA splicing factors in living cells. Mol. Biol. Cell. 11:413418.
Faugeras, O. 1993. Three-Dimensional Computer Vision. MIT Press, Cambridge, MA. 695 pp.
Ferrant, M., A. Nabavi, B. Macq, F.A. Jolesz, R. Kikinis, and S.K. Warfield. 2001. Registration of 3-D intraoperative MR images of the brain using a finite-element biomechanical model. IEEE Trans. Med. Imaging. 20:13841397.[CrossRef][Medline]
Gebhard, M., R. Eils, and J. Mattes. 2002. Segmentation of 3D objects using NURBS surfaces for quantification of surface and volume dynamics. Conference on Diagnostic Imaging and Analysis (ICDIA). Shanghai, China 125130.
Gerlich, D., J. Beaudouin, M. Gebhard, J. Ellenberg, and R. Eils. 2001. Four-dimensional imaging and quantitative reconstruction to analyse complex spatiotemporal processes in live cells. Nat. Cell Biol. 3:852855.[CrossRef][Medline]
Gerlich, D., J. Beaudouin, B. Kalbfuss, N. Daigle, R. Eils, and J. Ellenberg. 2003. Global chromosome positions are transmitted through mitosis in mammalian cells. Cell. 112:751764.[Medline]
Germain, F., A. Doisy, X. Ronot, and P. Tracqui. 1999. Characterization of cell deformation and migration using a parametric estimation of image motion. IEEE Trans. Biomed. Eng. 46:584600.[CrossRef][Medline]
Guilak, F. 1995. Compression-induced changes in the shape and volume of the chondrocyte nucleus. J. Biomech. 28:15291541.[CrossRef][Medline]
Heun, P., T. Laroche, K. Shimada, P. Furrer, and S.M. Gasser. 2001. Chromosome dynamics in the yeast interphase nucleus. Science. 294:21812186.
Hirschberg, K., C.M. Miller, J. Ellenberg, J.F. Presley, E.D. Siggia, R.D. Phair, and J. Lippincott-Schwartz. 1998. Kinetic analysis of secretory protein traffic and characterization of golgi to plasma membrane transport intermediates in living cells. J. Cell Biol. 143:14851503.
Inoue, S. 1981. Video image processing greatly enhances contrast, quality, and speed in polarization-based microscopy. J. Cell Biol. 89:346356.[Abstract]
Jähne, B. 2002. Digital Image Processing. Springer Verlag, New York.
Kitano, H. 2002. Computational systems biology. Nature. 420:206210.[CrossRef][Medline]
Konig, K., P.T. So, W.W. Mantulin, B.J. Tromberg, and E. Gratton. 1996. Two-photon excited lifetime imaging of autofluorescence in cells during UVA and NIR photostress. J. Microsc. 183:197204.[Medline]
Lavallee, S., and R. Szeliski. 1995. Recovering the position and orientation of free-form objects from image contours using 3d distance maps. T-PAMI. 17:195201.
Loew, L.M. 1992. Voltage-sensitive dyes: measurement of membrane potentials induced by DC and AC electric fields. Bioelectromagnetics. 1:179189.
Maintz, J.B., and M.A. Viergever. 1998. A survey of medical image registration. Med. Image Anal. 2:136.[Medline]
Marshall, W.F., A. Straight, J.F. Marko, J. Swedlow, A. Dernburg, A. Belmont, A.W. Murray, D.A. Agard, and J.W. Sedat. 1997. Interphase chromosomes undergo constrained diffusional motion in living cells. Curr. Biol. 7:930939.[Medline]
Mattes, J., J. Fieres, J. Beaudouin, D. Gerlich, J. Ellenberg, and R. Eils. 2001. New tools for visualization and quantification in dynamic processes: Application to the nuclear envelope dynamics during mitosis. MICCAI 2001 Lecture Notes in Computer Science. 2208:13231325.
Mitiche, A., and P. Bouthemy. 1996. Computation and analysis of image motion: a synopsis of current problems and methods. Int. J. Comp. Vis. 19:2955.
Muratani, M., D. Gerlich, S.M. Janicki, M. Gebhard, R. Eils, and D.L. Spector. 2002. Metabolic-energy-dependent movement of PML bodies within the mammalian cell nucleus. Nat. Cell Biol. 4:106110.[CrossRef][Medline]
Oldenbourg, R., K. Katoh, and G. Danuser. 2000. Mechanism of lateral movement of filopodia and radial actin bundles across neuronal growth cones. Biophys. J. 78:11761182.
Phair, R.D., and T. Misteli. 2001. Kinetic modelling approaches to in vivo imaging. Nat. Rev. Mol. Cell Biol. 2:898907.[CrossRef][Medline]
Platani, M., I. Goldberg, A.I. Lamond, and J.R. Swedlow. 2002. Cajal body dynamics and association with chromatin are ATP-dependent. Nat. Cell Biol. 4:502508.[CrossRef][Medline]
Qian, H., M.P. Sheetz, and E.L. Elson. 1991. Single particle tracking. Analysis of diffusion and flow in two-dimensional systems. Biophys. J. 60:910921.[Abstract]
Rohr, K. 1997. On 3D differential operators for detecting point landmarks. Image and Vision Computing. 15:219233.[CrossRef]
Sekar, R.B., and A. Periasamy. 2003. Fluorescence resonance energy transfer (FRET) microscopy imaging of live cell protein localizations. J. Cell Biol. 160:629633.
Siggia, E.D., J. Lippincott-Schwartz, and S. Bekiranov. 2000. Diffusion in inhomogeneous media: theory and simulations applied to whole cell photobleach recovery. Biophys. J. 79:17611770.
Szeliski, R. 1996. Video mosaics for virtual environments. IEEE Computer Graphics and Applications. 16:2230.[CrossRef]
Terzopoulos, D., J. Platt, and K. Fleischer. 1991. Heating and melting deformable models. The Journal of Visualization and Computer Animation. 2:6873.
Thomas, C., P. DeVries, J. Hardin, and J. White. 1996. Four-dimensional imaging: computer visualization of 3D movements in living specimens. Science. 273:603607.
Tvarusko, W., M. Bentele, T. Misteli, R. Rudolf, C. Kaether, D.L. Spector, H.H. Gerdes, and R. Eils. 1999. Time-resolved analysis and visualization of dynamic processes in living cells. Proc. Natl. Acad. Sci. USA. 96:79507955.
Uttenweiler, D., C. Veigel, R. Steubing, C. Gotz, S. Mann, H. Haussecker, B. Jahne, and R.H. Fink. 2000. Motion determination in actin filament fluorescence images with a spatio-temporal orientation analysis method. Biophys. J. 78:27092715.
Vazquez, J., A.S. Belmont, and J.W. Sedat. 2001. Multiple regimes of constrained chromosome motion are regulated in the interphase Drosophila nucleus. Curr. Biol. 11:12271239.[CrossRef][Medline]
White, N.S. 1995. Visualization systems for multidimensional CLSM images. Handbook of Biological Confocal Microscopy. J.B. Pawley, editor. Plenum Press, New York. 211254.
Wright, S.J., V.E. Centonze, S.A. Stricker, P.J. DeVries, S.W. Paddock, and G. Schatten. 1993. Introduction to confocal microscopy and three-dimensional reconstruction. Methods Cell Biol. 38:145.[Medline]