Computational image analysis of cellular dynamics: a case study based on particle tracking.

Obtaining quantitative data from live cell images is the key to testing mechanistic hypotheses of molecular and cellular processes. The importance of using computer vision-based methods to accomplish this task is well recognized (Eils and Athale 2003; Swedlow et al. 2003). However, in practice, investigators often encounter obstacles that render the application of computational image processing in cell biology far from routine: First, it is not always clear which measurements are necessary to characterize a molecular system, and whether these measurements are sufficient to characterize the cellular process investigated. Second, even if the requirements for measurements are well-defined, it is often difficult to find a software tool to extract these data. It is even more challenging to find software tools that can answer specific questions that are raised by the hypotheses underlying the experiments. One solution is for investigators to develop their own software tools. This is feasible for some applications with the assistance of commercial and open source software packages that support the assembly and integration of custom-designed algorithms, even for users with limited computational expertise. Another solution is for investigators to develop interdisciplinary collaboration with computer scientists. Such collaborations require close interaction between the computer scientists and experimental biologists to jointly optimize the data acquisition and analysis procedures, which must be tightly coupled in any project applying computational analysis to biological image data. This chapter aims to introduce basic concepts that make the application of computational image processing to live cell image data successful. While the concepts are general, examples will be taken from the case study of particle tracking (PT), one of the most frequently encountered problems in cell biology. For a broader discussion of computer vision in live cell imaging, we refer to (Dorn et al. 2008).

[1]  H. Erfle,et al.  High-throughput RNAi screening by time-lapse imaging of live human cells , 2006, Nature Methods.

[2]  Petros Koumoutsakos,et al.  Single-particle tracking of murine polyoma virus-like particles on live cells and artificial membranes. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[3]  Michael Unser,et al.  Automatic tracking of individual fluorescence particles: application to the study of chromosome dynamics , 2005, IEEE Transactions on Image Processing.

[4]  Akihiro Kusumi,et al.  Phospholipids undergo hop diffusion in compartmentalized cell membrane , 2002, The Journal of cell biology.

[5]  Mubarak Shah,et al.  A noniterative greedy algorithm for multiframe point correspondence , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  C. Conrad,et al.  Automatic identification of subcellular phenotypes on human cell arrays. , 2004, Genome research.

[7]  D. Zenisek,et al.  Transport, capture and exocytosis of single synaptic vesicles at active zones , 2000, Nature.

[8]  Erik Brauner,et al.  Informatics and Quantitative Analysis in Biological Imaging , 2003, Science.

[9]  M. Davidson,et al.  Dual-color superresolution imaging using genetically expressed probes , 2008, 2008 Conference on Lasers and Electro-Optics and 2008 Conference on Quantum Electronics and Laser Science.

[10]  Rainer E. Burkard,et al.  Linear Assignment Problems and Extensions , 1999, Handbook of Combinatorial Optimization.

[11]  W Tvaruskó,et al.  Time-resolved analysis and visualization of dynamic processes in living cells. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[12]  Mark S. Nixon,et al.  Feature Extraction and Image Processing , 2002 .

[13]  T. Mitchison,et al.  Microtubule plus-end dynamics in Xenopus egg extract spindles. , 2004, Molecular biology of the cell.

[14]  Daniel Choquet,et al.  Differential activity-dependent regulation of the lateral mobilities of AMPA and NMDA receptors , 2004, Nature Neuroscience.

[15]  Gaudenz Danuser,et al.  Computational processing and analysis of dynamic fluorescence image data. , 2008, Methods in cell biology.

[16]  Stephen T. C. Wong,et al.  Tracking molecular particles in live cells using fuzzy rule‐based system , 2007, Cytometry. Part A : the journal of the International Society for Analytical Cytology.

[17]  Cor J. Veenman,et al.  Resolving Motion Correspondence for Densely Moving Points , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  Kartik Chandran,et al.  Endocytosis by Random Initiation and Stabilization of Clathrin-Coated Pits , 2004, Cell.

[19]  J. Henry,et al.  Analysis of transient behavior in complex trajectories: application to secretory vesicle dynamics. , 2006, Biophysical journal.

[20]  Kenneth R. Spring,et al.  Video Microscopy: The Fundamentals , 1986 .

[21]  Paul R Selvin,et al.  Fluorescence imaging with one nanometer accuracy: application to molecular motors. , 2005, Accounts of chemical research.

[22]  E. Meijering,et al.  Tracking in molecular bioimaging , 2006, IEEE Signal Processing Magazine.

[23]  Michael W. Davidson,et al.  Dual-color superresolution imaging of genetically expressed probes within individual adhesion complexes , 2007, Proceedings of the National Academy of Sciences.

[24]  Jean-Christophe Olivo-Marin,et al.  Extraction of spots in biological images using multiscale products , 2002, Pattern Recognit..

[25]  D. Grier,et al.  Methods of Digital Video Microscopy for Colloidal Studies , 1996 .

[26]  J. Olivo-Marin,et al.  Split and merge data association filter for dense multi-target tracking , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[27]  G Danuser,et al.  Tracking quasi‐stationary flow of weak fluorescent signals by adaptive multi‐frame correlation , 2005, Journal of microscopy.

[28]  K. Jaqaman,et al.  Robust single particle tracking in live cell time-lapse sequences , 2008, Nature Methods.

[29]  P. Vallotton,et al.  Computational analysis of F-actin turnover in cortical actin meshworks using fluorescent speckle microscopy. , 2003, Biophysical journal.

[30]  Robert F Murphy,et al.  Automated interpretation of protein subcellular location patterns. , 2006, International review of cytology.

[31]  Mark Bates,et al.  Multicolor Super-Resolution Imaging with Photo-Switchable Fluorescent Probes , 2007, Science.

[32]  G Danuser,et al.  Periodic patterns of actin turnover in lamellipodia and lamellae of migrating epithelial cells analyzed by quantitative Fluorescent Speckle Microscopy. , 2005, Biophysical journal.

[33]  P. Vallotton,et al.  Recovery, visualization, and analysis of actin and tubulin polymer flow in live cells: a fluorescent speckle microscopy study. , 2003, Biophysical journal.

[34]  Y. Kalaidzidis.  Intracellular objects tracking. , 2007, European journal of cell biology.

[35]  Dmitry Chetverikov,et al.  Feature Point Tracking for Incomplete Trajectories , 1999, Computing.

[36]  Laurent D. Cohen,et al.  Single quantum dot tracking based on perceptual Grouping using minimal paths in a spatiotemporal volume , 2005, IEEE Transactions on Image Processing.

[37]  D. L. Taylor,et al.  High content screening applied to large-scale cell biology. , 2004, Trends in biotechnology.

[38]  A. Trubuil,et al.  Visualization and quantification of vesicle trafficking on a three‐dimensional cytoskeleton network in living cells , 2007, Journal of microscopy.

[39]  M. Eisenstein,et al.  Automated imaging: data as far as the eye can see , 2005, Nature Methods.

[40]  G. Danuser,et al.  Quantitative fluorescent speckle microscopy of cytoskeleton dynamics. , 2006, Annual review of biophysics and biomolecular structure.

[41]  J. Olivo-Marin,et al.  Multiple Particle Tracking in 3-D+ Microscopy: Method and Application to the Tracking of Endocytosed Quantum Dots , 2006 .

[42]  R. Eils,et al.  Computational imaging in cell biology , 2003, The Journal of cell biology.

[43]  W. Webb,et al.  Automated detection and tracking of individual and clustered cell surface low density lipoprotein receptor molecules. , 1994, Biophysical journal.

[44]  C. Bakal,et al.  Quantitative Morphological Signatures Define Local Signaling Networks Regulating Cell Morphology , 2007, Science.

[45]  Sandra L Schmid,et al.  Cargo and Dynamin Regulate Clathrin-Coated Pit Maturation , 2009, PLoS biology.

[46]  P. Koumoutsakos,et al.  Feature point tracking and trajectory analysis for video imaging in cell biology. , 2005, Journal of structural biology.

[47]  P. Sorger,et al.  Automatic fluorescent tag detection in 3D with super‐resolution: application to the analysis of chromosome movement , 2002, Journal of microscopy.

[48]  Toshio Yanagida,et al.  Single-molecule imaging of EGFR signalling on the surface of living cells , 2000, Nature Cell Biology.

[49]  G Danuser,et al.  Yeast kinetochore microtubule dynamics analyzed by high-resolution three-dimensional microscopy. , 2005, Biophysical journal.