Concluded Projects

Concluded Projects

Analysis of transport dynamics in the Endoplasmic Reticulum (ER) of budding Yeast

with Prof. Yves Barral, Institute of Biochemistry, ETH Zurich.


The project is concerned with the investigation of the transport processes between the cortical ER of the mother cell and the cortical ER of the daughter cell (bud) before nuclear division in budding yeast (Saccharomyces cervisiae) cells. In the mother cell the ER is concentrated in the perinuclear region as well as in the cortical region with weak connections between the two. In the daughter cell the ER is located in the cortical region and no nucleus is present at this stage of division. It is known that the cortical ER of mother and daughter are somehow connected, but the mechanism by which they are connected as well as the conection strength are so far unknown.


To investigate this, the Barral group performed FLIP experiments (Fluorescence Loss In Photobleaching) with two different protein species: a GFP-tagged ER membrane protein (Sec61) and a soluble ER lumen protein (pure GFP with a cleavable signal sequence for ER residence). Several experiments were conduced where either the mother cell or the daughter cell was repetedly bleached and the fluorescence loss in both the mother and the daughter cell’s cortical ER was monitored over time.


The experiments clearly indicate that Sec61 is drained more slowly from the non-bleached part of the budding pair than GFP. The question arises whether this difference can be explained if one assumes normal passive diffusion in connections between the mother’s cortical ER and the one of the daughter (e.g. due to the bottleneck effect in the transition zone, different diffusion constants of the two proteins or the fact that one is diffusing on the surface whereas the other uses the lumenal space). The related question is: ”How strong is the connection between the cortical ER of the mother cell and the one of the daughter cell when compared to the connection between the perinuclear ER and the cortical ER in the mother cell?”


We developed both a second and a third order lumped parameter model for transport processes between the mother cell and the daughter cell in budding yeast. The models are systematically derived as simple low order ”control models” (as opposed to high order PDE models) such as to allow subsequent parameter identification by means of data fitting.


We found that diffusion as a transport process could possibly explain the experimental data, but that the connetions between the two cortical ERs are several orders of magnitude weaker that the ones between the perinuclear and the cortical ER in the mother cell. This suggests that the two ER parts in the mother cell act as a single compartment when compared to the daughter cell. Also, no significant directionality of the transport could be found.


For the first time this project could show evidence for membrane compartmentalization in intracellular organelles. It was known that the plasma membrane of budding yeast is compartmentalized by means of a diffusion barrier ("Septin ring"). Using a combination of experimental studies and computational modeling, the present project could show that this is also the case for the ER membrane. This opened the door for future research on the molecular basis of the observed compartmentalization.

C. Luedeke, S. Buvelot Frei, I. Sbalzarini, H. Schwarz, A. Spang, and Y. Barral. Septin-dependent compartmentalization of the endoplasmic reticulum during yeast polarized growth. J. Cell Biol., 169(6):897–908, 2005. (Paper PDF, Supplement PDF)

I. F. Sbalzarini. Lumped parameter models for ER diffusion in budding yeast cells. ICoS technical report, Institute of Computational Science (ICoS), ETH Zürich, 2003. (Report PDF)

 

Accurate real-time single-particle tracking for noisy video recordings

with Prof. Ari Helenius, Institute of Biochemistry, ETH Zurich,
and Prof. Urs Greber, Institute of Zoology, University of Zurich,
and Prof. Petros Koumoutsakos, CSElab, ETH Zurich.


Single-particle tracking is rapidly becoming an indispensable tool to study the dynamics of intracellular processes and the quantitative analysis of the resulting trajectories provides important information about working mechanisms and structures in living cells. With the availability of new microscopy techniques (confical microscopy, TIRF microscopy), it has become possible to classify modes of motion in live cells, determine diffusion constants, or measure the step displacements of molecular motors.


The feature point-tracking problem consists of automatically extracting the trajectories of moving particles from digital video sequences. This entails detecting the images of the desired particles in each frame and linking these detections over time to follow individual traces.


Biological applications often involve the tracking of objects whose type of motion is not known in advance. The tracking task is thus hindered by the absence of a suitable motion model, or by trajectories contatinin several modes of motion. In addition, videos from biological experiments are often very noisy (SNR < 3), and large amounts of data are collected.


We developed and implemented a fast and robust image processing algorithm for feature point tracking. The algorithm is self-initializing and capable of handling occlusion, entry, and exit of particles. Unlike previous work, it makes no assumption about the type or the smoothness of the motion (For software implementations of the algorithm see the downloads section.)


Accuracy and precision of the algorithm were shown to be comparable to computationally more involved methods. At the same time, the developed algorithm provides unprecedented robustness against imaging noise and is several orders of magnitude faster than previous approaches. It is suitable for processing of large numbers of long videos and was successfully applied to virus particle tracking (>50'000 trajectories), tracking of fast directed motion along microtubules, and tracking of Quantum Dots. The latter application was made possible by virtue of a novel multi-frame linking algorithm which still yields connected trajectories even for the intermittent detections of blinking Quantum Dots.

I. F. Sbalzarini and P. Koumoutsakos. Feature point tracking and trajectory analysis for video imaging in cell biology. J. Struct. Biol., 151(2):182–195, 2005. (Paper PDF, Software Download, ImageJ plug-in Download)

H. Ewers, A. E. Smith, I. F. Sbalzarini, H. Lilie, P. Koumoutsakos, and A. Helenius. Single-particle tracking of murine polyoma virus-like particles on live cells and artificial membranes. Proc. Natl. Acad. Sci. USA, 102(42):15110–15115, 2005. (Paper PDF, Supplementary PDF)

I. F. Sbalzarini, H. Ewers, A. Smith, A. Helenius, and P. Koumoutsakos. Heimtückische Viren auf lebenden Zellen. ETH Bulletin, 298:48–50, September 2005. (Paper PDF)

I. F. Sbalzarini. A MATLAB toolbox for virus particle tracking. ICoS technical report, Institute of Computational Science (ICoS), ETH Zürich, 2003. (Report PDF)

 

Correcting for geometry artifacts in Fluorescence Recovery After Photobleaching (FRAP) experiments in complex-shaped organelles

with Prof. Ari Helenius, Insitute of Biochemistry, ETH Zurich,
and Prof. Petros Koumoutsakos, CSElab, ETH Zurich.


With the wide-spread availability of fluorescence microscopy and the ability to tag intracellular components with fluorescent dyes, FRAP has rapidly become the standard experimental technique to investigate intracellular dynamics.


Molecular transport within live biological cells is dominated by diffusion in confined compartments with complex geometries. Quantitative evaluations of FRAP assays in cell biology (e.g. to measure diffusion coefficients of proteins in vivo) require knowledge of the solution of the diffusion equation in such geometries.


We developed and applied a high-performance parallel implementation of a particle method to solve the diffusion equation in 3D reconstructions of real samples obtained by fluorescence confocal microscopy. The employed particle method of PSE is grid-free and combines the advantages of high-order convergence and geometric flexibility. It enables efficient simulations of solute diffusion in complex-shaped biological structures.


The computational domain is obtained from 3D reconstructions of confocal section stacks from live cells. To efficiently parallelize the resulting inhomogeneous problem, we developed a general-purpose Parallel Particle Mesh (PPM) library. The PSE solver, which is implemented on the basis of this library, thus provides different adaptive domain decomposition techniques, dynamic load-balancing in imhomogeneous clusters, parallel file I/O, and on-line optimization of the network communication. It was tested using arbitrary geometries on both shared memory and distributed memory computers, and exhibited state-of-the-art parallel scaling (84% efficiency on 242 processors).


Combining FRAP experiments in the ER with direct numerical simulations of the same process in the very same geometry (reconstructed from the experimental images), allowed us to identify and quantify the geometric averaging artifacts in FRAP experiments in complex shapes. By using the same geometry for both the experiment and the simulation, we can however correct for these artifacts and obtain for the first time geometry-corrected molecular diffusion constants in live cells.

I. F. Sbalzarini, J. H. Walther, M. Bergdorf, S. E. Hieber, E. M. Kotsalis, and P. Koumoutsakos. PPM – a highly efficient parallel particle-mesh library for the simulation of continuum systems. J. Comput. Phys., in print, 2006. (Paper PDF, Software Download)

I. F. Sbalzarini, A. Hayer, A. Helenius, and P. Koumoutsakos. Simulations of (an)isotropic diffusion on curved biological surfaces. Biophys. J., 90(3):878–885, 2006. (Paper PDF, Software Download)

I. F. Sbalzarini, A. Mezzacasa, A. Helenius, and P. Koumoutsakos. Effects of organelle shape on fluorescence recovery after photobleaching. Biophys. J., 89(3):1482–1492, 2005. (Paper PDF, Supplementary PDF, Software Download)

I. F. Sbalzarini. Why diffusion on a domain with complex boundary appears anomalous. ICoS technical report, Institute of Computational Science (ICoS), ETH Zürich, 2005. (Report PDF)

I. F. Sbalzarini, A. Mezzacasa, A. Helenius, and P. Koumoutsakos. Large-scale simulations of diffusion in cell biology. ERCIM News, 59:69–70, 2004. (Paper PDF, Software Download)

 

A particle method to simulate diffusion processes on complex and moving surfaces

with Prof. Petros Koumoutsakos, CSElab, ETH Zurich.


We combined recent advances in scientific computing (particle level-set methods: Hieber & Koumoutsakos, J. Comput. Phys. 210:342-367, 2005) and computer graphics (image inpainting on implicit surfaces: Bertalmio et al., J. Comput. Phys. 174:759-780, 2001) to construct a particle method to simulate diffusion processes on complex-shaped and moving surfaces. The computer implementation is efficiently parallelized using the PPM library.


The method was then successfully applied to study diffusion of membrane-bound proteins in the complex-shaped surface of real ER samples, obtained by 3D reconstruction of confocal images. This provided a natural extension of the ER FRAP project described above to membrane proteins.

I. F. Sbalzarini, A. Hayer, A. Helenius, and P. Koumoutsakos. Simulations of (an)isotropic diffusion on curved biological surfaces. Biophys. J., 90(3):878–885, 2006. (Paper PDF, Software Download)

M. Bergdorf, I. F. Sbalzarini, and P. Koumoutsakos. A lagrangian particle method for reaction-diffusion systems on deforming surfaces. J. Math. Biol., to appear, 2010. (Paper PDF)

 

Statistical trajectory analysis for anomalous and weakly self-similar processes

Statistical analysis of trajectories based on the Mean Square Displacement (MSD) constitutes the classical way of analyzing single-particle tracking data. This method of analysis is however limited to cases of normal and strongly self-similar diffusion.


Many stochastic motion processes in biology do however exhibit anomalous or weakly self-similar behavior. We used the theory of Ferrari et al. (Physica D 154:111-137, 2001) to provide an extension of the MSD analysis to these cases. This so-called Moment Scaling Spectrum (MSS) analysis has significantly reduced the uncertainties in biological trajectory analysis and has for the first time enabled an unambiguous classification of virus motion types on the plasma membrane.

I. F. Sbalzarini and P. Koumoutsakos. Feature point tracking and trajectory analysis for video imaging in cell biology. J. Struct. Biol., 151(2):182–195, 2005. (Paper PDF, Software Download, ImageJ plug-in Download)

H. Ewers, A. E. Smith, I. F. Sbalzarini, H. Lilie, P. Koumoutsakos, and A. Helenius. Single-particle tracking of murine polyoma virus-like particles on live cells and artificial membranes. Proc. Natl. Acad. Sci. USA, 102(42):15110–15115, 2005. (Paper PDF, Supplementary PDF)

I. F. Sbalzarini. Moments of displacement and their spectrum. ICoS technical report, Institute of Computational Science (ICoS), ETH Zürich, 2005. (Report PDF)

 

Supervised detection of motion patterns in trajectory data

Many trajectories can be characterized by transient patterns that may provide insight into the interactions of the moving object with its immediate environment. The accurate and automated identification of trajectory motifs is important for the understanding of the underlying mechanisms. In this work, we developed a novel trajectory segmentation algorithm based on supervised support vector classification. The algorithm was validated on synthetic data and applied to the identification of trajectory fingerprints of fluorescently tagged human adenovirus particles in live cells. In virus trajectories on the cell surface, periods of confined motion, slow drift, and fast drift were efficiently detected. Additionally, directed motion was found for viruses in the cytoplasm. The algorithm enables the linking of microscopic observations to molecular phenomena that are critical in many biological processes, including infectious pathogen entry and signal transduction.

J. A. Helmuth, C. J. Burckhardt, P. Koumoutsakos, U. F. Greber, and I. F. Sbalzarini. A novel supervised trajectory segmentation algorithm identifies distinct types of human adenovirus motion in host cells, Journal of Structural Biology, 159(3):347-358, 2007. (PDF, Supplementary Material PDF, Software Download)