Gaudenz Danuser

Bio: Gaudenz Danuser is an academic researcher from University of Texas Southwestern Medical Center. The author has contributed to research in topics: Microtubule & Lamellipodium. The author has an hindex of 73, co-authored 229 publications receiving 18875 citations. Previous affiliations of Gaudenz Danuser include Chalmers University of Technology & Marine Biological Laboratory.

Robust single-particle tracking in live-cell time-lapse sequences.

Abstract: Single-particle tracking (SPT) is often the rate-limiting step in live-cell imaging studies of subcellular dynamics. Here we present a tracking algorithm that addresses the principal challenges of SPT, namely high particle density, particle motion heterogeneity, temporary particle disappearance, and particle merging and splitting. The algorithm first links particles between consecutive frames and then links the resulting track segments into complete trajectories. Both steps are formulated as global combinatorial optimization problems whose solution identifies the overall most likely set of particle trajectories throughout a movie. Using this approach, we show that the GTPase dynamin differentially affects the kinetics of long- and short-lived endocytic structures and that the motion of CD36 receptors along cytoskeleton-mediated linear tracks increases their aggregation probability. Both applications indicate the requirement for robust and complete tracking of dense particle fields to dissect the mechanisms of receptor organization at the level of the plasma membrane.

Coordination of Rho GTPase activities during cell protrusion

Abstract: The GTPases Rac1, RhoA and Cdc42 act together to control cytoskeleton dynamics. Recent biosensor studies have shown that all three GTPases are activated at the front of migrating cells, and biochemical evidence suggests that they may regulate one another: Cdc42 can activate Rac1 (ref. 8), and Rac1 and RhoA are mutually inhibitory. However, their spatiotemporal coordination, at the seconds and single-micrometre dimensions typical of individual protrusion events, remains unknown. Here we examine GTPase coordination in mouse embryonic fibroblasts both through simultaneous visualization of two GTPase biosensors and using a 'computational multiplexing' approach capable of defining the relationships between multiple protein activities visualized in separate experiments. We found that RhoA is activated at the cell edge synchronous with edge advancement, whereas Cdc42 and Rac1 are activated 2 micro-m behind the edge with a delay of 40 s. This indicates that Rac1 and RhoA operate antagonistically through spatial separation and precise timing, and that RhoA has a role in the initial events of protrusion, whereas Rac1 and Cdc42 activate pathways implicated in reinforcement and stabilization of newly expanded protrusions.

Two distinct actin networks drive the protrusion of migrating cells

Abstract: Cell migration initiates by extension of the actin cytoskeleton at the leading edge. Computational analysis of fluorescent speckle microscopy movies of migrating epithelial cells revealed this process is mediated by two spatially colocalized but kinematically, kinetically, molecularly, and functionally distinct actin networks. A lamellipodium network assembled at the leading edge but completely disassembled within 1 to 3 micrometers. It was weakly coupled to the rest of the cytoskeleton and promoted the random protrusion and retraction of the leading edge. Productive cell advance was a function of the second colocalized network, the lamella, where actomyosin contraction was integrated with substrate adhesion.

Positional stability of single double-strand breaks in mammalian cells

Abstract: Formation of cancerous translocations requires the illegitimate joining of chromosomes containing double-strand breaks (DSBs). It is unknown how broken chromosome ends find their translocation partners within the cell nucleus. Here, we have visualized and quantitatively analysed the dynamics of single DSBs in living mammalian cells. We demonstrate that broken ends are positionally stable and unable to roam the cell nucleus. Immobilization of broken chromosome ends requires the DNA-end binding protein Ku80, but is independent of DNA repair factors, H2AX, the MRN complex and the cohesion complex. DSBs preferentially undergo translocations with neighbouring chromosomes and loss of local positional constraint correlates with elevated genomic instability. These results support a contact-first model in which chromosome translocations predominantly form among spatially proximal DSBs.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Cancer nanomedicine: progress, challenges and opportunities.

Abstract: The intrinsic limits of conventional cancer therapies prompted the development and application of various nanotechnologies for more effective and safer cancer treatment, herein referred to as cancer nanomedicine. Considerable technological success has been achieved in this field, but the main obstacles to nanomedicine becoming a new paradigm in cancer therapy stem from the complexities and heterogeneity of tumour biology, an incomplete understanding of nano-bio interactions and the challenges regarding chemistry, manufacturing and controls required for clinical translation and commercialization. This Review highlights the progress, challenges and opportunities in cancer nanomedicine and discusses novel engineering approaches that capitalize on our growing understanding of tumour biology and nano-bio interactions to develop more effective nanotherapeutics for cancer patients.