TY - JOUR A1 - Dieterich, Peter A1 - Lindemann, Otto A1 - Moskopp, Mats Leif A1 - Tauzin, Sebastien A1 - Huttenlocher, Anna A1 - Klages, Rainer A1 - Chechkin, Aleksei V. A1 - Schwab, Albrecht T1 - Anomalous diffusion and asymmetric tempering memory in neutrophil chemotaxis JF - PLoS Computational Biology : a new community journal N2 - Neutrophil granulocytes are essential for the first host defense. After leaving the blood circulation they migrate efficiently towards sites of inflammation. They are guided by chemoattractants released from cells within the inflammatory foci. On a cellular level, directional migration is a consequence of cellular front-rear asymmetry which is induced by the concentration gradient of the chemoattractants. The generation and maintenance of this asymmetry, however, is not yet fully understood. Here we analyzed the paths of chemotacting neutrophils with different stochastic models to gain further insight into the underlying mechanisms. Wildtype chemotacting neutrophils show an anomalous superdiffusive behavior. CXCR2 blockade and TRPC6-knockout cause the tempering of temporal correlations and a reduction of chemotaxis. Importantly, such tempering is found both in vitro and in vivo. These findings indicate that the maintenance of anomalous dynamics is crucial for chemotactic behavior and the search efficiency of neutrophils. The motility of neutrophils and their ability to sense and to react to chemoattractants in their environment are of central importance for the innate immunity. Neutrophils are guided towards sites of inflammation following the activation of G-protein coupled chemoattractant receptors such as CXCR2 whose signaling strongly depends on the activity of Ca2+ permeable TRPC6 channels. It is the aim of this study to analyze data sets obtained in vitro (murine neutrophils) and in vivo (zebrafish neutrophils) with a stochastic mathematical model to gain deeper insight into the underlying mechanisms. The model is based on the analysis of trajectories of individual neutrophils. Bayesian data analysis, including the covariances of positions for fractional Brownian motion as well as for exponentially and power-law tempered model variants, allows the estimation of parameters and model selection. Our model-based analysis reveals that wildtype neutrophils show pure superdiffusive fractional Brownian motion. This so-called anomalous dynamics is characterized by temporal long-range correlations for the movement into the direction of the chemotactic CXCL1 gradient. Pure superdiffusion is absent vertically to this gradient. This points to an asymmetric 'memory' of the migratory machinery, which is found both in vitro and in vivo. CXCR2 blockade and TRPC6-knockout cause tempering of temporal correlations in the chemotactic gradient. This can be interpreted as a progressive loss of memory, which leads to a marked reduction of chemotaxis and search efficiency of neutrophils. In summary, our findings indicate that spatially differential regulation of anomalous dynamics appears to play a central role in guiding efficient chemotactic behavior. KW - neutrophils KW - chemotaxis KW - autocorrelation KW - zebrafish KW - cell migration KW - covariance KW - brownian motion KW - stochastic processes Y1 - 2022 U6 - https://doi.org/10.1371/journal.pcbi.1010089 SN - 1553-734X SN - 1553-7358 VL - 18 IS - 5 PB - PLoS CY - San Fransisco ER - TY - JOUR A1 - Seyrich, Maximilian A1 - Alirezaeizanjani, Zahra A1 - Beta, Carsten A1 - Stark, Holger T1 - Statistical parameter inference of bacterial swimming strategies JF - New journal of physics : the open-access journal for physics N2 - We provide a detailed stochastic description of the swimming motion of an E. coli bacterium in two dimension, where we resolve tumble events in time. For this purpose, we set up two Langevin equations for the orientation angle and speed dynamics. Calculating moments, distribution and autocorrelation functions from both Langevin equations and matching them to the same quantities determined from data recorded in experiments, we infer the swimming parameters of E. coli. They are the tumble rate lambda, the tumble time r(-1), the swimming speed v(0), the strength of speed fluctuations sigma, the relative height of speed jumps eta, the thermal value for the rotational diffusion coefficient D-0, and the enhanced rotational diffusivity during tumbling D-T. Conditioning the observables on the swimming direction relative to the gradient of a chemoattractant, we infer the chemotaxis strategies of E. coli. We confirm the classical strategy of a lower tumble rate for swimming up the gradient but also a smaller mean tumble angle (angle bias). The latter is realized by shorter tumbles as well as a slower diffusive reorientation. We also find that speed fluctuations are increased by about 30% when swimming up the gradient compared to the reversed direction. KW - E.coli KW - run and tumble KW - chemotaxis KW - stochastic processes KW - bacterial swimming strategies KW - parameter inference Y1 - 2018 U6 - https://doi.org/10.1088/1367-2630/aae72c SN - 1367-2630 VL - 20 PB - IOP Publ. Ltd. CY - Bristol ER - TY - JOUR A1 - Nagel, Oliver A1 - Frey, Manuel A1 - Gerhardt, Matthias A1 - Beta, Carsten T1 - Harnessing Motile Amoeboid Cells as Trucks for Microtransport and -Assembly JF - Advanced science N2 - Cell-driven microtransport is one of the most prominent applications in the emerging field of biohybrid systems. While bacterial cells have been successfully employed to drive the swimming motion of micrometer-sized cargo particles, the transport capacities of motile adherent cells remain largely unexplored. Here, it is demonstrated that motile amoeboid cells can act as efficient and versatile trucks to transport microcargo. When incubated together with microparticles, cells of the social amoeba Dictyostelium discoideum readily pick up and move the cargo particles. Relying on the unspecific adhesive properties of the amoeba, a wide range of different cargo materials can be used. The cell-driven transport can be directionally guided based on the chemotactic responses of amoeba to chemoattractant gradients. On the one hand, the cargo can be assembled into clusters in a self-organized fashion, relying on the developmentally induced chemotactic aggregation of cells. On the other hand, chemoattractant gradients can be externally imposed to guide the cellular microtrucks to a desired location. Finally, larger cargo particles of different shapes that exceed the size of a single cell by more than an order of magnitude, can also be transported by the collective effort of large numbers of motile cells. KW - biohybrid microsystems KW - chemotaxis KW - Dictyostelium discoideum KW - microtransport and -assembly Y1 - 2018 U6 - https://doi.org/10.1002/advs.201801242 SN - 2198-3844 VL - 6 IS - 3 PB - Wiley CY - Hoboken ER - TY - JOUR A1 - Hsu, H. F. A1 - Krekhov, Andrey A1 - Tarantola, Marco A1 - Beta, Carsten A1 - Bodenschatz, Eberhardt T1 - Interplay between myosin II and actin dynamics in chemotactic amoeba JF - New journal of physics : the open-access journal for physics N2 - The actin cytoskeleton and its response to external chemical stimuli is fundamental to the mechano-biology of eukaryotic cells and their functions. One of the key players that governs the dynamics of the actin network is the motor protein myosin II. Based on a phase space embedding we have identified from experiments three phases in the cytoskeletal dynamics of starved Dictyostelium discoideum in response to a precisely controlled chemotactic stimulation. In the first two phases the dynamics of actin and myosin II in the cortex is uncoupled, while in the third phase the time scale for the recovery of cortical actin is determined by the myosin II dynamics. We report a theoretical model that captures the experimental observations quantitatively. The model predicts an increase in the optimal response time of actin with decreasing myosin II-actin coupling strength highlighting the role of myosin II in the robust control of cell contraction. KW - actin KW - myosin II KW - chemotaxis KW - oscillations KW - coupling KW - delay differential equation KW - contraction Y1 - 2019 U6 - https://doi.org/10.1088/1367-2630/ab5822 SN - 1367-2630 VL - 21 IS - 11 PB - IOP Publ. Ltd. CY - Bristol ER -