Navigation of Chemotactic Cells by Parallel Signaling to Pseudopod Persistence and Orientation

Bosgraaf, Leonard; Haastert, Peter J.M. van

doi:10.1371/journal.pone.0006842

Cited by 100 publications

(146 citation statements)

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“…Chemotactic cells can also undergo spontaneous polarization and motility in the absence of an external gradient, likely because of activation by extracellular matrix components such as fibronectin (13,14). Observation of cells in 0-0 environments (C 0 = 0 nM, ΔC = 0 nM) revealed cells with frequent switching of orientation and very few cells that maintained persistence across the entire channel ( Fig.…”

Section: Resultsmentioning

confidence: 99%

Directional memory arises from long-lived cytoskeletal asymmetries in polarized chemotactic cells

Prentice-Mott

Meroz

Carlson

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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Chemotaxis, the directional migration of cells in a chemical gradient, is robust to fluctuations associated with low chemical concentrations and dynamically changing gradients as well as high saturating chemical concentrations. Although a number of reports have identified cellular behavior consistent with a directional memory that could account for behavior in these complex environments, the quantitative and molecular details of such a memory process remain unknown. Using microfluidics to confine cellular motion to a 1D channel and control chemoattractant exposure, we observed directional memory in chemotactic neutrophil-like cells. We modeled this directional memory as a long-lived intracellular asymmetry that decays slower than observed membrane phospholipid signaling. Measurements of intracellular dynamics revealed that moesin at the cell rear is a longlived element that when inhibited, results in a reduction of memory. Inhibition of ROCK (Rho-associated protein kinase), downstream of RhoA (Ras homolog gene family, member A), stabilized moesin and directional memory while depolymerization of microtubules (MTs) disoriented moesin deposition and also reduced directional memory. Our study reveals that long-lived polarized cytoskeletal structures, specifically moesin, actomyosin, and MTs, provide a directional memory in neutrophil-like cells even as they respond on short time scales to external chemical cues. . In particular, chemical cues activate signaling at the cell surface and are integrated within the cell cytoplasm to give rise to a polarization in the direction of the local gradient. Although these processes have been the subject of detailed experimental study and theoretical and computational modeling, the mechanisms by which this orientation is achieved and maintained in the face of environmental noise remains incomplete.Although migration might be thought of as being very sensitive to the variations in the external environment, instead, we see robust migration in a variety of fluctuating environments. These observations all point to the existence of a directional memory in chemotactic cells-a biochemical pathway that stores information about cellular orientation and prevents the loss of orientation in the face of fluctuations, transient loss of polarization, or saturation of the receptors. For example, recent work has demonstrated memory-based behavior in Dictyostelium amoebae under fluctuating waves of chemoattractant (6, 7), although the authors do not identify potential molecular elements that store this information.Here, we use microchannel-based microfluidic devices to observe cell polarization and movement in confined mammalian neutrophil-like cells. Cells in this environment exhibit a strong bias to repolarize in the previous direction of motion after a period of depolarization. This memory is time-dependent and decays when the cell is unstimulated. To describe these results, we construct a minimal phenomenological model coupling membrane and cytoskeletal polarization lifetimes and show that thi...

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Section: Resultsmentioning

confidence: 99%

Directional memory arises from long-lived cytoskeletal asymmetries in polarized chemotactic cells

Prentice-Mott

Meroz

Carlson

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

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“…In the second case, the new pseudopodia start as very lean protrusions that dilate as soon as they include the cell body. Previous studies have shown that the angle between two splitting pseudopodia is approximately 55°and that very often a split to the right is followed by a split to the left and vice versa, which leads to a zig-zag trajectory (Bosgraaf and Van Haastert 2009a). On the contrary, pseudopodia that form de novo may protrude in any direction without any preference relative to the left or right previous or next pseudopod, which induces a random trajectory.…”

Section: Pseudopodia Characteristicsmentioning

confidence: 98%

“…Pseudopodia formation is triggered by different types of external signals: chemicals (chemotaxis) (Hoeller and Kay 2007;Weiner 2002) or temperature (thermotaxis) gradients, electric fields (Bahat and Eisenbach 2006;Zhao 2009), or surface heterogeneity (durotaxis). Such external cues may then control the time and the position at the cell boundary where the pseudopod will form, but growth time and length of the pseudopodia are instead independent properties of the false feet (Andrew and Insall 2007;Bosgraaf and Van Haastert 2009a;Karsenti 2008). In order to elucidate how the cell senses the external attractants, two models are generally used.…”

Section: Pseudopodia Characteristicsmentioning

confidence: 99%

“…In general, cells may extend the pseudopodia in two different ways (Andrew and Insall 2007): either by splitting an existing pseudopod or by extending the membrane from areas of the cells not previously active (often referred to as "lateral" or de novo pseudopodia because they appear at the side and in the rear of the cell, Bosgraaf and Van Haastert 2009a). In the first case, few ruffles appear at the base of the existing pseudopod that successively becomes a major pseudopod to which the cell body flows (one way split).…”

Section: Pseudopodia Characteristicsmentioning

confidence: 99%

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Cell Migration with Multiple Pseudopodia: Temporal and Spatial Sensing Models

Allena

2013

Bull Math Biol

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is an open access repository that collects the work of Arts et Métiers ParisTech researchers and makes it freely available over the web where possible. Abstract Cell migration triggered by pseudopodia (or "false feet") is the most used method of locomotion. A 3D finite element model of a cell migrating over a 2D substrate is proposed, with a particular focus on the mechanical aspects of the biological phenomenon. The decomposition of the deformation gradient is used to reproduce the cyclic phases of protrusion and contraction of the cell, which are tightly synchronized with the adhesion forces at the back and at the front of the cell, respectively. First, a steady active deformation is considered to show the ability of the cell to simultaneously initiate multiple pseudopodia. Here, randomness is considered as a key aspect, which controls both the direction and the amplitude of the false feet. Second, the migration process is described through two different strategies: the temporal and the spatial sensing models. In the temporal model, the cell "sniffs" the surroundings by extending several pseudopodia and only the one that receives a positive input will become the new leading edge, while the others retract. In the spatial model instead, the cell senses the external sources at different spots of the membrane and only protrudes one pseudopod in the direction of the most attractive one.

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“…Cells were biased to extend in the direction of their previous extension, which is consistent with the biological mechanism behind pseudopod formation [Bos09]. This was accomplished by calculating a motion vector derived from the difference between the center of mass of a cell before and after a stochastic step.…”

Section: Model Adaptationsmentioning

confidence: 97%

Scientific report: Training workshop interdisciplinary life sciences

Akudibillah

Boas

Carrères

et al. 2014

Preprint

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This preprint is the outcome of the “Training Workshop Interdisciplinary Life Sciences”, held in October 2013 in the Lorentz Center, Leiden, The Netherlands. The motivation to organize this event stems from the following considerations: The enormous progress in laboratory techniques and facilities leads to the availability of huge amounts of data at all levels of complexity (molecules, cells, tissues, organs, organisms, populations, ecosystems). Especially data at the cellular level reveal details of life processes we were unconscious of until recently. However, it becomes clear that huge amounts of data alone do not automatically lead to understanding. The data explosion in Life Sciences teaches one lesson: life processes are of a highly intricate and integrative nature. To really understand the dynamic processes in living organisms one must integrate experimental data sets in quantitative and predictive models. Only then one may hope to grasp the functioning of these complex systems and be able to convert information in understanding. In the field of physics, for instance, this strong interaction between experiment and theory is already common practice since centuries, culminating in the 20th century being called the ’Century of Physics’. In contrast to physics, the complex nature of the Life Sciences forces us to work in an interdisciplinary fashion. The necessary expertise is available, but scattered over many scientific disciplines. Only the combined efforts of biologists, chemists, mathematicians, physicists, engineers, and informaticians will lead to progress in tackling the huge challenge of understanding the complexity of life. Researchers in the Life Sciences often focus their research on a rather narrow research field. However, the majority of the upcoming generation of researchers in the Life Sciences should be trained to expand their skills, becoming able to tackle complex, multi-dimensional systems. The knowledge they have to incorporate in their research will stem from a diverse range of disciplines, So, they should be trained to integrate a broad range of modelling approaches in order to deduce quantitative, predictive and often multi-scale models from highly diverse data sets. Present curricula in the Life Sciences hardly offer this kind of training yet. This workshop intends to start filling this gap. Three teams worked on the following open problems: 1) Modeling the influence of temperature on the Regulation of flowering time in Arabidopsis thaliana; 2) Validation of computational models of angiogenesis to experimental data; 3) Reconstructing the gene network that regulates branching in Tomato. This preprint bundles the reports of the three teams.

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Navigation of Chemotactic Cells by Parallel Signaling to Pseudopod Persistence and Orientation

Cited by 100 publications

References 36 publications

Directional memory arises from long-lived cytoskeletal asymmetries in polarized chemotactic cells

Directional memory arises from long-lived cytoskeletal asymmetries in polarized chemotactic cells

Cell Migration with Multiple Pseudopodia: Temporal and Spatial Sensing Models

Scientific report: Training workshop interdisciplinary life sciences

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