Efficient Front-Rear Coupling in Neutrophil Chemotaxis by Dynamic Myosin II Localization

Tsai, Tony; Collins, Sean R.; Chan, Caleb; Hadjitheodorou, Amalia; Lam, Pui-ying; Lou, Sunny S.; Yang, Hee Won; Jorgensen, Julianne; Ellett, Felix; Irimia, Daniel; Davidson, Michael W.; Fischer, R.; Huttenlocher, Anna; Meyer, Tobias; Ferrell, James E.; Theriot, Julie A.

doi:10.1016/j.devcel.2019.03.025

Cited by 74 publications

(103 citation statements)

References 48 publications

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“…Actomyosin-based contractility is essential for the establishment of the cell of the rear. The finding that the formation of the actin brushes at the cell front was followed by the accumulation of the membrane-cortex and membrane-ER linker proteins and Septin9a at the cell back can potentially be explained by front-to-rear actomyosin-dependent retrograde flow 32,[34][35][36] . To examine whether such a flow could be responsible for the formation of the cell rear in ligand-independent cell polarization, we expressed a dominant negative form of the Rho kinase protein (ROCK), which is known to inhibit actomyosin contraction (DN-ROCK) 22,37 .…”

Section: The Establishment the Front-rear Axis In Blebbing Cells By Lmentioning

confidence: 99%

Chemokine-biased robust self-organizing polarization of migrating cells in vivo

Olguín-Olguín

Aalto

Maugis

et al. 2019

Preprint

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The mechanisms facilitating the establishment of front-rear polarity in migrating cells are not fully understood, in particular in the context of bleb-driven directional migration. To gain further insight into this issue we utilized the migration of zebrafish primordial germ cells (PGCs) as an in vivo model. We followed the molecular and morphological cascade that converts apolar cells into polarized bleb-forming motile cells and analyzed the cross dependency among the different cellular functions we identified. Our results underline the critical role of antagonistic interactions between the front and the rear, in particular the role of biophysical processes including formation of barriers and transport of specific proteins to the back of the cell. These interactions direct the formation of blebs to a specific part of the cell that is specified as the cell front. In this way, spontaneous cell polarization facilitates non-directional cell motility and when biased by chemokine signals leads to migration towards specific locations.

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Section: The Establishment the Front-rear Axis In Blebbing Cells By Lmentioning

confidence: 99%

Chemokine-biased robust self-organizing polarization of migrating cells in vivo

Olguín-Olguín

Aalto

Maugis

et al. 2019

Preprint

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“…Neutrophils are highly dynamic 115 cells that use a variety of strategies to enter and efficiently maneuver through a range of 116 complex physiological environments (26,27). Their rapid migration is characterized by an 117 amoeboid style of locomotion, featuring dynamic pseudopod protrusion and retraction, 118 flexible oscillatory shape changes, and low adhesion affinity to their substrate (26,28). 119…”

Section: Introduction 40mentioning

confidence: 99%

“…When confined to a quasi-two dimensional environment, they are able to vary their 120 movement speed and direction, and also change shape dramatically, while maintaining a 121 remarkably constant projected area (28). Therefore, these cells represent an ideal test case 122 for examining whether and how changes in cell shape might be correlated with changes 123 in movement behavior.…”

Section: Introduction 40mentioning

confidence: 99%

Quantitative comparison of principal component analysis and unsupervised deep learning using variational autoencoders for shape analysis of motile cells

Chan

Hadjitheodorou

Tsai

et al. 2020

Preprint

Self Cite

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Cell motility is a crucial biological function for many cell types, including the immune cells in our body that act as first responders to foreign agents. In this work we consider the amoeboid motility of human neutrophils, which show complex and continuous morphological changes during locomotion. We imaged live neutrophils migrating on a 2D plane and extracted unbiased shape representations using cell contours and binary masks. We were able to decompose these complex shapes into low-dimensional encodings with both principal component analysis (PCA) and an unsupervised deep learning technique using variational autoencoders (VAE), enhanced with generative adversarial networks (GANs). We found that the neural network architecture, the VAE-GAN, was able to encode complex cell shapes into a low-dimensional latent space that encodes the same shape variation information as PCA, but much more efficiently. Contrary to the conventional viewpoint that the latent space is a “black box”, we demonstrated that the information learned and encoded within the latent space is consistent with PCA and is reproducible across independent training runs. Furthermore, by including cell speed into the training of the VAE-GAN, we were able to incorporate cell shape and speed into the same latent space. Our work provides a quantitative framework that connects biological form, through cell shape, to a biological function, cell movement. We believe that our quantitative approach to calculating a compact representation of cell shape using the VAE-GAN provides an important avenue that will support further mechanistic dissection of cell motility.AUTHOR SUMMARYDeep convolutional neural networks have recently enjoyed a surge in popularity, and have found useful applications in many fields, including biology. Supervised deep learning, which involves the training of neural networks using existing labeled data, has been especially popular in solving image classification problems. However, biological data is often highly complex and continuous in nature, where prior labeling is impractical, if not impossible. Unsupervised deep learning promises to discover trends in the data by reducing its complexity while retaining the most relevant information. At present, challenges in the extraction of meaningful human-interpretable information from the neural network’s nonlinear discovery process have earned it a reputation of being a “black box” that can perform impressively well at prediction but cannot be used to shed any meaningful insight on underlying mechanisms of variation in biological data sets. Our goal in this paper is to establish unsupervised deep learning as a practical tool to gain scientific insight into biological data by first establishing the interpretability of our particular data set (images of the shapes of motile neutrophils) using more traditional techniques. Using the insight gained from this as a guide allows us to shine light into the “black box” of unsupervised deep learning.

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“…Fragmentation of a polymer filament is accelerated when externally applied stresses become locally concentrated in specific regions. This principle underlies, for instance, the fragmentation of DNA at discrete folding points under extensional flow [8], the rupture of microtubules through buckling during spindle reorganization [9] and traumatic axonal injury [10], and the severing of actin bundles by myosin-driven compression in motile cells [11,12]. Local discontinuities in mechanical properties tend to concentrate externally applied stress, leading to preferential fracture of materials at these discontinuous regions [13,14].…”

Section: Introductionmentioning

confidence: 99%

Thermal fracture kinetics of heterogeneous semiflexible polymers

2020

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The fracture and severing of polymer chains plays a critical role in the failure of fibrous materials and the regulated turnover of intracellular filaments. Using continuum wormlike chain models, we investigate the fracture of semiflexible polymers via thermal bending fluctuations, focusing on the role of filament flexibility and dynamics. Our results highlight a previously unappreciated consequence of mechanical heterogeneity in the filament, which enhances the rate of thermal fragmentation particularly in cases where constraints hinder the movement of the chain ends. Although generally applicable to semiflexible chains with regions of different bending stiffness, the model is motivated by a specific biophysical system: the enhanced severing of actin filaments at the boundary between stiff bare regions and mechanically softened regions that are coated with cofilin regulatory proteins. The results presented here point to a potential mechanism for disassembly of polymeric materials in general and cytoskeletal actin networks in particular by the introduction of locally softened chain regions, as occurs with cofilin binding.

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Efficient Front-Rear Coupling in Neutrophil Chemotaxis by Dynamic Myosin II Localization

Cited by 74 publications

References 48 publications

Chemokine-biased robust self-organizing polarization of migrating cells in vivo

Chemokine-biased robust self-organizing polarization of migrating cells in vivo

Quantitative comparison of principal component analysis and unsupervised deep learning using variational autoencoders for shape analysis of motile cells

Thermal fracture kinetics of heterogeneous semiflexible polymers

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