Cell body rocking is a dominant mechanism for flagellar synchronization in a swimming alga

The unicellular green algae Chlamydomonas swims with two flagella, which can synchronize their beat. Synchronized beating is required to swim both fast and straight. A long-standing hypothesis proposes that synchronization of flagella results from hydrodynamic coupling, but the details are not understood. Here, we present realistic hydrodynamic computations and high-speed tracking experiments of swimming cells that show how a perturbation from the synchronized state causes rotational motion of the cell body. This rotation feeds back on the flagellar dynamics via hydrodynamic friction forces and rapidly restores the synchronized state in our theory. We calculate that this `cell body rocking' provides the dominant contribution to synchronization in swimming cells, whereas direct hydrodynamic interactions between the flagella contribute negligibly. We experimentally confirmed the coupling between flagellar beating and cell body rocking predicted by our theory. This work appeared also in the Proceedings of the National Academy of Science of the U.S.A as: Geyer et al., PNAS 110(45), p. 18058(6), 2013.Comment: 40 pages, 15 color figure

Analyzing Neuronal Dendritic Trees with Convolutional Neural Networks

In the biological sciences, image analysis software are used to detect, segment or classify a variety of features encountered in living matter. However, the algorithms that accomplish these tasks are often designed for a specific dataset, making them hardly portable to accomplish the same tasks on images of different biological structures. Recently, convolutional neural networks have been used to perform complex image analysis on a multitude of datasets. While applications of these networks abound in the technology industry and computer science, use cases are not as common in the academic sciences. Motivated by the generalizability of neural networks, we aim to develop a machine learning algorithm to detect morphological features in the dendritic trees of Drosophila Melanogaster class IV neurons. Our approach is based on the Single Shot Multibox Detector (Liu et. al.) and our training dataset is synthesized from simulations of dendritic trees that we previously developed. Our preliminary results show that the network performs well on the training set. However, on the test set, it sometimes misses objects of interest, which calls for further improvements

Dynamic curvature regulation accounts for the symmetric and asymmetric beats of Chlamydomonas flagella

Axonemal dyneins are the molecular motors responsible for the beating of cilia and flagella. These motors generate sliding forces between adjacent microtubule doublets within the axoneme, the motile cytoskeletal structure inside the flagellum. To create regular, oscillatory beating patterns, the activities of the axonemal dyneins must be coordinated both spatially and temporally. It is thought that coordination is mediated by stresses or strains that build up within the moving axoneme, but it is not known which components of stress or strain are involved, nor how they feed back on the dyneins. To answer this question, we used isolated, reactivate axonemes of the unicellular alga Chlamydomonas as a model system. We derived a theory for beat regulation in a two-dimensional model of the axoneme. We then tested the theory by measuring the beat waveforms of wild type axonemes, which have asymmetric beats, and mutant axonemes, in which the beat is nearly symmetric, using high-precision spatial and temporal imaging. We found that regulation by sliding forces fails to account for the measured beat, due to the short lengths of Chlamydomonas cilia. We found that regulation by normal forces (which tend to separate adjacent doublets) cannot satisfactorily account for the symmetric waveforms of the mbo2 mutants. This is due to the model's failure to produce reciprocal inhibition across the axes of the symmetrically beating axonemes. Finally, we show that regulation by curvature accords with the measurements. Unexpectedly, we found that the phase of the curvature feedback indicates that the dyneins are regulated by the dynamic (i.e. time-varying) component of axonemal curvature, but not by the static one. We conclude that a high-pass filtered curvature signal is a good candidate for the signal that feeds back to coordinate motor activity in the axoneme

Slow local movements of collagen fibers by fibroblasts drive the rapid global self-organization of collagen gels

Aclassic model for tissue morphogenesis is the formation of ligament-like straps between explants of fibroblasts placed in collagen gels. The patterns arise from mechanical forces exerted by cells on their substrates (Harris et al., 1981). However, where do such straps come from, and how are slow local movements of cells transduced into dramatic long-distance redistributions of collagen? We embedded primary mouse skin and human periodontal ligament fibroblasts in collagen gels and measured the time course of patterning by using a novel computer algorithm to calculate anisotropy, and by tracking glass beads dispersed in the gel. As fibroblasts began to spread into their immediate environments, a coordinated rearrangement of collagen commenced throughout the gel, producing a strap on a time scale of minutes. Killing of cells afterwards resulted in a partial relaxation of the matrix strain. Surprisingly, relatively small movements of collagen molecules on the tensile axis between two pulling explants induced a much larger concomitant compression of the gel perpendicular to the axis, organizing and aligning fibers into a strap. We propose that this amplification is due to the geometry of the collagen matrix, and that analogous amplified movements may drive morphological changes in other biological meshes, both outside and inside the cell

Contribution of increasing plasma membrane to the energetic cost of early zebrafish embryogenesis

© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Rodenfels, J., Sartori, P., Golfier, S., Nagendra, K., Neugebauer, K. M., & Howard, J. Contribution of increasing plasma membrane to the energetic cost of early zebrafish embryogenesis. Molecular Biology of the Cell, 31(7), (2020): 520-526, doi:10.1091/mbc.E19-09-0529.How do early embryos allocate the resources stored in the sperm and egg? Recently, we established isothermal calorimetry to measure heat dissipation by living zebra­fish embryos and to estimate the energetics of specific developmental events. During the reductive cleavage divisions, the rate of heat dissipation increases from ∼60 nJ · s−1 at the two-cell stage to ∼90 nJ · s−1 at the 1024-cell stage. Here we ask which cellular process(es) drive this increasing energetic cost. We present evidence that the cost is due to the increase in the total surface area of all the cells of the embryo. First, embryo volume stays constant during the cleavage stage, indicating that the increase is not due to growth. Second, the heat increase is blocked by nocodazole, which inhibits DNA replication, mitosis, and cell division; this suggests some aspect of cell proliferation contributes to these costs. Third, the heat increases in proportion to the total cell surface area rather than total cell number. Fourth, the heat increase falls within the range of the estimated costs of maintaining and assembling plasma membranes and associated proteins. Thus, the increase in total plasma membrane associated with cell proliferation is likely to contribute appreciably to the total energy budget of the embryo.The analysis of these data was initiated in the 2019 Physical Biology of the Cell course at the Marine Biological Laboratory in Woods Hole, MA. We acknowledge the support and feedback from the course directors and participants. This work was supported by funding from EMBO Long-Term Fellowship ALTF 754–2015 (to J.R.), the Eric and Wendy Schmidt Membership in Biology at the Institute for Advanced Study (to P.S.), National Institutes of Health (NIH) R21 HD094013 (to K.M.N.), and NIH R01 GM110386 (to J.H.). Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIH