Biology subjects

Yates, C. A.

Publications and source records attributed to Yates, C. A..

4 recordsLinked to original sources

bioRxiv2023

Newly born mesenchymal cells disperse through a rapid mechanosensitive migration

Embryonic mesenchymal cells are dispersed within an extracellular matrix but can coalesce to form condensates with key developmental roles. Cells within condensates undergo fate and morphological changes, and induce cell fate changes in nearby epithelia to produce structures including hair follicles, feathers or intestinal villi. Here, by imaging of mouse and chicken embryonic skin, we find that mesenchymal cells undergo much of their dispersal in early interphase, in a stereotyped process of displacement driven by three hours of rapid and persistent migration, followed by a long period of low motility. The cell division plane and the elevated migration speed and persistence of newly born mesenchymal cells are mechanosensitive, aligning with tension in the tissue. This early G1 migratory behaviour disperses mesenchymal cells and allows the daughters of recent divisions to travel long distances to enter dermal condensates, demonstrating an unanticipated effect of a cell cycle sub-phase on core mesenchymal behaviour. HighlightsO_LIAfter mesenchymal cell division the speed and persistence of daughter cell migration is elevated for 180 minutes C_LIO_LIMesenchymal cell division and migration are directed by tissue tension C_LIO_LINewly born mesenchymal cells are uniquely responsive to tissue strain C_LIO_LINewly born mesenchymal cells are preferentially recruited to dermal condensates C_LIO_LIIncreased dispersal of newly born cells enables long distance travel to dermal condensates C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/525849v1_ufig1.gif" ALT="Figure 1"> View larger version (46K): org.highwire.dtl.DTLVardef@c69b4dorg.highwire.dtl.DTLVardef@fe0295org.highwire.dtl.DTLVardef@5b9f61org.highwire.dtl.DTLVardef@13ca1aa_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

bioRxiv2021

Differential growth is a critical determinant of zebrafish pigment pattern formation

The skin patterns of vertebrates are formed by complex interactions between pigment-producing cells during development. Adult zebrafish (Danio rerio), a model organism for investigating the underlying patterning processes, display alternating horizontal blue and golden stripes, generated by the self-organisation of three pigment cell-types. Mathematical studies in which these cells are modelled as individual agents communicating via short- and long-range interactions have produced breakthroughs in the understanding of pattern development. These models, incorporating all experimentally evidenced cell-cell interactions, replicate many aspects of wild-type and mutant zebrafish patterns. Although received wisdom suggested that initial iridophore distribution was pivotal in orienting patterning, here we show that growth can override its influence. Altered growth sequences can generate further pattern modulation, including vertical stripes and maze-like patterns. We demonstrate that ventrally-biased (asymmetric) growth of the skin field explains two key zebrafish pattern development features which are otherwise obscure (dorso-ventral pattern asymmetry, and predominant ventral-to-dorsal migration of melanophores) in wild-type and multiple zebrafish mutants, and in the related species Danio nigrofasciatus. By identifying biased growth as a novel patterning mechanism, our study will inform future investigations into the mechanisms and evolution of fish pigment patterning and vertebrate pigment pattern formation. Furthermore, our work has implications for the mechanistic basis of human pigmentation defects.

developmental biology↗

bioRxiv2020

Synchronised oscillations in growing cell populations are explained by demographic noise

Understanding synchrony in growing populations is important for applications as diverse as epidemiology and cancer treatment. Recent experiments employing fluorescent reporters in melanoma cell lines have uncovered growing subpopulations exhibiting sustained oscillations, with nearby cells appearing to synchronise their cycles. In this study we demonstrate that the behaviour observed is consistent with long-lasting transient phenomenon initiated, and amplified by the finite-sample effects and demographic noise. We present a novel mathematical analysis of a multi-stage model of cell growth which accurately reproduces the synchronised oscillations. As part of the analysis, we elucidate the transient and asymptotic phases of the dynamics and derive an analytical formula to quantify the effect of demographic noise in the appearance of the oscillations. The implications of these findings are broad, such as providing insight into experimental protocols that are used to study the growth of asynchronous populations and, in particular, those investigations relating to anti-cancer drug discovery. Statement of SignificanceRecent experiments have reported strong evidence of periodic oscillations in the proportion of young and old melanoma cells. The biological mechanism generating this synchronisation and the potential impact that can have on commonly used experimental protocols is still unclear. Here we studied a population of melanoma cells for which we found oscillations in the proportions of cells in each phase of the cell cycle. We demonstrate that these observations may be triggered by intrinsic demographic noise alone, rather than any active synchronisation mechanism requiring cell-cell communication. Our findings may have implications for typical experimental protocols which aim to produce asynchronous cell populations.

cell biology↗

bioRxiv2017

A Multi-Stage Representation Of Cell Proliferation As A Markov Process

The stochastic simulation algorithm commonly known as Gillespies algorithm (originally derived for modelling well-mixed systems of chemical reactions) is now used ubiquitously in the modelling of biological processes in which stochastic effects play an important role. In well mixed scenarios at the sub-cellular level it is often reasonable to assume that times between successive reaction/interaction events are exponentially distributed and can be appropriately modelled as a Markov process and hence simulated by the Gillespie algorithm. However, Gillespies algorithm is routinely applied to model biological systems for which it was never intended. In particular, processes in which cell proliferation is important (e.g. embryonic development, cancer formation) should not be simulated naively using the Gillespie algorithm since the history-dependent nature of the cell cycle breaks the Markov process. The variance in experimentally measured cell cycle times is far less than in an exponential cell cycle time distribution with the same mean.\n\nHere we suggest a method of modelling the cell cycle that restores the memoryless property to the system and is therefore consistent with simulation via the Gillespie algorithm. By breaking the cell cycle into a number of independent exponentially distributed stages we can restore the Markov property at the same time as more accurately approximating the appropriate cell cycle time distributions. The consequences of our revised mathematical model are explored analytically as far as possible. We demonstrate the importance of employing the correct cell cycle time distribution by recapitulating the results from two models incorporating cellular proliferation (one spatial and one non-spatial) and demonstrating that changing the cell cycle time distribution makes quantitative and qualitative differences to the outcome of the models. Our adaptation will allow modellers and experimentalists alike to appropriately represent cellular proliferation - vital to the accurate modelling of many biological processes - whilst still being able to take advantage of the power and efficiency of the popular Gillespie algorithm.

cell biology↗