Biology subjects

Kursawe, J.

Publications and source records attributed to Kursawe, J..

12 recordsLinked to original sources

bioRxiv2024

An active matter model captures the spatial dynamics of actomyosin oscillations during morphogenesis

The apicomedial actomyosin network is crucial for generating mechanical forces in cells. Oscillatory behaviour of this contractile network is commonly observed before or during significant morphogenetic events. For instance, during the development of the Drosophila adult abdominal epidermis, larval epithelial cells (LECs) undergo pulsed contractions before being replaced by histoblasts. These contractions involve the formation of contracted regions of concentrated actin and myosin. However, the emergence and control of pulsed contractions are not fully understood. Here, we combined in vivo 4D microscopy with numerical simulations of an active elastomer model applied to realistic cell geometries and boundary conditions to study LEC actomyosin dynamics. The active elastomer model was able to reproduce in vivo observations quantitatively. We also characterised the relationship between cell shape, cell polarity, and actomyosin network parameters with the spatiotemporal characteristics of the contractile network both in vivo and in simulations. Our results show that cell geometry, accompanied by boundary conditions which reflect the cells polarity, is essential to understand the dynamics of the apicomedial actomyosin network. Moreover, our findings support the notion that spatiotemporal oscillatory behaviour of the actomyosin network is an emergent property of the actomyosin network, rather than driven by upstream signalling.

biophysics↗

bioRxiv2024

Estimating full-field displacement in biological images using deep learning

The estimation of full-field displacement between biological image frames or in videos is important for quantitative analyses of motion, dynamics and biophysics. However, the often weak signals, poor biological contrast and a multitude of noise processes typical to microscopy make this a formidable challenge for many contemporary methods. Here, we present a deep-learning method, termed Displacement Estimation FOR Microscopy (DEFORM-Net), that outperforms traditional digital image correlation and optical flow methods, as well as recent learned approaches, offering simultaneous high accuracy, spatial sampling and speed. DEFORM-Net is experimentally unsupervised, relying on displacement simulation based on a random fractal Perlin-noise process and optimised training loss functions, without the need for experimental ground truth. We demonstrate its performance on real biological videos of beating neonatal mouse cardiomyocytes and pulsed contractions in Drosophila pupae, and in various microscopy modalities. We provide DEFORM-Net as open source, including inference in the ImageJ/FIJI platform, for rapid evaluation, which will empower new quantitative applications in biology and medicine.

biophysics↗

bioRxiv2023

Simulating BRAFV600E-MEK-ERK signalling dynamics in response to vertical inhibition treatment strategies

In vertical inhibition treatment strategies, multiple components of an intracellular pathway are simulta-neously inhibited. Vertical inhibition of the BRAFV600E-MEK-ERK signalling pathway is a standard of care for treating BRAFV600E-mutated melanoma where two targeted cancer drugs, a BRAFV600E-inhibitor, and a MEK-inhibitor, are administered in combination. Targeted therapies have been linked to early onsets of drug resistance, and thus treatment strategies of higher complexities and lower doses have been proposed as alternatives to current clinical strategies. However, finding optimal complex, low-dose treatment strategies is a challenge, as it is possible to design more treatment strategies than are feasibly testable in experimental settings. To quantitatively address this challenge, we develop a mathematical model of BRAFV600E-MEK-ERK signalling dynamics in response to combinations of the BRAFV600E-inhibitor dabrafenib (DBF), the MEK-inhibitor trametinib (TMT), and the ERK-inhibitor SCH772984 (SCH). From a model of the BRAFV600E-MEK-ERK pathway, and a set of molecular-level drug-protein interactions, we extract a system of chemical reactions that is parameterized by in vitro data and converted to a system of ordinary differential equations (ODEs) using the law of mass action. The ODEs are solved numerically to produce simulations of how pathway-component concentrations change over time in response to different treatment strategies, i.e., inhibitor combinations and doses. The model can thus be used to limit the search space for effective treatment strategies that target the BRAFV600E-MEK-ERK pathway and warrant further experimental investigation. The results demonstrate that DBF and DBF-TMT-SCH therapies show marked sensitivity to BRAFV600E concentrations in silico, whilst TMT and SCH monotherapies do not.

cancer biology↗

bioRxiv2022

Contributions of cell behavior to geometric order in embryonic cartilage

During early development, cartilage provides shape and stability to the embryo while serving as a precursor for the skeleton. Correct formation of embryonic cartilage is hence essential for healthy development. In vertebrate cranial cartilage, it has been observed that a flat and laterally extended macroscopic geometry is linked to regular microscopic structure consisting of tightly packed, short, transversal clonar columns. However, it remains an ongoing challenge to identify how individual cells coordinate to successfully shape the tissue, and more precisely which mechanical interactions and cell behaviors contribute to the generation and maintenance of this columnar cartilage geometry during embryogenesis. Here, we apply a three-dimensional cell-based computational model to investigate mechanical principles contributing to column formation. The model accounts for clonal expansion, anisotropic proliferation and the geometrical arrangement of progenitor cells in space. We confirm that oriented cell divisions and repulsive mechanical interactions between cells are key drivers of column formation. In addition, the model suggests that column formation benefits from the spatial gaps created by the extracellular matrix in the initial configuration, and that column maintenance is facilitated by sequential proliferative phases. Our model thus correctly predicts the dependence of local order on division orientation and tissue thickness. The present study presents the first cell-based simulations of cell mechanics during cranial cartilage formation and we anticipate that it will be useful in future studies on the formation and growth of other cartilage geometries. Author SummaryIn embryos, the initial skeleton is made out of cartilage. As the embryo grows, this cartilage needs to increase in size while correctly maintaining shape. A recent study revealed that for cartilage found in growing skulls, a flat sheet-like geometry is reflected in a distinct arrangement of cells at the microscopic level. Cells sharing a common ancestor are arranged into short columns such that the sheet grows in thickness by lengthening columns, and expands length-wise by adding new columns from single precursor cells. In this work we investigate the mechanical principles underlying column formation and insertion using a computational model that individually represents cells and their behavior. We confirm that arrangement of clonal columns perpendicular to the main expansion direction of the sheet requires oriented cell division. Moreover, we find that column order benefits from an increased amount of extracellular matrix between cells. Similarly, our model suggests that new clonal columns are able to insert themselves into pre-existing cartilage if sufficient matrix is available. Our model constitutes an important step to study cartilage formation and growth in different geometries which will be useful for understanding skeletal developmental disorders as well as for applications in tissue engineering.

developmental biology↗

bioRxiv2021

Inferring kinetic parameters of oscillatory gene regulation from single cell time series data

Gene expression dynamics, such as stochastic oscillations and aperiodic fluctuations, have been associated with cell fate changes in multiple contexts, including development and cancer. Single cell live imaging of protein expression with endogenous reporters is widely used to observe such gene expression dynamics. However, the experimental investigation of regulatory mechanisms underlying the observed dynamics is challenging, since these mechanisms include complex interactions of multiple processes, including transcription, translation, and protein degradation. Here, we present a Bayesian method to infer kinetic parameters of oscillatory gene expression regulation using an auto-negative feedback motif with delay. Specifically, we use a delay-adapted nonlinear Kalman filter within a Metropolis-adjusted Langevin algorithm to identify posterior probability distributions. Our method can be applied to time series data on gene expression from single cells and is able to infer multiple parameters simultaneously. We apply it to published data on murine neural progenitor cells and show that it outperforms alternative methods. We further analyse how parameter uncertainty depends on the duration and time resolution of an imaging experiment, to make experimental design recommendations. This work demonstrates the utility of parameter inference on time course data from single cells and enables new studies on cell fate changes and population heterogeneity.

developmental biology↗

bioRxiv2021

Phase register of Hes1 oscillations with mitoses underlies cell-cycle heterogeneity in ER+ breast cancer cells.

Here, we study the dynamical expression of endogenously labelled Hes1, a transcriptional repressor implicated in controlling cell proliferation, to understand how cell-cycle length heterogeneity is generated in ER+ breast cancer cells. We find that Hes1 shows oscillatory expression with approximately 25h periodicity and during each cell-cycle has a variable peak in G1, a trough around G1-S transition and a less variable second peak in G2/M. Compared to other subpopulations, the cell-cycle in CD44HighCD24Low cancer stem cells is longest and most variable. Most cells divide around the peak of the Hes1 expression wave but preceding mitoses in slow dividing CD44HighCD24Low cells appear phase-shifted, resulting in a late-onset Hes1 peak in G1. The position, duration and shape of this peak, rather than the Hes1 expression levels, are good predictors of cell-cycle length. Diminishing Hes1 oscillations by enforcing sustained expression slows down the cell-cycle, impairs proliferation, abolishes the dynamic expression of p21, and increases the percentage of CD44HighCD24Low cells. Reciprocally, blocking the cell-cycle causes an elongation of Hes1 periodicity, suggesting a bidirectional interaction of the Hes1 oscillator and the cell-cycle. We propose that Hes1 oscillations are functionally important for the efficient progression of the cell-cycle and that the position of mitosis in relation to the Hes1 wave underlies cell-cycle length heterogeneity in cancer cell subpopulations. Significance statementTumours exhibit heterogeneities that are not due to mutations, including Cancer Stem Cells with different potencies. We show that the cancer stem cell state predisposed to dormancy in vivo has a highly variable and long cell-cycle. Using single-cell live-imaging for the transcriptional repressor Hes1 (a key molecule in cancer), we show a new type of circadian-like oscillatory expression of Hes1 in all cells in the population. The most potent cancer stem cells tend to divide around the trough of the Hes1 oscillatory wave, a feature predictive of a long cell-cycle. A novel concept proposed here is that the position of cell division with respect to the Hes1 wave is predictive of its prospective cell-cycle length and cancer cellular sub-state.

cell biology↗

bioRxiv2020

A dynamic and spatially periodic micro-pattern of HES5 expression underlies the probability of neuronal differentiation in the mouse spinal cord

Ultradian oscillations of HES Transcription Factors (TFs) at the single cell level, enable cell state transitions. However, the tissue level organisation of HES5 dynamics in neurogenesis is unknown. Here, we analyse the expression of HES5 ex-vivo in the developing mouse ventral spinal cord and identify microclusters of 4-6 cells with positively correlated HES5 level and ultradian dynamics. These microclusters are spatially periodic along the dorsoventral axis and temporally dynamic, alternating between high and low expression with a supra-ultradian persistence time. We show that Notch signaling is required for temporal dynamics but not the spatial periodicity of HES5. Few Neurogenin-2 cells are observed per cluster, irrespective of high or low state, suggesting that the microcluster organization of HES5 enables the stable selection of differentiating cells. Computational modelling predicts that different cell coupling strengths underlie the HES5 spatial patterns and rate of differentiation, which is consistent with comparison between the motoneuron and interneuron progenitor domains. Our work shows a previously unrecognised spatiotemporal organisation of neurogenesis, emergent at the tissue level from the synthesis of single cell dynamics. SynopsisLive imaging of HES5 expression in the ventral mouse spinal cord together with computational modelling is used to identify and analyse spatially periodic HES5 micro-patterns that emerge from the synthesis of single cell dynamics. O_LIHES5 is expressed in spatially periodic microclusters along the dorsal-ventral axis in spinal cord that are dynamically maintained by Notch signalling. C_LIO_LIMicroclusters can arise, in part, from single cell oscillators that are synchronous and weakly coupled via Notch. C_LIO_LISpatial patterns are different between motorneuron and interneuron progenitor domains and the probability for progenitor differentiation is regulated by the coupling strength between cells. C_LIO_LINGN2 is also spatially periodic along the dorso-ventral axis and microclusters of HES5 may act to pick a single NGN2 high cell for differentiation. C_LI

developmental biology↗

bioRxiv2019

miR-9 mediated noise optimization of the her6 oscillator is needed for cell state progression in the Zebrafish hindbrain

Ultradian oscillations of key transcription factors, such as members of the Hes family, are thought to be important in Neural Progenitor Cell (NPC) maintenance and miR-9 acts as a tuner of these oscillations in vitro. However, the existence and the role of such dynamic oscillatory expression in vivo is poorly understood. Here, we have generated a Zebrafish CRISPR knock-in Her6::venus fusion (Hes1 orthologue) to study endogenous dynamic gene expression in the embryonic hindbrain. We show that Her6 undergoes a transition from irregular, noisy, fluctuations to periodic oscillations as neurogenesis proceeds. In the absence of miR-9 input, noise in the Her6 oscillator increases and NPCs are unable to transit away from an intermediary state where they co-express progenitor and early differentiation markers. Thus, Her6 oscillations are facilitated by noise optimization mediated by miR-9 and this noise-tuning step is functionally important for cells to transition to differentiation.

developmental biology↗

bioRxiv2018

Quantitative, real-time, single cell analysis in tissue reveals expression dynamics of neurogenesis

During embryogenesis cells make fate decisions within complex tissue environments. The levels and dynamics of transcription factor expression regulate these decisions. Here we use single cell live imaging of an endogenous HES5 reporter and absolute protein quantification to gain a dynamic view of neurogenesis in the embryonic mammalian spinal cord. We report that dividing neural progenitors show both aperiodic and periodic HES5 protein fluctuations. Mathematical modelling suggests that in progenitor cells the HES5 oscillator operates close to its bifurcation boundary where stochastic conversions between dynamics are possible. HES5 expression becomes more frequently periodic as cells transition to differentiation which, coupled with an overall decline in HES5 expression, creates a transient period of oscillations with higher fold expression change. This increases the decoding capacity of HES5 oscillations and correlates with interneuron versus motor neuron cell fate. Thus, HES5 undergoes complex changes in gene expression dynamics as cells differentiate.

developmental biology↗

bioRxiv2017

Approximate Bayesian computation reveals the importance of repeated measurements for parameterising cell-based models of growing tissues

The growth and dynamics of epithelial tissues govern many morphogenetic processes in embryonic development. A recent quantitative transition in data acquisition, facilitated by advances in genetic and live-imaging techniques, is paving the way for new insights to these processes. Computational models can help us understand and interpret observations, and then make predictions for future experiments that can distinguish between hypothesised mechanisms. Increasingly, cell-based modelling approaches such as vertex models are being used to help understand the mechanics underlying epithelial morphogenesis. These models typically seek to reproduce qualitative phenomena, such as cell sorting or tissue buckling. However, it remains unclear to what extent quantitative data can be used to constrain these models so that they can then be used to make quantitative, experimentally testable predictions. To address this issue, we perform an in silico study to investigate whether vertex model parameters can be inferred from imaging data, and explore methods to quantify the uncertainty of such estimates. Our approach requires the use of summary statistics to estimate parameters. Here, we focus on summary statistics of cellular packing and of laser ablation experiments, as are commonly reported from imaging studies. We find that including data from repeated experiments is necessary to generate reliable parameter estimates that can facilitate quantitative model predictions.

developmental biology↗

bioRxiv2016

Impact of implementation choices on quantitative predictions of cell-based computational models

Cell-based models provide a powerful computational tool for studying the mechanisms underlying the growth and dynamics of biological tissues in health and disease. An increasing amount of quantitative data with cellular resolution has paved the way for the quantitative parameterisation and validation of such models. However, the numerical implementation of cell-based models remains challenging, and little work has been done to understand to what extent implementation choices may influence model predictions. Here, we consider the numerical implementation of a popular class of cell-based models called vertex models, which are often used to study epithelial tissues. In two-dimensional vertex models, a tissue is approximated as a tessellation of polygons and the vertices of these polygons move due to mechanical forces originating from the cells. Such models have been used extensively to study the mechanical regulation of tissue topology in the literature. Here, we analyse how the model predictions may be affected by numerical parameters, such as the size of the time step, and non-physical model parameters, such as length thresholds for cell rearrangement. We find that vertex positions and summary statistics are sensitive to several of these implementation parameters. For example, the predicted tissue size decreases with decreasing cell cycle durations, and cell rearrangement may be suppressed by large time steps. These findings are counter-intuitive and illustrate that model predictions need to be thoroughly analysed and implementation details carefully considered when applying cell-based computational models in a quantitative setting.

biophysics↗

bioRxiv2016

QuickStitch for seamless stitching of confocal mosaics through high-pass filtering and recursive normalization

Fluorescence micrographs naturally exhibit darkening around their edges (vignetting), which makes seamless stitching challenging. If vignetting is not corrected for, a stitched image will have visible seams where the individual images (tiles) overlap, introducing a systematic error into any quantitative analysis of the image. Although multiple vignetting correction methods exist, there remains no open-source tool that robustly handles large 2D immunofluorescence-based mosaic images. Here, we develop and validate QuickStitch, a tool that applies a recursive normalization algorithm to stitch large-scale immunofluorescence-based mosaics without incurring vignetting seams. We demonstrate how the tool works successfully for tissues of differing size, morphology, and fluorescence intensity. QuickStitch requires no specific information about the imaging system. It is provided as an open-source tool that is both user friendly and extensible, allowing straightforward incorporation into existing image processing pipelines. This enables studies that require accurate segmentation and analysis of high-resolution datasets when parameters of interest include both cellular-level phenomena and larger tissue-level regions of interest.

bioinformatics↗