Biology subjects

Weissbart, G.

Publications and source records attributed to Weissbart, G..

3 recordsLinked to original sources

bioRxiv2024

A common pathway controls cell size in the sepal and leaf epidermis leading to a non-random pattern of giant cells

Arabidopsis leaf epidermal cells have a wide range of sizes and ploidies, but the mechanisms patterning their size and spatial distribution remain unclear. Here, we show that the same genetic pathway creating giant cells in sepals also regulates cell size in the leaf epidermis, leading to the formation of giant cells. In both sepals and leaves, giant cells are scattered among smaller cells; therefore, we asked whether their spatial arrangement is random. By comparing sepal and leaf epidermises with computationally generated randomized tissues we show that the giant cell pattern becomes less random across the epidermis as the cells surrounding giant cells divide, leading to clustered patterns in mature tissues. Our cell-autonomous and stochastic computational model reproduces the giant cell organization, suggesting that random giant cell initiation together with the divisions of surrounding cells lead to the observed clustered pattern. These findings reveal that cell-size patterning is developmentally regulated by common mechanisms in leaves and sepals, and the spatial pattern of giant cells emerges from the interplay between stochastic cell- autonomous gene expression and tissue growth.

plant biology↗

bioRxiv2024

An unsupervised deep learning framework encodes super-resolved image features to decode bacterial cell cycle

Super-resolution microscopy can resolve cellular features at the nanoscale. However, increased spatial resolution comes with increased phototoxicity, and reduced temporal resolution. As a result, studies that require the highest spatial resolutions often rely on static or fixed images, lacking dynamic information. This is particularly true of bacteria, whose lateral dimensions approach the scale of the diffraction limit. In this work, we present Enso, a method based on unsupervised machine learning to recover bacterial cell cycle and cell type information from static single molecule localization microscopy (SMLM) images, whilst retaining nanoscale spatial resolution. Enso uses single-cell images as input, and orders cells according to their spatial pattern progression, ultimately linked to the cell cycle. Our method requires no a priori knowledge or categories, and is validated on both simulated and user-annotated experimental data.

bioinformatics↗

bioRxiv2018

Shaping the zebrafish myotome by differential friction and active stress

Organ formation is an inherently biophysical process, requiring large-scale tissue deformations. Yet, understanding how complex organ shape emerges during development remains a major challenge. During fish embryogenesis, large muscle segments, called myotomes, acquire a characteristic chevron morphology, which is believed to play a role in swimming. The final myotome shape can be altered by perturbing muscle cell differentiation or by altering the interaction between myotomes and surrounding tissues during morphogenesis. To disentangle the mechanisms contributing to shape formation of the myotome, we combine single-cell resolution live imaging with quantitative image analysis and theoretical modeling. We find that, soon after its segmentation from the presomitic mesoderm, the future myotome spreads across the underlying tissues. The mechanical coupling between the myotome and the surrounding tissues is spatially varying, resulting in spatially heterogeneous friction. Using a vertex model, we show that the interplay of differential spreading and friction is sufficient to drive the initial phase of myotome shape formation. However, we find that active stresses, generated during muscle cell differentiation, are necessary to reach the acute angle of the myotome observed in wildtype embryos. A final ingredient for formation and maintenance of the chevron shape is tissue plasticity, which is mediated by orientated cellular rearrangements. Our work sheds a new light on how a spatio-temporal sequence of local cellular events can have a non-local and irreversible mechanical impact at the tissue scale, leading to robust organ shaping.

biophysics↗