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Schimmenti, V. M.

Publications and source records attributed to Schimmenti, V. M..

2 recordsLinked to original sources

Apical extracellular matrix regulates fold morphogenesis in the Drosophila wing disc

Tissue folding is a fundamental process occurring often in animal organ development. Here, we study the progression of fold shape and the underlying mechanics in the development of the Drosophila wing disc. We present a 3D segmentation of the apical surface of the wing disc proper from larval stages, when folds grow, to early pupariation, when the tissue unfolds and remodels into a bilayer. We establish morphological metrics to quantify and resolve fold shape in this dataset, introducing a definition of fold depth and width that can be used to characterize folds on a curved surface. Furthermore, we identify fibrous extracellular matrix on the apical side (aECM) that physically connects the two opposing sides of the folds. By modeling a tissue fold with a lateral vertex model endowed by an adhesive layer representing the aECM, we predict that unfolding in the wing disc is preceded by the removal of aECM. Using genetic perturbations, we confirm that aECM adhesion affects fold stability and mechanics: loss of aECM leads to abnormal fold shape and unfolding dynamics, whereas failure to remove aECM at pupariation inhibits unfolding. Finally, we show that these aECM perturbations in larval stages cause morphological phenotypes in the adult wing, demonstrating that the fold morphology of the wing disc helps to define adult wing shape. In total, our work establishes a key mechanical role for aECM in wing disc growth and morphogenesis and advances our general understanding of how epithelial tissue folds can be mechanically stabilized during development.

developmental biology↗

Collective Gene Expression Fluctuations Encode the Regulatory State of Cells

Gene expression is inherently stochastic, leading to substantial cell-to-cell variability in mRNA and protein abundances. Variability in the expression of individual genes has been associated both with impaired signal processing and with facilitation of stress responses and differentiation. Here, combining machine learning, theory, and analysis of scRNA-seq data across various organisms and tissues, we show that variability in gene expression can be coordinated cell-wide. We define a statistical score that quantifies this coordination in single-cell data and demonstrate that distinct coordination patterns reflect the regulatory state of cells. We further develop a physics-informed machine-learning framework that identifies and predicts such variability patterns. Coordinated gene-expression variability emerges as a hallmark of stem and progenitor cells and distinguishes intrinsic stochasticity from cell-population heterogeneity. Together, our results establish the structure of gene expression variability as a cellular-scale signature of cell identity and regulatory organization.

biophysics↗