Biology subjects

Kiehart, D. P.

Publications and source records attributed to Kiehart, D. P..

3 recordsLinked to original sources

bioRxiv2023

Minimal vertex model explains how the amnioserosa avoids fluidization during Drosophila dorsal closure

Dorsal closure is a process that occurs during embryogenesis of Drosophila melanogaster. During dorsal closure, the amnioserosa (AS), a one-cell thick epithelial tissue that fills the dorsal opening, shrinks as the lateral epidermis sheets converge and eventually merge. During this process, both shape index and aspect ratio of amnioserosa cells increase markedly. The standard 2-dimensional vertex model, which successfully describes tissue sheet mechanics in multiple contexts, would in this case predict that the tissue should fluidize via cell neighbor changes. Surprisingly, however, the amnioserosa remains an elastic solid with no such events. We here present a minimal extension to the vertex model that explains how the amnioserosa can achieve this unexpected behavior. We show that continuous shrinkage of the preferred cell perimeter and cell perimeter polydispersity lead to the retention of the solid state of the amnioserosa. Our model accurately captures measured cell shape and orientation changes and predicts non-monotonic junction tension that we confirm with laser ablation experiments. Significance StatementDuring embryogenesis, cells in tissues can undergo significant shape changes. Many epithelial tissues fluidize, i.e. cells exchange neighbors, when the average cell shape index increases above a threshold value, consistent with the standard vertex model. During dorsal closure in Drosophila melanogaster, however, the amnioserosa tissue remains solid even as the average cell shape index increases well above threshold. We introduce perimeter polydispersity and allow the preferred cell perimeters, usually held fixed in vertex models, to decrease linearly with time as seen experimentally. With these extensions to the standard vertex model, we capture experimental observations quantitatively. Our results demonstrate that vertex models can describe the behavior of the amnioserosa in dorsal closure by allowing normally fixed parameters to vary with time.

biophysics↗

bioRxiv2023

Axial segmentation by iterative mechanical signaling

In bony fishes, formation of the vertebral column, or spine, is guided by a metameric blueprint established in the epithelial sheath of the notochord. Generation of the notochord template begins days after somitogenesis and even occurs in the absence of somite segmentation. However, patterning defects in the somites lead to imprecise notochord segmentation, suggesting these processes are linked. Here, we reveal that spatial coordination between the notochord and the axial musculature is necessary to ensure segmentation of the zebrafish spine both in time and space. We find that the connective tissues that anchor the axial skeletal musculature, known as the myosepta in zebrafish, transmit spatial patterning cues necessary to initiate notochord segment formation, a critical pre-patterning step in spine morphogenesis. When an irregular pattern of muscle segments and myosepta interact with the notochord sheath, segments form non-sequentially, initiate at atypical locations, and eventually display altered morphology later in development. We determine that locations of myoseptum-notochord connections are hubs for mechanical signal transmission, which are characterized by localized sites of deformation of the extracellular matrix (ECM) layer encasing the notochord. The notochord sheath responds to the external mechanical changes by locally augmenting focal adhesion machinery to define the initiation site for segmentation. Using a coarse-grained mathematical model that captures the spatial patterns of myoseptum-notochord interactions, we find that a fixed-length scale of external cues is critical for driving sequential segment patterning in the notochord. Together, this work identifies a robust segmentation mechanism that hinges upon mechanical coupling of adjacent tissues to control patterning dynamics.

developmental biology↗

bioRxiv2021

DeepProjection: Rapid and structure-specific projections of tissue sheets embedded in 3D microscopy stacks using deep learning

The efficient extraction of local high-resolution content from massive amounts of imaging data remains a serious and unsolved problem in studies of complex biological tissues. Here we present DeepProjection, a trainable projection algorithm based on deep learning. This algorithm rapidly and robustly extracts image content contained in curved manifolds from time-lapse recorded 3D image stacks by binary masking of background content, stack by stack. The masks calculated for a given movie, when predicted, e.g., on fluorescent cell boundaries on one channel, can subsequently be applied to project other fluorescent channels from the same manifold. We apply DeepProjection to follow the dynamic movements of 2D-tissue sheets in embryonic development. We show that we can selectively project the amnioserosa cell sheet during dorsal closure in Drosophila melanogaster embryos and the periderm layer in the elongating zebrafish embryo while masking highly fluorescent out-of-plane artifacts.

developmental biology↗