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Shvartsman, S. Y.

Publications and source records attributed to Shvartsman, S. Y..

2 recordsLinked to original sources

Signaling dynamics control cell fate in the early Drosophila embryo

The Erk mitogen-activated protein kinase plays diverse roles in animal development, where its activity is associated with phenomena including cell migration, proliferation and differentiation. Its widespread reuse raises a conundrum: when a single kinase like Erk is activated, how does a developing cell know which fate to adopt? Here, we combine precise optogenetic control with genetic perturbations to dissect Erk-dependent cellular responses in the early Drosophila embryo. We find that light-stimulated Erk activity is sufficient to posterior-ize the majority of the embryo, leading to massive apical constriction through expression of the autocrine receptor-ligand pair mist and fog. Ectopic contraction at non-terminal positions requires at least 1 h of high-amplitude Erk signaling, whereas a 30 min pulse of Erk activity patterns non-contractile neurogenic ectoderm during the same time window. In contrast to the canonical transient versus sustained model, the cell fate switch is triggered by the cumulative load of Erk signaling, not the duration of a single persistent pulse. Our results reveal that the early fly embryo harbors a classic example of dynamic cell fate control, where the total dose of Erk activity selects between two distinct physiological outcomes.

developmental biology

Synthesizing developmental trajectories

Dynamical processes in biology are studied using an ever-increasing number of techniques, each of which brings out unique features of the system. One of the current challenges is to develop systematic approaches for fusing heterogeneous datasets into an integrated view of multivariable dynamics. We demonstrate that heterogeneous data fusion can be successfully implemented within a semi-supervised learning framework that exploits the intrinsic geometry of high-dimensional datasets. We illustrate our approach using a dataset from studies of pattern formation in Drosophila. The result is a continuous trajectory that reveals the joint dynamics of gene expression, subcellular protein localization, protein phosphorylation, and tissue morphogenesis. Our approach can be readily adapted to other imaging modalities and forms a starting point for further steps of data analytics and modeling of biological dynamics.

developmental biology