As a sedentary epithelium turns motile during wound healing, morphogenesis, and metastasis, the Golgi apparatus moves from an apical position, above the nucleus, to a basal position. This apical-to-basal repositioning of Golgi is critical for epithelial cell migration. Yet the molecular mechanism underlying it remains elusive, although microtubules are believed to play a role. Using live-cell and super-resolution imaging, we show that at the onset of collective migration of epithelial cells, Golgi stacks get dispersed to create an unpolarized transitional structure, and surprisingly, this dispersal process depends not on microtubules but on actin cytoskeleton. Golgi–actin interaction involves Arp2/3-driven actin projections emanating from the actin cortex, and a Golgi-localized actin elongation factor, MENA. While in sedentary epithelial cells, actin projections intermittently interact with the apically located Golgi, and the frequency of this event increases before the dispersion of Golgi stacks, at the onset of cell migration. Preventing Golgi–actin interaction with MENA-mutants eliminates Golgi dispersion and reduces the persistence of cell migration. Taken together, we show a process of actin-driven Golgi dispersion that is mechanistically different from the well-known Golgi apparatus fragmentation during mitosis and is essential for collective migration of epithelial cells.

Single molecule Fluorescence in situ Hybridization (smFISH) for mRNA provides a powerful quantitative handle on expression from endogenous gene loci. While the method has been widely applied in cells in culture, applications to primary tissue samples remain fewer, and often use involved cryosectioning. Even apart from quantitative access to absolute transcript counts in specific tissue volumes, many other advantages of smFISH can be envisaged in tissue samples. Primary among these are the ability to report on subtle differences in expression among different cell types within a tissue, and the ability to correlate the expression from different target genes. Here, we present a modified method of smFISH applicable on various primary wholemount tissues from the fruit fly Drosophila melanogaster, and show the efficacy of the method in a variety of larval and adult tissue, and embryos. We also combine smFISH in tissue with immunofluorescence to demonstrate the possibility of capturing transcriptional and translational aspects of gene expression in the same tissue. Given the widespread use of Drosophila melanogaster as a model system in Developmental Biology and Genetics, such methods are likely to be of wide interest and could yield rich information about gene expression in tissues from this organism.

Collective cell migration during morphogenesis, cancer metastasis, and wound healing depends on the emergence of leader cells at the migration front. However, the cellular changes that enable only a few cells to become the leader cells, remain elusive. Here we show that the leader cells in Drosophila embryo and mammalian epithelial monolayer emerge through a mechanosensitive relocalization of lysosomes. Before the leader cells display their characteristic lamellipodial protrusions, lysosomes accumulate at their leading periphery. Promoting this lysosome accumulation augments the leader cell emergence, while inhibiting it suppresses the latter. Moreover, experiments modulating cellular forces by chemical inhibition, optogenetics, and micropatterning show that peripheral accumulation of lysosomes depends on the actomyosin contractility. Mechanistically, peripheral lysosomes associate with the inactive form of small RhoGTPase Rac1 and remove it from the vicinity of plasma membrane. Removal of inactive Rac1 molecules leads to an increased Rac1-activity at the leading periphery, triggering actin polymerization and lamellipodium formation. Taken together, lysosome appears as a unique intracellular platform that links mechanical and biochemical signals and thereby controls the emergence of leader cells. We, therefore, discover a previously unknown function of lysosome in collective cell migration, significantly expanding its scope in cell and developmental biology.

In the stationary epithelium, the Golgi apparatus assumes an apical position, above the cell nucleus. However, during wound healing and morphogenesis, as the epithelial cells starts migrating, it relocalizes closer to the basal plane. On this plane, the position of Golgi with respect to the cell nucleus defines the organizational polarity of a migrating epithelial cell, which is crucial for an efficient collective migration. Yet, factors influencing the Golgi polarity remain elusive. Here we constructed a graph neural network-based deep learning model to systematically analyze the dependency of Golgi polarity on multiple geometric and physical factors. In spite of the complexity of a migrating epithelial monolayer, our simple model was able to predict the Golgi polarity with 75% accuracy. Moreover, the model predicted that Golgi polarity predominantly correlates with the orientation of maximum principal stress. Finally, we found that this correlation operates locally since progressive coarsening of the stress field over multiple cell-lengths reduced the stress polarity-Golgi polarity correlation as well as the predictive accuracy of the neural network model. Taken together, our results demonstrated that graph neural networks could be a powerful tool towards understanding how different physical factors influence collective cell migration. They also highlighted a previously unknown role of physical cues in defining the intracellular organization.

In the stationary epithelium, the Golgi apparatus assumes an apical position, above the cell nucleus. However, during wound healing and morphogenesis, as the epithelial cells starts migrating, it relocalizes closer to the basal plane. On this plane, the position of Golgi with respect to the cell nucleus defines the organizational polarity of a migrating epithelial cell, which is crucial for an efficient collective migration. Yet, factors influencing the Golgi polarity remain elusive. Here we constructed a graph neural network-based deep learning model to systematically analyze the dependency of Golgi polarity on multiple geometric and physical factors. In spite of the complexity of a migrating epithelial monolayer, our simple model was able to predict the Golgi polarity with 75% accuracy. Moreover, the model predicted that Golgi polarity predominantly correlates with the orientation of maximum principal stress. Finally, we found that this correlation operates locally since progressive coarsening of the stress field over multiple cell-lengths reduced the stress polarity-Golgi polarity correlation as well as the predictive accuracy of the neural network model. Taken together, our results demonstrated that graph neural networks could be a powerful tool towards understanding how different physical factors influence collective cell migration. They also highlighted a previously unknown role of physical cues in defining the intracellular organization.