Essential roles for lines in mediating leg and antennal proximodistal patterning and generating a stable Notch signaling interface at segment borders

AbstractThe Drosophila leg imaginal disc provides a paradigm with which to understand the fundamental developmental mechanisms that generate an intricate appendage structure. Leg formation depends on the subdivision of the leg proximodistal (PD) axis into broad domains by the leg gap genes. The leg gap genes act combinatorially to initiate the expression of the Notch ligands Delta (Dl) and Serrate (Ser) in a segmental pattern. Dl and Ser induce the expression of a set of transcriptional regulators along the segment border, which mediate leg segment growth and joint morphogenesis. Here we show that Lines accumulates in nuclei in the presumptive tarsus and the inter-joints of proximal leg segments and governs the formation of these structures by destabilizing the nuclear protein Bowl. Across the presumptive tarsus, lines modulates the opposing expression landscapes of the leg gap gene dachshund (dac) and the tarsal PD genes, bric-a-brac 2 (bab), apterous (ap) and BarH1 (Bar). In this manner, lines inhibits proximal tarsal fates and promotes medial and distal tarsal fates. Across proximal leg segments, lines antagonizes bowl to promote Dl expression by relief-of-repression. In turn, Dl signals asymmetrically to stabilize Bowl in adjacent distal cells. Bowl, then, acts cell-autonomously, together with one or more redundant factors, to repress Dl expression. Together, lines and bowl act as a binary switch to generate a stable Notch signaling interface between Dl-expressing cells and adjacent distal cell. lines plays analogous roles in developing antennae, which are serially homologous to legs, suggesting evolutionarily conserved roles for lines in ventral appendage formation

In Situ Transcriptome Accessibility Sequencing (INSTA-seq)

Subcellular RNA localization regulates spatially polarized cellular processes, but unbiased investigation of its control in vivo remains challenging. Current hybridization-based methods cannot differentiate small regulatory variants, while in situ sequencing is limited by short reads. We solved these problems using a bidirectional sequencing chemistry to efficiently image transcript-specific barcode in situ , which are then extracted and assembled into longer reads using NGS. In the Drosophila retina, genes regulating eye development and cytoskeletal organization were enriched compared to methods using extracted RNA. We therefore named our method In Situ Transcriptome Accessibility sequencing (INSTA-seq). Sequencing reads terminated near 3’ UTR cis -motifs (e.g. Zip48C, stau ), revealing RNA-protein interactions. Additionally, Act5C polyadenylation isoforms retaining zipcode motifs were selectively localized to the optical stalk, consistent with their biology. Our platform provides a powerful way to visualize any RNA variants or protein interactions in situ to study their regulation in animal development

RhoGAP68F controls transport of adhesion proteins in Rab4 endosomes to modulate epithelial morphogenesis of Drosophila leg discs

AbstractElongation and invagination of epithelial tissues are fundamental developmental processes that contribute to the morphogenesis of embryonic and adult structures and are dependent on coordinated remodeling of cell–cell contacts. The morphogenesis of Drosophila leg imaginal discs depends on extensive remodeling of cell contacts and thus provides a useful system with which to investigate the underlying mechanisms. The small Rho GTPase regulator RhoGAP68F has been previously implicated in leg morphogenesis. It consists of on an N-terminal Sec14 domain and a C-terminal GAP domain. Here we examined the molecular function and role of RhoGAP68F in epithelial remodeling. We find that depletion of RhoGAP68F impairs epithelial remodeling from a pseudostratified to simple, while overexpression of RhoGAP68F causes tears of lateral cell–cell contacts and thus impairs epithelial integrity. We show that the RhoGAP68F protein localizes to Rab4 recycling endosomes and forms a complex with the Rab4 protein. The Sec14 domain is sufficient for localizing to Rab4 endosomes, while the activity of the GAP domain is dispensable. RhoGAP68F, in turn, inhibits the scission and movement of Rab4 endosomes involved in transport the adhesion proteins Fasciclin3 and E-cadherin back to cell–cell contacts. Expression of RhoGAP68F is upregulated during prepupal development suggesting that RhoGAP68F decreases the transport of key adhesion proteins to the cell surface during this developmental stage to decrease the strength of adhesive cell–cell contacts and thereby facilitate epithelial remodeling and leg morphogenesis

The Drumstick/Lines/Bowl regulatory pathway links antagonistic Hedgehog and Wingless signaling inputs to epidermal cell differentiation

Hedgehog and Wingless signaling in the Drosophila embryonic epidermis represents one paradigm for organizer function. In patterning this epidermis, Hedgehog and Wingless act asymmetrically, and consequently otherwise equivalent cells on either side of the organizer follow distinct developmental fates. To better understand the downstream mechanisms involved, we have investigated mutations that disrupt dorsal epidermal pattern. We have previously demonstrated that the gene lines contributes to this process. Here we show that the Lines protein interacts functionally with the zinc-finger proteins Drumstick (Drm) and Bowl. Competitive protein-protein interactions between Lines and Bowl and between Drm and Lines regulate the steady-state accumulation of Bowl, the downstream effector of this pathway. Lines binds directly to Bowl and decreases Bowl abundance. Conversely, Drm allows Bowl accumulation in drm-expressing cells by inhibiting Lines. This is accomplished both by outcompeting Bowl in binding to Lines and by redistributing Lines to the cytoplasm, thereby segregating Lines away from nuclearly localized Bowl. Hedgehog and Wingless affect these functional interactions by regulating drm expression. Hedgehog promotes Bowl protein accumulation by promoting drm expression, while Wingless inhibits Bowl accumulation by repressing drm expression anterior to the source of Hedgehog production. Thus, Drm, Lines, and Bowl are components of a molecular regulatory pathway that links antagonistic and asymmetric Hedgehog and Wingless signaling inputs to epidermal cell differentiation. Finally, we show that Drm and Lines also regulate Bowl accumulation and consequent patterning in the epithelia of the foregut, hindgut, and imaginal discs. Thus, in all these developmental contexts, including the embryonic epidermis, the novel molecular regulatory pathway defined here is deployed in order to elaborate pattern across a field of cells

Sidekick Is a Key Component of Tricellular Adherens Junctions that Acts to Resolve Cell Rearrangements

Tricellular adherens junctions are points of high tension that are central to the rearrangement of epithelial cells. However, the molecular composition of these junctions is unknown, making it difficult to assess their role in morphogenesis. Here, we show that Sidekick, an immunoglobulin family cell adhesion protein, is highly enriched at tricellular adherens junctions in Drosophila. This localization is modulated by tension, and Sidekick is itself necessary to maintain normal levels of cell bond tension. Loss of Sidekick causes defects in cell and junctional rearrangements in actively remodeling epithelial tissues like the retina and tracheal system. The adaptor proteins Polychaetoid and Canoe are enriched at tricellular adherens junctions in a Sidekick-dependent manner; Sidekick functionally interacts with both proteins and directly binds to Polychaetoid. We suggest that Polychaetoid and Canoe link Sidekick to the actin cytoskeleton to enable tricellular adherens junctions to maintain or transmit cell bond tension during epithelial cell rearrangements.This work was supported by the National Institutes of Health (grants EY025540 to J.E.T. and GM129151 to V.H.) and by Ministerio de Economía y Competitividad of the Spanish Government (BFU2012-39509-C02, BFU2015-68098-P to M.L.)

Segmentation and Tracking of Adherens Junctions in 3D for the Analysis of Epithelial Tissue Morphogenesis

<div><p>Epithelial morphogenesis generates the shape of tissues, organs and embryos and is fundamental for their proper function. It is a dynamic process that occurs at multiple spatial scales from macromolecular dynamics, to cell deformations, mitosis and apoptosis, to coordinated cell rearrangements that lead to global changes of tissue shape. Using time lapse imaging, it is possible to observe these events at a system level. However, to investigate morphogenetic events it is necessary to develop computational tools to extract quantitative information from the time lapse data. Toward this goal, we developed an image-based computational pipeline to preprocess, segment and track epithelial cells in 4D confocal microscopy data. The computational pipeline we developed, for the first time, detects the adherens junctions of epithelial cells in 3D, without the need to first detect cell nuclei. We accentuate and detect cell outlines in a series of steps, symbolically describe the cells and their connectivity, and employ this information to track the cells. We validated the performance of the pipeline for its ability to detect vertices and cell-cell contacts, track cells, and identify mitosis and apoptosis in surface epithelia of <i>Drosophila</i> imaginal discs. We demonstrate the utility of the pipeline to extract key quantitative features of cell behavior with which to elucidate the dynamics and biomechanical control of epithelial tissue morphogenesis. We have made our methods and data available as an open-source multiplatform software tool called TTT (<a href="http://github.com/morganrcu/TTT" target="_blank">http://github.com/morganrcu/TTT</a>)</p></div

The developed system for the preprocessing, segmentation and tracking of epithelial cells.

<p>A) Schematic of the computational pipeline from the acquisition of 3D time lapse data to image preprocessing, cell segmentation and tracking. After segmenting the cells, we define symbolically their structure using a planar graph connecting detected AJ vertices with edges (green). Then we identify the cells in the tissue as the faces of the AJ graph and build the Cell graph to describe cell connectivity (blue). Finally, we establish correspondence between cells among frames (colored lines connecting cell centroids) obtaining cell trajectories. (B-G) Part of an epithelium of a <i>Drosophila</i> leg at early pupal stages. This tissue dramatically narrows and elongates at this stage to generate a narrow and hollow cylinder while the epithelium at presumptive joints invaginates. B) Maximum intensity projection of an image stack through the leg epithelium marked with E-cad∷GFP to highlight cell outlines. Distal up, narrow region—presumptive joint; wider regions part of the presumptive segment. C) Projection of the denoised and deconvoluted volume. D) The output of the filters employed to detect AJs (green) and AJ vertices (red). E) AJ graph representing the AJs structure. F) Cell graph representing neighborhood relationships among cells in the tissue. G) Polygonal representation of the cells, colored according to assigned temporal identifiers.</p

Performance assessment of the automated detection of vertices, cell-cell contacts and cell tracks.

<p>A-D) Evaluation of the vertex detector, E-H) edge detector, and I-J) cell tracker. C-D), G-H) Green—true detections, blue—missed detections, red—false detections. A) Precision-Recall curve for AJ vertex detection. B) Variation of the <i>F</i>1 score of the vertex detector relative to changes in detection threshold <i>T</i>_<i>V</i>. Vertex detection of C) Notum and D) Leg datasets. Vertex location accuracy highly depends on properly tuning up <i>T</i>_<i>v</i>. E) Precision-Recall curve for AJ edge detection. F) Variation of the <i>F</i>1 score relative to changes in propagation threshold <i>T</i>_<i>E</i>. Edge detection in G) Notum and H) Leg datasets. Edge detection is more robust than vertex detection. I), J), K) and L) 2D projection of the trajectories respectively found for the cells in E) Notum, F) Leg, G) Mitosis in notum and H) Apoptosis in notum datasets. The system recovers accurate cell trajectories in different scenarios.</p