Forces controlling tissue morphogenesis are attributed to cellular-driven activities, and any role for extracellular matrix (ECM) is assumed to be passive. However, all polymer networks, including ECM, can develop autonomous stresses during their assembly. Here, we examine the morphogenetic function of an ECM before reaching homeostatic equilibrium by analyzing de novo ECM assembly during Drosophila ventral nerve cord (VNC) condensation. Asymmetric VNC shortening and a rapid decrease in surface area correlate with the exponential assembly of collagen IV (Col4) surrounding the tissue. Concomitantly, a transient developmentally induced Col4 gradient leads to coherent long-range flow of ECM, which equilibrates the Col4 network. Finite element analysis and perturbation of Col4 network formation through the generation of dominant Col4 mutations that affect assembly reveal that VNC morphodynamics is partially driven by a sudden increase in ECM-driven surface tension. These data suggest that ECM assembly stress and associated network instabilities can actively participate in tissue morphogenesis.

Summary This review describes how direct visualization of the dynamic interactions of cells with different extracellular matrix microenvironments can provide novel insights into complex biological processes. Recent studies have moved characterization of cell migration and invasion from classical 2D culture systems into 1D and 3D model systems, revealing multiple differences in mechanisms of cell adhesion, migration and signalling—even though cells in 3D can still display prominent focal adhesions. Myosin II restrains cell migration speed in 2D culture but is often essential for effective 3D migration. 3D cell migration modes can switch between lamellipodial, lobopodial and/or amoeboid depending on the local matrix environment. For example, “nuclear piston” migration can be switched off by local proteolysis, and proteolytic invadopodia can be induced by a high density of fibrillar matrix. Particularly, complex remodelling of both extracellular matrix and tissues occurs during morphogenesis. Extracellular matrix supports self-assembly of embryonic tissues, but it must also be locally actively remodelled. For example, surprisingly focal remodelling of the basement membrane occurs during branching morphogenesis—numerous tiny perforations generated by proteolysis and actomyosin contractility produce a microscopically porous, flexible basement membrane meshwork for tissue expansion. Cells extend highly active blebs or protrusions towards the surrounding mesenchyme through these perforations. Concurrently, the entire basement membrane undergoes translocation in a direction opposite to bud expansion. Underlying this slowly moving 2D basement membrane translocation are highly dynamic individual cell movements. We conclude this review by describing a variety of exciting research opportunities for discovering novel insights into cell-matrix interactions.

Biological tissue is often composed of cells with similar morphologies replicated throughout large volumes and many biological applications rely on the accurate identification of these cells and their locations from image data. Here we develop a generative model that captures the regularities present in images composed of repeating elements of a few different types. Formally, the model can be described as convolutional sparse block coding. For inference we use a variant of convolutional matching pursuit adapted to block-based representations. We extend the K-SVD learning algorithm to subspaces by retaining several principal vectors from the SVD decomposition instead of just one. Good models with little cross-talk between subspaces can be obtained by learning the blocks incrementally. We perform extensive experiments on simulated images and the inference algorithm consistently recovers a large proportion of the cells with a small number of false positives. We fit the convolutional model to noisy GCaMP6 two-photon images of spiking neurons and to Nissl-stained slices of cortical tissue and show that it recovers cell body locations without supervision. The flexibility of the block-based representation is reflected in the variability of the recovered cell shapes.

An often-overlooked aspect of neural plasticity is the plasticity of neuronal composition, in which the numbers of neurons of particular classes are altered in response to environment and experience. The Drosophila brain features several well-characterized lineages in which a single neuroblast gives rise to multiple neuronal classes in a stereotyped sequence during development [1]. We find that in the intrinsic mushroom body neuron lineage, the numbers for each class are highly plastic, depending on the timing of temporal fate transitions and the rate of neuroblast proliferation. For example, mushroom body neuroblast cycling can continue under starvation conditions, uncoupled from temporal fate transitions that depend on extrinsic cues reflecting organismal growth and development. In contrast, the proliferation rates of antennal lobe lineages are closely associated with organismal development, and their temporal fate changes appear to be cell cycle-dependent, such that the same numbers and types of uniglomerular projection neurons innervate the antennal lobe following various perturbations. We propose that this surprising difference in plasticity for these brain lineages is adaptive, given their respective roles as parallel processors versus discrete carriers of olfactory information.

Many animals rely on vision to navigate through their environment. The pattern of changes in the visual scene induced by self-motion is the optic flow1, which is first estimated in local patches by directionally selective (DS) neurons2–4. But how should the arrays of DS neurons, each responsive to motion in a preferred direction at a specific retinal position, be organized to support robust decoding of optic flow by downstream circuits? Understanding this global organization is challenging because it requires mapping fine, local features of neurons across the animal’s field of view3. In Drosophila, the asymmetric dendrites of the T4 and T5 DS neurons establish their preferred direction, making it possible to predict DS responses from anatomy4,5. Here we report that the preferred directions of fly DS neurons vary at different retinal positions and show that this spatial variation is established by the anatomy of the compound eye. To estimate the preferred directions across the visual field, we reconstructed hundreds of T4 neurons in a full brain EM volume6 and discovered unexpectedly stereotypical dendritic arborizations that are independent of location. We then used whole-head μCT scans to map the viewing directions of all compound eye facets and found a non-uniform sampling of visual space that explains the spatial variation in preferred directions. Our findings show that the organization of preferred directions in the fly is largely determined by the compound eye, exposing an intimate and unexpected connection between the peripheral structure of the eye, functional properties of neurons deep in the brain, and the control of body movements.

In Drosophila, pattern formation at multiple stages of embryonic and imaginal development depends on the same intercellular signaling pathways. We have identified a novel gene, eyelid (eld), which is required for embryonic segmentation, development of the notum and wing margin, and photoreceptor differentiation. In these tissues, eld mutations have effects opposite to those caused by wingless (wg) mutations. eld encodes a widely expressed nuclear protein with a region homologous to a novel family of DNA-binding domains. Based on this homology and on the phenotypic analysis, we suggest that Eld could act as a transcription factor antagonistic to the Wg pathway.

Visualizing the formation of multinucleated giant cells (MGCs) from living specimens has been challenging due to the fact that most live imaging techniques require propagation of light through glass, but on glass macrophage fusion is a rare event. This protocol presents the fabrication of several optical-quality glass surfaces where adsorption of compounds containing long-chain hydrocarbons transforms glass into a fusogenic surface. First, preparation of clean glass surfaces as starting material for surface modification is described. Second, a method is provided for the adsorption of compounds containing long-chain hydrocarbons to convert non-fusogenic glass into a fusogenic substrate. Third, this protocol describes fabrication of surface micropatterns that promote a high degree of spatiotemporal control over MGC formation. Finally, fabricating glass bottom dishes is described. Examples of use of this in vitro cell system as a model to study macrophage fusion and MGC formation are shown.

Recent studies in mice have shown that orofacial behaviors drive a large fraction of neural activity across the brain. To understand the nature and function of these signals, we need better computational models to characterize the behaviors and relate them to neural activity. Here we developed Facemap, a framework consisting of a keypoint tracking algorithm and a deep neural network encoder for predicting neural activity. We used the Facemap keypoints as input for the deep neural network to predict the activity of ∼50,000 simultaneously-recorded neurons and in visual cortex we doubled the amount of explained variance compared to previous methods. Our keypoint tracking algorithm was more accurate than existing pose estimation tools, while the inference speed was several times faster, making it a powerful tool for closed-loop behavioral experiments. The Facemap tracker was easy to adapt to data from new labs, requiring as few as 10 annotated frames for near-optimal performance. We used Facemap to find that the neuronal activity clusters which were highly driven by behaviors were more spatially spread-out across cortex. We also found that the deep keypoint features inferred by the model had time-asymmetrical state dynamics that were not apparent in the raw keypoint data. In summary, Facemap provides a stepping stone towards understanding the function of the brainwide neural signals and their relation to behavior.