Understanding the structure and operation of any nervous system has been a subject of research for well over a century. A near-term opportunity in this quest is to understand the brain of a model species, the fruit fly Drosophila melanogaster. This is an enticing target given its relatively small size (roughly 200,000 neurons), coupled with the behavioral richness that this brain supports, and the wide variety of techniques now available to study both brain and behavior. It is clear that within a few years we will possess a connectome for D. melanogaster: an electron-microscopy-level description of all neurons and their chemical synaptic connections. Given what we will soon have, what we already know and the research that is currently underway, what more do we need to know to enable us to understand the fly's brain? Here, we itemize the data we will need to obtain, collate and organize in order to build an integrated model of the brain of D. melanogaster.

Understanding cellular architecture is essential for understanding biology. Electron microscopy (EM) uniquely visualizes cellular structures with nanometre resolution. However, traditional methods, such as thin-section EM or EM tomography, have limitations in that they visualize only a single slice or a relatively small volume of the cell, respectively. Focused ion beam-scanning electron microscopy (FIB-SEM) has demonstrated the ability to image small volumes of cellular samples with 4-nm isotropic voxels. Owing to advances in the precision and stability of FIB milling, together with enhanced signal detection and faster SEM scanning, we have increased the volume that can be imaged with 4-nm voxels by two orders of magnitude. Here we present a volume EM atlas at such resolution comprising ten three-dimensional datasets for whole cells and tissues, including cancer cells, immune cells, mouse pancreatic islets and Drosophila neural tissues. These open access data (via OpenOrganelle) represent the foundation of a field of high-resolution whole-cell volume EM and subsequent analyses, and we invite researchers to explore this atlas and pose questions.

Animal behavior is shaped both by evolution and by individual experience. Parallel brain pathways encode innate and learned valences of cues, but the way in which they are integrated during action-selection is not well understood. We used electron microscopy to comprehensively map with synaptic resolution all neurons downstream of all Mushroom Body output neurons (encoding learned valences) and characterized their patterns of interaction with Lateral Horn neurons (encoding innate valences) in larva. The connectome revealed multiple types that receive convergent Mushroom Body and Lateral Horn inputs. A subset of these receives excitatory input from positive-valence MB and LH pathways and inhibitory input from negative-valence MB pathways. We confirmed functional connectivity from LH and MB pathways and behavioral roles of two of these neurons. These neurons encode integrated odor value and bidirectionally regulate turning. Based on this we speculate that learning could potentially skew the balance of excitation and inhibition onto these neurons and thereby modulate turning. Together, our study provides insights into the circuits that integrate learned and innate valences to modify behavior.

The nanoscale connectomics community has recently generated automated and semi-automated "wiring diagrams" of brain subregions from terabytes and petabytes of dense 3D neuroimagery. This process involves many challenging and imperfect technical steps, including dense 3D image segmentation, anisotropic nonrigid image alignment and coregistration, and pixel classification of each neuron and their individual synaptic connections. As data volumes continue to grow in size, and connectome generation becomes increasingly commonplace, it is important that the scientific community is able to rapidly assess the quality and accuracy of a connectome product to promote dataset analysis and reuse. In this work, we share our scalable toolkit for assessing the quality of a connectome reconstruction via targeted inquiry and large-scale graph analysis, and to provide insights into how such connectome proofreading processes may be improved and optimized in the future. We illustrate the applications and ecosystem on a recent reference dataset.Clinical relevance- Large-scale electron microscopy (EM) data offers a novel opportunity to characterize etiologies and neurological diseases and conditions at an unprecedented scale. EM is useful for low-level analyses such as biopsies; this increased scale offers new possibilities for research into areas such as neural networks if certain bottlenecks and problems are overcome.

Sculpting a flat patch of membrane into an endocytic vesicle requires curvature generation on the cell surface, which is the primary function of the endocytosis machinery. Using super-resolved live cell fluorescence imaging, we demonstrate that curvature generation by individual clathrin-coated pits can be detected in real time within cultured cells and tissues of developing organisms. Our analyses demonstrate that the footprint of clathrin coats increases monotonically during the formation of pits at different levels of plasma membrane tension. These findings are only compatible with models that predict curvature generation at the early stages of endocytic clathrin pit formation. We also found that CALM adaptors associated with clathrin plaques form clusters, whereas AP2 distribution is more homogenous. Considering the curvature sensing and driving roles of CALM, we propose that CALM clusters may increase the strain on clathrin lattices locally, eventually giving rise to rupture and subsequent pit completion at the edges of plaques.

Pigmentation divergence between Drosophila species has emerged as a model trait for studying the genetic basis of phenotypic evolution, with genetic changes contributing to pigmentation differences often mapping to genes in the pigment synthesis pathway and their regulators. These studies of Drosophila pigmentation have tended to focus on pigmentation changes in one body part for a particular pair of species, but changes in pigmentation are often observed in multiple body parts between the same pair of species. The similarities and differences of genetic changes responsible for divergent pigmentation in different body parts of the same species thus remain largely unknown. Here we compare the genetic basis of pigmentation divergence between Drosophila elegans and D. gunungcola in the wing, legs, and thorax. Prior work has shown that regions of the genome containing the pigmentation genes yellow and ebony influence the size of divergent male-specific wing spots between these two species. We find that these same two regions of the genome underlie differences in leg and thorax pigmentation; however, divergent alleles in these regions show differences in allelic dominance and epistasis among the three body parts. These complex patterns of inheritance can be explained by a model of evolution involving tissue-specific changes in the expression of Yellow and Ebony between D. elegans and D. gunungcola.

In the central nervous system (CNS), functional tasks are often allocated to distinct compartments. This is also evident in the Drosophila CNS where synapses and dendrites are clustered in distinct neuropil regions. The neuropil is separated from neuronal cell bodies by ensheathing glia, which as we show using dye injection experiments, contribute to the formation of an internal diffusion barrier. We find that ensheathing glia are polarized with a basolateral plasma membrane rich in phosphatidylinositol-(3,4,5)-triphosphate (PIP) and the Na/K-ATPase Nervana2 (Nrv2) that abuts an extracellular matrix formed at neuropil-cortex interface. The apical plasma membrane is facing the neuropil and is rich in phosphatidylinositol-(4,5)-bisphosphate (PIP) that is supported by a sub-membranous ß-Spectrin cytoskeleton. ß-spectrin mutant larvae affect ensheathing glial cell polarity with delocalized PIP and Nrv2 and exhibit an abnormal locomotion which is similarly shown by ensheathing glia ablated larvae. Thus, polarized glia compartmentalizes the brain and is essential for proper nervous system function.

Processing bodies (p-bodies) are a prototypical phase-separated RNA-containing granule. Their abundance is highly dynamic and has been linked to translation. Yet, the molecular mechanisms responsible for coordinate control of the two processes are unclear. Here, we uncover key roles for eEF2 kinase (eEF2K) in the control of ribosome availability and p-body abundance. eEF2K acts on a sole known substrate, eEF2, to inhibit translation. We find that the eEF2K agonist nelfinavir abolishes p-bodies in sensory neurons and impairs translation. To probe the latter, we used cryo-electron microscopy. Nelfinavir stabilizes vacant 80S ribosomes. They contain SERBP1 in place of mRNA and eEF2 in the acceptor site. Phosphorylated eEF2 associates with inactive ribosomes that resist splitting in vitro. Collectively, the data suggest that eEF2K defines a population of inactive ribosomes resistant to recycling and protected from degradation. Thus, eEF2K activity is central to both p-body abundance and ribosome availability in sensory neurons.

Internal models are nowadays customarily used in different domains of science and engineering to describe how living organisms or artificial computational units embed their acquired knowledge about recurring events taking place in the surrounding environment. This article reviews the internal model principle in control theory, bioengineering, and neuroscience, illustrating the fundamental concepts and theoretical developments of the few last decades of research.