Intercepting a moving object requires prediction of its future location. This complex task has been solved by dragonflies, who intercept their prey in midair with a 95% success rate. In this study, we show that a group of 16 neurons, called target-selective descending neurons (TSDNs), code a population vector that reflects the direction of the target with high accuracy and reliability across 360°. The TSDN spatial (receptive field) and temporal (latency) properties matched the area of the retina where the prey is focused and the reaction time, respectively, during predatory flights. The directional tuning curves and morphological traits (3D tracings) for each TSDN type were consistent among animals, but spike rates were not. Our results emphasize that a successful neural circuit for target tracking and interception can be achieved with few neurons and that in dragonflies this information is relayed from the brain to the wing motor centers in population vector form.

The endosomal-sorting complex required for transport (ESCRT) is evolutionarily conserved from Archaea to eukaryotes. The complex drives membrane scission events in a range of processes, including cytokinesis in Metazoa and some Archaea. CdvA is the protein in Archaea that recruits ESCRT-III to the membrane. Using electron cryotomography (ECT), we find that CdvA polymerizes into helical filaments wrapped around liposomes. ESCRT-III proteins are responsible for the cinching of membranes and have been shown to assemble into helical tubes in vitro, but here we show that they also can form nested tubes and nested cones, which reveal surprisingly numerous and versatile contacts. To observe the ESCRT-CdvA complex in a physiological context, we used ECT to image the archaeon Sulfolobus acidocaldarius and observed a distinct protein belt at the leading edge of constriction furrows in dividing cells. The known dimensions of ESCRT-III proteins constrain their possible orientations within each of these structures and point to the involvement of spiraling filaments in membrane scission.

A central problem in neuroscience is reconstructing neuronal circuits on the synapse level. Due to a wide range of scales in brain architecture such reconstruction requires imaging that is both high-resolution and high-throughput. Existing electron microscopy (EM) techniques possess required resolution in the lateral plane and either high-throughput or high depth resolution but not both. Here, we exploit recent advances in unsupervised learning and signal processing to obtain high depth-resolution EM images computationally without sacrificing throughput. First, we show that the brain tissue can be represented as a sparse linear combination of localized basis functions that are learned using high-resolution datasets. We then develop compressive sensing-inspired techniques that can reconstruct the brain tissue from very few (typically 5) tomographic views of each section. This enables tracing of neuronal processes and, hence, high throughput reconstruction of neural circuits on the level of individual synapses.

Internal state as well as environmental conditions influence choice behavior. The neural circuits underpinning state-dependent behavior remain largely unknown. Carbon dioxide (CO2) is an important olfactory cue for many insects, including mosquitoes, flies, moths, and honeybees [1]. Concentrations of CO2 higher than 0.02% above atmospheric level trigger a strong innate avoidance in the fly Drosophila melanogaster [2, 3]. Here, we show that the mushroom body (MB), a brain center essential for olfactory associative memories [4-6] but thought to be dispensable for innate odor processing [7], is essential for CO2 avoidance behavior only in the context of starvation or in the context of a food-related odor. Consistent with this, CO2 stimulation elicits Ca(2+) influx into the MB intrinsic cells (Kenyon cells: KCs) in vivo. We identify an atypical projection neuron (bilateral ventral projection neuron, biVPN) that connects CO2 sensory input bilaterally to the MB calyx. Blocking synaptic output of the biVPN completely abolishes CO2 avoidance in food-deprived flies, but not in fed flies. These findings show that two alternative neural pathways control innate choice behavior, and they are dependent on the animal’s internal state. In addition, they suggest that, during innate choice behavior, the MB serves as an integration site for internal state and olfactory input.

Transcriptional regulation is achieved through combinatorial interactions between regulatory elements in the human genome and a vast range of factors that modulate the recruitment and activity of RNA polymerase. Experimental approaches for studying transcription in vivo now extend from single-molecule techniques to genome-wide measurements. Parallel to these developments is the need for testable quantitative and predictive models for understanding gene regulation. These conceptual models must also provide insight into the dynamics of transcription and the variability that is observed at the single-cell level. In this Review, we discuss recent results on transcriptional regulation and also the models those results engender. We show how a non-equilibrium description informs our view of transcription by explicitly considering time- and energy-dependence at the molecular level.

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.

Optical flow is a key method used for quantitative motion estimation of biological structures in light microscopy. It has also been used as a key module in segmentation and tracking systems and is considered a mature technology in the field of computer vision. However, most of the research focused on 2D natural images, which are small in size and rich in edges and texture information. In contrast, 3D time-lapse recordings of biological specimens comprise up to several terabytes of image data and often exhibit complex object dynamics as well as blurring due to the point-spread-function of the microscope. Thus, new approaches to optical flow are required to improve performance for such data. We solve optical flow in large 3D time-lapse microscopy datasets by defining a Markov random field (MRF) over super-voxels in the foreground and applying motion smoothness constraints between super-voxels instead of voxel-wise. This model is tailored to the specific characteristics of light microscopy datasets: super-voxels help registration in textureless areas, the MRF over super-voxels efficiently propagates motion information between neighboring cells and the background subtraction and super-voxels reduce the dimensionality of the problem by an order of magnitude. We validate our approach on large 3D time-lapse datasets of Drosophila and zebrafish development by analyzing cell motion patterns. We show that our approach is, on average, 10 x faster than commonly used optical flow implementations in the Insight Tool-Kit (ITK) and reduces the average flow end point error by 50% in regions with complex dynamic processes, such as cell divisions.

Conventional acquisition of three-dimensional (3D) microscopy data requires sequential z scanning and is often too slow to capture biological events. We report an aberration-corrected multifocus microscopy method capable of producing an instant focal stack of nine 2D images. Appended to an epifluorescence microscope, the multifocus system enables high-resolution 3D imaging in multiple colors with single-molecule sensitivity, at speeds limited by the camera readout time of a single image.

Gap junctions (GJs) represent connexin-rich membrane domains that connect interiors of adjoining cells in mammalian tissues. How fast GJs can respond to bacterial pathogens has not been known previously. Using Bessel beam plane illumination and confocal spinning disk microscopy, we found fast ( 500 ms) formation of connexin-depleted regions (CDRs) inside GJ plaques between cells exposed to AB5 toxins. CDR formation appears as a fast redistribution of connexin channels within GJ plaques with minor changes in outline or geometry. CDR formation does not depend on membrane trafficking or submembrane cytoskeleton and has no effect on GJ conductance. However, CDR responses depend on membrane lipids, can be modified by cholesterol-clustering agents and extracellular K(+) ion concentration, and influence cAMP signaling. The CDR response of GJ plaques to bacterial toxins is a phenomenon observed for all tested connexin isoforms. Through signaling, the CDR response may enable cells to sense exposure to AB5 toxins. CDR formation may reflect lipid-phase separation events in the biological membrane of the GJ plaque, leading to increased connexin packing and lipid reorganization. Our data demonstrate very fast dynamics (in the millisecond-to-second range) within GJ plaques, which previously were considered to be relatively stable, long-lived structures.