The molecular mechanism responsible for capturing, sorting and retrieving vesicle membrane proteins following triggered exocytosis is not understood. Here we image the post-fusion release and then capture of a vesicle membrane protein, the vesicular acetylcholine transporter, from single vesicles in living neuroendocrine cells. We combine these measurements with super-resolution interferometric photo-activation localization microscopy and electron microscopy, and modelling to map the nanometer-scale topography and architecture of the structures responsible for the transporter’s capture following exocytosis. We show that after exocytosis, the transporter rapidly diffuses into the plasma membrane, but most travels only a short distance before it is locally captured over a dense network of membrane-resident clathrin-coated structures. We propose that the extreme density of these structures acts as a short-range diffusion trap. They quickly sequester diffusing vesicle material and limit its spread across the membrane. This system could provide a means for clathrin-mediated endocytosis to quickly recycle vesicle proteins in highly excitable cells.

Dendritic ion channels play a critical role in shaping synaptic input and are fundamentally important for synaptic integration and plasticity. In the hippocampal region CA1, somato-dendritic gradients of AMPA receptors and the hyperpolarization-activated cation conductance (I(h)) counteract the effects of dendritic filtering on the amplitude, time-course, and temporal integration of distal Schaffer collateral (SC) synaptic inputs within stratum radiatum (SR). While ion channel gradients in CA1 distal apical trunk dendrites within SR have been well characterized, little is known about the patterns of ion channel expression in the distal apical tuft dendrites within stratum lacunosum moleculare (SLM) that receive distinct input from the entorhinal cortex via perforant path (PP) axons. Here, we measured local ion channels densities within these distal apical tuft dendrites to determine if the somato-dendritic gradients of I(h) and AMPA receptors extend into distal tuft dendrites. We also determined the densities of voltage-gated sodium channels and NMDA receptors. We found that the densities of AMPA receptors, I(h,) and voltage-gated sodium channels are similar in tuft dendrites in SLM when compared with distal apical dendrites in SR, while the ratio of NMDA receptors to AMPA receptors increases in tuft dendrites relative to distal apical dendrites within SR. These data indicate that the somato-dendritic gradients of I(h) and AMPA receptors in apical dendrites do not extend into the distal tuft, and the relative densities of voltage-gated sodium channels and NMDA receptors are poised to support nonlinear integration of correlated SC and PP input.

Wikipedia, the online encyclopedia, is the most famous wiki in use today. It contains over 3.7 million pages of content; with many pages written on scientific subject matters that include peer-reviewed citations, yet are written in an accessible manner and generally reflect the consensus opinion of the community. In this, the 19th Annual Database Issue of Nucleic Acids Research, there are 11 articles that describe the use of a wiki in relation to a biological database. In this commentary, we discuss how biological databases can be integrated with Wikipedia, thereby utilising the pre-existing infrastructure, tools and above all, large community of authors (or Wikipedians). The limitations to the content that can be included in Wikipedia are highlighted, with examples drawn from articles found in this issue and other wiki-based resources, indicating why other wiki solutions are necessary. We discuss the merits of using open wikis, like Wikipedia, versus other models, with particular reference to potential vandalism. Finally, we raise the question about the future role of dedicated database biocurators in context of the thousands of crowdsourced, community annotations that are now being stored in wikis.

The GFP reconstitution across synaptic partners (GRASP) technique, based on functional complementation between two nonfluorescent GFP fragments, can be used to detect the location of synapses quickly, accurately and with high spatial resolution. The method has been previously applied in the nematode and the fruit fly but requires substantial modification for use in the mammalian brain. We developed mammalian GRASP (mGRASP) by optimizing transmembrane split-GFP carriers for mammalian synapses. Using in silico protein design, we engineered chimeric synaptic mGRASP fragments that were efficiently delivered to synaptic locations and reconstituted GFP fluorescence in vivo. Furthermore, by integrating molecular and cellular approaches with a computational strategy for the three-dimensional reconstruction of neurons, we applied mGRASP to both long-range circuits and local microcircuits in the mouse hippocampus and thalamocortical regions, analyzing synaptic distribution in single neurons and in dendritic compartments.

The ability to automatically segment an image into distinct regions is a critical aspect in many visual processing applications. Because inaccuracies often exist in automatic segmentation, manual segmentation is necessary in some application domains to correct mistakes, such as required in the reconstruction of neuronal processes from microscopic images. The goal of the automated segmentation tool is traditionally to produce the highest-quality segmentation, where quality is measured by the similarity to actual ground truth, so as to minimize the volume of manual correction necessary. Manual correction is generally orders-of-magnitude more time consuming than automated segmentation, often making handling large images intractable. Therefore, we propose a more relevant goal: minimizing the turn-around time of automated/manual segmentation while attaining a level of similarity with ground truth. It is not always necessary to inspect every aspect of an image to generate a useful segmentation. As such, we propose a strategy to guide manual segmentation to the most uncertain parts of segmentation. Our contributions include 1) a probabilistic measure that evaluates segmentation without ground truth and 2) a methodology that leverages these probabilistic measures to significantly reduce manual correction while maintaining segmentation quality.

Cell recognition requires interactions through molecules located on cell surface. The insect homolog of Down syndrome cell adhesion molecule (Dscam) manifests huge molecular diversity in its extracellular domain. High-affinity Dscam-Dscam interactions only occur between isoforms that carry identical extracellular domains. Homophilic Dscam signaling can, thus, vary in strength depending on the compositions of Dscams present on the opposing cell surfaces. Dscam abundantly exists in the developing nervous system and governs arborization and proper elaboration of neurites. Notably, individual neurons may stochastically and dynamically express a small subset of Dscam isoforms such that any given neurite can be endowed with a unique repertoire of Dscams. This allows individual neurites to recognize their sister branches. Self-recognition leads to self-repulsion, ensuring divergent migration of sister processes. By contrast, weak homophilic Dscam interactions may promote fasciculation of neurites that express analogous, but not identical, Dscams. Differential Dscam binding may provide graded cell recognition that in turn governs complex neuronal morphogenesis.

Genetically encoded calcium indicators (GECIs), together with modern microscopy, allow repeated activity measurement, in real time and with cellular resolution, of defined cellular populations. Recent efforts in protein engineering have yielded several high-quality GECIs that facilitate new applications in neuroscience. Here, we summarize recent progress in GECI design, optimization, and characterization, and provide guidelines for selecting the appropriate GECI for a given biological application. We focus on the unique challenges associated with imaging in behaving animals.

We explore the hypothesis that the neuronal spike generation mechanism is an analog-to-digital converter, which rectifies low-pass filtered summed synaptic currents and encodes them into spike trains linearly decodable in post-synaptic neurons. To digitally encode an analog current waveform, the sampling rate of the spike generation mechanism must exceed its Nyquist rate. Such oversampling is consistent with the experimental observation that the precision of the spike-generation mechanism is an order of magnitude greater than the cut-off frequency of dendritic low-pass filtering. To achieve additional reduction in the error of analog-to-digital conversion, electrical engineers rely on noise-shaping. If noise-shaping were used in neurons, it would introduce correlations in spike timing to reduce low-frequency (up to Nyquist) transmission error at the cost of high-frequency one (from Nyquist to sampling rate). Using experimental data from three different classes of neurons, we demonstrate that biological neurons utilize noise-shaping. We also argue that rectification by the spike-generation mechanism may improve energy efficiency and carry out de-noising. Finally, the zoo of ion channels in neurons may be viewed as a set of predictors, various subsets of which are activated depending on the statistics of the input current.

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.