Pixel and superpixel classifiers have become essential tools for EM segmentation algorithms. Training these classifiers remains a major bottleneck primarily due to the requirement of completely annotating the dataset which is tedious, error-prone and costly. In this paper, we propose an interactive learning scheme for the superpixel classifier for EM segmentation. Our algorithm is "active semi-supervised" because it requests the labels of a small number of examples from user and applies label propagation technique to generate these queries. Using only a small set (<20%) of all datapoints, the proposed algorithm consistently generates a classifier almost as accurate as that estimated from a complete groundtruth. We provide segmentation results on multiple datasets to show the strength of these classifiers.

This paper proposes a novel agglomerative framework for Electron Microscopy (EM) image (or volume) segmentation. For the overall segmentation methodology, we propose a context-aware algorithm that clusters the over-segmented regions of different sub-classes (representing different biological entities) in different stages. Furthermore, a delayed scheme for agglomerative clustering, which postpones the merge of newly formed bodies, is also proposed to generate a more confident boundary prediction. We report significant improvements in both segmentation accuracy and speed attained by the proposed approaches over existing standard methods on both 2D and 3D datasets.

The self-assembly of proteins into highly ordered nanoscale architectures is a hallmark of biological systems. The sophisticated functions of these molecular machines have inspired the development of methods to engineer self-assembling protein nanostructures; however, the design of multi-component protein nanomaterials with high accuracy remains an outstanding challenge. Here we report a computational method for designing protein nanomaterials in which multiple copies of two distinct subunits co-assemble into a specific architecture. We use the method to design five 24-subunit cage-like protein nanomaterials in two distinct symmetric architectures and experimentally demonstrate that their structures are in close agreement with the computational design models. The accuracy of the method and the number and variety of two-component materials that it makes accessible suggest a route to the construction of functional protein nanomaterials tailored to specific applications.

Acetylation of α-tubulin Lys40 by tubulin acetyltransferase (TAT) is the only known posttranslational modification in the microtubule lumen. It marks stable microtubules and is required for polarity establishment and directional migration. Here, we elucidate the mechanistic underpinnings for TAT activity and its preference for microtubules with slow turnover. 1.35 Å TAT cocrystal structures with bisubstrate analogs constrain TAT action to the microtubule lumen and reveal Lys40 engaged in a suboptimal active site. Assays with diverse tubulin polymers show that TAT is stimulated by microtubule interprotofilament contacts. Unexpectedly, despite the confined intraluminal location of Lys40, TAT efficiently scans the microtubule bidirectionally and acetylates stochastically without preference for ends. First-principles modeling and single-molecule measurements demonstrate that TAT catalytic activity, not constrained luminal diffusion, is rate limiting for acetylation. Thus, because of its preference for microtubules over free tubulin and its modest catalytic rate, TAT can function as a slow clock for microtubule lifetimes.

A remaining challenge in protein modeling is to predict structures for sequences with no sequence similarity to any experimentally solved structure. Based on earlier observations, the library of protein backbone supersecondary structure motifs (Smotifs) saturated about a decade ago. Therefore, it should be possible to build any structure from a combination of existing Smotifs with the help of limited experimental data that are sufficient to relate the backbone conformations of Smotifs between target proteins and known structures. Here, we present a hybrid modeling algorithm that relies on an exhaustive Smotif library and on nuclear magnetic resonance chemical shift patterns without any input of primary sequence information. In a test of 102 proteins, the algorithm delivered 90 homology-model-quality models, among them 24 high-quality ones, and a topologically correct solution for almost all cases. The current approach opens a venue to address the modeling of larger protein structures for which chemical shifts are available.

Electron diffraction of extremely small three-dimensional crystals (MicroED) allows for structure determination from crystals orders of magnitude smaller than those used for X-ray crystallography. MicroED patterns, which are collected in a transmission electron microscope, were initially not amenable to indexing and intensity extraction by standard software, which necessitated the development of a suite of programs for data processing. The MicroED suite was developed to accomplish the tasks of unit-cell determination, indexing, background subtraction, intensity measurement and merging, resulting in data that can be carried forward to molecular replacement and structure determination. This ad hoc solution has been modified for more general use to provide a means for processing MicroED data until the technique can be fully implemented into existing crystallographic software packages. The suite is written in Python and the source code is available under a GNU General Public License.

The evolution of behavior seems inconsistent with the deep homology of neuromodulatory signaling. G protein coupled receptors (GPCRs) evolved slowly from a common ancestor through a process involving gene duplication, neofunctionalization, and loss. Neuropeptides co-evolved with their receptors and exhibit many conserved functions. Furthermore, brain areas are highly conserved with suggestions of deep anatomical homology between arthropods and vertebrates. Yet, behavior evolved more rapidly; even members of the same genus or species can differ in heritable behavior. The solution to the paradox involves changes in the compartmentalization, or subfunctionalization, of neuromodulation; neurons shift their expression of GPCRs and the content of monoamines and neuropeptides. Furthermore, parallel evolution of neuromodulatory signaling systems suggests a route for repeated evolution of similar behaviors.

Brain function is mediated by neural circuit connectivity, and elucidating the role of connections is aided by techniques to block their output. We developed cell-type-selective, reversible synaptic inhibition tools for mammalian neural circuits by leveraging G protein signaling pathways to suppress synaptic vesicle release. Here, we find that the pharmacologically selective designer Gi-protein-coupled receptor hM4D is a presynaptic silencer in the presence of its cognate ligand clozapine-N-oxide (CNO). Activation of hM4D signaling sharply reduced synaptic release probability and synaptic current amplitude. To demonstrate the utility of this tool for neural circuit perturbations, we developed an axon-selective hM4D-neurexin variant and used spatially targeted intracranial CNO injections to localize circuit connections from the hypothalamus to the midbrain responsible for feeding behavior. This synaptic silencing approach is broadly applicable for cell-type-specific and axon projection-selective functional analysis of diverse neural circuits.

Dysfunction of the basal ganglia produces severe deficits in the timing, initiation, and vigor of movement. These diverse impairments suggest a control system gone awry. In engineered systems, feedback is critical for control. By contrast, models of the basal ganglia highlight feedforward circuitry and ignore intrinsic feedback circuits. In this study, we show that feedback via axon collaterals of substantia nigra projection neurons control the gain of the basal ganglia output. Through a combination of physiology, optogenetics, anatomy, and circuit mapping, we elaborate a general circuit mechanism for gain control in a microcircuit lacking interneurons. Our data suggest that diverse tonic firing rates, weak unitary connections and a spatially diffuse collateral circuit with distinct topography and kinetics from feedforward input is sufficient to implement divisive feedback inhibition. The importance of feedback for engineered systems implies that the intranigral microcircuit, despite its absence from canonical models, could be essential to basal ganglia function. DOI: http://dx.doi.org/10.7554/eLife.02397.001.

An important strategy for efficient neural coding is to match the range of cellular responses to the distribution of relevant input signals. However, the structure and relevance of sensory signals depend on behavioral state. Here, we show that behavior modifies neural activity at the earliest stages of fly vision. We describe a class of wide-field neurons that provide feedback to the most peripheral layer of the Drosophila visual system, the lamina. Using in vivo patch-clamp electrophysiology, we found that lamina wide-field neurons respond to low-frequency luminance fluctuations. Recordings in flying flies revealed that the gain and frequency tuning of wide-field neurons change during flight, and that these effects are mimicked by the neuromodulator octopamine. Genetically silencing wide-field neurons increased behavioral responses to slow-motion stimuli. Together, these findings identify a cell type that is gated by behavior to enhance neural coding by subtracting low-frequency signals from the inputs to motion detection circuits.