Intracellular recording allows measurement and perturbation of the membrane potential of identified neurons with sub-millisecond and sub-millivolt precision. This gives intracellular recordings a unique capacity to provide rich information about individual cells (e.g., high-resolution characterization of inputs, outputs, excitability, and structure). Hence, such recordings can elucidate the mechanisms that underlie fundamental phenomena, such as brain state, sparse coding, gating, gain modulation, and learning. Technical developments have increased the range of behaviors during which intracellular recording methods can be employed, such as in freely moving animals and head-fixed animals actively performing tasks, including in virtual environments. Such advances, and the combination of intracellular recordings with genetic and imaging techniques, have enabled investigation of the mechanisms that underlie neural computations during natural and trained behaviors.

Success in the projects aimed at providing an advanced understanding of the brain is directly predicated on making critical advances in nanotechnology. This Perspective addresses the unique interface of neuroscience and nanomaterials by considering the foundational problem of sensing neuron membrane voltage and offers a potential solution that may be facilitated by a prototypical nanomaterial. Despite substantial improvements, the visualization of instantaneous voltage changes within individual neurons, whether in cell culture or in vivo, at both the single-cell and network level at high speed remains complex and problematic. The unique properties of semiconductor quantum dots (QDs) have made them powerful fluorophores for bioimaging. What is not widely appreciated, however, is that QD photoluminescence is exquisitely sensitive to proximal electric fields. This property should be suitable for sensing voltage changes that occur in the active neuronal membrane. Here, we examine the potential role of QDs in addressing the important challenge of real-time optical voltage imaging.

Courtship rituals serve to reinforce reproductive barriers between closely related species. Drosophila melanogaster and Drosophila simulans exhibit reproductive isolation, owing in part to the fact that D. melanogaster females produce 7,11-heptacosadiene, a pheromone that promotes courtship in D. melanogaster males but suppresses courtship in D. simulans males. Here we compare pheromone-processing pathways in D. melanogaster and D. simulans males to define how these sister species endow 7,11-heptacosadiene with the opposite behavioural valence to underlie species discrimination. We show that males of both species detect 7,11-heptacosadiene using homologous peripheral sensory neurons, but this signal is differentially propagated to P1 neurons, which control courtship behaviour. A change in the balance of excitation and inhibition onto courtship-promoting neurons transforms an excitatory pheromonal cue in D. melanogaster into an inhibitory cue in D. simulans. Our results reveal how species-specific pheromone responses can emerge from conservation of peripheral detection mechanisms and diversification of central circuitry, and demonstrate how flexible nodes in neural circuits can contribute to behavioural evolution.

Targeted manipulation of activity in specific populations of neurons is important for investigating the neural circuit basis of behavior. Optogenetic approaches using light-sensitive microbial rhodopsins have permitted manipulations to reach a level of temporal precision that is enabling functional circuit dissection. As demand for more precise perturbations to serve specific experimental goals increases, a palette of opsins with diverse selectivity, kinetics, and spectral properties will be needed. Here, we introduce a novel approach of "topological engineering"-inversion of opsins in the plasma membrane-and demonstrate that it can produce variants with unique functional properties of interest for circuit neuroscience. In one striking example, inversion of a Channelrhodopsin variant converted it from a potent activator into a fast-acting inhibitor that operates as a cation pump. Our findings argue that membrane topology provides a useful orthogonal dimension of protein engineering that immediately permits as much as a doubling of the available toolkit.

Expansion microscopy (ExM) is a recently developed technique that enables nanoscale-resolution imaging of preserved cells and tissues on conventional diffraction-limited microscopes via isotropic physical expansion of the specimens before imaging. In ExM, biomolecules and/or fluorescent labels in the specimen are linked to a dense, expandable polymer matrix synthesized evenly throughout the specimen, which undergoes 3-dimensional expansion by ∼4.5 fold linearly when immersed in water. Since our first report, versions of ExM optimized for visualization of proteins, RNA, and other biomolecules have emerged. Here we describe best-practice, step-by-step ExM protocols for performing analysis of proteins (protein retention ExM, or proExM) as well as RNAs (expansion fluorescence in situ hybridization, or ExFISH), using chemicals and hardware found in a typical biology lab. Furthermore, a detailed protocol for handling and mounting expanded samples and for imaging them with confocal and light-sheet microscopes is provided. © 2018 by John Wiley & Sons, Inc.

A central tenet of biology is that globular proteins have a unique 3D structure under physiological conditions. Recent work has challenged this notion by demonstrating that some proteins switch folds, a process that involves remodeling of secondary structure in response to a few mutations (evolved fold switchers) or cellular stimuli (extant fold switchers). To date, extant fold switchers have been viewed as rare byproducts of evolution, but their frequency has been neither quantified nor estimated. By systematically and exhaustively searching the Protein Data Bank (PDB), we found ∼100 extant fold-switching proteins. Furthermore, we gathered multiple lines of evidence suggesting that these proteins are widespread in nature. Based on these lines of evidence, we hypothesized that the frequency of extant fold-switching proteins may be underrepresented by the structures in the PDB. Thus, we sought to identify other putative extant fold switchers with only one solved conformation. To do this, we identified two characteristic features of our ∼100 extant fold-switching proteins, incorrect secondary structure predictions and likely independent folding cooperativity, and searched the PDB for other proteins with similar features. Reassuringly, this method identified dozens of other proteins in the literature with indication of a structural change but only one solved conformation in the PDB. Thus, we used it to estimate that 0.5-4% of PDB proteins switch folds. These results demonstrate that extant fold-switching proteins are likely more common than the PDB reflects, which has implications for cell biology, genomics, and human health.

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.

Cellular esterases catalyze many essential biological functions by performing hydrolysis reactions on diverse substrates. The promiscuity of esterases complicates assignment of their substrate preferences and biological functions. To identify universal factors controlling esterase substrate recognition, we designed a 32-member structure-activity relationship (SAR) library of fluorogenic ester substrates and used this library to systematically interrogate esterase preference for chain length, branching patterns, and polarity to differentiate common classes of esterase substrates. Two structurally homologous bacterial esterases were screened against this library, refining their previously broad overlapping substrate specificity. esterase ybfF displayed a preference for γ-position thioethers and ethers, whereas Rv0045c from displayed a preference for branched substrates with and without thioethers. We determined that this substrate differentiation was partially controlled by individual substrate selectivity residues Tyr119 in ybfF and His187 in Rv0045c; reciprocal substitution of these residues shifted each esterase's substrate preference. This work demonstrates that the selectivity of esterases is tuned based on transition state stabilization, identifies thioethers as an underutilized functional group for esterase substrates, and provides a rapid method for differentiating structural isozymes. This SAR library could have multi-faceted future applications including in vivo imaging, biocatalyst screening, molecular fingerprinting, and inhibitor design.

Electron crystallography is widespread in material science applications, but for biological samples its use has been restricted to a handful of examples where two-dimensional (2D) crystals or helical samples were studied either by electron diffraction and/or imaging. Electron crystallography in cryoEM, was developed in the mid-1970s and used to solve the structure of several membrane proteins and some soluble proteins. In 2013, a new method for cryoEM was unveiled and named Micro-crystal Electron Diffraction, or MicroED, which is essentially three-dimensional (3D) electron crystallography of microscopic crystals. This method uses truly 3D crystals, that are about a billion times smaller than those typically used for X-ray crystallography, for electron diffraction studies. There are several important differences and some similarities between electron crystallography of 2D crystals and MicroED. In this review, we describe the development of these techniques, their similarities and differences, and offer our opinion of future directions in both fields.

Extracting a connectome from an electron microscopy (EM) data set requires identification of neurons and determination of synapses between neurons. As manual extraction of this information is very time-consuming, there has been extensive research effort to automatically segment the neurons to help guide and eventually replace manual tracing. Until recently, there has been comparatively less research on automatically detecting the actual synapses between neurons. This discrepancy can, in part, be attributed to several factors: obtaining neuronal shapes is a prerequisite first step in extracting a connectome, manual tracing is much more time-consuming than annotating synapses, and neuronal contact area can be used as a proxy for synapses in determining connections.
However, recent research has demonstrated that contact area alone is not a sufficient predictor of synaptic connection. Moreover, as segmentation has improved, we have observed that synapse annotation is consuming a more significant fraction of overall reconstruction time. This ratio will only get worse as segmentation improves, gating overall possible speed-up. Therefore, we address this problem by developing algorithms that automatically detect pre-synaptic neurons and their post-synaptic partners. In particular, pre-synaptic structures are detected using a Deep and Wide Multiscale Recursive Network, and post-synaptic partners are detected using a MLP with features conditioned on the local segmentation.
This work is novel because it requires minimal amount of training, leverages advances in image segmentation directly, and provides a complete solution for polyadic synapse detection. We further introduce novel metrics to evaluate our algorithm on connectomes of meaningful size. These metrics demonstrate that complete automatic prediction can be used to effectively characterize most connectivity correctly.