Serial-section electronmicroscopy (ssEM) is themethod of choice for studyingmacroscopic biological samples at extremely high resolution in three dimensions. In the nervous system, nanometer-scale images are necessary to reconstruct dense neural wiring diagrams in the brain, so called connectomes. In order to use this data, consisting of up to 10 individual EM images, it must be assembled into a volume, requiring seamless 2D stitching from each physical section followed by 3D alignment of the stitched sections. The high throughput of ssEM necessitates 2D stitching to be done at the pace of imaging, which currently produces tens of terabytes per day. To achieve this, we present a modular volume assembly software pipeline ASAP (Assembly Stitching and Alignment Pipeline) that is scalable to datasets containing petabytes of data and parallelized to work in a distributed computational environment. The pipeline is built on top of the Render (27) services used in the volume assembly of the brain of adult Drosophilamelanogaster (30). It achieves high throughput by operating on themeta-data and transformations of each image stored in a database, thus eliminating the need to render intermediate output. ASAP ismodular, allowing for easy incorporation of new algorithms without significant changes in the workflow. The entire software pipeline includes a complete set of tools for stitching, automated quality control, 3D section alignment, and final rendering of the assembled volume to disk. ASAP has been deployed for continuous stitching of several large-scale datasets of the mouse visual cortex and human brain samples including one cubic millimeter of mouse visual cortex (28; 8) at speeds that exceed imaging. The pipeline also has multi-channel processing capabilities and can be applied to fluorescence and multi-modal datasets like array tomography.

Training spiking recurrent neural networks on neuronal recordings or behavioral tasks has become a prominent tool to study computations in the brain. With an increasing size and complexity of neural recordings, there is a need for fast algorithms that can scale to large datasets. We present optimized CPU and GPU implementations of the recursive least-squares algorithm in spiking neural networks. The GPU implementation allows training networks to reproduce neural activity of an order of millions neurons at order of magnitude times faster than the CPU implementation. We demonstrate this by applying our algorithm to reproduce the activity of > 66, 000 recorded neurons of a mouse performing a decision-making task. The fast implementation enables efficient training of large-scale spiking models, thus allowing for in-silico study of the dynamics and connectivity underlying multi-area computations.

Morphogen gradients are used in developing embryos, where they subdivide a field of cells into territories characterized by distinct cell fate potentials. Such systems require both a spatially-graded distribution of the morphogen, and an ability to encode different responses at different target genes. However, the potential for different temporal responses is also present because morphogen gradients typically provide temporal cues, which may be a potential source of conflict. Thus, a low threshold response adapted for an early temporal onset may be inappropriate when the desired spatial response is a spatially-limited, high-threshold expression pattern. Here, we identify such a case with the Drosophila vnd locus, which is a target of the dorsal (dl) nuclear concentration gradient that patterns the dorsal/ventral (D/V) axis of the embryo. The vnd gene plays a critical role in the "ventral dominance" hierarchy of vnd, ind, and msh, which individually specify distinct D/V neural columnar fates in increasingly dorsal ectodermal compartments. The role of vnd in this regulatory hierarchy requires early temporal expression, which is characteristic of low-threshold responses, but its specification of ventral neurogenic ectoderm demands a relatively high-threshold response to dl. We show that the Neurogenic Ectoderm Enhancer (NEE) at vnd takes additional input from the complementary Dpp gradient via a conserved Schnurri/Mad/Medea silencer element (SSE) unlike NEEs at brk, sog, rho, and vn. These results show how requirements for conflicting temporal and spatial responses to the same gradient can be solved by additional inputs from complementary gradients.

Climbing over chasms larger than step size is vital to fruit flies, since foraging and mating are achieved while walking. Flies avoid futile climbing attempts by processing parallax-motion vision to estimate gap width. To identify neuronal substrates of climbing control, we screened a large collection of fly lines with temporarily inactivated neuronal populations in a novel high-throughput assay described here. The observed climbing phenotypes were classified; lines in each group are reported. Selected lines were further analysed by high-resolution video cinematography. One striking class of flies attempts to climb chasms of unsurmountable width; expression analysis guided us to C2 optic-lobe interneurons. Inactivation of C2 or the closely related C3 neurons with highly specific intersectional driver lines consistently reproduced hyperactive climbing whereas strong or weak artificial depolarization of C2/C3 neurons strongly or mildly decreased climbing frequency. Contrast-manipulation experiments support our conclusion that C2/C3 neurons are part of the distance-evaluation system.

Precise, repeatable genetic access to specific neurons via GAL4/UAS and related methods is a key advantage of Drosophila neuroscience. Neuronal targeting is typically documented using light microscopy of full GAL4 expression patterns, which generally lack the single-cell resolution required for reliable cell type identification. Here we use stochastic GAL4 labeling with the MultiColor FlpOut approach to generate cellular resolution confocal images at large scale. We are releasing aligned images of 74,000 such adult central nervous systems. An anticipated use of this resource is to bridge the gap between neurons identified by electron or light microscopy. Identifying individual neurons that make up each GAL4 expression pattern improves the prediction of split-GAL4 combinations targeting particular neurons. To this end we have made the images searchable on the NeuronBridge website. We demonstrate the potential of NeuronBridge to rapidly and effectively identify neuron matches based on morphology across imaging modalities and datasets.

Controlling the propagation of electromagnetic waves is important to a broad range of applications. Recent advances in controlling wave propagation in random scattering media have enabled optical focusing and imaging inside random scattering media. In this work, we propose and demonstrate a new method to deliver optical power more efficiently through scattering media. Drastically different from the random matrix characterization approach, our method can rapidly establish high efficiency communication channels using just a few measurements, regardless of the number of optical modes, and provides a practical and robust solution to boost the signal levels in optical or short wave communications. We experimentally demonstrated analog and digital signal transmission through highly scattering media with greatly improved performance. Besides scattering, our method can also reduce the loss of signal due to absorption. Experimentally, we observed that our method forced light to go around absorbers, leading to even higher signal improvement than in the case of purely scattering media. Interestingly, the resulting signal improvement is highly directional, which provides a new means against eavesdropping.

Enzyme kinetics measurements are a standard component of undergraduate biochemistry laboratories. The combination of serine hydrolases and fluorogenic enzyme substrates provides a rapid, sensitive, and general method for measuring enzyme kinetics in an undergraduate biochemistry laboratory. In this method, the kinetic activity of multiple protein variants is determined in parallel using a microplate reader, multichannel pipets, serial dilutions, and fluorogenic ester substrates. The utility of this methodology is illustrated by the measurement of differential enzyme activity in microplate volumes in triplicate with small protein samples and low activity enzyme variants. Enzyme kinetic measurements using fluorogenic substrates are, thus, adaptable for use with student-purified enzyme variants and for comparative enzyme kinetics studies. The rapid setup and analysis of these kinetic experiments not only provides advanced undergraduates with experience in a fundamental biochemical technique, but also provides the adaptability for use in inquiry-based laboratories.

Potassium ion (K+) plays a critical role as an essential electrolyte in all biological systems. Genetically encoded fluorescent K+ biosensors are promising tools to further improve our understanding of K+-dependent processes under normal and pathological conditions. Here, we report the crystal structure of a previously reported genetically encoded fluorescent K+ biosensor, GINKO1, in the K+-bound state. Using structure-guided optimization and directed evolution, we have engineered an improved K+ biosensor, designated GINKO2, with higher sensitivity and specificity. We have demonstrated the utility of GINKO2 for in vivo detection and imaging of K+ dynamics in multiple model organisms, including bacteria, plants, and mice.

Although most proteins conform to the classical one-structure/one-function paradigm, an increasing number of proteins with dual structures and functions are emerging. These fold-switching proteins remodel their secondary structures in response to cellular stimuli, fostering multi-functionality and tight cellular control. Accurate predictions of fold-switching proteins could both suggest underlying mechanisms for uncharacterized biological processes and reveal potential drug targets. Previously, we developed a prediction method for fold-switching proteins based on secondary structure predictions and structure-based thermodynamic calculations. Given the large number of genomic sequences without homologous experimentally characterized structures, however, we sought to predict fold-switching proteins from their sequences alone. To do this, we leveraged state-of-the-art secondary structure predictions, which require only amino acid sequences but are not currently designed to identify structural duality in proteins. Thus, we hypothesized that incorrect and inconsistent secondary structure predictions could be good initial predictors of fold-switching proteins. We found that secondary structure predictions of fold-switching proteins with solved structures are indeed less accurate than secondary structure predictions of non-fold-switching proteins with solved structures. These inaccuracies result largely from the conformations of fold-switching proteins that are underrepresented in the Protein Data Bank (PDB), and, consequently, the training sets of secondary structure predictors. Given that secondary structure predictions are homology-based, we hypothesized that decontextualizing the inaccurately-predicted regions of fold-switching proteins could weaken the homology relationships between these regions and their overpopulated structural representatives. Thus, we reran secondary structure predictions on these regions in isolation and found that they were significantly more inconsistent than in regions of non-fold-switching proteins. Thus, inconsistent secondary structure predictions can serve as a preliminary marker of fold switching. These findings have implications for genomics and the future development of secondary structure predictors.

Extant fold-switching proteins remodel their secondary structures and change their functions in response to cellular stimuli, regulating biological processes and affecting human health. In spite of their biological importance, these proteins remain understudied. Few representative examples of fold switchers are available in the Protein Data Bank, and they are difficult to predict. In fact, all 96 experimentally validated examples of extant fold switchers were stumbled upon by chance. Thus, predictive methods are needed to expedite the process of discovering and characterizing more of these shapeshifting proteins. Previous approaches require a solved structure or all-atom simulations, greatly constraining their use. Here, we propose a high-throughput sequence-based method for predicting extant fold switchers that transition from α-helix in one conformation to β-strand in the other. This method leverages two previous observations: (1) α-helix <-> β-strand prediction discrepancies from JPred4 are a robust predictor of fold switching, and (2) the fold-switching regions (FSRs) of some extant fold switchers have different secondary structure propensities when expressed in isolation (isolated FSRs) than when expressed within the context of their parent protein (contextualized FSRs). Combining these two observations, we ran JPred4 on the sequences of isolated and contextualized FSRs from 14 known extant fold switchers and found α-helix <->β-strand prediction discrepancies in every case. To test the overall robustness of this finding, we randomly selected regions of proteins not expected to switch folds (single-fold proteins) and found significantly fewer α-helix <-> β-strand prediction discrepancies (p < 4.2*10−20, Kolmogorov-Smirnov test). Combining these discrepancies with the overall percentage of predicted secondary structure, we developed a classifier that often robustly identifies extant fold switchers (Matthews Correlation Coefficient of 0.70). Although this classifier had a high false negative rate (6/14), its false positive rate was very low (1/211), suggesting that it can be used to predict a subset of extant fold switchers from billions of available genomic sequences.