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Structural variants (SVs) rearrange large segments of DNA1 and can have profound consequences in evolution and human disease2,3. As national biobanks, disease-association studies, and clinical genetic testing have grown increasingly reliant on genome sequencing, population references such as the Genome Aggregation Database (gnomAD)4 have become integral in the interpretation of single-nucleotide variants (SNVs)5. However, there are no reference maps of SVs from high-coverage genome sequencing comparable to those for SNVs. Here we present a reference of sequence-resolved SVs constructed from 14,891 genomes across diverse global populations (54% non-European) in gnomAD. We discovered a rich and complex landscape of 433,371 SVs, from which we estimate that SVs are responsible for 25–29% of all rare protein-truncating events per genome. We found strong correlations between natural selection against damaging SNVs and rare SVs that disrupt or duplicate protein-coding sequence, which suggests that genes that are highly intolerant to loss-of-function are also sensitive to increased dosage6. We also uncovered modest selection against noncoding SVs in cis-regulatory elements, although selection against protein-truncating SVs was stronger than all noncoding effects. Finally, we identified very large (over one megabase), rare SVs in 3.9% of samples, and estimate that 0.13% of individuals may carry an SV that meets the existing criteria for clinically important incidental findings7. This SV resource is freely distributed via the gnomAD browser8 and will have broad utility in population genetics, disease-association studies, and diagnostic screening., A large empirical assessment of sequence-resolved structural variants from 14,891 genomes across diverse global populations in the Genome Aggregation Database (gnomAD) provides a reference map for disease-association studies, population genetics, and diagnostic screening.

A Correction to this paper has been published: https://doi.org/10.1038/s41586-020-03176-6.

Transmembrane voltage and intracellular calcium concentration are coupled parameters essential to the function of neurons, cardiomyocytes, and other excitable cells. Here we introduce the Firefly-HR microscope for simultaneous optogenetic stimulation and voltage and calcium imaging with fluorescent proteins using three spectrally distinct visible color bands. Firefly-HR combines patterned stimulation, near-total internal reflection laser excitation through a prism located between the sample and a water-immersion objective, and concurrent imaging of three color channels. The microscope has efficient light collection, low fluorescent background, and a large field of view (0.24 x 1.2 mm @ 1000 frames/sec). We characterize optical crosstalk and demonstrate capabilities with three applications: (1) probing synaptically connected neuronal microcircuits, (2) examining the coupling between neuronal action potentials and calcium influx, and (3) studying the pharmacology of paced human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) via simultaneous recordings of voltage, calcium, and contraction.

A key challenge for establishing a phenotypic screen for neuronal excitability is measurement of membrane potential changes with high throughput and accuracy. Most approaches for probing excitability rely on low-throughput, invasive methods or lack cell-specific information. These limitations stimulated the development of novel strategies for characterizing the electrical properties of cultured neurons. Among these was the development of optogenetic technologies (Optopatch) that allow for stimulation and recording of membrane voltage signals from cultured neurons with single-cell sensitivity and millisecond temporal resolution. Neuronal activity is elicited using blue light activation of the channelrhodopsin variant 'CheRiff'. Action potentials and synaptic signals are measured with 'QuasAr', a rapid and sensitive voltage-indicating protein with near-infrared fluorescence that scales proportionately with transmembrane potential. This integrated technology of optical stimulation and recording of electrical signals enables investigation of neuronal electrical function with unprecedented scale and precision. © 2017 by John Wiley & Sons, Inc.

Neuronal physiology depends on a neuron’s ion channel composition and unique morphology. Variable ion channel compositions can produce similar neuronal physiologies across animals. Less is known regarding the morphological precision required to produce reliable neuronal physiology. Theoretical studies suggest that moraphology is tightly tuned to minimize wiring and conduction delay of synaptic events. We utilize high-resolution confocal microscopy and custom computational tools to characterize the morphologies of four neuron types in the stomatogastric ganglion (STG) of the crab Cancer borealis. Macroscopic branching patterns and fine cable properties are variable within and across neuron types. We compare these neuronal structures to synthetic minimal spanning neurite trees constrained by a wiring cost equation and find that STG neurons do not adhere to prevailing hypotheses regarding wiring optimization principles. In this highly modulated and oscillating circuit, neuronal structures appear to be governed by a space-filling mechanism that outweighs the cost of inefficient wiring. DOI: http://dx.doi.org/10.7554/eLife.22352.001

Recent theoretical and experimental work indicates that neurons tune themselves to maintain target levels of excitation by modulating ion channel expression and synaptic strengths. As a result, functionally equivalent circuits can produce similar activity despite disparate underlying network and cellular properties. To experimentally test the extent to which synaptic and intrinsic conductances can produce target activity in the presence of variability in neuronal intrinsic properties, we used the dynamic clamp to create hybrid two-cell circuits built from four types of stomatogastric neurons coupled to the same model Morris-Lecar neuron by reciprocal inhibition. We measured six intrinsic properties (input resistance, minimum membrane potential, firing rate in response to +1 nA of injected current, slope of the frequency-current curve, spike height, and spike voltage threshold) of dorsal gastric, gastric mill, lateral pyloric, and pyloric dilator neurons from male crabs of the species Cancer borealis. The intrinsic properties varied twofold to sevenfold in each cell type. We coupled each biological neuron to the Morris-Lecar model with seven different values of inhibitory synaptic conductance and also used the dynamic clamp to add seven different values of an artificial h-conductance, thus creating 49 different circuits for each biological neuron. Despite the variability in intrinsic excitability, networks formed from each neuron produced similar circuit performance at some values of synaptic and h-conductances. This work experimentally confirms results from previous modeling studies; tuning synaptic and intrinsic conductances can yield similar circuit outputs from neurons with variable intrinsic excitability.

Recent work argues that similar network performance can result from highly variable sets of network parameters, raising the question of whether neuromodulation can be reliable across individuals with networks with different sets of synaptic strengths and intrinsic membrane conductances. To address this question, we used the dynamic clamp to construct 2-cell reciprocally inhibitory networks from gastric mill (GM) neurons of the crab stomatogastric ganglion. When the strength of the artificial inhibitory synapses ( g syn ) and the conductance of an artificial I h ( g h ) were varied with the dynamic clamp, a variety of network behaviors resulted, including regions of stable alternating bursting. Maps of network output as a function of g syn and g h were constructed in normal saline and again in the presence of serotonin or oxotremorine. Both serotonin and oxotremorine depolarize and excite isolated individual GM neurons, but by different cellular mechanisms. Serotonin and oxotremorine each increased the size of the parameter regions that supported alternating bursting, and, on average, increased burst frequency. Nonetheless, in both cases some parameter sets within the sample space deviated from the mean population response and decreased in frequency. These data provide insight into why pharmacological treatments that work in most individuals can generate anomalous actions in a few individuals, and they have implications for understanding the evolution of nervous systems.

We develop two techniques to solve for the spatio-temporal neural activity patterns using Electroencephalogram (EEG) and Functional Magnetic Resonance Imaging (fMRI) data. EEG-only source localization is an inherently underconstrained problem, whereas fMRI by itself suffers from poor temporal resolution. Combining the two modalities transforms source localization into an overconstrained problem, and produces a solution with the high temporal resolution of EEG and the high spatial resolution of fMRI. Our first method uses fMRI to regularize the EEG solution, while our second method uses Independent Components Analysis (ICA) and realistic models of Blood Oxygen-Level Dependent (BOLD) signal to relate the EEG and fMRI data. The second method allows us to treat the fMRI and EEG data on equal footing by fitting simultaneously a solution to both data types. Both techniques avoid the need for ad hoc assumptions about the distribution of neural activity, although ultimately the second method provides more accurate inverse solutions.

We describe a new technique to fit conductance-based neuron models to intracellular voltage traces from isolated biological neurons. The biological neurons are recorded in current-clamp with pink (1/ f) noise injected to perturb the activity of the neuron. The new algorithm finds a set of parameters that allows a multicompartmental model neuron to match the recorded voltage trace. Attempting to match a recorded voltage trace directly has a well-known problem: mismatch in the timing of action potentials between biological and model neuron is inevitable and results in poor phenomenological match between the model and data. Our approach avoids this by applying a weak control adjustment to the model to promote alignment during the fitting procedure. This approach is closely related to the control theoretic concept of a Luenberger observer. We tested this approach on synthetic data and on data recorded from an anterior gastric receptor neuron from the stomatogastric ganglion of the crab Cancer borealis. To test the flexibility of this approach, the synthetic data were constructed with conductance models that were different from the ones used in the fitting model. For both synthetic and biological data, the resultant models had good spike-timing accuracy.

In complex nervous systems patterns of neuronal activity and measures of intrinsic neuronal excitability are often used as criteria for identifying and/or classifying neurons. We asked how well identification of neurons by conventional measures of intrinsic excitability compares with a measure of neuronal excitability derived from a neuron’s behavior in a dynamic clamp constructed two cell network. We used four cell types from the crab stomatogastric ganglion, the Pyloric Dilator (PD), Lateral Pyloric (LP), Gastric Mill (GM), and Dorsal Gastric (DG) neurons. Each neuron was evaluated for six conventional measures of intrinsic excitability (Intrinsic Properties; IPs). Additionally, each neuron was coupled by reciprocal inhibitory synapses made with the dynamic clamp to a Morris-Lecar model neuron and the resulting network was assayed for four measures of network activity (network activity properties; NAPs). We searched for linear combinations of IPs that correlated with each NAP, and combinations of NAPs that correlated with each IP. In the process we developed a method to correct for multiple correlations while searching for correlating features. When properly controlled for multiple correlations, four of the IPs were correlated with NAPs, and all four NAPs were correlated with IPs. Neurons were classified into cell types by training a linear classifier on sets of properties, or using k-medoids clustering. The IPs were modestly successful in classifying the neurons, and the NAPs were more successful. Combining the two measures did better than either measure alone, but not well enough to classify neurons with perfect accuracy, thus reiterating the need to combine electrophysiology with another, independent criterion for cell identification.

We compare preferential growth, critical phase transitions, and highly optimized tolerance (HOT) as mechanisms for generating power laws in the familiar and analytically tractable context of lattice percolation and forest fire models on the Cayley tree. All three mechanisms have been widely discussed in the context of complexity in natural and technological systems. This parallel study enables direct comparison of the mechanisms and associated lattice solutions. Criticality fits most naturally into the category of random processes, where power laws are a consequence of fluctuations in an ensemble with no intrinsic scale. The power laws in preferential growth can be understood in the context of competing exponential growth and decay processes. HOT generalizes this functional mechanism involving exponentials of exponentials to a broader class of nonexponential functions, which arise from optimization.