BACKGROUND: Recent advancements with induced pluripotent stem cell-derived (iPSC) retinal pigment epithelium (RPE) have made disease modeling and cell therapy for macular degeneration feasible. However, current techniques for intracellular electrophysiology - used to validate epithelial function - are painstaking and require manual skill; limiting experimental throughput.

NEW METHOD: A five-stage algorithm, leveraging advances in automated patch clamping, systematically derived and optimized, improves yield and reduces skill when compared to conventional, manual techniques.

RESULTS: The automated algorithm improves yield per attempt from 17% (manually, n = 23) to 22% (automated, n = 120) (chi-squared, p = 0.004). Specifically for RPE, depressing the local cell membrane by 6 μm and electroporating (buzzing) just prior to this depth (5 μm) maximized yield.

COMPARISON WITH EXISTING METHOD: Conventionally, intracellular epithelial electrophysiology is performed by manually lowering a pipette with a micromanipulator, blindly, towards a monolayer of cells and spontaneously stopping when the magnitude of the instantaneous measured membrane potential decreased below a predetermined threshold. The new method automatically measures the pipette tip resistance during the descent, detects the cell surface, indents the cell membrane, and briefly buzzes to electroporate the membrane while descending, overall achieving a higher yield than conventional methods.

CONCLUSIONS: This paper presents an algorithm for high-yield, automated intracellular electrophysiology in epithelia; optimized for human RPE. Automation reduces required user skill and training while, simultaneously, improving yield. This algorithm could enable large-scale exploration of drug toxicity and physiological function verification for numerous kinds of epithelia.

We demonstrate gas cluster ion beam scanning electron microscopy (SEM), in which wide-area ion milling is performed on a series of thick tissue sections. This three-dimensional electron microscopy technique acquires datasets with <10 nm isotropic resolution of each section, and these can then be stitched together to span the sectioned volume. Incorporating gas cluster ion beam SEM into existing single-beam and multibeam SEM workflows should be straightforward, increasing reliability while improving z resolution by a factor of three or more.

Optogenetics allows manipulations of genetically and spatially defined neuronal populations with excellent temporal control. However, neurons are coupled with other neurons over multiple length scales, and the effects of localized manipulations thus spread beyond the targeted neurons. We benchmarked several optogenetic methods to inactivate small regions of neocortex. Optogenetic excitation of GABAergic neurons produced more effective inactivation than light-gated ion pumps. Transgenic mice expressing the light-dependent chloride channel GtACR1 produced the most potent inactivation. Generally, inactivation spread substantially beyond the photostimulation light, caused by strong coupling between cortical neurons. Over some range of light intensity, optogenetic excitation of inhibitory neurons reduced activity in these neurons, together with pyramidal neurons, a signature of inhibition-stabilized neural networks ('paradoxical effect'). The offset of optogenetic inactivation was followed by rebound excitation in a light dose-dependent manner, limiting temporal resolution. Our data offer guidance for the design of optogenetics experiments.

Classically, G-protein-coupled receptors (GPCRs) are thought to activate G protein from the plasma membrane and are subsequently desensitized by β-arrestin (β-arr). However, some GPCRs continue to signal through G protein from internalized compartments, mediated by a GPCR-G protein-β-arr 'megaplex'. Nevertheless, the molecular architecture of the megaplex remains unknown. Here, we present its cryo-electron microscopy structure, which shows simultaneous engagement of human G protein and bovine β-arr to the core and phosphorylated tail, respectively, of a single active human chimeric β-adrenergic receptor with the C-terminal tail of the arginine vasopressin type 2 receptor (βVR). All three components adopt their canonical active conformations, suggesting that a single megaplex GPCR is capable of simultaneously activating G protein and β-arr. Our findings provide a structural basis for GPCR-mediated sustained internalized G protein signaling.

Animals employ diverse learning rules and synaptic plasticity dynamics to record temporal and statistical information about the world. However, the molecular mechanisms underlying this diversity are poorly understood. The anatomically defined compartments of the insect mushroom body function as parallel units of associative learning, with different learning rates, memory decay dynamics and flexibility (Aso & Rubin 2016). Here we show that nitric oxide (NO) acts as a neurotransmitter in a subset of dopaminergic neurons in . NO's effects develop more slowly than those of dopamine and depend on soluble guanylate cyclase in postsynaptic Kenyon cells. NO acts antagonistically to dopamine; it shortens memory retention and facilitates the rapid updating of memories. The interplay of NO and dopamine enables memories stored in local domains along Kenyon cell axons to be specialized for predicting the value of odors based only on recent events. Our results provide key mechanistic insights into how diverse memory dynamics are established in parallel memory systems.

In pursuit of food, hungry animals mobilize significant energy resources and overcome exhaustion and fear. How need and motivation control the decision to continue or change behavior is not understood. Using a single fly treadmill, we show that hungry flies persistently track a food odor and increase their effort over repeated trials in the absence of reward suggesting that need dominates negative experience. We further show that odor tracking is regulated by two mushroom body output neurons (MBONs) connecting the MB to the lateral horn. These MBONs, together with dopaminergic neurons and Dop1R2 signaling, control behavioral persistence. Conversely, an octopaminergic neuron, VPM4, which directly innervates one of the MBONs, acts as a brake on odor tracking by connecting feeding and olfaction. Together, our data suggest a function for the MB in internal state-dependent expression of behavior that can be suppressed by external inputs conveying a competing behavioral drive.

R7 UV photoreceptors (PRs) are divided into yellow (y) and pale (p) subtypes. yR7 PRs express the Dpr11 cell surface protein and are presynaptic to Dm8 amacrine neurons (yDm8) that express Dpr11's binding partner DIP-g, while pR7 PRs synapse onto DIP-g-negative pDm8. Dpr11 and DIP-g expression patterns define 'yellow' and 'pale' color vision circuits. We examined Dm8 neurons in these circuits by electron microscopic reconstruction and expansion microscopy. and mutations affect the morphologies of yDm8 distal ('home column') dendrites. yDm8 neurons are generated in excess during development and compete for presynaptic yR7 PRs, and interactions between Dpr11 and DIP-g are required for yDm8 survival. These interactions also allow yDm8 neurons to select yR7 PRs as their appropriate home column partners. yDm8 and pDm8 neurons do not normally compete for survival signals or R7 partners, but can be forced to do so by manipulation of R7 subtype fate.

Brains are notoriously hard to understand, and neuroscientists need all the tools they can get their hands on to have a realistic shot at it. Advances in machine learning are proving instrumental, illustrated by their recent use to shed light on navigational strategies implemented by zebrafish brains.

Neurons perform computations by integrating inputs from thousands of synapses-mostly in the dendritic tree-to drive action potential firing in the axon. One fruitful approach to studying this process is to record from neurons using patch-clamp electrodes, fill the recorded neurons with a substance that allows subsequent staining, reconstruct the three-dimensional architectures of the dendrites, and use the resulting functional and structural data to develop computer models of dendritic integration. Accurately producing quantitative reconstructions of dendrites is typically a tedious process taking many hours of manual inspection and measurement. Here we present ShuTu, a new software package that facilitates accurate and efficient reconstruction of dendrites imaged using bright-field microscopy. The program operates in two steps: (1) automated identification of dendritic processes, and (2) manual correction of errors in the automated reconstruction. This approach allows neurons with complex dendritic morphologies to be reconstructed rapidly and efficiently, thus facilitating the use of computer models to study dendritic structure-function relationships and the computations performed by single neurons.