Mutations in the retinal protein retinoschisin (RS1) cause progressive loss of vision in young males, a form of macular degeneration called X-linked retinoschisis (XLRS). We previously solved the structure of RS1, a 16-mer composed of paired back-to-back octameric rings. Here, we show by cryo-electron microscopy that RS1 16-mers can assemble into extensive branched networks. We classified the different configurations, finding four types of interaction between the RS1 molecules. The predominant configuration is a linear strand with a wavy appearance. Three less frequent types constitute the branch points of the network. In all cases, the "spikes" around the periphery of the double rings are involved in these interactions. In the linear strand, a loop (usually referred to as spike 1) occurs on both sides of the interface between neighboring molecules. Mutations in this loop suppress secretion, indicating the possibility of intracellular higher-order assembly. These observations suggest that branched networks of RS1 may play a stabilizing role in maintaining the integrity of the retina.

Optical imaging has become a powerful tool for studying brains . The opacity of adult brains makes microendoscopy, with an optical probe such as a gradient index (GRIN) lens embedded into brain tissue to provide optical relay, the method of choice for imaging neurons and neural activity in deeply buried brain structures. Incorporating a Bessel focus scanning module into two-photon fluorescence microendoscopy, we extended the excitation focus axially and improved its lateral resolution. Scanning the Bessel focus in 2D, we imaged volumes of neurons at high-throughput while resolving fine structures such as synaptic terminals. We applied this approach to the volumetric anatomical imaging of dendritic spines and axonal boutons in the mouse hippocampus, and functional imaging of GABAergic neurons in the mouse lateral hypothalamus .

Understanding the circuit mechanisms behind motion detection is a long-standing question in visual neuroscience. In , recent synapse-level connectomes in the optic lobe, particularly in ON-pathway (T4) receptive-field circuits, in concert with physiological studies, suggest an increasingly intricate motion model compared with the ubiquitous Hassenstein-Reichardt model, while our knowledge of OFF-pathway (T5) has been incomplete. Here we present a conclusive and comprehensive connectome that for the first time integrates detailed connectivity information for inputs to both T4 and T5 pathways in a single EM dataset covering the entire optic lobe. With novel reconstruction methods using automated synapse prediction suited to such a large connectome, we successfully corroborate previous findings in the T4 pathway and comprehensively identify inputs and receptive fields for T5. While the two pathways are likely evolutionarily linked and indeed exhibit many similarities, we uncover interesting differences and interactions that may underlie their distinct functional properties.

The insect mushroom body (MB) is a conserved brain structure that plays key roles in a diverse array of behaviors. The MB is the primary invertebrate model of neural circuits related to memory formation and storage, and its development, morphology, wiring, and function has been extensively studied. MBs consist of intrinsic Kenyon Cells that are divided into three major neuron classes (γ, α'/β' and α/β) and 7 cell subtypes (γd, γm, α'/β'ap, α'/β'm, α/βp, α/βs and α/βc) based on their birth order, morphology, and connectivity. These subtypes play distinct roles in memory processing, however the underlying transcriptional differences are unknown. Here, we used RNA sequencing (RNA-seq) to profile the nuclear transcriptomes of each MB neuronal cell subtypes. We identified 350 MB class- or subtype-specific genes, including the widely used α/β class marker and the α'/β' class marker Immunostaining corroborates the RNA-seq measurements at the protein level for several cases. Importantly, our data provide a full accounting of the neurotransmitter receptors, transporters, neurotransmitter biosynthetic enzymes, neuropeptides, and neuropeptide receptors expressed within each of these cell types. This high-quality, cell type-level transcriptome catalog for the MB provides a valuable resource for the fly neuroscience community.

Goal-directed animal behaviors are typically composed of sequences of motor actions whose order and timing are critical for a successful outcome. Although numerous theoretical models for sequential action generation have been proposed, few have been supported by the identification of control neurons sufficient to elicit a sequence. Here, we identify a pair of descending neurons that coordinate a stereotyped sequence of engagement actions during Drosophila melanogaster male courtship behavior. These actions are initiated sequentially but persist cumulatively, a feature not explained by existing models of sequential behaviors. We find evidence consistent with a ramp-to-threshold mechanism, in which increasing neuronal activity elicits each action independently at successively higher activity thresholds.

The ability of animals to accumulate sensory information across time is fundamental to decision-making. Using a mouse behavioral paradigm where navigational decisions are based on accumulating pulses of visual cues, I compared neural activity in primary visual (V1) to secondary visual and retrosplenial cortices. Even in V1, only a small fraction of neurons had sensory-like responses to cues. Instead, all areas were grossly similar in how neural populations contained a large variety of task-related information from sensory to cognitive, including cue timings, accumulated counts, place/time, decision and reward outcome. Across-trial influences were prevalent, possibly relevant to how animal behavior incorporates past contexts. Intriguingly, all these variables also modulated the amplitudes of sensory responses. While previous work often modeled the accumulation process as integration, the observed scaling of sensory responses by accumulated counts instead suggests a recursive process where sensory responses are gradually amplified. I show that such a multiplicative feedback-loop algorithm better explains psychophysical data than integration, particularly in how the performance transitions into following Weber-Fechner's Law only at high counts.