Optogenetics is routinely used to activate and inactivate genetically defined neuronal populations in vivo. A second optogenetic revolution will occur when spatially distributed and sparse neural assemblies can be precisely manipulated in behaving animals.

Recent behavioral studies have given rise to two contrasting models for limited working memory capacity: a "discrete-slot" model in which memory items are stored in a limited number of slots, and a "shared-resource" model in which the neural representation of items is distributed across a limited pool of resources. To elucidate the underlying neural processes, we investigated a continuous network model for working memory of an analog feature. Our model network fundamentally operates with a shared resource mechanism, and stimuli in cue arrays are encoded by a distributed neural population. On the other hand, the network dynamics and performance are also consistent with the discrete-slot model, because multiple objects are maintained by distinct localized population persistent activity patterns (bump attractors). We identified two phenomena of recurrent circuit dynamics that give rise to limited working memory capacity. As the working memory load increases, a localized persistent activity bump may either fade out (so the memory of the corresponding item is lost) or merge with another nearby bump (hence the resolution of mnemonic representation for the merged items becomes blurred). We identified specific dependences of these two phenomena on the strength and tuning of recurrent synaptic excitation, as well as network normalization: the overall population activity is invariant to set size and delay duration; therefore, a constant neural resource is shared by and dynamically allocated to the memorized items. We demonstrate that the model reproduces salient observations predicted by both discrete-slot and shared-resource models, and propose testable predictions of the merging phenomenon.

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.

Collective behavior in cellular populations is coordinated by biochemical signaling networks within individual cells. Connecting the dynamics of these intracellular networks to the population phenomena they control poses a considerable challenge because of network complexity and our limited knowledge of kinetic parameters. However, from physical systems, we know that behavioral changes in the individual constituents of a collectively behaving system occur in a limited number of well-defined classes, and these can be described using simple models. Here, we apply such an approach to the emergence of collective oscillations in cellular populations of the social amoeba Dictyostelium discoideum. Through direct tests of our model with quantitative in vivo measurements of single-cell and population signaling dynamics, we show how a simple model can effectively describe a complex molecular signaling network at multiple size and temporal scales. The model predicts novel noise-driven single-cell and population-level signaling phenomena that we then experimentally observe. Our results suggest that like physical systems, collective behavior in biology may be universal and described using simple mathematical models.

Assigning valence—appeal or aversion—to gustatory stimuli and relaying it to higher-order brain regions to guide flexible behaviors is crucial to survival. Yet the neural circuit that transforms gustatory input into motivationally relevant signals remains poorly defined in any model system. In Drosophila melanogaster, substantial progress has been made in mapping the sensorimotor pathway for feeding and the architecture of the dopaminergic reinforcement system. However, where and how valence is first assigned to a taste has long been a mystery. Here, we identified a pair of subesophageal zone interneurons in Drosophila, termed Fox, that impart positive valence to sweet taste and convey this signal to the mushroom body, the fly’s associative learning center. We show that Fox neuron activity is necessary and sufficient to drive appetitive behaviors and can override a tastant’s intrinsic valence without impairing taste quality discrimination. Furthermore, Fox neurons transmit the positive valence to specific dopaminergic neurons that mediate appetitive memory formation. Our findings reveal a circuit mechanism that transforms sweet sensation into a reinforcing signal to support learned sugar responses. The Fox neurons exhibit a convergent–divergent “hourglass” circuit motif, acting as a bottleneck for valence assignment and distributing motivational signals to higher-order centers. This architecture confers both robustness and flexibility in reward processing—an organizational principle that may generalize across species.

Circadian rhythms play an essential role in many biological processes and surprisingly only three prokaryotic proteins are required to constitute a true post-translational circadian oscillator. The evolutionary history of the three Kai proteins indicates that KaiC is the oldest member and central component of the clock, with subsequent additions of KaiB and KaiA to regulate its phosphorylation state for time synchronization. The canonical KaiABC system in cyanobacteria is well understood, but little is known about more ancient systems that possess just KaiBC, except for reports that they might exhibit a basic, hourglass-like timekeeping mechanism. Here, we investigate the primordial circadian clock in Rhodobacter sphaeroides (RS) that contains only KaiBC to elucidate its inner workings despite the missing KaiA. Using a combination X-ray crystallography and cryo-EM we find a novel dodecameric fold for KaiCRS where two hexamers are held together by a coiled-coil bundle of 12 helices. This interaction is formed by the C-terminal extension of KaiCRS and serves as an ancient regulatory moiety later superseded by KaiA. A coiled-coil register shift between daytime- and nighttime-conformations is connected to the phosphorylation sites through a long-range allosteric network that spans over 160 Å. Our kinetic data identify the difference in ATP-to-ADP ratio between day and night as the environmental cue that drives the clock and further unravels mechanistic details that shed light on the evolution of self-sustained oscillators.

A cognitive compass enabling spatial navigation requires neural representation of heading direction (HD), yet the neural circuit architecture enabling this representation remains unclear. While various network models have been proposed to explain HD systems, these models rely on simplified circuit architectures that are incompatible with empirical observations from connectomes. Here we construct a novel network model for the fruit fly HD system that satisfies both connectome-derived architectural constraints and the functional requirement of continuous heading representation. We characterize an ensemble of continuous attractor networks where compass neurons providing local mutual excitation are coupled to inhibitory neurons. We discover a new mechanism where continuous heading representation emerges from combining symmetric and anti-symmetric activity patterns. Our analysis reveals three distinct realizations of these networks that all match observed compass neuron activity but differ in their predictions for inhibitory neuron activation patterns. Further, we found that deviations from these realizations can be compensated by cell-type-specific rescaling of synaptic weights, which could be potentially achieved through neuromodulation. This framework can be extended to incorporate the complete fly central complex connectome and could reveal principles of neural circuits representing other continuous quantities, such as spatial location, across insects and vertebrates.

Detailed descriptions of brain-scale sensorimotor circuits underlying vertebrate behavior remain elusive. Recent advances in zebrafish neuroscience offer new opportunities to dissect such circuits via whole-brain imaging, behavioral analysis, functional perturbations, and network modeling. Here, we harness these tools to generate a brain-scale circuit model of the optomotor response, an orienting behavior evoked by visual motion. We show that such motion is processed by diverse neural response types distributed across multiple brain regions. To transform sensory input into action, these regions sequentially integrate eye- and direction-specific sensory streams, refine representations via interhemispheric inhibition, and demix locomotor instructions to independently drive turning and forward swimming. While experiments revealed many neural response types throughout the brain, modeling identified the dimensions of functional connectivity most critical for the behavior. We thus reveal how distributed neurons collaborate to generate behavior and illustrate a paradigm for distilling functional circuit models from whole-brain data.

Neutrophil and macrophage (Mϕ) migration underpin the inflammatory response. However, the fast velocity, multidirectional instantaneous movement, and plastic, ever-changing shape of phagocytes confound high-resolution intravital imaging. Lattice lightsheet microscopy (LLSM) captures highly dynamic cell morphology at exceptional spatiotemporal resolution. We demonstrate the first extensive application of LLSM to leukocytes in vivo, utilizing optically transparent zebrafish, leukocyte-specific reporter lines that highlighted subcellular structure, and a wounding assay for leukocyte migration. LLSM revealed details of migrating leukocyte morphology, and permitted intricate, volumetric interrogation of highly dynamic activities within their native physiological setting. Very thin, recurrent uropod extensions must now be considered a characteristic feature of migrating neutrophils. LLSM resolved trailing uropod extensions, demonstrating their surprising length, and permitting quantitative assessment of cytoskeletal contributions to their evanescent form. Imaging leukocytes in blood vessel microenvironments at LLSM's spatiotemporal resolution displayed blood-flow-induced neutrophil dynamics and demonstrated unexpected leukocyte-endothelial interactions such as leukocyte-induced endothelial deformation against the intravascular pressure. LLSM of phagocytosis and cell death provided subcellular insights and uncovered novel behaviors. Collectively, we provide high-resolution LLSM examples of leukocyte structures (filopodia lamellipodia, uropod extensions, vesicles), and activities (interstitial and intravascular migration, leukocyte rolling, phagocytosis, cell death, and cytoplasmic ballooning). Application of LLSM to intravital leukocyte imaging sets the stage for transformative studies into the cellular and subcellular complexities of phagocyte biology.

Differential detergent fractionation (DDF) is frequently used to partition fresh cells and tissues into distinct compartments. We have tested whether DDF can reproducibly extract and fractionate cellular protein components from frozen tissues. Frozen kidneys were sequentially extracted with three different buffer systems. Analysis of the three fractions with liquid chromatography-tandem mass spectrometry (LC-MS/MS) identified 1693 proteins, some of which were common to all fractions and others of which were unique to specific fractions. Normalized spectral index (SI(N)) values obtained from these data were compared to evaluate both the reproducibility of the method and the efficiency of enrichment. SI(N) values between replicate fractions demonstrated a high correlation, confirming the reproducibility of the method. Correlation coefficients across the three fractions were significantly lower than those for the replicates, supporting the capability of DDF to differentially fractionate proteins into separate compartments. Subcellular annotation of the proteins identified in each fraction demonstrated a significant enrichment of cytoplasmic, cell membrane, and nuclear proteins in the three respective buffer system fractions. We conclude that DDF can be applied to frozen tissue to generate reproducible proteome coverage discriminating subcellular compartments. This demonstrates the feasibility of analyzing cellular compartment-specific proteins in archived tissue samples with the simple DDF method.