Persistent internal states are important for maintaining survival-promoting behaviors, such as aggression. In female Drosophila melanogaster, we have previously shown that individually activating either aIPg or pC1d cell types can induce aggression. Here we investigate further the individual roles of these cholinergic, sexually dimorphic cell types, and the reciprocal connections between them, in generating a persistent aggressive internal state. We find that a brief 30-second optogenetic stimulation of aIPg neurons was sufficient to promote an aggressive internal state lasting at least 10 minutes, whereas similar stimulation of pC1d neurons did not. While we previously showed that stimulation of pC1e alone does not evoke aggression, persistent behavior could be promoted through simultaneous stimulation of pC1d and pC1e, suggesting an unexpected synergy of these cell types in establishing a persistent aggressive state. Neither aIPg nor pC1d show persistent neuronal activity themselves, implying that the persistent internal state is maintained by other mechanisms. Moreover, inactivation of pC1d did not significantly reduce aIPg-evoked persistent aggression, arguing that the aggressive state did not depend on pC1d-aIPg recurrent connectivity. Our results suggest the need for alternative models to explain persistent female aggression.

Preprint: https://www.biorxiv.org/content/10.1101/2023.06.07.543722v2

When foraging for resources, animals must often sample many options that yield reward with different probabilities. In such scenarios, many animals have been shown to exhibit “matching”, an empirical behavioral observation in which the fraction of rewarded samples is the same across all options. While previous work has shown that matching can be optimal in environments with diminishing returns, this condition is not sufficient to determine optimality. Furthermore, while diminishing returns naturally arise when resources in the environment deplete and take time to be replenished, the specific form of diminishing returns depends on the temporal structure and statistics of the replenishment process. Here, we explore how these environmental properties affect whether matching is optimal. By considering an agent that samples different options with fixed sampling rates, we derive the probability of collecting a reward as a function of these sampling rates for different types of environments, and we analytically determine the conditions under which the optimal sampling-rate policy exhibits matching. When all options are governed by the same replenishment dynamics, we find that optimality gives rise to matching across a wide range of environments. However, when these dynamics differ across options, the optimal policy can deviate from matching. In such cases, the rank-ordering of observed reward probabilities depends only on the qualitative nature of the replenishment process, but not on the specific replenishment rates. As a result, the optimal policy can exhibit underor over-matching depending on how rewarding the different options are. We use this result to identify environmental settings under which performance differs substantially between matching and optimality. Finally, we show how fluctuations in these replenishment rates—which can represent either environmental stochasticity or the agent’s internal uncertainty about the environment—can accentuate deviations between optimality and matching. Together, these findings deepen our understand of the relationship between environmental variability and behavioral optimality, and they provide testable experimental predictions across a wide range of environmental settings.

Fluorescence microscopy, a key driver for progress in the life sciences, faces limitations due to the microscope’s optics, fluorophore chemistry, and photon exposure limits, necessitating trade-offs in imaging speed, resolution, and depth. Here, we introduce MicroSplit, a computational multiplexing technique based on deep learning that allows multiple cellular structures to be imaged in a single fluorescent channel and then unmixed computationally, allowing faster imaging and reduced photon exposure. We show that MicroSplit efficiently separates up to four superimposed noisy structures into distinct denoised fluorescent image channels. Furthermore, using Variational Splitting Encoder-Decoder (VSE) networks, our approach can sample diverse predictions from a trained posterior of solutions. The diversity of these samples scales with the uncertainty in a given input, allowing us to estimate the true prediction errors by computing the variability between posterior samples. We demonstrate the robustness of MicroSplit across various datasets and noise levels and show its utility to image more, image faster, and improve downstream analysis. We provide MicroSplit along with all associated training and evaluation datasets as open resources, enabling life scientists to benefit from the potential of computational multiplexing and accelerate the pace of scientific discovery.

Understanding how neural circuits give rise to behavior requires comprehensive knowledge of neuronal morphology, connectivity, and function. Atlas platforms play a critical role in enabling the visualization, exploration, and dissemination of such information. Here, we present FishExplorer, an interactive and expandable community platform designed to integrate and analyze multimodal brain data from larval zebrafish. FishExplorer supports datasets acquired through light microscopy (LM), electron microscopy (EM), and X-ray imaging, all co-registered within a unified spatial coordinate system which enables seamless comparison of neuronal morphologies and synaptic connections. To further assist circuit analysis, FishExplorer includes a suite of tools for querying and visualizing connectivity at the whole-brain scale. By integrating data from recent large-scale EM reconstructions (presented in companion studies), FishExplorer enables researchers to validate circuit models, explore wiring principles, and generate new hypotheses. As a continuously evolving resource, FishExplorer is designed to facilitate collaborative discovery and serve the growing needs of the teleost neuroscience community.

In most animals, a relatively small number of descending neurons (DNs) connect higher brain centers in the animal’s head to circuits and motor neurons (MNs) in the nerve cord of the animal’s body that effect movement of the limbs. To understand how brain signals generate behavior, it is critical to understand how these descending pathways are organized onto the body MNs. In the fly, Drosophila melanogaster, MNs controlling muscles in the leg, wing, and other motor systems reside in a ventral nerve cord (VNC), analogous to the mammalian spinal cord. In companion papers, we introduced a densely-reconstructed connectome of the Drosophila Male Adult Nerve Cord (MANC, (Takemura et al., 2024)), including cell type and developmental lineage annotation (Marin et al., 2024), which provides complete VNC connectivity at synaptic resolution. Here, we present a first look at the organization of the VNC networks connecting DNs to MNs based on this new connectome information. We proofread and curated all DNs and MNs to ensure accuracy and reliability, then systematically matched DN axon terminals and MN dendrites with light microscopy data to link their VNC morphology with their brain inputs or muscle targets. We report both broad organizational patterns of the entire network and fine-scale analysis of selected circuits of interest. We discover that direct DN-MN connections are infrequent and identify communities of intrinsic neurons linked to control of different motor systems, including putative ventral circuits for walking, dorsal circuits for flight steering and power generation, and intermediate circuits in the lower tectulum for coordinated action of wings and legs. Our analysis generates hypotheses for future functional experiments and, together with the MANC connectome, empowers others to investigate these and other circuits of the Drosophila ventral nerve cord in richer mechanistic detail.

Spatial multiomic profiling has been transforming the understanding of local tumor ecosystems. Yet, the spatial analyses of tumor-immune interactions at systemic levels, such as in liquid biopsies, are challenging. Within the last 10 years, we have longitudinally collected nearly 3,000 patient blood samples for multiplexing imaging of circulating tumor cells (CTCs) and their interactions with white blood cells (WBCs). Multicellular CTC clusters exhibit enhanced metastatic potential. The detection of CTCs and characterization of tumor immune ecosystems are constrained by (1) low frequency of CTCs in blood samples; (2) specific lineages of immune cells are not recognized by limited channels of current imaging methods, (3) reliance on labor-intensive manual analysis slows down the discovery of biomarkers for predicting therapy response and survival in cancer patients. We hypothesize that an AI-powered platform will accelerate the lineage and spatial characterization of tumor immune ecosystems for prognostic evaluations.

Understanding how neurons integrate into developing circuits and contribute to functional activity is essential for decoding brain development and plasticity. However, current methods to study neuronal integration often suffer from low throughput, limited spatiotemporal resolution, or invasive procedures that hinder in vivo functional analysis. To overcome these challenges, we present a birthdate-labeling strategy, named CHLOK, based on HaloTag technology and a broad palette of fluorescent synthetic dyes. This approach enables precise multicolor labeling of neurons according to their maturation stage and allows flexible integration into functional assays through compatibility with calcium imaging and optogenetics. We validated CHLOK by mapping birthdate-resolved neuronal activity in the developing visual and motor systems of zebrafish larvae. Our results reveal distinct functional contributions of early- versus late-born neurons, providing new insights into the temporal dynamics of circuit formation. Furthermore, we demonstrate the versatility of this approach, showcasing age-specific multicolor calcium and voltage imaging as well as optogenetic manipulation. By overcoming key limitations of existing techniques, CHLOK offers a powerful, versatile and non-invasive tool for studying neural integration, circuit development and function in vivo.

Liposomes are essential vehicles for membrane protein reconstitution and drug delivery, making them vital tools in both in vivo and in vitro studies. However, the lack of robust techniques for the precise arrangement of these synthetic vesicles limits their potential applications. Here, we present a modular polymerization platform based on square DNA origami to template the formation and organization of liposomes. By programming the sequence, number, position, chirality, and flexibility of sticky ends on each square, we assemble uniformly sized liposomes into diverse two-dimensional (2D) arrays, as well as finite lattices and rings. Additionally, we demonstrate stepwise assembly and targeted disassembly, enabling dynamic structural control. These complex liposome architectures represent a significant advancement in the fields of biotechnology, nanotechnology, and bottom-up biology.

Synchronous neuronal ensembles play a pivotal role in the consolidation of long-term memory in the hippocampus. However, their organization during the acquisition of spatial memory remains less clear. In this study, we used neuronal population voltage imaging to investigate the synchronization patterns of CA1 pyramidal neuronal ensembles during the exploration of a new environment, a critical phase for spatial memory acquisition. We found synchronous ensembles comprising approximately 40% of CA1 pyramidal neurons, firing simultaneously in brief windows (∼25ms) during immobility and locomotion in novel exploration. Notably, these synchronous ensembles were not associated with contralateral ripple oscillations but were instead phase-locked to theta waves recorded in the contralateral CA1 region. Moreover, the subthreshold membrane potentials of neurons exhibited coherent intracellular theta oscillations with a depolarizing peak at the moment of synchrony. Among newly formed place cells, pairs with more robust synchronization during locomotion displayed more distinct place-specific activities. These findings underscore the role of synchronous ensembles in coordinating place cells of different place fields.

During brain development, synapses are initially formed in excess and are later eliminated in an activity-dependent manner, with weak synapses being preferentially removed. Previous studies identified glia as mediators of synapse removal, but it is unclear how glia specifically target weak synapses. Here we show that, in the developing mouse visual pathway, inhibiting synaptic transmission induces postsynaptic activation of caspase-3. Caspase-3 is essential for synapse elimination driven by both spontaneous and experience-dependent neural activity. Synapse weakening-induced caspase-3 activation determines the specificity of synapse elimination mediated by microglia but not astrocytes. Furthermore, in a mouse model of Alzheimer’s disease, caspase-3 deficiency protects against synapse loss induced by amyloid-β deposition. Our results reveal caspase-3 activation as a key step in activity-dependent synapse elimination during development and synapse loss in neurodegeneration.

bioRxiv preprint: https://doi.org/10.1101/2024.08.02.606316