Epithelial polarity is essential for proper tissue organization and function, yet the molecular mechanisms governing apical membrane formation during secretory epithelial development remain incompletely understood. Here, we investigate the role of the small GTPase Cdc42 in salivary gland acinar cell development using a mouse model designed to knock out Cdc42 specifically at the onset of acinar cell formation. Loss of Cdc42 resulted in defective apical membrane formation accompanied by accumulation of vesicles around the apical lumen. These vesicles contained the apical water channel AQP5 and the apical recycling endosome (ARE) marker Rab11a, while the basolateral transporter NKCC1 retained normal localization, indicating an apical-selective trafficking defect. Importantly, Cdc42 deficiency caused a selective 40% reduction in the expression of the SNARE protein VAMP2, while other vesicle trafficking proteins including VAMP8, SNAP23, and EEA1 remained unchanged. Our findings reveal that Cdc42 controls apical membrane formation by maintaining VAMP2 expression, which is essential for the fusion of Rab11a-positive recycling endosomes. The accumulation of fusion-incompetent AREs near the apical surface demonstrates the critical role of the Cdc42-VAMP2 pathway in epithelial development. These results provide new insights into how polarity regulators integrate vesicle trafficking and fusion machinery, and may have implications for understanding glandular diseases involving epithelial polarity defects.

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

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.

Power-law scaling in coarse-grained data suggests critical dynamics, but the true source of this scaling often remains unclear. Here, we analyze neural activity recorded during spatial navigation, reproducing power-law scaling under a phenomenological renormalization group (PRG) procedure that clusters units by activity similarity. Such scaling was previously linked to criticality. Here, we show that the iterative nature of the procedure itself leads to the emergence of power laws when applied to heterogeneous, non-interacting units obeying spatially structured activity without requiring critical interactions. Furthermore, the scaling exponents produced by heteregeneous non-interacting units match the observed exponents in recorded neural data. A simplified version of the PRG further reveals how heterogeneity smooths transitions across scales, mimicking critical behavior. The resulting exponents depend systematically on system and population size, predictions confirmed by subsampling the data.

Cryo-ultramicrotomy, developed by Bernhard in 1965 [1], has long been regarded as the pinnacle of achievement for electron microscopists. This technique allows biological samples to be sliced into ultrathin sections and examined in a cryo-electron microscope, revealing the most intricate subcellular structures without chemical fixation or staining. The advent of vitrification [2,3] and high-pressure freezing (HPF) technology [4,5] provided reliable methods for preserving cellular structures, and the introduction of diamond knife to cryo-ultramicrotomy [6] offering cryo-ultramicrotomists reassurance in consistency of the quality [7].

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.