Biology subjects

Wei, D.

Publications and source records attributed to Wei, D..

At least 19 recordsLinked to original sources

bioRxiv2026

RnR-ExM: Robust Non-rigid Registration Challenge for Expansion Microscopy Volumes

Expansion Microscopy (ExM) enables nanoscale imaging of biological samples while preserving structural and molecular details. The Robust Non-rigid Registration Challenge for ExM (RnR-ExM) was launched to benchmark 3D registration algorithms on real-world ExM datasets. The challenge released 24 pairs of high-resolution 3D volumes from zebrafish, mouse, and C.~elegans, deliberately selected for their difficulty with standard alignment methods. To evaluate performance, we provided curated segmentation masks of nuclei and blood vessels as landmarks for half of the dataset. To encourage learning-based approaches, we released code for generating synthetic training data reflecting the deformation and appearance of the unlabeled pairs. By submission, 168 participants registered, with six teams submitting results by the deadline; the top three were invited to present at ISBI 2023. Accurate registration of ExM volumes is essential for downstream biological analyses such as multiplexed molecular mapping and cell-type-specific structural profiling. In this paper, we detail the methods and results of these top teams, highlighting current capabilities and future directions for 3D registration in ExM. All datasets and code are available at https://rnr-exm.grand-challenge.org/

neuroscience↗

bioRxiv2026

Charting sex divergence in the human brain across the lifespan

Biological sex fundamentally shapes human brain organization, but sex-specific normative neuroanatomical trajectories across the lifespan remain largely uncharted. Here, we constructed independent, sex-specific lifespan brain charts using structural neuroimaging data from 59,915 healthy individuals (29,760 males and 30,155 females) ranging in age from 266 postconception days to 100 years. By examining 296 structural phenotypes across global, cortical, and subcortical measures, these models revealed widespread sex differences in maturational timing, with males reaching peak milestones later than females. These trajectories demonstrate that sex differences evolve dynamically, with phenotype-specific windows of emergence and maximal separation. Compared with conventional sex-pooled references, sex-specific models achieved superior predictive accuracy and reduced misestimation of individual deviations in healthy populations. Across five neuropsychiatric conditions, sex-specific models improved the detection of extreme deviations and revealed both shared and sex-dependent patterns of disorder-related neuroanatomical abnormalities. These sex-specific charts establish tailored normative references for assessing brain development, ageing, and disease.

neuroscience↗

bioRxiv2026

DVT-primed neutrophils reshape the brain microenvironment to promote brain metastasis

Brain metastasis (BrM) is a devastating complication of triple-negative breast cancer (TNBC), yet how host macroenvironmental conditions influence brain metastatic susceptibility remains poorly understood. Here, we investigated the impact of deep vein thrombosis (DVT), a common complication in patients with cancer, on TNBC brain metastasis. Analysis of a large clinical cohort identified DVT as an independent risk factor for BrM in patients with metastatic breast cancer. Using two syngeneic TNBC models, we demonstrated that DVT selectively enhanced brain metastasis without significantly affecting primary tumor growth or lung metastasis. Mechanistically, DVT increased neutrophil accumulation in the brain metastatic microenvironment, and neutrophil depletion completely abrogated the metastasis-promoting effect of DVT. Single-cell RNA sequencing of CD45+ cells in peripheral blood revealed that DVT reprogrammed circulating neutrophils toward migratory and inflammatory transcriptomic profiles characterized by neutrophil extracellular trap (NET) formation. DVT increased CXCR2 expression on circulating neutrophils and enhanced neutrophil recruitment in brain. Moreover, DVT significantly increased circulating NETs. Collectively, our findings identify DVT as a systemic driver of TNBC brain metastasis and reveal that DVT primes circulating neutrophils toward enhanced vascular recruitment and NET formation, providing a mechanistic link between cancer-associated thrombosis and brain metastatic susceptibility.

molecular biology↗

bioRxiv2026

Searching across working memory and perception

Searching for objects of interest is an essential and pervasive activity in adaptive cognition. In everyday situations, we often search for relevant content in our surroundings and in mind at the same time, yet most experimental tasks consider search behavior in perception or in working memory separately. To address the gap, we designed tasks to investigate perceptual and working-memory search proceeding in tandem. Participants viewed successive arrays of two real-world objects, separated by a delay, and then were prompted to search for a specific object, while the second array remained visible. The probed target was equally likely to be in the previously encoded array, available only in working memory, or in the visible array. Behavioral performance across four experiments with different response demands and delays yielded a consistent pattern of findings. Accuracy was better for detecting perceptual targets than working-memory targets. However, search speed was equivalent for targets in both domains in all cases, suggesting parallel search functions co-existing across the two domains. The final experiment combined continuous color reproduction with eye tracking and neural recordings. Neural measures of posterior alpha lateralization, tracking the neural selection of working-memory and perceptual targets, suggested parallel search functions developing with similar time courses. Intriguingly, oculomotor gaze shifts showed a divergent pattern, with earlier gaze shifts associated with perceptual targets and later shifts with working-memory targets. Overall, the present studies establish a means for investigating search across working-memory and perceptual domains, suggest the co-existence of parallel search mechanisms, and invite new lines of enquiry.

neuroscience↗

bioRxiv2026

The transcription factor Bhlhe40 promotes inflammatory functions of ILC2s through inducing GM-CSF while inhibiting IL-10 expression

Group 2 innate lymphoid cells (ILC2s) regulate type 2 immune responses partly by recruiting eosinophils, but the mechanisms underlying this process remain incompletely understood. Although both ILC2s and type 2 T helper (Th2) cells are capable of expressing IL-5, a cytokine critical for recruiting eosinophils, ILC2s are more potent than Th2 cells in this process. Here, we show that the transcription factor Bhlhe40 promotes GM-CSF production in ILC2s, and Bhlhe40 expression in ILC2s is required for efficient eosinophil recruitment during both papain- and helminth-induced type 2 immune responses. However, Bhlhe40 deficiency in ILC2s had no effect on the production of classical type 2 cytokines, including IL-4, IL-5 and IL-13, despite Bhlhe40 being required for type 2 cytokine production by Th2 cells. Furthermore, in contrast to ILC2s, Th2 cells produced little GM-CSF and administration of GM-CSF rescued eosinophil recruitment in ILC2-specific Bhlhe40-deficient mice. In addition to promoting GM-CSF expression, Bhlhe40 repressed IL-10 production in ILC2s as it did in Th2 cells, particularly during chronic inflammation. Single-cell transcriptomic analyses further supported this regulatory network, and the ChIP-Seq data revealed direct binding of Bhlhe40 to the enhancer and promoter regions within the Il10 and Csf2 loci. Collectively, our findings show that Bhlhe40 exerts distinct gene regulatory functions in ILC2s and Th2 cells, and Bhlhe40 modulates the inflammatory and anti-inflammatory functions of ILC2s by promoting GM-CSF while suppressing IL-10 production.

immunology↗

bioRxiv2026

Simultaneous brain-wide single-cell recording resolves spatiotemporal memory architecture

Many fundamental mammalian brain functions emerge from the coordinated activity of cells distributed across large, brain-wide networks. To understand these processes in healthy and diseased states, ideally one would simultaneously measure and analyze single-cell activity at the brain-wide scale, an enduring challenge for live-measurement approaches that often face an inherent tradeoff between spatial resolution and scale. Here, we present GLOBE (sinGle-cell spatiotemporaL recOrding Brain-widE), a technology for brain-wide single-cell recording of cellular activity in vivo with spatiotemporal resolution, physiological sensitivity, and parallelization-accelerated readout. GLOBE leverages genetically encoded intracellular protein tape recorders and a high-throughput computational platform for integrated image and signal analysis. GLOBE records analog signal amplitudes across a continuous time axis, requires only standard light microscopy for in situ readout, and is compatible with expansion microscopy and RNA readouts. We applied GLOBE to simultaneously record transcriptional activity of the immediate early gene Fos in up to 219,703 neurons simultaneously across a single mouse brain over 5.5 continuous days, with a timestamp precision of 3.1-6.7 hours (median absolute error), a local recording density of 69-90% of neurons per imaging field of view, and a post-mortem imaging readout speed of 2.9 seconds per neuron on average. GLOBE resolves the brain-wide spatiotemporal structure of single-cell activity, revealing that Fos transcriptional dynamics associated with fear learning and memory retrieval are distributed across the brain with region-specific temporal heterogeneity, and that the variance of this structure scales down as the number of sampled cells increases. We envision GLOBE to have broad applications for dissecting and decoding physiological and pathological processes at the brain-wide scale.

neuroscience↗

bioRxiv2026

NuGraph: Graph-Based Reasoning over 3D Primitives for Nucleus Segmentation Correction

Correcting segmentation errors in large-scale 3D nuclei reconstructions requires reasoning about which fragments belong to the same nucleus across densely packed regions. Existing correction methods rely on local pairwise fragment matching, which cannot resolve the global topology of nuclear clusters and fails to recover missing morphology. We propose NO_SCPLOWUC_SCPLOWGO_SCPLOWRAPHC_SCPLOW, a graph-based reasoning framework that operates over atomic 3D primitives obtained by decomposing erroneous masks. NO_SCPLOWUC_SCPLOWGO_SCPLOWRAPHC_SCPLOW encodes primitive geometry via a 3D point-cloud backbone and performs global relational reasoning through graph attention, capturing inter-primitive dependencies across entire clusters rather than isolated pairs. A primitive-proposal contrastive loss aligns local primitive features with nucleuslevel semantics, improving grouping accuracy in dense regions. The resulting proposals are then refined by a shaperefinement network that predicts signed distance fields to restore smooth morphology. To train without manual error annotations, we develop a self-supervised data engine that synthesizes realistic segmentation errors from clean nuclei labels. To benchmark correction at brain scale, we curate NucEMFix, the first brain-wide EM benchmark of nuclei error cases across FAFB and MICrONS (8,000+ annotated error nuclei). NO_SCPLOWUC_SCPLOWGO_SCPLOWRAPHC_SCPLOW attains 87.99% F1 on NucEMFix-F (FAFB) and 86.20% on NucEMFix-M (MICrONS), outperforming both re-segmentation baselines (e.g., +8.6% over nnU-Net) and pairwise correction methods, while reducing curation effort by over 100x relative to manual proofreading. Code and data are available at https://mingzhiwang618.github.io/NucEMFix.

neuroscience↗

bioRxiv2026

NucleoNet and DropNet: Generalist deep learning models for instance segmentation of nuclei and lipid droplets from electron microscopy images

Automating cellular organelle segmentation is key to increasing the throughput in electron microscopy (EM) and volume EM (vEM) workflows. Deep learning (DL) has accelerated this process, but model development has predominately centered on mitochondria, partly because of a scarcity of suitable training datasets for other features. Here, we crowdsourced the manual step of labeling nuclei and lipid droplets (LDs) from complex cellular EM images and trained Panoptic DeepLab (PDL) models on these large, heterogenous annotated datasets as well as on publicly available vEM datasets. NucleoNet and DropNet, the resulting instance segmentation models for nuclei and LDs, respectively, yield high-quality results on varied benchmarks. We applied these models to quantify differences between 2D and 3D in vitro cancer models versus in vivo tumors, highlighting a path toward robust quantitation in EM. NucleoNet and DropNet are publicly available on our napari plugin, empanada v1.2, for easy point-and-click segmentation of 2D and 3D cellular EM images.

cell biology↗

bioRxiv2025

Molecular mechanism decipher of Alzheimer's disease based on single-cell RNA-seq information

BackgroundAlzheimers disease (AD) is a most common age-related neurodegenerative disease with no effective treatments, is one of the leading contributions to global disease burden. AD is represented by complicated biological mechanisms and complexity of brain tissues, often manifested histologically by the parenchymal deposition of amyloid-beta (A{beta}) plaques, the formation of neurofibrillary tangles and neuroinflammation. The etiology of AD is likely due to complex interactions among different brain cell types leading to interconnected cellular pathologies. Single-cell RNA sequencing (RNA-seq) technologies enable unbiased characterization of cell types and states, transitions from normal to disease and responses to therapies, providing an effective tool to systematically resolve cellular heterogeneity in AD. MethodsIn this study, to investigate the molecular mechanisms of AD, we collected single cell RNA-seq data of disease and control samples and designed an precise computational analysis pipeline for the dataset. We further proposed a consensus non-negative matrix factorization model to extract disease cellular gene expression programs. ResultsAccording to the computational pipeline, we get the most disease-related sub-clusters, cellular process gene expression programs, and disease-related gene programs. We further investigated the enrichment pathways of disease gene programs. DiscussionThis research might provide a cell type specific gene program targets therapeutic approach to combat the disease. HighlightsO_LIWe designed a consensus non-negative matrix factorization method, which could extract cellular process gene expression programs, and disease-related gene programs. C_LIO_LIWe further investigated the enrichment pathways of the most disease-related sub-clusters. C_LIO_LIWe provide a cellular foundation for a new perspective on AD molecular mechanism that informs personalized therapeutic development, targeting the disease-related gene program. C_LI

bioinformatics↗

bioRxiv2025

MitoEM 2.0 - A Benchmark for Challenging 3D MitochondriaInstance Segmentation from EM Images

We present MitoEM 2.0, a curated data resource for training and evaluating three-dimensional (3D) mitochondria instance segmentation in volume electron microscopy. The collection assembles multiscale vEM datasets (FIB-SEM, SBF-SEM, ssSEM) spanning diverse tissues and species, with expert-verified instance labels emphasizing biologically difficult scenarios, including dense mitochondrial packing, hyperfused networks, and thin filamentous connections with ambiguous boundaries. All releases include native-resolution volumes and standardized processed versions, per-volume metadata (voxel size, modality, tissue, splits), and official train/validation/test partitions to enable reproducible benchmarking. Annotations follow a consistent protocol with quality checks and instance reindexing. Data are provided in NIfTI with nnU-Net-compatible layout, alongside machine-readable split files and checksums. Baseline scripts support common training pipelines and size-stratified evaluation. By consolidating challenging volumes and harmonized labels, MitoEM 2.0 facilitates robust model development and fair comparison across methods while supporting reuse in bioimage analysis, algorithm benchmarking, and teaching.

neuroscience↗

bioRxiv2025

Pan-cancer analysis reveals that tumor microbiomes generate specific epitopes through transcriptional reprogramming

The therapeutic promise of tumor microbial peptides faces a critical safety paradox: tumors and peritumor tissues harbor compositionally similar microbiomes, yet safe immunotherapy demands tumor-specific targeting. We performed comprehensive transcriptomic analysis of microbial communities from 1,868 tumor and peritumor samples across 11 cancer types. Although 88.5% of microbial species are present in both tissue types, tumor and peritumor tissues exhibit profound transcriptional divergence: only 34.1% of microbial genes are expressed in both tissue types, and merely 18.2% of predicted HLA-binding peptides overlap. This transcriptional divergence represents microbial transcriptional reprogramming (MTR) in tumor tissues, through which microbes activate distinct genomic regions while adapting to tumor microenvironmental pressures, thereby generating tumor-specific peptide repertoires. Using Microbial Transcriptional Reprogramming Index (MiTRI) to quantify MTR, we found that higher MiTRI values correlate with expanded immunogenic peptide repertoires. Mass spectrometry-based immunopeptidomics confirmed that 25% of naturally presented microbial peptides originate from MTR regions. Crucially, in microsatellite-stable colorectal cancer, neo-adjuvant therapy responders exhibited significantly higher MiTRI values. T cell receptors (TCRs) recognized MTR-derived epitopes at 2.10-fold higher rates than genome-wide epitopes, accounting for 79.1% of clonally expanded TCRs. These findings establish MTR as the principle that ensures the tumor-specificity and safety of micorbiome-directed immunotherapy.

cancer biology↗

bioRxiv2025

A Comprehensive Mathematical Model Simulating the Adaptive Immune Response

Adaptive immunity plays a crucial role in defending against invading pathogenic microorganisms. It encompasses both humoral and cellular immune responses. For the first time, we have systematically developed a mathematical model of adaptive immunity that comprehensively incorporates the effects of both humoral and cellular immunity. Our model successfully explains several key immunological phenomena, including the formation of immune memory (involving both B-cell and T-cell memory), the mechanism of original antigenic sin, the basis of secondary infections, and the processes of activation and exhaustion in cellular immunity. Furthermore, we employed a discrete time-scale agent-based modeling approach to simulate the dynamics of the adaptive immune response following pathogen invasion, with a specific focus on elucidating the mechanisms underlying chronic infection. Finally, we have established a novel mathematical model of the interaction between cancer cells and the immune system, providing a more robust theoretical foundation for cancer immunotherapy.

immunology↗

bioRxiv2025

VesiclePy: A Machine Learning Vesicle Analysis Toolbox for Volume Electron Microscopy

Vesicles are critical components of neurons that package neurotransmitters and neuropeptides for their release, in order to communicate with other neurons and cells. However, due to their small size, the reconstruction of the full vesicle endowment across an entire neuronal morphology remains challenging. To achieve this, we have used, as a tool to identify and visualize vesicles, Volume Electron Microscopy (vEM), a method that has the nanoscale resolution to detect individual vesicle boundaries, content, and 3D locations. However, the large volume of vEM datasets poses a challenge in the segmentation, classification, and spatial analysis of tens of thousands of vesicles and their target cell in 3D. Here we report the development of VesiclePy, an integrated pipeline for automated segmentation, classification, proofreading, and spatial analysis of vesicles, relative to neuron masks in large-volume electron microscopy data. Our package integrates the efficiency of deep learning and the accuracy of human proofreading and provides a streamlined package in chunked processing and accurate indexing, localization, and visualization of single vesicle resolution in large vEM data. We demonstrate the viability of VesiclePy using high-pressure frozen serial EM data of Hydra vulgaris and quantify the performance of the package using ground truth manual annotations. We show that VesiclePy can process a multiterabyte serial EM dataset, efficiently annotate 53,851 vesicles from 20 complete neurons, and classify vesicles into 5 types. Each vesicle has a unique ID and 3D location for further spatial analysis in relation to neuron or non-neuronal targets nearby. Finally, by combining vesicle data and morphological information of each neuron, we can quantitatively cluster neurons into subtypes. VesiclePy is available at https://github.com/PytorchConnectomics/VesiclePy under an MIT license.

neuroscience↗

bioRxiv2025

Developmental Connectomics of the Mouse Cerebellum

To uncover the developmental processes that establish the precise patterns of synaptic connectivity in the CNS, we employed a connectomic approach in the mouse cerebellar cortex between birth and 2 weeks of age. There were dramatic quantitative and qualitative changes in the structure and connectivity of cerebellar cells. Parallel fiber synapses onto Purkinje cells increased [~]500-fold, with the most rapid growth taking place a week after birth. To support this profound synaptogenesis, Purkinje cells generated thousands of transient parallel fiber-oriented filopodia that received nascent synapses from parallel fibers. Importantly, we find that granule cells initiate synaptic output onto Purkinje cells only after receiving mossy fiber input, revealing a sequential, input-dependent logic for circuit assembly. In sharp contrast to the concurrent pruning of climbing fiber inputs, parallel fiber connectivity expanded and became highly individualized during development. Despite anatomical overlap, neighboring Purkinje cells share significantly fewer parallel fiber inputs than expected by chance. Moreover, parallel fibers themselves diverged spatially, further enforcing selective input allocation and resulting in highly specific parallel fiber cohorts for each Purkinje cell. Our findings uncover a mechanistic sequence in which early afferent activity and transient cellular structures guide the selective wiring and expansion of parallel fiber input to Purkinje cells, establishing developmental principles that ensure functional specificity in the mature brain.

neuroscience↗

bioRxiv2025

ProStab: Prediction of protein stability change upon mutations by protein language and inverse folding models

Predicting protein stability change upon mutation is critical for protein engineering, yet remains limited by the modeling assumptions of physics-based methods and the generalization bottlenecks of data-driven approaches. We present ProStab, a deep learning framework that integrates sequence- and structure-based information, including the mutation-aware sequence embeddings from protein language models and the geometric features extracted via an inverse folding model. Trained on the large-scale Megascale dataset, ProStab demonstrates strong performance across diverse test sets and robust generalization across distribution shifts between the training and test sets. In head-to-head comparisons, ProStab outperforms all state-of-the-art methods with consistently higher Spearman correlation and precision. To evaluate its practical utility, we experimentally validated ProStab-predicted mutations on the model enzyme transaminase. Among the 16 successfully expressed variants, 4 exhibited improved thermal stability. Remarkably, the 1st top-ranked predicted mutation yielded the highest observed enzymatic activity, retaining three-fold that of the wild type after 10 minutes at 40 {degrees}C. To facilitate broader application, a publicly accessible web server has been developed. We envisage that ProStab provides a scalable and accurate platform for intelligent protein stability design.

bioinformatics↗

bioRxiv2025

SynAnno: Interactive Guided Proofreading of Synaptic Annotations

Connectomics, a subfield of neuroscience, aims to map and analyze synapse-level wiring diagrams of the nervous system. While recent advances in deep learning have accelerated automated neuron and synapse segmentation, reconstructing accurate connectomes still demands extensive human proofreading to correct segmentation errors. We present SynAnno, an interactive tool designed to streamline and enhance the proofreading of synaptic annotations in large-scale connectomics datasets. SynAnno integrates into existing neuroscience workflows by enabling guided, neuron-centric proofreading. To address the challenges posed by the complex spatial branching of neurons, it introduces a structured workflow with an optimized traversal path and a 3D mini-map for tracking progress. In addition, SynAnno incorporates fine-tuned machine learning models to assist with error detection and correction, reducing the manual burden and increasing proofreading efficiency. We evaluate SynAnno through a user and case study involving seven neuroscience experts. Results show that SynAnno significantly accelerates synapse proofreading while reducing cognitive load and annotation errors through structured guidance and visualization support. The source code and interactive demo are available at: https://github.com/PytorchConnectomics/SynAnno.

neuroscience↗

bioRxiv2025

IL-6 is a Key Factor in the Formation of Gut Tissue Resident Memory T Cells from Naïve T cells

Tissue resident memory CD4+ T cells (TRMs) populate mucosal sites and play a critical role in local immune responses. Gut TRM cells persist for extended periods in the gut mucosa where they rapidly respond to invading pathogens and provide long lasting protection. This study investigates the factors that mediate differentiation of naive CD4+ T cells into cells presenting a gut TRM phenotype. Naive CD4+ T cells were cultured under conditions that mimicked mucosal environments. This included signaling through MAdCAM-1 in the presence of Retinoic Acid (RA) and TGF-{beta}. This combination of stimuli primed naive CD4+ T cells to adopt a TRM phenotype. However, to fully differentiate into TRMs an additional soluble factor provided by memory T cells was required. Our results identified IL-6 as a key factor that induces the expression of TRM -associated markers, including CD69, CD103 and CCR5. This unique combination of stimuli promoted TRM differentiation despite low level proliferation. TRM differentiation was mediated through JAK/STAT signaling, and antagonists that target JAK/STAT pathways suppressed MAdCAM-1 mediated TRM cell formation. Our findings revealed that MAdCAM-1 works together with TGF-{beta}, RA and IL-6 in this process. Such information may aid in the design of next generation adjuvants and effective mucosal vaccines. Additionally, each of these factors may be targeted to treat excessive gut inflammation associated with conditions like inflammatory bowel disease. Overall, these findings provide new strategies aimed at modulating immune responses to invading pathogens and identify therapeutic approaches toward regulating gut inflammation. Author SummaryImmunologists and vaccinologists have long been interested in strategies aimed at boosting immune responses in mucosal tissues where pathogens are first encountered. Gut Tissue resident memory T cells (TRMs) play a central role in gut homeostasis and immunity. They provide a rapid long-lasting protection against pathogens. TRMs must target harmful pathogens and at the same time tolerate harmless commensal bacteria. This balance between immunity and tolerance in the complex environment of gut tissues is essential to the maintenance of gut homeostasis. In this study we have identified key factors present in the gut tissue milieu that are involved in TRM differentiation from naive T cells. These findings point to new therapeutic approaches that can potentially target immune responses in gut tissues and may help in developing effective mucosal vaccines. Conversely, these factors may be targeted in excessive gut inflammation involved in conditions like inflammatory bowel disease (IBD). Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=145 SRC="FIGDIR/small/660664v1_ufig1.gif" ALT="Figure 1"> View larger version (65K): org.highwire.dtl.DTLVardef@14e6015org.highwire.dtl.DTLVardef@860b93org.highwire.dtl.DTLVardef@61f8c8org.highwire.dtl.DTLVardef@142b48_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2025

A connectomic resource for neural cataloguing and circuit dissection of the larval zebrafish brain

We present a correlated light and electron microscopy (CLEM) dataset from a 7-day-old larval zebrafish, integrating confocal imaging of genetically labeled excitatory (vglut2a) and inhibitory (gad1b) neurons with nanometer-resolution serial section EM. The dataset spans the brain and anterior spinal cord, capturing >180,000 segmented soma, >40,000 molecularly annotated neurons, and 30 million synapses, most of which were classified as excitatory, inhibitory, or modulatory. To characterize the directional flow of activity across the brain, we leverage the synaptic and cell body annotations to compute region-wise input and output drive indices at single cell resolution. We illustrate the datasets utility by dissecting and validating circuits in three distinct systems: water flow direction encoding in the lateral line, recurrent excitation and contralateral inhibition in a hindbrain motion integrator, and functionally relevant targeted long-range projections from a tegmental excitatory nucleus, demonstrating that this resource enables rigorous hypothesis testing as well as exploratory-driven circuit analysis. The dataset is integrated into an open-access platform optimized to facilitate community reconstruction and discovery efforts throughout the larval zebrafish brain.

neuroscience↗