Biology subjects

Januszewski, M.

Publications and source records attributed to Januszewski, M..

At least 19 recordsLinked to original sources

bioRxiv2026

The female larval fly CNS connectome: first draft

We release an enhanced FIB-SEM volume and associated neuron, mitochondria, synapse and glia automated segmentations for the complete, gap- and artifact-free central nervous system of the first instar female larval Drosophila melanogaster. Segmentation data is largely unproofread besides select neurons in the premotor and proprioceptive system and select subvolumes where synapses have been manually annotated, serving as training and evaluation data. The dataset represents the first complete central nervous system of the larval fly, and now awaits substantial improvement of machine vision approaches that effectively leverage domain knowledge for the automated yet data-guided proofreading, or substantial and costly human proofreading. All the data and methods are publicly available for further improvement and analysis.

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bioRxiv2026

Fine ultrastructural organization of glomerular neuropil in the developing zebrafish olfactory bulb

Across animal phyla, odor information is represented in the first olfactory processing center by combinatorial activation of discrete glomeruli. To examine how this organization of olfactory processing channels emerges during development we combined 2-photon calcium imaging, volume electron microscopy, and large-scale automated neuron reconstruction in zebrafish larvae. We focused on a developmental stage (130 hours post fertilization) when the olfactory bulb (OB) is structured into ~16 large neuropil regions referred to as protoglomeruli but most glomeruli are not yet differentiated. Our ultrastructural reconstructions revealed that protoglomeruli are organized into smaller units referred to as microglomeruli, each of which is defined by a distinct set of mitral cells with overlapping, spatially confined dendritic arbors. Neighboring microglomeruli were innervated by distinct subsets of sensory axons and activity was more highly correlated among mitral cells associated with the same microglomerulus than across microglomeruli of the same or different protoglomeruli. These results reveal a precise subcellular organization of the neuropil in the developing OB into distinct processing channels, indicating that the layout of the adult glomerular array is established already early in development.

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bioRxiv2026

Deep anatomical and ultrastructural classification of neurons in the zebrafish olfactory bulb

Neuronal circuits in the olfactory bulb (OB) perform computations fundamental to pattern classification including a decorrelation and normalization of odor-evoked activity. These computations are mediated by diverse interneurons but a comprehensive picture of interneuron types and their microcircuit organization is lacking. We provide a deep anatomical classification of neuron types and their synaptic connectivity in the OB of adult zebrafish, a well-established model to analyze olfactory computations. We reconstructed 459 neurons in an image volume acquired by serial block face scanning electron microscopy and defined 13 neuron classes based on morphological and ultrastructural features. These comprised two classes of projection neurons and 11 interneuron classes, some of which were further separated into subclasses. Ultrastructural information including spine shape, variations in neurite diameter and synaptic arrangements contributed significantly to the distinction of cell types. As in other species, reciprocal synaptic connections were abundant. Targeted synapse annotation revealed systematic connectivity between projection neurons and interneurons. These included microcircuit motifs combining reciprocal and unidirectional connectivity that provide possible structural substrates for gain control and lateral inhibition. The results provide detailed insights into the structural organization of the OB and an anatomical foundation for physiological and computational studies of information processing in olfaction.

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bioRxiv2026

Olfactory Loss Enhances Visual Learning in Drosophila through Structural and Functional Reorganisation

Loss of a sensory modality can enhance performance in the remaining senses. However, the circuit mechanisms by which such cross-modal compensation can improve cognitive functions, including learning, are unknown. Here, we show that compromising olfaction in both larval and adult Drosophila enhances visual associative learning. Using behavioural analysis, functional imaging, and comparative connectomics, we reveal the circuit mechanisms that underlie this improvement. Animals with improved learning ability have enhanced responses to visual stimuli in the higher-order learning circuit. The complementary circuit mechanisms that can enhance these responses are structural reweighting of inputs in the larva, resulting in an increased fraction of synaptic inputs from visual pathways onto neurons in the learning circuit, and a reduction in cross-modal inhibition in the adult. Together, these findings reveal synaptic and disinhibitory circuit mechanisms that enhance learning in higher-order associative networks following sensory loss.

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bioRxiv2025

The songbird basal ganglia connectome

The basal ganglia (BG) play an essential role in shaping vertebrate behavior, ranging from motor learning to emotions, but comprehensive maps of their canonical synaptic architecture are missing. In mammals, three main neuronal pathways through the BG have been described - the direct, indirect, and hyperdirect pathways - which together orchestrate many aspects of learning and behavior. In songbirds, cell types associated with striatal and pallidal components appear intermingled in a single basal ganglia nucleus, Area X, essential for song learning. This allows for the dense reconstruction of the entire circuit within a compact volume. Here, we introduce the first vertebrate basal ganglia connectome, comprising over 8,500 automated neuron reconstructions connected by about 20 million synapses. High image quality and automated reconstruction allowed analysis with minimal manual proofreading. Based on direct anatomical measurement of synaptic connectivity, we confirm that a direct, indirect and hyperdirect pathway can be traced through Area X. However, detailed morphological and connectomic analysis revealed no clearly distinct direct and indirect medium spiny neuron subpopulations, and a dominance of the direct and hyperdirect pathway. In addition to previously identified neuron types in Area X, we could distinguish three novel GABAergic neuron types, two of which are major output targets of GPe neurons, leading to novel feedback circuitry within Area X. We further found unexpectedly strong neuronal interconnectivity and recurrency between neurons associated with all pathways. Our data thus challenge the universality of the view of the basal ganglia as an information processor organized into discrete feedforward pathways.

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bioRxiv2025

Sexual dimorphism in the complete connectome of the Drosophila male central nervous system

Sex differences in behaviour exist across all animals, typically under strong genetic regulation. In Drosophila, fruitless/doublesex transcription factors can identify dimorphic neurons but their organisation into functional circuits remains unclear. We present the connectome of the entire Drosophila male central nervous system. This contains 166,691 neurons spanning the brain and nerve cord, fully proofread and annotated including fruitless/doublesex expression and 11,691 types. We provide the first comprehensive comparison between male and female brain connectomes to synaptic resolution, finding 7,205 isomorphic, 114 dimorphic, 262 male-specific and 69 female-specific types. This resource enables analysis of full sensory-to-motor circuits underlying complex behaviours and the impact of dimorphic elements. Sex-specific/dimorphic neurons are concentrated in higher brain centres while the sensory and motor periphery are largely isomorphic. Within higher centres, male-specific connections are organised into hotspots defined by male-specific neurons or arbours. Numerous circuit switches reroute sensory information to form antagonistic circuits controlling opposing behaviours.

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bioRxiv2025

Connectome analysis of a cerebellum-like circuit for sensory prediction

Stable and accurate perception involves comparing incoming sensory input with internally- generated predictions 1-3. A mechanistic understanding of this process has been elusive due to the size and complexity of the relevant brain regions in mammals. Here we leverage connectomics to comprehensively map the cell types and synaptic connections underlying a well-characterized and ecologically relevant form of predictive sensory processing in the cerebellum-like electrosensory lobe (ELL) of weakly electric fish 4,5. Connectome analysis reveals highly-structured feedforward and recurrent synaptic connectivity mediating the cancellation of predictable electrosensory input. A computational model constrained by prior electrophysiological recordings shows how this connectivity supports the formation of predictions at multiple sites within the network and how the ELL solves a continual learning problem by maintaining fast and accurate predictions despite noise and changes in environmental context. Overall, these findings provide a blueprint for using connectomics to elucidate learning in vertebrate nervous systems.

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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.

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bioRxiv2025

Accelerating Neuron Reconstruction with PATHFINDER

Comprehensive mapping of neural connections is essential for understanding brain function. Existing automated methods for connectome reconstruction from high-resolution images of brain tissue introduce errors that require extensive and time-consuming manual correction, a critical bottleneck in the field. To address this, we developed PATHFINDER, an AI system that segments volumetric image data, identifies potential ways to assemble neuron fragments, and evaluates the plausibility of resulting shapes to reconstruct complete neurons. Using a dataset of all axons in an IBEAM-mSEM volume of mouse cortex, we show that PATHFINDER reduces the error rate in axon reconstruction by an order of magnitude over previous state of the art, leading to an improvement in proofreading throughput of up to 84x relative to prior estimates in the context of a whole mouse brain. By drastically reducing the manual e"ort required for analysis, this advance unlocks the potential for both large-scale connectome mapping and routine investigation of smaller volumes.

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bioRxiv2025

An ultrastructural map of a spinal sensorimotor circuit reveals the potential of astroglia modulation

Information flow through circuits is dictated by the precise connectivity of neurons and glia. While a single astrocyte can contact many synapses, how glial-synaptic interactions are arranged within a single circuit to impact information flow remains understudied. Here, we use the local spinal sensorimotor circuit in zebrafish as a model to understand how neurons and astroglia are connected in a vertebrate circuit. With semi-automated cellular reconstructions and automated approaches to map all the synaptic connections, we identified the precise synaptic connections of the local sensorimotor circuit, from dorsal root ganglia neurons to spinal interneurons and finally to motor neurons. This revealed a complex network of interneurons that interact in the local sensorimotor circuit. We then mapped the glial processes within tripartite synapses in the circuit. We demonstrate that tripartite synapses are equally distributed across the circuit, supporting the idea that glia can modulate information flow through the circuit at different levels. We show that multiple astroglia, including bona fide astrocytes, contact synapses within a single sensory neurons circuit and that each of these astroglia can contact multiple parts of the circuit. This detailed map reveals an extensive network of connected neurons and astroglia that process sensory stimuli in a vertebrate. We then utilized this ultrastructural map to model how synaptic thresholding and glial modulation could alter information flow in circuits. We validated this circuit map with GCaMP6s imaging of dorsal root ganglia, spinal neurons and astroglia. This work provides a foundational resource detailing the ultrastructural organization of neurons and glia in a vertebrate circuit, offering insights in how glia could influence information flow in complex neural networks.

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bioRxiv2024

Paired and solitary ionocytes in the zebrafish olfactory epithelium

The sense of smell is generated by electrical currents that are influenced by the concentration of ions in olfactory sensory neurons and mucus. In contrast to the extensive morphological and molecular characterization of sensory neurons, there has been little description of the cells that control ion concentrations in the zebrafish olfactory system. Here, we report the molecular and ultrastructural characterization of zebrafish olfactory ionocytes. Transcriptome analysis suggests that the zebrafish olfactory epithelium contains at least three different ionocyte types, which resemble Na+/K+-ATPase-rich (NaR), H+-ATPase-rich (HR), and Na+/Cl- cotransporter (NCC) cells, responsible for calcium, pH, and chloride regulation, respectively, in the zebrafish skin. In the olfactory epithelium, NaR-like and HR-like ionocytes are usually adjacent to one another, whereas NCC-like cells are usually solitary. The distinct subtypes are differentially distributed: NaR-like/HR-like cell pairs are found broadly within the olfactory epithelium, whereas NCC-like cells reside within the peripheral non-sensory multiciliated cell zone. Comparison of gene expression and serial-section electron microscopy analysis indicates that the NaR-like cells wrap around the HR-like cells and are connected to them by shallow tight junctions. The development of olfactory ionocyte subtypes is also differentially regulated, as pharmacological Notch inhibition leads to a loss of NaR-like and HR-like cells, but does not affect NCC-like ionocyte number. These results provide a molecular and anatomical characterization of olfactory ionocytes in a stenohaline freshwater teleost. The paired ionocytes suggest that both transcellular and paracellular transport regulate ion concentrations in the olfactory epithelium, while the solitary ionocytes may enable independent regulation of ciliary beating.

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bioRxiv2024

Connectomic Analysis of Mitochondria in the Central Brain of Drosophila

Mitochondria are integral to the metabolism and cell biology of a neuron. Electron microscopy images of fly brain volumes, taken for connectomics, can be analyzed for mitochondria as well as the cells and synapses already reported. Here, from the Drosophila Hemibrain connectome dataset, we extract, classify, and measure approximately 6 million mitochondria, with the majority located among more than 20 thousand neurons and over 5500 cell types. Each mitochondrion is annotated with its location, orientation, voxel size, and appearance (dark and dense, light and sparse, or medium), and each synapse is linked to its closest mitochondrion. Using these data, we show how the most basic characteristics of mitochondria--volume, distance from synapses, and appearance--vary considerably between cell types and between brain regions. Mitochondria are larger and closer at presynapses than at postsynapses, and presynapses typically have a mitochondrion within one micron. However, cells important for learning and memory, Kenyon cells, have unusually small and sparse mitochondria that are placed with unusual precision near only half of presynapses. We find that mitochondria occupy a greater fraction of cell volume in inhibitory neurons than excitatory cells, dopaminergic neurons have a distinct synaptic positioning of mitochondria, and that glutamatergic neurons have a high fraction of mitochondria with a light appearance. We also find that synapses with more postsynaptic partners have larger presynaptic mitochondria and more distant postsynaptic mitochondria. Finally, we have extended the data model of our public web interface, neuPrint, to record our mitochondria data as searchable subcellular components of neurons in a connectomic database. These results indicate wide-ranging principles for how mitochondrial organization varies by cell type in a dataset of unprecedented size and coverage.

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bioRxiv2024

Connectome-driven neural inventory of a complete visual system

Vision provides animals with detailed information about their surroundings, conveying diverse features such as color, form, and movement across the visual scene. Computing these parallel spatial features requires a large and diverse network of neurons, such that in animals as distant as flies and humans, visual regions comprise half the brains volume. These visual brain regions often reveal remarkable structure-function relationships, with neurons organized along spatial maps with shapes that directly relate to their roles in visual processing. To unravel the stunning diversity of a complex visual system, a careful mapping of the neural architecture matched to tools for targeted exploration of that circuitry is essential. Here, we report a new connectome of the right optic lobe from a male Drosophila central nervous system FIB-SEM volume and a comprehensive inventory of the flys visual neurons. We developed a computational framework to quantify the anatomy of visual neurons, establishing a basis for interpreting how their shapes relate to spatial vision. By integrating this analysis with connectivity information, neurotransmitter identity, and expert curation, we classified the [~]53,000 neurons into 727 types, about half of which are systematically described and named for the first time. Finally, we share an extensive collection of split-GAL4 lines matched to our neuron type catalog. Together, this comprehensive set of tools and data unlock new possibilities for systematic investigations of vision in Drosophila, a foundation for a deeper understanding of sensory processing.

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bioRxiv2024

Light-microscopy based dense connectomic reconstruction of mammalian brain tissue

The information-processing capability of the brains cellular network depends on the physical wiring pattern between neurons and their molecular and functional characteristics. Mapping neurons and resolving their individual synaptic connections can be achieved by volumetric imaging at nanoscale resolution with dense cellular labeling. Light microscopy is uniquely positioned to visualize specific molecules but dense, synapse-level circuit reconstruction by light microscopy has been out of reach due to limitations in resolution, contrast, and volumetric imaging capability. Here we developed light-microscopy based connectomics (LICONN). We integrated specifically engineered hydrogel embedding and expansion with comprehensive deep-learning based segmentation and analysis of connectivity, thus directly incorporating molecular information in synapse-level brain tissue reconstructions. LICONN will allow synapse-level brain tissue phenotyping in biological experiments in a readily adoptable manner. One-Sentence SummaryHydrogel expansion enables molecularly informed reconstruction of brain tissue at synaptic resolution with light microscopy.

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bioRxiv2023

A Connectome of the Male Drosophila Ventral Nerve Cord

Animal behavior is principally expressed through neural control of muscles. Therefore understanding how the brain controls behavior requires mapping neuronal circuits all the way to motor neurons. We have previously established technology to collect large-volume electron microscopy data sets of neural tissue and fully reconstruct the morphology of the neurons and their chemical synaptic connections throughout the volume. Using these tools we generated a dense wiring diagram, or connectome, for a large portion of the Drosophila central brain. However, in most animals, including the fly, the majority of motor neurons are located outside the brain in a neural center closer to the body, i.e. the mammalian spinal cord or insect ventral nerve cord (VNC). In this paper, we extend our effort to map full neural circuits for behavior by generating a connectome of the VNC of a male fly.

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bioRxiv2022

Multi-Layered Maps of Neuropil with Segmentation-Guided Contrastive Learning

Maps of the nervous system that identify individual cells along with their type, subcellular components, and connectivity have the potential to reveal fundamental organizational principles of neural circuits. Volumetric nanometer-resolution imaging of brain tissue provides the raw data needed to build such maps, but inferring all the relevant cellular and subcellular annotation layers is challenging. Here, we present Segmentation-Guided Contrastive Learning of Representations ("SegCLR"), a self-supervised machine learning technique that produces highly informative representations of cells directly from 3d electron microscope imagery and segmentations. When applied to volumes of human and mouse cerebral cortex, SegCLR enabled the classification of cellular subcompartments (axon, dendrite, soma, astrocytic process) with 4,000-fold less labeled data compared to fully supervised approaches. Surprisingly, SegCLR also enabled inference of cell types (neurons, glia, and subtypes of each) from fragments with lengths as small as 10 micrometers, a task that can be difficult for humans to perform and whose feasibility greatly enhances the utility of imaging portions of brains in which many neuron fragments terminate at a volume boundary. These predictions were further augmented via Gaussian process uncertainty estimation to enable analyses restricted to high confidence subsets of the data. Finally, SegCLR enabled detailed exploration of layer-5 pyramidal cell subtypes and automated large-scale statistical analysis of upstream and downstream synaptic partners in mouse visual cortex.

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bioRxiv2021

Visual recognition of social signals by a tecto-thalamic neural circuit

Social affiliation emerges from individual-level behavioral rules that are driven by conspecific signals1-5. Long-distance attraction and short-distance repulsion, for example, are rules that jointly set a preferred inter-animal distance in swarms6-8. However, little is known about their perceptual mechanisms and executive neuronal circuits3. Here we trace the neuronal response to self-like biological motion9,10 (BM), a visual trigger for affiliation in developing zebrafish2,11. Unbiased activity mapping and targeted volumetric two-photon calcium imaging revealed 19 activity hotspots distributed throughout the brain and clustered BM-tuned neurons in a multimodal, socially activated nucleus of the dorsal thalamus (DT). Individual DT neurons encode fish-like local acceleration but are insensitive to global or continuous motion. Electron microscopic reconstruction of DT neurons revealed synaptic input from the optic tectum (TeO/superior colliculus) and projections into nodes of the conserved social behavior network12,13. Chemogenetic ablation of the TeO selectively disrupted DT responses to BM and social attraction without affecting short-distance repulsion. Together, we discovered a tecto-thalamic pathway that drives a core network for social affiliation. Our findings provide an example of visual social processing, and dissociate neuronal control of attraction from repulsion during affiliation, thus revealing neural underpinnings of collective behavior.

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bioRxiv2021

Denoising-based Image Compression for Connectomics

Connectomic reconstruction of neural circuits relies on nanometer resolution microscopy which produces on the order of a petabyte of imagery for each cubic millimeter of brain tissue. The cost of storing such data is a significant barrier to broadening the use of connectomic approaches and scaling to even larger volumes. We present an image compression approach that uses machine learning-based denoising and standard image codecs to compress raw electron microscopy imagery of neuropil up to 17-fold with negligible loss of 3d reconstruction and synaptic detection accuracy.

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