Biology subjects

Kemnitz, N.

Publications and source records attributed to Kemnitz, N..

At least 19 recordsLinked to original sources

bioRxiv2026

Cell type-resolved connectomic reconstruction of the spinal dorsal horn reveals sensory neuron synaptic organization and modality-specific inhibition

Touch, temperature, chemical, and noxious stimuli are detected by more than a dozen peripheral sensory neuron subtypes that together mediate somatosensation. Somatosensory neurons form synapses in the spinal cord dorsal horn, the first site of central somatosensory processing, to engage the downstream circuitry that drives both spinal reflexes and ascending pathways for perception. Yet how sensory neuron synapses are organized into the spinal cord circuitry remains poorly understood. Here we present two connectomic tools for studying somatosensory circuits of the spinal cord dorsal horn: dSC1, a large-scale, synapse-resolved electron microscopy (EM) dataset of the mouse dorsal horn, and SPINE (SPinal cord Integrated NEuron classifier), a deep-learning pipeline that identifies major sensory neuron subtypes directly from EM images. Applying SPINE to the dSC1 dataset, we found marked heterogeneity in synaptic architecture across physiologically distinct sensory neuron subtypes: low-threshold mechanoreceptors form substantially more synapses and receive far more GABAergic presynaptic inhibition than high-threshold mechanoreceptors, thermoreceptors and nociceptors. Cell-type-resolved circuit reconstruction further identified five populations of spinal inhibitory neurons (IhNs) that form synapses onto sensory neuron axons, each preferentially driven by and presynaptically inhibiting a single sensory subtype - a homotypic, modality-specific inhibitory motif. Most of these IhNs also feedforwardly inhibit other sensory pathways - a heterotypic, cross modality motif. Together, dSC1 and SPINE enable cell-type-resolved spinal cord circuit reconstruction and reveal a dual-mode logic of sensory-evoked inhibition that shapes modality-specific signaling at the earliest stage of somatosensory processing.

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bioRxiv2026

A synaptic wiring diagram underlying modality-specific ascending pathways of the superficial spinal dorsal horn

The spinal cord dorsal horn transforms tactile, thermal, pruritic, and nociceptive signals from peripheral somatosensory neurons into outputs to the brain, but how dorsal horn circuits organize diverse inputs into modality-selective ascending pathways is poorly understood. Using dSC1, a large-scale electron microscopy dataset of the mouse dorsal horn, we mapped the circuitry connecting primary sensory afferents to projection neurons of the anterolateral tract (ALT), a principal pathway conveying somatosensory information to the brain. We find that afferents encoding distinct modalities are routed onto connectivity-defined populations of ALT projection neurons. Nociceptors and cold thermoreceptors directly innervate distinct lamina I ALT projection neuron populations specialized for conveying noxious and cooling signals, respectively, whereas tactile and pruritic signals instead pass through a recurrent, multilayered network involving interneurons called vertical cells before reaching a separate ALT projection population. This distinction, direct versus polysynaptic wiring, underlies marked differences in sensory convergence and establishes segregated ascending channels for conveying signals encoding the principal somatosensory modalities. Together, our findings define an architectural logic by which the dorsal horn integrates and routes tactile, thermal, pruritic, and nociceptive information into distinct ALT output pathways to the brain.

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bioRxiv2025

Visual pathway origins: an electron microscopic volume for connectomic analysis of the human foveal retina

The fovea is a unique specialization of the primate retina and is a promising site for obtaining the first complete connectome of a human central nervous system (CNS) structure. Within the fovea, neural cells and circuits have been miniaturized and compressed during evolution to sample the visual image at highest spatial resolution and begin the neural processing that serves human form, color, and motion perception. Here we present a comprehensive analysis of a sample of human foveal retina using deep learning-based segmentation to reconstruct all cells and synaptic connections at nanoscale resolution. We classified [~]3,000 cells into 51 distinct morphological types based on their structural features and connectivity patterns. Our observations reveal novel synaptic pathways absent in non-human primates, suggesting specialized circuits contribute uniquely to human trichromatic color vision. A biophysical model of the distinct connectomes made by gap junctions (electrical synapses) between short- (S) and medium-long- (ML) wavelength-sensitive cone photoreceptors, suggests chromatic interactions between S and ML cones prior to the first chemical synapse. Segmentation of retinal ganglion cells (RGCs) suggests the presence of only 11 visual pathways, with 5 high-density RGC pathways accounting for over 95% of foveal output to the brain: a dramatic contrast to the 40+ ganglion cell types recognized in mouse retina. Our connectomic analysis reveals distinctive features of human neural circuitry and demonstrates how AI-based computational approaches can advance understanding of human brain structure and function. Significant statementVision begins in the retina, an accessible outpost of the brain located at the back of the eye. When we directly view an object, we use the fovea--a tiny region containing over 50 distinct, densely packed neuron types. How these diverse cells interconnect through synapses to transmit visual information has been difficult to determine. Here, we used artificial intelligence-based computational methods to create a complete wiring diagram--a connectome--mapping every neuron and synapse in a sample of human fovea. This connectomic resource will be freely available to researchers worldwide. Discoveries so far include synaptic circuits linked to human color vision, demonstrating how connectomics can reveal what makes the human nervous system unique.

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bioRxiv2023

The Synaptic Architecture of Layer 5 Thick Tufted Excitatory Neurons in the Visual Cortex of Mice

The neocortex is one of the most critical structures that makes us human, and it is involved in a variety of cognitive functions from perception to sensory integration and motor control. Composed of repeated modules, or microcircuits, the neocortex relies on distinct cell types as its fundamental building blocks. Despite significant progress in characterizing these cell types 1-5, an understanding of the complete synaptic partners associated with individual excitatory cell types remain elusive. Here, we investigate the connectivity of arguably the most well recognized and studied excitatory neuron in the neocortex: the thick tufted layer 5 pyramidal cell 6-10 also known as extra telencephalic (ET) 11 neurons. Although the synaptic interactions of ET neurons have been extensively explored, a comprehensive characterization of their local connectivity remains lacking. To address this knowledge gap, we leveraged a 1 mm3 electron microscopic (EM) dataset. We found that ET neurons primarily establish connections with inhibitory cells in their immediate vicinity. However, when they extend their axons to other cortical regions, they tend to connect more with excitatory cells. We also find that the inhibitory cells targeted by ET neurons are a specific group of cell types, and they preferentially inhibit ET cells. Finally, we observed that the most common excitatory targets of ET neurons are layer 5 IT neurons and layer 6 pyramidal cells, whereas synapses with other ET neurons are not as common. These findings challenge current views of the connectivity of ET neurons and suggest a circuit design that involves local competition among ET neurons and collaboration with other types of excitatory cells. Our results also highlight a specific circuit pattern where a subclass of excitatory cells forms a network with specific inhibitory cell types, offering a framework for exploring the connectivity of other types of excitatory cells.

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bioRxiv2023

CAVE: Connectome Annotation Versioning Engine

Advances in Electron Microscopy, image segmentation and computational infrastructure have given rise to large-scale and richly annotated connectomic datasets which are increasingly shared across communities. To enable collaboration, users need to be able to concurrently create new annotations and correct errors in the automated segmentation by proofreading. In large datasets, every proofreading edit relabels cell identities of millions of voxels and thousands of annotations like synapses. For analysis, users require immediate and reproducible access to this constantly changing and expanding data landscape. Here, we present the Connectome Annotation Versioning Engine (CAVE), a computational infrastructure for immediate and reproducible connectome analysis in up-to petascale datasets ([~]1mm3) while proofreading and annotating is ongoing. For segmentation, CAVE provides a distributed proofreading infrastructure for continuous versioning of large reconstructions. Annotations in CAVE are defined by locations such that they can be quickly assigned to the underlying segment which enables fast analysis queries of CAVEs data for arbitrary time points. CAVE supports schematized, extensible annotations, so that researchers can readily design novel annotation types. CAVE is already used for many connectomics datasets, including the largest datasets available to date.

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bioRxiv2023

Neuronal wiring diagram of an adult brain

Connections between neurons can be mapped by acquiring and analyzing electron microscopic (EM) brain images. In recent years, this approach has been applied to chunks of brains to reconstruct local connectivity maps that are highly informative, yet inadequate for understanding brain function more globally. Here, we present the first neuronal wiring diagram of a whole adult brain, containing 5x107 chemical synapses between [~]130,000 neurons reconstructed from a female Drosophila melanogaster. The resource also incorporates annotations of cell classes and types, nerves, hemilineages, and predictions of neurotransmitter identities. Data products are available by download, programmatic access, and interactive browsing and made interoperable with other fly data resources. We show how to derive a projectome, a map of projections between regions, from the connectome. We demonstrate the tracing of synaptic pathways and the analysis of information flow from inputs (sensory and ascending neurons) to outputs (motor, endocrine, and descending neurons), across both hemispheres, and between the central brain and the optic lobes. Tracing from a subset of photoreceptors all the way to descending motor pathways illustrates how structure can uncover putative circuit mechanisms underlying sensorimotor behaviors. The technologies and open ecosystem of the FlyWire Consortium set the stage for future large-scale connectome projects in other species.

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bioRxiv2023

Synaptic architecture of leg and wing motor control networks in Drosophila

Animal movement is controlled by motor neurons (MNs), which project out of the central nervous system to activate muscles. MN activity is coordinated by complex premotor networks that allow individual muscles to contribute to many different behaviors. Here, we use connectomics to analyze the wiring logic of premotor circuits controlling the Drosophila leg and wing. We find that both premotor networks cluster into modules that link MNs innervating muscles with related functions. Within most leg motor modules, the synaptic weights of each premotor neuron are proportional to the size of their target MNs, establishing a circuit basis for hierarchical MN recruitment. In contrast, wing premotor networks lack proportional synaptic connectivity, which may allow wing steering muscles to be recruited with different relative timing. By comparing the architecture of distinct limb motor control systems within the same animal, we identify common principles of premotor network organization and specializations that reflect the unique biomechanical constraints and evolutionary origins of leg and wing motor control.

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bioRxiv2023

Integrating EM and Patch-seq data: Synaptic connectivity and target specificity of predicted Sst transcriptomic types

Neural circuit function is shaped both by the cell types that comprise the circuit and the connections between those cell types1. Neural cell types have previously been defined by morphology 2, 3, electrophysiology 4, 5, transcriptomic expression 6-8, connectivity 9-13, or even a combination of such modalities 14-16. More recently, the Patch-seq technique has enabled the characterization of morphology (M), electrophysiology (E), and transcriptomic (T) properties from individual cells 17-20. Using this technique, these properties were integrated to define 28, inhibitory multimodal, MET-types in mouse primary visual cortex 21. It is unknown how these MET-types connect within the broader cortical circuitry however. Here we show that we can predict the MET-type identity of inhibitory cells within a large-scale electron microscopy (EM) dataset and these MET-types have distinct ultrastructural features and synapse connectivity patterns. We found that EM Martinotti cells, a well defined morphological cell type 22, 23 known to be Somatostatin positive (Sst+) 24, 25, were successfully predicted to belong to Sst+ MET-types. Each identified MET-type had distinct axon myelination patterns and synapsed onto specific excitatory targets. Our results demonstrate that morphological features can be used to link cell type identities across imaging modalities, which enables further comparison of connectivity in relation to transcriptomic or electrophysiological properties. Furthermore, our results show that MET-types have distinct connectivity patterns, supporting the use of MET-types and connectivity to meaningfully define cell types.

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bioRxiv2023

NEURD: A mesh decomposition framework forautomated proofreading and morphologicalanalysis of neuronal EM reconstructions

We are now in the era of millimeter-scale electron microscopy (EM) volumes collected at nanometer resolution (Shapson-Coe et al., 2021; Consortium et al., 2021). Dense reconstruction of cellular compartments in these EM volumes has been enabled by recent advances in Machine Learning (ML) (Lee et al., 2017; Wu et al., 2021; Lu et al., 2021; Macrina et al., 2021). Automated segmentation methods produce exceptionally accurate reconstructions of cells, but post-hoc proofreading is still required to generate large connectomes free of merge and split errors. The elaborate 3-D meshes of neurons in these volumes contain detailed morphological information at multiple scales, from the diameter, shape, and branching patterns of axons and dendrites, down to the fine-scale structure of dendritic spines. However, extracting these features can require substantial effort to piece together existing tools into custom workflows. Building on existing open-source software for mesh manipulation, here we present "NEURD", a software package that decomposes meshed neurons into compact and extensively-annotated graph representations. With these feature-rich graphs, we automate a variety of tasks such as state of the art automated proofreading of merge errors, cell classification, spine detection, axon-dendritic proximities, and other annotations. These features enable many downstream analyses of neural morphology and connectivity, making these massive and complex datasets more accessible to neuroscience researchers focused on a variety of scientific questions.

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bioRxiv2023

Functional connectomics reveals general wiring rule in mouse visual cortex

Understanding the relationship between circuit connectivity and function is crucial for uncovering how the brain implements computation. In the mouse primary visual cortex (V1), excitatory neurons with similar response properties are more likely to be synaptically connected, but previous studies have been limited to within V1, leaving much unknown about broader connectivity rules. In this study, we leverage the millimeter-scale MICrONS dataset to analyze synaptic connectivity and functional properties of individual neurons across cortical layers and areas. Our results reveal that neurons with similar responses are preferentially connected both within and across layers and areas -- including feedback connections -- suggesting the universality of the like-to-like connectivity across the visual hierarchy. Using a validated digital twin model, we separated neuronal tuning into feature (what neurons respond to) and spatial (receptive field location) components. We found that only the feature component predicts fine-scale synaptic connections, beyond what could be explained by the physical proximity of axons and dendrites. We also found a higher-order rule where postsynaptic neuron cohorts downstream of individual presynaptic cells show greater functional similarity than predicted by a pairwise like-to-like rule. Notably, recurrent neural networks (RNNs) trained on a simple classification task develop connectivity patterns mirroring both pairwise and higher-order rules, with magnitude similar to those in the MICrONS data. Lesion studies in these RNNs reveal that disrupting like-to-like connections has a significantly greater impact on performance compared to lesions of random connections. These findings suggest that these connectivity principles may play a functional role in sensory processing and learning, highlighting shared principles between biological and artificial systems.

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bioRxiv2023

Cell-type-specific inhibitory circuitry from a connectomic census of mouse visual cortex

Mammalian cortex features a vast diversity of neuronal cell types, each with characteristic anatomical, molecular and functional properties. Synaptic connectivity powerfully shapes how each cell type participates in the cortical circuit, but mapping connectivity rules at the resolution of distinct cell types remains difficult. Here, we used millimeter-scale volumetric electron microscopy1 to investigate the connectivity of all inhibitory neurons across a densely-segmented neuronal population of 1352 cells spanning all layers of mouse visual cortex, producing a wiring diagram of inhibitory connections with more than 70,000 synapses. Taking a data-driven approach inspired by classical neuroanatomy, we classified inhibitory neurons based on the relative targeting of dendritic compartments and other inhibitory cells and developed a novel classification of excitatory neurons based on the morphological and synaptic input properties. The synaptic connectivity between inhibitory cells revealed a novel class of disinhibitory specialist targeting basket cells, in addition to familiar subclasses. Analysis of the inhibitory connectivity onto excitatory neurons found widespread specificity, with many interneurons exhibiting differential targeting of certain subpopulations spatially intermingled with other potential targets. Inhibitory targeting was organized into "motif groups," diverse sets of cells that collectively target both perisomatic and dendritic compartments of the same excitatory targets. Collectively, our analysis identified new organizing principles for cortical inhibition and will serve as a foundation for linking modern multimodal neuronal atlases with the cortical wiring diagram.

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bioRxiv2022

Large-scale unsupervised discovery of excitatory morphological cell types in mouse visual cortex

Neurons in the neocortex exhibit astonishing morphological diversity which is critical for properly wiring neural circuits and giving neurons their functional properties. However, the organizational principles underlying this morphological diversity remain an open question. Here, we took a data-driven approach using graph-based machine learning methods to obtain a low-dimensional morphological "bar code" describing more than 30,000 excitatory neurons in mouse visual areas V1, AL and RL that were reconstructed from the millimeter scale MICrONS serial-section electron microscopy volume. Contrary to previous classifications into discrete morphological types (m-types), our data-driven approach suggests that the morphological landscape of cortical excitatory neurons is better described as a continuum, with a few notable exceptions in layers 5 and 6. Dendritic morphologies in layers 2-3 exhibited a trend towards a decreasing width of the dendritic arbor and a smaller tuft with increasing cortical depth. Inter-area differences were most evident in layer 4, where V1 contained more atufted neurons than higher visual areas. Moreover, we discovered neurons in V1 on the border to layer 5 which avoided deeper layers with their dendrites. In summary, we suggest that excitatory neurons morphological diversity is better understood by considering axes of variation than using distinct m-types.

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bioRxiv2022

Tools for comprehensive reconstruction and analysis of Drosophila motor circuits

Like the vertebrate spinal cord, the insect ventral nerve cord (VNC) mediates limb sensation and motor control. Here, we apply automated tools for electron microscopy volume alignment, neuron segmentation, and synapse prediction toward creating a connectome of an adult female Drosophila VNC. To interpret a connectome, it is crucial to know its relationship with the rest of the body. We therefore mapped the muscle targets of leg and wing motor neurons in the connectome by comparing their morphology to genetic driver lines, dye fills, and X-ray nano-tomography of the fly leg and wing. Knowing the outputs of the connectome allowed us to identify neural circuits that coordinate the wings and legs during escape takeoff. We provide the reconstruction of VNC circuits and motor neuron atlas, along with tools for programmatic and interactive access, as community resources to support experimental and theoretical studies of how the fly nervous system controls behavior.

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bioRxiv2022

Fast imaging of millimeter-scale areas with beam deflection transmission electron microscopy

Serial section transmission electron microscopy (TEM) has proven to be one of the leading methods for millimeter-scale 3D imaging of brain tissues at nanoscale resolution. It is important to further improve imaging efficiency to acquire larger and more brain volumes. We report here a three fold increase in the speed of TEM by using a beam deflecting mechanism to enable highly efficient acquisition of multiple image tiles (nine) for each motion of the mechanical stage. For millimeter-scale areas, the duty cycle of imaging doubles to more than 30%, yielding a net average imaging rate of 0.3 gigapixels per second. If fully utilized, an array of beam deflection TEMs should be capable of imaging a dataset of cubic millimeter scale in several weeks.

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bioRxiv2022

Special Nuclear Layer Contacts Among Starburst Amacrine Cells in the Mouse Retina

We observed novel classes of cell-cell contacts between retinal starburst amacrine neurons, from finely detailed morphological reconstructions of cells from an electron microscopic image volume of a mouse retina. These contacts have peculiar morphological patterns and traits, different among the respective On and Off starburst amacrine subpopulations, but both occur within the soma layers as opposed to their regular laminae of contact within the inner plexiform layer.

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bioRxiv2022

Quantitative Census of Local Somatic Features in Mouse Visual Cortex

Mammalian neocortex contains a highly diverse set of cell types. These types have been mapped systematically using a variety of molecular, electrophysiological and morphological approaches. Each modality offers new perspectives on the variation of biological processes underlying cell type specialization. Cellular scale electron microscopy (EM) provides dense ultrastructural examination and an unbiased perspective into the subcellular organization of brain cells, including their synaptic connectivity and nanometer scale morphology. It also presents a clear challenge for analysis to identify cell-types in data that contains tens of thousands of neurons, most of which have incomplete reconstructions. To address this challenge, we present the first systematic survey of the somatic region of all cells within a cubic millimeter of cortex using quantitative features obtained from EM. This analysis demonstrates a surprising sufficiency of the perisomatic region to identify cell-types, including types defined primarily based on their connectivity patterns. We then describe how this classification facilitates cell type specific connectivity characterization and locating cells with rare connectivity patterns in the dataset.

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bioRxiv2022

Petascale pipeline for precise alignment of images from serial section electron microscopy

The reconstruction of neural circuits from serial section electron microscopy (ssEM) images is being accelerated by automatic image segmentation methods. Segmentation accuracy is often limited by the preceding step of aligning 2D section images to create a 3D image stack. Precise and robust alignment in the presence of image artifacts is challenging, especially as datasets are attaining the petascale. We present a computational pipeline for aligning ssEM images with several key elements. Self-supervised convolutional nets are trained via metric learning to encode and align image pairs, and they are used to initialize iterative fine-tuning of alignment. A procedure called vector voting increases robustness to image artifacts or missing image data. For speedup the series is divided into blocks that are distributed to computational workers for alignment. The blocks are aligned to each other by composing transformations with decay, which achieves a global alignment without resorting to a time-consuming global optimization. We apply our pipeline to a whole fly brain dataset, and show improved accuracy relative to prior state of the art. We also demonstrate that our pipeline scales to a cubic millimeter of mouse visual cortex. Our pipeline is publicly available through two open source Python packages.

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bioRxiv2021

3D reconstruction of cell nuclei in a full Drosophila brain

We reconstructed all cell nuclei in a 3D image of a Drosophila brain acquired by serial section electron microscopy (EM). The total number of nuclei is approximately 133,000, at least 87% of which belong to neurons. Neuronal nuclei vary from several hundred down to roughly 5 cubic micrometers. Glial nuclei can be even smaller. The optic lobes contain more than two times the number of cells than the central brain. Our nuclear reconstruction serves as a spatial map and index to the cells in a Drosophila brain.

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