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.

How to Build the Virtual Cell with Artificial Intelligence: Priorities and Opportunities.

The cell is arguably the most fundamental unit of life and is central to understanding biology. Accurate modeling of cells is important for this understanding as well as for determining the root causes of disease. Recent advances in artificial intelligence (AI), combined with the ability to generate large-scale experimental data, present novel opportunities to model cells.

Multiplexed volumetric CLEM enabled by scFvs provides insights into the cytology of cerebellar cortex.

Mapping neuronal networks is a central focus in neuroscience. While volume electron microscopy (vEM) can reveal the fine structure of neuronal networks (connectomics), it does not provide molecular information to identify cell types or functions. We developed an approach that uses fluorescent single-chain variable fragments (scFvs) to perform multiplexed detergent-free immunolabeling and volumetric-correlated-light-and-electron-microscopy on the same sample.

A petavoxel fragment of human cerebral cortex reconstructed at nanoscale resolution.

To fully understand how the human brain works, knowledge of its structure at high resolution is needed. Presented here is a computationally intensive reconstruction of the ultrastructure of a cubic millimeter of human temporal cortex that was surgically removed to gain access to an underlying epileptic focus. It contains about 57,000 cells, about 230 millimeters of blood vessels, and about 150 million synapses and comprises 1.

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 elucidate fundamental organizational principles of neural circuits. Nanometer-resolution imaging of brain tissue provides the necessary raw data, but inferring cellular and subcellular annotation layers is challenging. We present segmentation-guided contrastive learning of representations (SegCLR), a self-supervised machine learning technique that produces representations of cells directly from 3D imagery and segmentations.

Mapping Alzheimer's Molecular Pathologies in Large-Scale Connectomics Data: A Publicly Accessible Correlative Microscopy Resource.

Connectomics using volume-electron-microscopy enables mapping and analysis of neuronal networks, revealing insights into neural circuit function and dysfunction. In Alzheimer's disease (AD), where amyloid-β (Aβ) and hyperphosphorylated-Tau (pTau) are implicated, connectomics offers an approach to unravel how these molecules contribute to circuit alterations by enabling the study of these molecules within the context of the complete local neuronal and glial milieu. We present a volumetric-correlated-light-and-electron microscopy (vCLEM) protocol using fluorescent nanobodies to localize Aβ and pTau within a large-scale connectomics dataset from the hippocampus of the 3xTg AD mouse model.

Mapping of neuronal and glial primary cilia contactome and connectome in the human cerebral cortex.

Primary cilia act as antenna receivers of environmental signals and enable effective neuronal or glial responses. Disruption of their function is associated with circuit disorders. To understand the signals these cilia receive, we comprehensively mapped cilia's contacts within the human cortical connectome using serial-section EM reconstruction of a 1 mm cortical volume, spanning the entire cortical thickness.

Multiplexed volumetric CLEM enabled by antibody derivatives provides new insights into the cytology of the mouse cerebellar cortex.

Mapping neuronal networks that underlie behavior has become a central focus in neuroscience. While serial section electron microscopy (ssEM) can reveal the fine structure of neuronal networks (connectomics), it does not provide the molecular information that helps identify cell types or their functional properties. Volumetric correlated light and electron microscopy (vCLEM) combines ssEM and volumetric fluorescence microscopy to incorporate molecular labeling into ssEM datasets.

Multiplexed volumetric CLEM enabled by antibody derivatives provides new insights into the cytology of the mouse cerebellar cortex.

Mapping neuronal networks that underlie behavior has become a central focus in neuroscience. While serial section electron microscopy (ssEM) can reveal the fine structure of neuronal networks (connectomics), it does not provide the molecular information that helps identify cell types or their functional properties. Volumetric correlated light and electron microscopy (vCLEM) combines ssEM and volumetric fluorescence microscopy to incorporate molecular labeling into ssEM datasets.

SyConn2: dense synaptic connectivity inference for volume electron microscopy.

The ability to acquire ever larger datasets of brain tissue using volume electron microscopy leads to an increasing demand for the automated extraction of connectomic information. We introduce SyConn2, an open-source connectome analysis toolkit, which works with both on-site high-performance compute environments and rentable cloud computing clusters. SyConn2 was tested on connectomic datasets with more than 10 million synapses, provides a web-based visualization interface and makes these data amenable to complex anatomical and neuronal connectivity queries.

Structured sampling of olfactory input by the fly mushroom body.

Associative memory formation and recall in the fruit fly Drosophila melanogaster is subserved by the mushroom body (MB). Upon arrival in the MB, sensory information undergoes a profound transformation from broadly tuned and stereotyped odorant responses in the olfactory projection neuron (PN) layer to narrowly tuned and nonstereotyped responses in the Kenyon cells (KCs). Theory and experiment suggest that this transformation is implemented by random connectivity between KCs and PNs.

The Mind of a Mouse.

Large scientific projects in genomics and astronomy are influential not because they answer any single question but because they enable investigation of continuously arising new questions from the same data-rich sources. Advances in automated mapping of the brain's synaptic connections (connectomics) suggest that the complicated circuits underlying brain function are ripe for analysis. We discuss benefits of mapping a mouse brain at the level of synapses.

A connectome and analysis of the adult central brain.

The neural circuits responsible for animal behavior remain largely unknown. We summarize new methods and present the circuitry of a large fraction of the brain of the fruit fly . Improved methods include new procedures to prepare, image, align, segment, find synapses in, and proofread such large data sets.

Learning cellular morphology with neural networks.

Reconstruction and annotation of volume electron microscopy data sets of brain tissue is challenging but can reveal invaluable information about neuronal circuits. Significant progress has recently been made in automated neuron reconstruction as well as automated detection of synapses. However, methods for automating the morphological analysis of nanometer-resolution reconstructions are less established, despite the diversity of possible applications.

High-precision automated reconstruction of neurons with flood-filling networks.

Reconstruction of neural circuits from volume electron microscopy data requires the tracing of cells in their entirety, including all their neurites. Automated approaches have been developed for tracing, but their error rates are too high to generate reliable circuit diagrams without extensive human proofreading. We present flood-filling networks, a method for automated segmentation that, similar to most previous efforts, uses convolutional neural networks, but contains in addition a recurrent pathway that allows the iterative optimization and extension of individual neuronal processes.

Connectomic reconstruction of the inner plexiform layer in the mouse retina.

Comprehensive high-resolution structural maps are central to functional exploration and understanding in biology. For the nervous system, in which high resolution and large spatial extent are both needed, such maps are scarce as they challenge data acquisition and analysis capabilities. Here we present for the mouse inner plexiform layer--the main computational neuropil region in the mammalian retina--the dense reconstruction of 950 neurons and their mutual contacts.

Machines that learn to segment images: a crucial technology for connectomics.

Connections between neurons can be found by checking whether synapses exist at points of contact, which in turn are determined by neural shapes. Finding these shapes is a special case of image segmentation, which is laborious for humans and would ideally be performed by computers. New metrics properly quantify the performance of a computer algorithm using its disagreement with 'true' segmentations of example images.

Convolutional networks can learn to generate affinity graphs for image segmentation.

Many image segmentation algorithms first generate an affinity graph and then partition it. We present a machine learning approach to computing an affinity graph using a convolutional network (CN) trained using ground truth provided by human experts. The CN affinity graph can be paired with any standard partitioning algorithm and improves segmentation accuracy significantly compared to standard hand-designed affinity functions.