NEURD: automated proofreading and feature extraction for connectomics

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 can now yield exceptionally accurate reconstructions of cells, but despite this accuracy, laborious post-hoc proofreading is still required to generate large connectomes free of merge and split errors. The elaborate 3-D meshes of neurons produced by these segmentations contain detailed morphological information, from the diameter, shape, and branching patterns of axons and dendrites, down to the fine-scale structure of dendritic spines. However, extracting information about 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 each meshed neuron into a compact and extensively-annotated graph representation. With these feature-rich graphs, we implement workflows to automate a variety of tasks that would otherwise require extensive manual effort, such as state of the art automated post-hoc proofreading of merge errors, cell classification, spine detection, axon-dendritic proximities, and computation of other features. These features enable many downstream analyses of neural morphology and connectivity, making these new massive and complex datasets more accessible to neuroscience researchers focused on a variety of scientific questions.

Comparative connectomics of dauer reveals developmental plasticity

A fundamental question in neurodevelopmental biology is how flexibly the nervous system changes during development. To address this, we reconstructed the chemical connectome of dauer, an alternative developmental stage of nematodes with distinct behavioral characteristics, by volumetric reconstruction and automated synapse detection using deep learning. With the basic architecture of the nervous system preserved, structural changes in neurons, large or small, were closely associated with connectivity changes, which in turn evoked dauer-specific behaviors such as nictation. Graph theoretical analyses revealed significant dauer-specific rewiring of sensory neuron connectivity and increased clustering within motor neurons in the dauer connectome. We suggest that the nervous system in the nematode has evolved to respond to harsh environments by developing a quantitatively and qualitatively differentiated connectome.

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.

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.

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 CAVE's 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.

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 5×107 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.

Functional connectomics reveals general wiring rule in mouse visual cortex

To understand how the brain computes, it is important to unravel the relationship between circuit connectivity and function. Previous research has shown that excitatory neurons in layer 2/3 of the primary visual cortex of mice with similar response properties are more likely to form connections. However, technical challenges of combining synaptic connectivity and functional measurements have limited these studies to few, highly local connections. Utilizing the millimeter scale and nanometer resolution of the MICrONS dataset, we studied the connectivity-function relationship in excitatory neurons of the mouse visual cortex across interlaminar and interarea projections, assessing connection selectivity at the coarse axon trajectory and fine synaptic formation levels. A digital twin model of this mouse, that accurately predicted responses to arbitrary video stimuli, enabled a comprehensive characterization of the function of neurons. We found that neurons with highly correlated responses to natural videos tended to be connected with each other, not only within the same cortical area but also across multiple layers and visual areas, including feedforward and feedback connections, whereas we did not find that orientation preference predicted connectivity. The digital twin model separated each neuron's tuning into a feature component (what the neuron responds to) and a spatial component (where the neuron's receptive field is located). We show that the feature, but not the spatial component, predicted which neurons were connected at the fine synaptic scale. Together, our results demonstrate the "like-to-like" connectivity rule generalizes to multiple connection types, and the rich MICrONS dataset is suitable to further refine a mechanistic understanding of circuit structure and function.

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 types ¹ . 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.

Igneous: Distributed dense 3D segmentation meshing, neuron skeletonization, and hierarchical downsampling

Three-dimensional electron microscopy images of brain tissue and their dense segmentations are now petascale and growing. These volumes require the mass production of dense segmentation-derived neuron skeletons, multi-resolution meshes, image hierarchies (for both modalities) for visualization and analysis, and tools to manage the large amount of data. However, open tools for large-scale meshing, skeletonization, and data management have been missing. Igneous is a Python-based distributed computing framework that enables economical meshing, skeletonization, image hierarchy creation, and data management using cloud or cluster computing that has been proven to scale horizontally. We sketch Igneous's computing framework, show how to use it, and characterize its performance and data storage.

Reconstruction of neocortex: Organelles, compartments, cells, circuits, and activity

We assembled a semi-automated reconstruction of L2/3 mouse primary visual cortex from ∼250 × 140 × 90 μm3 of electron microscopic images, including pyramidal and non-pyramidal neurons, astrocytes, microglia, oligodendrocytes and precursors, pericytes, vasculature, nuclei, mitochondria, and synapses. Visual responses of a subset of pyramidal cells are included. The data are publicly available, along with tools for programmatic and three-dimensional interactive access. Brief vignettes illustrate the breadth of potential applications relating structure to function in cortical circuits and neuronal cell biology. Mitochondria and synapse organization are characterized as a function of path length from the soma. Pyramidal connectivity motif frequencies are predicted accurately using a configuration model of random graphs. Pyramidal cells receiving more connections from nearby cells exhibit stronger and more reliable visual responses. Sample code shows data access and analysis.

RealNeuralNetworks.jl: An Integrated Julia Package for Skeletonization, Morphological Analysis, and Synaptic Connectivity Analysis of Terabyte-Scale 3D Neural Segmentations

Benefiting from the rapid development of electron microscopy imaging and deep learning technologies, an increasing number of brain image datasets with segmentation and synapse detection are published. Most of the automated segmentation methods label voxels rather than producing neuron skeletons directly. A further skeletonization step is necessary for quantitative morphological analysis. Currently, several tools are published for skeletonization as well as morphological and synaptic connectivity analysis using different computer languages and environments. Recently the Julia programming language, notable for elegant syntax and high performance, has gained rapid adoption in the scientific computing community. Here, we present a Julia package, called RealNeuralNetworks.jl, for efficient sparse skeletonization, morphological analysis, and synaptic connectivity analysis. Based on a large-scale Zebrafish segmentation dataset, we illustrate the software features by performing distributed skeletonization in Google Cloud, clustering the neurons using the NBLAST algorithm, combining morphological similarity and synaptic connectivity to study their relationship. We demonstrate that RealNeuralNetworks.jl is suitable for use in terabyte-scale electron microscopy image segmentation datasets.

FlyWire: online community for whole-brain connectomics

Due to advances in automated image acquisition and analysis, whole-brain connectomes with 100,000 or more neurons are on the horizon. Proofreading of whole-brain automated reconstructions will require many person-years of effort, due to the huge volumes of data involved. Here we present FlyWire, an online community for proofreading neural circuits in a Drosophila melanogaster brain and explain how its computational and social structures are organized to scale up to whole-brain connectomics. Browser-based three-dimensional interactive segmentation by collaborative editing of a spatially chunked supervoxel graph makes it possible to distribute proofreading to individuals located virtually anywhere in the world. Information in the edit history is programmatically accessible for a variety of uses such as estimating proofreading accuracy or building incentive systems. An open community accelerates proofreading by recruiting more participants and accelerates scientific discovery by requiring information sharing. We demonstrate how FlyWire enables circuit analysis by reconstructing and analyzing the connectome of mechanosensory neurons.

KITENIN recruits Dishevelled/PKC delta to form a functional complex and controls the migration and invasiveness of colorectal cancer cells

BACKGROUND AND AIMS

KITENIN was previously reported to promote metastasis in mouse colon tumour models; however, the signalling mechanism of KITENIN at the cellular level was unknown. Here the functional role of KITENIN with respect to colorectal cancer (CRC) cell invasion and its expression in CRC tissues were investigated.

METHODS

The effect of KITENIN on cell motility was analysed in a migration and invasion assay upon its overexpression and knockdown. Immunoprecipitation was used to elucidate binding partners, and immunohistochemistry was used to study expression levels.

RESULTS

KITENIN overexpression enhanced the migration of rat intestinal epithelial cells, whereas a loss of invasiveness was observed in CRC cells after KITENIN knockdown. Mechanically, KITENIN served as a scaffolding molecule that simultaneously recruited both Dishevelled (Dvl) and protein kinase C delta (PKC delta) through the membrane-spanning C-terminal region to form a complex that stimulated extracellular signal-regulated kinase (ERK)/activating protein-1 (AP-1) via a PKC delta component but also organised the actin filament via a Dvl component. The KITENIN complex controlled the invasiveness of CRC cells aetiologically harbouring various mutations in APC, beta-catenin or K-ras, in which AP-1 activation is redundant but the organisation of the actin filament is indispensable for cell motility. Clinically, KITENIN expression was significantly higher in colon cancer tissues from advanced stage (III, IV) than that of stage I CRC and also in corresponding metastatic tissues.

CONCLUSIONS

The functional KITENIN complex acts as an executor with regard to cell motility and thereby controls CRC cell invasion, which may contribute to promoting metastasis.