DeepBouton: Automated Identification of Single-Neuron Axonal Boutons at the Brain-Wide Scale

A Multi-Scale Neuron Morphometry Dataset from Peta-voxel Mouse Whole-Brain Images

Neuron morphology and sub-neuronal patterns offer vital insights into cell typing and the structural organization of brain networks. The community-collaborative BRAIN Initiative Cell Census Network (BICCN) project has yielded a vast amount of whole-brain imaging data. However, reconstructing multi-scale neuron morphometry at a whole-brain scale requires not only the integration of diverse hardware devices, tools, and algorithms but also a dedicated production workflow. To address these challenges, we developed a cloud-based, collaborative platform capable of handling peta-scale imaging data. Using this platform, we generated the largest multi-scale morphometry dataset from hundreds of sparsely labeled mouse brains. The morphometry dataset comprises 182,497 annotated cell bodies, 15,441 locally traced morphologies, and 1,876 fully reconstructed morphologies. We also identified sub-neuronal arborizations for both axons and dendrites, along with the primary axonal tracts connecting them. In addition, we identified 2.63 million putative boutons. All morphometric data were registered to the Allen Common Coordinate Framework (CCF) atlas. The morphometry dataset has proven to be an invaluable resource for whole-brain cross-scale morphological studies in mouse.

Neuronal diversity and stereotypy at multiple scales through whole brain morphometry

We conducted a large-scale whole-brain morphometry study by analyzing 3.7 peta-voxels of mouse brain images at the single-cell resolution, producing one of the largest multi-morphometry databases of mammalian brains to date. We registered 204 mouse brains of three major imaging modalities to the Allen Common Coordinate Framework (CCF) atlas, annotated 182,497 neuronal cell bodies, modeled 15,441 dendritic microenvironments, characterized the full morphology of 1876 neurons along with their axonal motifs, and detected 2.63 million axonal varicosities that indicate potential synaptic sites. Our analyzed six levels of information related to neuronal populations, dendritic microenvironments, single-cell full morphology, dendritic and axonal arborization, axonal varicosities, and sub-neuronal structural motifs, along with a quantification of the diversity and stereotypy of patterns at each level. This integrative study provides key anatomical descriptions of neurons and their types across a multiple scales and features, contributing a substantial resource for understanding neuronal diversity in mammalian brains.

Microglia regulate sleep through calcium-dependent modulation of norepinephrine transmission

Sleep interacts reciprocally with immune system activity, but its specific relationship with microglia-the resident immune cells in the brain-remains poorly understood. Here, we show in mice that microglia can regulate sleep through a mechanism involving Gi-coupled GPCRs, intracellular Ca2+ signaling and suppression of norepinephrine transmission. Chemogenetic activation of microglia Gi signaling strongly promoted sleep, whereas pharmacological blockade of Gi-coupled P2Y12 receptors decreased sleep. Two-photon imaging in the cortex showed that P2Y12-Gi activation elevated microglia intracellular Ca2+, and blockade of this Ca2+ elevation largely abolished the Gi-induced sleep increase. Microglia Ca2+ level also increased at natural wake-to-sleep transitions, caused partly by reduced norepinephrine levels. Furthermore, imaging of norepinephrine with its biosensor in the cortex showed that microglia P2Y12-Gi activation significantly reduced norepinephrine levels, partly by increasing the adenosine concentration. These findings indicate that microglia can regulate sleep through reciprocal interactions with norepinephrine transmission.

Excitatory nucleo-olivary pathway shapes cerebellar outputs for motor control

The brain generates predictive motor commands to control the spatiotemporal precision of high-velocity movements. Yet, how the brain organizes automated internal feedback to coordinate the kinematics of such fast movements is unclear. Here we unveil a unique nucleo-olivary loop in the cerebellum and its involvement in coordinating high-velocity movements. Activating the excitatory nucleo-olivary pathway induces well-timed internal feedback complex spike signals in Purkinje cells to shape cerebellar outputs. Anatomical tracing reveals extensive axonal collaterals from the excitatory nucleo-olivary neurons to downstream motor regions, supporting integration of motor output and internal feedback signals within the cerebellum. This pathway directly drives saccades and head movements with a converging direction, while curtailing their amplitude and velocity via the powerful internal feedback mechanism. Our finding challenges the long-standing dogma that the cerebellum inhibits the inferior olivary pathway and provides a new circuit mechanism for the cerebellar control of high-velocity movements.

BoutonNet: an automatic method to detect anterogradely labeled presynaptic boutons in brain tissue sections

Neurons emit axons, which form synapses, the fundamental unit of the nervous system. Neuroscientists use genetic anterograde tracing methods to label the synaptic output of specific neuronal subpopulations, but the resulting data sets are too large for manual analysis, and current automated methods have significant limitations in cost and quality. In this paper, we describe a pipeline optimized to identify anterogradely labeled presynaptic boutons in brain tissue sections. Our histologic pipeline labels boutons with high sensitivity and low background. To automatically detect labeled boutons in slide-scanned tissue sections, we developed BoutonNet. This detector uses a two-step approach: an intensity-based method proposes possible boutons, which are checked by a neural network-based confirmation step. BoutonNet was compared to expert annotation on a separate validation data set and achieved a result within human inter-rater variance. This open-source technique will allow quantitative analysis of the fundamental unit of the brain on a whole-brain scale.

Petabyte-Scale Multi-Morphometry of Single Neurons for Whole Brains

Recent advances in brain imaging allow producing large amounts of 3-D volumetric data from which morphometry data is reconstructed and measured. Fine detailed structural morphometry of individual neurons, including somata, dendrites, axons, and synaptic connectivity based on digitally reconstructed neurons, is essential for cataloging neuron types and their connectivity. To produce quality morphometry at large scale, it is highly desirable but extremely challenging to efficiently handle petabyte-scale high-resolution whole brain imaging database. Here, we developed a multi-level method to produce high quality somatic, dendritic, axonal, and potential synaptic morphometry, which was made possible by utilizing necessary petabyte hardware and software platform to optimize both the data and workflow management. Our method also boosts data sharing and remote collaborative validation. We highlight a petabyte application dataset involving 62 whole mouse brains, from which we identified 50,233 somata of individual neurons, profiled the dendrites of 11,322 neurons, reconstructed the full 3-D morphology of 1,050 neurons including their dendrites and full axons, and detected 1.9 million putative synaptic sites derived from axonal boutons. Analysis and simulation of these data indicate the promise of this approach for modern large-scale morphology applications.

Niwaki Instead of Random Forests: Targeted Serial Sectioning Scanning Electron Microscopy With Reimaging Capabilities for Exploring Central Nervous System Cell Biology and Pathology

Ultrastructural analysis of discrete neurobiological structures by volume scanning electron microscopy (SEM) often constitutes a "needle-in-the-haystack" problem and therefore relies on sophisticated search strategies. The appropriate SEM approach for a given relocation task not only depends on the desired final image quality but also on the complexity and required accuracy of the screening process. Block-face SEM techniques like Focused Ion Beam or serial block-face SEM are "one-shot" imaging runs by nature and, thus, require precise relocation prior to acquisition. In contrast, "multi-shot" approaches conserve the sectioned tissue through the collection of serial sections onto solid support and allow reimaging. These tissue libraries generated by Array Tomography or Automated Tape Collecting Ultramicrotomy can be screened at low resolution to target high resolution SEM. This is particularly useful if a structure of interest is rare or has been predetermined by correlated light microscopy, which can assign molecular, dynamic and functional information to an ultrastructure. As such approaches require bridging mm to nm scales, they rely on tissue trimming at different stages of sample processing. Relocation is facilitated by endogenous or exogenous landmarks that are visible by several imaging modalities, combined with appropriate registration strategies that allow overlaying images of various sources. Here, we discuss the opportunities of using multi-shot serial sectioning SEM approaches, as well as suitable trimming and registration techniques, to slim down the high-resolution imaging volume to the actual structure of interest and hence facilitate ambitious targeted volume SEM projects.

Chemical sectioning fluorescence tomography: high-throughput, high-contrast, multicolor, whole-brain imaging at subcellular resolution

A thorough neuroanatomical study of the brain architecture is crucial for understanding its cellular compositions, connections, and working mechanisms. However, the fine- and multiscale features of neuron structures make it challenging for microscopic imaging, as it requires high contrast and high throughput simultaneously. Here, we propose chemical sectioning fluorescence tomography (CSFT) to solve this problem. By chemically switching OFF/ON the fluorescent state of the labeled proteins (FPs), we light only the top layer as thin as submicron for imaging without background interference. Combined with the wide-field fluorescence micro-optical sectioning tomography (fMOST) system, we have shown multicolor CSFT imaging. We also demonstrate mouse whole-brain imaging at the subcellular resolution, as well as the power for quantitative acquisition of synaptic-connection-related pyramidal dendritic spines and axon boutons on the brain-wide scale at the complete single-neuron level. We believe that the CSFT method would greatly facilitate our understanding of the brain-wide neuron networks.

Skeleton optimization of neuronal morphology based on three-dimensional shape restrictions

Background

Neurons are the basic structural unit of the brain, and their morphology is a key determinant of their classification. The morphology of a neuronal circuit is a fundamental component in neuron modeling. Recently, single-neuron morphologies of the whole brain have been used in many studies. The correctness and completeness of semimanually traced neuronal morphology are credible. However, there are some inaccuracies in semimanual tracing results. The distance between consecutive nodes marked by humans is very long, spanning multiple voxels. On the other hand, the nodes are marked around the centerline of the neuronal fiber, not on the centerline. Although these inaccuracies do not seriously affect the projection patterns that these studies focus on, they reduce the accuracy of the traced neuronal skeletons. These small inaccuracies will introduce deviations into subsequent studies that are based on neuronal morphology files.

Results

We propose a neuronal digital skeleton optimization method to evaluate and make fine adjustments to a digital skeleton after neuron tracing. Provided that the neuronal fiber shape is smooth and continuous, we describe its physical properties according to two shape restrictions. One restriction is designed based on the grayscale image, and the other is designed based on geometry. These two restrictions are designed to finely adjust the digital skeleton points to the neuronal fiber centerline. With this method, we design the three-dimensional shape restriction workflow of neuronal skeleton adjustment computation. The performance of the proposed method has been quantitatively evaluated using synthetic and real neuronal image data. The results show that our method can reduce the difference between the traced neuronal skeleton and the centerline of the neuronal fiber. Furthermore, morphology metrics such as the neuronal fiber length and radius become more precise.

Conclusions

This method can improve the accuracy of a neuronal digital skeleton based on traced results. The greater the accuracy of the digital skeletons that are acquired, the more precise the neuronal morphologies that are analyzed will be.

A Single-Neuron: Current Trends and Future Prospects

The brain is an intricate network with complex organizational principles facilitating a concerted communication between single-neurons, distinct neuron populations, and remote brain areas. The communication, technically referred to as connectivity, between single-neurons, is the center of many investigations aimed at elucidating pathophysiology, anatomical differences, and structural and functional features. In comparison with bulk analysis, single-neuron analysis can provide precise information about neurons or even sub-neuron level electrophysiology, anatomical differences, pathophysiology, structural and functional features, in addition to their communications with other neurons, and can promote essential information to understand the brain and its activity. This review highlights various single-neuron models and their behaviors, followed by different analysis methods. Again, to elucidate cellular dynamics in terms of electrophysiology at the single-neuron level, we emphasize in detail the role of single-neuron mapping and electrophysiological recording. We also elaborate on the recent development of single-neuron isolation, manipulation, and therapeutic progress using advanced micro/nanofluidic devices, as well as microinjection, electroporation, microelectrode array, optical transfection, optogenetic techniques. Further, the development in the field of artificial intelligence in relation to single-neurons is highlighted. The review concludes with between limitations and future prospects of single-neuron analyses.