Morphological diversity of single neurons in molecularly defined cell types

Single-cell and spatial omics: exploring hypothalamic heterogeneity

Elucidating the complex dynamic cellular organization in the hypothalamus is critical for understanding its role in coordinating fundamental body functions. Over the past decade, single-cell and spatial omics technologies have significantly evolved, overcoming initial technical challenges in capturing and analyzing individual cells. These high-throughput omics technologies now offer a remarkable opportunity to comprehend the complex spatiotemporal patterns of transcriptional diversity and cell-type characteristics across the entire hypothalamus. Current single-cell and single-nucleus RNA sequencing methods comprehensively quantify gene expression by exploring distinct phenotypes across various subregions of the hypothalamus. However, single-cell/single-nucleus RNA sequencing requires isolating the cell/nuclei from the tissue, potentially resulting in the loss of spatial information concerning neuronal networks. Spatial transcriptomics methods, by bypassing the cell dissociation, can elucidate the intricate spatial organization of neural networks through their imaging and sequencing technologies. In this review, we highlight the applicative value of single-cell and spatial transcriptomics in exploring the complex molecular-genetic diversity of hypothalamic cell types, driven by recent high-throughput achievements.

Linking brain activity across scales with simultaneous opto- and electrophysiology

The brain enables adaptive behavior via the dynamic coordination of diverse neuronal signals across spatial and temporal scales: from fast action potential patterns in microcircuits to slower patterns of distributed activity in brain-wide networks. Understanding principles of multiscale dynamics requires simultaneous monitoring of signals in multiple, distributed network nodes. Combining optical and electrical recordings of brain activity is promising for collecting data across multiple scales and can reveal aspects of coordinated dynamics invisible to standard, single-modality approaches. We review recent progress in combining opto- and electrophysiology, focusing on mouse studies that shed new light on the function of single neurons by embedding their activity in the context of brain-wide activity patterns. Optical and electrical readouts can be tailored to desired scales to tackle specific questions. For example, fast dynamics in single cells or local populations recorded with multi-electrode arrays can be related to simultaneously acquired optical signals that report activity in specified subpopulations of neurons, in non-neuronal cells, or in neuromodulatory pathways. Conversely, two-photon imaging can be used to densely monitor activity in local circuits while sampling electrical activity in distant brain areas at the same time. The refinement of combined approaches will continue to reveal previously inaccessible and under-appreciated aspects of coordinated brain activity.

Novel strategies in Parkinson's disease treatment: a review

An unprecedented extension of life expectancy observed during the past century drastically increased the number of patients diagnosed with Parkinson's diseases (PD) worldwide. Estimated costs of PD alone reached $52 billion per year, making effective neuroprotective treatments an urgent and unmet need. Current treatments of both AD and PD focus on mitigating the symptoms associated with these pathologies and are not neuroprotective. In this review, we discuss the most advanced therapeutic strategies that can be used to treat PD. We also critically review the shift of the therapeutic paradigm from a small molecule-based inhibition of protein aggregation to the utilization of natural degradation pathways and immune cells that are capable of degrading toxic amyloid deposits in the brain of PD patients.

High-throughput analysis of dendrite and axonal arbors reveals transcriptomic correlates of neuroanatomy

Neuronal anatomy is central to the organization and function of brain cell types. However, anatomical variability within apparently homogeneous populations of cells can obscure such insights. Here, we report large-scale automation of neuronal morphology reconstruction and analysis on a dataset of 813 inhibitory neurons characterized using the Patch-seq method, which enables measurement of multiple properties from individual neurons, including local morphology and transcriptional signature. We demonstrate that these automated reconstructions can be used in the same manner as manual reconstructions to understand the relationship between some, but not all, cellular properties used to define cell types. We uncover gene expression correlates of laminar innervation on multiple transcriptomically defined neuronal subclasses and types. In particular, our results reveal correlates of the variability in Layer 1 (L1) axonal innervation in a transcriptomically defined subpopulation of Martinotti cells in the adult mouse neocortex.

A systematic evaluation of computational methods for cell segmentation

Cell segmentation is a fundamental task in analyzing biomedical images. Many computational methods have been developed for cell segmentation and instance segmentation, but their performances are not well understood in various scenarios. We systematically evaluated the performance of 18 segmentation methods to perform cell nuclei and whole cell segmentation using light microscopy and fluorescence staining images. We found that general-purpose methods incorporating the attention mechanism exhibit the best overall performance. We identified various factors influencing segmentation performances, including image channels, choice of training data, and cell morphology, and evaluated the generalizability of methods across image modalities. We also provide guidelines for choosing the optimal segmentation methods in various real application scenarios. We developed Seggal, an online resource for downloading segmentation models already pre-trained with various tissue and cell types, substantially reducing the time and effort for training cell segmentation models.

Molecular and cellular mechanisms of teneurin signaling in synaptic partner matching

In developing brains, axons exhibit remarkable precision in selecting synaptic partners among many non-partner cells. Evolutionarily conserved teneurins are transmembrane proteins that instruct synaptic partner matching. However, how intracellular signaling pathways execute teneurins' functions is unclear. Here, we use in situ proximity labeling to obtain the intracellular interactome of a teneurin (Ten-m) in the Drosophila brain. Genetic interaction studies using quantitative partner matching assays in both olfactory receptor neurons (ORNs) and projection neurons (PNs) reveal a common pathway: Ten-m binds to and negatively regulates a RhoGAP, thus activating the Rac1 small GTPases to promote synaptic partner matching. Developmental analyses with single-axon resolution identify the cellular mechanism of synaptic partner matching: Ten-m signaling promotes local F-actin levels and stabilizes ORN axon branches that contact partner PN dendrites. Combining spatial proteomics and high-resolution phenotypic analyses, this study advanced our understanding of both cellular and molecular mechanisms of synaptic partner matching.

Making Ramón y Cajal proud: Development of cell identity and diversity in the cerebral cortex

Since the beautiful images of Santiago Ramón y Cajal provided a first glimpse into the immense diversity and complexity of cell types found in the cerebral cortex, neuroscience has been challenged and inspired to understand how these diverse cells are generated and how they interact with each other to orchestrate the development of this remarkable tissue. Some fundamental questions drive the field's quest to understand cortical development: what are the mechanistic principles that govern the emergence of neuronal diversity? How do extrinsic and intrinsic signals integrate with physical forces and activity to shape cell identity? How do the diverse populations of neurons and glia influence each other during development to guarantee proper integration and function? The advent of powerful new technologies to profile and perturb cortical development at unprecedented resolution and across a variety of modalities has offered a new opportunity to integrate past knowledge with brand new data. Here, we review some of this progress using cortical excitatory projection neurons as a system to draw out general principles of cell diversification and the role of cell-cell interactions during cortical development.

Sex-specific and cell-specific regulation of ER stress and neuroinflammation after traumatic brain injury in juvenile mice

Endoplasmic reticulum (ER) stress and neuroinflammation play an important role in secondary brain damage after traumatic brain injury (TBI). Due to the complex brain cytoarchitecture, multiple cell types are affected by TBI. However, cell type-specific and sex-specific responses to ER stress and neuroinflammation remain unclear. Here we investigated differential regulation of ER stress and neuroinflammatory pathways in neurons and microglia during the acute phase post-injury in a mouse model of impact acceleration TBI in both males and females. We found that TBI resulted in significant weight loss only in males, and sensorimotor impairment and depressive-like behaviors in both males and females at the acute phase post-injury. By concurrently isolating neurons and microglia from the same brain sample of the same animal, we were able to evaluate the simultaneous responses in neurons and microglia towards ER stress and neuroinflammation in both males and females. We discovered that the ER stress and anti-inflammatory responses were significantly stronger in microglia, especially in female microglia, compared with the male and female neurons. Whereas the degree of phosphorylated-tau (pTau) accumulation was significantly higher in neurons, compared with the microglia. In conclusion, TBI resulted in behavioral deficits and cell type-specific and sex-specific responses to ER stress and neuroinflammation, and abnormal protein accumulation at the acute phase after TBI in immature mice.

Single-neuron projectome-guided analysis reveals the neural circuit mechanism underlying endogenous opioid antinociception

Endogenous opioid antinociception is a self-regulatory mechanism that reduces chronic pain, but its underlying circuit mechanism remains largely unknown. Here, we showed that endogenous opioid antinociception required the activation of mu-opioid receptors (MORs) in GABAergic neurons of the central amygdala nucleus (CEA) in a persistent-hyperalgesia mouse model. Pharmacogenetic suppression of these CEAMOR neurons, which mimics the effect of MOR activation, alleviated the persistent hyperalgesia. Furthermore, single-neuron projection analysis revealed multiple projectome-based subtypes of CEAMOR neurons, each innervating distinct target brain regions. We found that the suppression of axon branches projecting to the parabrachial nucleus (PB) of one subtype of CEAMOR neurons alleviated persistent hyperalgesia, indicating a subtype- and axonal-branch-specific mechanism of action. Further electrophysiological analysis revealed that suppression of a distinct CEA-PB disinhibitory circuit controlled endogenous opioid antinociception. Thus, this study identified the central neural circuit that underlies endogenous opioid antinociception, providing new insight into the endogenous pain modulatory mechanisms.

Deciphering brain cellular and behavioral mechanisms: Insights from single-cell and spatial RNA sequencing

The brain is a complex computing system composed of a multitude of interacting neurons. The computational outputs of this system determine the behavior and perception of every individual. Each brain cell expresses thousands of genes that dictate the cell's function and physiological properties. Therefore, deciphering the molecular expression of each cell is of great significance for understanding its characteristics and role in brain function. Additionally, the positional information of each cell can provide crucial insights into their involvement in local brain circuits. In this review, we briefly overview the principles of single-cell RNA sequencing and spatial transcriptomics, the potential issues and challenges in their data processing, and their applications in brain research. We further outline several promising directions in neuroscience that could be integrated with single-cell RNA sequencing, including neurodevelopment, the identification of novel brain microstructures, cognition and behavior, neuronal cell positioning, molecules and cells related to advanced brain functions, sleep-wake cycles/circadian rhythms, and computational modeling of brain function. We believe that the deep integration of these directions with single-cell and spatial RNA sequencing can contribute significantly to understanding the roles of individual cells or cell types in these specific functions, thereby making important contributions to addressing critical questions in those fields. This article is categorized under: RNA Evolution and Genomics > Computational Analyses of RNA RNA in Disease and Development > RNA in Development RNA in Disease and Development > RNA in Disease.

Claustrum neurons projecting to the anterior cingulate restrict engagement during sleep and behavior

The claustrum has been linked to attention and sleep. We hypothesized that this reflects a shared function, determining responsiveness to stimuli, which spans the axis of engagement. To test this hypothesis, we recorded claustrum population dynamics from male mice during both sleep and an attentional task ('ENGAGE'). Heightened activity in claustrum neurons projecting to the anterior cingulate cortex (ACCp) corresponded to reduced sensory responsiveness during sleep. Similarly, in the ENGAGE task, heightened ACCp activity correlated with disengagement and behavioral lapses, while low ACCp activity correlated with hyper-engagement and impulsive errors. Chemogenetic elevation of ACCp activity reduced both awakenings during sleep and impulsive errors in the ENGAGE task. Furthermore, mice employing an exploration strategy in the task showed a stronger correlation between ACCp activity and performance compared to mice employing an exploitation strategy which reduced task complexity. Our results implicate ACCp claustrum neurons in restricting engagement during sleep and goal-directed behavior.

Automated neuronal reconstruction with super-multicolour Tetbow labelling and threshold-based clustering of colour hues

Fluorescence imaging is widely used for the mesoscopic mapping of neuronal connectivity. However, neurite reconstruction is challenging, especially when neurons are densely labelled. Here, we report a strategy for the fully automated reconstruction of densely labelled neuronal circuits. Firstly, we establish stochastic super-multicolour labelling with up to seven different fluorescent proteins using the Tetbow method. With this method, each neuron is labelled with a unique combination of fluorescent proteins, which are then imaged and separated by linear unmixing. We also establish an automated neurite reconstruction pipeline based on the quantitative analysis of multiple dyes (QDyeFinder), which identifies neurite fragments with similar colour combinations. To classify colour combinations, we develop unsupervised clustering algorithm, dCrawler, in which data points in multi-dimensional space are clustered based on a given threshold distance. Our strategy allows the reconstruction of neurites for up to hundreds of neurons at the millimetre scale without using their physical continuity.

A suite of enhancer AAVs and transgenic mouse lines for genetic access to cortical cell types

The mammalian cortex is comprised of cells with different morphological, physiological, and molecular properties that can be classified according to shared properties into cell types. Defining the contribution of each cell type to the computational and cognitive processes that are guided by the cortex is essential for understanding its function in health and disease. We use transcriptomic and epigenomic cortical cell type taxonomies from mice and humans to define marker genes and enhancers, and to build genetic tools for cortical cell types. Here, we present a large toolkit for selective targeting of cortical populations, including mouse transgenic lines and recombinant adeno-associated virus (AAV) vectors containing genomic enhancers. We report evaluation of fifteen new transgenic driver lines and over 680 different enhancer AAVs covering all major subclasses of cortical cells, with many achieving a high degree of specificity, comparable with existing transgenic lines. We find that the transgenic lines based on marker genes can provide exceptional specificity and completeness of cell type labeling, but frequently require generation of a triple-transgenic cross for best usability/specificity. On the other hand, enhancer AAVs are easy to screen and use, and can be easily modified to express diverse cargo, such as recombinases. However, their use depends on many factors, such as viral titer and route of administration. The tools reported here as well as the scaled process of tool creation provide an unprecedented resource that should enable diverse experimental strategies towards understanding mammalian cortex and brain function.

Endogenous opioids facilitate stress-induced binge eating via an insular cortex-claustrum pathway

Stress has been shown to promote the development and persistence of binge eating behaviors. However, the neural circuit mechanisms for stress-induced binge-eating behaviors are largely unreported. The endogenous dynorphin (dyn)/kappa opioid receptor (KOR) opioid neuropeptide system has been well established to be a crucial mediator of the anhedonic component of stress. Here, we aimed to dissect the basis of dynorphinergic control of stress-induced binge-like eating behavior. We first established a mouse behavioral model for stress-induced binge-like eating behaviors. We found that mice exposed to stress increased their food intake of familiar palatable food (high fat, high sugar, HPD) compared to non-stressed mice. Following a brain-wide analysis, we isolated robust cFos-positive cells in the Claustrum (CLA), a subcortical structure with highly abundant KOR expression, following stress-induced binge-eating behavior. We report that KOR signaling in CLA is necessary for this elevated stress-induced binge eating behavior using local pharmacology and local deletion of KOR. In vivo calcium recordings using fiber photometry revealed a disinhibition circuit structure in the CLA during the initiation of HPD feeding bouts. We further established the dynamics of endogenous dynorphinergic control of this behavior using a genetically encoded dynorphin biosensor, Klight. Combined with 1-photon single-cell calcium imaging, we report significant heterogeneity with the CLA population during stress-induced binge eating and such behavior attenuates local dynorphin tone. Furthermore, we isolate the anterior Insular cortex (aIC) as the potential source of endogenous dynorphin afferents in the CLA. By characterizing neural circuits and peptidergic mechanisms within the CLA, we uncover a pathway that implicates endogenous opioid regulation stress-induced binge eating.

Connecto-informatics at the mesoscale: current advances in image processing and analysis for mapping the brain connectivity

Mapping neural connections within the brain has been a fundamental goal in neuroscience to understand better its functions and changes that follow aging and diseases. Developments in imaging technology, such as microscopy and labeling tools, have allowed researchers to visualize this connectivity through high-resolution brain-wide imaging. With this, image processing and analysis have become more crucial. However, despite the wealth of neural images generated, access to an integrated image processing and analysis pipeline to process these data is challenging due to scattered information on available tools and methods. To map the neural connections, registration to atlases and feature extraction through segmentation and signal detection are necessary. In this review, our goal is to provide an updated overview of recent advances in these image-processing methods, with a particular focus on fluorescent images of the mouse brain. Our goal is to outline a pathway toward an integrated image-processing pipeline tailored for connecto-informatics. An integrated workflow of these image processing will facilitate researchers' approach to mapping brain connectivity to better understand complex brain networks and their underlying brain functions. By highlighting the image-processing tools available for fluroscent imaging of the mouse brain, this review will contribute to a deeper grasp of connecto-informatics, paving the way for better comprehension of brain connectivity and its implications.

A robust transformer-based pipeline of 3D cell alignment, denoise and instance segmentation on electron microscopy sequence images

Germline cells are critical for transmitting genetic information to subsequent generations in biological organisms. While their differentiation from somatic cells during embryonic development is well-documented in most animals, the regulatory mechanisms initiating plant germline cells are not well understood. To thoroughly investigate the complex morphological transformations of their ultrastructure over developmental time, nanoscale 3D reconstruction of entire plant tissues is necessary, achievable exclusively through electron microscopy imaging. This paper presents a full-process framework designed for reconstructing large-volume plant tissue from serial electron microscopy images. The framework ensures end-to-end direct output of reconstruction results, including topological networks and morphological analysis. The proposed 3D cell alignment, denoise, and instance segmentation pipeline (3DCADS) leverages deep learning to provide a cell instance segmentation workflow for electron microscopy image series, ensuring accurate and robust 3D cell reconstructions with high computational efficiency. The pipeline involves five stages: the registration of electron microscopy serial images; image enhancement and denoising; semantic segmentation using a Transformer-based neural network; instance segmentation through a supervoxel-based clustering algorithm; and an automated analysis and statistical assessment of the reconstruction results, with the mapping of topological connections. The 3DCADS model's precision was validated on a plant tissue ground-truth dataset, outperforming traditional baseline models and deep learning baselines in overall accuracy. The framework was applied to the reconstruction of early meiosis stages in the anthers of Arabidopsis thaliana, resulting in a topological connectivity network and analysis of morphological parameters and characteristics of cell distribution. The experiment underscores the 3DCADS model's potential for biological tissue identification and its significance in quantitative analysis of plant cell development, crucial for examining samples across different genetic phenotypes and mutations in plant development. Additionally, the paper discusses the regulatory mechanisms of Arabidopsis thaliana's germline cells and the development of stamen cells before meiosis, offering new insights into the transition from somatic to germline cell fate in plants.

Discovery of 2-deoxy glucose surfaced mixed layer dendrimer: a smart neuron targeted systemic drug delivery system for brain diseases

The availability of non-invasive drug delivery systems capable of efficiently transporting bioactive molecules across the blood-brain barrier to specific cells at the injury site in the brain is currently limited. Delivering drugs to neurons presents an even more formidable challenge due to their lower numbers and less phagocytic nature compared to other brain cells. Additionally, the diverse types of neurons, each performing specific functions, necessitate precise targeting of those implicated in the disease. Moreover, the complex synthetic design of drug delivery systems often hinders their clinical translation. The production of nanomaterials at an industrial scale with high reproducibility and purity is particularly challenging. However, overcoming this challenge is possible by designing nanomaterials through a straightforward, facile, and easily reproducible synthetic process. Methods: In this study, we have developed a third-generation 2-deoxy-glucose functionalized mixed layer dendrimer (2DG-D) utilizing biocompatible and cost-effective materials via a highly facile convergent approach, employing copper-catalyzed click chemistry. We further evaluated the systemic neuronal targeting and biodistribution of 2DG-D, and brain delivery of a neuroprotective agent pioglitazone (Pio) in a pediatric traumatic brain injury (TBI) model. Results: The 2DG-D exhibits favorable characteristics including high water solubility, biocompatibility, biological stability, nanoscale size, and a substantial number of end groups suitable for drug conjugation. Upon systemic administration in a pediatric mouse model of traumatic brain injury (TBI), the 2DG-D localizes in neurons at the injured brain site, clears rapidly from off-target locations, effectively delivers Pio, ameliorates neuroinflammation, and improves behavioral outcomes. Conclusions: The promising in vivo results coupled with a convenient synthetic approach for the construction of 2DG-D makes it a potential nanoplatform for addressing brain diseases.

Multimodal cortical neuronal cell type classification

Since more than a century, neuroscientists have distinguished excitatory (glutamatergic) neurons with long-distance projections from inhibitory (GABAergic) neurons with local projections and established layer-dependent schemes for the ~ 80% excitatory (principal) cells as well as the ~ 20% inhibitory neurons. Whereas, in the early days, mainly morphological criteria were used to define cell types, later supplemented by electrophysiological and neurochemical properties, nowadays. single-cell transcriptomics is the method of choice for cell type classification. Bringing recent insight together, we conclude that despite all established layer- and area-dependent differences, there is a set of reliably identifiable cortical cell types that were named (among others) intratelencephalic (IT), extratelencephalic (ET), and corticothalamic (CT) for the excitatory cells, which altogether comprise ~ 56 transcriptomic cell types (t-types). By the same means, inhibitory neurons were subdivided into parvalbumin (PV), somatostatin (SST), vasoactive intestinal polypeptide (VIP), and "other (i.e. Lamp5/Sncg)" subpopulations, which altogether comprise ~ 60 t-types. The coming years will show which t-types actually translate into "real" cell types that show a common set of multimodal features, including not only transcriptome but also physiology and morphology as well as connectivity and ultimately function. Only with the better knowledge of clear-cut cell types and experimental access to them, we will be able to reveal their specific functions, a task which turned out to be difficult in a part of the brain being so much specialized for cognition as the cerebral cortex.

Subregion preference in the long-range connectome of pyramidal neurons in the medial prefrontal cortex

BACKGROUND

The medial prefrontal cortex (mPFC) is involved in complex functions containing multiple types of neurons in distinct subregions with preferential roles. The pyramidal neurons had wide-range projections to cortical and subcortical regions with subregional preferences. Using a combination of viral tracing and fluorescence micro-optical sectioning tomography (fMOST) in transgenic mice, we systematically dissected the whole-brain connectomes of intratelencephalic (IT) and pyramidal tract (PT) neurons in four mPFC subregions.

RESULTS

IT and PT neurons of the same subregion projected to different target areas while receiving inputs from similar upstream regions with quantitative differences. IT and PT neurons all project to the amygdala and basal forebrain, but their axons target different subregions. Compared to subregions in the prelimbic area (PL) which have more connections with sensorimotor-related regions, the infralimbic area (ILA) has stronger connections with limbic regions. The connection pattern of the mPFC subregions along the anterior-posterior axis showed a corresponding topological pattern with the isocortex and amygdala but an opposite orientation correspondence with the thalamus.

CONCLUSIONS

By using transgenic mice and fMOST imaging, we obtained the subregional preference whole-brain connectomes of IT and pyramidal tract PT neurons in the mPFC four subregions. These results provide a comprehensive resource for directing research into the complex functions of the mPFC by offering anatomical dissections of the different subregions.

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.

Distinct subpopulations of ventral pallidal cholinergic projection neurons encode valence of olfactory stimuli

To better understand the function of cholinergic projection neurons in the ventral pallidum (VP), we examined behavioral responses to appetitive (APP) and aversive (AV) odors that elicited approach or avoidance, respectively. Exposure to each odor increased cFos expression and calcium signaling in VP cholinergic neurons. Activity and Cre-dependent viral vectors selectively labeled VP cholinergic neurons that were activated and reactivated in response to either APP or AV odors, but not both, identifying two non-overlapping populations of VP cholinergic neurons differentially activated by the valence of olfactory stimuli. These two subpopulations showed differences in electrophysiological properties, morphology, and projections to the basolateral amygdala. Although VP neurons are engaged in both approach and avoidance behavioral responses, cholinergic signaling is only required for approach behavior. Thus, two distinct subpopulations of VP cholinergic neurons differentially encode valence of olfactory stimuli and play distinct roles in approach and avoidance behaviors.

Cross-hemispheric communication: Insights on lateralized brain functions

On the surface, the two hemispheres of vertebrate brains look almost perfectly symmetrical, but several motor, sensory, and cognitive systems show a deeply lateralized organization. Importantly, the two hemispheres are connected by various commissures, white matter tracts that cross the brain's midline and enable cross-hemispheric communication. Cross-hemispheric communication has been suggested to play an important role in the emergence of lateralized brain functions. Here, we review current advances in understanding cross-hemispheric communication that have been made using modern neuroscientific tools in rodents and other model species, such as genetic labeling, large-scale recordings of neuronal activity, spatiotemporally precise perturbation, and quantitative behavior analyses. These findings suggest that the emergence of lateralized brain functions cannot be fully explained by largely static factors such as genetic variation and differences in structural brain asymmetries. In addition, learning-dependent asymmetric interactions between the left and right hemispheres shape lateralized brain functions.

Top-down modulation in canonical cortical circuits with short-term plasticity

Cortical dynamics and computations are strongly influenced by diverse GABAergic interneurons, including those expressing parvalbumin (PV), somatostatin (SST), and vasoactive intestinal peptide (VIP). Together with excitatory (E) neurons, they form a canonical microcircuit and exhibit counterintuitive nonlinear phenomena. One instance of such phenomena is response reversal, whereby SST neurons show opposite responses to top-down modulation via VIP depending on the presence of bottom-up sensory input, indicating that the network may function in different regimes under different stimulation conditions. Combining analytical and computational approaches, we demonstrate that model networks with multiple interneuron subtypes and experimentally identified short-term plasticity mechanisms can implement response reversal. Surprisingly, despite not directly affecting SST and VIP activity, PV-to-E short-term depression has a decisive impact on SST response reversal. We show how response reversal relates to inhibition stabilization and the paradoxical effect in the presence of several short-term plasticity mechanisms demonstrating that response reversal coincides with a change in the indispensability of SST for network stabilization. In summary, our work suggests a role of short-term plasticity mechanisms in generating nonlinear phenomena in networks with multiple interneuron subtypes and makes several experimentally testable predictions.

Unravelling the genetic basis of Schizophrenia

Neuronal development is a highly regulated mechanism that is central to organismal function in animals. In humans, disruptions to this process can lead to a range of neurodevelopmental phenotypes, including Schizophrenia (SCZ). SCZ has a significant genetic component, whereby an individual with an SCZ affected family member is eight times more likely to develop the disease than someone with no family history of SCZ. By examining a combination of genomic, transcriptomic and epigenomic datasets, large-scale 'omics' studies aim to delineate the relationship between genetic variation and abnormal cellular activity in the SCZ brain. Herein, we provide a brief overview of some of the key omics methods currently being used in SCZ research, including RNA-seq, the assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq) and high-throughput chromosome conformation capture (3C) approaches (e.g., Hi-C), as well as single-cell/nuclei iterations of these methods. We also discuss how these techniques are being employed to further our understanding of the genetic basis of SCZ, and to identify associated molecular pathways, biomarkers, and candidate drug targets.

The geometry of photopolymerized topography influences neurite pathfinding by directing growth cone morphology and migration

Objective. Cochlear implants provide auditory perception to those with severe to profound sensorineural hearing loss: however, the quality of sound perceived by users does not approximate natural hearing. This limitation is due in part to the large physical gap between the stimulating electrodes and their target neurons. Therefore, directing the controlled outgrowth of processes from spiral ganglion neurons (SGNs) into close proximity to the electrode array could provide significantly increased hearing function.Approach.For this objective to be properly designed and implemented, the ability and limits of SGN neurites to be guided must first be determined. In this work, we engineer precise topographical microfeatures with angle turn challenges of various geometries to study SGN pathfinding and use live imaging to better understand how neurite growth is guided by these cues.Main Results.We find that the geometry of the angled microfeatures determines the ability of neurites to navigate the angled microfeature turns. SGN neurite pathfinding fidelity is increased by 20%-70% through minor increases in microfeature amplitude (depth) and by 25% if the angle of the patterned turn is made obtuse. Further, we see that dorsal root ganglion neuron growth cones change their morphology and migration to become more elongated within microfeatures. Our observations also indicate complexities in studying neurite turning. First, as the growth cone pathfinds in response to the various cues, the associated neurite often reorients across the angle topographical microfeatures. Additionally, neurite branching is observed in response to topographical guidance cues, most frequently when turning decisions are most uncertain.Significance.Overall, the multi-angle channel micropatterned substrate is a versatile and efficient system to assess neurite turning and pathfinding in response to topographical cues. These findings represent fundamental principles of neurite pathfinding that will be essential to consider for the design of 3D systems aiming to guide neurite growthin vivo.

Morphological heterogeneity of neurons in the human central amygdaloid nucleus

The central amygdaloid nucleus (CeA) has an ancient phylogenetic development and functions relevant for animal survival. Local cells receive intrinsic amygdaloidal information that codes emotional stimuli of fear, integrate them, and send cortical and subcortical output projections that prompt rapid visceral and social behavior responses. We aimed to describe the morphology of the neurons that compose the human CeA (N = 8 adult men). Cells within CeA coronal borders were identified using the thionine staining and were further analyzed using the "single-section" Golgi method followed by open-source software procedures for two-dimensional and three-dimensional image reconstructions. Our results evidenced varied neuronal cell body features, number and thickness of primary shafts, dendritic branching patterns, and density and shape of dendritic spines. Based on these criteria, we propose the existence of 12 morphologically different spiny neurons in the human CeA and discuss the variability in the dendritic architecture within cellular types, including likely interneurons. Some dendritic shafts were long and straight, displayed few collaterals, and had planar radiation within the coronal neuropil volume. Most of the sampled neurons showed a few to moderate density of small stubby/wide spines. Long spines (thin and mushroom) were observed occasionally. These novel data address the synaptic processing and plasticity in the human CeA. Our morphological description can be combined with further transcriptomic, immunohistochemical, and electrophysiological/connectional approaches. It serves also to investigate how neurons are altered in neurological and psychiatric disorders with hindered emotional perception, in anxiety, following atrophy in schizophrenia, and along different stages of Alzheimer's disease.

Fine-tuning TrailMap: The utility of transfer learning to improve the performance of deep learning in axon segmentation of light-sheet microscopy images

Light-sheet microscopy has made possible the 3D imaging of both fixed and live biological tissue, with samples as large as the entire mouse brain. However, segmentation and quantification of that data remains a time-consuming manual undertaking. Machine learning methods promise the possibility of automating this process. This study seeks to advance the performance of prior models through optimizing transfer learning. We fine-tuned the existing TrailMap model using expert-labeled data from noradrenergic axonal structures in the mouse brain. By changing the cross-entropy weights and using augmentation, we demonstrate a generally improved adjusted F1-score over using the originally trained TrailMap model within our test datasets.

Non-homogenous axonal bouton distribution in whole-brain single-cell neuronal networks

We examined the distribution of pre-synaptic contacts in axons of mouse neurons and constructed whole-brain single-cell neuronal networks using an extensive dataset of 1,891 fully reconstructed neurons. We found that bouton locations were not homogeneous throughout the axon and among brain regions. As our algorithm was able to generate whole-brain single-cell connectivity matrices from full morphology reconstruction datasets, we further found that non-homogeneous bouton locations have a significant impact on network wiring, including degree distribution, triad census, and community structure. By perturbing neuronal morphology, we further explored the link between anatomical details and network topology. In our in silico exploration, we found that dendritic and axonal tree span would have the greatest impact on network wiring, followed by synaptic contact deletion. Our results suggest that neuroanatomical details must be carefully addressed in studies of whole-brain networks at the single-cell level.

2P-NucTag: on-demand phototagging for molecular analysis of functionally identified cortical neurons

Neural circuits are characterized by genetically and functionally diverse cell types. A mechanistic understanding of circuit function is predicated on linking the genetic and physiological properties of individual neurons. However, it remains highly challenging to map the functional properties of transcriptionally heterogeneous neuronal subtypes in mammalian cortical circuits in vivo. Here, we introduce a high-throughput two-photon nuclear phototagging (2P-NucTag) approach optimized for on-demand and indelible labeling of single neurons via a photoactivatable red fluorescent protein following in vivo functional characterization in behaving mice. We demonstrate the utility of this function-forward pipeline by selectively labeling and transcriptionally profiling previously inaccessible 'place' and 'silent' cells in the mouse hippocampus. Our results reveal unexpected differences in gene expression between these hippocampal pyramidal neurons with distinct spatial coding properties. Thus, 2P-NucTag opens a new way to uncover the molecular principles that govern the functional organization of neural circuits.

Neuronal subtype-specific transcriptomic changes in the cerebral neocortex associated with sleep pressure

Sleep is homeostatically regulated by sleep pressure, which increases during wakefulness and dissipates during sleep. Recent studies have suggested that the cerebral neocortex, a six-layered structure composed of various layer- and projection-specific neuronal subtypes, is involved in the representation of sleep pressure governed by transcriptional regulation. Here, we examined the transcriptomic changes in neuronal subtypes in the neocortex upon increased sleep pressure using single-nucleus RNA sequencing datasets and predicted the putative intracellular and intercellular molecules involved in transcriptome alterations. We revealed that sleep deprivation (SD) had the greatest effect on the transcriptome of layer 2 and 3 intratelencephalic (L2/3 IT) neurons among the neocortical glutamatergic neuronal subtypes. The expression of mutant SIK3 (SLP), which is known to increase sleep pressure, also induced profound changes in the transcriptome of L2/3 IT neurons. We identified Junb as a candidate transcription factor involved in the alteration of the L2/3 IT neuronal transcriptome by SD and SIK3 (SLP) expression. Finally, we inferred putative intercellular ligands, including BDNF, LSAMP, and PRNP, which may be involved in SD-induced alteration of the transcriptome of L2/3 IT neurons. We suggest that the transcriptome of L2/3 IT neurons is most impacted by increased sleep pressure among neocortical glutamatergic neuronal subtypes and identify putative molecules involved in such transcriptional alterations.

Microfluidic platform for omics analysis on single cells with diverse morphology and size: A review

BACKGROUND

Microfluidic techniques have emerged as powerful tools in single-cell research, facilitating the exploration of omics information from individual cells. Cell morphology is crucial for gene expression and physiological processes. However, there is currently a lack of integrated analysis of morphology and single-cell omics information. A critical challenge remains: what platform technologies are the best option to decode omics data of cells that are complex in morphology and size?

RESULTS

This review highlights achievements in microfluidic-based single-cell omics and isolation of cells based on morphology, along with other cell sorting methods based on physical characteristics. Various microfluidic platforms for single-cell isolation are systematically presented, showcasing their diversity and adaptability. The discussion focuses on microfluidic devices tailored to the distinct single-cell isolation requirements in plants and animals, emphasizing the significance of considering cell morphology and cell size in optimizing single-cell omics strategies. Simultaneously, it explores the application of microfluidic single-cell sorting technologies to single-cell sequencing, aiming to effectively integrate information about cell shape and size.

SIGNIFICANCE AND NOVELTY

The novelty lies in presenting a comprehensive overview of recent accomplishments in microfluidic-based single-cell omics, emphasizing the integration of different microfluidic platforms and their implications for cell morphology-based isolation. By underscoring the pivotal role of the specialized morphology of different cells in single-cell research, this review provides robust support for delving deeper into the exploration of single-cell omics data.

A Novel Method for Clustering Cellular Data to Improve Classification

Many fields, such as neuroscience, are experiencing the vast proliferation of cellular data, underscoring the need for organizing and interpreting large datasets. A popular approach partitions data into manageable subsets via hierarchical clustering, but objective methods to determine the appropriate classification granularity are missing. We recently introduced a technique to systematically identify when to stop subdividing clusters based on the fundamental principle that cells must differ more between than within clusters. Here we present the corresponding protocol to classify cellular datasets by combining data-driven unsupervised hierarchical clustering with statistical testing. These general-purpose functions are applicable to any cellular dataset that can be organized as two-dimensional matrices of numerical values, including molecular, physiological, and anatomical datasets. We demonstrate the protocol using cellular data from the Janelia MouseLight project to characterize morphological aspects of neurons.

Cross-layer transmission realized by light-emitting memristor for constructing ultra-deep neural network with transfer learning ability

Deep neural networks have revolutionized several domains, including autonomous driving, cancer detection, and drug design, and are the foundation for massive artificial intelligence models. However, hardware neural network reports still mainly focus on shallow networks (2 to 5 layers). Implementing deep neural networks in hardware is challenging due to the layer-by-layer structure, resulting in long training times, signal interference, and low accuracy due to gradient explosion/vanishing. Here, we utilize negative ultraviolet photoconductive light-emitting memristors with intrinsic parallelism and hardware-software co-design to achieve electrical information's optical cross-layer transmission. We propose a hybrid ultra-deep photoelectric neural network and an ultra-deep super-resolution reconstruction neural network using light-emitting memristors and cross-layer block, expanding the networks to 54 and 135 layers, respectively. Further, two networks enable transfer learning, approaching or surpassing software-designed networks in multi-dataset recognition and high-resolution restoration tasks. These proposed strategies show great potential for high-precision multifunctional hardware neural networks and edge artificial intelligence.

Drug targeting in psychiatric disorders - how to overcome the loss in translation?

In spite of major efforts and investment in development of psychiatric drugs, many clinical trials have failed in recent decades, and clinicians still prescribe drugs that were discovered many years ago. Although multiple reasons have been discussed for the drug development deadlock, we focus here on one of the major possible biological reasons: differences between the characteristics of drug targets in preclinical models and the corresponding targets in patients. Importantly, based on technological advances in single-cell analysis, we propose here a framework for the use of available and newly emerging knowledge from single-cell and spatial omics studies to evaluate and potentially improve the translational predictivity of preclinical models before commencing preclinical and, in particular, clinical studies. We believe that these recommendations will improve preclinical models and the ability to assess drugs in clinical trials, reducing failure rates in expensive late-stage trials and ultimately benefitting psychiatric drug discovery and development.

Delayed escape behavior requires claustral activity

Animals are known to exhibit innate and learned forms of defensive behaviors, but it is unclear whether animals can escape through methods other than these forms. In this study, we develop the delayed escape task, in which male rats temporarily hold the information required for future escape, and we demonstrate that this task, in which the subject extrapolates from past experience without direct experience of its behavioral outcome, does not fall into either of the two forms of behavior. During the holding period, a subset of neurons in the rostral-to-striatum claustrum (rsCla), only when pooled together, sustain enhanced population activity without ongoing sensory stimuli. Transient inhibition of rsCla neurons during the initial part of the holding period produces prolonged inhibition of the enhanced activity. The transient inhibition also attenuates the delayed escape behavior. Our data suggest that the rsCla activity bridges escape-inducing stimuli to the delayed onset of escape.

Unsupervised classification of brain-wide axons reveals the presubiculum neuronal projection blueprint

We present a quantitative strategy to identify all projection neuron types from a given region with statistically different patterns of anatomical targeting. We first validate the technique with mouse primary motor cortex layer 6 data, yielding two clusters consistent with cortico-thalamic and intra-telencephalic neurons. We next analyze the presubiculum, a less-explored region, identifying five classes of projecting neurons with unique patterns of divergence, convergence, and specificity. We report several findings: individual classes target multiple subregions along defined functions; all hypothalamic regions are exclusively targeted by the same class also invading midbrain and agranular retrosplenial cortex; Cornu Ammonis receives input from a single class of presubicular axons also projecting to granular retrosplenial cortex; path distances from the presubiculum to the same targets differ significantly between classes, as do the path distances to distinct targets within most classes; the identified classes have highly non-uniform abundances; and presubicular somata are topographically segregated among classes. This study thus demonstrates that statistically distinct projections shed light on the functional organization of their circuit.

Rabies virus-based barcoded neuroanatomy resolved by single-cell RNA and in situ sequencing

Mapping the connectivity of diverse neuronal types provides the foundation for understanding the structure and function of neural circuits. High-throughput and low-cost neuroanatomical techniques based on RNA barcode sequencing have the potential to map circuits at cellular resolution and a brain-wide scale, but existing Sindbis virus-based techniques can only map long-range projections using anterograde tracing approaches. Rabies virus can complement anterograde tracing approaches by enabling either retrograde labeling of projection neurons or monosynaptic tracing of direct inputs to genetically targeted postsynaptic neurons. However, barcoded rabies virus has so far been only used to map non-neuronal cellular interactions in vivo and synaptic connectivity of cultured neurons. Here we combine barcoded rabies virus with single-cell and in situ sequencing to perform retrograde labeling and transsynaptic labeling in the mouse brain. We sequenced 96 retrogradely labeled cells and 295 transsynaptically labeled cells using single-cell RNA-seq, and 4130 retrogradely labeled cells and 2914 transsynaptically labeled cells in situ. We found that the transcriptomic identities of rabies virus-infected cells can be robustly identified using both single-cell RNA-seq and in situ sequencing. By associating gene expression with connectivity inferred from barcode sequencing, we distinguished long-range projecting cortical cell types from multiple cortical areas and identified cell types with converging or diverging synaptic connectivity. Combining in situ sequencing with barcoded rabies virus complements existing sequencing-based neuroanatomical techniques and provides a potential path for mapping synaptic connectivity of neuronal types at scale.

Transcriptional regulation in the development and dysfunction of neocortical projection neurons

Glutamatergic projection neurons generate sophisticated excitatory circuits to integrate and transmit information among different cortical areas, and between the neocortex and other regions of the brain and spinal cord. Appropriate development of cortical projection neurons is regulated by certain essential events such as neural fate determination, proliferation, specification, differentiation, migration, survival, axonogenesis, and synaptogenesis. These processes are precisely regulated in a tempo-spatial manner by intrinsic factors, extrinsic signals, and neural activities. The generation of correct subtypes and precise connections of projection neurons is imperative not only to support the basic cortical functions (such as sensory information integration, motor coordination, and cognition) but also to prevent the onset and progression of neurodevelopmental disorders (such as intellectual disability, autism spectrum disorders, anxiety, and depression). This review mainly focuses on the recent progress of transcriptional regulations on the development and diversity of neocortical projection neurons and the clinical relevance of the failure of transcriptional modulations.

Impaired synaptic plasticity and decreased excitability of hippocampal glutamatergic neurons mediated by BDNF downregulation contribute to cognitive dysfunction in mice induced by repeated neonatal exposure to ketamine

AIM

Repeated exposure to ketamine during the neonatal period in mice leads to cognitive impairments in adulthood. These impairments are likely caused by synaptic plasticity and excitability damage. We investigated the precise role of brain-derived neurotrophic factor (BDNF) in the cognitive impairments induced by repeated ketamine exposure during the neonatal period.

METHODS

We evaluated the cognitive function of mice using the Morris water maze test and novel object recognition test. Western blotting and immunofluorescence were used to detect the protein levels of BDNF. Western blotting, Golgi-Cox staining, transmission electron microscopy, and long-term potentiation (LTP) recordings were used to assess synaptic plasticity in the hippocampus. The excitability of neurons was evaluated using c-Fos. In the intervention experiment, pAdeno-CaMKIIα-BDNF-mNeuronGreen was injected into the hippocampal CA1 region of mice to increase the level of BDNF. The excitability of neurons was enhanced using a chemogenetic approach.

RESULTS

Our findings suggest that cognitive impairments in mice repeatedly exposed to ketamine during the neonatal period are associated with downregulated BDNF protein level, synaptic plasticity damage, and decreased excitability of glutamatergic neurons in the hippocampal CA1 region. Furthermore, the specific upregulation of BDNF in glutamatergic neurons of the hippocampal CA1 region and the enhancement of excitability can improve impaired synaptic plasticity and cognitive function in mice.

CONCLUSION

BDNF downregulation mediates synaptic plasticity and excitability damage, leading to cognitive impairments in adulthood following repeated ketamine exposure during the neonatal period.

A systematic evaluation of computation methods for cell segmentation

Cell segmentation is a fundamental task in analyzing biomedical images. Many computational methods have been developed for cell segmentation, but their performances are not well understood in various scenarios. We systematically evaluated the performance of 18 segmentation methods to perform cell nuclei and whole cell segmentation using light microscopy and fluorescence staining images. We found that general-purpose methods incorporating the attention mechanism exhibit the best overall performance. We identified various factors influencing segmentation performances, including training data and cell morphology, and evaluated the generalizability of methods across image modalities. We also provide guidelines for choosing the optimal segmentation methods in various real application scenarios. We developed Seggal, an online resource for downloading segmentation models already pre-trained with various tissue and cell types, which substantially reduces the time and effort for training cell segmentation models.

NMNAT2 supports vesicular glycolysis via NAD homeostasis to fuel fast axonal transport

BACKGROUND

Bioenergetic maladaptations and axonopathy are often found in the early stages of neurodegeneration. Nicotinamide adenine dinucleotide (NAD), an essential cofactor for energy metabolism, is mainly synthesized by Nicotinamide mononucleotide adenylyl transferase 2 (NMNAT2) in CNS neurons. NMNAT2 mRNA levels are reduced in the brains of Alzheimer's, Parkinson's, and Huntington's disease. Here we addressed whether NMNAT2 is required for axonal health of cortical glutamatergic neurons, whose long-projecting axons are often vulnerable in neurodegenerative conditions. We also tested if NMNAT2 maintains axonal health by ensuring axonal ATP levels for axonal transport, critical for axonal function.

METHODS

We generated mouse and cultured neuron models to determine the impact of NMNAT2 loss from cortical glutamatergic neurons on axonal transport, energetic metabolism, and morphological integrity. In addition, we determined if exogenous NAD supplementation or inhibiting a NAD hydrolase, sterile alpha and TIR motif-containing protein 1 (SARM1), prevented axonal deficits caused by NMNAT2 loss. This study used a combination of techniques, including genetics, molecular biology, immunohistochemistry, biochemistry, fluorescent time-lapse imaging, live imaging with optical sensors, and anti-sense oligos.

RESULTS

We provide in vivo evidence that NMNAT2 in glutamatergic neurons is required for axonal survival. Using in vivo and in vitro studies, we demonstrate that NMNAT2 maintains the NAD-redox potential to provide "on-board" ATP via glycolysis to vesicular cargos in distal axons. Exogenous NAD+ supplementation to NMNAT2 KO neurons restores glycolysis and resumes fast axonal transport. Finally, we demonstrate both in vitro and in vivo that reducing the activity of SARM1, an NAD degradation enzyme, can reduce axonal transport deficits and suppress axon degeneration in NMNAT2 KO neurons.

CONCLUSION

NMNAT2 ensures axonal health by maintaining NAD redox potential in distal axons to ensure efficient vesicular glycolysis required for fast axonal transport.

Novel Approaches to Studying SLC13A5 Disease

The role of the sodium citrate transporter (NaCT) SLC13A5 is multifaceted and context-dependent. While aberrant dysfunction leads to neonatal epilepsy, its therapeutic inhibition protects against metabolic disease. Notably, insights regarding the cellular and molecular mechanisms underlying these phenomena are limited due to the intricacy and complexity of the latent human physiology, which is poorly captured by existing animal models. This review explores innovative technologies aimed at bridging such a knowledge gap. First, I provide an overview of SLC13A5 variants in the context of human disease and the specific cell types where the expression of the transporter has been observed. Next, I discuss current technologies for generating patient-specific induced pluripotent stem cells (iPSCs) and their inherent advantages and limitations, followed by a summary of the methods for differentiating iPSCs into neurons, hepatocytes, and organoids. Finally, I explore the relevance of these cellular models as platforms for delving into the intricate molecular and cellular mechanisms underlying SLC13A5-related disorders.

Single-neuron analysis of axon arbors reveals distinct presynaptic organizations between feedforward and feedback projections

The morphology and spatial distribution of axon arbors and boutons are crucial for neuron presynaptic functions. However, the principles governing their whole-brain organization at the single-neuron level remain unclear. We developed a machine-learning method to separate axon arbors from passing axons in single-neuron reconstruction from fluorescence micro-optical sectioning tomography imaging data and obtained 62,374 axon arbors that displayed distinct morphology, spatial patterns, and scaling laws dependent on neuron types and targeted brain areas. Focusing on the axon arbors in the thalamus and cortex, we revealed the segregated spatial distributions and distinct morphology but shared topographic gradients between feedforward and feedback projections. Furthermore, we uncovered an association between arbor complexity and microglia density. Finally, we found that the boutons on terminal arbors show branch-specific clustering with a log-normal distribution that again differed between feedforward and feedback terminal arbors. Together, our study revealed distinct presynaptic structural organizations underlying diverse functional innervation of single projection neurons.

Efficient mapping of the thalamocortical monosynaptic connectivity in vivo by tangential insertions of high-density electrodes in the cortex

The thalamus provides the principal input to the cortex and therefore understanding the mechanisms underlying cortical integration of sensory inputs requires to characterize the thalamocortical connectivity in behaving animals. Here, we propose tangential insertions of high-density electrodes into mouse cortical layer 4 as a method to capture the activity of thalamocortical axons simultaneously with their synaptically connected cortical neurons. This technique can reliably monitor multiple parallel thalamic synaptic inputs to cortical neurons, providing an efficient approach to map thalamocortical connectivity in both awake and anesthetized mice.

Spatially contrastive variational autoencoder for deciphering tissue heterogeneity from spatially resolved transcriptomics

Recent advances in spatially resolved transcriptomics (SRT) have brought ever-increasing opportunities to characterize expression landscape in the context of tissue spatiality. Nevertheless, there still exist multiple challenges to accurately detect spatial functional regions in tissue. Here, we present a novel contrastive learning framework, SPAtially Contrastive variational AutoEncoder (SpaCAE), which contrasts transcriptomic signals of each spot and its spatial neighbors to achieve fine-grained tissue structures detection. By employing a graph embedding variational autoencoder and incorporating a deep contrastive strategy, SpaCAE achieves a balance between spatial local information and global information of expression, enabling effective learning of representations with spatial constraints. Particularly, SpaCAE provides a graph deconvolutional decoder to address the smoothing effect of local spatial structure on expression's self-supervised learning, an aspect often overlooked by current graph neural networks. We demonstrated that SpaCAE could achieve effective performance on SRT data generated from multiple technologies for spatial domains identification and data denoising, making it a remarkable tool to obtain novel insights from SRT studies.

Layer 1 neocortex: Gating and integrating multidimensional signals

Layer 1 (L1) of the neocortex acts as a nexus for the collection and processing of widespread information. By integrating ascending inputs with extensive top-down activity, this layer likely provides critical information regulating how the perception of sensory inputs is reconciled with expectation. This is accomplished by sorting, directing, and integrating the complex network of excitatory inputs that converge onto L1. These signals are combined with neuromodulatory afferents and gated by the wealth of inhibitory interneurons that either are embedded within L1 or send axons from other cortical layers. Together, these interactions dynamically calibrate information flow throughout the neocortex. This review will primarily focus on L1 within the primary sensory cortex and will use these insights to understand L1 in other cortical areas.

Neural heterogeneity controls computations in spiking neural networks

The brain is composed of complex networks of interacting neurons that express considerable heterogeneity in their physiology and spiking characteristics. How does this neural heterogeneity influence macroscopic neural dynamics, and how might it contribute to neural computation? In this work, we use a mean-field model to investigate computation in heterogeneous neural networks, by studying how the heterogeneity of cell spiking thresholds affects three key computational functions of a neural population: the gating, encoding, and decoding of neural signals. Our results suggest that heterogeneity serves different computational functions in different cell types. In inhibitory interneurons, varying the degree of spike threshold heterogeneity allows them to gate the propagation of neural signals in a reciprocally coupled excitatory population. Whereas homogeneous interneurons impose synchronized dynamics that narrow the dynamic repertoire of the excitatory neurons, heterogeneous interneurons act as an inhibitory offset while preserving excitatory neuron function. Spike threshold heterogeneity also controls the entrainment properties of neural networks to periodic input, thus affecting the temporal gating of synaptic inputs. Among excitatory neurons, heterogeneity increases the dimensionality of neural dynamics, improving the network's capacity to perform decoding tasks. Conversely, homogeneous networks suffer in their capacity for function generation, but excel at encoding signals via multistable dynamic regimes. Drawing from these findings, we propose intra-cell-type heterogeneity as a mechanism for sculpting the computational properties of local circuits of excitatory and inhibitory spiking neurons, permitting the same canonical microcircuit to be tuned for diverse computational tasks.

Meet the authors: Hanchuan Peng, Peng Xie, and Feng Xiong

In a recent paper at Patterns, Hanchuan Peng, Peng Xie, and Feng Xiong from Southeast University describe a deep learning method to characterize complete single-neuron morphologies, which can discover neuron projection patterns of diverse cells and learn neuronal morphology representation. In this interview, the authors shared the story behind the paper and their research experience. This interview is a companion to these authors' recent paper, "DSM: Deep sequential model for complete neuronal morphology representation and feature extraction."1.

DSM: Deep sequential model for complete neuronal morphology representation and feature extraction

The full morphology of single neurons is indispensable for understanding cell types, the basic building blocks in brains. Projecting trajectories are critical to extracting biologically relevant information from neuron morphologies, as they provide valuable information for both connectivity and cell identity. We developed an artificial intelligence method, deep sequential model (DSM), to extract concise, cell-type-defining features from projections across brain regions. DSM achieves more than 90% accuracy in classifying 12 major neuron projection types without compromising performance when spatial noise is present. Such remarkable robustness enabled us to efficiently manage and analyze several major full-morphology data sources, showcasing how characteristic long projections can define cell identities. We also succeeded in applying our model to both discovering previously unknown neuron subtypes and analyzing exceptional co-expressed genes involved in neuron projection circuits.

Analyzing Thalamocortical Tract-Tracing Experiments in a Common Reference Space

Current mesoscale connectivity atlases provide limited information about the organization of thalamocortical projections in the mouse brain. Labeling the projections of spatially restricted neuron populations in thalamus can provide a functionally relevant level of connectomic analysis, but these need to be integrated within the same common reference space. Here, we present a pipeline for the segmentation, registration, integration and analysis of multiple tract-tracing experiments. The key difference with other workflows is that the data is transformed to fit the reference template. As a test-case, we investigated the axonal projections and intranuclear arrangement of seven neuronal populations of the ventral posteromedial nucleus of the thalamus (VPM), which we labeled with an anterograde tracer. Their soma positions corresponded, from dorsal to ventral, to cortical representations of the whiskers, nose and mouth. They strongly targeted layer 4, with the majority exclusively targeting one cortical area and the ones in ventrolateral VPM branching to multiple somatosensory areas. We found that our experiments were more topographically precise than similar experiments from the Allen Institute and projections to the primary somatosensory area were in agreement with single-neuron morphological reconstructions from publicly available databases. This pilot study sets the basis for a shared virtual connectivity atlas that could be enriched with additional data for studying the topographical organization of different thalamic nuclei. The pipeline is accessible with only minimal programming skills via a Jupyter Notebook, and offers multiple visualization tools such as cortical flatmaps, subcortical plots and 3D renderings and can be used with custom anatomical delineations.