A multimodal cell census and atlas of the mammalian primary motor cortex

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.

Regulation of perisomatic synapses from cholecystokinin basket interneurons through NrCAM and Ankyrin B

The perisomatic region of cortical pyramidal neurons (PNs) integrates local and long-range inputs and regulates firing. This domain receives GABAergic inputs from cholecystokinin (CCK)- and Parvalbumin (PV)-expressing basket cells (BCs) but how synaptic contacts are established is unclear. Neuron-glial related cell adhesion molecule (NrCAM) is a homophilic transmembrane protein that binds the scaffold protein Ankyrin B. Here we show that NrCAM and Ankyrin B mediate perisomatic synaptic contact between CCK-BCs and PNs in mouse medial prefrontal cortex (mPFC). Immunolabeling of CCK-BC terminals for vesicular glutamate transporter-3 (VGLUT3) or vesicular GABA transporter (VGAT) revealed a significant decrease in CCK-BC synaptic puncta on PN soma in NrCAM-null mice, however no decrease in PV-BC puncta or cell loss. VGLUT3+ CCK-BC puncta were also decreased by Ankyrin B deletion from PNs in Nex1Cre-ERT2:Ank2flox/flox:EGFP mice. A novel CCK-BC reporter mouse expressing tdTomato (tdT) at the Synuclein-γ (Sncg) locus showed NrCAM localized to Sncg + CCK-BCs, and to postsynaptic PN soma in Nex1Cre-ERT2:Ank2+/+:EGFP mice. Results suggest that NrCAM and Ankyrin B contribute to the establishment of connectivity between CCK-BCs and excitatory neurons of the mPFC.

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.

Integrated analysis of molecular atlases unveils modules driving developmental cell subtype specification in the human cortex

Human brain development requires generating diverse cell types, a process explored by single-cell transcriptomics. Through parallel meta-analyses of the human cortex in development (seven datasets) and adulthood (16 datasets), we generated over 500 gene co-expression networks that can describe mechanisms of cortical development, centering on peak stages of neurogenesis. These meta-modules show dynamic cell subtype specificities throughout cortical development, with several developmental meta-modules displaying spatiotemporal expression patterns that allude to potential roles in cell fate specification. We validated the expression of these modules in primary human cortical tissues. These include meta-module 20, a module elevated in FEZF2+ deep layer neurons that includes TSHZ3, a transcription factor associated with neurodevelopmental disorders. Human cortical chimeroid experiments validated that both FEZF2 and TSHZ3 are required to drive module 20 activity and deep layer neuron specification but through distinct modalities. These studies demonstrate how meta-atlases can engender further mechanistic analyses of cortical fate specification.

A molecular brain atlas reveals cellular shifts during the repair phase of stroke

Ischemic stroke triggers a cascade of pathological events that affect multiple cell types and often lead to incomplete functional recovery. Despite advances in single-cell technologies, the molecular and cellular responses that contribute to long-term post-stroke impairment remain poorly understood. To gain better insight into the underlying mechanisms, we generated a single-cell transcriptomic atlas from distinct brain regions using a mouse model of permanent focal ischemia at one month post-injury. Our findings reveal cell- and region-specific changes within the stroke-injured and peri-infarct brain tissue. For instance, GABAergic and glutamatergic neurons exhibited upregulated genes in signaling pathways involved in axon guidance and synaptic plasticity, and downregulated pathways associated with aerobic metabolism. Using cell-cell communication analysis, we identified increased strength in predicted interactions within stroke tissue among both neural and non-neural cells via signaling pathways such as those involving collagen, protein tyrosine phosphatase receptor, neuronal growth regulator, laminin, and several cell adhesion molecules. Furthermore, we found a strong correlation between mouse transcriptome responses after stroke and those observed in human nonfatal brain stroke lesions. Common molecular features were linked to inflammatory responses, extracellular matrix organization, and angiogenesis. Our findings provide a detailed resource for advancing our molecular understanding of stroke pathology and for discovering therapeutic targets in the repair phase of stroke recovery.

Single-cell and spatial RNA sequencing identify divergent microenvironments and progression signatures in early- versus late-onset prostate cancer

The clinical and pathological outcomes differ between early-onset (diagnosed in men ≤55 years of age) and late-onset prostate cancer, potentially attributed to the changes in hormone levels and immune activities associated with aging. Exploring the heterogeneity therein holds potential for developing age-specific precision interventions. Here, through single-cell and spatial transcriptomic analyses of prostate cancer tissues, we identified that an androgen response-related transcriptional meta-program (AR-MP) might underlie the age-related heterogeneity of tumor cells and microenvironment. APOE+ tumor-associated macrophages infiltrated AR-MP-activated tumor cells in early-onset prostate cancer, potentially facilitating tumor progression and immunosuppression. By contrast, inflammatory cancer-associated fibroblasts in late-onset prostate cancer correlated with downregulation of AR-MP of tumor cells and increased epithelial-to-mesenchymal transition and pre-existing castration resistance, which may also be linked to smoking. This study provides potential insights for tailoring precision treatments by age groups, emphasizing interventions that include targeting AR and tumor-associated macrophages in young patients but anchoring epithelial-to-mesenchymal transition and inflammatory cancer-associated fibroblasts in old counterparts.

Developmental overproduction of cortical superficial neurons impairs adult auditory cortical processing

While evolutionary cortical expansion is thought to underlie the evolution of human cognitive capabilities, excessive developmental expansion can lead to megalencephaly, often found in neurodevelopmental disorders. Still, little is known about how the overproduction of cortical neurons during development affects cortical processing and behavior in later life. Here we show that developmental overproduction of cortical superficial neurons impairs auditory processing in adult mice. We applied XAV939 to overproduce cortical superficial excitatory neurons during development. XAV939-treated adult mice exhibited auditory behavioral deficits and abnormal auditory cortical processing. Furthermore, we found fewer functional monosynaptic connections between cortical putative excitatory neurons. Altogether, our results suggest that abnormal auditory cortical processing contributes to the atypical auditory detectability in XAV939-treated mice. Although the expansion of cortical size is evolutionarily advantageous, an abnormal expansion during development can result in detrimental effects on cortical processing and perceptual behavior in adulthood.

A large-scale integrated transcriptomic atlas for soybean organ development

Soybean is one of the most important crops globally, and its production must be significantly increased to meet increasing demand. Elucidating the genetic regulatory networks underlying soybean organ development is essential for breeding elite and resilient varieties to ensure increased soybean production under climate change. An integrated transcriptomic atlas that leverages multiple types of transcriptomics data can facilitate the characterization of temporal-spatial expression patterns of most organ development-related genes and thereby help us to understand organ developmental processes. Here, we constructed a comprehensive, integrated transcriptomic atlas for soybeans, integrating bulk RNA sequencing (RNA-seq) datasets from 314 samples across the soybean life cycle, along with single-nucleus RNA-seq and spatially enhanced resolution omics sequencing datasets from five organs: root, nodule, shoot apex, leaf, and stem. Investigating genes related to organ specificity, blade development, and nodule formation, we demonstrate that the atlas has robust power for exploring key genes involved in organ formation. In addition, we developed a user-friendly panoramic database for the transcriptomic atlas, enabling easy access and queries, which will serve as a valuable resource to significantly advance future soybean functional studies.

Single-Cell Analysis in Cerebrovascular Research: Primed for Breakthroughs and Clinical Impact

Data generated using single-cell RNA-sequencing has the potential to transform understanding of the cerebral circulation and advance clinical care. However, the high volume of data, sometimes generated and presented without proper pathophysiological context, can be difficult to interpret and integrate into current understanding of the cerebral circulation and its disorders. Furthermore, heterogeneity in the representation of brain regions and vascular segments makes it difficult to compare results across studies. There are currently no standards for tissue collection and processing that allow easy comparisons across studies and analytical platforms. There are no standards either for single-cell data analysis and presentation. This topical review introduces single-cell RNA-sequencing to physicians and scientists in the cerebrovascular field, with the goals of highlighting opportunities and challenges of applying this technology in the cerebrovascular field and discussing key concepts and knowledge gaps that can be addressed by single-cell RNA-sequencing.

Global modules robustly emerge from local interactions and smooth gradients

Modular structure and function are ubiquitous in biology, from the organization of animal brains and bodies to the scale of ecosystems. However, the mechanisms of modularity emergence from non-modular precursors remain unclear. Here we introduce the principle of peak selection, a process by which purely local interactions and smooth gradients can drive the self-organization of discrete global modules. The process combines strengths of the positional and Turing pattern-formation mechanisms into a model for morphogenesis. Applied to the grid-cell system of the brain, peak selection results in the self-organization of functionally distinct modules with discretely spaced spatial periods. Applied to ecological systems, it results in discrete multispecies niches and synchronous spawning across geographically distributed coral colonies. The process exhibits self-scaling with system size and 'topological robustness'1, which renders module emergence and module properties insensitive to most parameters. Peak selection ameliorates the fine-tuning requirement for continuous attractor dynamics in single grid-cell modules and it makes a detail-independent prediction that grid module period ratios should approximate adjacent integer ratios, providing a highly accurate match to the available data. Predictions for grid cells at the transcriptional, connectomic and physiological levels promise to elucidate the interplay of molecules, connectivity and function emergence in brains.

ALS molecular subtypes are a combination of cellular and pathological features learned by deep multiomics classifiers

Amyotrophic lateral sclerosis (ALS) is a complex syndrome with multiple genetic causes and wide variation in disease presentation. Despite this heterogeneity, large-scale genomics studies revealed that ALS postmortem samples can be grouped into a small number of subtypes, defined by transcriptomic signatures of mitochondrial dysfunction and oxidative stress (ALS-Ox), microglial activation and neuroinflammation (ALS-Glia), or TDP-43 pathology and associated transposable elements (ALS-TE). In this study, we present a deep ALS neural net classifier (DANCer) for ALS molecular subtypes. Applying DANCer to an expanded cohort from the NYGC ALS Consortium highlights two subtypes that strongly correlate with disease duration: ALS-TE in cortex and ALS-Glia in spinal cord. Finally, single-nucleus transcriptomes demonstrate that ALS subtypes are recapitulated in neurons and glia, with both ALS-wide and subtype-specific alterations in all cell types. In summary, ALS molecular subtypes represent a combination of cellular and pathological features that correlate with clinical features of ALS.

The diversity and plasticity of descending motor pathways rewired after stroke and trauma in rodents

Descending neural pathways to the spinal cord plays vital roles in motor control. They are often damaged by brain injuries such as stroke and trauma, which lead to severe motor impairments. Due to the limited capacity for regeneration of neural circuits in the adult central nervous system, currently no essential treatments are available for complete recovery. Notably, accumulating evidence shows that residual circuits of the descending pathways are dynamically reorganized after injury and contribute to motor recovery. Furthermore, recent technological advances in cell-type classification and manipulation have highlighted the structural and functional diversity of these pathways. Here, we focus on three major descending pathways, namely, the corticospinal tract from the cerebral cortex, the rubrospinal tract from the red nucleus, and the reticulospinal tract from the reticular formation, and summarize the current knowledge of their structures and functions, especially in rodent models (mice and rats). We then review and discuss the process and patterns of reorganization induced in these pathways following injury, which compensate for lost connections for recovery. Understanding the basic structural and functional properties of each descending pathway and the principles of the induction and outcome of the rewired circuits will provide therapeutic insights to enhance interactive rewiring of the multiple descending pathways for motor recovery.

A Comprehensive Atlas of Cell Type Density Patterns and Their Role in Brain Organization

Cell-type composition across brain regions is a critical structural factor shaping both local and long-range brain circuits. Here, we employed single-cell resolution imaging of the mouse brain, combined with computational analyses, to map the distribution of 30 cell classes and types defined by gene marker expression in Cre recombinase-based genetic mouse models. This approach generated a comprehensive atlas of cell type-specific densities across the male and female brain, revealing (1) surprisingly broad sex differences in cells tagged by developmental cell-type markers, (2) shared cell type composition signatures among functionally related brain structures, and (3) close associations not only between specific cell types but also discrete cell type densities and anatomical regions and subregions. In summary, despite the relatively broad cell type classification enabled by the Cre mouse models, our findings highlight intricate relationships between brain cell type distribution and anatomical organization, associating distinct local cell densities with region-specific brain functions.

Advances in Spatial Omics Technologies

Rapidly developing spatial omics technologies provide us with new approaches to deeply understanding the diversity and functions of cell types within organisms. Unlike traditional approaches, spatial omics technologies enable researchers to dissect the complex relationships between tissue structure and function at the cellular or even subcellular level. The application of spatial omics technologies provides new perspectives on key biological processes such as nervous system development, organ development, and tumor microenvironment. This review focuses on the advancements and strategies of spatial omics technologies, summarizes their applications in biomedical research, and highlights the power of spatial omics technologies in advancing the understanding of life sciences related to development and disease.

Developing a multiscale neural connectivity knowledgebase of the autonomic nervous system

The Stimulating Peripheral Activity to Relieve Conditions (SPARC) program is a U.S. National Institutes of Health (NIH) funded effort to enhance our understanding of the neural circuitry responsible for visceral control. SPARC's mission is to identify, extract, and compile our overall existing knowledge and understanding of the autonomic nervous system (ANS) connectivity between the central nervous system and end organs. A major goal of SPARC is to use this knowledge to promote the development of the next generation of neuromodulation devices and bioelectronic medicine for nervous system diseases. As part of the SPARC program, we have been developing the SPARC Connectivity Knowledge Base of the Autonomic Nervous System (SCKAN), a dynamic resource containing information about the origins, terminations, and routing of ANS projections. The distillation of SPARC's connectivity knowledge into this knowledge base involves a rigorous curation process to capture connectivity information provided by experts, published literature, textbooks, and SPARC scientific data. SCKAN is used to automatically generate anatomical and functional connectivity maps on the SPARC portal. In this article, we present the design and functionality of SCKAN, including the detailed knowledge engineering process developed to populate the resource with high quality and accurate data. We discuss the process from both the perspective of SCKAN's ontological representation as well as its practical applications in developing information systems. We share our techniques, strategies, tools and insights for developing a practical knowledgebase of ANS connectivity that supports continual enhancement.

Regulation of Perisomatic Synapses from Cholecystokinin Basket Interneurons through NrCAM and Ankyrin B

The perisomatic region of cortical pyramidal neurons (PNs) integrates local and long-range inputs and regulates firing. This domain receives GABAergic inputs from cholecystokinin (CCK)- and Parvalbumin (PV)-expressing basket cells (BCs) but how synaptic contacts are established is unclear. Neuron-glial related cell adhesion molecule (NrCAM) is a homophilic transmembrane protein that binds the scaffold protein Ankyrin B. Here we show that NrCAM and Ankyrin B mediate perisomatic synaptic contact between CCK-BCs and PNs in mouse medial prefrontal cortex (mPFC). Immunolabeling of CCK-BC terminals for vesicular glutamate transporter-3 (VGLUT3) or vesicular GABA transporter (VGAT) revealed a significant decrease in CCK-BC synaptic puncta on PN soma in NrCAM-null mice, however no decrease in PV-BC puncta or cell loss. VGLUT3+ CCK-BC puncta were also decreased by Ankyrin B deletion from PNs in Nex1Cre-ERT2:Ank2 flox/flox :EGFP mice. A novel CCK-BC reporter mouse expressing tdTomato (tdT) at the Synuclein-γ ( Sncg ) locus showed NrCAM localized to Sncg+ CCK-BCs, and to postsynaptic PN soma in Nex1Cre-ERT2:Ank2 +/+ :EGFP mice. Results suggest that NrCAM and Ankyrin B contribute to the establishment of connectivity between CCK-BCs and excitatory neurons of the mPFC.

Massively parallel reporter assay investigates shared genetic variants of eight psychiatric disorders

A meta-genome-wide association study across eight psychiatric disorders has highlighted the genetic architecture of pleiotropy in major psychiatric disorders. However, mechanisms underlying pleiotropic effects of the associated variants remain to be explored. We conducted a massively parallel reporter assay to decode the regulatory logic of variants with pleiotropic and disorder-specific effects. Pleiotropic variants differ from disorder-specific variants by exhibiting chromatin accessibility that extends across diverse cell types in the neuronal lineage and by altering motifs for transcription factors with higher connectivity in protein-protein interaction networks. We mapped pleiotropic and disorder-specific variants to putative target genes using functional genomics approaches and CRISPR perturbation. In vivo CRISPR perturbation of a pleiotropic and a disorder-specific gene suggests that pleiotropy may involve the regulation of genes expressed broadly across neuronal cell types and with higher network connectivity.

Organizational Principles of the Primate Cerebral Cortex at the Single-Cell Level

The primate cerebral cortex, the major organ for cognition, consists of an immense number of neurons. However, the organizational principles governing these neurons remain unclear. By accessing the single-cell spatial transcriptome of over 25 million neuron cells across the entire macaque cortex, it is discovered that the distribution of neurons within cortical layers is highly non-random. Strikingly, over three-quarters of these neurons are located in distinct neuronal clusters. Within these clusters, different cell types tend to collaborate rather than function independently. Typically, excitatory neuron clusters mainly consist of excitatory-excitatory combinations, while inhibitory clusters primarily contain excitatory-inhibitory combinations. Both cluster types have roughly equal numbers of neurons in each layer. Importantly, most excitatory and inhibitory neuron clusters form spatial partnerships, indicating a balanced local neuronal network and correlating with specific functional regions. These organizational principles are conserved across mouse cortical regions. These findings suggest that different brain regions of the cortex may exhibit similar mechanisms at the neuronal population level.

Community modularity structure promotes the evolution of phase clusters and chimeralike states

Community modularity structure is widely observed across various brain scales, reflecting a balance between information processing efficiency and neural wiring metabolic efficiency. Revealing the relationship between community structure and brain function facilitates our further understanding of the brain. Here, we construct an adaptive neural network (ANN) consisting of leaky integrate-and-fire neurons with adaptivity governed by spike-time-dependent plasticity rules. The ANN demonstrates diverse dynamic collective behaviors, including traveling waves dominated by initial states, phase-cluster formations, and chimeralike states. In addition to functional clustering, ANN spontaneously organizes into community structures characterized by densely interconnected modules with sparse interconnections. Neurons within modules synchronize, while those across modules remain asynchronous, forming phase-cluster states. By encoding neural rhythms, the ANN segments into asynchronous and synchronous structural modules, leading to chimeralike states. These findings provide further evidence supporting the perspective that function emerges from structure and that structure is influenced by function in complex dynamic processes.

Cell type-specific 3D-genome organization and transcription regulation in the brain

3D organization of the genome plays a critical role in regulating gene expression. How 3D-genome organization differs among different cell types and relates to cell type-dependent transcriptional regulation remains unclear. Here, we used genome-scale DNA and RNA imaging to investigate 3D-genome organization in transcriptionally distinct cell types in the mouse cerebral cortex. We uncovered a wide spectrum of differences in the nuclear architecture and 3D-genome organization among different cell types, ranging from the size of the cell nucleus to higher-order chromosome structures and radial positioning of chromatin loci within the nucleus. These cell type-dependent variations in nuclear architecture and chromatin organization exhibit strong correlations with both the total transcriptional activity of the cell and transcriptional regulation of cell type-specific marker genes. Moreover, we found that the methylated DNA binding protein MeCP2 promotes active-inactive chromatin segregation and regulates transcription in a nuclear radial position-dependent manner that is highly correlated with its function in modulating active-inactive chromatin compartmentalization.

Topic modeling analysis of the Allen Human Brain Atlas

The human brain is a complex interconnected structure controlling all elementary and high-level cognitive tasks. It is composed of many regions that exhibit specific distributions of cell types and distinct patterns of functional connections. This complexity is rooted in differential transcription. The constituent cell types of different brain regions express distinctive combinations of genes as they develop and mature, ultimately shaping their functional state in adulthood. How precisely the genetic information of anatomical structures is connected to their underlying biological functions remains an open question in modern neuroscience. A major challenge is the identification of "universal patterns", which do not depend on the particular individual, but are instead basic structural properties shared by all brains. Despite the vast amount of gene expression data available at both the bulk and single-cell levels, this task remains challenging, mainly due to the lack of suitable data mining tools. In this paper, we propose an approach to address this issue based on a hierarchical version of Stochastic Block Modeling. Thanks to its specific choice of priors, the method is particularly effective in identifying these universal features. We use as a laboratory to test our algorithm a dataset obtained from six independent human brains from the Allen Human Brain Atlas. We show that the proposed method is indeed able to identify universal patterns much better than more traditional algorithms such as Latent Dirichlet Allocation or Weighted Correlation Network Analysis. The probabilistic association between genes and samples that we find well represents the known anatomical and functional brain organization. Moreover, leveraging the peculiar "fuzzy" structure of the gene sets obtained with our method, we identify examples of transcriptional and post-transcriptional pathways associated with specific brain regions, highlighting the potential of our approach.

Cell Type-Agnostic Transcriptomic Signatures Enable Uniform Comparisons of Neurodevelopment

Single-cell transcriptomics has revolutionized our understanding of neurodevelopmental cell identities, yet, predicting a cell type's developmental state from its transcriptome remains a challenge. We perform a meta-analysis of developing human brain datasets comprising over 2.8 million cells, identifying both tissue-level and cell-autonomous predictors of developmental age. While tissue composition predicts age within individual studies, it fails to generalize, whereas specific cell type proportions reliably track developmental time across datasets. Training regularized regression models to infer cell-autonomous maturation, we find that a cell type-agnostic model achieves the highest accuracy (error = 2.6 weeks), robustly capturing developmental dynamics across diverse cell types and datasets. This model generalizes to human neural organoids, accurately predicting normal developmental trajectories (R = 0.91) and disease-induced shifts in vitro. Furthermore, it extends to the developing mouse brain, revealing an accelerated developmental tempo relative to humans. Our work provides a unified framework for comparing neurodevelopment across contexts, model systems, and species.

A single-cell transcriptomic atlas of developing inhibitory neurons reveals expanding and contracting modes of diversification

The cerebral cortex relies on vastly different types of inhibitory neurons to compute. How this diversity emerges during development remains an open question. The rarity of individual inhibitory neuron types often leads to their underrepresentation in single-cell RNA sequencing (scRNAseq) datasets, limiting insights into their developmental trajectories. To address this problem, we developed a computational pipeline to enrich and integrate rare cell types across multiple datasets. Applying this approach to somatostatin-expressing (SST+) inhibitory neurons-the most diverse inhibitory cell class in the cortex-we constructed the Dev-SST-Atlas, a comprehensive resource containing mouse transcriptomic data of over 51,000 SST+ neurons. We identify three principal groups-Martinotti cells (MCs), non-Martinotti cells (nMCs), and long-range projecting neurons (LRPs)-each following distinct diversification trajectories. MCs commit early, with distinct embryonic and neonatal clusters that map directly to adult counterparts. In contrast, nMCs diversify gradually, with each developmental cluster giving rise to multiple adult cell types. LRPs follow a unique 'contracting' mode. Initially, two clusters are present until postnatal day 5 (P5), but by P7, one type is eliminated through programmed cell death, leaving a single surviving population. This transient LRP type is also found in the fetal human cortex, revealing an evolutionarily conserved feature of cortical development. Together, these findings highlight three distinct modes of SST+ neuron diversification-invariant, expanding, and contracting-offering a new framework to understand how the large repertoire of inhibitory neurons emerges during development.

Sleep need driven oscillation of glutamate synaptic phenotype

Sleep loss increases AMPA-synaptic strength and number in the neocortex. However, this is only part of the synaptic sleep loss response. We report an increased AMPA/NMDA EPSC ratio in frontal-cortical pyramidal neurons of layers 2-3. Silent synapses are absent, decreasing the plastic potential to convert silent NMDA to active AMPA synapses. These sleep loss changes are recovered by sleep. Sleep genes are enriched for synaptic shaping cellular components controlling glutamate synapse phenotype, overlap with autism risk genes, and are primarily observed in excitatory pyramidal neurons projecting intra-telencephalically. These genes are enriched with genes controlled by the transcription factor, MEF2c, and its repressor, HDAC4. Sleep genes can thus provide a framework within which motor learning and training occur mediated by the sleep-dependent oscillation of glutamate-synaptic phenotypes.

A Massively Parallel CRISPR-Based Screening Platform for Modifiers of Neuronal Activity

Understanding the complex interplay between gene expression and neuronal activity is crucial for unraveling the molecular mechanisms underlying cognitive function and neurological disorders. Here, we developed pooled screens for neuronal activity, using CRISPR interference (CRISPRi) and the fluorescent calcium integrator CaMPARI2. Using this screening method, we evaluated 1343 genes for their effect on excitability in human iPSC-derived neurons, revealing potential links to neurodegenerative and neurodevelopmental disorders. These genes include known regulators of neuronal excitability, such as TARPs and ion channels, as well as genes associated with autism spectrum disorder and Alzheimer's disease not previously described to affect neuronal excitability. This CRISPRi-based screening platform offers a versatile tool to uncover molecular mechanisms controlling neuronal activity in health and disease.

Transcriptome size matters for single-cell RNA-seq normalization and bulk deconvolution

The variation of transcriptome size across cell types significantly impacts single-cell RNA sequencing (scRNA-seq) data normalization and bulk RNA-seq cellular deconvolution, yet this intrinsic feature is often overlooked. Here we introduce ReDeconv, a computational algorithm that incorporates transcriptome size into scRNA-seq normalization and bulk deconvolution. ReDeconv introduces a scRNA-seq normalization approach, Count based on Linearized Transcriptome Size (CLTS), which corrects differential expressed genes typically misidentified by standard count per 10 K normalization, as confirmed by orthogonal validations. By maintaining transcriptome size variation, CLTS-normalized scRNA-seq enhances the accuracy of bulk deconvolution. Additionally, ReDeconv mitigates gene length effects and models expression variances, thereby improving deconvolution outcomes, particularly for rare cell types. Evaluated with both synthetic and real datasets, ReDeconv surpasses existing methods in precision. ReDeconv alters the practice and provides a new standard for scRNA-seq analyses and bulk deconvolution. The software packages and a user-friendly web portal are available.

Co-Conservation of Synaptic Gene Expression and Circuitry in Collicular Neurons

The superior colliculus (SC), a midbrain sensorimotor hub, is anatomically and functionally similar across vertebrates, but how its cell types have evolved is unclear. Using single-nucleus transcriptomics, we compared the SC's molecular and cellular organization in mice, tree shrews, and humans. Despite over 96 million years of evolutionary divergence, we identified ~30 consensus neuronal subtypes, including Cbln2+ neurons that form the SC-pulvinar circuit in mice and tree shrews. Synapse-related genes were among the most conserved, unlike neocortex, suggesting co-conservation of synaptic genes and circuitry. In contrast, cilia-related genes diverged significantly across species, highlighting the potential importance of the neuronal primary cilium in SC evolution. Additionally, we identified a novel inhibitory SC neuron in tree shrews and humans but not mice. Our findings reveal that the SC has evolved by conserving neuron subtypes, synaptic genes, and circuitry, while diversifying ciliary gene expression and an inhibitory neuron subtype.

Inferring disease progression stages in single-cell transcriptomics using a weakly supervised deep learning approach

Application of single-cell/nucleus genomic sequencing to patient-derived tissues offers potential solutions to delineate disease mechanisms in humans. However, individual cells in patient-derived tissues are in different pathological stages, and hence, such cellular variability impedes subsequent differential gene expression analyses. To overcome such a heterogeneity issue, we present a novel deep learning approach, scIDST, that infers disease progression levels of individual cells with weak supervision framework. The disease progression-inferred cells display significant differential expression of disease-relevant genes, which cannot be detected by comparative analysis between patients and healthy donors. In addition, we demonstrate that pretrained models by scIDST are applicable to multiple independent data resources and are advantageous to infer cells related to certain disease risks and comorbidities. Taken together, scIDST offers a new strategy of single-cell sequencing analysis to identify bona fide disease-associated molecular features.

The cell-type underpinnings of the human functional cortical connectome

The functional properties of the human brain arise, in part, from the vast assortment of cell types that pattern the cerebral cortex. The cortical sheet can be broadly divided into distinct networks, which are embedded into processing streams, or gradients, that extend from unimodal systems through higher-order association territories. Here using microarray data from the Allen Human Brain Atlas and single-nucleus RNA-sequencing data from multiple cortical territories, we demonstrate that cell-type distributions are spatially coupled to the functional organization of cortex, as estimated through functional magnetic resonance imaging. Differentially enriched cells follow the spatial topography of both functional gradients and associated large-scale networks. Distinct cellular fingerprints were evident across networks, and a classifier trained on postmortem cell-type distributions was able to predict the functional network allegiance of cortical tissue samples. These data indicate that the in vivo organization of the cortical sheet is reflected in the spatial variability of its cellular composition.

Challenges and Opportunities in the Clinical Translation of High-Resolution Spatial Transcriptomics

Pathology has always been fueled by technological advances. Histology powered the study of tissue architecture at single-cell resolution and remains a cornerstone of clinical pathology today. In the last decade, next-generation sequencing has become informative for the targeted treatment of many diseases, demonstrating the importance of genome-scale molecular information for personalized medicine. Today, revolutionary developments in spatial transcriptomics technologies digitalize gene expression at subcellular resolution in intact tissue sections, enabling the computational analysis of cell types, cellular phenotypes, and cell-cell communication in routinely collected and archival clinical samples. Here we review how such molecular microscopes work, highlight their potential to identify disease mechanisms and guide personalized therapies, and provide guidance for clinical study design. Finally, we discuss remaining challenges to the swift translation of high-resolution spatial transcriptomics technologies and how integration of multimodal readouts and deep learning approaches is bringing us closer to a holistic understanding of tissue biology and pathology.

Advances and applications in single-cell and spatial genomics

The applications of single-cell and spatial technologies in recent times have revolutionized the present understanding of cellular states and the cellular heterogeneity inherent in complex biological systems. These advancements offer unprecedented resolution in the examination of the functional genomics of individual cells and their spatial context within tissues. In this review, we have comprehensively discussed the historical development and recent progress in the field of single-cell and spatial genomics. We have reviewed the breakthroughs in single-cell multi-omics technologies, spatial genomics methods, and the computational strategies employed toward the analyses of single-cell atlas data. Furthermore, we have highlighted the advances made in constructing cellular atlases and their clinical applications, particularly in the context of disease. Finally, we have discussed the emerging trends, challenges, and opportunities in this rapidly evolving field.

Statistical analysis supports pervasive RNA subcellular localization and alternative 3' UTR regulation

Targeted low-throughput studies have previously identified subcellular RNA localization as necessary for cellular functions including polarization, and translocation. Furthermore, these studies link localization to RNA isoform expression, especially 3' Untranslated Region (UTR) regulation. The recent introduction of genome-wide spatial transcriptomics techniques enables the potential to test if subcellular localization is regulated in situ pervasively. In order to do this, robust statistical measures of subcellular localization and alternative poly-adenylation (APA) at single-cell resolution are needed. Developing a new statistical framework called SPRAWL, we detect extensive cell-type specific subcellular RNA localization regulation in the mouse brain and to a lesser extent mouse liver. We integrated SPRAWL with a new approach to measure cell-type specific regulation of alternative 3' UTR processing and detected examples of significant correlations between 3' UTR length and subcellular localization. Included examples, Timp3, Slc32a1, Cxcl14, and Nxph1 have subcellular localization in the mouse brain highly correlated with regulated 3' UTR processing that includes the use of unannotated, but highly conserved, 3' ends. Together, SPRAWL provides a statistical framework to integrate multi-omic single-cell resolved measurements of gene-isoform pairs to prioritize an otherwise impossibly large list of candidate functional 3' UTRs for functional prediction and study. In these studies of data from mice, SPRAWL predicts that 3' UTR regulation of subcellular localization may be more pervasive than currently known.

RNA-programmable cell type monitoring and manipulation in the human cortex with CellREADR

Reliable and systematic experimental access to diverse cell types is necessary for understanding the neural circuit organization, function, and pathophysiology of the human brain. Methods for targeting human neural populations are scarce and currently center around identifying and engineering transcriptional enhancers and viral capsids. Here we demonstrate the utility of CellREADR, a programmable RNA sensor-effector technology that couples cellular RNA sensing to effector protein translation, for accessing, monitoring, and manipulating specific neuron types in ex vivo human cortical tissues. We designed CellREADR constructs to target two distinct human neuron types, CALB2+ (calretinin) GABAergic interneurons and FOXP2+ (forkhead box protein P2) glutamatergic projection neurons, and validated cell targeting using histological, electrophysiological, and transcriptomic methods. CellREADR-mediated expression of optogenetic effectors and genetically-encoded calcium indicators allowed us to manipulate and monitor these neuronal populations in cortical microcircuits. We further demonstrate that AAV-based CellREADR and enhancer vectors can be jointly used to target different subpopulations in the same preparation. By demonstrating specific, reliable, and programmable experimental access to targeted cell types, our results highlight CellREADR's potential for studying human neural circuits and treating brain disorders with cell type resolution.

Chromatin accessibility: biological functions, molecular mechanisms and therapeutic application

The dynamic regulation of chromatin accessibility is one of the prominent characteristics of eukaryotic genome. The inaccessible regions are mainly located in heterochromatin, which is multilevel compressed and access restricted. The remaining accessible loci are generally located in the euchromatin, which have less nucleosome occupancy and higher regulatory activity. The opening of chromatin is the most important prerequisite for DNA transcription, replication, and damage repair, which is regulated by genetic, epigenetic, environmental, and other factors, playing a vital role in multiple biological progresses. Currently, based on the susceptibility difference of occupied or free DNA to enzymatic cleavage, solubility, methylation, and transposition, there are many methods to detect chromatin accessibility both in bulk and single-cell level. Through combining with high-throughput sequencing, the genome-wide chromatin accessibility landscape of many tissues and cells types also have been constructed. The chromatin accessibility feature is distinct in different tissues and biological states. Research on the regulation network of chromatin accessibility is crucial for uncovering the secret of various biological processes. In this review, we comprehensively introduced the major functions and mechanisms of chromatin accessibility variation in different physiological and pathological processes, meanwhile, the targeted therapies based on chromatin dynamics regulation are also summarized.

Transcriptomic cell-type specificity of local cortical circuits

Complex neocortical functions rely on networks of diverse excitatory and inhibitory neurons. While local connectivity rules between major neuronal subclasses have been established, the specificity of connections at the level of transcriptomic subtypes remains unclear. We introduce single transcriptome assisted rabies tracing (START), a method combining monosynaptic rabies tracing and single-nuclei RNA sequencing to identify transcriptomic cell types, providing inputs to defined neuron populations. We employ START to transcriptomically characterize inhibitory neurons providing monosynaptic input to 5 different layer-specific excitatory cortical neuron populations in mouse primary visual cortex (V1). At the subclass level, we observe results consistent with findings from prior studies that resolve neuronal subclasses using antibody staining, transgenic mouse lines, and morphological reconstruction. With improved neuronal subtype granularity achieved with START, we demonstrate transcriptomic subtype specificity of inhibitory inputs to various excitatory neuron subclasses. These results establish local connectivity rules at the resolution of transcriptomic inhibitory cell types.

A temporal cortex cell atlas highlights gene expression dynamics during human brain maturation

The human brain undergoes protracted postnatal maturation, guided by dynamic changes in gene expression. Most studies exploring these processes have used bulk tissue analyses, which mask cell-type-specific gene expression dynamics. Here, using single-nucleus RNA sequencing on temporal lobe tissue, including samples of African ancestry, we build a joint pediatric and adult atlas of 75 cell subtypes, which we verify with spatial transcriptomics. We explore the differences between pediatric and adult cell subtypes, revealing the genes and pathways that change during brain maturation. Our results highlight excitatory neuron subtypes, including the LTK and FREM subtypes, that show elevated expression of genes associated with cognition and synaptic plasticity in pediatric tissue. The resources we present here improve our understanding of the brain during its development and contribute to global efforts to build an inclusive brain cell map.

Translational Insights From Cell Type Variation Across Amygdala Subnuclei in Rhesus Monkeys and Humans

OBJECTIVE

Theories of amygdala function are central to our understanding of psychiatric and neurodevelopmental disorders. However, limited knowledge of the molecular and cellular composition of the amygdala impedes translational research aimed at developing new treatments and interventions. The aim of this study was to characterize and compare the composition of amygdala cells to help bridge the gap between preclinical models and human psychiatric and neurodevelopmental disorders.

METHODS

Tissue was dissected from multiple amygdala subnuclei in both humans (N=3, male) and rhesus macaques (N=3, male). Single-nucleus RNA sequencing was performed to characterize the transcriptomes of individual nuclei.

RESULTS

The results reveal substantial heterogeneity between regions, even when restricted to inhibitory or excitatory neurons. Consistent with previous work, the data highlight the complexities of individual marker genes for uniquely targeting specific cell types. Cross-species analyses suggest that the rhesus monkey model is well-suited to understanding the human amygdala, but also identify limitations. For example, a cell cluster in the ventral lateral nucleus of the amygdala (vLa) is enriched in humans relative to rhesus macaques. Additionally, the data describe specific cell clusters with relative enrichment of disorder-related genes. These analyses point to the human-enriched vLa cell cluster as relevant to autism spectrum disorder, potentially highlighting a vulnerability to neurodevelopmental disorders that has emerged in recent primate evolution. Further, a cluster of cells expressing markers for intercalated cells is enriched for genes reported in human genome-wide association studies of neuroticism, anxiety disorders, and depressive disorders.

CONCLUSIONS

Together, these findings shed light on the composition of the amygdala and identify specific cell types that can be prioritized in basic science research to better understand human psychopathology and guide the development of potential treatments.

The single-cell opioid responses in the context of HIV (SCORCH) consortium

Substance use disorders (SUD) and drug addiction are major threats to public health, impacting not only the millions of individuals struggling with SUD, but also surrounding families and communities. One of the seminal challenges in treating and studying addiction in human populations is the high prevalence of co-morbid conditions, including an increased risk of contracting a human immunodeficiency virus (HIV) infection. Of the ~15 million people who inject drugs globally, 17% are persons with HIV. Conversely, HIV is a risk factor for SUD because chronic pain syndromes, often encountered in persons with HIV, can lead to an increased use of opioid pain medications that in turn can increase the risk for opioid addiction. We hypothesize that SUD and HIV exert shared effects on brain cell types, including adaptations related to neuroplasticity, neurodegeneration, and neuroinflammation. Basic research is needed to refine our understanding of these affected cell types and adaptations. Studying the effects of SUD in the context of HIV at the single-cell level represents a compelling strategy to understand the reciprocal interactions among both conditions, made feasible by the availability of large, extensively-phenotyped human brain tissue collections that have been amassed by the Neuro-HIV research community. In addition, sophisticated animal models that have been developed for both conditions provide a means to precisely evaluate specific exposures and stages of disease. We propose that single-cell genomics is a uniquely powerful technology to characterize the effects of SUD and HIV in the brain, integrating data from human cohorts and animal models. We have formed the Single-Cell Opioid Responses in the Context of HIV (SCORCH) consortium to carry out this strategy.

An Honest Reckoning With the Amygdala and Mental Illness

Anxiety disorders are a leading source of human misery, morbidity, and premature mortality. Existing treatments are far from curative for many, underscoring the need to clarify the underlying neural mechanisms. Although many brain regions contribute, the amygdala has received the most intense scientific attention. Over the past several decades, this scrutiny has yielded a detailed understanding of amygdala function, but it has failed to produce new clinical assays, biomarkers, or cures. Rising to this urgent public health challenge demands an honest reckoning with the functional-neuroanatomical complexity of the amygdala and a shift from theories anchored on "the amygdala" to models centered on specific amygdala nuclei and cell types. This review begins by examining evidence from studies of rodents, monkeys, and humans for the "canonical model," the idea that the amygdala plays a central role in fear- and anxiety-related states, traits, and disorders. Next, the authors selectively highlight work indicating that the canonical model, while true, is overly simplistic and fails to adequately capture the actual state of the evidentiary record, the breadth of amygdala-associated functions and illnesses, or the complexity of the amygdala's functional architecture. The authors describe the implications of these facts for basic and clinical neuroimaging research. The review concludes with some general recommendations for grappling with the complexity of the amygdala and accelerating efforts to understand and more effectively treat amygdala-related psychopathology.

Spatial and single-nucleus transcriptomic analysis of genetic and sporadic forms of Alzheimer's disease

The pathogenesis of Alzheimer's disease (AD) depends on environmental and heritable factors, with its molecular etiology still unclear. Here we present a spatial transcriptomic (ST) and single-nucleus transcriptomic survey of late-onset sporadic AD and AD in Down syndrome (DSAD). Studying DSAD provides an opportunity to enhance our understanding of the AD transcriptome, potentially bridging the gap between genetic mouse models and sporadic AD. We identified transcriptomic changes that may underlie cortical layer-preferential pathology accumulation. Spatial co-expression network analyses revealed transient and regionally restricted disease processes, including a glial inflammatory program dysregulated in upper cortical layers and implicated in AD genetic risk and amyloid-associated processes. Cell-cell communication analysis further contextualized this gene program in dysregulated signaling networks. Finally, we generated ST data from an amyloid AD mouse model to identify cross-species amyloid-proximal transcriptomic changes with conformational context.

Integrated multimodal cell atlas of Alzheimer's disease

Alzheimer's disease (AD) is the leading cause of dementia in older adults. Although AD progression is characterized by stereotyped accumulation of proteinopathies, the affected cellular populations remain understudied. Here we use multiomics, spatial genomics and reference atlases from the BRAIN Initiative to study middle temporal gyrus cell types in 84 donors with varying AD pathologies. This cohort includes 33 male donors and 51 female donors, with an average age at time of death of 88 years. We used quantitative neuropathology to place donors along a disease pseudoprogression score. Pseudoprogression analysis revealed two disease phases: an early phase with a slow increase in pathology, presence of inflammatory microglia, reactive astrocytes, loss of somatostatin+ inhibitory neurons, and a remyelination response by oligodendrocyte precursor cells; and a later phase with exponential increase in pathology, loss of excitatory neurons and Pvalb+ and Vip+ inhibitory neuron subtypes. These findings were replicated in other major AD studies.

Circuits and mechanisms for TMS-induced corticospinal waves: Connecting sensitivity analysis to the network graph

Transcranial magnetic stimulation (TMS) is a non-invasive, FDA-cleared treatment for neuropsychiatric disorders with broad potential for new applications, but the neural circuits that are engaged during TMS are still poorly understood. Recordings of neural activity from the corticospinal tract provide a direct readout of the response of motor cortex to TMS, and therefore a new opportunity to model neural circuit dynamics. The study goal was to use epidural recordings from the cervical spine of human subjects to develop a computational model of a motor cortical macrocolumn through which the mechanisms underlying the response to TMS, including direct and indirect waves, could be investigated. An in-depth sensitivity analysis was conducted to identify important pathways, and machine learning was used to identify common circuit features among these pathways. Sensitivity analysis identified neuron types that preferentially contributed to single corticospinal waves. Single wave preference could be predicted using the average connection probability of all possible paths between the activated neuron type and L5 pyramidal tract neurons (PTNs). For these activations, the total conduction delay of the shortest path to L5 PTNs determined the latency of the corticospinal wave. Finally, there were multiple neuron type activations that could preferentially modulate a particular corticospinal wave. The results support the hypothesis that different pathways of circuit activation contribute to different corticospinal waves with participation of both excitatory and inhibitory neurons. Moreover, activation of both afferents to the motor cortex as well as specific neuron types within the motor cortex initiated different I-waves, and the results were interpreted to propose the cortical origins of afferents that may give rise to certain I-waves. The methodology provides a workflow for performing computationally tractable sensitivity analyses on complex models and relating the results to the network structure to both identify and understand mechanisms underlying the response to acute stimulation.

Implementation and validation of single-cell genomics experiments in neuroscience

Single-cell or single-nucleus transcriptomics is a powerful tool for identifying cell types and cell states. However, hypotheses derived from these assays, including gene expression information, require validation, and their functional relevance needs to be established. The choice of validation depends on numerous factors. Here, we present types of orthogonal and functional validation experiment to strengthen preliminary findings obtained using single-cell and single-nucleus transcriptomics as well as the challenges and limitations of these approaches.

Developing a Multiscale Neural Connectivity Knowledgebase of the Autonomic Nervous System

The Stimulating Peripheral Activity to Relieve Conditions (SPARC) program is a U.S. National Institutes of Health (NIH) funded effort to enhance our understanding of the neural circuitry responsible for visceral control. SPARC's mission is to identify, extract, and compile our overall existing knowledge and understanding of the autonomic nervous system (ANS) connectivity between the central nervous system and end organs. A major goal of SPARC is to use this knowledge to promote the development of the next generation of neuromodulation devices and bioelectronic medicine for nervous system diseases. As part of the SPARC program, we have been developing SCKAN, a dynamic knowledge base of ANS connectivity that contains information about the origins, terminations, and routing of ANS projections. The distillation of SPARC's connectivity knowledge into this knowledge base involves a rigorous curation process to capture connectivity information provided by experts, published literature, textbooks, and SPARC scientific data. SCKAN is used to automatically generate anatomical and functional connectivity maps on the SPARC portal. In this article, we present the design and functionality of SCKAN, including the detailed knowledge engineering process developed to populate the resource with high quality and accurate data. We discuss the process from both the perspective of SCKAN's ontological representation as well as its practical applications in developing information systems. We share our techniques, strategies, tools and insights for developing a practical knowledgebase of ANS connectivity that supports continual enhancement.

Sleep need driven oscillation of glutamate synaptic phenotype

Sleep loss increases AMPA-synaptic strength and number in the neocortex. However, this is only part of the synaptic sleep loss response. We report increased AMPA/NMDA EPSC ratio in frontal-cortical pyramidal neurons of layers 2-3. Silent synapses are absent, decreasing the plastic potential to convert silent NMDA to active AMPA synapses. These sleep loss changes are recovered by sleep. Sleep genes are enriched for synaptic shaping cellular components controlling glutamate synapse phenotype, overlap with autism risk genes and are primarily observed in excitatory pyramidal neurons projecting intra-telencephalically. These genes are enriched with genes controlled by the transcription factor, MEF2c and its repressor, HDAC4. Sleep genes can thus provide a framework within which motor learning and training occurs mediated by sleep-dependent oscillation of glutamate-synaptic phenotypes.

scPair: Boosting single cell multimodal analysis by leveraging implicit feature selection and single cell atlases

Multimodal single-cell assays profile multiple sets of features in the same cells and are widely used for identifying and mapping cell states between chromatin and mRNA and linking regulatory elements to target genes. However, the high dimensionality of input features and shallow sequencing depth compared to unimodal assays pose challenges in data analysis. Here we present scPair, a multimodal single-cell data framework that overcomes these challenges by employing an implicit feature selection approach. scPair uses dual encoder-decoder structures trained on paired data to align cell states across modalities and predict features from one modality to another. We demonstrate that scPair outperforms existing methods in accuracy and execution time, and facilitates downstream tasks such as trajectory inference. We further show scPair can augment smaller multimodal datasets with larger unimodal atlases to increase statistical power to identify groups of transcription factors active during different stages of neural differentiation.

The Brain Image Library: A Community-Contributed Microscopy Resource for Neuroscientists

Advancements in microscopy techniques and computing technologies have enabled researchers to digitally reconstruct brains at micron scale. As a result, community efforts like the BRAIN Initiative Cell Census Network (BICCN) have generated thousands of whole-brain imaging datasets to trace neuronal circuitry and comprehensively map cell types. This data holds valuable information that extends beyond initial analyses, opening avenues for variation studies and robust classification of cell types in specific brain regions. However, the size and heterogeneity of these imaging data have historically made storage, sharing, and analysis difficult for individual investigators and impractical on a broad community scale. Here, we introduce the Brain Image Library (BIL), a public resource serving the neuroscience community that provides a persistent centralized repository for brain microscopy data. BIL currently holds thousands of brain datasets and provides an integrated analysis ecosystem, allowing for exploration, visualization, and data access without the need to download, thus encouraging scientific discovery and data reuse.

Neuronal fate resulting from indirect neurogenesis in the mouse neocortex

Excitatory cortical neurons originate from cortical radial glial cells (RGCs). Initially, these neurons were thought to derive directly from RGCs (direct neurogenesis) and be distributed in an inside-out fashion. However, the discovery of indirect neurogenesis, whereby intermediate neuronal progenitors (INPs) generate neurons, challenged this view. To investigate the integration of neurons via these two modes, we developed a method to identify INP progeny and analyze their fate using transgenic mice expressing tamoxifen-inducible Cre recombinase under the neurogenin-2 promoter, alongside thymidine analog incorporation. Their fate was further analyzed using mosaic analysis with double markers in mice. Indirect neurogenesis was prominent during early neurogenesis, generating neuron types that would emerge slightly later than those produced via direct neurogenesis. Despite the timing difference, both neurogenic modes produced fundamentally similar neuron types, as evidenced by marker expression and cortical-depth location. Furthermore, INPs generated pairs of similar phenotype neurons. These findings suggest that indirect neurogenesis, like direct neurogenesis, generates neuron types in a temporally ordered sequence and increases the number of similar neuron types, particularly in deep layers. Thus, both neurogenic modes cooperatively generate a diverse array of neuron types in a similar order, and their progeny populate together to form a coherent cortical structure.

Genetically defined neuron types underlying visuomotor transformation in the superior colliculus

The superior colliculus (SC) is a conserved midbrain structure that is important for transforming visual and other sensory information into motor actions. Decades of investigations in numerous species have made the SC and its nonmammalian homologue, the optic tectum, one of the best studied structures in the brain, with rich information now available regarding its anatomical organization, its extensive inputs and outputs and its important functions in many reflexive and cognitive behaviours. Excitingly, recent studies using modern genomic and physiological approaches have begun to reveal the diverse neuronal subtypes in the SC, as well as their unique functions in visuomotor transformation. Studies have also started to uncover how subtypes of SC neurons form intricate circuits to mediate visual processing and visually guided behaviours. Here, we review these recent discoveries on the cell types and neuronal circuits underlying visuomotor transformations mediated by the SC. We also highlight the important future directions made possible by these new developments.