Transcriptomic diversity of cell types across the adult human brain

A novel selective phosphodiesterase 9 inhibitor, irsenontrine (E2027), enhances GluA1 phosphorylation in neurons and improves learning and memory via cyclic GMP elevation

Phosphodiesterase 9 (PDE9) plays a critical role in synaptic plasticity and cognitive function by modulating cyclic GMP (cGMP). Many reports have shown that PDE9 inhibition improves cognitive function and synaptic plasticity in rodents. Several studies have found that the NO/cGMP/PKG pathway is downregulated in patients with Alzheimer's disease (AD) or dementia with Lewy bodies (DLB) and in older individuals. A PDE9 inhibitor could therefore be a potential therapeutic approach for improving cognitive dysfunction in dementia, including in AD and DLB. We previously discovered a novel PDE9 inhibitor, irsenontrine (E2027). In the current study, irsenontrine showed highly selective affinity for PDE9 with more than 1800-fold selectivity over other PDEs. Irsenontrine maleate significantly increased intracellular cGMP levels in rat cortical primary neurons, and phosphorylation of AMPA receptor subunit GluA1 was induced following cGMP elevation. Oral administration of irsenontrine significantly upregulated cGMP levels in the hippocampus and cerebrospinal fluid (CSF) of naïve rats, and a novel object recognition test showed that irsenontrine administration also significantly improved learning and memory. The effects of irsenontrine were confirmed in rats treated with Nω-nitro-l-arginine methyl ester hydrochloride (l-NAME), a model of learning and memory impairment due to downregulation of the cGMP pathway. l-NAME downregulated cGMP in the CSF and hippocampus and impaired novel object recognition, but oral administration of irsenontrine clearly attenuated these phenotypes. These results indicate that irsenontrine improves learning and memory via the elevation of cGMP levels, and they strongly suggest that irsenontrine could be a novel therapeutic approach against cognitive dysfunction.

Tumors and their microenvironments: Learning from pediatric brain pathologies

Early clues to tumors and their microenvironments come from embryonic development. Here we review the literature and consider whether the embryonic brain and its pathologies can serve as a better model. Among embryonic organs, the brain is the most heterogenous and complex, with multiple lineages leading to wide spectrum of cell states and types. Its dysregulation promotes neurodevelopmental brain pathologies and pediatric tumors. Embryonic brain pathologies point to the crucial importance of spatial heterogeneity over time, akin to the tumor microenvironment. Tumors dedifferentiate through genetic mutations and epigenetic modulations; embryonic brains differentiate through epigenetic modulations. Our innovative review proposes learning developmental brain pathologies to target tumor evolution-and vice versa. We describe ways through which tumor pharmacology can learn from embryonic brains and their pathologies, and how learning tumor, and its microenvironment, can benefit targeting neurodevelopmental pathologies. Examples include pediatric low-grade versus high-grade brain tumors as in rhabdomyosarcomas and gliomas.

Neural stem cells of the subventricular zone: A potential stem cell pool for brain repair in Parkinson's disease

Parkinson's disease is a neurodegenerative disease caused by the degeneration of dopaminergic neurons in the substantia nigra. There are no curative treatments, and therefore, there is an urgent need for new approaches. One potential strategy being investigated is stem cell-based approaches to replace lost neurons, by, for example, harnessing endogenous neural stem cells (NSCs). These cells are found in the subventricular zone (SVZ) aligning the lateral ventricles and remain in a dormant state in the aged and diseased mammalian brain. However, with the appropriate stimuli, NSCs can shift into an activated state, proliferate, and differentiate. In this review, we discuss how PD pathology affects the behavior of NSCs and current pharmacological strategies to boost regeneration in PD. NSCs of the SVZ could be a stem cell source for brain repair, and future studies should shed light on whether these stem cells have the potential to produce functional neuronal cells.

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.

Cell-death pathways and tau-associated neuronal vulnerability in Alzheimer's disease

Neuronal loss is the ultimate driver of neural system dysfunction in Alzheimer's disease (AD). We used single-nucleus RNA sequencing and neuropathological phenotyping to elucidate mechanisms of neurodegeneration in AD by identifying vulnerable neuronal populations and probing for their differentially expressed genes. Evidenced by transcriptomic analyses and quantitative tau immunoassays of human AD and non-AD brain tissue, we identified a neuronal population especially vulnerable to tau pathology. Multiplexed immunohistochemistry and in situ hybridization (CBLN2 and LINC00507) validated the presence of the tau-vulnerable neuronal population and revealed a propensity of this population to bear tau pathology. Differentially expressed genes associated with phospho-tau pathology in these neurons revealed genes involved in apoptosis, cell-component dissociation (e.g., autophagosome maturation and actin filament depolymerization), and regulation of vesicle-mediated transport.

Shared genetics and causal relationship between sociability and the brain's default mode network

BACKGROUND

The brain's default mode network (DMN) plays a role in social cognition, with altered DMN function being associated with social impairments across various neuropsychiatric disorders. However, the genetic basis linking sociability with DMN function remains underexplored. This study aimed to elucidate the shared genetics and causal relationship between sociability and DMN-related resting-state functional MRI (rs-fMRI) traits.

METHODS

We conducted a comprehensive genomic analysis using large-scale genome-wide association study (GWAS) summary statistics for sociability and 31 activity and 64 connectivity DMN-related rs-fMRI traits (N = 34,691-342,461). We performed global and local genetic correlations analyses and bi-directional Mendelian randomization (MR) to assess shared and causal effects. We prioritized genes influencing both sociability and rs-fMRI traits by combining expression quantitative trait loci MR analyses, the CELLECT framework - integrating single-nucleus RNA sequencing (snRNA-seq) data with GWAS - and network propagation within a protein-protein interaction network.

RESULTS

Significant local genetic correlations were identified between sociability and two rs-fMRI traits, one representing spontaneous activity within the temporal cortex, the other representing connectivity between the cingulate and angular/temporal cortices. MR analyses suggested potential causal effects of sociability on 12 rs-fMRI traits. Seventeen genes were highly prioritized, with LINGO1, ELAVL2, and CTNND1 emerging as top candidates. Among these, DRD2 was also identified, serving as a robust internal validation of our approach.

CONCLUSIONS

By combining genomic and transcriptomic data, our gene prioritization strategy may serve as a blueprint for future studies. Our findings can guide further research into the biological mechanisms underlying sociability and its role in the development, prognosis, and treatment of neuropsychiatric disorders.

Early establishment and life course stability of sex biases in the human brain transcriptome

To elaborate on the origins of the established male-female differences in several brain-related phenotypes, we assessed the patterns of transcriptomic sex biases in the developing and adult human forebrain. We find an abundance of sex differences in expression (sex-DEs) in the prenatal brain, driven by both hormonal and sex-chromosomal factors, and considerable consistency in the sex effects between the developing and adult brain, with little sex-DE exclusive to the adult forebrain. Sex-DE was not enriched in genes associated with brain disorders, consistent with systematic differences in the characteristics of these genes (e.g., constraint). Yet, the genes with persistent sex-DE across the lifespan were overrepresented in disease gene co-regulation networks, pointing to their potential to mediate sex biases in brain phenotypes. Altogether, our work highlights prenatal development as a crucial time point for the establishment of brain sex differences.

Enhancer AAV toolbox for accessing and perturbing striatal cell types and circuits

We present an enhancer-AAV toolbox for accessing and perturbing striatal cell types and circuits. Best-in-class vectors were curated for accessing major striatal neuron populations including medium spiny neurons (MSNs), direct- and indirect-pathway MSNs, Sst-Chodl, Pvalb-Pthlh, and cholinergic interneurons. Specificity was evaluated by multiple modes of molecular validation, by three different routes of virus delivery, and with diverse transgene cargos. Importantly, we provide detailed information necessary to achieve reliable cell-type-specific labeling under different experimental contexts. We demonstrate direct pathway circuit-selective optogenetic perturbation of behavior and multiplex labeling of striatal interneuron types for targeted analysis of cellular features. Lastly, we show conserved in vivo activity for exemplary MSN enhancers in rats and macaques. This collection of striatal enhancer AAVs offers greater versatility compared to available transgenic lines and can readily be applied for cell type and circuit studies in diverse mammalian species beyond the mouse model.

A dynamic and multimodal framework to define microglial states

The widespread use of single-cell RNA sequencing has generated numerous purportedly distinct and novel subsets of microglia. Here, we challenge this fragmented paradigm by proposing that microglia exist along a continuum rather than as discrete entities. We identify a methodological over-reliance on computational clustering algorithms as the fundamental issue, with arbitrary cluster numbers being interpreted as biological reality. Evidence suggests that the observed transcriptional diversity stems from a combination of microglial plasticity and technical noise, resulting in terminology describing largely overlapping cellular states. We introduce a continuous model of microglial states, where cell positioning along the continuum is determined by biological aging and cell-specific molecular contexts. The model accommodates the dynamic nature of microglia. We advocate for a parsimonious approach toward classification and terminology that acknowledges the continuous spectrum of microglial states, toward a robust framework for understanding these essential immune cells of the CNS.

Evaluating methods for the prediction of cell-type-specific enhancers in the mammalian cortex

Identifying cell-type-specific enhancers is critical for developing genetic tools to study the mammalian brain. We organized the "Brain Initiative Cell Census Network (BICCN) Challenge: Predicting Functional Cell Type-Specific Enhancers from Cross-Species Multi-Omics" to evaluate machine learning and feature-based methods for nominating enhancer sequences targeting mouse cortical cell types. Methods were assessed using in vivo data from hundreds of adeno-associated virus (AAV)-packaged, retro-orbitally delivered enhancers. Open chromatin was the strongest predictor of functional enhancers, while sequence models improved prediction of non-functional enhancers and identified cell-type-specific transcription factor codes to inform in silico enhancer design. This challenge establishes a benchmark for enhancer prioritization and highlights computational and molecular features critical for identifying functional cortical enhancers, advancing efforts to map and manipulate gene regulation in the mammalian cortex.

How radial glia progenitor lineages generate cell-type diversity in the developing cerebral cortex

The cerebral cortex is arguably the most complex organ in humans. The cortical architecture is characterized by a remarkable diversity of neuronal and glial cell types that make up its neuronal circuits. Following a precise temporally ordered program, radial glia progenitor (RGP) cells generate all cortical excitatory projection neurons and glial cell-types. Cortical excitatory projection neurons are produced either directly or via intermediate progenitors, through indirect neurogenesis. How the extensive cortical cell-type diversity is generated during cortex development remains, however, a fundamental open question. How do RGPs quantitatively and qualitatively generate all the neocortical neurons? How does direct and indirect neurogenesis contribute to the establishment of neuronal and lineage heterogeneity? Whether RGPs represent a homogeneous and/or multipotent progenitor population, or if RGPs consist of heterogeneous groups is currently also not known. In this review, we will summarize the latest findings that contributed to a deeper insight into the above key questions.

Single nuclei RNA-sequencing of adult brain neurons derived from type 2 neuroblasts reveals transcriptional complexity in the insect central complex

In both Drosophila and mammals, the brain contains the most diverse population of cell types of any tissue. It is generally accepted that transcriptional diversity is an early step in generating neuronal and glial diversity, followed by the establishment of a unique gene expression profile that determines morphology, connectivity, and function. In Drosophila, there are two types of neural stem cells, called Type 1 (T1) and Type 2 (T2) neuroblasts. The diversity of T2-derived neurons contributes a large portion of the central complex (CX), a conserved brain region that plays a role in sensorimotor integration. Recent work has revealed much of the connectome of the CX, but how this connectome is assembled remains unclear. Mapping the transcriptional diversity of T2-derived neurons is a necessary step in linking transcriptional profile to the assembly of the adult brain. Here we perform single nuclei RNA sequencing of T2 neuroblast-derived adult neurons and glia. We identify clusters containing all known classes of glia, clusters that are male/female enriched, and 161 neuron-specific clusters. We map neurotransmitter and neuropeptide expression and identify unique transcription factor combinatorial codes for each cluster. This is a necessary step that directs functional studies to determine whether each transcription factor combinatorial code specifies a distinct neuron type within the CX. We map several columnar neuron subtypes to distinct clusters and identify two neuronal classes (NPF+ and AstA+) that both map to two closely related clusters. Our data support the hypothesis that each transcriptional cluster represents one or a few closely related neuron subtypes.

Distinct systemic impacts of Aβ42 and Tau revealed by whole-organism snRNA-seq

Both neuronal and peripheral tissues become disrupted in Alzheimer's disease (AD). However, a comprehensive understanding of how AD impacts different tissues across the whole organism is lacking. Using Drosophila, we generated an AD Fly Cell Atlas (AD-FCA) based on whole-organism single-nucleus transcriptomes of 219 cell types from flies expressing AD-associated proteins, either human amyloid-β 42 peptide (Aβ42) or Tau, in neurons. We found that Aβ42 primarily affects the nervous system, including sensory neurons, while Tau induces accelerated aging in peripheral tissues. We identified a neuronal cluster enriched in Aβ42 flies, which has high lactate dehydrogenase (LDH) expression. This LDH-high cluster is conserved in 5XFAD mouse and human AD datasets. We found a conserved defect in fat metabolism from both fly and mouse tauopathy models. The AD-FCA offers new insights into how Aβ42 or Tau systemically and differentially affects a whole organism and provides a valuable resource for understanding brain-body communication in neurodegeneration.

Molecular and spatial transcriptomic classification of midbrain dopamine neurons and their alterations in a LRRK2G2019S model of Parkinson's disease

Several studies have revealed that midbrain dopamine (DA) neurons, even within a single neuroanatomical area, display heterogeneous properties. In parallel, studies using singlecell profiling techniques have begun to cluster DA neurons into subtypes based on their molecular signatures. Recent work has shown that molecularly defined DA subtypes within the substantia nigra (SNc) display distinctive anatomic and functional properties, and differential vulnerability in Parkinson's disease (PD). Based on these provocative results, a granular understanding of these putative subtypes and their alterations in PD models, is imperative. We developed an optimized pipeline for single-nuclear RNA sequencing (snRNA-seq) and generated a high-resolution hierarchically organized map revealing 20 molecularly distinct DA neuron subtypes belonging to three main families. We integrated this data with spatial MERFISH technology to map, with high definition, the location of these subtypes in the mouse midbrain, revealing heterogeneity even within neuroanatomical sub-structures. Finally, we demonstrate that in the preclinical LRRK2G2019S knock-in mouse model of PD, subtype organization and proportions are preserved. Transcriptional alterations occur in many subtypes including those localized to the ventral tier SNc, where differential expression is observed in synaptic pathways, which might account for previously described DA release deficits in this model. Our work provides an advancement of current taxonomic schemes of the mouse midbrain DA neuron subtypes, a high-resolution view of their spatial locations, and their alterations in a prodromal mouse model of PD.

Genome-wide association studies of binge eating behaviour and anorexia nervosa yield insights into the unique and shared biology of eating disorder phenotypes

Eating disorders -including anorexia nervosa (AN), bulimia nervosa, and binge eating disorder-are clinically distinct but exhibit symptom overlap and diagnostic crossover. Genomic analyses have mostly examined AN. We conducted the first genomic meta-analysis of binge eating behaviour (BE; 39,279 cases, 1,227,436 controls), alongside new analyses of AN (24,223 cases, 1,243,971 controls) and its subtypes (all European ancestries). We identified six loci associated with BE, including loci associated with higher body mass index (BMI) and impulse-control behaviours. AN GWAS yielded eight loci, validating six loci. Subsequent polygenic risk score analysis demonstrated an association with AN in two East Asian ancestry cohorts. BE and AN exhibited similar positive genetic correlations with psychiatric disorders, but opposing genetic correlations with anthropometric traits. Most of the genetic signal in BE and AN was not shared with BMI. We have extended eating disorder genomics beyond AN; future work will incorporate multiple diagnoses and global ancestries.

Optimized enrichment of murine blood-brain barrier vessels with a critical focus on network hierarchy in post-collection analysis

Cerebrovascular networks contain a unique region of interconnected capillaries known as the blood-brain barrier (BBB). Positioned between upstream arteries and downstream veins, these microvessels have unique structural features, such as the absence of vascular smooth muscle cells (vSMCs) and a relatively thin basement membrane, to facilitate highly efficient yet selective exchange between the circulation and the brain interstitium. This vital role in neurological health and function has garnered significant attention from the scientific community and inspired methodology for enriching BBB capillaries. Extensive characterization of the isolates from such protocols is essential for framing the results of follow-on experiments and analyses, providing the most accurate interpretation and assignment of BBB properties. Seeking to aid in these efforts, here we visually screened output samples using fluorescent labels and found considerable reduction of non-vascular cells following density gradient centrifugation (DGC) and subsequent filtration. Comparatively, this protocol enriched brain capillaries, though larger diameter vessels associated with vSMCs could not be fully excluded. Protein analysis further underscored the enrichment of vascular markers following DGC, with filtration preserving BBB-associated markers and reducing - though not fully removing - arterial/venous contributions. Transcriptional profiling followed similar trends of DGC plus filtration generating isolates with less non-vascular and non-capillary material included. Considering vascular network hierarchy inspired a more comprehensive assessment of the material yielded from brain microvasculature isolation protocols. This approach is important for providing an accurate representation of the cerebrovascular segments being used for data collection and assigning BBB properties specifically to capillaries relative to other regions of the brain vasculature.

Single-Nucleus Transcriptomics Reveals Prenatal and Postnatal Pb Exposure-Induced Cell-Specific Neurotoxicity and Dysregulated Microglia-Neuron Communication in Mice Brain

Lead (Pb) is an environmental pollutant that has lasting effects on neurodevelopment. Children exhibit heightened sensitivity to Pb exposure compared to adults, and prenatal Pb exposure can harm the developing fetal nervous system. However, the specific regulatory effects of Pb across various developmental stages are not well understood. This study employed single-nucleus RNA sequencing (snRNA-seq) to analyze mice brains at different ages (2 and 8 weeks) following prenatal and postnatal Pb exposure. Blood lead level in exposed mice is comparable to those detected in human samples, implying its environmental implication. A total of 43,303 brain cells were sequenced for cell-specific analysis. Pb exposure was found to elevate the proportion of immature neurons in the brains of 2 week-old mice and to perturb neurodevelopment- and neural structure-related pathways within neurons. In 8 week-old mice, Pb primarily influenced pathways implicated in synaptic transmission, signal transduction, and learning and memory in both neurons and glial cells. The communication involving neurotransmitters glutamate and γ-aminobutyric acid (GABA), along with their receptors, was disrupted between neuron and microglia. Through the application of snRNA-seq, this study has demonstrated that the Pb-induced neurotoxicity is characterized by cellular heterogeneity and the disruption of neurotransmitter-related communication between microglia and neurons could be a critical factor in Pb-induced neurotoxicity.

Toward Principles of Brain Network Organization and Function

The brain is immensely complex, with diverse components and dynamic interactions building upon one another to orchestrate a wide range of behaviors. Understanding patterns of these complex interactions and how they are coordinated to support collective neural function is critical for parsing human and animal behavior, treating mental illness, and developing artificial intelligence. Rapid experimental advances in imaging, recording, and perturbing neural systems across various species now provide opportunities to distill underlying principles of brain organization and function. Here, we take stock of recent progress and review methods used in the statistical analysis of brain networks, drawing from fields of statistical physics, network theory, and information theory. Our discussion is organized by scale, starting with models of individual neurons and extending to large-scale networks mapped across brain regions. We then examine organizing principles and constraints that shape the biological structure and function of neural circuits. We conclude with an overview of several critical frontiers, including expanding current models, fostering tighter feedback between theory and experiment, and leveraging perturbative approaches to understand neural systems. Alongside these efforts, we highlight the importance of contextualizing their contributions by linking them to formal accounts of explanation and causation.

CHOIR improves significance-based detection of cell types and states from single-cell data

Clustering is a critical step in the analysis of single-cell data, enabling the discovery and characterization of cell types and states. However, most popular clustering tools do not subject results to statistical inference testing, leading to risks of overclustering or underclustering data and often resulting in ineffective identification of cell types with widely differing prevalence. To address these challenges, we present CHOIR (cluster hierarchy optimization by iterative random forests), which applies a framework of random forest classifiers and permutation tests across a hierarchical clustering tree to statistically determine clusters representing distinct populations. We demonstrate the performance of CHOIR through extensive benchmarking against 15 existing clustering methods across 230 simulated and five real single-cell RNA sequencing, assay for transposase-accessible chromatin sequencing, spatial transcriptomic and multi-omic datasets. CHOIR can be applied to any single-cell data type and provides a flexible, scalable and robust solution to the challenge of identifying biologically relevant cell groupings within heterogeneous single-cell data.

Transcriptomic Profiling Unveils EDN3+ Meningeal Fibroblasts as Key Players in Sturge-Weber Syndrome Pathogenesis

Sturge-Weber syndrome (SWS) is characterized by leptomeningeal vascular malformation, resulting in significant risks of life-threatening seizures and strokes. The current absence of specific treatments underscores the need to define the molecular and cellular mechanisms that drive the progression of SWS. Here, the transcriptome of 119 446 cells isolated from both malformed tissues and peri-lesion tissues from the brains of patients with SWS is examined. This comprehensive analysis finds a complex landscape of cell heterogeneity and distinct cell substate associated with the evolution of this disease are revealed. Notably, a unique fibroblast cluster and molecular mechanism are identified that contribute to the development of SWS. These findings not only expand the understanding of SWS but also open up promising avenues for therapeutic interventions.

Integrating Spatially-Resolved Transcriptomics Data Across Tissues and Individuals: Challenges and Opportunities

Advances in spatially-resolved transcriptomics (SRT) technologies have propelled the development of new computational analysis methods to unlock biological insights. The lowering cost of SRT data generation presents an unprecedented opportunity to create large-scale spatial atlases and enable population-level investigation, integrating SRT data across multiple tissues, individuals, species, or phenotypes. Here, unique challenges are described in the SRT data integration, where the analytic impact of varying spatial and biological resolutions is characterized and explored. A succinct review of spatially-aware integration methods and computational strategies is provided. Exciting opportunities to advance computational algorithms amenable to atlas-scale datasets along with standardized preprocessing methods, leading to improved sensitivity and reproducibility in the future are further highlighted.

Single-cell genomics of the mouse olfactory cortex reveals contrasts with neocortex and ancestral signatures of cell type evolution

Understanding the molecular logic of cortical cell-type diversity can illuminate cortical circuit function and evolution. Here, we performed single-nucleus transcriptome and chromatin accessibility analyses to compare neurons across three- to six-layered cortical areas of adult mice and across tetrapod species. We found that, in contrast to the six-layered neocortex, glutamatergic neurons of the three-layered mouse olfactory (piriform) cortex displayed continuous rather than discrete variation in transcriptomic profiles. Subsets of piriform and neocortical glutamatergic cells with conserved transcriptomic profiles were distinguished by distinct, area-specific epigenetic states. Furthermore, we identified a prominent population of immature neurons in piriform cortex and observed that, in contrast to the neocortex, piriform cortex exhibited divergence between glutamatergic cells in laboratory versus wild-derived mice. Finally, we showed that piriform neurons displayed greater transcriptomic similarity to cortical neurons of turtles, lizards and salamanders than to those of the neocortex. In summary, despite over 200 million years of coevolution alongside the neocortex, olfactory cortex neurons retain molecular signatures of ancestral cortical identity.

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.

Spatially resolved mapping of cells associated with human complex traits

Depicting spatial distributions of disease-relevant cells is crucial for understanding disease pathology1,2. Here we present genetically informed spatial mapping of cells for complex traits (gsMap), a method that integrates spatial transcriptomics data with summary statistics from genome-wide association studies to map cells to human complex traits, including diseases, in a spatially resolved manner. Using embryonic spatial transcriptomics datasets covering 25 organs, we benchmarked gsMap through simulation and by corroborating known trait-associated cells or regions in various organs. Applying gsMap to brain spatial transcriptomics data, we reveal that the spatial distribution of glutamatergic neurons associated with schizophrenia more closely resembles that for cognitive traits than that for mood traits such as depression. The schizophrenia-associated glutamatergic neurons were distributed near the dorsal hippocampus, with upregulated expression of calcium signalling and regulation genes, whereas depression-associated glutamatergic neurons were distributed near the deep medial prefrontal cortex, with upregulated expression of neuroplasticity and psychiatric drug target genes. Our study provides a method for spatially resolved mapping of trait-associated cells and demonstrates the gain of biological insights (such as the spatial distribution of trait-relevant cells and related signature genes) through these maps.

Glial changes and gene expression in Alzheimer's disease from snRNA-Seq and spatial transcriptomics

BackgroundAlzheimer's disease (AD) is characterized by cortical atrophy, glutamatergic neuron loss, and cognitive decline. However, large-scale quantitative assessments of cellular changes during AD pathology remain scarce.ObjectiveThis study aims to integrate single-nuclei sequencing data from the Seattle Alzheimer's Disease Cortical Atlas (SEA-AD) with spatial transcriptomics to quantify cellular changes in the prefrontal cortex and temporal gyrus, regions vulnerable to AD neuropathological changes (ADNC).MethodsWe mapped differentially expressed genes (DEGs) and analyzed their interactions with pathological factors such as APOE expression and Lewy bodies. Cellular proportions were assessed, focusing on neurons, glial cells, and immune cells.ResultsRORB-expressing L4-like neurons, though vulnerable to ADNC, exhibited stable cell numbers throughout disease progression. In contrast, astrocytes displayed increased reactivity, with upregulated cytokine signaling and oxidative stress responses, suggesting a role in neuroinflammation. A reduction in synaptic maintenance pathways indicated a decline in astrocytic support functions. Microglia showed heightened immune surveillance and phagocytic activity, indicating their role in maintaining cortical homeostasis.ConclusionsThe study underscores the critical roles of glial cells, particularly astrocytes and microglia, in AD progression. These findings contribute to a better understanding of cellular dynamics and may inform therapeutic strategies targeting glial cell function in AD.

Alcohol use disorder and body mass index show genetic pleiotropy and shared neural associations

Despite neurobiological overlap, alcohol use disorder (AUD) and body mass index (BMI) show minimal genetic correlation (rg), possibly due to mixed directions of shared variants. Here we applied MiXeR to investigate shared genetic architecture between AUD and BMI, conjunctional false discovery rate to detect shared loci and their directional effect, local analysis of (co)variant association for local rg, functional mapping and annotation to identify lead single-nucleotide polymorphisms, Genotype-Tissue Expression (GTEx) to examine tissue enrichment and BrainXcan to assess associations with brain phenotypes. MiXeR indicated 82.2% polygenic overlap, despite an rg of -0.03. The conjuctional false discovery rate method identified 132 shared lead single-nucleotide polymorphisms, with 53 novel, showing both concordant and discordant effects. GTEx analyses identified overexpression in multiple brain regions. Amygdala and caudate nucleus volumes were associated with AUD and BMI. Opposing variant effects explain the minimal rg between AUD and BMI, with implicated brain regions involved in executive function and reward, clarifying their polygenic overlap and neurobiological mechanisms.

Mapping the developmental profile of ventricular zone-derived neurons in the human cerebellum

The cerebellar ventricular zone (VZ) is the primary source of progenitors that generate cerebellar GABAergic neurons, including Purkinje cells (PCs) and interneurons (INs). This study provides detailed characterization of human cerebellar GABAergic neurogenesis using transcriptomic and histopathological analyses and reveals conserved and unique features compared to rodents. We show that the sequential progression of neurogenesis is conserved and occurs before 8 postconception weeks. Notably, PC differentiation occurs in the outer subventricular zone (SVZ), a region absent in the mouse cerebellum. Human PCs are generated during a compact two-week period before the onset of cerebral cortex histogenesis. A subset of human PCs retain proliferative marker expression weeks after leaving the VZ, another feature not observed in rodents. Human PC maturation is protracted with an extensive migration and reorganization throughout development with dendritic arborization developing in late gestation. We define a continuous transcriptional cascade of PC development from neuroepithelial cells to mature PCs. In contrast, while human interneuronal progenitors are born beginning in early fetal development, they exhibit an even more protracted differentiation across late gestation and into postnatal ages. These findings show dynamic developmental process for human cerebellar GABAergic neurons and underscore the importance of the embryonic environment, with early disruptions having potentially significant impacts.

Regional and aging-specific cellular architecture of non-human primate brains

BACKGROUND

Deciphering the functionality and dynamics of brain networks across different regions and age groups in non-human primates (NHPs) is crucial for understanding the evolution of human cognition as well as the processes underlying brain pathogenesis. However, systemic delineation of the cellular composition and molecular connections among multiple brain regions and their alterations induced by aging in NHPs remain largely unresolved.

METHODS

In this study, we performed single-nucleus RNA sequencing on 39 samples collected from 10 brain regions of two young and two aged rhesus macaques using the DNBelab C4 system. Validation of protein expression of signatures specific to particular cell types, brain regions, and aging was conducted through a series of immunofluorescence and immunohistochemistry staining experiments. Loss-of-function experiments mediated by short hairpin RNA (shRNA) targeting two age-related genes (i.e., VSNL1 and HPCAL4) were performed in U251 glioma cells to verify their aging effects. Senescence-associated beta-galactosidase (SA-β-gal) staining and quantitative PCR (qPCR) of senescence marker genes were employed to assess cellular senescence in U251 cells.

RESULTS

We have established a large-scale cell atlas encompassing over 330,000 cells for the rhesus macaque brain. Our analysis identified numerous gene expression signatures that were specific to particular cell types, subtypes, brain regions, and aging. These datasets greatly expand our knowledge of primate brain organization and highlight the potential involvement of specific molecular and cellular components in both the regionalization and functional integrity of the brain. Our analysis also disclosed extensive transcriptional alterations and cell-cell connections across brain regions in the aging macaques. Finally, by examining the heritability enrichment of human complex traits and diseases, we determined that neurological traits were significantly enriched in neuronal cells and multiple regions with aging-relevant gene expression signatures, while immune-related traits exhibited pronounced enrichment in microglia.

CONCLUSIONS

Taken together, our study presents a valuable resource for investigating the cellular and molecular architecture of the primate nervous system, thereby expanding our understanding of the mechanisms underlying brain function, aging, and disease.

Large-scale transcriptomic analyses of major depressive disorder reveal convergent dysregulation of synaptic pathways in excitatory neurons

Major Depressive Disorder (MDD) is a common, complex disorder that is a leading cause of disability worldwide and a significant risk factor for suicide. In this study, we have performed the largest molecular analysis of MDD in postmortem human brains (846 samples across 458 individuals) in the subgenual Anterior Cingulate Cortex (sACC) and the Amygdala, two regions central to mood regulation and the pathophysiology of MDD. We found extensive expression differences, particularly at the level of specific transcripts, with prominent enrichment for genes associated with the vesicular functioning, the postsynaptic density, GTPase signaling, and gene splicing. We find associated transcriptional features in 107 of 243 genome-wide significant loci for MDD and, through integrative analyses, highlight convergence of genetic risk, gene expression, and network-based analyses on dysregulated glutamatergic signaling and synaptic vesicular functioning. Together, these results provide an initial mechanistic understanding of MDD and highlight potential targets for novel drug discovery.

Mapping the spatial architecture of glioblastoma from core to edge delineates niche-specific tumor cell states and intercellular interactions

Treatment resistance in glioblastoma (GBM) is largely driven by the extensive multi-level heterogeneity that typifies this disease. Despite significant progress toward elucidating GBM's genomic and transcriptional heterogeneity, a critical knowledge gap remains in defining this heterogeneity at the spatial level. To address this, we employed spatial transcriptomics to map the architecture of the GBM ecosystem. This revealed tumor cell states that are jointly defined by gene expression and spatial localization, and multicellular niches whose composition varies along the tumor core-edge axis. Ligand-receptor interaction analysis uncovered a complex network of intercellular communication, including niche- and region-specific interactions. Finally, we found that CD8 positive GZMK positive T cells colocalize with LYVE1 positive CD163 positive myeloid cells in vascular regions, suggesting a potential mechanism for immune evasion. These findings provide novel insights into the GBM tumor microenvironment, highlighting previously unrecognized patterns of spatial organization and intercellular interactions, and novel therapeutic avenues to disrupt tumor-promoting interactions and overcome immune resistance.

Genetic association between gene expression profiles in oligodendrocyte precursor cells and psychiatric disorders

Background

Although neuronal dysfunction has been the focus of many studies on psychiatric disorders, accumulating evidence suggests that white matter abnormalities and oligodendrocyte lineage cells, including oligodendrocyte precursor cells (OPCs), play an important role. Beyond their established contribution to myelination, synaptic genes in OPCs form connections to neurons and influence neuronal circuits and plasticity, thereby potentially contributing to psychiatric pathology.

Methods

We analyzed publicly available single-nucleus RNA sequencing (snRNA-seq) data from white matter cells of healthy donors with SCZ genome-wide association study (GWAS) summary statistics. We assessed cell-type-specific enrichment of SCZ-associated genetic variants and performed weighted gene co-expression network analysis (WGCNA) to identify disease-related gene modules in implicated cell types.

Results

OPCs exhibited significant enrichment of SCZ-associated genetic risk variants and showed pronounced specificity in gene expression patterns. Through WGCNA, we identified a distinct co-expression module in OPCs that was enriched for synaptic genes associated with SCZ.

Conclusion

The present results highlight the previously underappreciated role of OPCs in psychiatric disorders, suggesting that OPC-involved synaptic interactions may contribute to the pathophysiology of SCZ. This work underscores the importance of considering OPCs as active players in neural network dysfunction, with potential implications for future therapeutic strategies.

Strengthening Rigor and Reproducibility in Epigenome-Wide Association Studies of Social Exposures and Brain-Based Health Outcomes

PURPOSE OF REVIEW

Studies examining the effects of social factors on the epigenome have proliferated over the last two decades. Social epigenetics research to date has broadly demonstrated that social factors spanning childhood adversity, to neighborhood disadvantage, educational attainment, and economic instability are associated with alterations to DNA methylation that may have a functional impact on health. These relationships are particularly relevant to brain-based health outcomes such as psychiatric disorders, which are strongly influenced by social exposures and are also the leading cause of disability worldwide. However, social epigenetics studies are limited by the many challenges faced by both epigenome-wide association studies (EWAS) and the study of social factors.

FINDINGS

In this manuscript, we provide a framework to achieve greater rigor and reproducibility in social epigenetics research. We discuss current limitations of the social epigenetics field, as well as existing and new solutions to improve rigor and reproducibility. Readers will gain a better understanding of the current considerations and processes that could maximize rigor when conducting social epigenetics research, as well as the technologies and approaches that merit attention and investment to propel continued discovery into the future.

Single-nucleus profiling decoding the subcortical visual pathway evolution of vertebrates

During the evolution of vertebrates, significant transformations have occurred in the visual transmission and processing pathways. However, our understanding of the differences between two primary visual pathways in vertebrates and their evolutionary changes remains limited. The emerging technologies and comparative analysis have provided us with a more comprehensive way to decipher this process. Here, we applied single-nucleus RNA sequencing (snRNA-seq) onto the avian optic tectum, one of the key visual subcortical hubs in birds, to construct its cellular landscape. By integrating these data with mammalian snRNA-seq datasets, we revealed differences in the density of two types of thalamic-projecting excitatory neurons within the retinotectal pathway of birds and mammals. Additionally, a series of shared molecules were identified between two types of dominant visual pathways in vertebrates. Overall, this work provides a novel focus on the evolution of visual pathways and establishes a framework for their comparative analysis.

A deep learning strategy to identify cell types across species from high-density extracellular recordings

High-density probes allow electrophysiological recordings from many neurons simultaneously across entire brain circuits but fail to reveal cell type. Here, we develop a strategy to identify cell types from extracellular recordings in awake animals and reveal the computational roles of neurons with distinct functional, molecular, and anatomical properties. We combine optogenetics and pharmacology using the cerebellum as a testbed to generate a curated ground-truth library of electrophysiological properties for Purkinje cells, molecular layer interneurons, Golgi cells, and mossy fibers. We train a semi-supervised deep learning classifier that predicts cell types with greater than 95% accuracy based on the waveform, discharge statistics, and layer of the recorded neuron. The classifier's predictions agree with expert classification on recordings using different probes, in different laboratories, from functionally distinct cerebellar regions, and across species. Our classifier extends the power of modern dynamical systems analyses by revealing the unique contributions of simultaneously recorded cell types during behavior.

Exploring human brain development and disease using assembloids

How the human brain develops and what goes awry in neurological disorders represent two long-lasting questions in neuroscience. Owing to the limited access to primary human brain tissue, insights into these questions have been largely gained through animal models. However, there are fundamental differences between developing mouse and human brain, and neural organoids derived from human pluripotent stem cells (hPSCs) have recently emerged as a robust experimental system that mimics self-organizing and multicellular features of early human brain development. Controlled integration of multiple organoids into assembloids has begun to unravel principles of cell-cell interactions. Moreover, patient-derived or genetically engineered hPSCs provide opportunities to investigate phenotypic correlates of neurodevelopmental disorders and to develop therapeutic hypotheses. Here, we outline the advances in technologies that facilitate studies by using assembloids and summarize their applications in brain development and disease modeling. Lastly, we discuss the major roadblocks of the current system and potential solutions.

Redefining the high variable genes by optimized LOESS regression with positive ratio

BACKGROUND

Single-cell RNA sequencing allows for the exploration of transcriptomic features at the individual cell level, but the high dimensionality and sparsity of the data pose substantial challenges for downstream analysis. Feature selection, therefore, is a critical step to reduce dimensionality and enhance interpretability.

RESULTS

We developed a robust feature selection algorithm that leverages optimized locally estimated scatterplot smoothing regression (LOESS) to precisely capture the relationship between gene average expression level and positive ratio while minimizing overfitting. Our evaluations showed that our algorithm consistently outperforms eight leading feature selection methods across three benchmark criteria and helps improve downstream analysis, thus offering a significant improvement in gene subset selection.

CONCLUSIONS

By preserving key biological information through feature selection, GLP provides informative features to enhance the accuracy and effectiveness of downstream analyses.

Benchmark of cellular deconvolution methods using a multi-assay dataset from postmortem human prefrontal cortex

Cellular deconvolution of bulk RNA-sequencing data using single cell/nuclei RNA-seq reference data is an important strategy for estimating cell type composition in heterogeneous tissues, such as the human brain. Here, we generate a multi-assay dataset in postmortem human dorsolateral prefrontal cortex from 22 tissue blocks, including bulk RNA-seq, reference snRNA-seq, and orthogonal measurement of cell type proportions with RNAScope/ImmunoFluorescence. We use this dataset to evaluate six deconvolution algorithms. Bisque and hspe were the most accurate methods. The dataset, as well as the Mean Ratio gene marker finding method, is made available in the DeconvoBuddies R/Bioconductor package.

scAMZI: attention-based deep autoencoder with zero-inflated layer for clustering scRNA-seq data

BACKGROUND

Clustering scRNA-seq data plays a vital role in scRNA-seq data analysis and downstream analyses. Many computational methods have been proposed and achieved remarkable results. However, there are several limitations of these methods. First, they do not fully exploit cellular features. Second, they are developed based on gene expression information and lack of flexibility in integrating intercellular relationships. Finally, the performance of these methods is affected by dropout event.

RESULTS

We propose a novel deep learning (DL) model based on attention autoencoder and zero-inflated (ZI) layer, namely scAMZI, to cluster scRNA-seq data. scAMZI is mainly composed of SimAM (a Simple, parameter-free Attention Module), autoencoder, ZINB (Zero-Inflated Negative Binomial) model and ZI layer. Based on ZINB model, we introduce autoencoder and SimAM to reduce dimensionality of data and learn feature representations of cells and relationships between cells. Meanwhile, ZI layer is used to handle zero values in the data. We compare the performance of scAMZI with nine methods (three shallow learning algorithms and six state-of-the-art DL-based methods) on fourteen benchmark scRNA-seq datasets of various sizes (from hundreds to tens of thousands of cells) with known cell types. Experimental results demonstrate that scAMZI outperforms competing methods.

CONCLUSIONS

scAMZI outperforms competing methods and can facilitate downstream analyses such as cell annotation, marker gene discovery, and cell trajectory inference. The package of scAMZI is made freely available at https://doi.org/10.5281/zenodo.13131559 .

Schizophrenia-associated changes in neuronal subpopulations in the human midbrain

Dysfunctional GABAergic and dopaminergic neurons are thought to exist in the ventral midbrain of patients with schizophrenia, yet transcriptional changes underpinning these abnormalities have not yet been localized to specific neuronal subsets. In the ventral midbrain, control over dopaminergic activity is maintained by both excitatory (glutamate) and inhibitory (GABA) input neurons. To elucidate neuron pathology at the single-cell level, we characterized the transcriptional diversity of distinct NEUN+ populations in the human ventral midbrain and then tested for schizophrenia-associated changes in neuronal subset proportions and gene activity changes within neuronal subsets. Combining single nucleus RNA-sequencing with fluorescence-activated sorting of NEUN+ nuclei, we analysed 31 669 nuclei. Initially, we detected 18 transcriptionally distinct neuronal populations in the human ventral midbrain, including two 'mixed' populations. The presence of neuronal populations in the midbrain was orthogonally validated with immunohistochemical stainings. 'Mixed' populations contained nuclei expressing transcripts for vesicular glutamate transporter 2 (SLC17A6) and glutamate decarboxylase 2 (GAD2), but these transcripts were not typically co-expressed by the same nucleus. Upon more fine-grained subclustering of the two 'mixed' populations, 16 additional subpopulations were identified that were transcriptionally classified as excitatory or inhibitory. In the midbrains of individuals with schizophrenia, we observed potential differences in the proportions of two (sub)populations of excitatory neurons, two subpopulations of inhibitory neurons, one 'mixed' subpopulation, and one subpopulation of TH-expressing neurons. This may suggest that transcriptional changes associated with schizophrenia broadly affect excitatory, inhibitory, and dopamine neurons. We detected 99 genes differentially expressed in schizophrenia compared to controls within neuronal subpopulations identified from the two 'mixed' populations, with most (67) changes within small GABAergic neuronal subpopulations. Overall, single-nucleus transcriptomic analyses profiled a high diversity of GABAergic neurons in the human ventral midbrain, identified putative shifts in the proportion of neuronal subpopulations, and suggested dysfunction of specific GABAergic subpopulations in schizophrenia, providing directions for future research.

Tracing the Shared Foundations of Gene Expression and Chromatin Structure

The three-dimensional organization of chromatin into topologically associating domains (TADs) may impact gene regulation by bringing distant genes into contact. However, many questions about TADs' function and their influence on transcription remain unresolved due to technical limitations in defining TAD boundaries and measuring the direct effect that TADs have on gene expression. Here, we develop consensus TAD maps for human and mouse with a novel "bag-of-genes" approach for defining the gene composition within TADs. This approach enables new functional interpretations of TADs by providing a way to capture species-level differences in chromatin organization. We also leverage a generative AI foundation model computed from 33 million transcriptomes to define contextual similarity, an embedding-based metric that is more powerful than co-expression at representing functional gene relationships. Our analytical framework directly leads to testable hypotheses about chromatin organization across cellular states. We find that TADs play an active role in facilitating gene co-regulation, possibly through a mechanism involving transcriptional condensates. We also discover that the TAD-linked enhancement of transcriptional context is strongest in early developmental stages and systematically declines with aging. Investigation of cancer cells show distinct patterns of TAD usage that shift with chemotherapy treatment, suggesting specific roles for TAD-mediated regulation in cellular development and plasticity. Finally, we develop "TAD signatures" to improve statistical analysis of single-cell transcriptomic data sets in predicting cancer cell-line drug response. These findings reshape our understanding of cellular plasticity in development and disease, indicating that chromatin organization acts through probabilistic mechanisms rather than deterministic rules.

Software availability

https://singhlab.net/tadmap.

Expanding the neuroimmune research toolkit with in vivo brain organoid technologies

Human pluripotent stem cell-derived microglia-like cells (MLCs) and brain organoid systems have revolutionized the study of neuroimmune interactions, providing new opportunities to model human-specific brain development and disease. Over the past decade, advances in protocol design have improved the fidelity, reproducibility and scalability of MLC and brain organoid generation. Co-culturing of MLCs and brain organoids have enabled direct investigations of human microglial interactions in vitro, although opportunities remain to improve microglial maturation and long-term survival. To address these limitations, innovative xenotransplantation approaches have introduced MLCs, organoids or neuroimmune organoids into the rodent brain, providing a vascularized environment that supports prolonged development and potential behavioral readouts. These expanding in vitro and in vivo toolkits offer complementary strategies to study neuroimmune interactions in health and disease. In this Perspective, we discuss the strengths, limitations and synergies of these models, highlighting important considerations for their future applications.

Human BioMolecular Atlas Program (HuBMAP): 3D Human Reference Atlas construction and usage

The Human BioMolecular Atlas Program (HuBMAP) aims to construct a 3D Human Reference Atlas (HRA) of the healthy adult body. Experts from 20+ consortia collaborate to develop a Common Coordinate Framework (CCF), knowledge graphs and tools that describe the multiscale structure of the human body (from organs and tissues down to cells, genes and biomarkers) and to use the HRA to characterize changes that occur with aging, disease and other perturbations. HRA v.2.0 covers 4,499 unique anatomical structures, 1,195 cell types and 2,089 biomarkers (such as genes, proteins and lipids) from 33 ASCT+B tables and 65 3D Reference Objects linked to ontologies. New experimental data can be mapped into the HRA using (1) cell type annotation tools (for example, Azimuth), (2) validated antibody panels or (3) by registering tissue data spatially. This paper describes HRA user stories, terminology, data formats, ontology validation, unified analysis workflows, user interfaces, instructional materials, application programming interfaces, flexible hybrid cloud infrastructure and previews atlas usage applications.

Adult Hippocampal Neurogenesis in the Human Brain: Updates, Challenges, and Perspectives

The existence of neurogenesis in the adult human hippocampus has been under considerable debate within the past three decades due to the diverging conclusions originating mostly from immunohistochemistry studies. While some of these reports conclude that hippocampal neurogenesis in humans occurs throughout physiologic aging, others indicate that this phenomenon ends by early childhood. More recently, some groups have adopted next-generation sequencing technologies to characterize with more acuity the extent of this phenomenon in humans. Here, we review the current state of research on adult hippocampal neurogenesis in the human brain with an emphasis on the challenges and limitations of using immunohistochemistry and next-generation sequencing technologies for its study.

Genetic causes of obesity: mapping a path forward

Over the past 30 years, significant progress has been made in understanding the genetic causes of obesity. In the coming years, catalogs that map each genetic variant to its genomic function are expected to accelerate variant-to-function (V2F) translation. Given that obesity is a heterogeneous disease, research will have to move beyond body mass index (BMI). Gene discovery efforts for more refined adiposity traits are poised to reveal additional genetic loci, pointing to new biological mechanisms. Obesity genetics research is reaching unprecedented heights and, along with a renewed interest in the development of weight-loss medication, it holds the potential to identify new drug targets. Polygenic scores (PGSs) that predict obesity risk are expected to further improve and will be particularly valuable early in life for timely prevention.

Transcriptional profiles of mouse oligodendrocyte precursor cells across the lifespan

Oligodendrocyte progenitor cells (OPCs) are highly dynamic, widely distributed glial cells of the central nervous system responsible for generating myelinating oligodendrocytes throughout life. However, the rates of OPC proliferation and differentiation decline dramatically with aging, which may impair homeostasis, remyelination and adaptive myelination during learning. To determine how aging influences OPCs, we generated a transgenic mouse line (Matn4-mEGFP) and performed single-cell RNA sequencing, providing enhanced resolution of transcriptional changes during key transitions from quiescence to proliferation and differentiation across the lifespan. We found that aging induces distinct transcriptomic changes in OPCs in different states, including enhanced activation of HIF-1α and WNT pathways. Pharmacological inhibition of these pathways in aged OPCs was sufficient to increase their ability to differentiate in vitro. Ultimately, Matn4-mEGFP mouse line and the sequencing dataset of cortical OPCs across ages will help to define the molecular changes guiding OPC behavior in various physiological and pathological contexts.

Comparative single-cell transcriptomic atlases of drosophilid brains suggest glial evolution during ecological adaptation

To explore how brains change upon species evolution, we generated single-cell transcriptomic atlases of the central brains of three closely related but ecologically distinct drosophilids: the generalists Drosophila melanogaster and Drosophila simulans, and the noni fruit specialist Drosophila sechellia. The global cellular composition of these species' brains is well-conserved, but we predicted a few cell types with different frequencies, notably perineurial glia of the blood-brain barrier, which we validate in vivo. Gene expression analysis revealed that distinct cell types evolve at different rates and patterns, with glial populations exhibiting the greatest divergence between species. Compared to the D. melanogaster brain, cellular composition and gene expression patterns are more divergent in D. sechellia than in D. simulans-despite their similar phylogenetic distance from D. melanogaster-indicating that the specialization of D. sechellia is reflected in the structure and function of its brain. Expression changes in D. sechellia include several metabolic signaling genes, suggestive of adaptations to its novel source of nutrition. Additional single-cell transcriptomic analysis on D. sechellia revealed genes and cell types responsive to dietary supplement with noni, pointing to glia as sites for both physiological and genetic adaptation to this fruit. Our atlases represent the first comparative datasets for "whole" central brains and provide a comprehensive foundation for studying the evolvability of nervous systems in a well-defined phylogenetic and ecological framework.

Cell2fate infers RNA velocity modules to improve cell fate prediction

RNA velocity exploits the temporal information contained in spliced and unspliced RNA counts to infer transcriptional dynamics. Existing velocity models often rely on coarse biophysical simplifications or numerical approximations to solve the underlying ordinary differential equations (ODEs), which can compromise accuracy in challenging settings, such as complex or weak transcription rate changes across cellular trajectories. Here we present cell2fate, a formulation of RNA velocity based on a linearization of the velocity ODE, which allows solving a biophysically more accurate model in a fully Bayesian fashion. As a result, cell2fate decomposes the RNA velocity solutions into modules, providing a biophysical connection between RNA velocity and statistical dimensionality reduction. We comprehensively benchmark cell2fate in real-world settings, demonstrating enhanced interpretability and power to reconstruct complex dynamics and weak dynamical signals in rare and mature cell types. Finally, we apply cell2fate to the developing human brain, where we spatially map RNA velocity modules onto the tissue architecture, connecting the spatial organization of tissues with temporal dynamics of transcription.

Rare mutations implicate CGE interneurons as a vulnerable axis of cognitive deficits across psychiatric disorders

Neuropsychiatric disorders such as autism spectrum disorder (ASD) and schizophrenia (SCZ) share genetic risk factors, including rare high penetrance single nucleotide variants and copy number variants (CNVs), and exhibit both overlapping and distinct clinical phenotypes. Cognitive deficits and intellectual disability-critical predictors of long-term outcomes-are common to both conditions. To investigate shared and disorder-specific neurobiological impact of highly penetrant rare mutations in ASD and SCZ, we analyzed human single-nucleus whole-brain sequencing data to identify strongly affected brain cell types. Our analysis revealed Caudal Ganglionic Eminence (CGE)-derived GABAergic interneurons as a key nexus for cognitive deficits across these disorders. Notably, genes within 22q11.2 deletions, known to confer a high risk of SCZ, ASD, and cognitive impairment, showed a strong expression bias toward vasoactive intestinal peptide-expressing cells (VIP+) among CGE subtypes. To explore VIP+ GABAergic interneuron perturbations in the 22q11.2 deletion syndrome in vivo , we examined their activity in the Df(16)A +/- mouse model during a spatial navigation task and observed reduced activity along with altered responses to random rewards. At the population level, VIP+ interneurons exhibited impaired spatial encoding and diminished subtype-specific activity suggesting deficient disinhibition in CA1 microcircuits in the hippocampus, a region essential for learning and memory. Overall, these results demonstrate the crucial role of CGE-derived interneurons in mediating cognitive processes that are disrupted across a range of psychiatric and neurodevelopmental disorders.

A Complex Network of Obesity-Risk Genes Revealed by Systematic Bioinformatics and Single-Cell Transcriptomic Analyses

The development of obesity is closely linked to genetic factors. Despite the identification of numerous genes associated with an increased risk of obesity in humans, a comprehensive understanding of their biological roles has not been achieved. In our extensive bioinformatics study, we identified 802 core genes implicated in obesity. Our protein-protein interaction (PPI) network analysis revealed that these genes form a tightly connected functional network primarily involved in neurological and metabolic regulatory processes. Moreover, our in-depth analysis of single-cell transcriptomic datasets from the human hypothalamus, pancreatic islets, adipose tissue, and liver has shed light on the distinct expression profiles of these obesity-linked genes across various tissue and cell types. This analysis also highlighted the biological processes they influence and the upstream transcriptional regulatory networks involved. Our study not only uncovers the complicated regulatory role of genetic factors in the pathogenesis and progression of obesity but also establishes a close link between the expression patterns and functional roles of these obesity-associated genes. This study provides crucial insights for advancing our understanding of the genetic mechanisms underlying obesity.

Single-cell spatial transcriptome atlas and whole-brain connectivity of the macaque claustrum

Claustrum orchestrates brain functions via its connections with numerous brain regions, but its molecular and cellular organization remains unresolved. Single-nucleus RNA sequencing of 227,750 macaque claustral cells identified 48 transcriptome-defined cell types, with most glutamatergic neurons similar to deep-layer insular neurons. Comparison of macaque, marmoset, and mouse transcriptomes revealed macaque-specific cell types. Retrograde tracer injections at 67 cortical and 7 subcortical regions defined four distinct distribution zones of retrogradely labeled claustral neurons. Joint analysis of whole-brain connectivity and single-cell spatial transcriptome showed that these four zones containing distinct compositions of glutamatergic (but not GABAergic) cell types preferentially connected to specific brain regions with a strong ipsilateral bias. Several macaque-specific glutamatergic cell types in ventral vs. dorsal claustral zones selectively co-projected to two functionally related areas-entorhinal cortex and hippocampus vs. motor cortex and putamen, respectively. These data provide the basis for elucidating the neuronal organization underlying diverse claustral functions.