Transcriptomic diversity of cell types across the adult human brain

Effects of pharmacological inhibition of FABP4 during gestation and lactation on offspring neurodevelopment and behavior

Fatty acid-binding protein 4 (FABP4), a key regulator of lipid metabolism and inflammation, has been implicated in neurodevelopmental disorders, including autism spectrum disorder (ASD). This study investigated the effects of FABP4 inhibition during gestation and lactation on offspring neurodevelopment using the selective FABP4 inhibitor BMS309403. Female mice received BMS309403 (15 mg/kg) via oral gavage from two weeks before mating to postnatal day 28 (P28). Administration of BMS309403 to mouse dams resulted in autism-like phenotypes in male offspring (behavioral tests: n = 7-10 per group; spine analysis: 6 mice per group, n = 26-38 dendrites per group), characterized by increased dendritic spine density in the prefrontal cortex, impaired vocal communication, increased repetitive behaviors, and depression-like symptoms. Fatty acid analysis (n = 4-6 per group) revealed significant alterations in maternal and fetal lipid profiles, including elevated arachidonic acid levels in maternal plasma and increased n6PUFAs in the fetal brain, suggesting a pro-inflammatory lipid environment. Principal component analysis demonstrated distinct clustering of lipid profiles between control and BMS309403-treated groups. Cytokine analysis (n = 6 per group) indicated reductions in IL-10 and IL-12(p40) in maternal plasma and decreased TNFα in the fetal plasma, suggesting dysregulation in systemic inflammatory signaling. These findings suggest that FABP4 inhibition during the perinatal period perturbs lipid metabolism and may influence neurodevelopment through systemic metabolic changes. Although the direct effects of BMS309403 on the fetal brain cannot be excluded, alteration in maternal metabolism and placental function may have contributed to the observed neurodevelopmental changes in offspring.

Defining the molecular identity and morphology of glia limitans superficialis astrocytes in vertebrates

Astrocytes are a highly abundant glial cell type and perform critical homeostatic functions in the central nervous system. Like neurons, astrocytes have many discrete heterogeneous subtypes. The subtype identity and functions are, at least in part, associated with their anatomical location and can be highly restricted to strategically important anatomical domains. Here, we report that astrocytes forming the glia limitans superficialis, the outermost border of the brain and spinal cord, are a highly specialized astrocyte subtype and can be identified by a single marker: myocilin (Myoc). We show that glia limitans superficialis astrocytes cover the entire brain and spinal cord surface, exhibit an atypical morphology, and are evolutionarily conserved from zebrafish, rodents, and non-human primates to humans. Identification of this highly specialized astrocyte subtype will advance our understanding of CNS homeostasis and potentially be targeted for therapeutic intervention to combat peripheral inflammatory effects on the CNS.

Transcriptional signatures of hippocampal tau pathology in primary age-related tauopathy and Alzheimer's disease

In primary age-related tauopathy (PART) and Alzheimer's disease (AD), tau aggregates share a similar structure and anatomic distribution, which is distinct from tau pathology in other diseases. However, transcriptional similarities between PART and AD and gene expression changes within tau-pathology-bearing neurons are largely unknown. Using GeoMx spatial transcriptomics, mRNA was quantified in hippocampal neurons with and without tau pathology in PART and AD. Synaptic genes were down-regulated in disease overall but up-regulated in tau-pathology-positive neurons. Two transcriptional signatures were associated with intraneuronal tau, both validated in a cortical AD dataset. Genes in the up-regulated signature were enriched in calcium regulation and synaptic function. Notably, transcriptional changes associated with intraneuronal tau in PART and AD were similar, suggesting a possible mechanistic relationship. These findings highlight the power of molecular analysis stratified by pathology and provide insight into common pathways associated with tau pathology in PART and AD.

Evaluating Methods for the Prediction of Cell Type-Specific Enhancers in the Mammalian Cortex

Identifying cell type-specific enhancers in the brain is critical to building genetic tools for investigating the mammalian brain. Computational methods for functional enhancer prediction have been proposed and validated in the fruit fly and not yet 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 assess machine learning and feature-based methods designed to nominate enhancer DNA sequences to target cell types in the mouse cortex. Methods were evaluated based on in vivo validation data from hundreds of cortical cell type-specific enhancers that were previously packaged into individual AAV vectors and retro-orbitally injected into mice. We find that open chromatin was a key predictor of functional enhancers, and sequence models improved prediction of non-functional enhancers that can be deprioritized as opposed to pursued for in vivo testing. Sequence models also identified cell type-specific transcription factor codes that can guide designs of in silico enhancers. This community challenge establishes a benchmark for enhancer prioritization algorithms and reveals computational approaches and molecular information that are crucial for identifying functional enhancers in mammalian cortical cell types. The results of this challenge bring us closer to understanding the complex gene regulatory landscape of the mammalian cortex and to designing more efficient genetic tools to target cortical cell types.

Molecular biology of the deadliest cancer - glioblastoma: what do we know?

Glioblastomas are the most prevalent primary brain tumors and are associated with a dramatically poor prognosis. Despite an intensive treatment approach, including maximal surgical tumor removal followed by radio- and chemotherapy, the median survival for glioblastoma patients has remained around 18 months for decades. Glioblastoma is distinguished by its highly complex mechanisms of immune evasion and pronounced heterogeneity. This variability is apparent both within the tumor itself, which can exhibit multiple phenotypes simultaneously, and in its surrounding microenvironment. Another key feature of glioblastoma is its "cold" microenvironment, characterized by robust immunosuppression. Recent advances in single-cell RNA sequencing have uncovered new promising insights, revealing previously unrecognized aspects of this tumor. In this review, we consolidate current knowledge on glioblastoma cells and its microenvironment, with an emphasis on their biological properties and unique patterns of molecular communication through signaling pathways. The evidence underscores the critical need for personalized poly-immunotherapy and other approaches to overcome the plasticity of glioblastoma stem cells. Analyzing the tumor microenvironment of individual patients using single-cell transcriptomics and implementing a customized immunotherapeutic strategy could potentially improve survival outcomes for those facing this formidable disease.

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, as well as Sst-Chodl, Pvalb-Pthlh, and cholinergic interneurons. Specificity was evaluated by multiple modes of molecular validation, 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 rat and macaque. 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.

Transcriptomic and genetic analysis suggests a role for mitochondrial dysregulation in schizophrenia

Schizophrenia is an often devastating disorder characterized by persistent and idiopathic cognitive deficits, delusions and hallucinations. Schizophrenia has been associated with impaired nervous system development and an excitation/inhibition imbalance in the prefrontal cortex. On a molecular level, schizophrenia is moderately heritable and genetically complex. Hundreds of risk genes have been identified, spanning a heterogeneous landscape dominated by loci that confer relatively small risk. Bioinformatic analyses of genetic associations point to a limited set of neurons, mainly excitatory cortical neurons, but other analyses suggest the importance of astrocytes and microglia. To understand different cell type roles in schizophrenia and reveal novel cell-type specific aetiologically relevant perturbations in schizophrenia, our study integrated genetic analysis with single nucleus RNA-seq of 536,618 nuclei from postmortem samples of dorsal prefrontal cortex (Brodmann Area 8/9) of 43 cases with schizophrenia and 42 neurotypical controls. We found no significant difference in cell type abundance. Gene expression in excitatory layer 2-3 intra-telencephalic neurons had the greatest number of differentially expressed transcripts and, together with excitatory deep layer intra-telencephalic neurons, conferred most of the genetic risk for schizophrenia. Most differential expression of genes was found in specific cell types and was dominated by down-regulated transcripts. Down-regulated transcripts were enriched in gene sets including transmembrane transport, mitochondrial function, protein folding, and cell-cell signaling whereas up-regulated transcripts were enriched in gene sets related to RNA processing, including RNA splicing in neurons. Co-regulation network analysis identified 40 schizophrenia-relevant programs across 13 cell types. A gene program largely shared between neuronal subtypes, astrocytes, and oligodendrocytes was significantly enriched for schizophrenia risk, supporting an aetiological role for perturbed protein modification, ion transport, and mitochondrial function. These results were largely consistent with cell-type expression quantitative trait locus and transcriptome-wide association analyses. Moreover, single-cell RNA sequencing results, most prominently mitochondrial dysfunction, had multiple points of convergence with proteomic and long-read RNA sequencing results from samples from the same donors. Our study integrates genetic analysis with transcriptomics to reveal novel cell-type specific aetiologically relevant perturbations in schizophrenia.

Antagonizing Il10 and Il4 signaling via intracerebral decoy receptor expression attenuates Aβ accumulation

Multiple lines of evidence indicate that immune signaling can impact the pathological progression in Alzheimer's disease (AD), including amyloid deposition, tau aggregation, synaptic pathology and neurodegenerative trajectory. In earlier studies, we reported that intracerebral expression of the anti-inflammatory cytokines, Interleukin-10 (Il10) and Interleukin-4 (Il4), increased amyloid β (Aβ) burden in TgCRND8 mice, a preclinical model of AD-type amyloidosis. As both Interleukin-10 receptor (IL10R) and Interleukin-4 receptor (IL4R) are upregulated in an age-progressive manner in rodent models of AD and in specific regions of human AD brains, we hypothesized that a decoy receptor strategy specifically targeting Il10 and Il4 signaling could have a disease-modifying effect. We derivatized the ectodomains of mouse Il10R (sIl10R) and mouse Il4R (sIl4R) into corresponding recombinant solubilized receptor forms and delivered these intracranially into neonatal TgCRND8 mice or hippocampally into adult TgCRND8 mice with pre-existing Aβ deposits. AAV-mediated expression of sIl10R and sIl4R robustly attenuated Aβ burden in TgCRND8 mice when expressed neonatally while in the hippocampus injection cohort, AAV-sIl4R, but not sIl10R, reduced Aβ burden. sIl10R and sIl4R had opposing effects on microglial and astrocyte proliferation, with sIl10R generally reducing gliosis. RNAseq analysis showed that sIl10R likely acts as a microglial immune checkpoint inhibitor while both sIl10R and sIl4R expression show unexpected impacts on genes related to circadian rhythm. Notably, neither Il10 nor sIl10R expression altered tau pathology in two tau transgenic models, despite robust expression and impacts on glial proliferation. Together, these data reveal that decoy receptor mediated targeting of physiological Il10 or Il4 signaling can beneficially impact amyloid deposition and thus represent novel immunomodulatory approaches for AD therapy.

Contrasting genetic predisposition and diagnosis in psychiatric disorders: A multi-omic single-nucleus analysis of the human OFC

Psychiatric disorders like schizophrenia, bipolar disorder, and major depressive disorder exhibit substantial genetic and clinical overlap. However, their molecular architecture remains elusive due to their polygenic nature and complex brain cell interactions. We integrated clinical data with genetic susceptibility to investigate gene expression and chromatin accessibility in the orbitofrontal cortex of 92 postmortem human brain samples at the single-nucleus (sn) level. Using snRNA-seq and snATAC-seq, we analyzed ~800,000 and 400,000 nuclei, respectively. We observed cell-type-specific dysregulation related to clinical diagnosis and genetic risk. Dysregulation in gene expression and chromatin accessibility associated with diagnosis was pronounced in excitatory neurons. Conversely, genetic risk predominantly affected glial and endothelial cells. Notably, INO80E and HCN2 genes exhibited dysregulation in excitatory neurons' superficial layers 2/3 influenced by schizophrenia polygenic risk. This study unveils the complex genetic and epigenetic landscape of psychiatric disorders, emphasizing the importance of cell-type-specific analyses in understanding their pathogenesis and contrasting genetic predisposition with clinical diagnosis.

Absolute Number of Three Populations of Interneurons and All GABAergic Synapses in the Human Hippocampus

The human hippocampus, essential for learning and memory, is implicated in numerous neurological and psychiatric disorders, each linked to specific neuronal subpopulations. Advancing our understanding of hippocampal function requires computational models grounded in precise quantitative neuronal data. While extensive data exist on the neuronal composition and synaptic architecture of the rodent hippocampus, analogous quantitative data for the human hippocampus remain very limited. Given the critical role of local GABAergic interneurons in modulating hippocampal functions, we employed unbiased stereological techniques to estimate the density and total number of three major GABAergic cell types in the male and female human hippocampus: parvalbumin (PV)-expressing, somatostatin (SOM)-positive, and calretinin (CR)-positive interneurons. Our findings reveal an estimated 49,400 PV-positive, 141,500 SOM-positive, and 250,600 CR-positive interneurons per hippocampal hemisphere. Notably, CR-positive interneurons, which are primarily interneuron-selective in rodents, were present in humans at a higher proportion. Additionally, using three-dimensional electron microscopy, we estimated ∼25 billion GABAergic synapses per hippocampal hemisphere, with PV-positive boutons comprising ∼3.5 billion synapses, or 14% of the total GABAergic synapses. These findings contribute crucial quantitative insights for modeling human hippocampal circuits and understanding its complex regulatory dynamics.

Transcriptional complexity in the insect central complex: single nuclei RNA-sequencing of adult brain neurons derived from type 2 neuroblasts

In both invertebrates such as Drosophila and vertebrates such as mouse or human, 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. In contrast to T1 neuroblasts, T2 neuroblasts generate intermediate neural progenitors (INPs) that expand the number and diversity of cell types. 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 neurons derived from T2 neuroblasts 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 (presumptive neuron subtype). 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.

Multiomic single-cell profiling identifies critical regulators of postnatal brain

Human brain development spans from embryogenesis to adulthood, with dynamic gene expression controlled by cell-type-specific cis-regulatory element activity and three-dimensional genome organization. To advance our understanding of postnatal brain development, we simultaneously profiled gene expression and chromatin accessibility in 101,924 single nuclei from four brain regions across ten donors, covering five key postnatal stages from infancy to late adulthood. Using this dataset and chromosome conformation capture data, we constructed enhancer-based gene regulatory networks to identify cell-type-specific regulators of brain development and interpret genome-wide association study loci for ten main brain disorders. Our analysis connected 2,318 cell-specific loci to 1,149 unique genes, representing 41% of loci linked to the investigated traits, and highlighted 55 genes influencing several disease phenotypes. Pseudotime analysis revealed distinct stages of postnatal oligodendrogenesis and their regulatory programs. These findings provide a comprehensive dataset of cell-type-specific gene regulation at critical timepoints in postnatal brain development.

Mouse-Geneformer: A deep learning model for mouse single-cell transcriptome and its cross-species utility

Deep learning techniques are increasingly utilized to analyze large-scale single-cell RNA sequencing (scRNA-seq) data, offering valuable insights from complex transcriptome datasets. Geneformer, a pre-trained model using a Transformer Encoder architecture and human scRNA-seq datasets, has demonstrated remarkable success in human transcriptome analysis. However, given the prominence of the mouse, Mus musculus, as a primary mammalian model in biological and medical research, there is an acute need for a mouse-specific version of Geneformer. In this study, we developed a mouse-specific Geneformer (mouse-Geneformer) by constructing a large transcriptome dataset consisting of 21 million mouse scRNA-seq profiles and pre-training Geneformer on this dataset. The mouse-Geneformer effectively models the mouse transcriptome and, upon fine-tuning for downstream tasks, enhances the accuracy of cell type classification. In silico perturbation experiments using mouse-Geneformer successfully identified disease-causing genes that have been validated in in vivo experiments. These results demonstrate the feasibility of analyzing mouse data with mouse-Geneformer and highlight the robustness of the Geneformer architecture, applicable to any species with large-scale transcriptome data available. Furthermore, we found that mouse-Geneformer can analyze human transcriptome data in a cross-species manner. After the ortholog-based gene name conversion, the analysis of human scRNA-seq data using mouse-Geneformer, followed by fine-tuning with human data, achieved cell type classification accuracy comparable to that obtained using the original human Geneformer. In in silico simulation experiments using human disease models, we obtained results similar to human-Geneformer for the myocardial infarction model but only partially consistent results for the COVID-19 model, a trait unique to humans (laboratory mice are not susceptible to SARS-CoV-2). These findings suggest the potential for cross-species application of the Geneformer model while emphasizing the importance of species-specific models for capturing the full complexity of disease mechanisms. Despite the existence of the original Geneformer tailored for humans, human research could benefit from mouse-Geneformer due to its inclusion of samples that are ethically or technically inaccessible for humans, such as embryonic tissues and certain disease models. Additionally, this cross-species approach indicates potential use for non-model organisms, where obtaining large-scale single-cell transcriptome data is challenging.

Multiple Sclerosis: Glial Cell Diversity in Time and Space

Multiple sclerosis (MS) is the most prevalent human inflammatory disease of the central nervous system with demyelination and glial scar formation as pathological hallmarks. Glial cells are key drivers of lesion progression in MS with roles in both tissue damage and repair depending on the surrounding microenvironment and the functional state of the individual glial subtype. In this review, we describe recent developments in the context of glial cell diversity in MS summarizing key findings with respect to pathological and maladaptive functions related to disease-associated glial subtypes. A particular focus is on the spatial and temporal dynamics of glial cells including subtypes of microglia, oligodendrocytes, and astrocytes. We contextualize recent high-dimensional findings suggesting that glial cells dynamically change with respect to epigenomic, transcriptomic, and metabolic features across the inflamed rim and during the progression of MS lesions. In summary, detailed knowledge of spatially restricted glial subtype functions is critical for a better understanding of MS pathology and its pathogenesis as well as the development of novel MS therapies targeting specific glial cell types.

A comprehensive spatio-cellular map of the human hypothalamus

The hypothalamus is a brain region that plays a key role in coordinating fundamental biological functions1. However, our understanding of the underlying cellular components and neurocircuitries have, until recently, emerged primarily from rodent studies2,3. Here we combine single-nucleus sequencing of 433,369 human hypothalamic cells with spatial transcriptomics, generating a comprehensive spatio-cellular transcriptional map of the hypothalamus, the 'HYPOMAP'. Although conservation of neuronal cell types between humans and mice, as based on transcriptomic identity, is generally high, there are notable exceptions. Specifically, there are significant disparities in the identity of pro-opiomelanocortin neurons and in the expression levels of G-protein-coupled receptors between the two species that carry direct implications for currently approved obesity treatments. Out of the 452 hypothalamic cell types, we find that 291 neuronal clusters are significantly enriched for expression of body mass index (BMI) genome-wide association study genes. This enrichment is driven by 426 'effector' genes. Rare deleterious variants in six of these (MC4R, PCSK1, POMC, CALCR, BSN and CORO1A) associate with BMI at population level, and CORO1A has not been linked previously to BMI. Thus, HYPOMAP provides a detailed atlas of the human hypothalamus in a spatial context and serves as an important resource to identify new druggable targets for treating a wide range of conditions, including reproductive, circadian and metabolic disorders.

Exome sequencing identifies novel genes associated with cerebellar volume and microstructure

Proteins encoded by exons are critical for cellular functions, and mutations in these genes often result in significant phenotypic effects. The cerebellum is linked to various heritable human disease phenotypes, yet genome-wide association studies have struggled to capture the effects of rare variants on cerebellar traits. This study conducts a large-scale exome association analysis using data from approximately 35,000 UK Biobank participants, examining seven cerebellar traits, including total cerebellar volume and white matter microstructure. We identify 90 genes associated with cerebellar traits, 60 of which were previously unreported in genome-wide association studies. Notable findings include the discovery of genes like PRKRA and TTK, as well as RASGRP3, linked to cerebellar volume and white matter microstructure. Gene enrichment analysis reveals associations with non-coding RNA processing, cognitive function, neurodegenerative diseases, and mental disorders, suggesting shared biological mechanisms between cerebellar phenotypes and neuropsychiatric diseases.

Genomics yields biological and phenotypic insights into bipolar disorder

Bipolar disorder is a leading contributor to the global burden of disease1. Despite high heritability (60-80%), the majority of the underlying genetic determinants remain unknown2. We analysed data from participants of European, East Asian, African American and Latino ancestries (n = 158,036 cases with bipolar disorder, 2.8 million controls), combining clinical, community and self-reported samples. We identified 298 genome-wide significant loci in the multi-ancestry meta-analysis, a fourfold increase over previous findings3, and identified an ancestry-specific association in the East Asian cohort. Integrating results from fine-mapping and other variant-to-gene mapping approaches identified 36 credible genes in the aetiology of bipolar disorder. Genes prioritized through fine-mapping were enriched for ultra-rare damaging missense and protein-truncating variations in cases with bipolar disorder4, highlighting convergence of common and rare variant signals. We report differences in the genetic architecture of bipolar disorder depending on the source of patient ascertainment and on bipolar disorder subtype (type I or type II). Several analyses implicate specific cell types in the pathophysiology of bipolar disorder, including GABAergic interneurons and medium spiny neurons. Together, these analyses provide additional insights into the genetic architecture and biological underpinnings of bipolar disorder.

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.

Are we there yet? Exploring astrocyte heterogeneity one cell at a time

Astrocytes are a highly abundant cell type in the brain and spinal cord. Like neurons, astrocytes can be molecularly and functionally distinct to fulfill specialized roles. Recent technical advances in sequencing-based single cell assays have driven an explosion of omics data characterizing astrocytes in the healthy, aged, injured, and diseased central nervous system. In this review, we will discuss recent studies which have furthered our understanding of astrocyte biology and heterogeneity, as well as discuss the limitations and challenges of sequencing-based single cell and spatial genomics methods and their potential future utility.

A Gene-Expression Based Comparison of Murine and Human Inhibitory Interneurons in the Cerebellar Cortex and Nuclei

Cerebellar information processing is critically shaped by several types of inhibitory interneurons forming various intra-cerebellar feed-forward and feed-back loops. Evidence gathered over the past decades has focused interest on a non-uniform set of cortical inhibitory interneurons distinct from "classical" Golgi, basket or stellate cells, summarily referred to as PLIs (for Purkinje cell layer interneurons). Similarly, cerebellar nuclear inhibitory interneurons have gained increasing attention. Our understanding of the functions of these cells is still fragmentary. For humans, we lack functional data, and even any dependable morphological classification for these cells. Here, I used publicly available single cell based gene expression data to compare inhibitory interneurons from the cerebellar cortex and inhibitory nuclear neurons of humans and mice. Integration of nuclear and cortical cells revealed transcriptomic similarities between subsets of these cells and suggest known characteristics of cortical cell types may be helpful to devise strategies for the further characterization of nuclear inhibitory interneurons. Comparison of human and murine PLIs indicate that these strongly differ by the expression of genes used to characterize these cells in mice. This limits their utility to identify and classify human PLIs, and leaves the question open as to the number and characteristics of non-Golgi inhibitory interneurons resident in the cerebellar granule cell and Purkinje cell layers in humans.

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.

Consequences of training data composition for deep learning models in single-cell biology

Foundation models for single-cell transcriptomics have the potential to augment (or replace) purpose-built tools for a variety of common analyses, especially when data are sparse. Recent work with large language models has shown that training data composition greatly shapes performance; however, to date, single-cell foundation models have ignored this aspect, opting instead to train on the largest possible corpus. We systematically investigate the consequences of training dataset composition on the behavior of deep learning models of single-cell transcriptomics, focusing on human hematopoiesis as a tractable model system and including cells from adult and developing tissues, disease states, and perturbation atlases. We find that (1) these models generalize poorly to unseen cell types, (2) adding malignant cells to a healthy cell training corpus does not necessarily improve modeling of unseen malignant cells, and (3) including an embryonic stem cell differentiation atlas during training improves performance on out-of-distribution tasks. Our results emphasize the importance of diverse training data and suggest strategies to optimize future single-cell foundation models.

Transcriptomic characterization of human lateral septum neurons reveals conserved and divergent marker genes across species

The lateral septum (LS) is a midline, subcortical structure that is a critical regulator of social behaviors. Mouse studies have identified molecularly distinct neuronal populations within the LS, which control specific facets of social behavior. Despite its known molecular heterogeneity in the mouse and critical role in regulating social behavior, comprehensive molecular profiling of the human LS has not been performed. Here, we conducted single-nucleus RNA sequencing (snRNA-seq) to generate transcriptomic profiles of the human LS and compared human LS profiles to recently collected mouse LS snRNA-seq datasets. Our analyses identified TRPC4 as a conserved molecular marker of the mouse and human LS, while FREM2 is enriched only in the human LS. We also identify a distinct neuronal cell type marked by OPRM1, the gene encoding the μ-opioid receptor. Together, these results highlight transcriptional heterogeneity of the human LS and identify robust marker genes for the human LS.

Purinergic System Transcript Changes in the Dorsolateral Prefrontal Cortex in Suicide and Major Depressive Disorder

Suicide is a major public health priority, and its molecular mechanisms appear to be related to imbalanced purine metabolism in the brain. This exploratory study investigates purinergic gene expression in the postmortem dorsolateral prefrontal cortex (DLPFC) tissue isolated from subjects with major depressive disorder (MDD) who died by suicide (MDD-S, n = 10), MDD subjects who did not die by suicide (MDD-NS, n = 6) and non-psychiatrically ill controls (CTL, n = 9-10). Purinergic system transcripts were assayed by quantitative polymerase chain reactions (qPCR) in superficial and deep gray matter as well as white matter DLPFC cortical layers using laser microdissection (LMD). Across all subjects, regardless of sex, P2RY12 (F(2,23) = 5.40, p = 0.004) and P2RY13 (KW statistic = 11.82, p = 0.001) transcript levels were significantly greater in MDD-S compared to MDD-NS subjects. Several other perturbations were observed in the white matter tissue isolated from females: NT5E (F(2,10) = 13.37, p = 0.001) and P2RY13 (F(2,9) = 3.99, p = 0.011, controlled for age) transcript expression was significantly greater in MDD-S vs. MDD-NS female groups. ENTPD2 (F(2,10) = 5.20, p = 0.03), ENTPD3 (F(2,10) = 28.99, p < 0.0001), and NT5E (F(2,10) = 13.37, p = 0.001) were among the transcripts whose expression was significantly elevated in MDD-S vs. CTL female groups. Transcripts that exhibited significantly altered expression in the superficial and deep gray matter included ENTPD2, NT5E, PANX1, and P2RY13 (p ≤ 0.05). Our medication analysis revealed that the expression of these transcripts was not significantly altered by antidepressants. This is the first study to holistically quantify the purinergic metabolic pathway transcripts in suicide and MDD utilizing human postmortem brain tissue. Our preliminary findings support evidence implicating changes in purinergic P2 receptors in the brain in suicide and provide support for broader purinergic system dysregulation in mood disorders.

Cell type transcriptomics reveal shared genetic mechanisms in Alzheimer's and Parkinson's disease

Historically, Alzheimer's disease (AD) and Parkinson's disease (PD) have been investigated as two distinct disorders of the brain. However, a few similarities in neuropathology and clinical symptoms have been documented over the years. Traditional single gene-centric genetic studies, including GWAS and differential gene expression analyses, have struggled to unravel the molecular links between AD and PD. To address this, we tailor a pattern-learning framework to analyze synchronous gene co-expression at sub-cell-type resolution. Utilizing recently published single-nucleus AD (70,634 nuclei) and PD (340,902 nuclei) datasets from postmortem human brains, we systematically extract and juxtapose disease-critical gene modules. Our findings reveal extensive molecular similarities between AD and PD gene cliques. In neurons, disrupted cytoskeletal dynamics and mitochondrial stress highlight convergence in key processes; glial modules share roles in T-cell activation, myelin synthesis, and synapse pruning. This multi-module sub-cell-type approach offers insights into the molecular basis of shared neuropathology in AD and PD.

Spatiotemporal analysis of gene expression in the human dentate gyrus reveals age-associated changes in cellular maturation and neuroinflammation

The dentate gyrus of the hippocampus is important for many cognitive functions, including learning, memory, and mood. Here, we present transcriptome-wide spatial gene expression maps of the human dentate gyrus and investigate age-associated changes across the lifespan. Genes associated with neurogenesis and the extracellular matrix are enriched in infants and decline throughout development and maturation. Following infancy, inhibitory neuron markers increase, and cellular proliferation markers decrease. We also identify spatio-molecular signatures that support existing evidence for protracted maturation of granule cells during adulthood and age-associated increases in neuroinflammation-related gene expression. Our findings support the notion that the hippocampal neurogenic niche undergoes major changes following infancy and identify molecular regulators of brain aging in glial- and neuropil-enriched tissue.

Integrative Epigenomic Landscape of Alzheimer's Disease Brains Reveals Oligodendrocyte Molecular Perturbations Associated with Tau

Alzheimer's disease (AD) brains are characterized by neuropathologic and biochemical changes that are highly variable across individuals. Capturing epigenetic factors that associate with this variability can reveal novel biological insights into AD pathophysiology. We conducted an epigenome-wide association study of DNA methylation (DNAm) in 472 AD brains with neuropathologic measures (Braak stage, Thal phase, and cerebral amyloid angiopathy score) and brain biochemical levels of five proteins (APOE, amyloid-β (Aβ)40, Aβ42, tau, and p-tau) core to AD pathogenesis. Using a novel regional methylation (rCpGm) approach, we identified 5,478 significant associations, 99.7% of which were with brain tau biochemical measures. Of the tau-associated rCpGms, 93 had concordant associations in external datasets comprising 1,337 brain samples. Integrative transcriptome-methylome analyses uncovered 535 significant gene expression associations for these 93 rCpGms. Genes with concurrent transcriptome-methylome perturbations were enriched in oligodendrocyte marker genes, including known AD risk genes such as BIN1 , myelination genes MYRF, MBP and MAG previously implicated in AD, as well as novel genes like LDB3 . We further annotated the top oligodendrocyte genes in an additional 6 brain single cell and 2 bulk transcriptome datasets from AD and two other tauopathies, Pick's disease and progressive supranuclear palsy (PSP). Our findings support consistent rCpGm and gene expression associations with these tauopathies and tau-related phenotypes in both bulk brain tissue and oligodendrocyte clusters. In summary, we uncover the integrative epigenomic landscape of AD and demonstrate tau-related oligodendrocyte gene perturbations as a common potential pathomechanism across different tauopathies.

Antisense oligonucleotides modulate aberrant inclusion of poison exons in SCN1A-related Dravet syndrome

Dravet syndrome is a developmental and epileptic encephalopathy associated with pathogenic variants in SCN1A. Most disease-causing variants are located within coding regions, but recent work has shed light on the role of noncoding variants associated with a poison exon in intron 20 of SCN1A. Discovery of the SCN1A poison exon known as 20N has led to the first potential disease-modifying therapy for Dravet syndrome in the form of an antisense oligonucleotide. Here, we demonstrate the existence of 2 additional poison exons in introns 1 and 22 of SCN1A through targeted, deep-coverage long-read sequencing of SCN1A transcripts. We show that inclusion of these poison exons is developmentally regulated in the human brain, and that deep intronic variants associated with these poison exons lead to their aberrant inclusion in vitro in a minigene assay or in iPSC-derived neurons. Additionally, we show that splice-modulating antisense oligonucleotides can ameliorate aberrant inclusion of poison exons. Our findings highlight the role of deep intronic pathogenic variants in disease and provide additional therapeutic targets for precision medicine in Dravet syndrome and other SCN1A-related disorders.

Annotation Comparison Explorer (ACE): connecting brain cell types across studies of health and Alzheimer's Disease

Single-cell multiomic technologies have allowed unprecedented access to gene profiles of individual cells across species and organ systems, including >1000 papers focused on brain cell types alone. The Allen Institute has created foundational atlases characterizing mammalian brain cell types in the adult mouse brain and the neocortex of aged humans with and without Alzheimer's disease (AD). With so many public cell type classifications (or 'taxonomies') available and many groups choosing to define their own, linking cell types and associated knowledge between studies remains a major challenge. Here, we introduce Annotation Comparison Explorer (ACE), a web application for comparing cell type assignments and other cell-based annotations (e.g., donor demographics, anatomic locations, batch variables, and quality control metrics). ACE allows filtering of cells and includes an interactive set of tools for comparing two or more taxonomy annotations alongside collected knowledge (e.g., increased abundance in disease conditions, cell type aliases, or other information about a specific cell type). We present three primary use cases for ACE. First, we demonstrate how a user can assign cell type labels from the Seattle Alzheimer's Disease Brain Cell Atlas (SEA-AD) taxonomy to cells from their own study and compare these cell type mappings to existing cell type assignments and cell metadata. Second, we extend this approach to ten published human AD studies which we previously reprocessed through a common data analysis pipeline. This allowed us to compare brain taxonomies across otherwise incomparable studies and identify congruent cell type abundance changes in AD, including a decrease in abundance of subsets of somatostatin interneurons. Finally, ACE includes translation tables between different mouse and human brain cell type taxonomies publicly accessible on Allen Brain Map, from initial studies in individual neocortical areas to more recent studies spanning the whole brain. ACE can be freely and publicly accessed as a web application (https://sea-ad.shinyapps.io/ACEapp/) and on GitHub (github.com/AllenInstitute/ACE).

Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium. Trans-ancestry genome-wide study of depression identifies 697 associations implicating cell types and pharmacotherapies

In a genome-wide association study (GWAS) meta-analysis of 688,808 individuals with major depression (MD) and 4,364,225 controls from 29 countries across diverse and admixed ancestries, we identify 697 associations at 635 loci, 293 of which are novel. Using fine-mapping and functional tools, we find 308 high-confidence gene associations and enrichment of postsynaptic density and receptor clustering. A neural cell-type enrichment analysis utilizing single-cell data implicates excitatory, inhibitory, and medium spiny neurons and the involvement of amygdala neurons in both mouse and human single-cell analyses. The associations are enriched for antidepressant targets and provide potential repurposing opportunities. Polygenic scores trained using European or multi-ancestry data predicted MD status across all ancestries, explaining up to 5.8% of MD liability variance in Europeans. These findings advance our global understanding of MD and reveal biological targets that may be used to target and develop pharmacotherapies addressing the unmet need for effective treatment.

Single-cell RNA sequencing of neonatal cortical astrocytes reveals versatile cell clusters during astrocyte-neuron conversion

BACKGROUND

Astrocytes are extensively utilized as starting cells for neuronal conversion. Our previous study discovered that a portion of primary cultured mouse neonatal cortical astrocytes can be directly converted into neurons after exposure to a neurogenic induction condition. Recent in vivo studies have demonstrated astrocyte heterogeneity in terms of their developmental origin, molecular profile, physiology, and functional outputs. We hypothesized that the heterogeneity of primary astrocytes in our study could influence their conversion potential.

METHODS AND RESULTS

We performed single-cell RNA sequencing on cells harvested at key time points during in vitro astrocyte-to-neuron conversion, specifically on Day 1 and Day 9. Through single-cell RNA sequencing analysis, we identified several subpopulations of astrocytes, labeled as Astrocyte 1 to Astrocyte 3, based on distinct gene expression patterns. Pseudotime trajectory analysis predicted the existence of three distinct cell states throughout the conversion process. Astrocyte 3 exhibited a higher propensity for neuronal conversion, with proliferation genes like Mki67 being highly expressed. Additionally, several candidate genes were identified as potentially crucial in the conversion process. Astrocyte 3 is considered a unique subtype population of astrocytes.

CONCLUSIONS

Our investigation underscores the diversity of primary neonatal cortical astrocytes and provides critical insights into the potential for astrocyte-to-neuron conversion, which may be harnessed to enhance the efficiency of this astrocyte-neuron conversion process.

Mapping the developmental trajectory of human astrocytes reveals divergence in glioblastoma

Glioblastoma (GBM) is defined by heterogeneous and resilient cell populations that closely reflect neurodevelopmental cell types. Although it is clear that GBM echoes early and immature cell states, identifying the specific developmental programmes disrupted in these tumours has been hindered by a lack of high-resolution trajectories of glial and neuronal lineages. Here we delineate the course of human astrocyte maturation to uncover discrete developmental stages and attributes mirrored by GBM. We generated a transcriptomic and epigenomic map of human astrocyte maturation using cortical organoids maintained in culture for nearly 2 years. Through this approach, we chronicled a multiphase developmental process. Our time course of human astrocyte maturation includes a molecularly distinct intermediate period that serves as a lineage commitment checkpoint upstream of mature quiescence. This intermediate stage acts as a site of developmental deviation separating IDH-wild-type neoplastic astrocyte-lineage cells from quiescent astrocyte populations. Interestingly, IDH1-mutant tumour astrocyte-lineage cells are the exception to this developmental perturbation, where immature properties are suppressed as a result of D-2-hydroxyglutarate oncometabolite exposure. We propose that this defiance is a consequence of IDH1-mutant-associated epigenetic dysregulation, and we identified biased DNA hydroxymethylation (5hmC) in maturation genes as a possible mechanism. Together, this study illustrates a distinct cellular state aberration in GBM astrocyte-lineage cells and presents developmental targets for experimental and therapeutic exploration.

Novel neural pathways targeted by GLP-1R agonists and bariatric surgery

The glucagon-like peptide 1 receptor (GLP-1R) agonist semaglutide has revolutionized the treatment of obesity, with other gut hormone-based drugs lined up that show even greater weight-lowering ability in obese patients. Nevertheless, bariatric surgery remains the mainstay treatment for severe obesity and achieves unparalleled weight loss that generally stands the test of time. While their underlying mechanisms of action remain incompletely understood, it is clear that the common denominator between GLP-1R agonists and bariatric surgery is that they suppress food intake by targeting the brain. In this Review, we highlight recent preclinical studies using contemporary neuroscientific techniques that provide novel concepts in the neural control of food intake and body weight with reference to endogenous GLP-1, GLP-1R agonists, and bariatric surgery. We start in the periphery with vagal, intestinofugal, and spinal sensory nerves and then progress through the brainstem up to the hypothalamus and finish at non-canonical brain feeding centers such as the zona incerta and lateral septum. Further defining the commonalities and differences between GLP-1R agonists and bariatric surgery in terms of how they target the brain may not only help bridge the gap between pharmacological and surgical interventions for weight loss but also provide a neural basis for their combined use when each individually fails.

Mapping the cellular etiology of schizophrenia and complex brain phenotypes

Psychiatric disorders are multifactorial and effective treatments are lacking. Probable contributing factors to the challenges in therapeutic development include the complexity of the human brain and the high polygenicity of psychiatric disorders. Combining well-powered genome-wide and brain-wide genetics and transcriptomics analyses can deepen our understanding of the etiology of psychiatric disorders. Here, we leverage two landmark resources to infer the cell types involved in the etiology of schizophrenia, other psychiatric disorders and informative comparison of brain phenotypes. We found both cortical and subcortical neuronal associations for schizophrenia, bipolar disorder and depression. These cell types included somatostatin interneurons, excitatory neurons from the retrosplenial cortex and eccentric medium spiny-like neurons from the amygdala. In contrast we found T cell and B cell associations with multiple sclerosis and microglial associations with Alzheimer's disease. We provide a framework for a cell-type-based classification system that can lead to drug repurposing or development opportunities and personalized treatments. This work formalizes a data-driven, cellular and molecular model of complex brain disorders.

Molecular and cellular characteristics of cerebrovascular cell types and their contribution to neurodegenerative diseases

Many diseases and disorders of the nervous system suffer from a lack of adequate therapeutics to halt or slow disease progression, and to this day, no cure exists for any of the fatal neurodegenerative diseases. In part this is due to the incredible diversity of cell types that comprise the brain, knowledge gaps in understanding basic mechanisms of disease, as well as a lack of reliable strategies for delivering new therapeutic modalities to affected areas. With the advent of single cell genomics, it is now possible to interrogate the molecular characteristics of diverse cell populations and their alterations in diseased states. More recently, much attention has been devoted to cell populations that have historically been difficult to profile with bulk single cell technologies. In particular, cell types that comprise the cerebrovasculature have become increasingly better characterized in normal and neurodegenerative disease contexts. In this review, we describe the current understanding of cerebrovasculature structure, function, and cell type diversity and its role in the mechanisms underlying various neurodegenerative diseases. We focus on human and mouse cerebrovasculature studies and discuss both origins and consequences of cerebrovascular dysfunction, emphasizing known cell type-specific vulnerabilities in neuronal and cerebrovascular cell populations. Lastly, we highlight how novel insights into cerebrovascular biology have impacted the development of modern therapeutic approaches and discuss outstanding questions in the field.

Tremor in the Age of Omics: An Overview of the Transcriptomic Landscape of Essential Tremor

Essential Tremor (ET) is the most common movement disorder and has a worldwide prevalence of 1%, including 5% of the population over 65 years old. It is characterized by an active, postural or kinetic tremor, primarily affecting the upper limbs, and is diagnosed based on clinical characteristics. The pathological mechanisms of ET, however, are mostly unknown. Moreover, despite its high heritability, genetic studies of ET genetics have yielded mixed results. Transcriptomics is a field that has the potential to reveal valuable insights about the processes and pathogenesis of ET thus providing an avenue for the development of more effective therapies. With the emergence of techniques such as single-cell and single-nucleus RNA sequencing (scRNA-seq and snRNA-seq), molecular and cellular events can now be more closely examined, providing valuable insights into potential causal mechanisms. In this review, we review the growing literature on transcriptomic studies in ET, aiming to identify biological pathways involved and explore possible avenues for further ET research. We emphasized the convergence on shared of biological pathways across several studies, specifically axonal guidance and calcium signaling. These findings posit multiple hypotheses linking both pathways through the regulation of axonal and synaptic plasticity. We conclude that increasing the sample size is vital to uncover the subtleties of ET clinical and pathological heterogeneity. Additionally, integrating Multiomics approaches should provide a comprehensive understanding of the disease's pathophysiology.

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.

Potassium ion channel modulation at cancer-neural interface enhances neuronal excitability in epileptogenic glioblastoma multiforme

The central nervous system (CNS) is increasingly recognized as a critical modulator in the oncogenesis of glioblastoma multiforme (GBM), with interactions between cancer and local neuronal circuits frequently leading to epilepsy; however, the relative contributions of these factors remain unclear. Here, we report a coordinated intratumor shift among distinct cancer subtypes within progenitor-like families of epileptic GBM patients, revealing an accumulation of oligodendrocyte progenitor (OPC)-like subpopulations at the cancer-neuron interface along with heightened electrical signaling activity in the surrounding neuronal networks. The OPC-like cells associated with epilepsy express KCND2, which encodes the voltage-gated K+ channel KV4.2, enhancing neuronal excitability via accumulation of extracellular K+, as demonstrated in patient-derived ex vivo slices, xenografting models, and engineering organoids. Together, we uncovered the essential local circuitry, cellular components, and molecular mechanisms facilitating cancer-neuron interaction at peritumor borders. KCND2 plays a crucial role in mediating nervous system-cancer electrical communication, suggesting potential targets for intervention.

Machine learning-driven identification of critical gene programs and key transcription factors in migraine

BACKGROUND

Migraine is a complex neurological disorder characterized by recurrent episodes of severe headaches. Although genetic factors have been implicated, the precise molecular mechanisms, particularly gene expression patterns in migraine-associated brain regions, remain unclear. This study applies machine learning techniques to explore region-specific gene expression profiles and identify critical gene programs and transcription factors linked to migraine pathogenesis.

METHODS

We utilized single-nucleus RNA sequencing (snRNA-seq) data from 43 brain regions, along with genome-wide association study (GWAS) data, to investigate susceptibility to migraine. The cell-type-specific expression (CELLEX) algorithm was employed to calculate specific expression profiles for each region, while non-negative matrix factorization (NMF) was applied to decompose gene programs within the single-cell data from these regions. Following the annotation of brain region expression profiles and gene programs to the genome, we employed stratified linkage disequilibrium score regression (S-LDSC) to assess the associations between brain regions, gene programs, and migraine-related SNPs. Key transcription factors regulating critical gene programs were identified using a random forest model based on regulatory networks derived from the GTEx consortium.

RESULTS

Our analysis revealed significant enrichment of migraine-associated single nucleotide polymorphisms (SNPs) in the posterior nuclear complex-medial geniculate nuclei (PoN_MG) of the thalamus, highlighting this region's crucial role in migraine pathogenesis. Gene program 1, identified through NMF, was enriched in the calcium signaling pathway, a known contributor to migraine pathophysiology. Random forest analysis predicted ARID3A as the top transcription factor regulating gene program 1, suggesting its potential role in modulating calcium-related genes involved in migraine.

CONCLUSION

This study provides new insights into the molecular mechanisms underlying migraine, emphasizing the importance of the PoN_MG thalamic region, calcium signaling pathways, and key transcription factors like ARID3A. These findings offer potential avenues for developing targeted therapeutic strategies for migraine treatment.

Rare Variant Analyses in Ancestrally Diverse Cohorts Reveal Novel ADHD Risk Genes

Attention-deficit/hyperactivity disorder (ADHD) is a highly heritable neurodevelopmental disorder, but its genetic architecture remains incompletely characterized. Rare coding variants, which can profoundly impact gene function, represent an underexplored dimension of ADHD risk. In this study, we analyzed large-scale DNA sequencing datasets from ancestrally diverse cohorts and observed significant enrichment of rare protein-truncating and deleterious missense variants in highly evolutionarily constrained genes. This analysis identified 15 high-confidence ADHD risk genes, including the previously implicated KDM5B. Integrating these findings with genome-wide association study (GWAS) data revealed nine enriched pathways, with strong involvement in synapse organization, neuronal development, and chromatin regulation. Protein-protein interaction analyses identified chromatin regulators as central network hubs, and single-cell transcriptomic profiling confirmed their expression in neurons and glial cells, with distinct patterns in oligodendrocyte subtypes. These findings advance our understanding of the genetic architecture of ADHD, uncover core molecular mechanisms, and provide promising directions for future therapeutic development.

Neurons Are Not All the Same: Diversity in Neuronal Populations and Their Intrinsic Responses to Spinal Cord Injury

Functional recovery following spinal cord injury will require the regeneration and repair of damaged neuronal pathways. It is well known that the tissue response to injury involves inflammation and the formation of a glial scar at the lesion site, which significantly impairs the capacity for neuronal regeneration and functional recovery. There are initial attempts by both supraspinal and intraspinal neurons to regenerate damaged axons, often influenced by the neighboring tissue pathology. Many experimental therapeutic strategies are targeted to further stimulate the initial axonal regrowth, with little consideration for the diversity of the affected neuronal populations. Notably, recent studies reveal that the neuronal response to injury is variable, based on multiple factors, including the location of the injury with respect to the neuronal cell bodies and the affected neuronal populations. New insights into regenerative mechanisms have shown that neurons are not homogenous but instead exhibit a wide array of diversity in their gene expression, physiology, and intrinsic responses to injury. Understanding this diverse intrinsic response is crucial, as complete functional recovery requires the successful coordinated regeneration and reorganization of various neuron pathways.

Molecular hallmarks of excitatory and inhibitory neuronal resilience and resistance to Alzheimer's disease

Background

A significant proportion of individuals maintain healthy cognitive function despite having extensive Alzheimer's disease (AD) pathology, known as cognitive resilience. Understanding the molecular mechanisms that protect these individuals can identify therapeutic targets for AD dementia. This study aims to define molecular and cellular signatures of cognitive resilience, protection and resistance, by integrating genetics, bulk RNA, and single-nucleus RNA sequencing data across multiple brain regions from AD, resilient, and control individuals.

Methods

We analyzed data from the Religious Order Study and the Rush Memory and Aging Project (ROSMAP), including bulk (n=631) and multi-regional single nucleus (n=48) RNA sequencing. Subjects were categorized into AD, resilient, and control based on β-amyloid and tau pathology, and cognitive status. We identified and prioritized protected cell populations using whole genome sequencing-derived genetic variants, transcriptomic profiling, and cellular composition distribution.

Results

Transcriptomic results, supported by GWAS-derived polygenic risk scores, place cognitive resilience as an intermediate state in the AD continuum. Tissue-level analysis revealed 43 genes enriched in nucleic acid metabolism and signaling that were differentially expressed between AD and resilience. Only GFAP (upregulated) and KLF4 (downregulated) showed differential expression in resilience compared to controls. Cellular resilience involved reorganization of protein folding and degradation pathways, with downregulation of Hsp90 and selective upregulation of Hsp40, Hsp70, and Hsp110 families in excitatory neurons. Excitatory neuronal subpopulations in the entorhinal cortex (ATP8B1+ and MEF2Chigh) exhibited unique resilience signaling through neurotrophin (modulated by LINGO1) and angiopoietin (ANGPT2/TEK) pathways. We identified MEF2C, ATP8B1, and RELN as key markers of resilient excitatory neuronal populations, characterized by selective vulnerability in AD. Protective rare variant enrichment highlighted vulnerable populations, including somatostatin (SST) inhibitory interneurons, validated through immunofluorescence showing co-expression of rare variant associated RBFOX1 and KIF26B in SST+ neurons in the dorsolateral prefrontal cortex. The maintenance of excitatory-inhibitory balance emerges as a key characteristic of resilience.

Conclusions

We identified molecular and cellular hallmarks of cognitive resilience, an intermediate state in the AD continuum. Resilience mechanisms include preservation of neuronal function, maintenance of excitatory/inhibitory balance, and activation of protective signaling pathways. Specific excitatory neuronal populations appear to play a central role in mediating cognitive resilience, while a subset of vulnerable SST interneurons likely provide compensation against AD-associated dysregulation. This study offers a framework to leverage natural protective mechanisms to mitigate neurodegeneration and preserve cognition in AD.

Multi-Omics Analysis for Identifying Cell-Type-Specific Druggable Targets in Alzheimer's Disease

Background

Analyzing disease-linked genetic variants via expression quantitative trait loci (eQTLs) is important for identifying potential disease-causing genes. Previous research prioritized genes by integrating Genome-Wide Association Study (GWAS) results with tissue-level eQTLs. Recent studies have explored brain cell type-specific eQTLs, but they lack a systematic analysis across various Alzheimer's disease (AD) GWAS datasets, nor did they compare effects between tissue and cell type levels or across different cell type-specific eQTL datasets. In this study, we integrated brain cell type-specific eQTL datasets with AD GWAS datasets to identify potential causal genes at the cell type level.

Methods

To prioritize disease-causing genes, we used Summary Data-Based Mendelian Randomization (SMR) and Bayesian Colocalization (COLOC) to integrate AD GWAS summary statistics with cell-type-specific eQTLs. Combining data from five AD GWAS, three single-cell eQTL datasets, and one bulk tissue eQTL meta-analysis, we identified and confirmed both novel and known candidate causal genes. We investigated gene regulation through enhancer activity using H3K27ac and ATAC-seq data, performed protein-protein interaction and pathway enrichment analyses, and conducted a drug/compound enrichment analysis with the Drug Signatures Database (DSigDB) to support drug repurposing for AD.

Results

We identified 27 candidate causal genes for AD using cell type-specific eQTL datasets, with the highest numbers in microglia, followed by excitatory neurons, astrocytes, inhibitory neurons, oligodendrocytes, and oligodendrocyte precursor cells (OPCs). PABPC1 emerged as a novel astrocyte-specific gene. Our analysis revealed protein-protein interaction (PPI) networks for these causal genes in microglia and astrocytes. We found the "regulation of aspartic-type peptidase activity" pathway being the most enriched among all the causal genes. AD-risk variants associated with candidate causal gene PABPC1 is located near or within enhancers only active in astrocytes. We classified the genes into three drug tiers and identified druggable interactions, with imatinib mesylate emerging as a key candidate. A drug-target gene network was created to explore potential drug targets for AD.

Conclusions

We systematically prioritized AD candidate causal genes based on cell type-specific molecular evidence. The integrative approach enhances our understanding of molecular mechanisms of AD-related genetic variants and facilitates the interpretation of AD GWAS results.

Decreased lipidated ApoE-receptor interactions confer protection against pathogenicity of ApoE and its lipid cargoes in lysosomes

While apolipoprotein E (APOE) is the strongest genetic modifier for late-onset Alzheimer's disease (LOAD), the molecular mechanisms underlying isoform-dependent risk and the relevance of ApoE-associated lipids remain elusive. Here, we report that impaired low-density lipoprotein (LDL) receptor (LDLR) binding of lipidated ApoE2 (lipApoE2) avoids LDLR recycling defects observed with lipApoE3/E4 and decreases the uptake of cholesteryl esters (CEs), which are lipids linked to neurodegeneration. In human neurons, the addition of ApoE carrying polyunsaturated fatty acids (PUFAs)-CE revealed an allelic series (ApoE4 > ApoE3 > ApoE2) associated with lipofuscinosis, an age-related lysosomal pathology resulting from lipid peroxidation. Lipofuscin increased lysosomal accumulation of tau fibrils and was elevated in the APOE4 mouse brain with exacerbation by tau pathology. Intrahippocampal injection of PUFA-CE-lipApoE4 was sufficient to induce lipofuscinosis in wild-type mice. Finally, the protective Christchurch mutation also reduced LDLR binding and phenocopied ApoE2. Collectively, our data strongly suggest decreased lipApoE-LDLR interactions minimize LOAD risk by reducing the deleterious effects of endolysosomal targeting of ApoE and associated pathogenic lipids.

MAPbrain: a multi-omics atlas of the primate brain

The brain is the central hub of the entire nervous system. Its development is a lifelong process guided by a genetic blueprint. Understanding how genes influence brain development is critical for deciphering the formation of human cognitive functions and the underlying mechanisms of neurological disorders. Recent advances in multi-omics techniques have now made it possible to explore these aspects comprehensively. However, integrating and analyzing extensive multi-omics data presents significant challenges. Here, we introduced MAPbrain (http://bigdata.ibp.ac.cn/mapBRAIN/), a multi-omics atlas of the primate brain. This repository integrates and normalizes both our own lab's published data and publicly available multi-omics data, encompassing 21 million brain cells from 38 key brain regions and 436 sub-regions across embryonic and adult stages, with 164 time points in humans and non-human primates. MAPbrain offers a unique, robust, and interactive platform that includes transcriptomics, epigenomics, and spatial transcriptomics data, facilitating a comprehensive exploration of brain development. The platform enables the exploration of cell type- and time point-specific markers, gene expression comparison between brain regions and species, joint analyses across transcriptome and epigenome, and navigation of cell types across species, brain regions, and development stages. Additionally, MAPbrain provides an online integration module for users to navigate and analyze their own data within the platform.

Enhanced yield and subtype identity of hPSC-derived midbrain dopamine neuron by modulation of WNT and FGF18 signaling

While clinical trials are ongoing using human pluripotent stem cell-derived midbrain dopamine (mDA) neuron precursor grafts in Parkinson's disease (PD), current protocols to derive mDA neurons remain suboptimal. In particular, the yield of TH+ mDA neurons after in vivo grafting and the expression of some mDA neuron and subtype-specific markers can be further improved. For example, characterization of mDA grafts by single cell transcriptomics has yielded only a small proportion of mDA neurons and a considerable fraction of contaminating cell populations. Here we present an optimized mDA neuron differentiation strategy that builds on our clinical grade ("Boost") protocol but includes the addition of FGF18 and IWP2 treatment ("Boost+") at the mDA neurogenesis stage. We demonstrate that Boost+ mDA neurons show higher expression of EN1, PITX3 and ALDH1A1. Improvements in both mDA neurons yield and transcriptional similarity to primary mDA neurons is observed both in vitro and in grafts. Furthermore, grafts are enriched in authentic A9 mDA neurons by single nucSeq. Functional studies in vitro demonstrate increased dopamine production and release and improved electrophysiological properties. In vivo analyses show increased percentages of TH+ mDA neurons resulting in efficient rescue of amphetamine induced rotation behavior in the 6-OHDA rat model and rescue of some motor deficits in non-drug induced assays, including the ladder rung assay that is not improved by Boost mDA neurons. The Boost+ conditions present an optimized protocol with advantages for disease modeling and mDA neuron grafting paradigms.

Connecting genomic results for psychiatric disorders to human brain cell types and regions reveals convergence with functional connectivity

Identifying cell types and brain regions critical for psychiatric disorders and brain traits is essential for targeted neurobiological research. By integrating genomic insights from genome-wide association studies with a comprehensive single-cell transcriptomic atlas of the adult human brain, we prioritized specific neuronal clusters significantly enriched for the SNP-heritabilities for schizophrenia, bipolar disorder, and major depressive disorder along with intelligence, education, and neuroticism. Extrapolation of cell-type results to brain regions reveals the whole-brain impact of schizophrenia genetic risk, with subregions in the hippocampus and amygdala exhibiting the most significant enrichment of SNP-heritability. Using functional MRI connectivity, we further confirmed the significance of the central and lateral amygdala, hippocampal body, and prefrontal cortex in distinguishing schizophrenia cases from controls. Our findings underscore the value of single-cell transcriptomics in understanding the polygenicity of psychiatric disorders and suggest a promising alignment of genomic, transcriptomic, and brain imaging modalities for identifying common biological targets.

Enhancer-driven cell type comparison reveals similarities between the mammalian and bird pallium

Combinations of transcription factors govern the identity of cell types, which is reflected by genomic enhancer codes. We used deep learning to characterize these enhancer codes and devised three metrics to compare cell types in the telencephalon across amniotes. To this end, we generated single-cell multiome and spatially resolved transcriptomics data of the chicken telencephalon. Enhancer codes of orthologous nonneuronal and γ-aminobutyric acid-mediated (GABAergic) cell types show a high degree of similarity across amniotes, whereas excitatory neurons of the mammalian neocortex and avian pallium exhibit varying degrees of similarity. Enhancer codes of avian mesopallial neurons are most similar to those of mammalian deep-layer neurons. With this study, we present generally applicable deep learning approaches to characterize and compare cell types on the basis of genomic regulatory sequences.

A joint analysis of single cell transcriptomics and proteomics using transformer

CITE-seq provides a powerful method for simultaneously measuring RNA and protein expression at the single-cell level. The integrated analysis of RNA and protein expression in identical cells is crucial for revealing cellular heterogeneity. However, the high experimental costs associated with CITE-seq limit its widespread application. In this paper, we propose scTEL, a deep learning framework based on Transformer encoder layers, to establish a mapping from sequenced RNA expression to unobserved protein expression in the same cells. This computation-based approach significantly reduces the experimental costs of protein expression sequencing. We are now able to predict protein expression using single-cell RNA sequencing (scRNA-seq) data, which is well-established and available at a lower cost. Moreover, our scTEL model offers a unified framework for integrating multiple CITE-seq datasets, addressing the challenge posed by the partial overlap of protein panels across different datasets. Empirical validation on public CITE-seq datasets demonstrates scTEL significantly outperforms existing methods.