The human neocortex underlies higher cognition and is the engine of complex thought. Yet our understanding of its neuronal diversity is limited by sparse access to tissue, inconsistent sampling across studies, and a lack of multiple modality data. Although single-cell transcriptomic taxonomies are an important framework for characterizing cell type diversity, transcriptomic information alone cannot reveal the cellular properties that define neuronal computations. To address this, we performed Patch-seq, a method for collecting Morphology, Electrophysiology, and Transcriptomic data from a single neuron. We focused on glutamatergic, neocortical, excitatory neurons, the principal long-range projecting neurons of the cortex, and systematically integrated their morphoelectric features with transcriptomic identity. In combination with spatial transcriptomic data, we interrogated 39 of 42 transcriptomically-defined neuron types with a layer-centric perspective. Morphoelectric properties, such as cortical depth, apical dendrite structure, and excitability clearly distinguish transcriptomic subclasses and support many finer transcriptomic types. Morphoelectric properties are influenced by spatial location in supragranular layers, while deeper layers exhibit greater heterogeneity. Cross-species comparisons reveal conserved subclass organization but pronounced differences in apical dendrite arborization between mouse and human, and surprising similarities between human and macaque. Together, these datasets provide a unified multimodal reference that advances our understanding of human cortical circuitry and establishes a foundation for experimental and computational studies of human brain function and disease.

The number of clinical investigations and approved applications of adeno-associated virus (AAV) based transgene product (TP) delivery has grown steadily. There also has been a growing interest in understanding how anti-AAV and anti-TP immune responses affect the safety and efficacy of these gene therapy treatments. While considerations related to anti-AAV immunity have been discussed in other works, this manuscript focuses on the assessment of anti-TP immune responses, including both humoral and cellular responses. The development of anti-TP antibodies or a cytotoxic cellular response may lead to increased clearance of the TP, elimination of AAV-transduced cells, and consequently, affect the overall durability and efficacy of the treatment. Additionally, the binding and neutralization of residual endogenous protein by anti-TP antibodies might further worsen the clinical condition under treatment. Several topics are explored in this manuscript, including immunogenicity risk factors that can be considered when evaluating the overall risk and impact of anti-TP immunogenicity, potential implications of anti-TP immunogenicity, the importance of assessing anti-TP immunogenicity, and the commonly used analytical methodologies. The manuscript proposes an approach to determining the scope of anti-TP immunogenicity assessment for clinical and non-clinical studies, based on the TP nature, other intrinsic and extrinsic risk factors. Authored by a group of scientists involved in AAV-based therapeutic development from various industry organizations, the manuscript aims to provide recommendations and guidance to industry sponsors, academic laboratories, and regulatory agencies working on AAV-based modalities, with the goal of achieving a more consistent approach to the assessment of anti-TP immune response.

Imaging-based spatially resolved transcriptomics can localize transcripts within tissue sections in three dimensions. However, cell segmentation, which assigns transcripts to cells, is usually performed in two dimensions and spatial doublets in the vertical dimension result in segmented cells containing transcripts originating from multiple cell types. Here we present a computational tool called ovrlpy that identifies overlapping cells, tissue folds and inaccurate cell segmentation by analyzing transcript localization in three dimensions.

Aβ presence in the caudate nucleus (Ca) partially defines Thal stage III in Alzheimer's disease (AD), but little is known about AD's cellular impact on the region. Leveraging a public basal ganglia taxonomy of cellular populations, we generated a cellular resolution atlas of AD-associated pathological changes in Ca. Unlike cortex, we found that Ca AD pathology is dominated by two key features: phosphorylated tau (pTau)-containing neuropil threads enriched near oligodendrocytes in white matter tracts and amyloid-β diffuse plaques enriched in gray matter. Although AD pathology in affected cortical regions results in neuronal loss, we find no AD-driven reductions in neuron proportions in Ca. However, there were observable changes in multiple cellular populations. Protoplasmic astrocytes and FLT1+/IL1B+ microglia increased in abundance with global pTau levels. We also observe gene expression changes in fast-spiking PTHLH-PVALB interneurons indicative of disrupted signaling pathways and altered intrinsic physiological properties. This work provides a cellular-resolution framework for understanding AD pathology in Ca.

A major goal of the BRAIN Initiative Cell Atlas Network (BICAN) is to create a suite of foundational reference cell atlases and associated standards for human and non-human primate brains. Central to this goal is the creation of cross-species harmonized cellular taxonomies and structural parcellations with formal ontologies that can be mapped into 3D reference frameworks bridging neuroimaging and cellular and histological resolutions. We describe here an iterative approach, focused initially on the basal ganglia, to co-create structural and cellular ontologies in human, macaque and marmoset brains, including a Harmonized Ontology of Mammalian Brain Anatomy (HOMBA), and to map and refine structural parcellations into neuroimaging-based common coordinate frameworks. These references provide the framework for documenting and mapping all experimental sampling in BICAN, allowing analyses of cellular and molecular variation as a function of topographic position, and enabling comparisons of cellular, molecular and neuroimaging-based functional variation within and between primate species.

Spatial transcriptomics enables the precise mapping of gene expression patterns within tissue architecture, offering unprecedented insights into cellular interactions, tissue heterogeneity, and disease pathology that are unattainable with traditional transcriptomic approaches. We present a tool for processing spatial transcriptomics data, SCALPEL (Spatial Cell Analysis, Labeling, Processing, and Expression Linking). SCALPEL is specifically designed to support the analysis of large, atlas-level datasets. Our new workflow features advanced 3D segmentation optimized for dense and heterogeneous tissues, refined filtering criteria, and transcriptome-based doublet detection to remove low-quality or artifactual cells. Cell type label transfer from existing taxonomies is further improved through updated filtering thresholds. Spatial domain detection is incorporated to capture local transcriptomic organization, and tissue sections are registered to the Allen Mouse Brain Common Coordinate Framework version 3 (CCFv3) for precise anatomical alignment. Genome-wide expression imputation from single-cell RNA-sequencing (scRNAseq) further enriches the dataset. Crucially, we benchmark the performance of this updated pipeline against a previously published version of our whole-mouse-brain (WMB) dataset (Yao et al., 2023b), demonstrating substantial improvements in cell number, expression profile clarity, and spatial registration. These advances provide a robust foundation for downstream spatial analyses and set a new standard for large-scale spatial transcriptomics studies.

Spatial omics technologies have revolutionized the study of tissue architecture and cellular heterogeneity by integrating molecular profiles with spatial localization. In spatially resolved transcriptomics, delineating higher-order anatomical structures is critical for understanding how cellular organization affects tissue and organ function. Since 2020, more than 50 spatially aware clustering (SAC) methods have been developed for this purpose. However, the reliability of current benchmarks is undermined by their narrow focus on Visium and brain tissue datasets, as well as incorrect interpretation of manual annotation as ground truth. Here, we present SACCELERATOR, a community-driven, extensible framework that standardizes data formatting, method integration, and metric evaluation, and is designed to rapidly incorporate new methods and datasets. SACCELERATOR currently includes 22 SAC methods applied to 15 datasets spanning 9 technologies and diverse tissue types. Our analysis revealed substantial limitations in the generalizability and reproducibility of SAC methods across tissues and platforms. We also demonstrate that anatomical labels commonly used as ground truths are often biased, potentially error-prone, and, in some cases, unsuitable for benchmarking efforts. Rather than scoring and comparing methods, we propose a consensus-guided workflow that aggregates clustering results to generate consensus representations. Descriptive spatial metrics highlight areas of high entropy where method disagreement is highest, enabling targeted feedback for tissue experts. Applied to brain and cancer datasets, this approach uncovered biologically meaningful patterns overlooked by individual methods and manual annotations. Our results underscore the need for iterative, expert-in-the-loop analysis and reveal that traditional evaluation metrics do not always capture the subjective qualities of results. By improving tissue annotation and addressing key benchmarking limitations, SACCELERATOR provides a robust foundation for advancing spatial omics research.

The basal ganglia (BG) are conserved brain regions essential for motor control, learning, emotion, and cognition, and are implicated in neurological and psychiatric disease. Yet a unified cross-species taxonomy of BG cell types is lacking, limiting translation of BG circuit mechanisms, interpretation of human genetic risk, and development of cell type-targeted tools. We present a multiomic consensus atlas of 1.8 million nuclei from human, macaque, and marmoset spanning eight BG structures. Integrating cross-species gene expression, open chromatin, and spatial profiling enables definition of conserved and divergent cell types. Alignment to existing mouse and human atlases identifies 61 homologous cell types conserved over 80 million years. We identify a STRd D2 StrioMat Hybrid medium spiny neuron (MSN) type with molecular, electrophysiological, and morphological features that clarify hybrid MSN identities. Comparative cis-regulatory analysis reveals conserved sequence grammars that encode cell identity and inform viral targeting strategies, providing a foundational resource for BG evolution, function, and disease.

Imaging-based spatially resolved transcriptomics can localise transcripts within cells in 3D. Cell egmentation precedes assignment of transcripts to cells and nnotation of cell function. However, cell segmentation is usually performed in 2D, thus unable to deal with spatial oublets arising from overlapping cells, resulting in segmented cells containing transcripts originating from multiple cell-types. Here we present a computational tool called ovrlpy that identifies overlapping cells, tissue folds and inaccurate cell-segmentation. ### Competing Interest Statement The authors have declared no competing interest.

Astrocytes shape synapses and circuits, yet human basal ganglia astrocyte diversity is incompletely defined. We built a multimodal atlas by integrating single-nucleus RNA-sequencing and chromatin accessibility with DNA methylation, 3D chromatin conformation, and spatial transcriptomics, then mapped basal ganglia programs onto a whole-brain reference. Astrocytes segregated into three anatomical subgroups spanning striatal gray matter, extra-striatal gray matter, and white matter, with subgroup-biased neurotransmitter transporters and synapse-associated programs consistent with differences in dominant afferent input. Within striatum, dorsal and ventral astrocyte populations aligned with distinct microcircuits and were conserved in nonhuman primates. A deep learning sequence model identified subgroup-associated enhancer code and, when benchmarked against published enhancer-AAV datasets, supported the design of candidate viral tools to target basal ganglia astrocyte programs in vivo. Together, these data define major axes of human astrocyte specialization and provide a framework for cell type-specific dissection of basal ganglia function. ### Competing Interest Statement The authors have declared no competing interest. National Institutes of Health, https://ror.org/01cwqze88, UM1MH130981 Research Foundation Flanders (FWO) PhD fellowship, 1SH6J24N & V428025N

Mammalian brain cell identity is shaped by intrinsic factors and external context. We present a spatially resolved transcriptomic and gene regulatory atlas of cell types found across all subcortical brain regions in a primate - the marmoset monkey. Dense sampling and cross-species integrations revealed spatially precise neuronal assemblies, including in complex structures such as hypothalamus. We find chromatin accessibility and transcriptional identity are spatially tuned within and across subcortical structures. Spatial gradients within subfields of the hippocampal formation are predominantly orchestrated by graded transcription factors coupled with graded enhancers. The primate-expanded population of GABAergic neurons in the thalamus shares transcriptional and regulatory syntax with neurons in superior colliculus, reflecting an evolutionary adaptation compared with rodents. We show that unexpected transcriptional convergence, such as between striatal GABAergic medium spiny neurons and telencephalic glutamatergic neurons, can arise when distinct gene regulatory networks impinge on the same downstream genes. ### Competing Interest Statement The authors have declared no competing interest. National Institute of Mental Health, https://ror.org/04xeg9z08, UM1MH130981 Netherlands Organization for Scientific Research, NWO:024.004.012

Abstract Image-based spatial transcriptomics platforms are powerful tools often used to identify cell populations and describe gene expression in intact tissue. Spatial experiments return large, high-dimensional datasets and several open-source software packages are available to facilitate analysis and visualization. The outputs of spatial transcriptomics platforms are typically imperfect. For example, local variations in transcript detection probability are common. Software tools to characterize imperfections and their impact on downstream analyses are lacking so the data quality is assessed manually, a laborious and often a subjective process. Here we describe imperfections in a dataset of 641 fresh-frozen adult mouse brain sections collected using the Vizgen MERSCOPE. Common imperfections included the local loss of tissue from the section, tissue outside the imaging volume due to detachment from the coverslip, transcripts missing due to dropped images, varying detection probability through space, and differences in transcript detection probability between experiments. We describe the incidence of each imperfection and the likely impact on the accuracy of cell type labels. We develop MerQuaCo, open-source code that detects and quantifies imperfections without user input, facilitating the selection of sections for further analysis with existing packages. Together, our results and MerQuaCo facilitate rigorous, objective assessment of the quality of spatial transcriptomics results.

Alzheimer’s disease (AD) is clinically characterized by a progressive cognitive decline associated with stereotyped accumulation of amyloid-beta (Aβ) plaques and hyperphosphorylated Tau (pTau) tangles across brain regions. While histopathology has revealed patterns of regional involvement, the molecular and cellular events that accompany and potentially drive this progression remain incompletely understood. We applied single nucleus RNA sequencing (RNAseq), ATAC-seq, and Multiome profiling to over 7 million high-quality nuclei from 10 brain regions—spanning medial and lateral entorhinal cortices, hippocampus, multiple temporal and frontal cortical areas, and primary visual cortex—sampled from the same cohort of 84 aged human donors across the AD spectrum (3 regions from all donors, 7 from those without severe co-morbidities). We predicted the cell-type for each nucleus by mapping to an expanded BRAIN initiative cell-type taxonomy, which included AD-associated non-neuronal states and ∼70 brain region-specific neuronal types. These datasets were paired with regional quantitative measurements of Aβ (6e10), pTau (AT8), and other protein pathologies, as well as cellular stains for neurons, microglia, and astrocytes. We inferred two distinct protein pathology accumulation patterns across brain regions: neocortical areas accumulated Aβ prior to pTau, whereas hippocampus and entorhinal cortex had early and, in some cases, substantial pTau burden independent of Aβ. Among neocortical regions, accumulation of AT8 beyond the temporal medial lobe strongly associated with dementia. In analyzing cell-type abundance differences associated with higher levels of pTau pathology, we identified shared motifs of selective neuronal loss consistent with our previous observations from the middle temporal gyrus. These included loss of L2/3 and L5 intratelencephalic excitatory neurons and several types of inhibitory interneurons (e.g., Vip, Sst, Pvalb). These same vulnerable inhibitory types were also reduced in hippocampal and entorhinal regions, when present, and we observed parallel increases in astrocyte, microglial, and oligodendrocyte precursor cells. Additionally, we observed a decrease in specific, regionally specialized neuron subtypes in the hippocampus, entorhinal cortex, and visual cortex. Our multimodal, multi-region single-cell atlas reveals common and region-specific patterns of cellular vulnerability in AD. These cell-types, particularly those commonly affected in distinct neural circuits, could serve as candidate therapeutic targets and biomarkers.

Dysfunction of the Basal Ganglia is implicated in several neurodegenerative diseases such as Parkinson’s and Huntington’s. A substructure of the Basal Ganglia, the caudate nucleus, is observed to have diffuse amyloid plaques in Alzheimer’s disease (AD), in Thal phase III. Additionally, literature suggests the presence of AD -related tangles. Functionally, the caudate is known to be involved in cognitive functions impacted by AD such as memory. The caudate also receives signals and has efferent projections to significantly affected regions in AD such as cortex and hippocampus respectively. Despite these connections, caudate nucleus remains understudied in AD. AT8 (pTau) and 6e10 (Aβ) immunohistochemical staining was performed on the caudate from 42 donors with only canonical proteionopathies and no comorbities. Single nucleus RNA and ATAC-seq (multiome or singleome) was collected for all donors in the cohort. Spatial transcriptomics was performed on a subset of 5 Thal I-III and 5 Thal IV-V donors, with post-hoc immunostaining of AT8 and 6e10. Cells were labeled using deep learning with a reference caudate dataset from healthy BRAIN Initiative donors. Changes in expression and cell type abundance were modeled in terms of levels of AT8 and 6e10 using Bayesian and general linear mixed effects models respectively. We identified caudate specific pTau associated abundance increases in astrocyte and microglia types. These microglia types were not the stereotypical disease associated types described in cortex. We also identified pTau associated abundance decreases in oliogodendrocyte subtypes consistent with cortex. Almost all neuronal populations in the caudate show little change in their cellular abundances. Most effects in cellular composition or differential expression were observed specifically with respect to level of pTau and not Aβ. AD’s impact in caudate head contrasts with established effects on the cortex. Regionally unique increases in certain non-neuronal populations suggest a caudate specific response to AD. Additionally, little neuronal loss, even with respect to significant pTau pathology suggests either environmental or cellular factors that confer resilience, or distinct pTau conditions in the caudate. Finally, our data suggests that the predominantly diffuse plaques in caudate are not sufficient for a plaque induced response in microglia.

The basal ganglia are interconnected subcortical nuclei with complex topographical organization that orchestrate goal-directed behaviors and are implicated in neurodegenerative movement disorders. We generated a cellular-resolution, spatial transcriptomic atlas of the basal ganglia in human, rhesus macaque, and common marmoset, sampling over one million cells in each species. By integrating spatial data with a cross-species, consensus snRNA-seq cell type taxonomy, this atlas reveals conserved principles of molecular organization within and across structures. The cellular architecture is complex but highly stereotyped, with gene expression gradients superimposed onto discrete compartments. Extensive spatial sampling illuminates 3D gradients of molecular organization in the striatum and reveals cell type-specific core and shell compartments in the primate internal globus pallidus, which is conserved with mouse. This unified, cross-species spatial transcriptomic atlas will be a foundational resource for characterizing the molecular and functional organization of the basal ganglia and their roles in health and disease. ### Competing Interest Statement H.Z. is on the scientific advisory board of MapLight Therapeutics, Inc. The other authors declare no competing interests. National Institute of Mental Health, https://ror.org/04xeg9z08, UM1MH130981-01 Allen Institute for Brain Science

The thalamus connects the sensory organs and major subcortical brain regions with the neocortex. The thalamus has long been divided into multiple discrete nuclei, based on cytoarchitecture, histochemical stains, and mesoscale connectivity. However, thalamic nuclei do not completely describe thalamic organization. For example, some boundaries between thalamic nuclei are disputed, whereas other nuclei are known to contain subdomains with distinct connectivity and function. Moreover, the correspondence between cellular gene expression and other properties of thalamic projection neurons remains to be established. Spatial analysis of single cell gene expression provides a basis for reevaluating thalamic organization. We present the THALMANAC, ( THAL amus M ERFISH AN alysis and AC cess) a Findable, Accessible, Interoperable, Reusable and Reproducible (FAIRR) resource for exploring and analyzing single-cell transcriptomic variation in the thalamus. The THALMANAC provides streamlined access to thalamic gene expression data registered to the common coordinate framework and tools for quantitative analysis and visualization of these data, all encapsulated in a reproducible, cloud computing platform. Using this resource, we find that gene expression generally supports the parcellation of thalamus into distinct nuclei. Some nuclei, such as the anteromedial nucleus, are additionally composed of discrete subdomains, while other nuclei share patterns of gene expression or are arrayed on a spatial gradient of gene expression. The THALMANAC establishes spatial transcriptomic data as a foundation for delineating thalamic organization. ![Figure][1] ### Competing Interest Statement The authors have declared no competing interest. National Institute of Neurological Disorders and Stroke, U19NS123714 National Institute of Mental Health, U19MH114830 [1]: pending:yes

Recombinant adeno-associated virus (AAV) vectors are the leading delivery vehicle used for in vivo gene therapies. AntiAAV antibodies (AAV Abs) can interact with the viral capsid component of an AAV-based gene therapy (GT). Therefore, patients with preexisting AAV Abs (seropositive patients) are often excluded from GT trials to prevent treatment of patients who are unlikely to bene fi t s or may have a higher risk for adverse events outweighing treatment bene fi ts. On the contrary, unnecessary exclusion of patients with high unmet medical need should be avoided. Instead, a risk -bene fi t assessment that weighs the potential risks due to seropositivity vs. severity of disease and available treatment options, should drive the decision if patient selection is required. Assays for patient selection must be validated according to their intended use following national regulations/standards for diagnostic assays in appropriate laboratories. In this review, we summarize the current process of patient selection, including assay cutoff criteria and related assay validation approaches. We further provide considerations on regulatory requirements for the development of in vitro diagnostic tests supporting market authorization of a corresponding GT.

Gene therapy for severe hemophilia A employs an adeno-associated virus (AAV) vector and liver-specific promoters that depend on healthy hepatocyte function to achieve safe and long-lasting increases in FVIII activity. Thus, hepatocyte health is an essential aspect of safe and successful gene therapy. Many people living with hemophilia A have current or past chronic hepatitis C virus infection, metabolic dysfunction-associated steatosis or steatohepatitis, or other conditions that may compromise the efficacy and safety of AAV-mediated gene therapy. In addition, gene therapy may induce an immune response to transduced hepatocytes, leading to liver inflammation and reduced FVIII activity. The immune response can be treated with immunosuppression, but close monitoring of liver function tests and factor levels is necessary. The long-term risk of hepatocellular carcinoma associated with gene therapy is unknown. Routine screening by imaging for hepatocellular carcinoma, preferable every 6 months, is essential in patients at high risk and recommended in all recipients of hemophilia A gene therapy. This paper describes our current understanding of the biologic underpinnings of how liver health affects hemophilia A gene therapy, and provides practical clinical guidance for assessing, monitoring, and managing liver health both before and after gene therapy.