Lydia Ng

Single-cell mapping methods convert raw, heterogeneous single-cell datasets into interpretable and comparable representations of biological identity. As reference cell-type taxonomies mature, mapping new datasets to shared references has become a central strategy for enabling cross-study integration, reproducible annotation, and cumulative biological knowledge. Here we present MapMyCells , an open-source framework designed to align diverse single-cell omics datasets to hierarchical reference taxonomies with minimal preprocessing. MapMyCells provides out-of-the-box support for an expanding set of high-quality brain cell-type references generated by the Allen Institute for Brain Science, the BRAIN Initiative, and the Seattle Alzheimer’s Disease Brain Cell Atlas, including whole-brain mouse and human atlases, aging and Alzheimer’s disease cohorts, and a cross-species consensus taxonomy initially focused on the basal ganglia. MapMyCells enables efficient mapping of hundreds of thousands of cells on standard workstations without specialized hardware, providing a deterministic, scalable, and modality-agnostic approach that is robust across species and molecular assays. The framework produces interpretable confidence metrics and quantitative summaries of mapping performance, allowing users to evaluate assignment precision and accuracy. We demonstrate the mapping of unlabeled transcriptomic, epigenomic, and spatial datasets to reference taxonomies and describe a general workflow for preparing arbitrary hierarchical taxonomies for reference-based mapping. As the ecosystem of single-cell reference atlases expands, MapMyCells offers a practical and reproducible solution for community-scale cell-type annotation and cross-dataset integration, supporting the development of unified and extensible brain cell atlases.

SUMMARY: The spinal cord contains evolutionarily conserved cell types critical for motor function, sensory processing, and autonomic regulation, many of which are implicated in diverse neurological diseases and injuries. Yet the field lacks a comprehensive molecular characterization of cellular diversity in human, macaque, and mouse spinal cord. Here, we present a unified, cross-species cell type atlas based on the integration of single-nucleus gene expression, chromatin accessibility, and spatial transcriptomic data from segments within cervical, thoracic, lumbar, and sacral regions, including motor neurons (MNs) sampled across the entire rostro-caudal axis of the macaque spinal cord. Leveraging the spatial distributions of our molecularly defined cell types, we generated a cell type-guided anatomical map of spinal cord laminae and nuclei. We identified both conserved and species-specific cellular features, including gene expression patterns across distinct MN subtypes in the primate spinal cord. Cross-species cis-regulatory analysis and deep learning sequence models dissected the enhancer logic underlying viral targeting, uncovering conserved transcription factor grammar encoding cellular identity. Together, these results establish a unifying molecular and anatomical taxonomy of spinal cord cell types across species.

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.

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.

The rapid growth of scientific publications and evolving experimental paradigms create significant challenges in staying up-to-date with current advances. Assertions are often unstructured and have limited provenance, which hinders reproducibility. Ontologies and knowledge graphs (KGs) offer structured solutions by capturing assertions, evidence, and provenance to support reproducibility. This paper reviews 23 ontologies - 13 focused on assertions and evidence and 10 on provenance - providing an overview of the current landscape while highlighting key challenges and opportunities for improvement.

Molecularly defined cortical cell types have recently been linked to whole neuronal morphology (WNM), particularly those characterized by whole-brain-wide projections, such as intratelencephalic (IT), extratelencephalic (ET), and corticothalamic (CT) neurons. In contrast, classical morphological classifications (e.g., tufted TPC, small tufted SPC, and stellate SSC) are based primarily on local dendrosomatic and axonal structures, especially apical dendrites. This study bridges these perspectives by establishing a new neuronal taxonomy, analyzing the connectomes of defined cortical cell types, and comparing them with those obtained from bulk anterograde injections. Neurons were sparsely labeled via tamoxifen-inducible Cre lines with GFP reporters, and 1,419 WNM cells were comprehensively reconstructed with Vaa3D-TeraVR from ∼15 areas across six functional regions of molecularly labeled brains imaged with 2p-fMOST. These cells were newly classified by integrating current molecular-WNM and classical morphological perspectives, with sample size augmented by 1,455 publicly available WNM cells reconstructed from the Mouse-Light project and CEBSIT. This effort defined ten combined molecular-WNM-classical morphological cell types: L5ET_TPC, L6CT_NPC, L6b_HPC, and seven IT types—L2/3IT_TPC, L4IT_SSC, L4IT_UPC, L4IT_TPC, L5IT_SPC, L6IT_IPC, and L6IT_car3PC. Clustering, quantitative analyses and random Forest classifier objectively validated these types and revealed their distinct connectomes, along with convergent, topographic, and hierarchical organizations across their projection brain regions. At the single-cell level, multiple organizational principles governing cortico-cortical (C-C) and cortico-subcortical (C-subC) connectomes emerged with unprecedented detail, offering a precise GPS-like tool for in vivo recordings and robust datasets for neuronal network modeling. Comparisons with bulk anterograde injection data underscored the limitations of traditional methods in identifying projection targets. Overall, our approach provides significant insights into cortical circuitry and elucidates the complex interplay between neuronal molecular identity, whole morphology, and classical morphological classification.

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.

The telencephalon of the mammalian brain comprises multiple regions and circuit pathways that play adaptive and integrative roles in a variety of brain functions. There is a wide array of GABAergic neurons in the telencephalon; they play a multitude of circuit functions, and dysfunction of these neurons has been implicated in diverse brain disorders. In this study, we conducted a systematic and in-depth analysis of the transcriptomic and spatial organization of GABAergic neuronal types in all regions of the mouse telencephalon and their developmental origins. This was accomplished by utilizing 611,423 single-cell transcriptomes from the comprehensive and high-resolution transcriptomic and spatial cell type atlas for the adult whole mouse brain we have generated, supplemented with an additional single-cell RNA-sequencing dataset containing 99,438 high-quality single-cell transcriptomes collected from the pre- and postnatal developing mouse brain. We present a hierarchically organized adult telencephalic GABAergic neuronal cell type taxonomy of 7 classes, 52 subclasses, 284 supertypes, and 1,051 clusters, as well as a corresponding developmental taxonomy of 450 clusters across different ages. Detailed charting efforts reveal extraordinary complexity where relationships among cell types reflect both spatial locations and developmental origins. Transcriptomically and developmentally related cell types can often be found in distant and diverse brain regions indicating that long-distance migration and dispersion is a common characteristic of nearly all classes of telencephalic GABAergic neurons. Additionally, we find various spatial dimensions of both discrete and continuous variations among related cell types that are correlated with gene expression gradients. Lastly, we find that cortical, striatal and some pallidal GABAergic neurons undergo extensive postnatal diversification, whereas septal and most pallidal GABAergic neuronal types emerge simultaneously during the embryonic stage with limited postnatal diversification. Overall, the telencephalic GABAergic cell type taxonomy can serve as a foundational reference for molecular, structural and functional studies of cell types and circuits by the entire community.

Summary: The mammalian cortex is comprised of cells classified into types according to shared properties. Defining the contribution of each cell type to the processes guided by the cortex is essential for understanding its function in health and disease. We used transcriptomic and epigenomic cortical cell type taxonomies from mouse and human to define marker genes and putative enhancers and created a large toolkit of transgenic lines and enhancer AAVs for selective targeting of cortical cell populations. We report evaluation of fifteen new transgenic driver lines, two new reporter lines, and >800 different enhancer AAVs covering most subclasses of cortical cells. The tools reported here as well as the scaled process of tool creation and modification enable diverse experimental strategies towards understanding mammalian cortex and brain function.

Technologies such as spatial transcriptomics offer unique opportunities to define the spatial organization of the mouse brain. We developed an unsupervised training scheme and novel transformer-based deep learning architecture to detect spatial domains in mouse whole-brain spatial transcriptomics data. Our model learns local representations of molecular and cellular statistical patterns. These local representations can be clustered to identify spatial domains within the brain from coarse to fine-grained. Discovered domains are spatially regular, even with several hundreds of spatial clusters. They are also consistent with existing anatomical ontologies such as the Allen Mouse Brain Common Coordinate Framework version 3 (CCFv3¹) and can be visually interpreted at the cell type or transcript level. We demonstrate our method can be used to identify previously uncatalogued subregions, such as in the midbrain, where we uncover gradients of inhibitory neuron complexity and abundance. We apply our method to a separate multi-animal whole-brain spatial transcriptomic dataset and observe that inclusion of both sagittal and coronal tissue slices in region identification improves correspondence of spatial domains to CCF.