Molecularly defined and spatially resolved cell atlas of the whole mouse brain

Decoding gene networks controlling hypothalamic and prethalamic neuron development

The hypothalamus and prethalamus regulate diverse physiological and behavioral processes, yet the gene regulatory networks guiding their development remain poorly defined. Using single-cell RNA and ATAC sequencing, we profile over 660,000 cells in the developing mouse hypothalamus and prethalamus between embryonic day 11 and postnatal day 8. This resource maps key transcriptional and chromatin dynamics underlying regionalization, neurogenesis, and neuronal subtype differentiation. We identify distinct neurogenic progenitor populations and uncover gene regulatory networks controlling their spatial and temporal identity. Integration with genome-wide association study data reveals that transcription factors active in supramammillary and prethalamic lineages are associated with metabolic and neuropsychiatric traits. Cross-repressive interactions among regional transcription factors reinforce hypothalamic boundaries. Functional analysis of Dlx1/2 shows that their loss disrupts GABAergic neuron specification, leading to impaired thalamic inhibition and hyperactivity. This study provides a foundational atlas of hypothalamic and prethalamic development and highlights the importance of early gene regulatory programs in health and disease.

Perturb-Multimodal: A platform for pooled genetic screens with imaging and sequencing in intact mammalian tissue

Metazoan life requires the coordinated activities of thousands of genes in spatially organized cell types. Understanding the basis of tissue function requires approaches to dissect the genetic control of diverse cellular and tissue phenotypes in vivo. Here, we present Perturb-Multimodal (Perturb-Multi), a paired imaging and sequencing method to construct large-scale, multimodal genotype-phenotype maps in tissues with pooled genetic perturbations. Using imaging, we identify perturbations in individual cells while simultaneously measuring their gene expression profiles and subcellular morphology. Using single-cell sequencing, we measure full transcriptomic responses to the same perturbations. We apply Perturb-Multi to study hundreds of genetic perturbations in the mouse liver. Our data suggest the genetic regulators and mechanisms underlying the dynamic control of hepatocyte zonation, the unfolded protein response, and steatosis. Perturb-Multi accelerates discoveries of the genetic basis of complex cell and tissue physiology and provides critical training data for emerging machine learning models of cellular function.

Future projections for mammalian whole-brain simulations based on technological trends in related fields

Large-scale brain simulation allows us to understand the interaction of vast numbers of neurons having nonlinear dynamics to help understand the information processing mechanisms in the brain. The scale of brain simulations continues to rise as computer performance improves exponentially. However, a simulation of the human whole brain has not yet been achieved as of 2024 due to insufficient computational performance and brain measurement data. This paper examines technological trends in supercomputers, cell type classification, connectomics, and large-scale activity measurements relevant to whole-brain simulation. Based on these trends, we attempt to predict the feasible timeframe for mammalian whole-brain simulation. Our estimates suggest that mouse whole-brain simulation at the cellular level could be realized around 2034, marmoset around 2044, and human likely later than 2044.

Single-cell and spatial omics: exploring hypothalamic heterogeneity

Elucidating the complex dynamic cellular organization in the hypothalamus is critical for understanding its role in coordinating fundamental body functions. Over the past decade, single-cell and spatial omics technologies have significantly evolved, overcoming initial technical challenges in capturing and analyzing individual cells. These high-throughput omics technologies now offer a remarkable opportunity to comprehend the complex spatiotemporal patterns of transcriptional diversity and cell-type characteristics across the entire hypothalamus. Current single-cell and single-nucleus RNA sequencing methods comprehensively quantify gene expression by exploring distinct phenotypes across various subregions of the hypothalamus. However, single-cell/single-nucleus RNA sequencing requires isolating the cell/nuclei from the tissue, potentially resulting in the loss of spatial information concerning neuronal networks. Spatial transcriptomics methods, by bypassing the cell dissociation, can elucidate the intricate spatial organization of neural networks through their imaging and sequencing technologies. In this review, we highlight the applicative value of single-cell and spatial transcriptomics in exploring the complex molecular-genetic diversity of hypothalamic cell types, driven by recent high-throughput achievements.

Single-nucleus and spatial signatures of the brainstem in normal brain and mild traumatic brain injury in male mice

The mammalian brainstem is particularly vulnerable to mild traumatic brain injury (mTBI), which is associated with prolonged autonomic dysfunction and coma. The spatial cellular connections within the brainstem or the mechanisms underlying its response to injury have been underestimated. Here, we performed single-nucleus RNA sequencing with spatial transcriptome sequencing in both normal and mTBI brainstems in male mice, revealing thirty-five neuron and non-neuron clusters. Typically, we identified subtypes of neurons that co-release multiple neurotransmitters, especially in the sagittal midline of the brainstem. Spatially adjacent neurons sharing similar gene expression patterns. The brainstem's response to mTBI has two features: (1) Oligodendrocytes around the fourth ventricle exhibit widespread disconnection at 1-h post-injury, and (2) Injury-related noradrenergic neurons, particularly in their interaction with neurons located in theIRt and the Sol. These findings provides a reference for further integrative investigations of cellular and circuit functions of brainstem.

Whole-brain mapping of basal forebrain cholinergic neurons reveals a long-range reciprocal input-output loop between distinct subtypes

Basal forebrain cholinergic neurons (BFCNs) influence cognition and emotion through specific acetylcholine release in various brain regions, including the prefrontal cortices and basolateral amygdala (BLA). Acetylcholine release is controlled by distinct BFCN subtypes, modulated by excitatory and inhibitory inputs. However, the organization of the whole-brain input-output networks of these subtypes remains unclear. Here, we identified two distinct BFCN subtypes-BFCN→ACA and BFCN→BLA-innervating the anterior cingulate cortex (ACA) and BLA, each with unique distributions, electrophysiological properties, and projection patterns. Combining rabies-virus-assisted mapping and triple-plex RNAscope hybridization, we characterized their whole-brain input networks, identifying unique excitatory and shared inhibitory inputs for these subtypes. Moreover, our results reveal a long-range reciprocal input-output loop: BFCN→ACA neurons target the isocortex, their shared excitatory-input source, whereas BFCN→BLA neurons target shared inhibitory-input sources such as the striatum and pallidum, thus enabling dynamic interactions among these BFCN subtypes. Our study deepens understanding of cholinergic modulation in cognition and emotion and provides insights into their functional interactions.

Specific targeting of brain endothelial cells using enhancer AAV vectors

Brain endothelial cells (BECs) in brain vasculature are critical structural and functional components of the blood brain barrier (BBB). Adeno-associated virus (AAV) capsids have previously been genetically engineered to confer specificity to endothelial cells, but these capsids show limited endothelial cell specificity that varies by delivery conditions. We developed a set of new BEC-enhancer AAV vectors that specifically target BECs based on the cis-regulatory elements identified from single-cell epigenetic datasets. Ex vivo and in vivo characterization of BEC-enhancer AAVs in wild-type, Ai9 reporter, and Alzheimer's disease model mouse brains show their utility for high transduction selectivity of the BECs with little off-target transduction in the liver. Our BEC-enhancer AAVs target the brain vasculature by systemic administration and can be minimally invasive in terms of the route of administration. They are useful new tools for delivering genetic payloads specifically to BECs for normal and diseased brain studies.

Microglia transcriptional states and their functional significance: Context drives diversity

In the brain, microglia are continuously exposed to a dynamic microenvironment throughout life, requiring them to adapt accordingly to specific developmental or disease-related demands. The advent of single-cell sequencing technologies has revealed the diversity of microglial transcriptional states. In this review, we explore the various contexts that drive transcriptional diversity in microglia and assess the extent to which non-homeostatic conditions induce context-specific signatures. We discuss our current understanding and knowledge gaps regarding the relationship between transcriptional states and microglial function, review the influence of complex microenvironments and prior experiences on microglial state induction, and highlight strategies to bridge the gap between mouse and human studies to advance microglia-targeting therapeutics.

Alternatively spliced mini-exon B in PTPδ regulates excitatory synapses through cell-type-specific trans-synaptic PTPδ-IL1RAP interaction

PTPδ, encoded by PTPRD, is implicated in various neurological, psychiatric, and neurodevelopmental disorders, but the underlying mechanisms remain unclear. PTPδ trans-synaptically interacts with multiple postsynaptic adhesion molecules, which involves its extracellular alternatively spliced mini-exons, meA and meB. While PTPδ-meA functions have been studied in vivo, PTPδ-meB has not been studied. Here, we report that, unlike homozygous PTPδ-meA-mutant mice, homozygous PTPδ-meB-mutant (Ptprd-meB-/-) mice show markedly reduced early postnatal survival. Heterozygous Ptprd-meB+/- male mice show behavioral abnormalities and decreased excitatory synaptic density and transmission in dentate gyrus granule cells (DG-GCs). Proteomic analyses identify decreased postsynaptic density levels of IL1RAP, a known trans-synaptic partner of meB-containing PTPδ. Accordingly, IL1RAP-mutant mice show decreased excitatory synaptic transmission in DG-GCs. Ptprd-meB+/- DG interneurons with minimal IL1RAP expression show increased excitatory synaptic density and transmission. Therefore, PTPδ-meB is important for survival, synaptic, and behavioral phenotypes and regulates excitatory synapses in cell-type-specific and IL1RAP-dependent manners.

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

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

Boosting multiplexing capabilities for error-robust spatial transcriptomic methods using a set exchange approach

In the last decades, image-based transcriptomic and proteomic experiments have moved from single-target probes to multiplexed experiments, allowing researchers to study hundreds or even thousands of mRNA and protein targets simultaneously. This large increase in scope necessitates methods in either increased specificity or in error correction, such as the Hamming codes used in the imaging-based spatial transcriptomic method MERFISH. For some experimental conditions, Hamming codes are efficient in encoding the highest possible number of genes for spatial analysis. However, for most experimental parameters, the optimal generation of error-robust codebooks is an unsolved mathematical problem. Here, we present a method to generate highly optimized extended Hamming codebooks compatible with established error-correctable methodologies such as MERFISH. Our method uses an iterative set-exchange approach and generally reaches over 90% of the theoretical maximum limit of gene set complexity. We also provide ready-to-use codebooks and discuss the advantages and disadvantages of changing probe density.

The Duke Mouse Brain Atlas: MRI and light sheet microscopy stereotaxic atlas of the mouse brain

Atlases of the brain are critical resources that make it possible to share data in a common reference frame. Unexpectedly, there is no three-dimensional (3D) stereotaxic atlas of the mouse brain that provides whole brain coverage at macro to single-cell levels. Diffusion tensor images from five perfusion-fixed (in skull) specimens were acquired at 15 micrometers, the highest resolution ever reported. Diffusion tensor imaging yields multiple 3D volumes, each of which highlights unique cytoarchitecture. The averages were mapped into micro-computed tomography of the mouse skull to create external landmarks (bregma and lambda). Light sheet images of the same brains were coregistered, providing cell maps in the same stereotaxic space. The Allen Reference Atlas was registered to the volume to correct the geometric distortion in that atlas and bring it into the stereotaxic space. The resulting multiscalar (13 terabytes) atlas provides a common spatial framework to anneal data across molecular, structural, and functional studies of mice.

Advances in Spatial Omics Technologies

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

Molecular states underlying neuronal cell type development and plasticity in the postnatal whisker cortex

Mouse whisker somatosensory cortex (wS1) is a major model system to study the experience-dependent plasticity of cortical neuron physiology, morphology, and sensory coding. However, the role of sensory experience in regulating neuronal cell type development and gene expression in wS1 remains poorly understood. We assembled a transcriptomic atlas of wS1 during postnatal development comprising 45 molecularly distinct neuronal types that can be grouped into eight excitatory and four inhibitory neuron subclasses. Between postnatal day (P) 12, the onset of active whisking, and P22, when classical critical periods close, ~ 250 genes were regulated in a neuronal subclass-specific fashion when whisker experience was normal. At the resolution of neuronal types, only the composition of layer (L) 2/3 glutamatergic neurons, but not other neuronal types, changed substantially between P12 and P22. These postnatal compositional changes in L2/3 neuronal types resemble those observed previously in the primary visual cortex (V1), and the temporal gene expression changes were also highly conserved between the regions. Unlike V1, however, cell type maturation in wS1 is not substantially dependent on sensory experience, as 10-day full-face whisker deprivation from P12 to P22 did not influence the transcriptomic identity nor composition of L2/3 neuronal types. A one-day competitive whisker deprivation protocol from P21 to P22 also did not affect cell type identity but induced moderate changes in plasticity-related gene expression. Thus, developmental maturation of cell types is similar in V1 and wS1, but sensory deprivation minimally affects cell type development in wS1.

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

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

Sainsc: A Computational Tool for Segmentation-Free Analysis of In Situ Capture Data

Spatially resolved transcriptomics (SRT) has become the method of choice for characterising the complexity of biomedical tissue samples. Until recently, scientists were restricted to SRT methods that can profile a limited set of target genes at high spatial resolution or transcriptome-wide but at a low spatial resolution. Through recent developments, there are now methods that offer both subcellular spatial resolution and full transcriptome coverage. However, utilising these new methods' high spatial resolution and gene resolution remains elusive due to several factors, including low detection efficiency and high computational costs. Here, we present Sainsc (Segmentation-free analysis of in situ capture data), which combines a cell-segmentation-free approach with efficient data processing of transcriptome-wide nanometre-resolution spatial data. Sainsc can generate cell-type maps with accurate cell-type assignment at the nanometre scale, together with corresponding maps of the assignment scores that facilitate interpretation of the local confidence of cell-type assignment. We demonstrate its utility and accuracy for different tissues and technologies. Compared to other methods, Sainsc requires lower computational resources and has scalable performance, enabling interactive data exploration. Sainsc is compatible with common data analysis frameworks and is available as open-source software in multiple programming languages.

Improving Spatial Transcriptomics with Membrane-Based Boundary Definition and Enhanced Single-Cell Resolution

Accurately defining cell boundaries for spatial transcriptomics is technically challenging. The current major approaches are nuclear staining or mathematical inference, which either exclude the cytoplasm or determine a hypothetical boundary. Here, a new method is introduced for defining cell boundaries: labeling cell membranes using genetically coded fluorescent proteins, which allows precise indexing of sequencing spots and transcripts within cells on sections. Use of this membrane-based method greatly increases the number of genes captured in cells compared to the number captured using nucleus-based methods; the numbers of genes are increased by 67% and 119% in mouse and axolotl livers, respectively. The obtained expression profiles are more consistent with single-cell RNA-seq data, demonstrating more rational clustering and apparent cell type-specific markers. Furthermore, improved single-cell resolution is achieved to better identify rare cell types and elaborate spatial domains in the axolotl brain and intestine. In addition to regular cells, accurate recognition of multinucleated cells and cells lacking nuclei in the mouse liver is achieved, demonstrating its ability to analyze complex tissues and organs, which is not achievable using previous methods. This study provides a powerful tool for improving spatial transcriptomics that has broad potential for its applications in the biological and medical sciences.

Spatial Transcriptomics: Biotechnologies, Computational Tools, and Neuroscience Applications

Spatial transcriptomics (ST) represents a revolutionary approach in molecular biology, providing unprecedented insights into the spatial organization of gene expression within tissues. This review aims to elucidate advancements in ST technologies, their computational tools, and their pivotal applications in neuroscience. It is begun with a historical overview, tracing the evolution from early image-based techniques to contemporary sequence-based methods. Subsequently, the computational methods essential for ST data analysis, including preprocessing, cell type annotation, spatial clustering, detection of spatially variable genes, cell-cell interaction analysis, and 3D multi-slices integration are discussed. The central focus of this review is the application of ST in neuroscience, where it has significantly contributed to understanding the brain's complexity. Through ST, researchers advance brain atlas projects, gain insights into brain development, and explore neuroimmune dysfunctions, particularly in brain tumors. Additionally, ST enhances understanding of neuronal vulnerability in neurodegenerative diseases like Alzheimer's and neuropsychiatric disorders such as schizophrenia. In conclusion, while ST has already profoundly impacted neuroscience, challenges remain issues such as enhancing sequencing technologies and developing robust computational tools. This review underscores the transformative potential of ST in neuroscience, paving the way for new therapeutic insights and advancements in brain research.

Spatially resolved mapping of cells associated with human complex traits

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

Segregated basal ganglia output pathways correspond to genetically divergent neuronal subclasses

The basal ganglia control multiple sensorimotor behaviors through anatomically segregated and topographically organized subcircuits with outputs to specific downstream circuits. However, it is unclear how the anatomical organization of basal ganglia output circuits relates to the molecular diversity of cell types. Here, we demonstrate that the major output nucleus of the basal ganglia, the substantia nigra pars reticulata (SNr), is comprised of transcriptomically distinct subclasses that reflect its distinct progenitor lineages. We show that these subclasses are topographically organized within the SNr, project to distinct targets in the midbrain and hindbrain, and receive inputs from different striatal subregions. Finally, we show that these mouse subclasses are also identifiable in human SNr neurons, suggesting that the genetic organization of the SNr is evolutionarily conserved. These findings provide a unifying logic for how the developmental specification of diverse SNr neurons relates to the anatomical organization of basal ganglia circuits controlling specialized downstream brain regions.

Unified integration of spatial transcriptomics across platforms

Spatial transcriptomics (ST) has transformed our understanding of tissue architecture and cellular interactions, but integrating ST data across platforms remains challenging due to differences in gene panels, data sparsity, and technical variability. Here, we introduce L loki , a novel framework for integrating imaging-based ST data from diverse platforms without requiring shared gene panels. L loki addresses ST integration through two key alignment tasks: feature alignment across technologies and batch alignment across datasets. Optimal transport-guided feature propagation adjusts data sparsity to match scRNA-seq references through graph-based imputation, enabling single-cell foundation models such as scGPT to generate unified features. Batch alignment then refines scGPT-transformed embeddings, mitigating batch effects while preserving biological variability. Evaluations on mouse brain samples from five different technologies demonstrate that L loki outperforms existing methods and is effective for cross-technology spatial gene program identification and tissue slice alignment. Applying L loki to five ovarian cancer datasets, we identify an integrated gene program indicative of tumor-infiltrating T cells across gene panels. Together, L loki provides a robust foundation for cross-platform ST studies, with the potential to scale to large atlas datasets, enabling deeper insights into cellular organization and tissue environments.

Hypothalamic PNOC/NPY neurons constitute mediators of leptin-controlled energy homeostasis

Leptin acts in the brain to suppress appetite, yet the responsible neurocircuitries underlying leptin's anorectic effect are incompletely defined. Prepronociceptin (PNOC)-expressing neurons mediate diet-induced hyperphagia and weight gain in mice. Here, we show that leptin regulates appetite and body weight via PNOC neurons, and that loss of leptin receptor (Lepr) expression in PNOC-expressing neurons in the arcuate nucleus of the hypothalamus (ARC) causes hyperphagia and obesity. Restoring Lepr expression in PNOC neurons on a Lepr-null obese background substantially reduces body weight. Lepr inactivation in PNOC neurons increases neuropeptide Y (Npy) expression in a subset of hypothalamic PNOC neurons that do not express agouti-related peptide (Agrp). Selective chemogenetic activation of PNOC/NPY neurons promotes feeding to the same extent as activating all PNOCARC neurons, and overexpression of Npy in PNOCARC neurons promotes hyperphagia and obesity. Thus, we introduce PNOC/NPYARC neurons as an additional critical mediator of leptin action and as a promising target for obesity therapeutics.

CellSP: Module discovery and visualization for subcellular spatial transcriptomics data

Spatial transcriptomics has enabled the study of mRNA distributions within cells, a key aspect of cellular function. However, there is a dearth of tools that can identify and interpret functionally relevant spatial patterns of subcellular transcript distribution. To address this, we present CellSP, a computational framework for identifying, visualizing, and characterizing consistent subcellular spatial patterns of mRNA. CellSP introduces the concept of "gene-cell modules", which are gene sets with coordinated subcellular transcript distributions in many cells. It provides intuitive visualizations of the captured patterns and offers functional insights into each discovered module. We demonstrate that CellSP reliably identifies functionally significant modules across diverse tissues and technologies. We use the tool to discover subcellular spatial phenomena related to myelination, axonogenesis and synapse formation in the mouse brain. We find immune response-related modules that change between kidney cancer and healthy samples, and myelination-related modules specific to mouse models of Alzheimer's Disease.

A single-cell atlas of spatial and temporal gene expression in the mouse cranial neural plate

The formation of the mammalian brain requires regionalization and morphogenesis of the cranial neural plate, which transforms from an epithelial sheet into a closed tube that provides the structural foundation for neural patterning and circuit formation. Sonic hedgehog (SHH) signaling is important for cranial neural plate patterning and closure, but the transcriptional changes that give rise to the spatially regulated cell fates and behaviors that build the cranial neural tube have not been systematically analyzed. Here, we used single-cell RNA sequencing to generate an atlas of gene expression at six consecutive stages of cranial neural tube closure in the mouse embryo. Ordering transcriptional profiles relative to the major axes of gene expression predicted spatially regulated expression of 870 genes along the anterior-posterior and mediolateral axes of the cranial neural plate and reproduced known expression patterns with over 85% accuracy. Single-cell RNA sequencing of embryos with activated SHH signaling revealed distinct SHH-regulated transcriptional programs in the developing forebrain, midbrain, and hindbrain, suggesting a complex interplay between anterior-posterior and mediolateral patterning systems. These results define a spatiotemporally resolved map of gene expression during cranial neural tube closure and provide a resource for investigating the transcriptional events that drive early mammalian brain development.

Connectivity of single neurons classifies cell subtypes in mouse brains

Classification of single neurons at a brain-wide scale is a way to characterize the structural and functional organization of brains. Here we acquired and standardized a large morphology database of 20,158 mouse neurons and generated a potential connectivity map of single neurons based on their dendritic and axonal arbors. With such an anatomy-morphology-connectivity mapping, we defined neuron connectivity subtypes for neurons in 31 brain regions. We found that cell types defined by connectivity show distinct separation from each other. Within this context, we were able to characterize the diversity in secondary motor cortical neurons, and subtype connectivity patterns in thalamocortical pathways. Our findings underscore the importance of connectivity in characterizing the modularity of brain anatomy at the single-cell level. These results highlight that connectivity subtypes supplement conventionally recognized transcriptomic cell types, electrophysiological cell types and morphological cell types as factors to classify cell classes and their identities.

Quantitative characterization of cell niches in spatially resolved omics data

Spatial omics enable the characterization of colocalized cell communities that coordinate specific functions within tissues. These communities, or niches, are shaped by interactions between neighboring cells, yet existing computational methods rarely leverage such interactions for their identification and characterization. To address this gap, here we introduce NicheCompass, a graph deep-learning method that models cellular communication to learn interpretable cell embeddings that encode signaling events, enabling the identification of niches and their underlying processes. Unlike existing methods, NicheCompass quantitatively characterizes niches based on communication pathways and consistently outperforms alternatives. We show its versatility by mapping tissue architecture during mouse embryonic development and delineating tumor niches in human cancers, including a spatial reference mapping application. Finally, we extend its capabilities to spatial multi-omics, demonstrate cross-technology integration with datasets from different sequencing platforms and construct a whole mouse brain spatial atlas comprising 8.4 million cells, highlighting NicheCompass' scalability. Overall, NicheCompass provides a scalable framework for identifying and analyzing niches through signaling events.

Transcriptomic and spatial GABAergic neuron subtypes in zona incerta mediate distinct innate behaviors

Understanding the anatomical connection and behaviors of transcriptomic neuron subtypes is critical to delineating cell type-specific functions in the brain. Here we integrated single-nucleus transcriptomic sequencing, in vivo circuit mapping, optogenetic and chemogenetic approaches to dissect the molecular identity and function of heterogeneous GABAergic neuron populations in the zona incerta (ZI) in mice, a region involved in modulating various behaviors. By microdissecting ZI for transcriptomic and spatial gene expression analyses, our results revealed two non-overlapping Ecel1- and Pde11a-expressing GABAergic neurons with dominant expression in the rostral and medial zona incerta (ZIrEcel1 and ZImPde11a), respectively. The GABAergic projection from ZIrEcel1 to periaqueductal gray mediates self-grooming, while the GABAergic projection from ZImPde11a to the oral part of pontine reticular formation promotes transition from sleep to wakefulness. Together, our results revealed the molecular markers, spatial organization and specific neuronal circuits of two discrete GABAergic projection neuron populations in segregated subregions of the ZI that mediate distinct innate behaviors, advancing our understanding of the functional organization of the brain.

Optimizing Xenium In Situ data utility by quality assessment and best-practice analysis workflows

The Xenium In Situ platform is a new spatial transcriptomics product commercialized by 10x Genomics, capable of mapping hundreds of genes in situ at subcellular resolution. Given the multitude of commercially available spatial transcriptomics technologies, recommendations in choice of platform and analysis guidelines are increasingly important. Herein, we explore 25 Xenium datasets generated from multiple tissues and species, comparing scalability, resolution, data quality, capacities and limitations with eight other spatially resolved transcriptomics technologies and commercial platforms. In addition, we benchmark the performance of multiple open-source computational tools, when applied to Xenium datasets, in tasks including preprocessing, cell segmentation, selection of spatially variable features and domain identification. This study serves as an independent analysis of the performance of Xenium, and provides best practices and recommendations for analysis of such datasets.

Integrating multimodal data to understand cortical circuit architecture and function

In recent years there has been a tremendous growth in new technologies that allow large-scale investigation of different characteristics of the nervous system at an unprecedented level of detail. There is a growing trend to use combinations of these new techniques to determine direct links between different modalities. In this Perspective, we focus on the mouse visual cortex, as this is one of the model systems in which much progress has been made in the integration of multimodal data to advance understanding. We review several approaches that allow integration of data regarding various properties of cortical cell types, connectivity at the level of brain areas, cell types and individual cells, and functional neural activity in vivo. The increasingly crucial contributions of computation and theory in analyzing and systematically modeling data are also highlighted. Together with open sharing of data, tools and models, integrative approaches are essential tools in modern neuroscience for improving our understanding of the brain architecture, mechanisms and function.

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

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

Comprehensive profiling of anaesthetised brain dynamics across phylogeny

The intrinsic dynamics of neuronal circuits shape information processing and cognitive function. Combining non-invasive neuroimaging with anaesthetic-induced suppression of information processing provides a unique opportunity to understand how local dynamics mediate the link between neurobiology and the organism's functional repertoire. To address this question, we compile a unique dataset of multi-scale neural activity during wakefulness and anesthesia encompassing human, macaque, marmoset, mouse and nematode. We then apply massive feature extraction to comprehensively characterize local neural dynamics across > 6 000 time-series features. Using dynamics as a common space for comparison across species, we identify a phylogenetically conserved dynamical profile of anaesthesia that encompasses multiple features, including reductions in intrinsic timescales. This dynamical signature has an evolutionarily conserved spatial layout, covarying with transcriptional profiles of excitatory and inhibitory neurotransmission across human, macaque and mouse cortex. At the network level, anesthetic-induced changes in local dynamics manifest as reductions in inter-regional synchrony. This relationship between local dynamics and global connectivity can be recapitulated in silico using a connectome-based computational model. Finally, this dynamical regime of anaesthesia is experimentally reversed in vivo by deep-brain stimulation of the centromedian thalamus in the macaque, resulting in restored arousal and behavioural responsiveness. Altogether, comprehensive dynamical phenotyping reveals that spatiotemporal isolation of local neural activity during anesthesia is conserved across species and anesthetics.

Synaptic architecture of a memory engram in the mouse hippocampus

Memory engrams are formed through experience-dependent plasticity of neural circuits, but their detailed architectures remain unresolved. Using three-dimensional electron microscopy, we performed nanoscale reconstructions of the hippocampal CA3-CA1 pathway after chemogenetic labeling of cellular ensembles recruited during associative learning. Neurons with a remote history of activity coinciding with memory acquisition showed no strong preference for wiring with each other. Instead, their connectomes expanded through multisynaptic boutons independently of the coactivation state of postsynaptic partners. The rewiring of ensembles representing an initial engram was accompanied by input-specific, spatially restricted upscaling of individual synapses, as well as remodeling of mitochondria, smooth endoplasmic reticulum, and interactions with astrocytes. Our findings elucidate the physical hallmarks of long-term memory and offer a structural basis for the cellular flexibility of information coding.

Single-cell spatial transcriptomic atlas of the whole mouse brain

A comprehensive atlas of genes, cell types, and their spatial distribution across a whole mammalian brain is fundamental for understanding the function of the brain. Here, using single-nucleus RNA sequencing (snRNA-seq) and Stereo-seq techniques, we generated a mouse brain atlas with spatial information for 308 cell clusters at single-cell resolution, involving over 4 million cells, as well as for 29,655 genes. We have identified cell clusters exhibiting preference for cortical subregions and explored their associations with brain-related diseases. Additionally, we pinpointed 155 genes with distinct regional expression patterns within the brainstem and unveiled 513 long non-coding RNAs showing region-enriched expression in the adult brain. Parcellation of brain regions based on spatial transcriptomic information revealed fine structure for several brain areas. Furthermore, we have uncovered 411 transcription factor regulons showing distinct spatiotemporal dynamics during neurodevelopment. Thus, we have constructed a single-cell-resolution spatial transcriptomic atlas of the mouse brain with genome-wide coverage.

SPARROW reveals microenvironment-zone-specific cell states in healthy and diseased tissues

Spatially resolved transcriptomics technologies have advanced our understanding of cellular characteristics within tissue contexts. However, current analytical tools often treat cell-type inference and cellular neighborhood identification as separate and hard clustering processes, limiting comparability across scales and samples. SPARROW addresses these challenges by jointly learning latent embeddings and soft clusterings of cell types and cellular organization. It outperformed state-of-the-art methods in cell-type inference and microenvironment zone delineation and uncovered zone-specific cell states in human and mouse tissues that competing methods missed. By integrating spatially resolved transcriptomics and single-cell RNA sequencing (scRNA-seq) data in a shared latent space, SPARROW achieves single-cell spatial resolution and whole-transcriptome coverage, enabling the discovery of both established and unknown microenvironment zone-specific ligand-receptor interactions in the human tonsil. Overall, SPARROW is a computational framework that provides a comprehensive characterization of tissue features across scales, samples, and conditions.

Decoding Gene Networks Controlling Hypothalamic and Prethalamic Neuron Development

Neuronal subtypes derived from the embryonic hypothalamus and prethalamus regulate many essential physiological processes, yet the gene regulatory networks controlling their development remain poorly understood. Using single-cell RNA- and ATAC-sequencing, we analyzed mouse hypothalamic and prethalamic development from embryonic day 11 to postnatal day 8, profiling 660,000 cells in total. This identified key transcriptional and chromatin dynamics driving regionalization, neurogenesis, and differentiation. This identified multiple distinct neural progenitor populations, as well as gene regulatory networks that control their spatial and temporal identities, and their terminal differentiation into major neuronal subtypes. Integrating these results with large-scale genome-wide association study data, we identified a central role for transcription factors controlling supramammillary hypothalamic development in a broad range of metabolic and cognitive traits. Recurring cross-repressive regulatory relationships were observed between transcription factors that induced prethalamic and tuberal hypothalamic identity on the one hand and mammillary and supramammillary hypothalamic identity on the other. In postnatal animals, Dlx1/2 was found to severely disrupt GABAergic neuron specification in both the hypothalamus and prethalamus, resulting in a loss of inhibition of thalamic neurons, hypersensitivity to cold, and behavioral hyperactivity. By identifying core gene regulatory networks controlling the specification and differentiation of major hypothalamic and prethalamic neuronal cell types, this study provides a roadmap for future efforts aimed at preventing and treating a broad range of homeostatic and cognitive disorders.

A single-cell atlas of spatial and temporal gene expression in the mouse cranial neural plate

The formation of the mammalian brain requires regionalization and morphogenesis of the cranial neural plate, which transforms from an epithelial sheet into a closed tube that provides the structural foundation for neural patterning and circuit formation. Sonic hedgehog (SHH) signaling is important for cranial neural plate patterning and closure, but the transcriptional changes that give rise to the spatially regulated cell fates and behaviors that build the cranial neural tube have not been systematically analyzed. Here we used single-cell RNA sequencing to generate an atlas of gene expression at six consecutive stages of cranial neural tube closure in the mouse embryo. Ordering transcriptional profiles relative to the major axes of gene expression predicted spatially regulated expression of 870 genes along the anterior-posterior and mediolateral axes of the cranial neural plate and reproduced known expression patterns with over 85% accuracy. Single-cell RNA sequencing of embryos with activated SHH signaling revealed distinct SHH-regulated transcriptional programs in the developing forebrain, midbrain, and hindbrain, suggesting a complex interplay between anterior-posterior and mediolateral patterning systems. These results define a spatiotemporally resolved map of gene expression during cranial neural tube closure and provide a resource for investigating the transcriptional events that drive early mammalian brain development.

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.

From morphology to single-cell molecules: high-resolution 3D histology in biomedicine

High-resolution three-dimensional (3D) tissue analysis has emerged as a transformative innovation in the life sciences, providing detailed insights into the spatial organization and molecular composition of biological tissues. This review begins by tracing the historical milestones that have shaped the development of high-resolution 3D histology, highlighting key breakthroughs that have facilitated the advancement of current technologies. We then systematically categorize the various families of high-resolution 3D histology techniques, discussing their core principles, capabilities, and inherent limitations. These 3D histology techniques include microscopy imaging, tomographic approaches, single-cell and spatial omics, computational methods and 3D tissue reconstruction (e.g. 3D cultures and spheroids). Additionally, we explore a wide range of applications for single-cell 3D histology, demonstrating how single-cell and spatial technologies are being utilized in the fields such as oncology, cardiology, neuroscience, immunology, developmental biology and regenerative medicine. Despite the remarkable progress made in recent years, the field still faces significant challenges, including high barriers to entry, issues with data robustness, ambiguous best practices for experimental design, and a lack of standardization across methodologies. This review offers a thorough analysis of these challenges and presents recommendations to surmount them, with the overarching goal of nurturing ongoing innovation and broader integration of cellular 3D tissue analysis in both biology research and clinical practice.

Single-cell technology grows up: Leveraging high-resolution omics approaches to understand neurodevelopmental disorders

The identification of hundreds of neurodevelopmental disorder (NDD) genes in the last decade led to numerous genetic models for understanding NDD gene mutation consequences and delineating putative neurobiological mediators of disease. In parallel, single-cell and single-nucleus genomic technologies have been developed and implemented to create high-resolution atlases of cell composition, gene expression, and circuit connectivity in the brain. Here, we discuss the opportunities to leverage mutant models (or human tissue, where available) and genomics approaches to systematically define NDD etiology at cellular resolution. We review progress in applying single-cell and spatial transcriptomics to interrogate developmental trajectories, cellular composition, circuit activity, and connectivity across human tissue and NDD models. We discuss considerations for implementing these approaches at scale to maximize insights and facilitate reproducibility. Finally, we highlight how standardized application of these technologies promises to not only define etiologies of individual disorders but also identify molecular, cellular, and circuit level convergence across NDDs.

Resolving tissue complexity by multimodal spatial omics modeling with MISO

Spatial molecular profiling has provided biomedical researchers valuable opportunities to better understand the relationship between cellular localization and tissue function. Effectively modeling multimodal spatial omics data is crucial for understanding tissue complexity and underlying biology. Furthermore, improvements in spatial resolution have led to the advent of technologies that can generate spatial molecular data with subcellular resolution, requiring the development of computationally efficient methods that can handle the resulting large-scale datasets. MISO (MultI-modal Spatial Omics) is a versatile algorithm for feature extraction and clustering, capable of integrating multiple modalities from diverse spatial omics experiments with high spatial resolution. Its effectiveness is demonstrated across various datasets, encompassing gene expression, protein expression, epigenetics, metabolomics and tissue histology modalities. MISO outperforms existing methods in identifying biologically relevant spatial domains, representing a substantial advancement in multimodal spatial omics analysis. Moreover, MISO's computational efficiency ensures its scalability to handle large-scale datasets generated by subcellular resolution spatial omics technologies.

Seq-Scope: repurposing Illumina sequencing flow cells for high-resolution spatial transcriptomics

Spatial transcriptomics technologies aim to advance gene expression studies by profiling the entire transcriptome with intact spatial information from a single histological slide. However, the application of spatial transcriptomics is limited by low resolution, limited transcript coverage, complex procedures, poor scalability and high costs of initial setup and/or individual experiments. Seq-Scope repurposes the Illumina sequencing platform for high-resolution, high-content spatial transcriptome analysis, overcoming these limitations. It offers submicrometer resolution, high capture efficiency, rapid turnaround time and precise annotation of histopathology at a much lower cost than commercial alternatives. This protocol details the implementation of Seq-Scope with an Illumina NovaSeq 6000 sequencing flow cell, allowing the profiling of multiple tissue sections in an area of 7 mm × 7 mm or larger. We describe the preparation of a fresh-frozen tissue section for both histological imaging and sequencing library preparation and provide a streamlined computational pipeline with comprehensive instructions to integrate histological and transcriptomic data for high-resolution spatial analysis. This includes the use of conventional software tools for single-cell and spatial analysis, as well as our recently developed segmentation-free method for analyzing spatial data at submicrometer resolution. Aside from array production and sequencing, which can be done in batches, tissue processing, library preparation and running the computational pipeline can be completed within 3 days by researchers with experience in molecular biology, histology and basic Unix skills. Given its adaptability across various biological tissues, Seq-Scope establishes itself as an invaluable tool for researchers in molecular biology and histology.

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.

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.

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

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

Characterization of direct Purkinje cell outputs to the brainstem

Purkinje cells (PCs) primarily project to cerebellar nuclei but also directly innervate the brainstem. Some PC-brainstem projections have been described previously, but most have not been thoroughly characterized. Here, we use a PC-specific cre line to anatomically and electrophysiologically characterize PC projections to the brainstem. PC synapses are surprisingly widespread, with the highest densities found in the vestibular and parabrachial nuclei. However, there are pronounced regional differences in synaptic densities within both the vestibular and parabrachial nuclei. Large optogenetically evoked PC-IPSCs are preferentially observed in subregions with the highest densities of putative PC boutons, suggesting that PCs selectively influence these areas and the behaviors they regulate. Unexpectedly, the pontine central gray and nearby subnuclei also contained a low density of putative PC boutons, and large PC-IPSCs are observed in a small fraction of cells. We combined electrophysiological recordings with immunohistochemistry to assess the molecular identities of two potential PC targets: PC synapses onto mesencephalic trigeminal neurons were not observed even though these cells are in close proximity to PC boutons; PC synapses onto locus coeruleus neurons are exceedingly rare or absent, even though previous studies concluded that PCs are a major input to these neurons. The availability of a highly selective cre line for PCs allowed us to study functional synapses, while avoiding complications that can accompany the use of viral approaches. We conclude that PCs directly innervate numerous brainstem nuclei, and in many nuclei they strongly inhibit a small fraction of cells. This suggests that PCs selectively target cell types with specific behavioral roles in the brainstem.

Data-driven fine-grained region discovery in the mouse brain with transformers

Spatial transcriptomics offers unique opportunities to define the spatial organization of tissues and organs, such as the mouse brain. We address a key bottleneck in the analysis of organ-scale spatial transcriptomic data by establishing a workflow for self-supervised spatial domain detection that is scalable to multimillion cell datasets. This workflow uses a self-supervised framework for learning latent representations of tissue spatial domains or niches. We use a novel encoder-decoder architecture, which we named CellTransformer, to hierarchically learn higher-order tissue features from lower-level cellular and molecular statistical patterns. Coupling our representation learning workflow with minibatched GPU-accelerated clustering algorithms allows us to scale to multi-million cell MERFISH datasets where other methods cannot. CellTransformer is effective at integrating cells across tissue sections, identifying domains highly similar to ones in existing ontologies such as Allen Mouse Brain Common Coordinate Framework (CCF) while allowing discovery of hundreds of uncataloged areas with minimal loss of domain spatial coherence. CellTransformer domains recapitulate previous neuroanatomical studies of areas in the subiculum and superior colliculus, and characterize putatively uncataloged subregions in subcortical areas which currently lack subregion annotation. CellTransformer is also capable of domain discovery in whole-brain Slide-seqV2 datasets. Our workflows enable complex multi-animal analyses, achieving nearly perfect consistency of up to 100 spatial domains in a dataset of four individual mice with nine million cells across more than 200 tissue sections. CellTransformer advances the state of the art for spatial transcriptomics, by providing a performant solution for detection of fine-grained tissue domains from spatial transcriptomics data.

Developing mRNA Nanomedicines with Advanced Targeting Functions

The emerging messenger RNA (mRNA) nanomedicines have sprung up for disease treatment. Developing targeted mRNA nanomedicines has become a thrilling research hotspot in recent years, as they can be precisely delivered to specific organs or tissues to enhance efficiency and avoid side effects. Herein, we give a comprehensive review on the latest research progress of mRNA nanomedicines with targeting functions. mRNA and its carriers are first described in detail. Then, mechanisms of passive targeting, endogenous targeting, and active targeting are outlined, with a focus on various biological barriers that mRNA may encounter during in vivo delivery. Next, emphasis is placed on summarizing mRNA-based organ-targeting strategies. Lastly, the advantages and challenges of mRNA nanomedicines in clinical translation are mentioned. This review is expected to inspire researchers in this field and drive further development of mRNA targeting technology.

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

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

Spatial profiling of the interplay between cell type- and vision-dependent transcriptomic programs in the visual cortex

How early sensory experience during "critical periods" of postnatal life affects the organization of the mammalian neocortex at the resolution of neuronal cell types is poorly understood. We previously reported that the functional and molecular profiles of layer 2/3 (L2/3) cell types in the primary visual cortex (V1) are vision-dependent [S. Cheng et al., Cell 185, 311-327.e24 (2022)]. Here, we characterize the spatial organization of L2/3 cell types with and without visual experience. Spatial transcriptomic profiling based on 500 genes recapitulates the zonation of L2/3 cell types along the pial-ventricular axis in V1. By applying multitasking theory, we suggest that the spatial zonation of L2/3 cell types is linked to the continuous nature of their gene expression profiles, which can be represented as a 2D manifold bounded by three archetypal cell types. By comparing normally reared and dark reared L2/3 cells, we show that visual deprivation-induced transcriptomic changes comprise two independent gene programs. The first, induced specifically in the visual cortex, includes immediate-early genes and genes associated with metabolic processes. It manifests as a change in cell state that is orthogonal to cell-type-specific gene expression programs. By contrast, the second program impacts L2/3 cell-type identity, regulating a subset of cell-type-specific genes and shifting the distribution of cells within the L2/3 cell-type manifold. Through an integrated analysis of spatial transcriptomics with single-nucleus RNA-seq data, we describe how vision patterns cortical L2/3 cell types during the critical period.