A molecularly defined and spatially resolved cell atlas of the whole mouse brain

Evolution of gene expression across brain regions in behaviourally divergent deer mice

The evolution of innate behaviours is ultimately due to genetic variation likely acting in the nervous system. Gene regulation may be particularly important because it can evolve in a modular brain-region specific fashion through the concerted action of cis- and trans-regulatory changes. Here, to investigate transcriptional variation and its regulatory basis across the brain, we perform RNA sequencing (RNA-Seq) on ten brain subregions in two sister species of deer mice (Peromyscus maniculatus and P. polionotus)-which differ in a range of innate behaviours, including their social system-and their F1 hybrids. We find that most of the variation in gene expression distinguishes subregions, followed by species. Interspecific differential expression (DE) is pervasive (52-59% of expressed genes), whereas the number of DE genes between sexes is modest overall (~3%). Interestingly, the identity of DE genes varies considerably across brain regions. Much of this modularity is due to cis-regulatory divergence, and while 43% of genes were consistently assigned to the same gene regulatory class across subregions (e.g. conserved, cis- or trans-regulatory divergence), a similar number were assigned to two or more different gene regulatory classes. Together, these results highlight the modularity of gene expression differences and divergence in the brain, which may be key to explain how the evolution of brain gene expression can contribute to the astonishing diversity of animal behaviours.

Spatial multi-omics: novel tools to study the complexity of cardiovascular diseases

Spatial multi-omic studies have emerged as a promising approach to comprehensively analyze cells in tissues, enabling the joint analysis of multiple data modalities like transcriptome, epigenome, proteome, and metabolome in parallel or even the same tissue section. This review focuses on the recent advancements in spatial multi-omics technologies, including novel data modalities and computational approaches. We discuss the advancements in low-resolution and high-resolution spatial multi-omics methods which can resolve up to 10,000 of individual molecules at subcellular level. By applying and integrating these techniques, researchers have recently gained valuable insights into the molecular circuits and mechanisms which govern cell biology along the cardiovascular disease spectrum. We provide an overview of current data analysis approaches, with a focus on data integration of multi-omic datasets, highlighting strengths and weaknesses of various computational pipelines. These tools play a crucial role in analyzing and interpreting spatial multi-omics datasets, facilitating the discovery of new findings, and enhancing translational cardiovascular research. Despite nontrivial challenges, such as the need for standardization of experimental setups, data analysis, and improved computational tools, the application of spatial multi-omics holds tremendous potential in revolutionizing our understanding of human disease processes and the identification of novel biomarkers and therapeutic targets. Exciting opportunities lie ahead for the spatial multi-omics field and will likely contribute to the advancement of personalized medicine for cardiovascular diseases.

Vision sculpts a continuum of L2/3 cell types in the visual cortex during the critical period

We previously reported that vision specifies Layer 2/3 (L2/3) glutamatergic cell-type identity in the primary visual cortex (V1). Using unsupervised clustering of single-nucleus RNA-sequencing data, we identified molecularly distinct L2/3 cell types in normal-reared (NR) and dark-reared (DR) mice, but the two sets exhibited poor correspondence. Here, we show that classification of cell types was confounded in DR by vision-dependent gene programs that are orthogonal to gene programs underlying cell-type identity. A focused clustering analysis successfully matches cell types between DR and NR, suggesting that cell identity-defining gene programs persist under vision deprivation but are overshadowed by vision-dependent transcriptomic variation. Using multi-tasking theory we show that L2/3 cell types form a continuum between three cell-archetypes. Visual deprivation markedly shifts this distribution along the continuum. Thus, dark-rearing markedly influences cell states thereby masking cell-type-identities and changes the distribution of L2/3 types along a transcriptomic continuum.

A spatially-resolved transcriptional atlas of the murine dorsal pons at single-cell resolution

The "dorsal pons", or "dorsal pontine tegmentum" (dPnTg), is part of the brainstem. It is a complex, densely packed region whose nuclei are involved in regulating many vital functions. Notable among them are the parabrachial nucleus, the Kölliker Fuse, the Barrington nucleus, the locus coeruleus, and the dorsal, laterodorsal, and ventral tegmental nuclei. In this study, we applied single-nucleus RNA-seq (snRNA-seq) to resolve neuronal subtypes based on their unique transcriptional profiles and then used multiplexed error robust fluorescence in situ hybridization (MERFISH) to map them spatially. We sampled ~1 million cells across the dPnTg and defined the spatial distribution of over 120 neuronal subtypes. Our analysis identified an unpredicted high transcriptional diversity in this region and pinpointed many neuronal subtypes' unique marker genes. We also demonstrated that many neuronal subtypes are transcriptionally similar between humans and mice, enhancing this study's translational value. Finally, we developed a freely accessible, GPU and CPU-powered dashboard (http://harvard.heavy.ai:6273/) that combines interactive visual analytics and hardware-accelerated SQL into a data science framework to allow the scientific community to query and gain insights into the data.

Developmental Mouse Brain Common Coordinate Framework

3D standard reference brains serve as key resources to understand the spatial organization of the brain and promote interoperability across different studies. However, unlike the adult mouse brain, the lack of standard 3D reference atlases for developing mouse brains has hindered advancement of our understanding of brain development. Here, we present a multimodal 3D developmental common coordinate framework (DevCCF) spanning mouse embryonic day (E) 11.5, E13.5, E15.5, E18.5, and postnatal day (P) 4, P14, and P56 with anatomical segmentations defined by a developmental ontology. At each age, the DevCCF features undistorted morphologically averaged atlas templates created from Magnetic Resonance Imaging and co-registered high-resolution templates from light sheet fluorescence microscopy. Expert-curated 3D anatomical segmentations at each age adhere to an updated prosomeric model and can be explored via an interactive 3D web-visualizer. As a use case, we employed the DevCCF to unveil the emergence of GABAergic neurons in embryonic brains. Moreover, we integrated the Allen CCFv3 into the P56 template with stereotaxic coordinates and mapped spatial transcriptome cell-type data with the developmental ontology. In summary, the DevCCF is an openly accessible resource that can be used for large-scale data integration to gain a comprehensive understanding of brain development.

Cell-type specific molecular signatures of aging revealed in a brain-wide transcriptomic cell-type atlas

Biological aging can be defined as a gradual loss of homeostasis across various aspects of molecular and cellular function. Aging is a complex and dynamic process which influences distinct cell types in a myriad of ways. The cellular architecture of the mammalian brain is heterogeneous and diverse, making it challenging to identify precise areas and cell types of the brain that are more susceptible to aging than others. Here, we present a high-resolution single-cell RNA sequencing dataset containing ~1.2 million high-quality single-cell transcriptomic profiles of brain cells from young adult and aged mice across both sexes, including areas spanning the forebrain, midbrain, and hindbrain. We find age-associated gene expression signatures across nearly all 130+ neuronal and non-neuronal cell subclasses we identified. We detect the greatest gene expression changes in non-neuronal cell types, suggesting that different cell types in the brain vary in their susceptibility to aging. We identify specific, age-enriched clusters within specific glial, vascular, and immune cell types from both cortical and subcortical regions of the brain, and specific gene expression changes associated with cell senescence, inflammation, decrease in new myelination, and decreased vasculature integrity. We also identify genes with expression changes across multiple cell subclasses, pointing to certain mechanisms of aging that may occur across wide regions or broad cell types of the brain. Finally, we discover the greatest gene expression changes in cell types localized to the third ventricle of the hypothalamus, including tanycytes, ependymal cells, and Tbx3+ neurons found in the arcuate nucleus that are part of the neuronal circuits regulating food intake and energy homeostasis. These findings suggest that the area surrounding the third ventricle in the hypothalamus may be a hub for aging in the mouse brain. Overall, we reveal a dynamic landscape of cell-type-specific transcriptomic changes in the brain associated with normal aging that will serve as a foundation for the investigation of functional changes in the aging process and the interaction of aging and diseases.

Single-cell analysis of the nervous system at small and large scales with instant partitions

Single-cell RNA sequencing is a new frontier across all biology, particularly in neuroscience. While powerful for answering numerous neuroscience questions, limitations in sample input size, and initial capital outlay can exclude some researchers from its application. Here, we tested a recently introduced method for scRNAseq across diverse scales and neuroscience experiments. We benchmarked against a major current scRNAseq technology and found that PIPseq performed similarly, in line with earlier benchmarking data. Across dozens of samples, PIPseq recovered many brain cell types at small and large scales (1,000-100,000 cells/sample) and was able to detect differentially expressed genes in an inflammation paradigm. Similarly, PIPseq could detect expected and new differentially expressed genes in a brain single cell suspension from a knockout mouse model; it could also detect rare, virally-la-belled cells following lentiviral targeting and gene knockdown. Finally, we used PIPseq to investigate gene expression in a nontraditional model species, the little skate (Leucoraja erinacea). In total, PIPSeq was able to detect single-cell gene expression changes across models and species, with an added benefit of large scale capture and sequencing of each sample.

A consensus cell type atlas from multiple connectomes reveals principles of circuit stereotypy and variation

The fruit fly Drosophila melanogaster combines surprisingly sophisticated behaviour with a highly tractable nervous system. A large part of the fly's success as a model organism in modern neuroscience stems from the concentration of collaboratively generated molecular genetic and digital resources. As presented in our FlyWire companion paper ¹ , this now includes the first full brain connectome of an adult animal. Here we report the systematic and hierarchical annotation of this ∼130,000-neuron connectome including neuronal classes, cell types and developmental units (hemilineages). This enables any researcher to navigate this huge dataset and find systems and neurons of interest, linked to the literature through the Virtual Fly Brain database ² . Crucially, this resource includes 4,179 cell types of which 3,166 consensus cell types are robustly defined by comparison with a second dataset, the "hemibrain" connectome ³ . Comparative analysis showed that cell type counts and strong connections were largely stable, but connection weights were surprisingly variable within and across animals. Further analysis defined simple heuristics for connectome interpretation: connections stronger than 10 unitary synapses or providing >1% of the input to a target cell are highly conserved. Some cell types showed increased variability across connectomes: the most common cell type in the mushroom body, required for learning and memory, is almost twice as numerous in FlyWire than in the hemibrain. We find evidence for functional homeostasis through adjustments of the absolute amount of excitatory input while maintaining the excitation-inhibition ratio. Finally, and surprisingly, about one third of the cell types recorded in the hemibrain connectome could not be robustly identified in the FlyWire connectome, cautioning against defining cell types based on single connectomes. We propose that a cell type should be robust to inter-individual variation, and therefore defined as a group of cells that are more similar to cells in a different brain than to any other cell in the same brain. We show that this new definition can be consistently applied to whole connectome datasets. Our work defines a consensus cell type atlas for the fly brain and provides both an intellectual framework and open source toolchain for brain-scale comparative connectomics.

High affinity of β-amyloid proteins to cerebral capillaries: implications in chronic lead exposure-induced neurotoxicity in rats

Lead (Pb) is a known environmental risk factor in the etiology of Alzheimer's disease (AD). The existing reports suggest that Pb exposure increases beta-amyloid (Aβ) levels in brain tissues and cerebrospinal fluid (CSF) and facilitates the formation of amyloid plaques, which is a pathological hallmark for AD. Pb exposure has long been associated with cerebral vasculature injury. Yet it remained unclear if Pb exposure caused excessive Ab buildup in cerebral vasculature, which may damage the blood-brain barrier and cause abnormal Ab accumulation. This study was designed to investigate the impact of chronic Pb exposure on Aβ accumulation in cerebral capillary and the expression of low-density lipoprotein receptor protein-1 (LRP1), a critical Aβ transporter, in brain capillary and parenchyma. Sprague-Dawley rats received daily oral gavage at doses of 0, 14 (low-dose), and 27 (high-dose) mg Pb/kg as Pb acetate, 5 d/wk, for 4 or 8 wks. At the end of Pb exposure, a solution containing Aβ40 was infused into the brain via the cannulated internal carotid artery. Data by ELISA showed a strikingly high affinity of Ab to cerebral vasculature, which was approximately 7-14 times higher than that to the parenchymal fractions collected from control brains. Pb exposure further aggravated the Aβ accumulation in cerebral vasculature in a dose-dependent manner. Western blot analyses revealed that Pb exposure decreased LRP1 expression in cortical capillaries and hippocampal parenchyma. Immunohistochemistry (IHC) studies further revealed a disrupted distribution of LRP1 alongside hippocampal vasculature accompanied with a decreased expression in hippocampal neurons by Pb exposure. Taken together, the current study demonstrated that the cerebral vasculature naturally possessed a high affinity to Aβ present in circulating blood. Pb exposure significantly increased Aβ accumulation in cerebral vasculature; such an increased Aβ accumulation was due partly to the diminished expression of LRP1 in response to Pb in tested brain regions. Perceivably, Pb-facilitated Ab aggravation in cerebral vasculature may contribute to Pb-associated amyloid alterations.

Single-cell DNA Methylome and 3D Multi-omic Atlas of the Adult Mouse Brain

Cytosine DNA methylation is essential in brain development and has been implicated in various neurological disorders. A comprehensive understanding of DNA methylation diversity across the entire brain in the context of the brain's 3D spatial organization is essential for building a complete molecular atlas of brain cell types and understanding their gene regulatory landscapes. To this end, we employed optimized single-nucleus methylome (snmC-seq3) and multi-omic (snm3C-seq1) sequencing technologies to generate 301,626 methylomes and 176,003 chromatin conformation/methylome joint profiles from 117 dissected regions throughout the adult mouse brain. Using iterative clustering and integrating with companion whole-brain transcriptome and chromatin accessibility datasets, we constructed a methylation-based cell type taxonomy that contains 4,673 cell groups and 261 cross-modality-annotated subclasses. We identified millions of differentially methylated regions (DMRs) across the genome, representing potential gene regulation elements. Notably, we observed spatial cytosine methylation patterns on both genes and regulatory elements in cell types within and across brain regions. Brain-wide multiplexed error-robust fluorescence in situ hybridization (MERFISH2) data validated the association of this spatial epigenetic diversity with transcription and allowed the mapping of the DNA methylation and topology information into anatomical structures more precisely than our dissections. Furthermore, multi-scale chromatin conformation diversities occur in important neuronal genes, highly associated with DNA methylation and transcription changes. Brain-wide cell type comparison allowed us to build a regulatory model for each gene, linking transcription factors, DMRs, chromatin contacts, and downstream genes to establish regulatory networks. Finally, intragenic DNA methylation and chromatin conformation patterns predicted alternative gene isoform expression observed in a companion whole-brain SMART-seq3 dataset. Our study establishes the first brain-wide, single-cell resolution DNA methylome and 3D multi-omic atlas, providing an unparalleled resource for comprehending the mouse brain's cellular-spatial and regulatory genome diversity.