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A leading cause of mortality after influenza infection is the development of a secondary bacterial pneumonia. In the absence of a bacterial superinfection, prescribing antibacterial therapies is not indicated but has become a common clinical practice for those presenting with a respiratory viral illness. In a murine model, we found that antibiotic use during influenza infection impaired the lung innate immunologic defenses toward a secondary challenge with methicillin-resistant Staphylococcus aureus (MRSA). Antibiotics augment lung eosinophils, which have inhibitory effects on macrophage function through the release of major basic protein. Moreover, we demonstrated that antibiotic treatment during influenza infection caused a fungal dysbiosis that drove lung eosinophilia and impaired MRSA clearance. Finally, we evaluated 3 cohorts of hospitalized patients and found that eosinophils positively correlated with antibiotic use, systemic inflammation, and worsened outcomes. Altogether, our work demonstrates a detrimental effect of antibiotic treatment during influenza infection that has harmful immunologic consequences via recruitment of eosinophils to the lungs, thereby increasing the risk of developing a secondary bacterial infection.

Cytosine DNA methylation is essential in brain development and is implicated in various neurological disorders. Understanding DNA methylation diversity across the entire brain in a spatial context is fundamental for a complete molecular atlas of brain cell types and their gene regulatory landscapes. Here we used single-nucleus methylome sequencing (snmC-seq3) and multi-omic sequencing (snm3C-seq) 1 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 taxonomy with 4,673 cell groups and 274 cross-modality-annotated subclasses. We identified 2.6 million differentially methylated regions across the genome that represent 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 spatial transcriptomics data validated the association of spatial epigenetic diversity with transcription and improved the anatomical mapping of our epigenetic datasets. Furthermore, chromatin conformation diversities occurred in important neuronal genes and were highly associated with DNA methylation and transcription changes. Brain-wide cell-type comparisons enabled the construction of regulatory networks that incorporate transcription factors, regulatory elements and their potential downstream gene targets. Finally, intragenic DNA methylation and chromatin conformation patterns predicted alternative gene isoform expression observed in a whole-brain SMART-seq 2 dataset. Our study establishes a brain-wide, single-cell DNA methylome and 3D multi-omic atlas and provides a valuable resource for comprehending the cellular–spatial and regulatory genome diversity of the mouse brain. Methylome-based clustering and cross-modality integration with companion datasets from the BRAIN Initiative Cell Census Network enabled the construction of a 3D multi-omic genome atlas of the adult mouse brain featuring thousands of cell-type-specific profiles.

Single-cell analyses parse the brain’s billions of neurons into thousands of ‘cell-type’ clusters residing in different brain structures 1 . Many cell types mediate their functions through targeted long-distance projections allowing interactions between specific cell types. Here we used epi-retro-seq 2 to link single-cell epigenomes and cell types to long-distance projections for 33,034 neurons dissected from 32 different regions projecting to 24 different targets (225 source-to-target combinations) across the whole mouse brain. We highlight uses of these data for interrogating principles relating projection types to transcriptomics and epigenomics, and for addressing hypotheses about cell types and connections related to genetics. We provide an overall synthesis with 926 statistical comparisons of discriminability of neurons projecting to each target for every source. We integrate this dataset into the larger BRAIN Initiative Cell Census Network atlas, composed of millions of neurons, to link projection cell types to consensus clusters. Integration with spatial transcriptomics further assigns projection-enriched clusters to smaller source regions than the original dissections. We exemplify this by presenting in-depth analyses of projection neurons from the hypothalamus, thalamus, hindbrain, amygdala and midbrain to provide insights into properties of those cell types, including differentially expressed genes, their associated cis -regulatory elements and transcription-factor-binding motifs, and neurotransmitter use. This study uses epi-retro-seq to link single-cell epigenomes and cell types to long-distance projections for neurons dissected from different regions projecting to different targets across the whole mouse brain.

Histone modifications underpin the cell-type-specific gene regulatory networks that drive the remarkable cellular heterogeneity of the adult mammalian brain. Here, we profiled four histone modifications jointly with transcriptome in 2.5 million nuclei across multiple adult mouse brain regions. By integrating these data with existing maps of chromatin accessibility, DNA methylation, and 3D genome organization, we established a unified regulatory framework for over 100 brain cell subclasses. This integrative epigenomic atlas annotates 81% of the genome, defining distinct active, primed, and repressive states. Notably, active chromatin states marked by combinatorial histone modifications more precisely identify functional enhancers than chromatin accessibility alone, while Polycomb- and H3K9me3-mediated repression contributes prominently to cell-type-specific regulation. Finally, this multi-modal resource enables deep learning models to predict epigenomic features and gene expression from DNA sequences. This work provides a comprehensive annotation of the mouse brain regulatory genome and a framework for interpreting non-coding variation in complex tissues.

Idiopathic pulmonary fibrosis (IPF) is an age-related, progressive, and fatal interstitial lung disease for which effective therapies remain limited. Alveolar type 2 (AT2) epithelial cells serve as facultative stem cells essential for alveolar repair; however, AT2 cell senescence disrupts epithelial regeneration and contributes to fibrotic remodeling in IPF. Syndecan-1 is a transmembrane heparan sulfate proteoglycan predominantly expressed by lung epithelial cells, but its role in AT2 dysfunction during fibrosis is poorly defined. Here, we demonstrate that syndecan-1 is robustly upregulated in AT2 cells in IPF and other fibrotic lung diseases, as well as in murine bleomycin-induced lung fibrosis. Syndecan-1 expression was further enhanced with aging and associated with increased fibrotic burden in aged mice. Using integrated human transcriptomic analyses, mouse genetic models, and epithelial cell-based systems, we show that excess syndecan-1 promotes cell-autonomous epithelial senescence and impairs AT2 progenitor function. Elevated syndecan-1 reduced AT2 renewal capacity, disrupted differentiation, and diminished surfactant protein C level, whereas genetic loss of syndecan-1 attenuated senescence and preserved epithelial function following injury. Together, these findings identify syndecan-1 as a critical epithelial regulator of AT2 senescence and maladaptive repair in pulmonary fibrosis and support targeting syndecan-1-driven epithelial dysfunction as a potential therapeutic strategy.

The mechanisms regulating transcriptional changes in brain aging remain poorly understood. Here, we use single-cell epigenomics to profile chromatin accessibility and gene expression across eight brain regions in the mouse brain at 2, 9, and 18 months of age. In addition to a significant decline in progenitor cell populations involved in neurogenesis and myelination, we observed widespread and concordant changes of transcription and chromatin accessibility during aging in glial and neuronal cell types. These alterations are accompanied by dysregulation of master transcription factors and a shift toward stress-responsive programs driven by AP-1, indicating a progressive loss of cell identity with aging. We also identify region- and cell-type-specific heterochromatin decay, characterized by increased accessibility at H3K9me3-marked domains, activation of transposable elements, and upregulation of long non-coding RNAs, particularly in glutamatergic neurons. Together, these results reveal age-related disruption of heterochromatin maintenance and transcriptional programs, identify vulnerable brain regions and cell types, and pinpoint key molecular pathways altered in brain aging.

Directly measuring chromatin states alongside transcription is essential for understanding how cell-type-specific regulatory programs are established and maintained in the adult human brain. We present a large-scale single-cell multimodal atlas generated by jointly profiling transcriptome with active (H3K27ac) and repressive (H3K27me3) histone modifications across 18 brain regions. We profile >750,000 nuclei spanning 160 cell types and integrate these data with chromatin accessibility, DNA methylation, 3D genome architecture, and spatial transcriptome. This framework annotates >500,000 regulatory elements and resolves cell-type-specific chromatin states. We link enhancers to target genes, infer gene regulatory networks, and classify chromatin interactions, revealing neuron-enriched long-range Polycomb repression of developmental genes. Integrating these maps with GWAS data and sequence-based model prioritizes noncoding variants, effector genes, and vulnerable cell types for neuropsychiatric disorders. Finally, cross-species comparisons show conserved activation but more divergent repression. Together, this study provides a functional reference for interpreting noncoding variants, epigenetic memory, and brain organization.

The basal ganglia play essential roles in motor control, emotion, learning and reward processing. Their dysfunction contributes to many neurological and psychiatric disorders. However, the gene regulatory programs defining basal ganglia cell-type identity and function remain poorly understood, limiting interpretation of disease-associated non-coding variants. Here, we present the first single-cell multiome atlas of histone modifications and transcriptomes across eight basal ganglia regions from neurotypical adult human donors. Joint profiling reveals cell-type-specific deployment of active and repressive -regulatory elements and gene regulatory networks, and suggests a combinatorial homeobox transcription factor code underlying cell identity. Integration with matched spatial transcriptomic MERFISH data uncovers regional heterogeneity of epigenomic landscapes. Comparative analysis between human and mouse medium spiny neurons uncovers conservation of core gene regulatory features. This atlas interprets non-coding risk variants of neuropsychiatric disorders and supports the development of a deep learning model to predict gene regulation and functional effects of disease-associated variants.

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-seq ) 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 (MERFISH ) 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-seq 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.

Aging is a major risk factor for neurodegenerative diseases, yet underlying epigenetic mechanisms remain unclear. Here, we generated a comprehensive single-nucleus cell atlas of brain aging across multiple brain regions, comprising 132,551 single-cell methylomes and 72,666 joint chromatin conformation-methylome nuclei. Integration with companion transcriptomic and chromatin accessibility data yielded a cross-modality taxonomy of 36 major cell types. We observed that age-related methylation changes were more pronounced in non-neuronal cells. Transposable element methylation alone distinguished age groups, showing cell-type-specific genome-wide demethylation. Chromatin conformation analysis demonstrated age-related increases in TAD boundary strength with enhanced accessibility at CTCF binding sites. Spatial transcriptomics across 895,296 cells revealed regional heterogeneity during aging within identical cell types. Finally, we developed novel deep-learning models that accurately predict age-related gene expression changes using multi-modal epigenetic features, providing mechanistic insights into gene regulation. This dataset advances our understanding of brain aging and offers potential translational applications. Single-cell multi-omic profiling maps the epigenetic and spatial transcriptomic landscape of brain aging across multiple regions. Cell-type-specific genome-wide demethylation of retrotransposable elements correlates with increased chromatin accessibility and expression. Elevated TAD boundary strength emerges as a unique marker of brain aging associated with CTCF gaining accessibility. A novel deep-learning model reveals the significance of epigenetic features on age-related transcriptomic changes across genes.