bioRxiv Science⌕ Search

bioRxiv · 10.1101/2025.01.24.634752

Single cell approaches define the murine leptomeninges:cortical brain interface as a distinct cellular neighborhood comprised of neural and nonneural cell types

Abstract

The interface between the brain surface and the adjacent meninges is a selective barrier regulating fluid, protein and immune cell exchange between the CNS and periphery. However, the cell types that form this important interface are not yet fully defined. To address this limitation, we have used single cell RNA-sequencing (scRNA-seq) and single cell spatial transcriptomics together with morphological lineage tracing and immunostaining to analyze the adult murine cortex. We show that the cortical interface is comprised of three major cell types, leptomeningeal cells, border astrocytes and tissue-resident macrophages. On the peripheral side the interface is comprised of transcriptionally-distinct PDGFR-positive leptomeningeal mesenchymal cells that are intermingled with macrophages. This leptomeningeal pial layer is lined by a population of transcriptionally-distinct border astrocytes. The interface neighborhood is rich in growth factor mRNAs, including many leptomeningeal ligands predicted to act on both the border astrocytes and macrophages. On the CNS side of the interface is the relatively cell-sparse cortical layer one containing interneurons, microglia, parenchymal astrocytes, oligodendrocyte precursor cells and oligodendrocytes. Except for the border astrocytes, layer one cells are not closely-associated with the interface, suggesting that secreted ligands may be the major way the brain interface communicates with the underlying cortical parenchyma. Thus, our data provide a molecular/cellular resource describing the brain interface cell types and their interactions, thereby enabling future studies asking how this distinct cellular compartment regulates CNS:periphery interactions. Significance StatementRecent years have seen significant progress in identifying the diverse cell types within the meningeal space. However, the mechanisms by which these cells interact with glial and neuronal cells in layer one of the adult murine cortex remain poorly understood. During development, communication between radial precursors and meningeal layers is crucial for proper brain formation, but the role of this interaction in adulthood is still unclear. Additionally, how resident immune cells in the leptomeningeal space signal to layer one cortical cells or meningeal mesenchymal cells during homeostasis remains an open question. Understanding the identity, location, and interactions of these cells is essential for unraveling the complex dynamics at this critical brain interface.

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Ebert, S. N., Eisner, C., Karamboulas, K., Bernier, L.-P., Kaplan, D. R., MacVicar, B. A., Miller, F. D.. 2025-01-25. Single cell approaches define the murine leptomeninges:cortical brain interface as a distinct cellular neighborhood comprised of neural and nonneural cell types. https://doi.org/10.1101/2025.01.24.634752

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

The Unreasonable Effectiveness of Cell Types in Describing Neuronal Physiological Features

Single-cell RNA sequencing (scRNA-seq) captures detailed gene expression profiles at scale, while patch-clamp recordings measure intrinsic neuronal electrophysiological properties. Modeling the relations between these two modalities remains a challenge. Here, we compare how well electrophysiological features can be predicted by traditional transcriptomic cell type classification, representations derived from a foundational model (scGPT) pretrained on large-scale scRNA-seq datasets, ion channel-coding genes, and highly variable genes. Using paired transcriptomic and electrophysiological patch-sequencing data from 495 human neurons from neurosurgical tissue, we find that cluster-level cell type representations consistently outperform highly variable gene selection, ion channel gene selection, and context-enriched scGPT embeddings. Notably, performance varies across model architectures and initializations, and the best results are obtained by combining the outputs of separate cell type and scGPT-based models. Together, these findings suggest that traditional discrete cellular classification is highly effective in predicting physiological features. For maximum performance it can be complemented by pretrained transformer models.

neuroscience↗

A nonlinear inhibition pathway underlying cortical responses to tuned holographic optogenetic perturbations

Optogenetics enables causal manipulation of cortical activity. Perturbation responses can be counterintuitive due to network interactions, making theory essential for predicting them. Existing approaches often rely on linear approximations, which fail for many biologically relevant perturbations. Here we develop a nonlinear theory of responses to holographic perturbations in cell-type-specific recurrent networks with structured connectivity. We fit a nonlinear model to mouse V1 data, which shows cotuned-ensemble suppression: perturbing spatially clustered neurons with similar preferred orientations yields markedly stronger short-range suppression than perturbing untuned ensembles. We show that cotuned-ensemble suppression arises from a feature-tuned, nonlinear inhibition pathway implicating somatostatin-positive (SST) interneurons. The theory predicts that cotuned ensembles suppress parvalbumin-positive (PV) neurons but facilitate SST neurons, and links the degree of cotuned-ensemble suppression or facilitation to the variance of the SST response. This framework identifies mechanisms by which nonlinear inhibition sculpts cortical dynamics and establishes a predictive basis for targeted optogenetic interventions.

neuroscience↗

Proteomic signatures of APOE ε4 across human tissues and cell types in Alzheimers disease

The apolipoprotein E {varepsilon}4 (APOE {varepsilon}4) allele is the strongest genetic risk factor for late-onset Alzheimers disease (AD). However, the underlying molecular mechanisms remain unclear. This study included 1691 participants from the Religious Orders Study and Rush Memory and Aging Project (ROSMAP), 1226 participants from the Accelerating Medicines Partnership - Alzheimers Disease (AMP-AD) Diverse Cohorts Study, and 735 participants from the Alzheimers Disease Neuroimaging Initiative (ADNI). To characterise APOE {varepsilon}4 molecular effects, we analysed proteomic data from plasma, cerebrospinal fluid (CSF), and induced pluripotent stem cell (iPSC)-derived astrocytes and neurons, as well as transcriptomic and proteomic data from multiple brain regions. The association of APOE {varepsilon}4 with AD neuropathology was also examined. APOE {varepsilon}4 carriers shared a plasma proteomic signature enriched for immune processes, irrespective of AD diagnosis. A machine learning classifier trained on this signature discriminated APOE {varepsilon}4 carriers from non-carriers in an independent cohort using CSF proteomics. APOE {varepsilon}4 carriage was associated with higher Braak stages and Consortium to Establish a Registry for Alzheimers Disease (CERAD) score. However, only limited APOE {varepsilon}4-associated transcriptomic and proteomic changes were observed in bulk brain tissue, with poor cross-layer concordance. Proteomic analyses of iPSC-derived astrocytes and neurons further revealed cell-type-specific APOE {varepsilon}4-associated changes. APOE {varepsilon}4 is associated with a consistent proteomic signature across plasma and CSF. Its molecular effects in the brain differ across cell types, brain regions and molecular layers. These findings support the need for cell-type-resolved multi-omic studies to elucidate how APOE {varepsilon}4 confers AD risk.

neuroscience↗