Search bioRxiv⌕ Search

bioRxiv · 10.1101/2024.07.27.605444

Characterizing microglial signaling dynamics during inflammation using single-cell mass cytometry

Abstract

Microglia play a critical role in maintaining central nervous system (CNS) homeostasis and display remarkable plasticity in their response to inflammatory stimuli. However, the specific signaling profiles that microglia adopt during such challenges remain incompletely understood. Traditional transcriptomic approaches provide valuable insights, but fail to capture dynamic post-translational changes. In this study, we utilized time-resolved single-cell mass cytometry (CyTOF) to measure distinct signaling pathways activated in microglia upon exposure to bacterial and viral mimetics--lipopolysaccharide (LPS) and polyinosinic-polycytidylic acid (Poly(I:C)), respectively. Furthermore, we evaluated the immunomodulatory role of astrocytes on microglial signaling in mixed cultures. Microglia or mixed cultures derived from neonatal mice were treated with LPS or Poly(I:C) for 48 hrs. Cultures were stained with a panel of 33 metal-conjugated antibodies targeting signaling and identity markers. High-dimensional clustering analysis was used to identify emergent signaling modules. We found that LPS treatment led to more robust early activation of pp38, pERK, pRSK, and pCREB compared to Poly(I:C). Despite these differences, both LPS and Poly(I:C) upregulated the classical activation markers CD40 and CD86 at later time-points. Strikingly, the presence of astrocytes significantly blunted microglial responses to both stimuli, particularly dampening CD40 upregulation. Our studies demonstrate that single-cell mass cytometry effectively captures the dynamic signaling landscape of microglia under pro-inflammatory conditions. This approach may pave the way for targeted therapeutic investigations of various neuroinflammatory disorders. Moreover, our findings underscore the necessity of considering cellular context, such as astrocyte presence, in interpreting microglial behavior during inflammation. Main PointsTime-resolved single cell mass cytometry delineates microglial signaling pathways following LPS or Poly(I:C) treatment. Astrocyte presence led to selective reduction of key microglial signaling nodes along with terminal inflammatory profiles.

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Kumar, S., Kahle, A. D., Keeler, A. B., Zunder, E. R., Deppmann, C. D.. 2024-07-29. Characterizing microglial signaling dynamics during inflammation using single-cell mass cytometry. https://doi.org/10.1101/2024.07.27.605444

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↗