IL-1ß Inhibition Partially Negates the Beneficial Effects of Diet-Induced Atherosclerosis Regression in Mice.

BACKGROUND: Thromboembolic events secondary to rupture or erosion of advanced atherosclerotic lesions is the global leading cause of death. The most common and effective means to reduce these major adverse cardiovascular events, including myocardial infarction and stroke, is aggressive lipid lowering via a combination of drugs and dietary modifications. However, we know little regarding the effects of reducing dietary lipids on the composition and stability of advanced atherosclerotic lesions, the mechanisms that regulate these processes, and what therapeutic approaches might augment the benefits of lipid lowering. METHODS: Smooth muscle cell lineage-tracing Apoe-/- mice were fed a high-cholesterol Western diet for 18 weeks and then a zero-cholesterol standard laboratory diet for 12 weeks before treating them with an IL (interleukin)-1ß or control antibody for 8 weeks. We assessed lesion size and remodeling indices, as well as the cellular composition of aortic and brachiocephalic artery lesions, indices of plaque stability, overall plaque burden, and phenotypic transitions of smooth muscle cell and other lesion cells by smooth muscle cell lineage tracing combined with single-cell RNA sequencing, cytometry by time-of-flight, and immunostaining plus high-resolution confocal microscopic z-stack analysis. RESULTS: Lipid lowering by switching Apoe-/- mice from a Western diet to a standard laboratory diet reduced LDL cholesterol levels by 70% and resulted in multiple beneficial effects including reduced overall aortic plaque burden, as well as reduced intraplaque hemorrhage and necrotic core area. However, contrary to expectations, IL-1ß antibody treatment after diet-induced reductions in lipids resulted in multiple detrimental changes including increased plaque burden and brachiocephalic artery lesion size, as well as increasedintraplaque hemorrhage, necrotic core area, and senescence as compared with IgG control antibody-treated mice. Furthermore, IL-1ß antibody treatment upregulated neutrophil degranulation pathways but downregulated smooth muscle cell extracellular matrix pathways likely important for the protective fibrous cap. CONCLUSIONS: Taken together, IL-1ß appears to be required for the maintenance of standard laboratory diet-induced reductions in plaque burden and increases in multiple indices of plaque stability.

Direct comparison of mass cytometry and single-cell RNA sequencing of human peripheral blood mononuclear cells.

Single-cell methods offer a high-resolution approach for characterizing cell populations. Many studies rely on single-cell transcriptomics to draw conclusions regarding cell state and behavior, with the underlying assumption that transcriptomic readouts largely parallel their protein counterparts and subsequent activity. However, the relationship between transcriptomic and proteomic measurements is imprecise, and thus datasets that probe the extent of their concordance will be useful to refine such conclusions. Additionally, novel single-cell analysis tools often lack appropriate gold standard datasets for the purposes of assessment. Integrative (combining the two data modalities) and predictive (using one modality to improve results from the other) approaches in particular, would benefit from transcriptomic and proteomic data from the same sample of cells. For these reasons, we performed single-cell RNA sequencing, mass cytometry, and flow cytometry on a split-sample of human peripheral blood mononuclear cells. We directly compare the proportions of specific cell types resolved by each technique, and further describe the extent to which protein and mRNA measurements correlate within distinct cell types.

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

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.

A hyperparameter-randomized ensemble approach for robust clustering across diverse datasets.

Clustering analysis is widely used to group objects by similarity, but for complex datasets such as those produced by single-cell analysis, the currently available clustering methods are limited by accuracy, robustness, ease of use, and interpretability. To address these limitations, we developed an ensemble clustering method with hyperparameter randomization that outperforms other methods across a broad range of single-cell and synthetic datasets, without the need for manual hyperparameter selection. In addition to hard cluster labels, it also outputs soft cluster memberships to characterize continuum-like regions and per cell overlap scores to quantify the uncertainty in cluster assignment. We demonstrate the improved clustering interpretability from these features by tracing the intermediate stages between handwritten digits in the MNIST dataset, and between tanycyte subpopulations in the hypothalamus. This approach improves the quality of clustering and subsequent downstream analyses for single-cell datasets, and may also prove useful in other fields of data analysis.