Single-cell Technologies in Atherosclerosis: Uncovering Cellular Heterogeneity, Mechanisms, and Therapeutic Opportunities.

The Hidden Architecture of Arterial Decay

Atherosclerosis—the buildup of lipids and chronic inflammation in the arteries—is the main driver of heart attacks and strokes. For decades, doctors viewed this mostly as a lipid-storage disorder. They saw it as a mechanical clogging of the pipes by excess fats. However, modern biology reframes it as a complex, immune-mediated inflammatory disease. It is driven by a chaotic dialogue between vascular, immune, and stromal (structural) cells.

To treat this disease, we must understand the behavior of individual cells within the arterial wall. Studying these cells has historically been difficult. It is like trying to understand a crowded stadium by listening to the roar from outside the gates. You can hear the volume, but you cannot distinguish individual voices. Researchers are now using advanced sequencing to look at individual cells inside artery plaques. This granular approach aims to reveal how different cells behave. This could eventually pave the way for personalized treatments for heart disease.

The limits of the "bulk" perspective

A fundamental question is how to map the biological state of individual cell populations within a plaque. Because human atherosclerotic tissue is hard to access, researchers struggle to characterize rare cell types. They also struggle to track the rapid shifts in cell identity that occur as a plaque progresses.

Previously, the field relied on "bulk" analysis. This measures the average gene expression across an entire tissue sample. This approach assumes that cells within a tissue behave somewhat uniformly. However, this creates significant blind spots. If a single, aggressive subtype of macrophage drives inflammation, its signal might be lost. A bulk sample would wash out that signal among thousands of quieter cells. The old model fails to account for cellular heterogeneity (the variety of cell types) and plasticity (the ability of a cell to change its identity).

Decoding the plaque cell by cell

The investigation into this complexity relies on "omics" technologies. These tools strip away the averaging effect of bulk studies. The most prominent is scRNA-seq (single-cell RNA sequencing). This profiles the gene-expression programs of thousands of individual cells. It helps identify unique cell subtypes. Researchers also employ scATAC-seq (single-cell Assay for Transposase-Accessible Chromatin). This technique maps chromatin accessibility (how accessible DNA is for reading). This allows scientists to see the regulatory blueprints of a cell.

To move beyond lists of genes, researchers use spatial transcriptomics and spatial proteomics. Standard single-cell methods require dissociating (breaking apart) the tissue into a liquid. This process loses all information about where a cell lived. Spatial methods preserve the tissue's architecture instead. This allows for the mapping of "neighborhoods." Examples include macrophage-rich necrotic cores or clusters of endothelial cells undergoing EndMT (endothelial-to-mesenchymal transition, where lining cells turn into scar-forming cells). By combining these layers, the authors describe a way to reconstruct the entire communication network of the plaque.

A spectrum of identity, not a binary choice

These tools have rewritten the census of the atherosclerotic plaque. As shown in, macrophages and smooth muscle cells (SMCs) are the dominant populations. However, their internal diversity is much greater than once thought.

The most striking finding involves the dismantling of the traditional M1/M2 macrophage classification. Instead of two simple categories—pro-inflammatory and anti-inflammatory—the authors report a continuous spectrum of subsets. Among these are TREM2+ foam cells (macrophages filled with lipids). There are also specialized subsets for iron handling (SPIC+) or matrix degradation (MMP9+).

The study also reveals profound plasticity in smooth muscle cells. Rather than staying static, SMCs can lose their contractile markers. They can assume entirely different identities, such as macrophage-like or fibroblast-like cells. This transition is governed by regulators like KLF4 and TCF21. The paper finds these shifts are regulated biological programs. They can either stabilize a plaque by forming a protective cap or destabilize it by promoting inflammation.

From high-resolution maps to precision medicine

Moving from seeing a "crowd" to seeing "individuals" changes clinical intervention. If these single-cell signatures are identified, the implications are twofold.

First, it enables the development of specific biomarkers. For example, doctors might detect circulating monocytes in the blood. These cells could carry the specific signature of an inflammatory plaque macrophage. This might help identify high-risk patients before a blockage is visible on a scan. Second, it opens the door to "precision immunomodulation." Instead of using broad anti-inflammatories, future therapies could target specific molecular pathways. One could target the KLF4 regulator to prevent harmful smooth muscle cell transitions.

However, moving from the lab to the clinic faces hurdles. The authors note that the field requires standardized protocols. This ensures results are reproducible across different labs. More importantly, we need longitudinal data (studies over time). We must understand how these cell states change throughout the disease lifecycle. The next step is likely developing real-time, in vivo (inside a living organism) single-cell imaging. This would allow us to observe cellular transformations as they happen.

Figures from the paper