Abstract Body: Aim
Indication for surgical intervention to treat carotid artery stenosis is predominantly based on the stenosis degree and occurrence of clinical symptoms. This approach overlooks crucial other aspects of plaque stability, including the imminent risk of rupture. Therefore, we aim to establish new stability-defining markers by integrating histological patterns, genomic and proteomic data, as well as imaging insights to redefine the clinical assessment of plaque stability.
Methods
We use histological image analysis (mainly Elastica-van-Gieson and Hematoxilin-Eosin staining) to detect features using clusters derived from Self-Supervised Learning models. This clustering highlights regions of interest, which are further examined to associate structural features to biological processes. These histological findings are integrated with transcriptomic data, including single-cell RNA sequencing, bulk RNA-sequencing, and targeted spatial transcriptomics. By combining the structural and molecular information, we are identifying key transcripts and build a “transcript-panel” to characterize the stability and rupture-prone nature of plaques. Spatial and subregion-specific proteomics (deep visual proteomics, subregional analysis of fibrous cap, necrotic core and media) complements this analysis, enabling a deeper understanding of biological processes within the identified clusters.
Results
Our analysis reveals strong correlations between histological features (such as elastin/collagen distribution and hemorrhagic patterns) and molecular profiles from sequencing data. These predefined clusters provide the framework for combining structural findings to transcriptomic and proteomic signatures, as well as the linkage to imaging data.
We are currently validating these findings using our CT and MRI imaging database to test whether our novel stability-defining patterns can also be detected in clinical imaging, enhancing their translational value for therapeutic decision-making.
Conclusions
By combining histological imaging with multi-omics, we aim to redefine human atherosclerotic plaque stability beyond the stenosis degree and occurrence of symptoms. This approach provides a robust framework for biomarker identification and offers the potential to bridge laboratory findings with clinical applications, ultimately improving patient outcomes.