What question did this study set out to answer?

The aim is to identify non-invasive biomarkers for diagnosing vulnerable plaques related to acute coronary syndrome.

February 8, 2026Journal of Cardiovascular Translational Research0 citationsOpen Access

Single-cell Transcriptomic Profiling Reveals Diagnostic of T Cell-platelet Aggregates in Peripheral Blood for Coronary Vulnerable Plaques

DYDiru YaoChinese Academy of Sciences PMPeina MengNanjing Medical University BHBin HuangSoutheast University

Key Points

The aim is to identify non-invasive biomarkers for diagnosing vulnerable plaques related to acute coronary syndrome.
Utilized single-cell RNA sequencing of peripheral blood mononuclear cells from CAD patients and controls.
Conducted high dimensional weighted gene co-expression network analysis to identify hub genes.
Employed machine learning techniques for biomarker screening, including LASSO and SVM-RFE.
Circulating T cell-platelet aggregates were significantly enriched in patients with vulnerable plaques.
Linked T cell-platelet aggregates to plaque instability through pro-inflammatory pathways.
Identified potential biomarkers EPHB6, STAT1, RPL23, IKZF3, and AHCY for non-invasive vulnurable plaque detection.

Abstract

Acute coronary syndrome, driven by vulnerable plaque (VP) instability, is a major cause of cardiovascular mortality. Current diagnostic methods for VPs are limited by invasiveness or low specificity, highlighting the need for non-invasive biomarkers. Using single-cell RNA sequencing (scRNA-seq) of peripheral blood mononuclear cells (PBMCs) from coronary artery disease (CAD) patients with VPs and controls, we identified circulating T cell-platelet aggregates (TPAs) significantly enriched in VP patients and linked to plaque instability via pro-inflammatory pathways. Through high dimensional weighted gene co-expression network analysis, we discovered TPAs' hub genes and demonstrated their role in plaque destabilization. Furthermore, employing machine learning, including Boruta, least absolute shrinkage and selection operator (LASSO) regression and support vector machine-recursive feature elimination (SVM-RFE), we screened for five blood biomarkers that can serve as diagnostic indicators for VPs. Our study demonstrates that TPAs are critically involved in VPs formation. Furthermore, we identified EPHB6, STAT1, RPL23, IKZF3 and AHCY as potential circulating biomarkers for non-invasive detection of VPs.

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