Multi‐omics integration and machine learning identify an ACKR1+ endothelial cell‐based lactate‐immune signature associated with carotid plaque vulnerability and progression
- 1Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College
- 2City University of Macau
Abstract
Background Early identification of vulnerable carotid plaques is crucial for stroke prevention, but the upstream metabolic processes linked to endothelial transition and immune recruitment remain poorly defined. We characterised lactate metabolism in ACKR1+ endothelial cells (ECs) in human carotid plaques and derived a machine‐learning signature for discriminating plaque vulnerability. Methods Internal and external (GSE253903) single‐cell datasets were integrated to identify endothelial subpopulations, and bulk transcriptomic (GSE163154) and in‐house proteomic data were used to assess metabolism–immune correlations. Least absolute shrinkage and selection operator (LASSO) regression selected core features for a logistic model, which was locked and applied to three independent cohorts: GSE43292 (Stary stage ≥ IV vs. I–II), GSE28829 (early vs. advanced lesions) and GSE100927 (normal vs. atherosclerotic arteries). Key molecules were validated in situ by immunohistochemistry (IHC) and spatial transcriptomics in independent plaques (five stable, five unstable). Results An ACKR1+ EC subpopulation linked to neovascularisation showed hyperactive glycolysis and enhanced lactate metabolism. Computational inference linked lactate accumulation to HIF‐1α stability, CXCL12 release and CD8+ T‐cell infiltration via the CXCL12–CXCR4 axis, and the monocarboxylate transporters SLC16A3 and SLC16A4 emerged as quantitative readouts of this ‘lactate–immune’ state. In unstable plaques, IHC confirmed higher atypical chemokine receptor 1 (ACKR1; geometric mean ratio [GMR] = 6.82), CD8+ T‐cell density (GMR = 4.63), SLC16A3 (GMR = 4.74) and HIF‐1α (GMR = 4.02; all q < .05), with lactylation elevated in the lipid core. A four‐feature model (ACKR1, SLC16A3, SLC16A4, CD8+ T‐cell cytotoxicity score) performed well in training and retained discrimination in the progression‐stratified cohorts, while in GSE100927 it showed high disease‐identification performance across three arterial beds. Conclusions Lactate metabolic reprogramming under hypoxia is associated with ACKR1+ EC‐related CD8+ T‐cell recruitment and plaque instability. This chain is an association‐based hypothesis requiring functional confirmation. The signature discriminates robustly at both the vulnerability‐stratification and disease‐identification levels, providing a quantifiable framework for molecular classification of atherosclerotic plaques.
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BibTeX
@article{zhang2026multiomics,
title = {{Multi‐omics integration and machine learning identify an ACKR1+ endothelial cell‐based lactate‐immune signature associated with carotid plaque vulnerability and progression}},
author = {Ji Zhang and Qijian Zhao and Yufei Wu and Keqiang Shu and Junye Chen and Kang Li and Yuyao Feng and Xu Zhang and Wenjian Liu and Zhichao Lai and Bao Liu},
journal = {Clinical and translational medicine},
year = {2026},
month = oct,
doi = {10.1002/ctm2.70856},
url = {https://europepmc.org/article/PMC/PMC13669426},
note = {CC BY},
}
RIS
TY - JOUR
TI - Multi‐omics integration and machine learning identify an ACKR1+ endothelial cell‐based lactate‐immune signature associated with carotid plaque vulnerability and progression
AU - Ji Zhang
AU - Qijian Zhao
AU - Yufei Wu
AU - Keqiang Shu
AU - Junye Chen
AU - Kang Li
AU - Yuyao Feng
AU - Xu Zhang
AU - Wenjian Liu
AU - Zhichao Lai
AU - Bao Liu
PY - 2026
DA - 2026/10/01
JO - Clinical and translational medicine
DO - 10.1002/ctm2.70856
UR - https://europepmc.org/article/PMC/PMC13669426
AB - Background Early identification of vulnerable carotid plaques is crucial for stroke prevention, but the upstream metabolic processes linked to endothelial transition and immune recruitment remain poorly defined. We characterised lactate metabolism in ACKR1+ endothelial cells (ECs) in human carotid plaques and derived a machine‐learning signature for discriminating plaque vulnerability. Methods Internal and external (GSE253903) single‐cell datasets were integrated to identify endothelial subpopulations, and bulk transcriptomic (GSE163154) and in‐house proteomic data were used to assess metabolism–immune correlations. Least absolute shrinkage and selection operator (LASSO) regression selected core features for a logistic model, which was locked and applied to three independent cohorts: GSE43292 (Stary stage ≥ IV vs. I–II), GSE28829 (early vs. advanced lesions) and GSE100927 (normal vs. atherosclerotic arteries). Key molecules were validated in situ by immunohistochemistry (IHC) and spatial transcriptomics in independent plaques (five stable, five unstable). Results An ACKR1+ EC subpopulation linked to neovascularisation showed hyperactive glycolysis and enhanced lactate metabolism. Computational inference linked lactate accumulation to HIF‐1α stability, CXCL12 release and CD8+ T‐cell infiltration via the CXCL12–CXCR4 axis, and the monocarboxylate transporters SLC16A3 and SLC16A4 emerged as quantitative readouts of this ‘lactate–immune’ state. In unstable plaques, IHC confirmed higher atypical chemokine receptor 1 (ACKR1; geometric mean ratio [GMR] = 6.82), CD8+ T‐cell density (GMR = 4.63), SLC16A3 (GMR = 4.74) and HIF‐1α (GMR = 4.02; all q < .05), with lactylation elevated in the lipid core. A four‐feature model (ACKR1, SLC16A3, SLC16A4, CD8+ T‐cell cytotoxicity score) performed well in training and retained discrimination in the progression‐stratified cohorts, while in GSE100927 it showed high disease‐identification performance across three arterial beds. Conclusions Lactate metabolic reprogramming under hypoxia is associated with ACKR1+ EC‐related CD8+ T‐cell recruitment and plaque instability. This chain is an association‐based hypothesis requiring functional confirmation. The signature discriminates robustly at both the vulnerability‐stratification and disease‐identification levels, providing a quantifiable framework for molecular classification of atherosclerotic plaques.
SN - 2001-1326
ER -