{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T13:31:23Z","timestamp":1787923883797,"version":"build-2784847793"},"reference-count":71,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T00:00:00Z","timestamp":1787875200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Immunol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Macrophages play an indispensable role in the pathogenesis of aortic dissection (AD). However, the underlying molecular and cellular mechanisms remain incompletely understood.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We integrated six publicly available single-cell RNA sequencing (scRNA-seq) datasets and constructed an atlas of AD, identifying an AD-enriched ANGPTL4+ macrophage subpopulation. Subsequent comprehensive bioinformatic analyses explored its potential mechanisms in AD. Using machine learning (ML) algorithms on integrated bulk RNA sequencing (bulk RNA-seq) data, we screened the signature genes of ANGPTL4+ macrophage and constructed a clinical prediction model. The correlation between the proportion of ANGPTL4+ macrophage and clinical features was investigated after CIBERSORTx deconvolution. Finally, we performed\n                      <jats:italic>in vitro<\/jats:italic>\n                      experiments to validate our findings.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Bioinformatic analyses revealed that ANGPTL4+ macrophage was predominantly present in AD, exhibited a distinct glycolytic phenotype, represented an early monocyte-derived macrophage state in the dissected aorta, predicted to be regulated by PPARG, and showed inferred ligand-receptor interactions with structural cells through ANGPTL and SPP1 signaling pathways. ML algorithms identified ANGPTL4, PHLDA1, and SERPINE1 as signatures of ANGPTL4+ macrophage. The proportion of ANGPTL4+ macrophage was positively associated with clinical metrics like monocyte count.\n                      <jats:italic>In vitro<\/jats:italic>\n                      experiments validated the remarkable enrichment of ANGPTL4+ macrophage and considerable up-regulation and co-localization of the signatures.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>In-depth investigation into the potential mechanisms of ANGPTL4+ macrophage, an AD-enriched macrophage subpopulation, and its signatures ANGPTL4, PHLDA1, and SERPINE1, provides novel insights into the pathogenesis and prospective therapeutic targets of AD.<\/jats:p>\n                  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