Three-dimensional mapping and functional analysis of sympathetic innervation in aortic perivascular adipose tissue
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ID: 316563
2026
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Abstract
Abstract Perivascular adipose tissue (PVAT) is a critical regulator of vascular homeostasis, and sympathetic nerves play a fundamental role in vascular function. However, the function of PVAT as an intermediary in neurovascular communication remains poorly understood. Due to the limitations of conventional two-dimensional (2D) imaging and the low tyrosine hydroxylase signal observed at room temperature, we re-evaluated the sympathetic neuroanatomy of aortic PVAT (aPVAT) using volume fluorescence imaging under cold conditions. This approach enabled whole-mount three-dimensional (3D) visualization of the sympathetic network in murine aPVAT. Retrograde tracing was performed to identify neural origins. Cold-exposed mice were assessed for sympathetic activity, plasma norepinephrine levels, and blood pressure fluctuations. The function of aPVAT sympathetic nerves was further examined via local ablation with 6-hydroxydopamine. Our results revealed an undescribed, hierarchically organized sympathetic network, characterized by a primary nerve trunk along the aortic arch that branches into secondary fibers penetrating into the adipose tissue. This innervation exhibited a significant increase in density under cold exposure. Retrograde tracing confirmed the left stellate ganglion as the predominant source, which was shared by major thoracic organs such as the heart and lung. Importantly, local ablation of sympathetic nerves within aPVAT abolished the cold-induced hypertensive response, while ablation of sympathetic nerves within inguinal white adipose tissue had no such effect on hypertension. These findings established sympathetic nerves within aPVAT as a critical source of perivascular innervation and identified this localized neuro-adipovascular circuit as a potential therapeutic target for neurogenic hypertension.
| Reference Key |
openalex_W7164053576
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| Authors | Zi Wang, Yan-Jue Song, Liang Tan, Zhenyu Xu, Ting Meng, Dai-Chen Yao, Ying Liu, Shu-Wen Qian, Qi-Qun Tang, Yan Tang |
| Journal | Life Metabolism |
| Year | 2026 |
| DOI |
10.1093/lifemeta/loag015
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| URL | |
| Keywords | Keywords not found |
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