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大尺度光声显微镜长期成像的伤口血管生成。

Long-Term Imaging of Wound Angiogenesis with Large Scale Optoacoustic Microscopy.

机构信息

Institute for Biomedical Engineering and Institute of Pharmacology and Toxicology Faculty of Medicine University of Zurich Zurich 8057 Switzerland.

Institute for Biomedical Engineering Department of Information Technology and Electrical Engineering ETH Zurich Zurich 8093 Switzerland.

出版信息

Adv Sci (Weinh). 2021 May 2;8(13):2004226. doi: 10.1002/advs.202004226. eCollection 2021 Jul.

Abstract

Wound healing is a well-coordinated process, necessitating efficient formation of new blood vessels. Vascularization defects are therefore a major risk factor for chronic, non-healing wounds. The dynamics of mammalian tissue revascularization, vessel maturation, and remodeling remain poorly understood due to lack of suitable in vivo imaging tools. A label-free large-scale optoacoustic microscopy (LSOM) approach is developed for rapid, non-invasive, volumetric imaging of tissue regeneration over large areas spanning up to 50 mm with a depth penetration of 1.5 mm. Vascular networks in dorsal mouse skin and full-thickness excisional wounds are imaged with capillary resolution during the course of healing, revealing previously undocumented views of the angiogenesis process in an unperturbed wound environment. Development of an automatic analysis framework enables the identification of key features of wound angiogenesis, including vessel length, diameter, tortuosity, and angular alignment. The approach offers a versatile tool for preclinical research in tissue engineering and regenerative medicine, empowering label-free, longitudinal, high-throughput, and quantitative studies of the microcirculation in processes associated with normal and impaired vascular remodeling, and analysis of vascular responses to pharmacological interventions in vivo.

摘要

伤口愈合是一个协调良好的过程,需要有效地形成新的血管。因此,血管化缺陷是慢性、不愈合伤口的主要风险因素。由于缺乏合适的体内成像工具,哺乳动物组织再血管化、血管成熟和重塑的动力学仍知之甚少。本研究开发了一种无标记的大规模光声显微镜(LSOM)方法,用于快速、非侵入性、对大区域(最大 50mm)的组织再生进行体积成像,深度穿透可达 1.5mm。在愈合过程中,用毛细血管分辨率对背部小鼠皮肤和全层切除伤口的血管网络进行成像,揭示了在未受干扰的伤口环境中以前未记录的血管生成过程的观点。开发了一种自动分析框架,能够识别伤口血管生成的关键特征,包括血管长度、直径、迂曲度和角向排列。该方法为组织工程和再生医学的临床前研究提供了一种通用工具,能够进行无标记、纵向、高通量和定量的微血管研究,分析正常和受损血管重塑过程中的血管反应,并分析体内药物干预的血管反应。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4f01/8261523/66ae354e4046/ADVS-8-2004226-g002.jpg

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