Warren Alpert Medical School, Brown University, Providence, RI, 02912, USA.
School of Engineering, Brown University, Providence, RI, 02912, USA.
Nat Commun. 2023 May 24;14(1):2982. doi: 10.1038/s41467-023-38609-z.
In age-related neurodegenerative diseases, pathology often develops slowly across the lifespan. As one example, in diseases such as Alzheimer's, vascular decline is believed to onset decades ahead of symptomology. However, challenges inherent in current microscopic methods make longitudinal tracking of such vascular decline difficult. Here, we describe a suite of methods for measuring brain vascular dynamics and anatomy in mice for over seven months in the same field of view. This approach is enabled by advances in optical coherence tomography (OCT) and image processing algorithms including deep learning. These integrated methods enabled us to simultaneously monitor distinct vascular properties spanning morphology, topology, and function of the microvasculature across all scales: large pial vessels, penetrating cortical vessels, and capillaries. We have demonstrated this technical capability in wild-type and 3xTg male mice. The capability will allow comprehensive and longitudinal study of a broad range of progressive vascular diseases, and normal aging, in key model systems.
在与年龄相关的神经退行性疾病中，病理学通常在整个生命周期中缓慢发展。例如，在阿尔茨海默病等疾病中，血管衰退据信在出现症状前几十年就已经开始。然而，当前微观方法中固有的挑战使得对这种血管衰退进行纵向跟踪变得困难。在这里，我们描述了一套用于在同一视野中对小鼠的大脑血管动力学和解剖结构进行长达七个多月的测量的方法。这种方法得益于光学相干断层扫描 (OCT) 和图像处理算法（包括深度学习）的进步。这些集成的方法使我们能够同时监测不同的血管特性，包括形态、拓扑和微血管功能的所有尺度：大脑膜血管、穿透性皮质血管和毛细血管。我们已经在野生型和 3xTg 雄性小鼠中证明了这种技术能力。该能力将允许在关键模型系统中对广泛的进行性血管疾病和正常衰老进行全面和纵向研究。
