目的:心脏受到自主神经系统的严格调节,在此期间,在健康的状态下,交感神经和副交感神经分支的作用是平衡的。近年来,人们对与心律失常发生有关的心脏自主神经的病理性重塑和生长越来越感兴趣。然而,心脏神经在相对较大的组织中的小尺寸使得使用这些神经的组织学量化的研究极具挑战性,并且通常仅依赖于所选择的感兴趣区域中的神经密度的量化。我们的目的是开发一种能够量化透壁组织切片中组织学神经密度的方法。
方法:在这里,我们描述了一种新颖的工作流程,该流程可以可视化和量化跨壁心肌组织切片中的可变神经支配类型及其异质性。一个自定义的半自动工作流程,用于量化涉及Python的心脏神经,MATLAB和ImageJ在本协议中以逐步和详细的方式提供和描述。
■本文表示了两个示例性组织切片的结果。取自梗死核心的示例性组织切片,具有0.20的高异质性值,63.3%正常神经支配,12.2%神经支配过度,3.6%低神经化和21.0%去神经化。从远离梗死的左心室区域获取的第二个示例组织切片显示出0.02的低异质性值,95.3%正常神经支配,3.8%神经支配过度,0.5%神经支配和0.5%神经支配。
结论:这种方法有可能广泛应用于任何涉及大组织神经高分辨率成像的研究。
OBJECTIVE: The heart is under strict regulation of the autonomic nervous system, during which, in a healthy state, the effects of sympathetic and parasympathetic branches are balanced. In recent years, there has been increasing interest in pathological remodeling and outgrowth of cardiac autonomic nerves in relation to arrhythmogenesis. However, the small size of the cardiac nerves in relatively large tissues renders research using histological quantification of these nerves extremely challenging and usually relies on quantification of the nerve density in selected regions of interest only. Our aim was to develop a method to be able to quantify the histological nerve density in transmural tissue sections.
METHODS: Here we describe a novel workflow that enables visualization and quantification of variable innervation types and their heterogeneity within transmural myocardial tissue sections. A custom semiautomatic workflow for the quantification of cardiac nerves involving Python, MATLAB and ImageJ is provided and described in this protocol in a stepwise and detailed manner.
UNASSIGNED: The results of two example tissue sections are represented in this paper. An example tissue section taken from the infarction core with a high heterogeneity value of 0.20, 63.3% normal innervation, 12.2% hyperinnervation, 3.6% hypoinnervation and 21.0% denervation. The second example tissue section taken from an area of the left ventricle remote from the infarction showed a low heterogeneity value of 0.02, 95.3% normal innervation, 3.8% hyperinnervation, 0.5% hypoinnervation and 0.5% denervation.
CONCLUSIONS: This approach has the potential to be broadly applied to any research involving high-resolution imaging of nerves in large tissues.