Willführ Alper, Brandenberger Christina, Piatkowski Tanja, Grothausmann Roman, Nyengaard Jens Randel, Ochs Matthias, Mühlfeld Christian
Institute of Functional and Applied Anatomy, Hannover Medical School, Hannover, Germany;
Institute of Functional and Applied Anatomy, Hannover Medical School, Hannover, Germany; Department of Cardiac Development and Remodelling, Max-Planck-Institute for Heart and Lung Research, Bad Nauheim, Germany;
Am J Physiol Lung Cell Mol Physiol. 2015 Dec 1;309(11):L1286-93. doi: 10.1152/ajplung.00410.2014. Epub 2015 Oct 2.
The lung parenchyma provides a maximal surface area of blood-containing capillaries that are in close contact with a large surface area of the air-containing alveoli. Volume and surface area of capillaries are the classic stereological parameters to characterize the alveolar capillary network (ACN) and have provided essential structure-function information of the lung. When loss (rarefaction) or gain (angiogenesis) of capillaries occurs, these parameters may not be sufficient to provide mechanistic insight. Therefore, it would be desirable to estimate the number of capillaries, as it contains more distinct and mechanistically oriented information. Here, we present a new stereological method to estimate the number of capillary loops in the ACN. One advantage of this method is that it is independent of the shape, size, or distribution of the capillaries. We used consecutive, 1 μm-thick sections from epoxy resin-embedded material as a physical disector. The Euler-Poincaré characteristic of capillary networks can be estimated by counting the easily recognizable topological constellations of "islands," "bridges," and "holes." The total number of capillary loops in the ACN can then be calculated from the Euler-Poincaré characteristic. With the use of the established estimator of alveolar number, it is possible to obtain the mean number of capillary loops per alveolus. In conclusion, estimation of alveolar capillaries by design-based stereology is an efficient and unbiased method to characterize the ACN and may be particularly useful for studies on emphysema, pulmonary hypertension, or lung development.
肺实质提供了一个含血毛细血管的最大表面积,这些毛细血管与含气肺泡的大表面积紧密接触。毛细血管的体积和表面积是表征肺泡毛细血管网络(ACN)的经典体视学参数,并提供了肺的基本结构-功能信息。当毛细血管发生丢失(稀疏化)或增加(血管生成)时,这些参数可能不足以提供机制性见解。因此,估计毛细血管数量是可取的,因为它包含更独特且具有机制导向性的信息。在此,我们提出一种新的体视学方法来估计ACN中毛细血管环的数量。该方法的一个优点是它与毛细血管的形状、大小或分布无关。我们使用来自环氧树脂包埋材料的连续1μm厚切片作为物理分割体。通过计数易于识别的“岛”“桥”和“洞”的拓扑构型,可以估计毛细血管网络的欧拉-庞加莱特征。然后可以根据欧拉-庞加莱特征计算ACN中毛细血管环的总数。利用已建立的肺泡数量估计器,可以获得每个肺泡的毛细血管环平均数。总之,基于设计的体视学方法估计肺泡毛细血管是一种有效且无偏的表征ACN的方法,可能对肺气肿、肺动脉高压或肺发育的研究特别有用。