Singapore-Massachusetts Institute of Technology Alliance for Research and Technology, Interdisciplinary Research Group in Infectious Diseases, 1 CREATE Way, #03-10/11 Innovation Wing; #03-12/13/14 Enterprise Wing, Singapore 138602, Singapore.
J Biomed Opt. 2013 Apr;18(4):046001. doi: 10.1117/1.JBO.18.4.046001.
Lung injury caused by influenza virus infection is widespread. Understanding lung damage and repair progression post infection requires quantitative spatiotemporal information on various cell types mapping into the tissue structure. Based on high content images acquired from an automatic slide scanner, we have developed algorithms to quantify cell infiltration in the lung, loss and recovery of Clara cells in the damaged bronchioles and alveolar type II cells (AT2s) in the damaged alveolar areas, and induction of pro-surfactant protein C (pro-SPC)-expressing bronchiolar epithelial cells (SBECs). These quantitative analyses reveal: prolonged immune cell infiltration into the lung that persisted long after the influenza virus was cleared and paralleled with Clara cell recovery; more rapid loss and recovery of Clara cells as compared to AT2s; and two stages of SBECs from Scgb1a1⁺ to Scgb1a1⁻. These results provide evidence supporting a new mechanism of alveolar repair where Clara cells give rise to AT2s through the SBEC intermediates and shed light on the understanding of the lung damage and repair process. The approach and algorithms in quantifying cell-level changes in the tissue context (cell-based tissue informatics) to gain mechanistic insights into the damage and repair process can be expanded and adapted in studying other disease models.
流感病毒感染导致的肺部损伤很常见。要了解感染后肺部损伤和修复的进展，需要对各种细胞类型在组织结构中的定量时空信息进行分析。我们基于自动载玻片扫描仪获取的高内涵图像，开发了定量分析肺浸润、受损细支气管中克拉拉细胞（Clara cells）和受损肺泡区 II 型肺泡细胞（AT2s）的丢失和恢复以及促表面活性蛋白 C（pro-SPC）表达的细支气管上皮细胞（SBECs）的诱导的算法。这些定量分析揭示了：流感病毒清除后，免疫细胞在肺部的持续浸润时间延长，与克拉拉细胞的恢复平行；与 AT2s 相比，克拉拉细胞的丢失和恢复更快；以及从 Scgb1a1⁺到 Scgb1a1⁻的 SBEC 两个阶段。这些结果为肺泡修复的新机制提供了证据，即克拉拉细胞通过 SBEC 中间产物产生 AT2s，并为理解肺部损伤和修复过程提供了线索。在定量分析组织中细胞水平变化（基于细胞的组织信息学）以深入了解损伤和修复过程的方法和算法可以扩展并适应于研究其他疾病模型。
