Spatiotemporal quantification of cell dynamics in the lung following influenza virus infection

• Main Authors: Yin, Lu, Xu, Shuoyu, Cheng, Jierong, Zheng, Dahai, Chen, Jianzhu, Yu, Hanry, Limmon, Gino V., Leung, Nicola H. N., Rajapakse, Jagath C., Chow, Vincent T. K.
• Other Authors: School of Computer Engineering
• Format: Article
• Language: English
• Published: 2013
• Subjects: DRNTU::Engineering::Computer science and engineering
• Online Access: https://hdl.handle.net/10356/98015; http://hdl.handle.net/10220/12234
• Institution: Nanyang Technological University

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.