Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/98015
Title: Spatiotemporal quantification of cell dynamics in the lung following influenza virus infection
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.
Keywords: DRNTU::Engineering::Computer science and engineering
Issue Date: 2013
Source: Yin, L., Xu, S., Cheng, J., Zheng, D., Limmon, G. V., Leung, N. H. N., et al. (2013). Spatiotemporal quantification of cell dynamics in the lung following influenza virus infection. Journal of Biomedical Optics, 18(4).
Series/Report no.: Journal of biomedical optics
Abstract: 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.
URI: https://hdl.handle.net/10356/98015
http://hdl.handle.net/10220/12234
ISSN: 1083-3668
DOI: http://dx.doi.org/10.1117/1.JBO.18.4.046001
Rights: © 2013 The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.[DOI: 10.1117/1.JBO.18.4.046001]
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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