期刊名称：Proceedings of the National Academy of Sciences
印刷版ISSN：0027-8424
电子版ISSN：1091-6490
出版年度：2010
卷号：107
期号：23
页码：10371-10376
DOI：10.1073/pnas.0909374107
语种：English
出版社：The National Academy of Sciences of the United States of America
摘要：Only little is known about how cells coordinately behave to establish functional tissue structure and restore microarchitecture during regeneration. Research in this field is hampered by a lack of techniques that allow quantification of tissue architecture and its development. To bridge this gap, we have established a procedure based on confocal laser scans, image processing, and three-dimensional tissue reconstruction, as well as quantitative mathematical modeling. As a proof of principle, we reconstructed and modeled liver regeneration in mice after damage by CCl4, a prototypical inducer of pericentral liver damage. We have chosen the regenerating liver as an example because of the tight link between liver architecture and function: the complex microarchitecture formed by hepatocytes and microvessels, i.e. sinusoids, ensures optimal exchange of metabolites between blood and hepatocytes. Our model captures all hepatocytes and sinusoids of a liver lobule during a 16 days regeneration process. The model unambiguously predicted a so-far unrecognized mechanism as essential for liver regeneration, whereby daughter hepatocytes align along the orientation of the closest sinusoid, a process which we named "hepatocyte-sinusoid alignment" (HSA). The simulated tissue architecture was only in agreement with the experimentally obtained data when HSA was included into the model and, moreover, no other likely mechanism could replace it. In order to experimentally validate the model of prediction of HSA, we analyzed the three-dimensional orientation of daughter hepatocytes in relation to the sinusoids. The results of this analysis clearly confirmed the model prediction. We believe our procedure is widely applicable in the systems biology of tissues.
关键词：agent based model ; image processing and analysis ; mathematical tissue modeling ; systems biology ; morphogenesis
