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  • 标题:A stochastic model dissects cell states in biological transition processes
  • 本地全文:下载
  • 作者:Jonathan W. Armond ; Krishanu Saha ; Anas A. Rana
  • 期刊名称:Scientific Reports
  • 电子版ISSN:2045-2322
  • 出版年度:2014
  • 卷号:4
  • DOI:10.1038/srep03692
  • 出版社:Springer Nature
  • 摘要:Many biological processes, including differentiation, reprogramming, and disease transformations, involve transitions of cells through distinct states. Direct, unbiased investigation of cell states and their transitions is challenging due to several factors, including limitations of single-cell assays. Here we present a stochastic model of cellular transitions that allows underlying single-cell information, including cell-state-specific parameters and rates governing transitions between states, to be estimated from genome-wide, population-averaged time-course data. The key novelty of our approach lies in specifying latent stochastic models at the single-cell level, and then aggregating these models to give a likelihood that links parameters at the single-cell level to observables at the population level. We apply our approach in the context of reprogramming to pluripotency. This yields new insights, including profiles of two intermediate cell states, that are supported by independent single-cell studies. Our model provides a general conceptual framework for the study of cell transitions, including epigenetic transformations.
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