A stochastic model dissects cell states in biological transition processes View Full Text


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Article Info

DATE

2014-01-17

AUTHORS

Jonathan W. Armond, Krishanu Saha, Anas A. Rana, Chris J. Oates, Rudolf Jaenisch, Mario Nicodemi, Sach Mukherjee

ABSTRACT

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. More... »

PAGES

3692

References to SciGraph publications

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  • 2007-06-06. In vitro reprogramming of fibroblasts into a pluripotent ES-cell-like state in NATURE
  • 2012-07-08. The H3K27 demethylase Utx regulates somatic and germ cell epigenetic reprogramming in NATURE
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    http://scigraph.springernature.com/pub.10.1038/srep03692

    DOI

    http://dx.doi.org/10.1038/srep03692

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1010808649

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/24435049


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