Molecular Interaction Networks to Select Factors for Cell Conversion
The process of identifying sets of transcription factors that can induce a cell conversion can be time-consuming and expensive. To help alleviate this, a number of computational tools have been developed which integrate gene expression data with molecular
- PDF / 3,752,505 Bytes
- 29 Pages / 504.567 x 720 pts Page_size
- 106 Downloads / 204 Views
1
Introduction The ability to transdifferentiate between cell types by overexpression of transcription factors is a phenomenon that is widely studied and is beginning to make an impact in the clinic. Despite the intense interest in this area, many groups are still using trial and error-based approaches in order to discover sets of transcription factors that can induce a cell conversion. This process is both timeconsuming and expensive and subsequently has led to the development of a number of computational techniques to help facilitate the
The original version of this chapter was revised. The correction to this chapter is available at https://doi.org/10. 1007/978-1-4939-9224-9_20 Patrick Cahan (ed.), Computational Stem Cell Biology: Methods and Protocols, Methods in Molecular Biology, vol. 1975, https://doi.org/10.1007/978-1-4939-9224-9_16, © Springer Science+Business Media, LLC, part of Springer Nature 2019
333
334
John F. Ouyang et al.
discovery. In general these approaches utilize gene expression data [1] and in some cases also combine gene regulatory information [2, 3] in order to predict the required factors for any given cell conversion. The motivation for these approaches is to make the identification of sets of transcription factors easier and in doing so facilitate experimental progress. One such approach is Mogrify, a technique which utilizes gene expression data and gene regulatory interactions to predict sets of transcription factors for cell conversion. These predictions can be tailored to specific experimental requirements, for instance, limiting the set of possible transcription factors, selectively removing or adding specific factors based on experimental evidence, or specifying the maximum number of transcription factors. The process of how these predictions are made involves several key steps: (1) calculating differential gene expression, (2) scoring each transcription factor based on its predicted regulatory influence, and (3) selecting transcription factors such that their regulatory control is not heavily redundant. In the following sections, we will discuss some important features of cell conversions, the Mogrify algorithm and the associated web resource. As with all computational and experimental protocols, understanding each of these aspects and the context in which they have been developed will provide the maximum utility. 1.1 Differentiation, Reprogramming, and Transdifferentiation: A Short History
Much of our understanding of cell fate changes originally came from studying the natural process of differentiation; early embryonic cells progressively confine their lineage toward specific cell types in response to external and internal cues. The earliest studies in this area focused on identifying populations of cells that maintain this ability to differentiate, the first of which in 1961. In this experiment, bone marrow cells were extracted and implanted into spleen samples taken from irradiated mice. These cells then formed “pluripotent colony-forming units” which could be made to d
Data Loading...
