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Systems biology of stem cell fate and cellular reprogramming

Key Points

  • Stem cell differentiation and the maintenance of self-renewal are intrinsically complex processes that require the coordinated dynamic expression of hundreds of genes and proteins in precise response to external signalling cues.

  • As computational tools can help identify patterns and elucidate structure in complex datasets, they are now beginning to be used in stem cell research to better understand this complexity.

  • The representation of complex molecular regulatory interactions as networks is useful in conceptualizing this complexity. However, it is difficult to relate network architecture to cell behaviour in a quantitative way.

  • The collective behaviour of complex regulatory networks can be explored by using techniques from dynamical systems theory and analysing cell types associated with attractors of underlying regulatory networks.

  • Robust heterogeneity at the population level can arise from stochastic transitions between coexisting attractors driven by widespread molecular noise.

  • Cellular reprogramming corresponds to navigation through a complex noisy attractor landscape. Understanding the relationships between stochasticity and determinism in defining cell fate might help decipher the molecular regulatory mechanisms of cellular reprogramming.

Abstract

Stem cell differentiation and the maintenance of self-renewal are intrinsically complex processes requiring the coordinated dynamic expression of hundreds of genes and proteins in precise response to external signalling cues. Numerous recent reports have used both experimental and computational techniques to dissect this complexity. These reports suggest that the control of cell fate has both deterministic and stochastic elements: complex underlying regulatory networks define stable molecular 'attractor' states towards which individual cells are drawn over time, whereas stochastic fluctuations in gene and protein expression levels drive transitions between coexisting attractors, ensuring robustness at the population level.

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Figure 1: Stem cell regulatory networks.
Figure 2: Cellular reprogramming as navigation through a complex attractor landscape.

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Acknowledgements

The authors thank J. Wang for supplying the NANOG interactome data used to create Fig. 1a and Y.-S. Ang for helping to compile the list of genes used to create the supplementary stem cell transcription network.

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FURTHER INFORMATION

The Black Family Stem Cell Institute homepage

Integrated Stem Cell Molecular Interactions database

Glossary

Inner cell mass

Early cells in the embryo that generate all lineages of the mature organism but do not give rise to the placenta.

Blastocyst

The embryo before implantation, which contains at least two distinct cell types: the trophectoderm and the inner cell mass.

Feedback loop

A closed path in a network starting and ending at the same node and passing through intermediary nodes only once.

Feedforward loop

The union of two distinct paths in a network from a source node to a target node, passing through intermediary nodes only once.

Attractor

A stable balanced state of a dynamical system towards which nearby configurations are drawn over time. Attractors can be stationary states, limit cycles (oscillators) or even strange (chaotic).

ChIP-on-chip

A high-throughput chromatin immunoprecipitation (ChIP) procedure that is used to identify binding sites for a specific transcription factor or other DNA-binding protein in the entire genome.

ChIP-seq

A procedure similar to ChIP-on-chip except that instead of hybridizing isolated DNA fragments bound by the protein of interest with a microarray, the fragments are amplified, size-selected and directly sequenced using massively parallel signature sequencing (MPSS)-based deep sequencing techniques.

ChIP-PET

A procedure that is similar to ChIP-on-chip and ChIP-seq. In this case isolated DNA from the ChIP portion of the experiment is digested into 18-nucleotide-long fragments that are concatenated, tagged and sequenced (known as paired-end ditags (PETs)). The sequences of the PETs are then reassembled and compared with the genome to identify actual binding sites.

Reverse engineering

Inferring regulatory interactions from high-throughput datasets using computational and statistical inference techniques.

Induced pluripotent stem (iPS) cell

A type of pluripotent stem cell that can be produced by various adult somatic cell types by forced expression of certain combinations of key embryonic stem cell-associated transcription factors.

Epigenetic modifier

A substance that causes a change in gene expression without changing DNA sequence.

Molecular noise

Stochastic fluctuations in molecular expression levels originating from the inherent the indeterminism of molecular processes and the unpredictable variability of the extracellular environment.

Multi-stable system

A dynamical system that supports the existence of two or more coexisting attractors for some region of parameter space.

26S proteasome

Large multi-subunit protease complex that selectively degrades multiubiquitylated proteins. It contains a 20S particle that carries the catalytic activity and two regulatory 19S particles.

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MacArthur, B., Ma'ayan, A. & Lemischka, I. Systems biology of stem cell fate and cellular reprogramming. Nat Rev Mol Cell Biol 10, 672–681 (2009). https://doi.org/10.1038/nrm2766

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