An animated landscape representation of CD4+ T‐cell differentiation, variability, and plasticity: Insights into the behavior of populations versus cells. Issue 8 (August 2014)

• Record Type: Journal Article
• Authors: Rebhahn, Jonathan A.; Deng, Nan; Sharma, Gaurav; Livingstone, Alexandra M.; Huang, Sui; Mosmann, Tim R.
• Is Part Of: European journal of immunology. Volume 44:Issue 8(2014:Aug.)
• Page Start: 2216
• Page End: 2229
• Publication Date: 2014-08
• Subjects: Immunology -- Periodicals; 616.079
• DOI: 10.1002/eji.201444645
• Languages: English
• ISSNs: 0014-2980

Abstract

<abstract abstract-type="main"> <title> <x xml:space="preserve">Abstract</x> </title> <p>Recent advances in understanding CD4<sup>+</sup> T‐cell differentiation suggest that previous models of a few distinct, stable effector phenotypes were too simplistic. Although several well‐characterized phenotypes are still recognized, some states display plasticity, and intermediate phenotypes exist. As a framework for reexamining these concepts, we use Waddington's landscape paradigm, augmented with explicit consideration of stochastic variations. Our animation program "LAVA" visualizes T‐cell differentiation as cells moving across a landscape of hills and valleys, leading to attractor basins representing stable or semistable differentiation states. The model illustrates several principles, including: (i) cell populations may behave more predictably than individual cells; (ii) analogous to reticulate evolution, differentiation may proceed through a network of interconnected states, rather than a single well‐defined pathway; (iii) relatively minor changes in the barriers between attractor basins can change the stability or plasticity of a population; (iv) intrapopulation variability of gene expression may be an important regulator of differentiation, rather than inconsequential noise; (v) the behavior of some populations may be defined mainly by the behavior of outlier cells. While not a quantitative representation of actual differentiation, our model is intended to provoke discussion of T‐cell differentiation pathways, particularly highlighting a probabilistic view of transitions between states.</p> </abstract>