URMC / Labs / Mosmann Lab / Projects / LAVA - Animating Stochastic Processes during T Cell Differentiation
LAVA - Animating Stochastic Processes during T Cell Differentiation
LAVA representation of potential reticulate differentiation
Recent advances in understanding CD4+ 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. For example, examining how chronic, weak influences may induce long-term effects.
In chronic inflammatory conditions, very slight effects may slowly alter populations if cells reach a transfer threshold at a very low rate. However, once the cells have transitioned to the inflammation‐induced state, “uphill” reversion to normal may be more difficult.
The LAVA software and instruction manual is available for free download.
Rebhahn JA, Deng N, Sharma G, Livingstone AM, Huang S, Mosmann TR. An animated landscape representation of CD4 T-cell differentiation, variability and plasticity: Insights into the behavior of populations versus cells. Eur J Immunol. 2014. PMID: 24945794.