An animated landscape representation of CD4⁺ T‐cell differentiation, variability, and plasticity: Insights into the behavior of populations versus cells

Abstract

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.

Competitive SWIFT cluster templates enhance detection of aging changes

“…Without exhaustive analysis of the hundreds of SWIFT sub‐populations it is difficult to know whether ALL of the sub‐populations are biologically meaningful, although it is increasingly clear that the full diversity of T cells, for example, is much greater than previously thought (recently reviewed in Ref. 42). …”

T follicular helper cell heterogeneity: Time, space, and function

“…Upon activation, naive CD4 + T cells initiate bifurcating differentiation down a Waddingtonian landscape (illustration of CD4 + T cell differentiation landscape adapted from Rebhahn et al 135 ). Upon activation, naive CD4 + T cells initiate bifurcating differentiation down a Waddingtonian landscape (illustration of CD4 + T cell differentiation landscape adapted from Rebhahn et al 135 ).…”

“…In between the two architypes are differentiated cells that have F I G U R E 4 Model of Tfh memory formation in type 1 responses. Upon activation, naive CD4 + T cells initiate bifurcating differentiation down a Waddingtonian landscape (illustration of CD4 + T cell differentiation landscape adapted from Rebhahn et al 135 ). As they receive signals that facilitate progression through their developmental programs, their phenotype becomes increasingly polarized in the direction of Th1 or Tfh.…”

Single-Cell-Based Analysis Highlights a Surge in Cell-to-Cell Molecular Variability Preceding Irreversible Commitment in a Differentiation Process

“…In this view, discrete identified cell states (e.g., self-renewing, differentiated) correspond to different regions of this space that could be seen as different attractor states. The transition process between attractors therefore first requires the exit from the original state that may be fueled by an increase in gene expression stochasticity [31]. Regardless of the differences between these models, they all assume that the differentiation process is represented by cell trajectories leading from one state to another through a phase of biased random walk in gene expression.…”