Mapping Differentiation under Mixed Culture Conditions Reveals a Tunable Continuum of T Cell Fates

Cell differentiation is typically directed by external signals that drive opposing regulatory pathways. Studying differentiation under polarizing conditions, with only one input signal provided, is limited in its ability to resolve the logic of interactions between opposing pathways. Dissection of this logic can be facilitated by mapping the system's response to mixtures of input signals, which are expected to occur in vivo, where cells are simultaneously exposed to various signals with potentially opposing effects. Here, we systematically map the response of naïve T cells to mixtures of signals driving differentiation into the Th1 and Th2 lineages. We characterize cell state at the single cell level by measuring levels of the two lineage-specific transcription factors (T-bet and GATA3) and two lineage characteristic cytokines (IFN-γ and IL-4) that are driven by these transcription regulators. We find a continuum of mixed phenotypes in which individual cells co-express the two lineage-specific master regulators at levels that gradually depend on levels of the two input signals. Using mathematical modeling we show that such tunable mixed phenotype arises if autoregulatory positive feedback loops in the gene network regulating this process are gradual and dominant over cross-pathway inhibition. We also find that expression of the lineage-specific cytokines follows two independent stochastic processes that are biased by expression levels of the master regulators. Thus, cytokine expression is highly heterogeneous under mixed conditions, with subpopulations of cells expressing only IFN-γ, only IL-4, both cytokines, or neither. The fraction of cells in each of these subpopulations changes gradually with input conditions, reproducing the continuous internal state at the cell population level. These results suggest a differentiation scheme in which cells reflect uncertainty through a continuously tuneable mixed phenotype combined with a biased stochastic decision rather than a binary phenotype with a deterministic decision

Transferability of interventions in health education: a review

<p>Abstract</p> <p>Background</p> <p>Health education interventions are generally complex. Their outcomes result from both the intervention itself and the context for which they are developed. Thus, when an intervention carried out in one context is reproduced in another, its transferability can be questionable. We performed a literature review to analyze the concept of transferability in the health education field.</p> <p>Methods</p> <p>Articles included were published between 2000 and 2010 that addressed the notion of transferability of interventions in health education. Articles were analyzed using a standardized grid based on four items: 1) terminology used; 2) factors that influenced transferability; 3) capacity of the research and evaluation designs to assess transferability; and 4) tools and criteria available to assess transferability.</p> <p>Results</p> <p>43 articles met the inclusion criteria. Only 13 of them used the exact term “transferability” and one article gave an explicit definition: the extent to which the measured effectiveness of an applicable intervention could be achieved in another setting. Moreover, this concept was neither clearly used nor distinguished from others, such as applicability. We highlight the levels of influence of transferability and their associated factors, as well as the limitations of research methods in their ability to produce transferable conclusions.</p> <p>Conclusions</p> <p>We have tried to clarify the concept by defining it along three lines that may constitute areas for future research: factors influencing transferability, research methods to produce transferable data, and development of criteria to assess transferability. We conclude this review with three propositions: 1) a conceptual clarification of transferability, especially with reference to other terms used; 2) avenues for developing knowledge on this concept and analyzing the transferability of interventions; and 3) in relation to research, avenues for developing better evaluation methods for assessing the transferability of interventions.</p