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

Antebi, Yaron E.; Reich-Zeliger, Shlomit; Hart, Yuval; Mayo, Avi; Eizenberg, Inbal; Rimer, Jacob; Putheti, Prabhakar; Pe’er, Dana; Friedman, Nir

doi:10.1371/journal.pbio.1001616

Cited by 94 publications

(106 citation statements)

References 55 publications

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“…However, this notion is challenged by evidence of population heterogeneity (29)(30)(31)(32) and Th cell plasticity (33)(34)(35)(36)(37), showing that, under various conditions, differentiated cells can adopt hybrid phenotypes, coexpressing signature TFs and cytokines of different lineages. We and others have recently shown that naïve CD4 + T cells can differentiate into a mixed Th1-Th2 phenotype, coexpressing lineage-specific TFs and cytokines of both cell types in varying levels depending on the relative levels of input cytokine signals (38)(39)(40). Taken together, these observations suggest a more complex picture of CD4 + T-cell fate determination with a substantial degree of phenotype plasticity and flexibility in response to different input stimuli.…”

Section: Significancementioning

confidence: 68%

“…To test this assumption, we attempted to populate the region in between Th1 and Th2 phenotypes in the differentiation space using varying concentrations of two input cytokines, IL-12 and IL-4. As we have previously shown (38), gradually changing concentrations of IL-12 and IL-4 result in mixed Th1-Th2 phenotypes. (Fig.…”

Section: The Segmented Linear Model Predicts Trajectories In the Diffmentioning

confidence: 74%

“…This notion is supported by previous data on mixed Th1-Th2 cells. We and others observed stability of a mixed Th1-Th2 phenotype for the duration of weeks (in vitro) (38) and months (in vivo) (39). Devising adequate in vivo models to test for stability and immune function of other mixed Th phenotypes is challenging but can be insightful for better understanding of their physiological significance.…”

Section: Discussionmentioning

confidence: 92%

“…Previous models described differentiation of Th cells toward a specific fate (55)(56)(57) or focused on key molecular components (38,(58)(59)(60). Other models describe Th differentiation at a higher level of detail by modeling regulatory networks of increasing complexity (61)(62)(63)(64).…”

Section: Discussionmentioning

confidence: 99%

“…Heterogeneity in T-cell responses was shown by a number of in vitro and in vivo studies to be averaged out for a large-enough initial cell population, such that the mean population response follows more deterministic rules (38,(43)(44)(45).…”

Section: Mapping Cd4 + T-cell Differentiation Under a Large Number Ofmentioning

confidence: 99%

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Diverse continuum of CD4⁺T-cell states is determined by hierarchical additive integration of cytokine signals

Eizenberg-Magar

Rimer

Zaretsky

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

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During cell differentiation, progenitor cells integrate signals from their environment that guide their development into specialized phenotypes. The ways by which cells respond to complex signal combinations remain difficult to analyze and model. To gain additional insight into signal integration, we systematically mapped the response of CD4 + T cells to a large number of input cytokine combinations that drive their differentiation. We find that, in response to varied input combinations, cells differentiate into a continuum of cell fates as opposed to a limited number of discrete phenotypes. Input cytokines hierarchically influence the cell population, with TGFβ being most dominant followed by IL-6 and IL-4. Mathematical modeling explains these results using additive signal integration within hierarchical groups of input cytokine combinations and correctly predicts cell population response to new input conditions. These findings suggest that complex cellular responses can be effectively described using a segmented linear approach, providing a framework for prediction of cellular responses to new cytokine combinations and doses, with implications to fine-tuned immunotherapies.C ell differentiation is controlled by complex gene regulatory networks that determine the expression of a large number of genes in response to external stimuli. CD4 + T cells, central regulators of immune responses, can differentiate into a number of phenotypes (or cell states), each defined by the expression of a panel of genes, such as lineage-specifying transcription factors (TFs) and cytokines (1). Differentiation outcome depends on a number of factors, including strength of antigen stimulation (2, 3), interactions with antigen presenting cells, and the signaling environment defined by secreted cytokines (1). By applying specific cytokine signals, it is possible to direct antigen-activated T cells toward differentiation into a number of specific phenotypes. Although the molecular interactions and regulatory networks that govern the differentiation process are being revealed at increasing resolution (4, 5), understanding the logic of operation of these complex networks and developing predictive mathematical models for cellular responses remain a challenge.Mathematical models that describe complex cellular processes are difficult to construct because of a lack of complete information about the underlying networks of molecular interactions and in particular, missing quantitative data regarding the parameters of biochemical interactions within these networks. Alternatively, a "black box" approach can be used, constructing a model of the system by analyzing its response to different input conditions (Fig. 1A). This technique enables study of complex systems in a simplified manner, because its implementation merely requires measurable inputs and outputs, without quantifying the response of each component inside the box. Approaches based on this concept are highly useful for describing engineered systems and were also applied for studying som...

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Section: Significancementioning

confidence: 68%

Section: The Segmented Linear Model Predicts Trajectories In the Diffmentioning

confidence: 74%

Section: Discussionmentioning

confidence: 92%

Section: Discussionmentioning

confidence: 99%

Section: Mapping Cd4 + T-cell Differentiation Under a Large Number Ofmentioning

confidence: 99%

See 3 more Smart Citations

Diverse continuum of CD4⁺T-cell states is determined by hierarchical additive integration of cytokine signals

Eizenberg-Magar

Rimer

Zaretsky

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

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

et al. 2014

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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.

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Human amnion favours tissue repair by inducing the M1-to-M2 switch and enhancing M2 macrophage features

Magatti

Vertua

Munari

et al. 2016

J Tissue Eng Regen Med

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Human amniotic mesenchymal cells (hAMTCs) possess interesting immunomodulatory properties, making them attractive candidates for regenerative medicine applications. Recent in vivo reports argue in favour of an important role for macrophages as targets of hAMTC‐mediated suppression of inflammation and the enhancement of tissue repair. However, a comprehensive study of the effects of hAMTCs and their conditioned medium (CM) on human macrophage differentiation and function is unavailable. In the present study we found that hAMTCs and CM induce the differentiation of myeloid cells (U937 and monocytes) towards macrophages. We then investigated their effects on monocytes differentiated toward pro‐inflammatory M1 and anti‐inflammatory M2 macrophages. Monocytes treated under M1 conditions in the presence of hAMTCs or CMs shifted towards M2‐like macrophages, which expressed CD14, CD209, CD23, CD163 and PM‐2 K, possessed higher phagocytic activity and produced higher IL‐10 and lower pro‐inflammatory cytokines. They were also poor T cell stimulators and Th1 inducers, while they were able to increase activated and naïve suppressive Treg subsets. We show that prostaglandins, and not IL‐6, play a role in determining the M2 activation status. Instead, monocytes treated under M2 conditions in the presence of hAMTCs or CM retained M2‐like features, but with an enhanced anti‐inflammatory profile, having a reduced expression of the co‐stimulatory molecule CD80, reduced phagocytosis activity and decreased the secretion of inflammatory chemokines. Importantly, we provide evidence that macrophages re‐educated by CM improve tissue regeneration/repair in wound‐healing models. In conclusion, we identified new cell targets of hAMTCs and their bioactive factors and here provide insight into the beneficial effects observed when these cells are used in therapeutic approaches in vivo. © 2016 The Authors Journal of Tissue Engineering and Regenerative Medicine Published by John Wiley & Sons Ltd.

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

Abstract: An experimental and theoretical study of T cell differentiation in response to mixed-input conditions reveals that cells can tune between Th1 and Th2 states through a continuum of mixed phenotypes.

Cited by 94 publications

References 55 publications

Diverse continuum of CD4⁺T-cell states is determined by hierarchical additive integration of cytokine signals

Diverse continuum of CD4⁺T-cell states is determined by hierarchical additive integration of cytokine signals

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

Human amnion favours tissue repair by inducing the M1-to-M2 switch and enhancing M2 macrophage features

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

Abstract: An experimental and theoretical study of T cell differentiation in response to mixed-input conditions reveals that cells can tune between Th1 and Th2 states through a continuum of mixed phenotypes.

Cited by 94 publications

References 55 publications

Diverse continuum of CD4+T-cell states is determined by hierarchical additive integration of cytokine signals

Diverse continuum of CD4+T-cell states is determined by hierarchical additive integration of cytokine signals

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

Human amnion favours tissue repair by inducing the M1-to-M2 switch and enhancing M2 macrophage features

Contact Info

Product

Resources

About

Diverse continuum of CD4⁺T-cell states is determined by hierarchical additive integration of cytokine signals

Diverse continuum of CD4⁺T-cell states is determined by hierarchical additive integration of cytokine signals

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