IL-4+TGF-β in presence of pbCD3/sCD28 activation induce generation of CD4⁺IL-9⁺ T cells.

<p><b>A)</b> CD4⁺CD25⁻ T cells (1.0×10⁶/ml in 24 well plates) were activated with plate bound-anti-CD3 mAb (pbCD3)/soluble-anti-CD28 mAb (sCD28) in presence or absence of IL-4 or TGF-β or IL-4+TGF-β for 96hrs and analyzed by flow cytometry for IL-9 expression. IL-4+TGF-β in combination induced significantly higher percentage of CD4⁺ T cells positive for IL-9, as compared to IL-4, TGF-β, or neither (<i>n = 14</i>). Data is expressed as the mean±SD. <b>B)</b> CD4⁺CD25⁻ T cells, CD4⁺CD25⁻CD45RA⁺ T cells (naïve T cells), CD4⁺CD25⁻CD45RO⁺ T cells (resting memory T cells) (2.0×10⁵/ml in 96 well plates) were activated with pbCD3/sCD28 in presence of IL-4+TGF-β in a 96 well plate for 96hrs. Cells were surface stained for CD4 PerCP-Cy5.5 and intracellular stained for IL-9 PE. IL-4+TGF-β in combination induced IL-9 expression by both naïve and memory T cells, but memory T cells expressed high levels of IL-9. Data are representative of six independent experiments (six different donors).</p

IL-1β amplifies IL-4+TGF-β induced IL-9 production by memory CD4⁺CD25⁻CD45RO⁺ T cells.

<p>Resting memory CD4⁺CD25⁻CD45RO⁺ T cells (2.0×10⁵/ml in 96 well plates) activated with pbCD3/sCD28 alone or with IL-4+TGF-β, in the presence or absence of IL-1β, IL-2, IL-6, IL-12, and IL-21 for 96hrs and supernatants were collected. Influence of IL-1β, IL-2, IL-6, IL-12, and IL-21 on IL-9 production of IL-4+TGF-β treated CD4⁺CD25⁻CD45RO⁺ T cells was examined. IL-1β, IL-12 or IL-21 significantly elevated IL-4+TGF-β induced IL-9 production, but IL-1β had significantly higher influence compared to IL-12 or IL-21 (<i>n = 3</i>). Data is expressed as the mean±SD.</p

CD4⁺CD25⁻ T cells activated with IL-4+TGF-β express more IL-9 than Th1, Th2, Th17, or iTregs.

<p>CD4⁺CD25⁻ T cells (1.0×10⁶/ml in 24 well plates) were activated with pbCD3/sCD28 in presence or absence of IL-4+TGF-β or Th1-, Th2-, Th17-, iTreg-polarizing condition for 96hrs. Cells were harvested, gated on CD4⁺ T cells, and were analyzed for IL-9⁺ cells by flow cytometry or were used to quantitate IL-9 transcripts by real-time PCR. Data is expressed as the mean±SD. (A) 2% of Th2 cells, 4% of iTregs, or 10% of cells treated with IL-4+TGF-β in combination were IL-9⁺, whereas Th1-, Th17-, or Th0-cells had negligible number of IL-9⁺ cells (<i>n = 7</i>); (B) Log-transformed ratios of IL-9 mRNA copies to 18S rRNA are shown. Cells treated with IL-4+TGF-β in combination had significantly higher levels of IL-9 mRNA as compared to polarized Th1-, Th2-, Th17-, iTreg-, or Th0-cells (<i>n</i> = 9).</p

Cytokine and transcription factor profile of memory CD4⁺CD25⁻CD45RO⁺ T cells activated with IL-4+TGF-β.

<p><b>A)</b> Log-transformed quantities of cytokines (pg/ml) are shown. CD4⁺CD25⁻CD45RO⁺ T cells (1.0×10⁶/ml in 24 well plates) were activated with pbCD3/sCD28 in presence or absence of IL-4+TGF-β. Supernatants were collected at 96hrs post activation and IFNγ, IL-2, IL-5, IL-9, IL-10, IL-13, and IL-17 were quantified by ELISA. IL-4+TGF-β treated CD4⁺ T cells produced significantly high IL-2 and IL-9, but significantly low IFNγ, IL-13, and IL-17, as compared to CD4⁺CD25⁻CD45RO⁺ T cells not treated with IL-4+TGF-β (<i>n = 3</i>). Data is expressed as the mean±SD. <b>B)</b> Log-transformed ratios of mRNA copies to GAPDH mRNA copies for GATA3, RORC, IL-9, and Tbet are shown. CD4⁺CD25⁻CD45RO⁺ T cells (1.0×10⁶/ml in 24 well plates) were activated with pbCD3/sCD28 in presence of IL-4+TGF-β. Cells were harvested and single cell sorted. IL-9 transcripts were quantified by qt-RT-PCR. 10,000 cells comprising of total cell population (TC) was also taken and the gene expression was averaged for single cell for reference. Cells positive for IL-9 transcripts were further quantitated for GATA3, RORC, and Tbet (<i>n</i> = 3). As IL-9 is expressed in only 10% of all CD4⁺ T cells activated with pbCD3/sCD28 in presence of IL-4+TGF-β, average IL-9 mRNA copies of TC are always lower than that of a single IL-9⁺ cell. CD4⁺IL-9⁺ T cells expressed GATA3 and RORC, but not Tbet. <b>C)</b> CD4⁺CD25⁻CD45RO⁺ T cells (1.0×10⁶/ml in 24 well plates) were activated with pbCD3/sCD28 in presence of IL-4+TGF-β. Cells were surface stained for CD4 and intracellular stained for IL-9 and FOXP3. Cells were gated for CD4 and then IL-9⁺ or/and FOXP3⁺ cells were analyzed. 25% of CD4⁺IL-9⁺ T cells were also FOXP3⁺. Data are representative of seven independent experiments. <b>D)</b> CD4⁺CD25⁻CD45RO⁺ T cells (1.0×10⁶/ml in 24 well plates) were activated with pbCD3/sCD28 in presence or absence of IL-4 or TGF-β or IL-4 plus TGF-β for 96hrs and analyzed by flow cytometry for FOXP3 expression. IL-4 significantly inhibited TGF-β induced FOXP3 expression (<i>n = 3</i>). Data is expressed as the mean±SD.</p

Mapping input function of cytokine expression reveals a highly heterogeneous population under mixed input conditions.

<p>(A) Histograms of IFN-γ secretion levels measured in a population of cells cultured with decreasing levels of IL-4 and increasing levels of IL-12 (bright to dark colour). Dashed curve, isotype control. (B) Histograms of IL-4 secretion levels measured in a population of cells cultured with increasing levels of IL-4 and a constant level of IL-12 (yellow to blue colour). Dashed curve, isotype control. (C, D) Measured MFI for IFN-γ (C) and IL-4 (D), in response to a matrix of orthogonal gradients of the two input signals IL-12 and IL-4. Regions 1, 2, and m are the same as in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1001616#pbio-1001616-g002" target="_blank">Figure 2C,D</a>. (E–G) Scatter plots showing normalized measured expression patterns of IFN-γ and IL-4. Under mixed conditions cell population is highly heterogeneous in cytokine expression, with subpopulations expressing only IFN-γ (+/−), only IL-4 (−/+), both cytokines (+/+), and neither one (−/−). (H) Distributions of the parameter α′, representing the ratio between expression levels of IFN-γ and IL-4 (see definition in the main text) for cell populations cultured under Th1, Th2, and mixed input conditions. As for the TFs, the distributions under Th1 and Th2 conditions show a single peak. However, in contrast with TF, under mixed input conditions the distribution is broad and covers the whole range of values between α′ = 90° (Th2) and α′ = 0° (Th1).</p

A two-stage scheme for continuously tunable Th1–Th2 differentiation.

<p>The two input signals (top) drive the GRN that controls differentiation of CD4⁺ T cells. The levels of the two lineage-specifying transcription factors, T-bet and GATA3, tune (bar graphs) from a Th1 state (left) to a Th2 state (right), through a continuum of intermediate states in which both factors are co-expressed. Cytokine expression upon restimulation is stochastic. The fraction of cells that express IFN-γ or IL-4 is biased by the levels of the corresponding transcription factors, as well as by other factors (dashed arrows). These two stochastic processes are independent. This model results in a heterogonous cell population (scatter plots, right), with cells expressing only IFN-γ (yellow ellipse), only IL-4 (blue), both cytokines (green), or neither (white). The fraction of cells in each of the four subpopulations continuously tunes with changing inputs. Expression levels of all four factors are represented schematically by the cell populations at the bottom. The internal color represents levels of T-bet and GATA3 tuning from Th1 (yellow, T-bet high, GATA3 low) to Th2 (blue, T-bet low, GATA3 high), through intermediate levels of green. The outer color represents cytokine expression upon restimulation, showing a higher level of heterogeneity. For clarity, we don't show here noise in gene expression (for example, cells cultured under Th1 conditions express different levels of T-bet, and similarly for the other proteins and conditions). Note that other factors influence this differentiation process (TCR stimulation strength and duration, other cytokines), which we assume here to be constant across all conditions.</p

Understanding the logic of cell fate decisions by studying response to a matrix of input combinations at the single cell level.

<p>(A) A schematic representation of cell differentiation through a binary cell fate decision. Signal A drives differentiation of a precursor cell into the differentiated state X. Signal B drives it into the state Y. Cell decision is mediated by a GRN that typically involves interacting signaling pathways that contain various positive and negative feedback loops. (B) Mutual exclusion model: The GRN has two stable states, each corresponding to the phenotype of a specific differentiated lineage. (C) Multiple states model: In a range of input conditions cells are found in a third state, co-expressing characteristic genes of both lineages. (D) Continuous transition model: As the input conditions vary, a single steady state continuously shifts between the two extreme phenotypes, giving rise to a continuum of differentiated cell states with mixed characteristics. (E) Single cell variability: Under mixed input conditions, the cell population can be either heterogeneous, with each cell in either the X or Y “pure state” (i), or in a “mixed state,” with cells co-expressing lineage-specific factors (ii). Note that under polarizing input conditions (top-left and bottom-right corners), all models are indistinguishable.</p

Input function for Th1/Th2 cell differentiation under mixed input conditions reveals a tunable mixed phenotype.

<p>(A) Histograms of T-bet levels measured in populations of cells cultured with decreasing levels of IL-4 and increasing levels of IL-12 (yellow to blue color). Dashed curve, cells from a T-bet knockout mouse. (B) Histograms of GATA3 levels measured in populations of cells cultured with increasing levels of IL-4 and a constant level of IL-12 (yellow to blue color). Dashed curve, GATA3 isotype control staining. (C, D) Measured median fluorescence intensities (MFI) for T-bet (C) and GATA3 (D), in response to a matrix of orthogonal gradients of the two input signals IL-12 and IL-4. Regions 1 and 2 represent standard polarizing conditions used to generate a Th1 or Th2 response, respectively. Region m represents a state with mixed inputs, resulting in expression of both T-bet and GATA3. (E–G) Scatter plots showing normalized measured expression patterns of T-bet and GATA3, under the conditions marked by 1,m,2 in panels (C, D) (see <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1001616#pbio.1001616.s020" target="_blank">Text S1</a> for details). A single, unimodal population is observed, which shifts in the T-bet-GATA3 plane in response to input signals. Colored dots in each panel show the population median. (H) Distributions of the parameter α, representing the ratio between expression levels of T-bet and GATA3 (see F and definition in the main text), for cell populations cultured under Th1, Th2, and mixed input conditions. The distributions all show a single peak, and continuously shift from a Th1 (α≈90°) to a Th2 state (α≈0°). (I) Representative images of cells cultured under various input conditions as indicated, fixed and stained for T-bet (blue, pseudo-color) and GATA3 (red, pseudo-color). Images were acquired using fluorescent flow microscopy (see <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1001616#s4" target="_blank">Materials and Methods</a>). Three cells for each condition are shown in the bright-field (BF), T-bet, and GATA3 channels. (J) The input function of T-bet is well described by separation of variables, with each input influencing the output in an independent manner. Shown are the calculated dependencies of T-bet on the two inputs, F₁(IL-12) and F₂(IL-4), and the calculated input function given by F₁(IL-12)×F₂(IL-4), which shows a high similarity with the measured data (C). See <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1001616#pbio.1001616.s003" target="_blank">Figure S3</a> for similar results for GATA3, IFN-γ, and IL-4.</p

A model for a continuously tunable mixed-state under mixed input conditions.

<p>(A) A schematic model for the effective GRN module regulating Th1–Th2 differentiation. (B) Analysis of the model for gradual feedback links (<i>n</i> = 1). The number and location of fixed points for given input signals depend on the ratio between the strength of negative and positive feedbacks, (see <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1001616#pbio.1001616.s020" target="_blank">Text S1</a> for details). In region I, the GRN has a single fixed point with a high level of x and a low level of y. In region II it has a single fixed point with low x and high y. In region III it has a single fixed point with co-expression of both TFs, whereas in region IV it has two stable fixed points (bifurcation). (C–D) TF levels shift continuously upon gradual changes in input signal mixtures. Measured levels (MFI) of T-bet and GATA3 (C) along a trajectory in input plane, which interpolates between a Th1 and a Th2 condition (shown in E, gray line). Continuous changes in TF levels are in agreement with model predictions for <i>n</i> = 1, region III (D) and do not show any bi-stability or sharp transitions as predicted by a high-<i>n</i> model, or low-<i>n</i> model region IV. (E–F) Mapping patterns of TF co-expression over the entire input plane, comparing experiment (E) and model (F). For each TF, we define a threshold level T at ∼50% of its maximal expression level. Regions' color represents patterns of co-expression, as shown in the legend.</p

Heterogeneity in cytokine expression is generated through independent and biased stochastic processes.

<p>(A, B) Probability of cytokine expression is biased by the level of the corresponding TF. Cells growing under Th1, Mixed, and Th2 conditions were binned according to their measured level of (A) T-bet or (B) GATA3 expression. For each bin, containing 500 cells, the fraction of cells expressing (A) IFN-γ or (B) IL-4 is plotted versus the mean TF level of cells in that bin. The red line shows the population average level of the TF, and the green line shows the fraction of cytokine positive cells in the entire cell population. (C) GATA3/T-bet ratio is plotted for the four subpopulations of cytokine expression in cells growing under mixed conditions, compared with this ratio measured in cells cultured under polarizing Th1 or Th2 conditions. All four subpopulations of cells that were cultured under mixed conditions show a similar T-bet/GATA3 ratio, irrespective of their cytokine secretion state (−/−, +/−, −/+, +/+: corresponding to the four quadrants of <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1001616#pbio-1001616-g004" target="_blank">Figure 4E</a>). This observation is insensitive to the threshold values used to define the subpopulations (<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1001616#pbio.1001616.s009" target="_blank">Figure S9</a>). (D) Cells were cultured under mixed conditions for 1 wk, and then viably sorted into four subpopulations according to their cytokine expression pattern, as indicated (subpopulations correspond to the four quadrants of <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.1001616#pbio-1001616-g004" target="_blank">Figure 4E</a>). Each subpopulation was re-cultured under mixed conditions for another week, restimulated, and levels of cytokine expression were measured, as shown. Within that week all subpopulations were able to re-populate all four quadrants, such that all cytokine expression patterns reappear.</p