Temporal perturbation of ERK dynamics reveals network architecture of FGF2/MAPK signaling
2019; Springer Nature; Volume: 15; Issue: 11 Linguagem: Inglês
10.15252/msb.20198947
ISSN1744-4292
AutoresYannick Blum, Jan Mikelson, Maciej Dobrzyński, Hyunryul Ryu, Marc‐Antoine Jacques, Noo Li Jeon, Mustafa Khammash, Olivier Pertz,
Tópico(s)Hippo pathway signaling and YAP/TAZ
ResumoArticle19 November 2019Open Access Source DataTransparent process Temporal perturbation of ERK dynamics reveals network architecture of FGF2/MAPK signaling Yannick Blum Yannick Blum Institute of Cell Biology, University of Bern, Bern, Switzerland Search for more papers by this author Jan Mikelson Jan Mikelson Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland Search for more papers by this author Maciej Dobrzyński Maciej Dobrzyński orcid.org/0000-0002-0208-7758 Institute of Cell Biology, University of Bern, Bern, Switzerland Search for more papers by this author Hyunryul Ryu Hyunryul Ryu Institute of Advanced Machinery and Design, Seoul National University, Seoul, Korea Search for more papers by this author Marc-Antoine Jacques Marc-Antoine Jacques Institute of Cell Biology, University of Bern, Bern, Switzerland Search for more papers by this author Noo Li Jeon Noo Li Jeon Institute of Advanced Machinery and Design, Seoul National University, Seoul, Korea Search for more papers by this author Mustafa Khammash Mustafa Khammash Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland Search for more papers by this author Olivier Pertz Corresponding Author Olivier Pertz [email protected] orcid.org/0000-0001-8579-4919 Institute of Cell Biology, University of Bern, Bern, Switzerland Search for more papers by this author Yannick Blum Yannick Blum Institute of Cell Biology, University of Bern, Bern, Switzerland Search for more papers by this author Jan Mikelson Jan Mikelson Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland Search for more papers by this author Maciej Dobrzyński Maciej Dobrzyński orcid.org/0000-0002-0208-7758 Institute of Cell Biology, University of Bern, Bern, Switzerland Search for more papers by this author Hyunryul Ryu Hyunryul Ryu Institute of Advanced Machinery and Design, Seoul National University, Seoul, Korea Search for more papers by this author Marc-Antoine Jacques Marc-Antoine Jacques Institute of Cell Biology, University of Bern, Bern, Switzerland Search for more papers by this author Noo Li Jeon Noo Li Jeon Institute of Advanced Machinery and Design, Seoul National University, Seoul, Korea Search for more papers by this author Mustafa Khammash Mustafa Khammash Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland Search for more papers by this author Olivier Pertz Corresponding Author Olivier Pertz [email protected] orcid.org/0000-0001-8579-4919 Institute of Cell Biology, University of Bern, Bern, Switzerland Search for more papers by this author Author Information Yannick Blum1,‡, Jan Mikelson2,‡, Maciej Dobrzyński1,‡, Hyunryul Ryu3,4, Marc-Antoine Jacques1, Noo Li Jeon3, Mustafa Khammash2 and Olivier Pertz *,1 1Institute of Cell Biology, University of Bern, Bern, Switzerland 2Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland 3Institute of Advanced Machinery and Design, Seoul National University, Seoul, Korea 4Present address: Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, MA, USA ‡These authors contributed equally to this work *Corresponding author. Tel: +41 31 631 46 37; E-mail: [email protected] Molecular Systems Biology (2019)15:e8947https://doi.org/10.15252/msb.20198947 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract Stimulation of PC-12 cells with epidermal (EGF) versus nerve (NGF) growth factors (GFs) biases the distribution between transient and sustained single-cell ERK activity states, and between proliferation and differentiation fates within a cell population. We report that fibroblast GF (FGF2) evokes a distinct behavior that consists of a gradually changing population distribution of transient/sustained ERK signaling states in response to increasing inputs in a dose response. Temporally controlled GF perturbations of MAPK signaling dynamics applied using microfluidics reveal that this wider mix of ERK states emerges through the combination of an intracellular feedback, and competition of FGF2 binding to FGF receptors (FGFRs) and heparan sulfate proteoglycan (HSPG) co-receptors. We show that the latter experimental modality is instructive for model selection using a Bayesian parameter inference. Our results provide novel insights into how different receptor tyrosine kinase (RTK) systems differentially wire the MAPK network to fine-tune fate decisions at the cell population level. Synopsis Analyses of single-cell MAPK/ERK signaling dynamics in response to temporally controlled EGF, NGF, FGF2 stimulations show that FGF2 evokes distinct signaling dynamics compared to EGF/NGF. A mathematical model that accounts for these responses is presented. FGF2 leads to distinct population distributions of dynamic ERK states compared to EGF/NGF. Increasing FGF2 inputs gradually shifts the population distribution of ERK states. Temporal perturbations provide new insights into FGF2/MAPK signaling network structure. FGFR/HSPG interactions enable the gradually changing distributions of ERK states. Introduction Signaling dynamics, rather than steady states, have been shown to control cell fate responses (Levine et al, 2013). For multiple systems including receptor tyrosine kinase signaling (RTK), signaling heterogeneity can explain the fate variability observed within a cell population (Cohen-Saidon et al, 2009; Chen et al, 2012). Both biological noise extrinsic to individual cells and intrinsic variability within signaling networks shape the cell fate. It has been proposed that the dynamic nature of signal transduction enables accurate information transmission in the presence of noise (Wollman, 2018). Measuring single-cell signaling dynamics is therefore key to understanding how cellular responses correlate with specific cell fate decisions. The extracellular signal-regulated kinase (ERK) is a key regulator of fates such as proliferation and differentiation. It functions within a mitogen-activated protein kinase (MAPK) signaling pathway in which growth factor (GF) receptors activate a membrane-resident Ras GTPase that subsequently triggers a MAPK cascade leading to ERK activation (Avraham & Yarden, 2011). Rat adrenal pheochromocytoma PC-12 cells have been widely used as a model system to study the regulation of cell fate by MAPK signaling (Marshall, 1995). Stimulation with EGF or NGF leads to population-averaged transient or sustained ERK states, which specifically trigger proliferation or differentiation. Thus, ERK signal duration has been proposed as a key determinant of cell fate (Marshall, 1995; Santos et al, 2007). These distinct ERK states result from GF-dependent control of the MAPK network (Santos et al, 2007), with negative and positive feedback producing all-or-none adaptive or bistable outputs, respectively (Xiong & Ferrell, 2003; Santos et al, 2007; Avraham & Yarden, 2011). More recently, single-cell assays have indicated that EGF/NGF induces heterogeneous dynamic signaling states across a cell population (Ryu et al, 2015). While EGF leads to transient ERK activity responses, NGF induces transient or sustained responses in an isogenic population due to variability in expression of signaling components and receptor-dependent modulation of the negative and positive feedback loops. This might explain how NGF can induce a heterogeneous mix of differentiating and proliferating cells (Chen et al, 2012). Further support that dynamic ERK signaling states control fate decisions stems from model-based prediction of dynamic GF stimulation schemes that induce synthetic ERK activity patterns that determine fate decision independently of GF identity (Ryu et al, 2015). An additional GF, FGF2, also activates ERK through FGF receptors (FGFRs) and regulates processes such as angiogenesis, wound healing, and development (Ornitz & Itoh, 2015). Upon FGF2 stimulation, FGFR dimerizes, autophosphorylates, recruits adaptors, and activates the Ras/RAF/MEK/ERK cascade (Ornitz & Itoh, 2015). In PC-12 cells, FGF2 induces sustained ERK activity, which correlates with differentiation (Qui & Green, 1992). FGF–FGFR interactions are further regulated by a heparan sulfate proteoglycan co-receptor (HSPG) (Ornitz, 2000; Matsuo & Kimura-Yoshida, 2013). FGF2 initially binds to HSPGs through a high-affinity interaction, followed by a 2nd lower affinity interaction leading to a HSPG/FGF2/FGFR trimeric complex. The latter subsequently dimerizes to a dimer of trimer complex that can autophosphorylate and signal downstream (Ornitz & Itoh, 2015). In marked contrast to signaling systems that exhibit sigmoidal dose responses, FGF2 elicits a biphasic dose response of signaling and cell fate outputs, where an intermediate concentration of FGF2 elicits higher activation of signaling and fate outputs compared to low and high FGF2 concentrations. For example, bell-shaped neuronal differentiation (Williams et al, 1994), or cell proliferation fate outputs (Zhu et al, 2010) are observed in FGF2 dose–response challenges in different cell systems. This correlates with a biphasic dose response of ERK activity outputs (Zhu et al, 2010; Kanodia et al, 2014). The ability of FGF2 to induce biphasic dose responses has been proposed to emerge from competition of FGF2 binding to HSPGs and the FGFR (Kanodia et al, 2014). However, the FGF2-dependent signaling network has been significantly less defined than the network downstream of EGF and NGF. An important question in the signaling field is how different RTKs can specify different cell fates by using the MAPK network. Here, we explore how FGF2 controls ERK activity dynamics at the single-cell level in PC-12 cells. We find that FGF2 induces a mix of dynamic ERK states that are distinct from those of EGF/NGF. An increase in FGF2 input gradually modulates the distribution of transient/sustained ERK states. Using microfluidics to temporally perturb the MAPK signaling network, we further explore the logic behind these different signaling states. Our data together with mathematical modeling show that the FGF2-dependent MAPK signaling network underlying these responses consists of an extracellular FGF2/FGFR/HSPG interaction layer coupled to an intracellular MAPK network layer with a simple negative feedback. We conclude that EGF, NGF, and FGF2 wire the MAPK network differently to induce distinct population distributions of ERK states that fine-tune fate decisions at the cell population level. Our data therefore provide new insights into how different RTKs decode binding of their cognate GF by engaging distinct MAPK network structures. Our results suggest that the FGF2/MAPK signaling network has evolved to translate increasing FGF2 inputs into gradual changes in the population distribution of dynamic ERK states. This might be important to regulate fate decisions during the interpretation of morphogen gradient. Results FGF2 induces dynamic signaling states distinct from those induced by EGF/NGF EGF/NGF-triggered ERK activity responses have been widely studied in PC-12 cells. However, single-cell studies have revealed a much higher signaling complexity than previously anticipated (Ryu et al, 2015). Here, we asked if FGF2 potentially induces ERK activity dynamics within a cell population that are distinct from those of EGF/NGF. To study FGF2 signaling at the single-cell level, we used a PC-12 cell line stably expressing EKAR2G, a fluorescence resonance energy transfer (FRET)-based biosensor for endogenous, cytosolic ERK activity. EKAR2G has been extensively validated elsewhere (Harvey et al, 2008; Fritz et al, 2013). To extract single-cell temporal ERK activity patterns, we used a CellProfiler-based (Kamentsky et al, 2011) image analysis pipeline for segmentation and tracking of single cells, and for computation of a per-cell average FRET biosensor ratio. We used a computer-programmable microfluidic device to temporally perturb cells using GF pulses (Fig 1A). Figure 1. FGF2 induces different dynamic ERK activity signaling states than EGF/NGF A. Flow-based, microfluidic device for temporal GF delivery. Computer-controlled, pressure pump enables mixing of medium and GFs to deliver GF pulses in cells cultured in the microfluidic device. The right panel illustrates typical GF stimulation patterns. B. Representative EKAR2G ratio images of cells treated with 25 ng/ml EGF, NGF, and FGF2. Ratio images are color-coded so that warm/cold colors represent high/low ERK activation levels. Scale bar = 50 μm. C, D. Population averages of ERK activity dynamics in response to stimulation with 25 ng/ml (C), or with a dose–response challenge using 0.25, 2.5, 25, and 250 ng/ml EGF, NGF, and FGF2 (D). Single-cell time series were normalized to their own means before GF stimulation, t = [0, 40]. Red curve at the bottom of panel C indicates GF stimulation profile measured simultaneously using an Alexa 546-labeled dextran. N = [48, 120] cells per GF concentration. ERK dynamics measured at 2′ intervals. E. Hierarchical clustering of pooled (N = 983) single-cell time series from panel (D). To focus on relevant ERK dynamics, we trimmed x-axis to t = [36, 100] min. Each row of the heatmap corresponds to a time series of a single cell. We used dynamic time warping and Ward's linkage method for building the dendrogram, which was then cut to distinguish 6 clusters that are color-coded on the left. F. Average ERK activity across 6 clusters identified in panel (E), color-coded as in (E). G. Distribution of ERK activity trajectories across 6 clusters from panels (E and F) in response to different GF dosages. H. Separability between populations of single-cell trajectories calculated as normalized area under the curve of Jeffries–Matusita distance along time (Materials and Methods, Appendix Fig S2B). The dendrogram was created using the complete-linkage method. Data information: In panels (C, D, and F), gray band indicates 95% CI for the mean, representative of 3 replicates. In panels (D and F), black horizontal bar indicates GF stimulation. Source data are available online for this figure. Source Data for Figure 1 [msb198947-sup-0006-SDataFig1.zip] Download figure Download PowerPoint First, we stimulated cells with a typical EGF/NGF/FGF2 concentration of 25 ng/ml (Fig 1B and C). We used a fluorescent dextran for quality control of the GF delivery by the microfluidic device (Fig 1C, lower red trace). As expected, when we evaluated population-averaged temporal ERK activity patterns, EGF led to transient ERK activity, while NGF induced a peak followed by sustained ERK activity with an amplitude lower than the peak. In contrast, FGF2 led to a transient ERK peak that was sharper than the one evoked by EGF. After this fast adaptation, ERK activity gradually increases over time. Since increasing FGF2 concentration induces a biphasic dose response in fate determination and ERK activity in a variety of cell systems (Zhu et al, 2010; Kanodia et al, 2014), we also tested such increase in our system. We stimulated PC-12 cells with EGF/NGF/FGF2 concentration in a 0.25–250 ng/ml range, as in previous works (Zhu et al, 2010; Kanodia et al, 2014; Fig 1D). On average, all EGF concentrations triggered an initial ERK peak with identical amplitude but with faster adaptation at higher GF concentrations. In contrast, 0.25 ng/ml NGF only induced moderate ERK activity without an initial ERK activity peak. 2.5 ng/ml NGF led to sustained ERK activity after a small initial peak. 25 and 250 ng/ml led to almost indistinguishable profiles of an ERK activity peak followed by sustained ERK activity. FGF2 stimulation led to different population-averaged temporal ERK activity patterns than both EGF and NGF. Indeed, 0.25 ng/ml FGF2 led to sustained ERK activity without a robust initial peak, whereas 2.5, 25, and 250 ng/ml FGF2 led to a clearly defined initial ERK transient. At 25 and 250 ng/ml FGF2, after the initial transient, we again observed slow ERK activity recovery. The previously described biphasic dose response of ERK activity is evident when we consider a time point after the initial ERK activity peak (Zhu et al, 2010; Kanodia et al, 2014). Additionally, we observe that the amplitude of the 1st ERK peak activity was highly similar across GF identity/concentration (Fig EV1A). Click here to expand this figure. Figure EV1. Raw single-cell ERK activity trajectories of the EGF, NGF, and FGF dose response Single-cell ERK activity measured at 10 min before and after sustained GF stimulation (T = 30′ and 50′). Single-cell trajectories were normalized to their own means before GF stimulation, t = [0, 40]. Lower and upper hinge correspond to 25th and 75th percentiles. Lower and upper whiskers correspond to 1.5*IQR from the hinge. Replicates: 1. ERK activity dynamics in response to stimulation with a dose–response challenge using 0.25, 2.5, 25, and 250 ng/ml EGF, NGF, and FGF2. Single-cell time series were normalized to their own means before GF stimulation, t = [36, 40] min. Red curve indicates the population mean; black horizontal bar indicates GF stimulation. N = [48, 120] cells per GF concentration. ERK dynamics measured at 2′ intervals. Single-cell ERK trajectories within 6 clusters identified in Fig 1F. We used hierarchical clustering with dynamic time warping and Ward's linkage method for building the dendrogram, which was then cut to distinguish 6 clusters. Hierarchical clustering of individual GF dose–response challenges from (B) with the same approach as (B) but for a longer interval (t = [36, 200] min). The left column indicates cluster means and 95% CI for the mean, and the right column is the distribution of single-cell trajectories across 4 clusters, representative of 3 replicates. Download figure Download PowerPoint FGF2 dose response leads to an ERK activity population distribution that is wider than that associated with EGF and NGF Heterogeneous single-cell dynamic signaling states were evident when single-cell temporal ERK activity patterns were overlaid over population-averaged temporal ERK activity patterns (Fig EV1B). To examine this heterogeneity, we pooled all trajectories (EGF, NGF, and FGF2—4 concentrations) using a time interval ranging from shortly before GF stimulation to 60′ after stimulation (Fig 1E). We then applied hierarchical clustering with dynamic time warping (DTW) (Giorgino, 2009) to extract classes of single-cell temporal ERK activity patterns. DTW calculates similarity between two time series by matching shape features that may be shifted in time between the two series. Visual inspection of the dendrogram obtained from this procedure led us to identify 6 major dynamic patterns/clusters, which are highlighted by vertical color bars in Fig 1E. Figure 1F summarizes population averages for each cluster, while Fig EV1C displays single-cell temporal ERK activity patterns for each cluster. Even though all clusters differ in amplitude, we can recognize adaptive behavior in clusters 1, 3, and 4, and sustained activation in clusters 2, 5, and 6. We then computed the population distribution of these representative single-cell temporal ERK activity patterns across all experimental conditions (Fig 1G). Low-amplitude adaptive and sustained ERK activities (clusters 1 and 2) were largely absent from responses to all EGF stimulations, indicating robust ERK signaling. With the increase of EGF, an adaptive cluster 3 replaced sustained clusters 5 and 6. In contrast, the lowest NGF dose induced a mix of low-amplitude adaptive and sustained responses. High NGF concentrations induced high-amplitude sustained clusters 5 and 6 with a decreasing contribution of intermediate responses. We observed a wider mix of cluster distribution for FGF2 dose response. 0.25 ng/ml FGF2 led to a mix of low- and high-amplitude sustained responses. 2.5 ng/ml FGF2 decreased sustained responses in favor of cluster 4 (high amplitude, intermediate adaptation). Then, with an increased FGF2 dosage the distribution shifted to strongly adaptive responses with low and high amplitudes. Intrigued by the fact that FGF2 induces slow ERK activity recovery after the 1st peak at 25 and 250 ng/ml in population-averaged measurements (Fig 1D), we tested if this was also the case at the single-cell level. For that purpose, we repeated the clustering analysis individually for EGF, NGF, and FGF2 dose–response experiments on a time ranging from shortly before GF addition to 160′ after stimulation (Fig EV1D). For FGF2, this again identified single-cell temporal ERK activity patterns that displayed a robust 1st ERK activity peak followed by different levels of adaptation (clusters 1–3), or sustained ERK activity. Importantly, the three adaptive clusters that were present at high FGF2 concentrations displayed slow ERK activity recovery after adaptation. This specific phenomenon is not present in EGF/NGF dose responses. To independently assess that FGF2 evokes a distinct and wider mix of single-cell temporal ERK activity patterns than EGF/NGF in a dose response, we applied PCA decomposition and calculated accumulated pairwise distances of the response distribution at different time points (Figs 1H and EV2). Both approaches showed that single-cell temporal ERK activity pattern population distributions for different GF dosages are more separated for FGF2 (Appendix Text). Click here to expand this figure. Figure EV2. Separability of ERK states in the EGF, NGF, and FGF2 dose–response challenge Principal component analysis (PCA) of a pooled dataset from Fig 1E. The first two components account for 85% of the overall variability. Here, we use the same data trimmed to t = [36,100] min as in clustering in Fig 1E. Note that in addition to GF responses, responses of untreated cells are also shown, although they were not used in the decomposition (red points). Schematic of distance calculation between two populations of single-cell time series. Two synthetic populations of noisy time series data consist of 100 trajectories each, with each trajectory spanning t = [0, 5] T with 0.1 T interval. Step 1: At every measured time point, calculate Jeffries–Matusita distance (dJM) between two distributions of a measured quantity. Step 2: Calculate area under the curve (AUC) of dJM and express it as fraction of the maximum AUC of dJM, which is 2dxN, where is the interval length and is the number of measured time points. The normalized AUC of dJM is used to construct dendrogram in Fig 1H. dJM over time for each GF concentration pair (0.25, 2.5, 25, 250 ng/ml) for EGF, NGF, and FGF2 stimulation. Here, we use the same data trimmed to t = [36,200] min as in Figure EV1B. Download figure Download PowerPoint Decoding FGF2/MAPK signaling network properties by temporal perturbation of ERK dynamics We then sought to identify the signaling network structure that explains how the FGFR/MAPK network evokes ERK states different from those evoked by EGF/NGF. For that purpose, we dynamically perturbed cells by delivering single or multiple GF pulses of different lengths and concentrations using our microfluidic device (Fig 1A). This approach captures salient features of the MAPK network not accessible with sustained GF stimulation and, in many cases, induces population-homogeneous signaling states that are simpler to interpret (Ryu et al, 2015). We stimulated PC-12 cells with pulses of 3′, 10′, and 60′ with the four concentrations of each GF used previously. We plotted the population-averaged temporal ERK activity patterns (Fig 2) and used hierarchical clustering to extract representative dynamic patterns for each GF pulse pattern (Fig EV3). Figure 2. ERK activity dynamics in response to single-pulse stimulation A–C. Population average of ERK activity dynamics in response to 3′, 10′, and 60′ EGF (A), NGF (B), and FGF2 (C) single-pulse stimulation. Single-cell time series were normalized to their own means before the GF stimulation, t = [0, 40]. Solid lines—population mean, N = [39, 166], replicates: EGF: 1, NGF: 1, FGF: representative of 3 replicates; gray bands—95% CI for the mean; black horizontal bars—duration of GF stimulation. Source data are available online for this figure. Source Data for Figure 2 [msb198947-sup-0007-SDataFig2.zip] Download figure Download PowerPoint Click here to expand this figure. Figure EV3. Clustering of dynamic ERK activity signaling states in response to single-pulse GF stimulation regimes A–C. Hierarchical clustering of dynamic ERK responses to a single 3′, 10′, and 60′ pulse of EGF (A), NGF (B), and FGF2 (C). We used dynamic time warping and Ward's linkage method for building the dendrogram, which was then cut to highlight 3 or 4 main branches with major dynamic patterns. GF dose responses in each panel were clustered independently; thus, cluster averages that share the same color across panels are unrelated. Download figure Download PowerPoint The pulsed EGF/NGF dose responses were consistent with our previous observations (Ryu et al, 2015). Population-averaged temporal ERK activity patterns exhibited a full-amplitude initial ERK activity peak followed by robust adaptation for all EGF concentrations for 3′ or 10′ pulse, except for a 3′ 0.25 ng/ml EGF pulse (Fig 2A) where the peak was less pronounced. The 60′ EGF pulse revealed distinct adaptation kinetics after the initial ERK activity peak with faster adaptation at higher EGF dose. As observed in sustained stimulation, full adaptation occurred concomitantly with EGF washout. Clustering of single-cell temporal ERK activity patterns revealed adaptive responses across the EGF doses and pulsing schemes (Fig EV3A). In the case of NGF, the 0.25 ng/ml concentration did not yield ERK activation across any pulsing scheme (Fig 3B). Above this concentration, high NGF input (achieved by increasing dose and/or pulse duration) gradually shifted the population-averaged temporal ERK activity patterns from transient to more sustained profile. Clustering of single-cell temporal ERK activity patterns revealed a mix of transient and sustained responses, with sustained clusters contributing more at high NGF inputs (Fig EV3B). Figure 3. ERK activity dynamics in response to multi-pulse stimulation Population average of ERK activity dynamics in response to a multi-pulse 3′–20′ EGF stimulation. Single-cell time series were normalized to their own means before the GF stimulation, t = [0, 40]. Cluster averages of ERK activity and distribution of single-cell trajectories across clusters. Population average of ERK activity dynamics in response to a multi-pulse 3′–20′ NGF stimulation. Single-cell time series were normalized to their own means before the GF stimulation, t = [0, 40]. Cluster averages of ERK activity and distribution of single-cell trajectories across clusters. Population average of ERK activity dynamics in response to a multi-pulse 3′–20′ FGF2 stimulation. Single-cell time series were normalized to their own means before the GF stimulation, t = [0, 40]. Cluster averages of ERK activity and distribution of single-cell trajectories across clusters. Data information: We performed hierarchical clustering with the Manhattan distance and the complete-linkage method; we cut the dendrogram at 3 (B, D) and 4 clusters (F) for visualization. Solid lines—population mean, N = [52, 91], replicates: EGF: 1, NGF: 1, FGF: representative of 3 replicates; gray bands—95% CI for the mean; black horizontal bars—duration of GF stimulation. Source data are available online for this figure. Source Data for Figure 3 [msb198947-sup-0008-SDataFig3.zip] Download figure Download PowerPoint Intriguingly, varying FGF2 dosage and pulse duration again revealed more complex population-averaged temporal ERK activity patterns than for EGF/NGF (Fig 2C). At a threshold input, we observed a new dynamic pattern, whereby an initial adaptive ERK activity peak was followed by a rebound that then decayed slowly. This dynamic pattern was visible at 25 ng/ml 10′ pulse and at 250 ng/ml 3′ and 10′ pulse. Lower or shorter FGF2 dosages induced only transient ERK activities. Clustering revealed a transient cluster as well as the characteristic ERK activity with a rebound (Fig EV3C). The latter cluster was enriched at high FGF2 inputs. The 60′ FGF2 pulse led to even more complex population-averaged temporal ERK activity patterns. The pattern with ERK activity rebound emerged at 2.5–250 ng/ml FGF2, and for these concentrations, the rebound ensued only after GF was washed away. The adaptation after the initial peak was stronger at higher GF dosages. In contrast, at the lower concentration of 0.25 ng/ml FGF2, we observed sustained activation during the GF pulse and a slow decay after GF washout. Clustering of responses to 60′ pulse confirms that the lowest FGF2 dosage induces high and low sustained responses without a rebound, while higher GF concentrations result in a much stronger adaptation after the initial peak. To probe network architectural features that might work at longer timescales and to test how the MAPK network responds to novel GF inputs before full adaptation, we subjected PC-12 cells to multiple 3′ pulses separated by 20′ pauses (Fig 3A). Multiple pulses of EGF led to transient population-averaged temporal ERK activity patterns with 20′ timescale adaptation that were in phase with the puls
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