Frequency modulation of ERK activation dynamics rewires cell fate

Article30 November 2015Open Access Source Data Frequency modulation of ERK activation dynamics rewires cell fate Hyunryul Ryu Hyunryul Ryu School of Mechanical and Aerospace Engineering, Seoul National University, Seoul, Korea Institute of Advanced Machinery and Design, Seoul National University, Seoul, Korea Search for more papers by this author Minhwan Chung Minhwan Chung School of Mechanical and Aerospace Engineering, Seoul National University, Seoul, Korea Search for more papers by this author Maciej Dobrzyński Maciej Dobrzyński System Biology Ireland, University College Dublin, Belfield, Dublin, Ireland Search for more papers by this author Dirk Fey Dirk Fey System Biology Ireland, University College Dublin, Belfield, Dublin, Ireland Search for more papers by this author Yannick Blum Yannick Blum Department of Biomedicine, University of Basel, Basel, Switzerland Search for more papers by this author Sung Sik Lee Sung Sik Lee Institute of Biochemistry, Zurich, Switzerland Search for more papers by this author Matthias Peter Matthias Peter Institute of Biochemistry, Zurich, Switzerland Search for more papers by this author Boris N Kholodenko Corresponding Author Boris N Kholodenko System Biology Ireland, University College Dublin, Belfield, Dublin, Ireland Search for more papers by this author Noo Li Jeon Corresponding Author Noo Li Jeon School of Mechanical and Aerospace Engineering, Seoul National University, Seoul, Korea Institute of Advanced Machinery and Design, Seoul National University, Seoul, Korea Search for more papers by this author Olivier Pertz Corresponding Author Olivier Pertz Department of Biomedicine, University of Basel, Basel, Switzerland Search for more papers by this author Hyunryul Ryu Hyunryul Ryu School of Mechanical and Aerospace Engineering, Seoul National University, Seoul, Korea Institute of Advanced Machinery and Design, Seoul National University, Seoul, Korea Search for more papers by this author Minhwan Chung Minhwan Chung School of Mechanical and Aerospace Engineering, Seoul National University, Seoul, Korea Search for more papers by this author Maciej Dobrzyński Maciej Dobrzyński System Biology Ireland, University College Dublin, Belfield, Dublin, Ireland Search for more papers by this author Dirk Fey Dirk Fey System Biology Ireland, University College Dublin, Belfield, Dublin, Ireland Search for more papers by this author Yannick Blum Yannick Blum Department of Biomedicine, University of Basel, Basel, Switzerland Search for more papers by this author Sung Sik Lee Sung Sik Lee Institute of Biochemistry, Zurich, Switzerland Search for more papers by this author Matthias Peter Matthias Peter Institute of Biochemistry, Zurich, Switzerland Search for more papers by this author Boris N Kholodenko Corresponding Author Boris N Kholodenko System Biology Ireland, University College Dublin, Belfield, Dublin, Ireland Search for more papers by this author Noo Li Jeon Corresponding Author Noo Li Jeon School of Mechanical and Aerospace Engineering, Seoul National University, Seoul, Korea Institute of Advanced Machinery and Design, Seoul National University, Seoul, Korea Search for more papers by this author Olivier Pertz Corresponding Author Olivier Pertz Department of Biomedicine, University of Basel, Basel, Switzerland Search for more papers by this author Author Information Hyunryul Ryu1,2, Minhwan Chung1, Maciej Dobrzyński3, Dirk Fey3, Yannick Blum4, Sung Sik Lee5, Matthias Peter5, Boris N Kholodenko 3, Noo Li Jeon 1,2 and Olivier Pertz 4,6 1School of Mechanical and Aerospace Engineering, Seoul National University, Seoul, Korea 2Institute of Advanced Machinery and Design, Seoul National University, Seoul, Korea 3System Biology Ireland, University College Dublin, Belfield, Dublin, Ireland 4Department of Biomedicine, University of Basel, Basel, Switzerland 5Institute of Biochemistry, Zurich, Switzerland 6Present address: Institute of Cell Biology, University of Bern, Bern, Switzerland *Corresponding author. Tel: +353 1716 6331; E-mail: [email protected] *Corresponding author. Tel: +82 2 880 71 11; E-mail: [email protected] *Corresponding author. Tel: +41 31 631 46 16; E-mail: [email protected] Molecular Systems Biology (2015)11:838https://doi.org/10.15252/msb.20156458 Correction(s) for this article Frequency modulation of ERK activation dynamics rewires cell fate23 April 2016 See also: N Blüthgen (November 2015) 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 Transient versus sustained ERK MAP kinase (MAPK) activation dynamics induce proliferation versus differentiation in response to epidermal (EGF) or nerve (NGF) growth factors in PC-12 cells. Duration of ERK activation has therefore been proposed to specify cell fate decisions. Using a biosensor to measure ERK activation dynamics in single living cells reveals that sustained EGF/NGF application leads to a heterogeneous mix of transient and sustained ERK activation dynamics in distinct cells of the population, different than the population average. EGF biases toward transient, while NGF biases toward sustained ERK activation responses. In contrast, pulsed growth factor application can repeatedly and homogeneously trigger ERK activity transients across the cell population. These datasets enable mathematical modeling to reveal salient features inherent to the MAPK network. Ultimately, this predicts pulsed growth factor stimulation regimes that can bypass the typical feedback activation to rewire the system toward cell differentiation irrespective of growth factor identity. Synopsis Dynamic manipulation of ERK signaling at the single cell level reveals new features of the MAPK network topology and induces robust signaling responses that rewire cell fate decision independently of growth factor identity. Sustained growth factor stimulation induces heterogeneous ERK dynamics, while pulsed growth factor stimulation homogenizes ERK dynamics in a cell population. Dynamic manipulation of ERK signaling using growth factor pulses in living cells, reveals novel features of MAPK network topology and enhances previous mathematical models of MAPK signaling. Pulsed growth factor stimulation at adequate frequencies predicted by an updated model of MAPK signaling rewires cell fate. Introduction Complex signaling networks allow cells to translate external stimuli into specific cell fates. In many cases, signaling dynamics rather than steady states control these fate decisions (Levine et al, 2013; Purvis & Lahav, 2013). Furthermore, signaling states of individual cells differ even across an isogenic population (Cohen-Saidon et al, 2009; Snijder & Pelkmans, 2011; Chen et al, 2012), due to broad distributions of protein abundances, as well as intrinsic noise present within all biochemical networks (Snijder & Pelkmans, 2011). Measuring single-cell signaling dynamics is therefore a key to understand how cellular responses correlate with fate decisions. The extracellular-regulated kinase (ERK) regulates cellular fates such as proliferation and differentiation. It functions within a mitogen-activated protein kinase (MAPK) signaling network in which growth factor (GF) receptors activate a Ras GTPase, subsequently triggering a MAPK cascade leading to ERK activation (Avraham & Yarden, 2011). Rat adrenal pheochromocytoma PC-12 cells have been used as a model system to study MAPK-dependent fate decisions (Marshall, 1995). Stimulation with EGF or NGF leads to transient or sustained ERK activation dynamics, respectively, triggering proliferation or differentiation (Marshall, 1995; Avraham & Yarden, 2011). These different ERK activation dynamics involve activation of different Ras isoforms (Sasagawa et al, 2005), as well as GF-dependent control of the MAPK network topology (Santos et al, 2007), with negative or positive feedbacks producing adaptive or bistable outputs (Xiong & Ferrell, 2003; Santos et al, 2007; Avraham & Yarden, 2011). Downstream, molecular interpretation of signal duration involves stabilization of ERK-induced immediate early gene (IEG) products by sustained ERK activity, ultimately instructing the differentiation fate (Murphy et al, 2002; Nakakuki et al, 2010). Single-cell analysis has, however, revealed that NGF does not lead to homogeneous PC-12 cell differentiation. Rather, a heterogeneous mix of differentiating and proliferating cells is observed, with the respective cell fate choices depending on a complex ERK and AKT signaling code (Chen et al, 2012). Here, we study ERK activation dynamics in GF-stimulated single PC-12 cells. We find that sustained GF stimulation induces heterogeneous cell responses different than the population average, with both GFs being able to produce transient and sustained ERK activation responses. We dynamically probe the ERK signaling flux through application of GF pulses, which homogenizes ERK activation responses throughout the cell population. This provides novel insight to understand the MAPK network structure and ultimately provides a rationale to rewire cell fate decisions independently of GF identity. Results Sustained GF stimulation induces heterogeneous ERK activation dynamics To study ERK activation dynamics in single PC-12 cells, we produced a stable cell line that expresses EKAR2G, a fluorescence resonance energy transfer-based biosensor for endogenous ERK activity (Fig 1A) (Harvey et al, 2008; Fritz et al, 2013). This biosensor specifically reports on ERK, but not on p38 mitogen-activated, neither on c-Jun N-terminal kinases (Harvey et al, 2008). By virtue of a nuclear export sequence, EKAR2G localizes to, and specifically measures ERK activity in the cytosol (Fig 1B). Although this does not seem to be true for all cell types (Ahmed et al, 2014), we assumed that cytosolic and nuclear pools of ERK activity are in equilibrium, since, at least for EGF-stimulated PC-12 cells, there is no apparent time lag between nuclear and cytosolic ERK activation dynamics (Herbst et al, 2011). Biosensor expression levels were homogeneous and displayed only small standard deviation with respect to the population median (Appendix Fig S1A). To match the temporal resolution enabled by our biosensor, we used a flow-based, computer-programmable microfluidic device to deliver GFs with precise kinetics (Fig 1C). We observed that flow induced transient ERK activation (Appendix Fig S1B and C). This most likely results from flow-induced mechanical stress, and/or exposure to low amount of serum required for cell survival in the live cell imaging experiments. Subsequent experiments were then performed after the flow effect has subsided, leading to a flat baseline (Appendix Fig S1D). Figure 1. Sustained GF stimulation induces heterogeneous ERK activity dynamics EKAR2G biosensor. Upon phosphorylation by ERK, binding of the WW phospho-recognition domain to the phosphorylated Cdc25 substrate sequence leads to spatial re-orientation of donor (mTFP1) and acceptor (Venus) fluorophores leading to a FRET change that can be ratiometrically measured. Ratiometric and mTFP1 donor images of EKAR2G in an EGF-stimulated PC-12 cell at the indicated time points, with t = 0′ corresponding to EGF application. Upper panel: FRET ratio image is color-coded for ERK activity. Lower panel: raw mTFP1 donor image in black/white contrast. Scale bar = 20 μm. Flow-based, microfluidic device for temporal GF delivery. Computer-controlled, pressure pump enables mixing of medium and GFs in the control part (left), and temporally defined GF delivery in the cell culture chamber (right and magnified inset). Population average of ERK activation dynamics measured by Western blot using a phosphoERK antibody. Population average of ERK activation dynamics cell-averaged EKAR2G emission ratios (ERs) from n= at least 111 cells. StDev are shown. Selected EKAR2G ratio time series illustrating, from top to bottom, sustained, oscillatory, or transient ERK activity dynamics. ER is color-coded as in bar. Scale bar = 20 μm. Single-cell ERK activity trajectories. Cell-averaged ERs for n = 10 cells, standard deviation range (StDev), population average for the indicated GFs and dosages. Experimental time courses were normalized to the mean of 5 time points immediately preceding GF application. Waterfall plots of single-cell ERK activity trajectories. Cell-averaged ER trajectories are color-coded (n = 78 cells), population average (bottom). Vertical dotted line indicates GF application. Source data are available online for this figure. Source Data for Figure 1 [msb156458-sup-0007-SDataFig1.zip] Download figure Download PowerPoint Throughout this study, we used high-dosage, 25 ng/ml EGF and 50 ng/ml NGF (representing equivalent GF molarities (Santos et al, 2007)), and low-dosage, 1 ng/ml EGF and 2 ng/ml NGF concentrations. As previously described (Santos et al, 2007), Western blot analysis showed that high-dosage EGF triggered a single ERK activation peak that almost nearly returned to baseline after 10–15 min (Fig 1D). In contrast, high-dosage NGF evoked one ERK activation peak followed by sustained, but reduced with respect to the peak, ERK activity. Similar ERK activation dynamics profiles were observed in population-averaged EKAR2G measurements (Fig 1E). At the single-cell level, however, a mix of transient, oscillatory, and sustained ERK activity trajectories were observed (Fig 1F–H, Video EV1), showing that EGF- and NGF-induced ERK activation kinetics are the result of heterogeneous cell responses distinct from the population average. This signaling heterogeneity was not a consequence of the small but existing heterogeneity in EKAR2G expression levels. Specifically, we have not observed any correlation between biosensor expression levels and NGF-triggered ERK activity at 40′ after stimulation, a time point and experimental condition at which highly heterogeneous ERK activity is observed in the cell population (Appendix Fig S1E). Furthermore, immunostaining experiments confirmed this heterogeneity—after the 1st activation peak, the phosphoERK signal displayed a higher average and amplitude spread for NGF in comparison with EGF (Fig EV1A–C). Comparison of population-averaged ERK activity measurements using Western blot (Fig 1D), or immunofluorescence (Fig EV1B), versus EKAR2G (Fig 1E), revealed somewhat slower desensitization kinetics of the 1st ERK activity peak for the latter. Immunofluorescence analysis of native versus EKAR2G-expressing PC-12 cells revealed a slight lag of 1st peak phosphoERK desensitization kinetics in the latter cells (Fig EV1D), without however affecting the amplitudes of EGF versus NGF phosphoERK signals after the first peak. This indicates that biosensor expression affects the MAPK signaling network to some extent. Biosensor FRET ratio measurements do not necessarily scale linearly with the signaling events they report on, and it has previously proven valuable to explicitly model this (Birtwistle et al, 2011; Fujita et al, 2014). However, given the strong similarity between the Western blot, immunofluorescence, and EKAR2G datasets, we assumed that FRET ratio measurements could be used directly as a proxy for ERK activity. These results indicate that, while EKAR2G expression has a slight impact on a specific phase of ERK activation dynamics, it remains a faithful indicator of ERK signaling. Click here to expand this figure. Figure EV1. Time course of single-cell ERK activation steady states in response to sustained GF stimulation PC-12 cells in conventional dishes were stimulated with 25 ng/ml EGF or 50 ng/ml NGF, fixed at the indicated time points after stimulation, and stained with anti-phosphoERK and anti-ERK2 antibodies. Images are color-coded for phosphoERK or ERK2 signal intensity. Note that the immunostaining intensity images have been scaled so as to slightly saturate phosphoERK signals at the 5′ time point. This provides the required dynamic range to clearly distinguish heterogeneous activation states within the cell population in response to NGF stimulation at later time points. At time points after the 1st ERK activity peak, red asterisks indicate specific cells that exhibit elevated ERK activity. Scale bar: 50 μm. Population distributions of phosphoERK signals. Boxplots of cell-averaged phosphoERK signals for 25 ng/ml EGF and 50 ng/ml NGF dosages, n= at least 1,200 cells/condition/time point; * < 0.05, bootstrapping randomization test for the observed difference in the mean. Median, interquartile range (box), and data within 1.5 IQR range of the lower and upper quartiles (whiskers) are shown. Measurements are normalized to t = 0′. DAPI-stained images were segmented in CellProfiler with global Otsu thresholding. Cell bodies were identified by expanding nuclei based on total ERK staining. Whole-cell mean intensity was calculated from phosphoERK channel after correcting images for uneven illumination and after subtracting mean background intensity. Density plot of phosphoERK signals distribution at 25 ng/ml EGF and 50 ng/ml NGF at 45′ post-stimulation. Measurements are normalized to t = 0′. Population distributions of phoshoERK signals in native PC-12 cells or PC-12 cells expressing the EKAR2G biosensor. PC-12 cells expressing or not expressing EKAR2G were mixed, stimulated with either 25 ng/ml EGF or 50 ng/ml NGF, fixed at the indicated time points, and immunostained using anti-phosphoERK antibodies. PC-12 cells expressing or not expressing EKAR2G were identified using the FRET channel and analyzed as in (B), n= at least 990 cells (* < 0.05, two sample t-test for the mean). Note that 1st ERK activity peak desensitizes slightly more slowly in PC-12 cells expressing EKAR2G versus native PC-12 cells. However, increased phosphoERK signals are still observed when NGF- and EGF-treated cells are compared at time points after 30′. Download figure Download PowerPoint To quantify cell heterogeneity, we pooled all high- and low-dosage, EGF- and NGF-triggered ERK activation trajectories described above (Fig 1E–H), and used k-means clustering to extract 5 representative temporal activation patterns (Fig 2A). We then determined their incidence in response to the different GF dosages (Fig 2B and C) and found that EGF favored adaptive responses, while NGF led to a larger number of sustained ERK activity trajectories. We also observed ultrasensitive and switch-like ERK responses for increasing dosages of EGF and NGF. The 1st peak amplitudes saturated and remained unchanged above threshold concentrations of 1 ng/ml EGF and 2 ng/ml NGF (Fig EV2A and B). At lower concentrations, lower and more heterogeneous 1st ERK activity peak amplitudes were observed. A striking observation was that the different representative ERK activity trajectories displayed distinct 1st ERK activity peak amplitudes that correlated with the ability to produce a transient or a sustained response—cells with low or high 1st peak amplitude, respectively, produced transient or sustained responses (Fig 2A–D). For additional quantification of this phenomenon, we correlated 1st peak amplitude with long-term, 40′, ERK activity (indicating transient or sustained ERK activity). Robust correlation was observed for high and low NGF dosages, suggesting that high 1st ERK peak amplitude leads to sustained responses (Appendix Fig S2). Non-significant correlations were observed for high and low EGF dosages. Another interesting observation was that a low EGF dosage significantly shifted the distribution of ERK activity trajectories toward more sustained profiles when compared to the high EGF dosage (Fig 2A–C). Importantly, such subtle shifts in the distribution of ERK activity profiles are not apparent when a population average is computed (Fig 1G), illustrating the value of our clustering approach to analyze our single-cell trajectories datasets. Together, these results show that GF stimulation produces heterogeneous ERK activity responses, with both GFs being able to induce transient and sustained responses in different cells of the population. This most likely results from different strengths of EGF-triggered negative and NGF-triggered positive feedbacks within different cells. In the case of NGF, the presence or absence of sustained ERK activation responses might depend on the strength of the positive feedback (Ferrell & Machleder, 1998), which is consistent with the finding that the amplitude of the 1st peak correlates with the establishment of a sustained response. Sustained responses might, however, also result from lack of adaptation through negative feedback and continuous signaling input due to constant GF exposure. Figure 2. Quantification of ERK activity trajectory heterogeneity Trajectories from all sustained GF stimulation experiments (n = 307 cells, same ERK activation trajectories as in Fig 1) were pooled and five representative trajectories were identified using k-means clustering with squared Euclidean distance. Raw (color-coded by cluster) and cluster representative trajectories (black). Overlaid cluster representative trajectories. Population distribution of representative ERK activity trajectories in response to different GF dosages. Data representative of n = 3 experiments. Peak emission ratio intensity for each cluster. Boxplots with median, interquartile (box) and 1.5 IQR (whiskers) range, and raw datasets are shown. Boxplot notches extend 1.58 IQR / √Nobs, which gives approximately 95% confidence interval for comparing medians. Download figure Download PowerPoint Click here to expand this figure. Figure EV2. Threshold GF concentrations required for robust ERK activationERK activation dynamics were evaluated in the microfluidic device at different EGF and NGF concentrations in response to sustained GF stimulation. Representative ERK activity trajectories in response to different GF dosages (n = 20). Distribution of cell-averaged ERs at 6′ (maximum of 1st ERK activity peak), or at 60′ after GF stimulation (long-term ERK activity). Notched boxplots with median, interquartile (box), and 1.5 IQR (whiskers) range are shown for at least n = 30 cells. Note ERK activity adaptation for EGF versus sustained ERK activity for NGF at the 60′ time point. Download figure Download PowerPoint Pulsed GF stimulation reveals novel features of the MAPK network Because continuous signaling input does not effectively probe the MAPK network to produce adaptive or bistable responses (Kholodenko et al, 2010; Avraham & Yarden, 2011), we applied single GF pulses using our microfluidic device. A high-dosage, 3′ and 10′ EGF, or 3′ NGF pulse elicited a robust peak of ERK activity that immediately adapted (Fig 3A and B, and Video EV2). In contrast, a 10′ NGF pulse induced sustained ERK activity within a cell subpopulation, potentially indicating bistability (Xiong & Ferrell, 2003), while the remaining cells exhibited transient responses (Fig 3B). As for sustained NGF stimulation, cell trajectory clustering again indicated a correlation between first peak amplitude, and the ability to produce a sustained response (Appendix Fig S3). Low-dosage, 3′ and 10′, EGF and NGF single pulses all led to adaptive responses (Fig 3C and D). All GF pulses, as well as sustained GF stimulation, evoked a robust 1st ERK activity peak, with similar amplitude distributions across the different dosages, indicating switch-like responses (Fig 3E and F). In marked contrast with sustained GF stimulation, population-homogeneous, rapid desensitization kinetics were observed for pulsed GF stimulation, except for cells and conditions in which bistable behavior was triggered by NGF (Fig 3E and G). These results show that EGF only induces adaptive, while NGF can produce both adaptive and bistable outputs depending on input strength/duration. Figure 3. ERK activity responses to single GF pulses A–D. Single GF pulse experiments. Cell-averaged ERs, population average, and StDev range for n = 10 cells. Pulse application indicated by black bars. 3′ (A, C) and 10′ (B, D) pulse. High (A, B) and low (C, D) GF concentrations. E. Rationale for measuring amplitude and duration of 1st ERK activity peak in response to GF stimulation. Peak amplitude was measured as the ER change from when the GF stimulation starts until the highest ERK activity before adaptation occurs. Peak duration was estimated as the time between the first point before reaching the half-maximum of the peak in the ascending phase, and the first point after the half-maximum in the descending phase. F. Amplitude of 1st ERK activity peak in response to different sustained or pulsed GF stimulation experiments. Notched boxplots of 1st ERK activity peak amplitude with median, interquartile range (box), and data within 1.5 IQR range of the lower and upper quartiles (whiskers) are shown (n = 20 cells per experiment). G. Duration of 1st ERK activity peak in response to sustained and pulsed GF stimulation. Boxplots of 1st ERK activity peak duration with median, interquartile range (box), and data within 1.5 IQR range of the lower and upper quartiles (whiskers) are shown (n = 20 cells per experiment). Source data are available online for this figure. Source Data for Figure 3 [msb156458-sup-0008-SDataFig3.zip] Download figure Download PowerPoint To explore the timescale at which adaptation occurs, we retriggered ERK activity by a delayed, 2nd 3′ EGF or NGF pulse, which only can induce adaptive ERK activity transients. Retriggering after a 3′ delay, when ERK activity still peaked, did not affect ERK activation kinetics (Fig 4A). Retriggering after a 10′ delay, when ERK activity already displayed significant desensitization, led to a 2nd ERK activity peak of lower amplitude (Fig 4B). Retriggering after a 20′ delay, when adaptation had occurred, enabled a 2nd activity peak of similar amplitude as the 1st one (Fig 4C). For both EGF and NGF, the MAPK network structure therefore makes ERK refractory to re-activation until adaptation has occurred. Figure 4. ERK activity responses to multiple GF pulses A–C. Double-pulse GF experiments. (A) 3′ GF/3′ pause/3′ GF. (B) 3′ GF/10′ pause/3′ GF. (C) 3′ GF/20′ pause/3′ GF. Cell-averaged ERs, population average, and StDev range for n = 10 cells. D–G. Multi-pulse GF experiments. Population average ERs (n= at least 30 cells) for different EGF/NGF dosages. (D) 3′ GF/3′pause. (E) 3′ GF/10′ pause. (F) 3′ GF/20′ pause. (G) 3′ GF/60′ pause. H. Decay kinetics of ERK activity maxima from (D–G). Boxplots with median, interquartile (box), and 1.5 IQR (whiskers) range are shown for at least n = 30 cells. Source data are available online for this figure. Source Data for Figure 4 [msb156458-sup-0009-SDataFig4.zip] Download figure Download PowerPoint In addition to the feedback loops to upstream components that operate on timescales of minutes, ERK also induces expression of dual-specificity phosphatases (DUSPs) which negatively regulate ERK on a timescale of hours (Patterson et al, 2009). Consistently, evaluation of ERK activity trajectories for 5 h after sustained GF stimulation displayed a global long-term trend to ERK adaptation for both EGF and NGF at high and low dosages (Appendix Fig S4). The heterogeneity inherent to these ERK responses makes it difficult to discern any patterns of regulation operating at the hour timescale. We therefore reasoned that repeatable GF pulses could trigger multiple ERK activity transients homogeneously across the cell population, as the refractory periods effectively reduced noise. Such a pulsing input might therefore provide a robust readout for long-term, hour-scale feedback loops. We therefore evaluated 3′ GF multi-pulse regimes with varying pause duration and GF dosages (Fig 4D–G, single-cell trajectories shown in Appendix Fig S5 and Video EV3). A high-dosage EGF, 3′ GF/3′ pause multi-pulse regime led to a robust single ERK activation peak that rapidly subsided, while the identical NGF pulse regime exhibited diminished ERK desensitization (Fig 4D). The 3′/10′ and 3′/20′ regimes triggered well-resolved ERK activity transients whose amplitude diminished over time, to a higher extent for EGF than for NGF (Fig 4E and F, and Video EV4). Lower desensitization appeared for the low versus the high EGF dosages, while this difference was much smaller for high and low NGF dosages (Fig 3D–F). The 3′/60′ regimes did not lead to pronounced long-term ERK activity desensitization (Fig 3G). Figure 3H quantifies the hour-scale decay of ERK activity maxima. These results clearly identify distinct hour-scale negative feedbacks that depend on GF concentration and identity and that are not distinguishable using sustained GF stimulation. Modeling MAPK network structure Dynamic responses of the MAPK signaling to sustained GF stimulations have been extensively modeled in the literature (Kholodenko, 2000, 2006; Santos et al, 2007; von Kriegsheim et al, 2009; Kholodenko et al, 2010; Nakakuki et al, 2010). Our dynamic probing of ERK signaling using GF pulses in single living cells provides for a unique opportunity to calibrate the models to a much higher detail and study the dynamics at temporal scales inaccessible through standard population-averaged measurements. This allowed us to refine established models by incorporating novel feedback and crosstalk structures that are required to explain the salient features identified in our experiments (Fig 5A). Our core mode