Signaling networks are key regulators of cellular function. Although the concentrations of signaling proteins are perturbed in disease states, such as cancer, and are modulated by drug therapies, our understanding of how such changes shape the properties of signaling networks is limited. Here we couple mass-cytometry-based single-cell analysis with overexpression of tagged signaling proteins to study the dependence of signaling relationships and dynamics on protein node abundance. Focusing on the epidermal growth factor receptor (EGFR) signaling network in HEK293T cells, we analyze 20 signaling proteins during a 1-h EGF stimulation time course using a panel of 35 antibodies. Data analysis with BP-R2, a measure that quantifies complex signaling relationships, reveals abundance-dependent network states and identifies novel signaling relationships. Further, we show that upstream signaling proteins have abundance-dependent effects on downstream signaling dynamics. Our approach elucidates the influence of node abundance on signal transduction networks and will further our understanding of signaling in health and disease.
Subscribe to Journal
Get full journal access for 1 year
only $4.92 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
Wolf-Yadlin, A. et al. Effects of HER2 overexpression on cell signaling networks governing proliferation and migration. Mol. Syst. Biol. 2, 54 (2006).
De Los Angeles, A. et al. Hallmarks of pluripotency. Nature 525, 469–478 (2015).
Feinberg, A.P. Phenotypic plasticity and the epigenetics of human disease. Nature 447, 433–440 (2007).
Bywater, M.J., Pearson, R.B., McArthur, G.A. & Hannan, R.D. Dysregulation of the basal RNA polymerase transcription apparatus in cancer. Nat. Rev. Cancer 13, 299–314 (2013).
Silvera, D., Formenti, S.C. & Schneider, R.J. Translational control in cancer. Nat. Rev. Cancer 10, 254–266 (2010).
Santarius, T., Shipley, J., Brewer, D., Stratton, M.R. & Cooper, C.S. A census of amplified and overexpressed human cancer genes. Nat. Rev. Cancer 10, 59–64 (2010).
Govindarajan, B. et al. Overexpression of Akt converts radial growth melanoma to vertical growth melanoma. J. Clin. Invest. 117, 719–729 (2007).
Eralp, Y. et al. MAPK overexpression is associated with anthracycline resistance and increased risk for recurrence in patients with triple-negative breast cancer. Ann. Oncol. 19, 669–674 (2008).
Han, T. et al. PTPN11/Shp2 overexpression enhances liver cancer progression and predicts poor prognosis of patients. J. Hepatol. 63, 651–660 (2015).
Davies, H. et al. Mutations of the BRAF gene in human cancer. Nature 417, 949–954 (2002).
Wang, M., Herrmann, C.J., Simonovic, M., Szklarczyk, D. & von Mering, C. Version 4.0 of PaxDb: Protein abundance data, integrated across model organisms, tissues, and cell-lines. Proteomics 15, 3163–3168 (2015).
Citri, A. & Yarden, Y. EGF-ERBB signalling: towards the systems level. Nat. Rev. Mol. Cell Biol. 7, 505–516 (2006).
Tebbutt, N., Pedersen, M.W. & Johns, T.G. Targeting the ERBB family in cancer: couples therapy. Nat. Rev. Cancer 13, 663–673 (2013).
Roberts, P.J. & Der, C.J. Targeting the Raf-MEK-ERK mitogen-activated protein kinase cascade for the treatment of cancer. Oncogene 26, 3291–3310 (2007).
Mendoza, M.C., Er, E.E. & Blenis, J. The Ras-ERK and PI3K-mTOR pathways: cross-talk and compensation. Trends Biochem. Sci. 36, 320–328 (2011).
Olayioye, M.A., Neve, R.M., Lane, H.A. & Hynes, N.E. The ErbB signaling network: receptor heterodimerization in development and cancer. EMBO J. 19, 3159–3167 (2000).
Manning, B.D. & Cantley, L.C. AKT/PKB signaling: navigating downstream. Cell 129, 1261–1274 (2007).
Bowman, T., Garcia, R., Turkson, J. & Jove, R. STATs in oncogenesis. Oncogene 19, 2474–2488 (2000).
Oliva, J.L., Griner, E.M. & Kazanietz, M.G. PKC isozymes and diacylglycerol-regulated proteins as effectors of growth factor receptors. Growth Factors 23, 245–252 (2005).
Kim, D., Rath, O., Kolch, W. & Cho, K.-H. A hidden oncogenic positive feedback loop caused by crosstalk between Wnt and ERK pathways. Oncogene 26, 4571–4579 (2007).
Massague, J. Integration of Smad and MAPK pathways: a link and a linker revisited. Genes Dev. 17, 2993–2997 (2003).
Zhang, Y. et al. Time-resolved mass spectrometry of tyrosine phosphorylation sites in the epidermal growth factor receptor signaling network reveals dynamic modules. Mol. Cell. Proteomics 4, 1240–1250 (2005).
Kim, S.Y. et al. AMP-activated protein kinase-α1 as an activating kinase of TGF-β-activated kinase 1 has a key role in inflammatory signals. Cell Death Dis. 3, e357 (2012).
Corcoran, R.B. et al. Synthetic lethal interaction of combined BCL-XL and MEK inhibition promotes tumor regressions in KRAS mutant cancer models. Cancer Cell 23, 121–128 (2013).
Tewari, M. et al. Systematic interactome mapping and genetic perturbation analysis of a C. elegans TGF-β signaling network. Mol. Cell 13, 469–482 (2004).
Sundqvist, A. et al. Specific interactions between Smad proteins and AP-1 components determine TGFβ-induced breast cancer cell invasion. Oncogene 32, 3606–3615 (2013).
Aoki, K. et al. Stochastic ERK activation induced by noise and cell-to-cell propagation regulates cell density-dependent proliferation. Mol. Cell 52, 529–540 (2013).
Bodenmiller, B. et al. Multiplexed mass cytometry profiling of cellular states perturbed by small-molecule regulators. Nat. Biotechnol. 30, 858–867 (2012).
Bendall, S.C. et al. Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum. Science 332, 687–696 (2011).
Krishnaswamy, S. et al. Systems biology. Conditional density-based analysis of T cell signaling in single-cell data. Science 346, 1250689 (2014).
Couzens, A.L. et al. Protein interaction network of the mammalian Hippo pathway reveals mechanisms of kinase-phosphatase interactions. Sci. Signal. 6, rs15 (2013).
Zhou, Y. et al. Chimeric mouse tumor models reveal differences in pathway activation between ERBB family- and KRAS-dependent lung adenocarcinomas. Nat. Biotechnol. 28, 71–78 (2010).
Redell, M.S. et al. FACS analysis of Stat3/5 signaling reveals sensitivity to G-CSF and IL-6 as a significant prognostic factor in pediatric AML: a Children's Oncology Group report. Blood 121, 1083–1093 (2013).
Perfetto, L. et al. SIGNOR: a database of causal relationships between biological entities. Nucleic Acids Res. 44, D548–D554 (2016).
Xu, Y., Li, N., Xiang, R. & Sun, P. Emerging roles of the p38 MAPK and PI3K/AKT/mTOR pathways in oncogene-induced senescence. Trends Biochem. Sci. 39, 268–276 (2014).
Rahman, M.T. et al. KRAS and MAPK1 gene amplification in type II ovarian carcinomas. Int. J. Mol. Sci. 14, 13748–13762 (2013).
Valtorta, E. et al. KRAS gene amplification in colorectal cancer and impact on response to EGFR-targeted therapy. Int. J. Cancer 133, 1259–1265 (2013).
Cepero, V. et al. MET and KRAS gene amplification mediates acquired resistance to MET tyrosine kinase inhibitors. Cancer Res. 70, 7580–7590 (2010).
Little, A.S. et al. Amplification of the driving oncogene, KRAS or BRAF, underpins acquired resistance to MEK1/2 inhibitors in colorectal cancer cells. Sci. Signal. 4, ra17 (2011).
Cohen, P. & Frame, S. The renaissance of GSK3. Nat. Rev. Mol. Cell Biol. 2, 769–776 (2001).
Shin, I., Kim, S., Song, H., Kim, H.-R.C. & Moon, A. H-Ras-specific activation of Rac-MKK3/6-p38 pathway: its critical role in invasion and migration of breast epithelial cells. J. Biol. Chem. 280, 14675–14683 (2005).
Seong, H.-A., Jung, H., Ichijo, H. & Ha, H. Reciprocal negative regulation of PDK1 and ASK1 signaling by direct interaction and phosphorylation. J. Biol. Chem. 285, 2397–2414 (2010).
Lee, Y.-K., Hwang, J.-T., Kwon, D.Y., Surh, Y.-J. & Park, O.J. Induction of apoptosis by quercetin is mediated through AMPKalpha1/ASK1/p38 pathway. Cancer Lett. 292, 228–236 (2010).
Cardaci, S., Filomeni, G. & Ciriolo, M.R. Redox implications of AMPK-mediated signal transduction beyond energetic clues. J. Cell Sci. 125, 2115–2125 (2012).
Rawlings, J.S., Rosler, K.M. & Harrison, D.A. The JAK/STAT signaling pathway. J. Cell Sci. 117, 1281–1283 (2004).
Nyati, M.K., Morgan, M.A., Feng, F.Y. & Lawrence, T.S. Integration of EGFR inhibitors with radiochemotherapy. Nat. Rev. Cancer 6, 876–885 (2006).
Mitra, S.K., Hanson, D.A. & Schlaepfer, D.D. Focal adhesion kinase: in command and control of cell motility. Nat. Rev. Mol. Cell Biol. 6, 56–68 (2005).
Hendriks, R.W., Yuvaraj, S. & Kil, L.P. Targeting Bruton's tyrosine kinase in B cell malignancies. Nat. Rev. Cancer 14, 219–232 (2014).
Marin, T.M. et al. Shp2 negatively regulates growth in cardiomyocytes by controlling focal adhesion kinase/Src and mTOR pathways. Circ. Res. 103, 813–824 (2008).
Wei, Z.Z. et al. Regulatory role of the JNK-STAT1/3 signaling in neuronal differentiation of cultured mouse embryonic stem cells. Cell. Mol. Neurobiol. 34, 881–893 (2014).
Yang, X. et al. A public genome-scale lentiviral expression library of human ORFs. Nat. Methods 8, 659–661 (2011).
Finck, R. et al. Normalization of mass cytometry data with bead standards. Cytometry A 83, 483–494 (2013).
Zunder, E.R. et al. Palladium-based mass tag cell barcoding with a doublet-filtering scheme and single-cell deconvolution algorithm. Nat. Protoc. 10, 316–333 (2015).
Ward, J.H. Hierarchical grouping to optimize an objective function. J. Am. Stat. Assoc. 58, 236–244 (1963).
Hagberg, A.A., Schult, D.A. & Swart, P.J. Exploring network structure, dynamics, and function using NetworkX. Proc. 7th Python Sci. Conf. 2008, 11–15 (2008).
We would like to thank the Bodenmiller laboratory for support and fruitful discussions, the Lehner laboratory and the Mosimann laboratory for sharing equipment. We would especially like to thank A.-C. Gingras, Lunenfeld-Tanenbaum Research Institute, for sharing the pDEST vectors used in this study. This work was supported by the Swiss National Science Foundation (SNSF) R'Equip grant 316030-139220, a SNSF Assistant Professorship grant PP00P3-144874, a Swiss Cancer League grant, the PhosphonetPPM SystemsX grant, and funding from the European Research Council (ERC) under the European Union's Seventh Framework Programme (FP/2007-2013)/ERC Grant Agreement no. 336921. The work of J.D.W. was supported by a National Science Foundation Graduate Research Fellowship under grant no. DGE-1650044 and a Whitaker International Fellowship awarded by the Institute of International Education. The work of D.S. is supported by the Forschungskredit of the University of Zurich Fellowship under grant no. FK-74419-01-01.
The authors declare no competing financial interests.
Integrated supplementary information
(a) Detection of GFP-N-terminal, FLAG-C-terminal, and FLAG-N-terminal tagged proteins. All GFP-tagged fusion proteins, but only 20 of the 25 FLAG-C-terminal tagged and only 22 of the 25 FLAG-N-terminal tagged proteins were detected using mass cytometry. (b) HEK293T cells overexpressing GFP-GFP, FLAG-C-terminal-GFP, and FLAG-N-terminal-GFP fusion proteins were co-stained with anti-GFP and anti-FLAG antibodies. The fusion protein FLAG-C-terminal-GFP was detected by the anti-GFP antibody but not by the anti-FLAG antibody. This indicates that in certain contexts the FLAG tag is not accessible to the anti-FLAG antibody. The FLAG epitope may be masked due to protein folding or by the denaturation process that is part of our experimental protocol.
HEK293T cells that overexpressed the GFP-tagged POIs used in this study were imaged with confocal microscopy. For each POI, the main panel shows the image in a given z-depth; the bottom panel and the side panel shows x-z and y-z cross-sectional images, respectively. POI-GFP subcellular localization was determined by overlapping with two control stains: Hoechst 33343 for the nucleus and Alexa Fluor 647 carboxylic acid succinimidyl ester indicating the cell outline. The POI-GFP localization was verified by comparison with information of the UniProt subcellular localization database (Supplementary Table 4).
(a-d) Catalytic activities of GFP-tagged POIs were compared with FLAG-C-terminal and FLAG-N-terminal tagged POIs. The examples shown here indicate that the GFP tag did not alter signaling relationships or signaling dynamics after EGF stimulation (the complete dataset with comparison of all constructs used in this study is shown in Supplementary Dataset 1). (e) Heat map showing abundance-dependent signaling relationship strengths from overexpressed POIs with three different tags as determined by BP-R2 analysis (Supplementary Figure 10 and Methods). Measured markers showing at least one strong relationship in any of the conditions were included in the heat map. Strong relationships were detected independently of tag. BP-R2 values slightly vary for the 3 tags, due to the antibody accessibility and differences in transfection efficiencies (Supplementary Figure 1).
Supplementary Figure 4 Total protein antibody staining of HEK293T cells overexpressing a GFP-tagged POI.
(a) HEK293T cells transfected with KRAS-GFP, HRAS-GFP, MEK1-GFP, ERK2-GFP, AKT1-GFP, GSK3β-GFP, or S6-GFP for 18 h were stained with anti-total POI and anti-GFP antibodies. A linear regression analysis for each pair was performed in the original scale. R2 ranges from 0.74 to 0.88, indicating the total POI is linearly correlated with GFP and that the POI overexpression does not alter the expression of the endogenous POI. (b) The same cells were stained with nine antibodies to quantify total protein as well as with a GFP antibody. Median ion counts for all measured markers are shown. Overexpression of a POI-GFP for 18 h does not cause notable changes in the measured network nodes. (c) ERK2-GFP transfected HEK293T cells and the untransfected control with or without EGF stimulation were stained for total-ERK and phospho-ERK (Thr202/Tyr204). The dynamic range in the overexpression condition allows observation of abundance-dependent signaling relationships. With total ERK staining, the same signaling relationships as shown in the Supplementary Figure 2b is recapitulated, verifying GFP as an indicator of POI expression level.
HEK293T cells were transfected with the FLAG-GFP overexpression vector. With flow cytometry, cells were gated into GFP low, medium, and high populations with the gating strategy shown in the left panel. With mass cytometry, each of the three sorted populations was measured independently to determine the gating windows. Unsorted cells were then assessed by the mass cytometry. The maximum difference in population percentage between mass cytometry and flow cytometry was less than 3%.
Supplementary Figure 6 Comparison of EGF stimulations in starved (FBS is absent) and non-starved (FBS is present) cell culture conditions.
HEK293T cells were stimulated with EGF with or without FBS over a 1-h time course. In the non-starved condition basal signaling states of the major MAPK/ERK or AKT pathway components were higher than in starved conditions, but these elevated levels did not affect the signaling responses to the EGF stimulation. Mean value of each sample is shown with circle. Standard deviation is indicated by shaded area.
HEK293T cells were treated with TrypLE for 30 s, 1 min, 2 min, or 4 min with or without EGF stimulation for 5 min (time from EGF addition to PFA crosslinking). Within the first 2-min TrypLE treatment (i.e., the time after which we quenched cells in all experiments), only phosphorylation of Ser167/170 on MARCKS varied relatively. Mean value of each sample is shown with circle. Standard deviation is indicated by shaded area.
Supplementary Figure 8 Live imaging of GFP fluorescence at 18 to 19 h after HEK293T cells were transfected with a FLAG-GFP construct.
Quantification of the GFP intensity showed a slight increase of 5.4% over the 1-h time course. There was a fluctuation in total GFP signal, indicating that the 5.4% increase is most likely attributable to technical variability of the measurement. The analysis of signaling relationships in our study was performed based on a binning strategy on arcsinh transformed GFP ion counts (mass cytometry). Thus, the measured change will not significantly affect the binning over the time course. Standard deviation is indicated by shaded area.
Supplementary Figure 9 Abundance-dependent signaling analyses performed in individual experiment replicates are highly reproducible.
(a) Different batches of HEK293T cells were transfected with JNK1-GFP, P38α-GFP, PDK1-GFP, or p90RSK-GFP constructs, stained, and analyzed by mass cytometry on three different days. Highly consistent signaling responses were observed among the three individual experiment replicates. Panels (b) and (c) show analyses of representative phosphorylation sites in the MAPK/ERK, AKT, stress pathways, and the STAT5 protein in cells in which (b) KRASG12V-GFP and (c) MEK1DD-GFP was overexpressed. Panels (d) and (e) show all relationships that passed the BP-R2 threshold (see Methods for details) for the (d) KRASG12V-GFP and (e) MEK1DD-GFP overexpression experiments.
(a) BP-R2 analysis considers deviation from bin median versus the global mean of bin medians. (b) Examples of BP-R2 and Spearman correlation of bin medians values. The top left and top right plots show examples of positive and negative Spearman correlations of bin medians. The top left and bottom left plots show replicates of the same overexpression condition and how a (supposedly) increased noisiness affects the BP-R2 values. The bottom right plot shows a complex signaling relationship with the corresponding BP-R2 value. The BP-R2 metric detects complex arbitrary relationship (bottom right). (c) Density distribution of the median BP-R2 values for the 700 POI-GFP-marker relationships from the negative controls (FLAG-GFP, untransfected) and the 3500 POI-GFP-marker relationships of the signaling node overexpression conditions. Cutoff for strong signaling relationships were determined at a median BP-R2 value of 0.11, the highest median BP-R2 of the negative controls.
Supplementary Figure 11 Benchmark of BP-R2 against other methods used to identify relationships in mass cytometry data.
(a) Venn diagram of strong relationships detected by BP-R2, Spearman correlation, and DREMI in our dataset using the same cutoff - the 99 percentile of the BP-R2 / Spearman correlation / DREMI score in the control groups (FLAG-GFP overexpression and the untransfected cells). BP-R2 outperforms the other two measures. (b) BP-R2, Spearman correlation, and DREMI measurements of signaling relationship strength between p-ERK1/2 and overexpressed ERK2-GFP. BP-R2 is suitable for analyzing non-monotonic signaling relationships and outperforms the other two measures in representing actual signaling activation status.
(a) Strategy to exclude spill over among mass channels. When strong signaling relationships as determined by BP-R2 were identified (measured phosphorylation of p70S6K in the p90RSK-GFP overexpression is shown here as a selected example), all other potentially affected channels (details in Methods) were evaluated for spillover that might have led to a high BP-R2 value. Using an experimental spillover filter (b), spillover-affected relationships were discarded. Here three groups of antibody stains were performed simultaneously: First, all antibodies; second, all antibodies except for the one that potentially causes spillover; third, only the antibody that potentially causes spillover. If spillover induced background was over 10% of the actual ion counts, the channel was discarded from the analysis.
(a) Heat map of median intensities of all measured markers at 0, 5, 15, 30, and 60 min post-EGF stimulation in all overexpression conditions (Table 1). Data visualized as log2 of the ratio of the median signals divided by the mean of median signals of the FLAG-GFP controls at time point 0. (b) Heat map of BP-R2 values of all measured markers versus GFP signals at 0, 5, 15, 30, and 60 min post-EGF stimulation in all overexpression conditions.
Supplementary Figure 14 Heat map of consistent and robust examples for overexpression-induced phosphorylation site abundance peak time changes after EGF stimulation for each of the three replicates.
(a) Coefficient of variation (CV) was computed for each strong signaling relationship that had a BP-R2 value above 0.11 (i.e., a strong signaling relationship), and CVs were plotted against BP-R2. No correlation was observed. (b) Overexpression ranges (median value of GFP in Bin10 minus the median value of GFP in Bin1) calculated for all POIs. (c) Maximum amplitudes of phosphorylation sites were independent of the level of overexpression of the POIs.
Supplementary Figures 1–15, Supplementary Tables 1–6 (PDF 4864 kb)
Supplementary Dataset 1 (PDF 23272 kb)
Supplementary Dataset 2 (PDF 28869 kb)
Supplementary Dataset 3 (PDF 1566 kb)
Supplementary Dataset 4 (PDF 8808 kb)
Supplementary Dataset 5 (PDF 23009 kb)
Supplementary Software (ZIP 374237 kb)
About this article
Cite this article
Lun, XK., Zanotelli, V., Wade, J. et al. Influence of node abundance on signaling network state and dynamics analyzed by mass cytometry. Nat Biotechnol 35, 164–172 (2017). https://doi.org/10.1038/nbt.3770
Scientific Reports (2020)
Scientific Reports (2019)
npj Systems Biology and Applications (2019)
Nature Communications (2018)