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Quantifying single-cell ERK dynamics in colorectal cancer organoids reveals EGFR as an amplifier of oncogenic MAPK pathway signalling

Abstract

Direct targeting of the downstream mitogen-activated protein kinase (MAPK) pathway to suppress extracellular-regulated kinase (ERK) activation in KRAS and BRAF mutant colorectal cancer (CRC) has proven clinically unsuccessful, but promising results have been obtained with combination therapies including epidermal growth factor receptor (EGFR) inhibition. To elucidate the interplay between EGF signalling and ERK activation in tumours, we used patient-derived organoids (PDOs) from KRAS and BRAF mutant CRCs. PDOs resemble in vivo tumours, model treatment response and are compatible with live-cell microscopy. We established real-time, quantitative drug response assessment in PDOs with single-cell resolution, using our improved fluorescence resonance energy transfer (FRET)-based ERK biosensor EKAREN5. We show that oncogene-driven signalling is strikingly limited without EGFR activity and insufficient to sustain full proliferative potential. In PDOs and in vivo, upstream EGFR activity rigorously amplifies signal transduction efficiency in KRAS or BRAF mutant MAPK pathways. Our data provide a mechanistic understanding of the effectivity of EGFR inhibitors within combination therapies against KRAS and BRAF mutant CRC.

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Fig. 1: Minimizing cell-cycle-dependent influences on EKAREV(Tq)-FRET.
Fig. 2: Comparative analyses of EKAREV, EKAREN4 and EKAREN5.
Fig. 3: Single-cell ERK dynamics in CRC PDOs expose cell-to-cell heterogeneity in response to MAPK pathway inhibitors.
Fig. 4: Pan-HER inhibitor afatinib eliminates ERK activity oscillations in KRAS, NRAS or BRAF mutant PDOs.
Fig. 5: Mutant KRAS molecules engage in EGFR-mediated ERK activation.
Fig. 6: Differential adaptation to long-term growth conditions without EGF.
Fig. 7: EGFR-mediated amplification of ERK signalling promotes tumour growth in vitro and in vivo.

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Data availability

Source data are provided with this paper. All other data supporting the findings of this study are available from the corresponding author on reasonable request.

Code availability

Custom-written ImageJ/Fiji-scripts that were used to analyse 2D and 3D FRET data are available from the corresponding author upon request.

References

  1. Ryan, M. B. & Corcoran, R. B. Therapeutic strategies to target RAS-mutant cancers. Nat. Rev. Clin. Oncol. 15, 709–720 (2018).

    Article  CAS  PubMed  Google Scholar 

  2. Yaeger, R. & Corcoran, R. B. Targeting alterations in the RAF-MEK pathway. Cancer Discov. 9, 329–341 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Karnoub, A. E. & Weinberg, R. A. Ras oncogenes: split personalities. Nat. Rev. Mol. Cell Biol. 9, 517–531 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Yao, Z. et al. BRAF mutants evade ERK-dependent feedback by different mechanisms that determine their sensitivity to pharmacologic inhibition. Cancer Cell 28, 370–383 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Corcoran, R. B. et al. Combined BRAF, EGFR and MEK inhibition in patients with BRAFV600E-mutant colorectal cancer. Cancer Discov. 8, 428–443 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. van Geel, R. et al. A phase Ib dose-escalation study of encorafenib and cetuximab with or without alpelisib in metastatic BRAF-mutant colorectal cancer. Cancer Discov. 7, 610–619 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  7. Kopetz, S. et al. Encorafenib, binimetinib and cetuximab in BRAF V600E-mutated colorectal cancer. New Engl. J. Med. 381, 1632–1643 (2019).

    Article  CAS  PubMed  Google Scholar 

  8. Amodio, V. et al. EGFR blockade reverts resistance to KRAS(G12C) inhibition in colorectal cancer. Cancer Discov. 10, 1129–1139 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Huijberts, S. et al. Phase I study of lapatinib plus trametinib in patients with KRAS-mutant colorectal, non-small cell lung and pancreatic cancer. Cancer Chemother. Pharmacol. 85, 917–930 (2020).

    Article  CAS  PubMed  Google Scholar 

  10. Prahallad, A. et al. Unresponsiveness of colon cancer to BRAF(V600E) inhibition through feedback activation of EGFR. Nature 483, 100–103 (2012).

    Article  CAS  PubMed  Google Scholar 

  11. Sun, C. et al. Intrinsic resistance to MEK inhibition in KRAS mutant lung and colon cancer through transcriptional induction of ERBB3. Cell Rep. 7, 86–93 (2014).

    Article  CAS  PubMed  Google Scholar 

  12. Corcoran, R. B. et al. EGFR-mediated re-activation of MAPK signaling contributes to insensitivity of BRAF mutant colorectal cancers to RAF inhibition with vemurafenib. Cancer Discov. 2, 227–235 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Moll, H. P. et al. Afatinib restrains K-RAS-driven lung tumorigenesis. Sci. Transl. Med. 10, eaao2301 (2018).

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  14. Ardito, C. M. et al. EGF receptor is required for KRAS-induced pancreatic tumorigenesis. Cancer Cell 22, 304–317 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Navas, C. et al. EGF receptor signaling is essential for K-RAS oncogene-driven pancreatic ductal adenocarcinoma. Cancer Cell 22, 318–330 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Albeck, J. G., Mills, G. B. & Brugge, J. S. Frequency-modulated pulses of ERK activity transmit quantitative proliferation signals. Mol. Cell 49, 249–261 (2013).

    Article  CAS  PubMed  Google Scholar 

  17. 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).

    Article  CAS  PubMed  Google Scholar 

  18. Muta, Y. et al. Composite regulation of ERK activity dynamics underlying tumour-specific traits in the intestine. Nat. Commun. 9, 2174 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  19. Bugaj, L. J. et al. Cancer mutations and targeted drugs can disrupt dynamic signal encoding by the Ras-Erk pathway. Science 361, eaao3048 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  20. Vlachogiannis, G. et al. Patient-derived organoids model treatment response of metastatic gastrointestinal cancers. Science 359, 920–926 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Ganesh, K. et al. A rectal cancer organoid platform to study individual responses to chemoradiation. Nat. Med. 25, 1607–1614 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Yao, Y. et al. Patient-derived organoids predict chemoradiation responses of locally advanced rectal cancer. Cell Stem Cell 26, 17–26 (2020).

    Article  CAS  PubMed  Google Scholar 

  23. van de Wetering, M. et al. Prospective derivation of a living organoid biobank of colorectal cancer patients. Cell 161, 933–945 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  24. Fujii, M. et al. A colorectal tumor organoid library demonstrates progressive loss of niche factor requirements during tumorigenesis. Cell Stem Cell 18, 827–838 (2016).

    Article  CAS  PubMed  Google Scholar 

  25. Pauli, C. et al. Personalized in vitro and in vivo cancer models to guide precision medicine. Cancer Discov. 7, 462–477 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Verissimo, C. S. et al. Targeting mutant RAS in patient-derived colorectal cancer organoids by combinatorial drug screening. eLife 5, e18489 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  27. Regot, S., Hughey, J. J., Bajar, B. T., Carrasco, S. & Covert, M. W. High-sensitivity measurements of multiple kinase activities in live single cells. Cell 157, 1724–1734 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Komatsu, N. et al. Development of an optimized backbone of FRET biosensors for kinases and GTPases. Mol. Biol. Cell 22, 4647–4656 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Kamioka, Y. et al. Live imaging of protein kinase activities in transgenic mice expressing FRET biosensors. Cell Struct. Funct. 37, 65–73 (2012).

    Article  CAS  PubMed  Google Scholar 

  30. Goedhart, J. et al. Structure-guided evolution of cyan fluorescent proteins towards a quantum yield of 93%. Nat. Commun. 3, 751 (2012).

    Article  PubMed  CAS  Google Scholar 

  31. Komatsubara, A. T., Matsuda, M. & Aoki, K. Quantitative analysis of recombination between YFP and CFP genes of FRET biosensors introduced by lentiviral or retroviral gene transfer. Sci. Rep. 5, 13283 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Vinkenborg, J. L., Evers, T. H., Reulen, S. W., Meijer, E. W. & Merkx, M. Enhanced sensitivity of FRET-based protease sensors by redesign of the GFP dimerization interface. ChemBioChem 8, 1119–1121 (2007).

    Article  CAS  PubMed  Google Scholar 

  33. Cost, H., Barreau, P., Basset, M., Le Peuch, C. & Geny, B. Phorbol myristate acetate inhibits phosphoinositol lipid-specific phospholipase C activity via protein kinase C activation in conditions inducing differentiation in HL-60 cells. Cell Biochem. Funct. 9, 263–273 (1991).

    Article  CAS  PubMed  Google Scholar 

  34. Bonnet, J., Mayonove, P. & Morris, M. C. Differential phosphorylation of Cdc25C phosphatase in mitosis. Biochem. Biophys. Res. Commun. 370, 483–488 (2008).

    Article  CAS  PubMed  Google Scholar 

  35. Franckhauser, C., Mamaeva, D., Heron-Milhavet, L., Fernandez, A. & Lamb, N. J. Distinct pools of cdc25C are phosphorylated on specific TP sites and differentially localized in human mitotic cells. PLoS ONE 5, e11798 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  36. Bao, Z. Q., Jacobsen, D. M. & Young, M. A. Briefly bound to activate: transient binding of a second catalytic magnesium activates the structure and dynamics of CDK2 kinase for catalysis. Structure 19, 675–690 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Holt, L. J. et al. Global analysis of Cdk1 substrate phosphorylation sites provides insights into evolution. Science 325, 1682–1686 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. King, C., Sarabipour, S., Byrne, P., Leahy, D. J. & Hristova, K. The FRET signatures of noninteracting proteins in membranes: simulations and experiments. Biophys. J. 106, 1309–1317 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Sparta, B. et al. Receptor level mechanisms are required for epidermal growth factor (EGF)-stimulated extracellular signal-regulated kinase (ERK) activity pulses. J. Biol. Chem. 290, 24784–24792 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Gillies, T. E., Pargett, M., Minguet, M., Davies, A. E. & Albeck, J. G. Linear integration of ERK activity predominates over persistence detection in Fra-1 regulation. Cell Syst. 5, 549–563 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Aoki, K. et al. Propagating wave of ERK activation orients collective cell migration. Dev. Cell 43, 305–317 (2017).

    Article  CAS  PubMed  Google Scholar 

  42. Harvey, C. D. et al. A genetically encoded fluorescent sensor of ERK activity. Proc. Natl Acad. Sci. USA 105, 19264–19269 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Dymond, A. W. et al. Metabolism, excretion and pharmacokinetics of selumetinib, an MEK1/2 inhibitor, in healthy adult male subjects. Clin. Ther. 38, 2447–2458 (2016).

    Article  CAS  PubMed  Google Scholar 

  44. Gerosa, L. et al. Receptor-driven ERK pulses reconfigure MAPK signaling and enable persistence of drug-adapted BRAF-mutant melanoma cells. Cell Syst. 11, 478–494.e9 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Meerbrey, K. L. et al. The pINDUCER lentiviral toolkit for inducible RNA interference in vitro and in vivo. Proc. Natl Acad. Sci. USA 108, 3665–3670 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. de Vries-Smits, A. M., Burgering, B. M., Leevers, S. J., Marshall, C. J. & Bos, J. L. Involvement of p21ras in activation of extracellular signal-regulated kinase 2. Nature 357, 602–604 (1992).

    Article  PubMed  Google Scholar 

  47. Canon, J. et al. The clinical KRAS(G12C) inhibitor AMG 510 drives anti-tumour immunity. Nature 575, 217–223 (2019).

    Article  CAS  PubMed  Google Scholar 

  48. Waters, A. M. et al. Evaluation of the selectivity and sensitivity of isoform- and mutation-specific RAS antibodies. Sci. Signal. 10, eaao3332 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  49. Matano, M. et al. Modeling colorectal cancer using CRISPR-Cas9-mediated engineering of human intestinal organoids. Nat. Med. 21, 256–262 (2015).

    Article  CAS  PubMed  Google Scholar 

  50. Bertotti, A. et al. A molecularly annotated platform of patient-derived xenografts (‘xenopatients’) identifies HER2 as an effective therapeutic target in cetuximab-resistant colorectal cancer. Cancer Discov. 1, 508–523 (2011).

    Article  CAS  PubMed  Google Scholar 

  51. Hiratsuka, T. et al. Intercellular propagation of extracellular signal-regulated kinase activation revealed by in vivo imaging of mouse skin. eLife 4, e05178 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Lavoie, H. & Therrien, M. Regulation of RAF protein kinases in ERK signalling. Nat. Rev. Mol. Cell Biol. 16, 281–298 (2015).

    Article  CAS  PubMed  Google Scholar 

  53. Ambrogio, C. et al. KRAS dimerization impacts MEK inhibitor sensitivity and oncogenic activity of mutant KRAS. Cell 172, 857–868 (2018).

    Article  CAS  PubMed  Google Scholar 

  54. Hunter, J. C. et al. Biochemical and structural analysis of common cancer-associated KRAS mutations. Mol. Cancer Res. 13, 1325–1335 (2015).

    Article  CAS  PubMed  Google Scholar 

  55. Sheffels, E., Sealover, N. E., Theard, P. L. & Kortum, R. L. Anchorage-independent growth conditions reveal a differential SOS2 dependence for transformation and survival in RAS-mutant cancer cells. Small GTPases 12, 67–78 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  56. Nichols, R. J. et al. RAS nucleotide cycling underlies the SHP2 phosphatase dependence of mutant BRAF-, NF1- and RAS-driven cancers. Nat. Cell Biol. 20, 1064–1073 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Mainardi, S. et al. SHP2 is required for growth of KRAS-mutant non-small-cell lung cancer in vivo. Nat. Med. 24, 961–967 (2018).

    Article  CAS  PubMed  Google Scholar 

  58. Durrant, D. E. & Morrison, D. K. Targeting the Raf kinases in human cancer: the Raf dimer dilemma. Br. J. Cancer 118, 3–8 (2018).

    Article  CAS  PubMed  Google Scholar 

  59. Blasco, R. B. et al. c-Raf, but not B-Raf, is essential for development of K-Ras oncogene-driven non-small cell lung carcinoma. Cancer Cell 19, 652–663 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Uhlitz, F. et al. A census of cell types and paracrine interactions in colorectal cancer. Preprint at bioRxiv https://doi.org/10.1101/2020.01.10.901579 (2020).

  61. Lupo, B. et al. Colorectal cancer residual disease at maximal response to EGFR blockade displays a druggable Paneth cell-like phenotype. Sci. Transl. Med. 12, eaax8313 (2020).

    Article  CAS  PubMed  Google Scholar 

  62. Zanella, E. R. et al. IGF2 is an actionable target that identifies a distinct subpopulation of colorectal cancer patients with marginal response to anti-EGFR therapies. Sci. Transl. Med. 7, 272ra212 (2015).

    Article  CAS  Google Scholar 

  63. Khambata-Ford, S. et al. Expression of epiregulin and amphiregulin and K-ras mutation status predict disease control in metastatic colorectal cancer patients treated with cetuximab. J. Clin. Oncol. 25, 3230–3237 (2007).

    Article  CAS  PubMed  Google Scholar 

  64. Huang, Y. M. & Chang, C. E. Mechanism of phosphothreonine/serine recognition and specificity for modular domains from all-atom molecular dynamics. BMC Biophys. 4, 12 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Roerink, S. F. et al. Intra-tumour diversification in colorectal cancer at the single-cell level. Nature 556, 457–462 (2018).

    Article  CAS  PubMed  Google Scholar 

  66. Kraulis, P. J. Similarity of protein G and ubiquitin. Science 254, 581–582 (1991).

    Article  CAS  PubMed  Google Scholar 

  67. Merritt, E. A. & Murphy, M. E. Raster3D Version 2.0. A program for photorealistic molecular graphics. Acta Crystallogr. D Biol. Crystallogr. 50, 869–873 (1994).

    Article  CAS  PubMed  Google Scholar 

  68. Borlinghaus, R. T. MRT letter: high speed scanning has the potential to increase fluorescence yield and to reduce photobleaching. Microsc. Res. Tech. 69, 689–692 (2006).

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

We thank members of the Snippert, Riquet and Trusolino laboratories for reagents, suggestions and discussions. We thank M. Gloerich for careful reading of the manuscript. This work is part of the Oncode Institute, which is partly financed by the Dutch Cancer Society and was funded by the Gravitation Program CancerGenomics.nl from the Netherlands Organisation for Scientific Research (NWO), by a grant from the Dutch Cancer Society (KWF/UU2013-6070, H.J.G.S.), ERC starting grant (IntratumoralNiche, H.J.G.S.) and a ‘Sta op tegen Kanker’ International Translational Cancer Research grant (J.L.B.). Stand Up to Cancer is a programme administered by the AACR. Research was further supported by EOS MODEL-IDI (FWO grant no. 30826052), iBOF ATLANTIS (BOF20/IBF/039), FWO research grants (G.0E04.16N, G.0C76.18N, G.0B71.18N and G.0B96.20N), Methusalem (BOF16/MET_V/007), Foundation against Cancer (F/2016/865 and F/2020/1505), CRIG and GIGG consortia, and VIB (to P.V.). This research was supported by the Agence Nationale pour la Recherche (ANR) via the G2Progress programme (ANR-13- BSV2-0016-02, F.B.R.). F.B.R. acknowledges the Nikon BELUX partnership and funding from Oseo–Ministère de l’enseignement supérieur et de la recherche via the national contest 2013 d’aide à la création d’entreprise de technologies innovantes catégorie émergence in the context of the KiBioS spin-off project. This collaborative work was encouraged by the CNRS Groupement de recherche (GDR) 2588 ‘Microscopie et Imagerie du Vivant’ scientific community via the biosensor workgroup initiative and especially during IMOB2018. Additional funding was provided by AIRC (Associazione Italiana per la Ricerca sul Cancro) Investigator grants 20697 (A.B.) and 22802 (L.T.), AIRC 5x1000 grant no. 21091 (A.B. and L.T.), AIRC/CRUK/FC AECC Accelerator Award 22795 (L.T.), European Research Council Consolidator Grant 724748—BEAT (A.B.), H2020 grant no. 754923 COLOSSUS (L.T.), H2020 INFRAIA grant no. 731105 EDIReX (A.B.) and Fondazione Piemontese per la Ricerca sul Cancro-ONLUS, 5x1000 Ministero della Salute 2014, 2015 and 2016 (L.T.). A.B. and L.T. are members of the EurOPDX Consortium.

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Authors and Affiliations

Authors

Contributions

B.P. and H.J.G.S. conceived and oversaw the project. B.P., J.R.B.d.A., H.R., F.B.R., J.L.B. and H.J.G.S. wrote the manuscript. F.B.R. and P.V. provided grant support and oversaw experiments. B.P., J.B.P., J.L.B., L.T., F.B.R. and H.J.G.S. designed experiments. B.P., R.L.v.I., S.K., J.R.B.d.A., D.L., F. Sipieter and B.C. performed FRET microscopy. J.B.P. and S.M. performed western blots. H.R. analysed crystallography data. A.B. and F. Sassi performed and analysed in vivo experiments. I.V.-K. assisted with organoid culture. R.G.J.V. and S.F.B. provided PDO material.

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Correspondence to Hugo J. G. Snippert.

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Extended data

Extended Data Fig. 1 CDK1/cyclinB phosphorylates EKAREV(Tq) during G2- and M-phase.

a, Typical mitotic EKAREV-FRET profile in HEK293 cells, including rising phase, steep increase at nuclear envelope breakdown (NEB) and sharp decline at anaphase (Supplementary Movie 1). Corresponding snapshots of above cells with H2B-mScarlet support cell-cycle phases (20 cells; 1 experiment). 1, G2; 2, NEB; 3, metaphase; 4, anaphase; 5, cytokinesis (c.k.); 6, G1. FRET-signal relative to PMA saturation (150nM). Black, FRET-ratio of YPet(yellow)/Turq2(blue) intensities. b, As a, with cell-cycle stages recognized by EKAREV(Tq) biosensor exclusion from condensed chromosomes; consistently observed (53 cells, 4 experiments). c, As a, but EKAREV(Tq) biosensor lacking nuclear localization. Observed in 28 cells, 2 experiments. d, As a, but EKAREV(TA) control biosensor that cannot be phosphorylated. Observed in 5 cells, 1 experiment. e, EKAREV(Tq) FRET signal in mitotic arrested HEK293 cells (nocodazol, 0.83μM; 2hrs) is sensitive to CDK1 inhibitor RO-3306. In mitotic cells, recognized by absence of nuclear localization (NEB) of NLS-tagged biosensor (insert images), FRET decreased upon 10µM RO-3306 (9 cells; 2 experiments with similar results), or 3x 1µM (1 cell). e’, Loss of normalized FRET signal (ΔR, %) upon RO-3306 (10µM) or MAPK pathway inhibitors (sel+SCH, 5μM each). Box-and-whisker plots: boxes represent quartile 2 and 3, horizontal line represents median, whiskers represent minimum and maximum within 1,5x interquartile range. RO-3306: n=23 cells, sel+SCH n=16 cells. f, EKAREV(Tq) FRET signal is sensitive to CDK1-inhibition in G2-phase. Synchronized cells were imaged before, during and after incubation with RO-3306 (10µM) and retrospectively analyzed if mitotic entry was observed <15 minutes after drug washout (n=23 cells). Right, similar experiment, here inhibiting MEK and ERK (n=20 cells). Graph shows mean±s.d. of baseline-normalized traces. g, As f, monitoring G2-phase in HeLa cells (n=19) co-expressing ERK-KTR-mCherry and EKAREV(Tq). ERK-KTR biosensor suffers from same undesired CDK1-sensitivity. ***, two-sided student’s T-test, p<0.0005. Scale bar, 10μm.

Source data

Extended Data Fig. 2 Improving ERK specificity, generating EKAREN4.

a, HeLa cells expressing EKAREV substrate variants Alt_ERK_Substr_1-6 (Extended Data Fig. 4b). PMA, 500nM; SCH, 10μM. Right, responses (mean±s.d.), normalized to EKAREV-GW4.0. b, HeLa cells expressing EKAREV-GW variants were arrested in mitosis (Extended Data Fig. 1e) to test CDK1/cyclinB sensitivity (RO-3306). Purple, mean of individual traces. Right, overview for several variants, mean ratio loss ± s.e.m. Best responder EKAREV-GW(Alt_substr._6) is compromised by RO-sensitivity. c, Repeat of Fig. 1e, quantifying FRET in G2- and M-phase HEK293 cells(mean ± s.d), complemented with results from third-residue-substitution variants (purple). No improvements compared to EKAREN4/EKAREN5. d, Sensor dephosphorylation kinetics, assessed by instant ERK inactivation (sel+SCH, 5μM) after initial sensor saturation (PMA). For identical experimental conditions, HEK293 stably expressing EKAREV(Tq) or EKAREN4 were mixed. H2B-mScarlet selectively marked EKAREV(Tq) (left) or EKAREN4 cells (right) (insets: scale, 25 μm). Cells analyzed individually and averaged after double normalization (baseline and PMA-plateau). e, Maximum FRET range (ΔR(%), approximated through saturation (PMA) in serum-starved HEK293 cells of widely variable expression levels. e’, Baseline and plateau ratios corresponding to cells in e. Increased FRET range of EKAREN4 likely results from elevated plateau ratios. Expression levels affect FRET range by differentially affecting baseline ratios (see slopes in a.u.). Experiment performed twice. f, As e, differential effect of expression level on FRET range is similar for ERK-insensitive control sensors EKAREV(TA) and EKAREN4(TA). g, Mean (± s.d.) ΔR per expression level category (see e). For panels a-g the n numbers represent cells and are indicated in the graph for each group. Box-and-whiskers: boxes represent quartile 2 and 3, horizontal line represents median, whiskers represent minimum and maximum within 1,5x interquartile range. Dots are outliers. P values in all relevant panels were calculated using a two-sided student’s T-test, * p<0.05; ***, p<0.0005. n.s., non-significant.

Source data

Extended Data Fig. 3 Multi-dimensional analyses comparing EKAREV, EKAREN4 and EKAREN5.

a, FRET-range versus sensor expression level, as in Extended Data Fig. 2e (EKAREN4, n=80 cells; EKAREN5, n=75 cells). a’, baseline and plateau ratios corresponding to cells plotted in a. b, Means (± s.d.) of ΔR from a, calculated in three expression level categories (as in Extended Data Fig. 2g). c, Dephosphorylation kinetics of EKAREN5 were directly compared with EKAREV(Tq) (as Extended Data Fig. 2d). Retrospective unmixing was based on clustering plateau amplitudes (PMA) (see Fig. 2a). Experiment performed once. d, As in c, comparing phosphorylation and dephosphorylation kinetics of EKAREN5 with ~33-fold expression level difference. Co-seeded high and low expressors were simultaneously monitored. Experiment performed three times. e-h, Various automated analyses on autonomous ERK fluctuations of HeLa cells (dataset of Fig. 2f,g), registered simultaneously by ERK-KTR-mCherry and either of EKAREV/EKAREN FRET sensors. EKAREV, n=15 single-cell traces; EKAREN4, n=10 single-cell traces; EKAREN5, n=17 single-cell traces. e, Automated peak counting per individual cell. f, Temporal matching of rising phases in KTR versus FRET signals. g, Temporal matching of falling phases in KTR versus FRET signals. h, Counted ‘inflection’ points per trace, that is points where ERK changes accelerate or decelerate(see Methods). i, Correlation between EKAREN5-FRET and ppERK staining (mean nuclear signal). After various ERK manipulations, HeLa-EKAREN5 cells were FRET-imaged and fixed instantly after acquisition, yielding various ERK activity states between complete inhibition (MEKi+ERKi) and pathway saturation (>7 min EGF). Grey line, regression analysis (y=ax+b). Traces are mean ratios ± s.d. For panels a-i the n numbers represent cells and are indicated in the graph for each group. Box-and-whisker plots: boxes represent quartile 2 and 3, horizontal line represents median, whiskers represent minimum and maximum within 1,5x interquartile range. Dots, outliers. Scale bar, 50μm. Two-sided student’s T-tests: *, p<0.05; **, p<0.005; n.s., non-significant.

Source data

Extended Data Fig. 4 Biosensor sequences.

a, Silent mutations introduced into Turquoise2 (insert) to minimize sequence homology with the YPet fluorophore in the same construct. Red ‘x’ marks silent mutations. Blue, residues discriminating Turquoise2 from parental eCFP (Goedhart et al.30). Green, residues rendering Turquoise2 prone to dimerize with YPet, in analogy to EKAREV design (Komatsu et al., 201128). Dark green, the V224L mutation was added to further enhance dimerization and, hence, FRET efficiency in ON-state (Vinkenborg et al., 200732). b, Alternative ERK substrate sequences were derived from ERK targets RSK1 (human) and ELK1 (human) using the Kinexus website and compared to parental EKAREV-GW-4.0 (GW = GateWay) with CDC25C substrate sequence. c, Overview of generated and tested point mutant variants of EKAREV. Red, central Threonine, target of ERK phosphorylation. Blue, the Lysine at position +4 mimics the general CDK1-consensus site, mutated to Proline (K->P). Purple, the Lysine at position +6 mimics the CDK1-consensus site and mutated to bulky Trp (K->W) to create steric hindrance with cyclinB. Underlined, ERK docking domain FQFP. Purple boxed characters, rational attempts to further eliminate CDK1-sensitivity with third amino acid replacements. V422T was aimed at favoring ERK over CDK1 consensus site; L427W was aimed at further augmenting sterical hinderance of cyclinB interaction; L427E was aimed at impeding cyclinB interaction through electrostatic repulsion. The Asp (D) at position+3 was left unchanged for its reported importance for the Pin1 affinity (Komatsu et al., 201128). d, Summary of available EKAREN4 and EKAREN5 plasmid constructs (including variable targeting motifs and Thr-Ala control versions), as well as adapted version of pInducer20 (Meerbrey et al., 201145) to initiate expression of HRASN17 and P2A-coupled reporter fluorophore mKate2-NLS. Constructs were deposited at Addgene.

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Extended Data Fig. 5 Geometric effects on raw FRET signals in 3D organoid models.

a, PDO model expressing the non-phosphorylatable (hence ERK-insensitive) control sensor EKAREV(TA) was FRET-imaged to assess non-biological geometry effects on raw signals in 3D organoid FRET microscopy. YFP/CFP ratios can differ between organoids situated either far away or close to the objective (distance difference ~150 μm). Experiment performed once, this direct comparison representing general observations. b, Performing FRET acquisition, Turq2 and YPet emissions were determined from all cells of a bulky organoid (>200 cells) and plotted against their z-coordinates. YFP/CFP ratios increase subtly with increasing depth within the organoid, likely due to differential scattering-induced loss of fluorescence between the two fluorophores. Experiment performed twice with same outcome.

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Extended Data Fig. 6 Single-cell ERK responses in organoids to various doses of selumetinib.

a, As in Fig. 3b, here using selumetinib at 50 nM concentration. Shown are 33 single-cell analyses from two p9T organoids. Right, waterfall plot summarizing cellular recovery from ERK inhibition (see also Fig. 3b). b, Exact copy of Fig. 3b, using selumetinib at 200 nM concentration. Shown are 59 single-cell analyses from three p9T organoids. Right, waterfall plot summarizing the cellular recovery from ERK inhibition. c, As in Fig. 3b, here using selumetinib at 1 μM concentration. Shown are 42 single-cell analyses from three p9T organoids. Right, waterfall plot summarizing the recovery from ERK inhibition. d, Three example FRET traces to illustrate the power of time-resolved signal transduction analysis. Trace colours correspond to bars indicated with coloured arrows in waterfall plot of c. Top, cell displaying onset of recovery, interrupted by super-inhibition. Middle, selumetinib-induced inhibition is followed by a sustained phase without apparent recovery. Bottom, inhibitory effect is more prolonged, explaining negative outcome for Recovery (= ‘Recov’ – ‘Inh’). Grey, green and purple colours correspond with bars in waterfall plot of Extended Data Fig. 6b.

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Extended Data Fig. 7 EGFR plays a central role in generating pulsatile ERK dynamics in PDO-KRASG12V.

a, Fluctuating ERK dynamics in PDO-KRASG12V, abolished by afatinib at 50nM concentration. Shown traces are representative for 14 single-cell analyses from 3 PDOs. Experiment performed twice. b, As a, loss of ERK activity oscillations, observed in PDO-KRASG12V upon administration of pan-HER inhibitors lapatinib (1 μM, 21 cells), dacometinib (500 nM, 12 cells), or EGFR-specific inhibitors erlotinib (500 nM, 17 cells) and gefinitib (500 nM, 18 cells). Two independent experiments performed. ~5 organoids per condition. c, As a, loss of ERK oscillations, observed in PDO-KRASG12V upon anti-EGFR antibody cetuximab (500ng/ml). Residual ERK activity was sensitive to trametinib (MEKi). Experiment performed once; 30 cells analyzed. d, Oscillating ERK dynamics persist in PDO-KRASG12V despite HER2-inhibitor CP-724714 (5 μM). Three traces representative for 24 single-cell analyses, from one experiment with 4 organoids. e, FRET-trace demonstrating that afatinib (200nM) instantly interrupts rising phase of pulsatile ERK (arrow). Representative for >20 observations in various PDO-lines. f, Shp2-inhibitor SHP099 (5μM) abrogates autonomous ERK activity in PDO-KRASG12C, but not BRAFV600E(#4). Shown are representative multi-cellular z-plane analyses. Experiment performed once; 3 organoids. f’ BRAFV600E(#4) is similarly unresponsive to drugs targeting Src (KX2, 500 nM), FAK (PND-1186, 500 nM) and cKit/ PDGFR/ Bcr-Abl (dovitinib, 100 nM; imatinib, 1.0 μM; masitinib, 100 nM; pazopanib, 250 nM). Shown are 2x-normalized ratios (mean±s.d.). n numbers represent PDOs and are indicated in the graph per group. Experiment performed once. g, Adapted pInducer20 for doxycyclin-inducible expression of HRASN17 and P2A-coupled reporter mKate2-NLS (TRE2, Tet-Responsive Element). Western blot demonstrating doxycyclin-mediated induction of HRASN17 expression (general anti-RAS antibody) in BRAFV600E(#3)(EKAREN5+pInducer). S.E., short exposure; L.E., long exposure. Vinculin as loading control. Experiment performed once. h, Western blot analyses on indicated PDOs illustrating pan-HER inhibition on components of the linear EGFR-MAPK-pathway. Pharmacological treatments as in Fig. 4e. Experiment performed once.

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Extended Data Fig. 8 Single-cell drug response upon long-term exposure to EGFR inhibitors.

a, Single-cell analyses from data set presented in Fig. 7b. After 72 hrs afatinib (1 μM), EKAREN5 showed constant, non-oscillatory basal ERK signal. Rare activity spikes in few cells were observed in PDO-NRASQ61H and BRAFV600E(#3). b, Single-cell drug response analysis after 8 days of afatinib treatment (1μM). ERK dynamics in EGFR-inhibited PDO-KRASG12C (63 cells in 6 organoids) and PDO-NRASQ61H (70 cells in 5 organoids) are constant, non-oscillatory in nature. Depletion of the continuous oscillatory dynamics unmasked a hidden pattern of (EGFR-independent) rare activity spikes in few cells (one per ~30 hr (19 pulses observed in 571 hours of single-cell signaling evaluation). Never were two pulses observed in one single-cell derivation. Plots show all single cell analyses from multiple organoids or from a single representative organoid. Dotted line: to indicate that the two pulses are from separate cells.

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Extended Data Fig. 9 Examining clinical relevant drug therapy on BRAF mutant PDO.

a, Growth assay performed as in Fig. 7c,d, here monitoring the BRAFV600E mutant PDOs #1, #2 and #3 under treatment with drugs from clinical trial by Kopetz et al. (NEJM, 20197). Cetuximab, 1000 ng/ml; binimetinib (MEK-inh), 100 nM; encorafenib (BRAF-inh), 300 nM. Mean number of objects per time point: BRAFV600E(#1), n=84; BRAFV600E(#2), n=62; BRAFV600E(#3), n=78. For the exact n-numbers of all presented points, see Source Data file. Data are represented as mean size ± s.e.m. Growth medium was supplemented with 200x reduced EGF concentration (0.25 ng/ml) to minimize competition between cetuximab and EGF ligand. Experiment performed once. b, In same PDO lines, ERK responses were recorded using EKAREN5-FRET. Data are represented as mean value ± s.d. Quantification scheme based on double calibrated multi-cellular z-plane analyses (n numbers represent PDOs and are indicated in the graph for each group). ERK levels were maximally reduced in presence of triple combination. Box-and-whisker plots: boxes represent quartile 2 and 3, horizontal line represents median, whiskers represent minimum and maximum within 1,5x interquartile range. Dots are outliers.

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Supplementary information

Supplementary Information

Supplementary discussion of the characterization and applications of ERK biosensors. This discussion summarizes and analyses observations made during the optimization of this ERK biosensor and discusses the application of FRET biosensors in 3D organoids.

Reporting Summary

Peer Review Information

Supplementary Table

Driver mutations present in the panel of CRC PDOs. Overview of driver mutations present in the panel of KRASG12X and BRAFV600E mutant CRC organoids used in this study. Left, CRC PDOs drawn from ref. 23. Right, CRC PDOs drawn from Foundation Hubrecht Organoid Technology (HUB).

Supplementary Video 1

EKAREV(Tq) ERK biosensor shows large ratio peak during G2 and M phase. Analysis of EKAREV(Tq) FRET in HEK293 cell during G2- and M-phase. The time-lapse movie corresponds to Extended Data Fig. 1a. From left to right: co-expressed H2B-mScarlet marks the mitotic stages, sensor fluorescence (Turquoise2), FRET ratio movie, normalized ratio curve (YFP/CFP) corresponding to the drawn ROIs. PMA was added to induce sensor saturation (time point t127).

Supplementary Video 2

Simultaneous readout of pulsatile ERK dynamics using EKAREN5-FRET and ERK-KTR-mCherry. Top, EKAREN5-FRET. From left to right: sensor fluorescence, FRET ratio, normalized ratio curve (YFP/CFP) corresponding to the drawn ROIs. Bottom, KTR analysis in same cell. From left to right: FRET sensor (used to demarcate the nucleus), KTR sensor (shown ROI defines cytosolic compartment), normalized ratio curve (cytosol/nucleus). Note the variety of peak amplitudes.

Supplementary Video 3

Single-cell analysis of selumetinib response in PDO-KRASG12V. Single-cell analyis of one of the two neighbor cells shown in Fig. 3b. From left to right: sensor fluorescence (Turquoise2), FRET ratio movie, normalized ratio curve (YFP/CFP) corresponding to the drawn ROIs.

Supplementary Video 4

Single-cell analysis of afatinib response in PDO-BRAFV600E(#3). Single-cell analyis shown in Fig. 4b′. From left to right: sensor fluorescence (Turquoise2), FRET ratio movie, normalized ratio curve (YFP/CFP) corresponding to the drawn ROIs. Before addition of afatinib (200nM), all cells show oscillatory ERK activity behavior.

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Ponsioen, B., Post, J.B., Buissant des Amorie, J.R. et al. Quantifying single-cell ERK dynamics in colorectal cancer organoids reveals EGFR as an amplifier of oncogenic MAPK pathway signalling. Nat Cell Biol 23, 377–390 (2021). https://doi.org/10.1038/s41556-021-00654-5

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