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Control of cell state transitions

Abstract

Understanding cell state transitions and purposefully controlling them is a longstanding challenge in biology. Here we present cell state transition assessment and regulation (cSTAR), an approach for mapping cell states, modelling transitions between them and predicting targeted interventions to convert cell fate decisions. cSTAR uses omics data as input, classifies cell states, and develops a workflow that transforms the input data into mechanistic models that identify a core signalling network, which controls cell fate transitions by influencing whole-cell networks. By integrating signalling and phenotypic data, cSTAR models how cells manoeuvre in Waddington’s landscape1 and make decisions about which cell fate to adopt. Notably, cSTAR devises interventions to control the movement of cells in Waddington’s landscape. Testing cSTAR in a cellular model of differentiation and proliferation shows a high correlation between quantitative predictions and experimental data. Applying cSTAR to different types of perturbation and omics datasets, including single-cell data, demonstrates its flexibility and scalability and provides new biological insights. The ability of cSTAR to identify targeted perturbations that interconvert cell fates will enable designer approaches for manipulating cellular development pathways and mechanistically underpinned therapeutic interventions.

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Fig. 1: Overview of cSTAR and experimental system.
Fig. 2: Separation of proliferation and differentiation signalling patterns and projection of the STV into the PCA space.
Fig. 3: Output of the DPD module.
Fig. 4: Computing signalling patterns and modelling cell manoeuvring in Waddington’s landscape using the DPD.
Fig. 5: Model-predicted DPD values and quantification of phenotypic responses of TrkA and TrkB cells to different inhibitors.
Fig. 6: Inhibition of ERBB and ERK modules synergistically induces differentiation of TrkB-expressing cells.
Fig. 7: cSTAR analysis of RAF inhibitor-resistant SKMEL-133 cells.

Data availability

Mass spectrometry proteomics data have been uploaded to the PRIDE database (accession number PXD028943). The RPPA data for SKMEL-133 cell line29 are available at http://projects.sanderlab.org/pertbio/. The scRNA-seq data for EMT in A549, DU145, MCF7 and OVCA420 cell lines31 are available at the NCBI Gene Expression Omnibus under accession GSE147405. The CYTOF data for EMT in Py2T cell line32 are available at https://community.cytobank.org/cytobank/experiments#project-id=1296.

Code availability

All codes for the data analysis, network reconstruction and modelling are available at https://github.com/OleksiiR/cSTAR_Nature.

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Acknowledgements

This work was supported by NIH/NCI grant R01CA244660, CRUK/Brain Tumour Charity grant C42454/A28596, EU NanoCommons grant 731032, IRC grant GOIPG/2020/1361 and Science Foundation Ireland grants 14/IA/2395 and 18/SPP/3522, the latter together with the Children's Health Foundation. We thank T. Santra for advising on Bayesian statistics, P. Cotter for IT assistance and C. Sander, B. Bodenmiller and S. Krishnaswamy for providing raw RPPA and CYTOF data.

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Authors and Affiliations

Authors

Contributions

B.N.K., O.S.R. and W.K. conceived the study. M.H. generated a biological model of TrkA- and TrkB-expressing cell lines, which specify different cell fate decisions. N.R., M.H., S.M., K.W., K.M. and N.O.C. performed experiments and primary RPPA data analysis. V.Z. performed clustering analysis of RPPA data and kinase enrichment analysis of scRNA-seq data. B.N.K. and O.S.R. derived equations and developed the mathematical approach. E.K. analysed the robustness of the results with respect to noise. O.S.R. and T.P. carried out numerical calculations. B.N.K., O.S.R. and W.K. wrote the manuscript. All authors read and approved the final version of the manuscript.

Corresponding author

Correspondence to Boris N. Kholodenko.

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Competing interests

A patent application (no. UK2107576.7) related to this work has been filed, with O.S.R., V.Z., W.K. and B.N.K. named as inventors. All other authors declare no competing interests.

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Extended data figures and tables

Extended Data Fig. 1 Proliferation responses of SH-SY5Y-TrkA and SH-SY5Y-TrkB cells to NGF, BDNF, and kinase inhibitors.

Proliferation of NGF-stimulated TrkA cells and BDNF-stimulated TrkB cells treated with different inhibitors was measured using the (A) ATP luminescence and (B) MTS assays following 72 h after treatment. Concentrations of inhibitors: TRKi (SP600125) – 5 μM, AKTi (AKT inhibitor IV) – 1 μM, JNKi (JNK-IN-8) – 1 μM, S6Ki (LY2584702) – 1 μM, MEKi (Trametinib) – 0.5 μM, RSKi (BI-D1870) – 1 μM. Data are presented as mean values +/− SEM for n = 3 biologically independent experiments.

Extended Data Fig. 2 RPPA phosphoproteomics data.

Heatmap of RPPA data obtained at 10 and 45 min stimulation of TrkA and TrkB cells with 100 ng/ml NGF and BDNF (the time and replicate numbers are indicated at the bottom, the proteins on the side). The data were clustered using Ward hierarchical clustering.

Extended Data Fig. 3 PCA compression of RPPA data and selection of core network components.

(A) PCA compressed RPPA data for TrkA and TrkB cells are plotted in the space of the first two principal components that are normalized by the data variance captured by these components. Following NGF or BDNF stimulation (100 ng/ml) the data points measured by RPPA were clustered in a 115-dimensional molecular dataspace using K-means clustering (K = 2). All data points from NGF-stimulated TrkA cells constitute a single cluster shown in blue, and all data points from BDNF-stimulated TrkB cells form a cluster shown in red. Unstimulated control cells are shown in black. (B) High ranked STV components determine the components of a core signaling network. The changes in individual protein activities or abundances between the centroids of the data point clouds that characterize two different cells states were projected onto the STV to determine protein ranks. Resulting high rank proteins constitute the nodes of a core signaling network.

Extended Data Fig. 4 Literature-based prior network and signaling responses to ligand stimulation.

(A) Prior topology of core network connections based on the existing knowledge16,37,62. (B) Time courses of Trk and ERK activation (measured with phosphospecific antibodies, pTrk and ppERK) in TrkA and TrkB cells after stimulation with 100 ng/ml NGF or BDNF, respectively. GAPDH staining was used as loading control. For gel source data, see Supplementary Fig. 1. Representative blot of 3 biological replicates is shown.

Extended Data Fig. 5 Reconstruction of core signaling networks by BMRA.

Inferred topologies of TrkA (A) and TrkB (B) core signaling networks. Edges that are specific to TrkA and TrkB are shown in blue and red, respectively. Arrowheads indicate activation, blunt ends indicate inhibition. Line widths indicate the absolute values of interaction strengths.

Extended Data Fig. 6 Model predicted time courses of responses to p70S6K and Trk inhibitors.

Experimental data (dots) are imposed on model predicted time course of signaling responses of TrkA and TrkB cells treated with (A) S6K inhibitor (LY2584702, 1 μM) or (B) Trk inhibitor (SP600125, 5 μM) and stimulated with 100 ng/ml NGF and BDNF, respectively. Dashed lines are the time courses in the absence of inhibitor. TrkA, blue; TrkB, red. Data are presented as mean values +/− SEM for n = 3 biologically independent experiments.

Extended Data Fig. 7 Model predicted time courses of responses to MEK and AKT inhibitors.

Experimental data are imposed on model predicted time courses of signaling responses of TrkA and TrkB cells treated with (A) MEK inhibitor (Trametinib, 0.5 μM) or (B) AKT inhibitor (AKT inhibitor IV, 1 μM) and stimulated with 100 ng/ml NGF and BDNF, respectively. Dashed lines show the time courses in the absence of inhibitor. TrkA, blue; TrkB, red. Data are presented as mean values +/− SEM for n = 3 biologically independent experiments.

Extended Data Fig. 8 Model predicted time courses of responses to JNK and RSK inhibitors.

Experimental data are imposed on model predicted time-courses of signaling responses of TrkA (blue) and TrkB (red) cells treated with (A) JNK inhibitor (1 μM) or (B) RSK inhibitor (1 μM) and stimulated with 100 ng/ml NGF and BDNF, respectively. Dashed lines show the time courses in the absence of inhibitor. TrkA, blue; TrkB, red. Data are presented as mean values +/− SEM for n = 3 biologically independent experiments.

Extended Data Fig. 9

(A) The restoring force f(S) is plotted versus the DPD output S. (B) Waddington’s landscape in the absence of the signaling driving force. The basins of attraction for differentiation and proliferation are colored blue and red, respectively. (C) Schematic diagram of the generation of cell fate decisions by the driving signaling force, which drives cell state changes, and the restoring force, which stabilizes a given cell state.

Extended Data Fig. 10 Live cell images of TrkA cells stimulated with NGF and treated with inhibitors.

Inhibitor concentrations are given in the legend to Extended Data Figs. 68. Representative images of 3 biological replicates are shown.

Extended Data Fig. 11 Live cell images of TrkB cells stimulated with BDNF and treated with inhibitors.

Inhibitor concentrations are given in the legend to Extended Data Figs. 68. Representative images of 3 biological replicates are shown.

Extended Data Fig. 12 Model predicted outcomes of TrkA cell inhibitor treatments are corroborated by cell images.

(A) Model predicted DPD responses of TrkA cells to ERBB and MEK inhibitors are shown at 45 min 100 ng/ml NGF stimulation by Loewe isoboles. The ERBB inhibitor applied alone has a negligible effect. (B) The percentages of differentiated TrkA cells show that a combination of ERBB (Gefitinib, GEF) and MEK inhibitors (Trametinib, TRAM) does not change the cell state, as correctly predicted by the model. Data are presented as mean values +/- SEM for n = 3 biologically independent experiments. (C) Live cell images of NGF-stimulated TrkA cells treated with 2.5 μM Gefitinib, 0.2 μM Trametinib and a combination of 1.25 μM Gefitinib and 0.1 μM Trametinib taken at 72 h. Representative images of 3 biological replicates are shown.

Extended Data Fig. 13 Model predicted outcomes of TrkB cell inhibitor treatments are corroborated by cell images.

Live cell images of BDNF-stimulated TrkB cells treated with 2.5 μM Gefitinib, 0.2 μM Trametinib and a combination of 1.2 μM Gefitinib and 0.1 μM Trametinib at 72 h. Representative images of 3 biological replicates are shown.

Extended Data Fig. 14 Inhibition of p38 does not change the percentage of differentiated TrkA and TrkB cells.

Live cell images of NGF-stimulated TrkA (A) cells and BDNF-stimulated TrkB (B) cells treated with 10 μM p38 inhibitor SB203580 for 72 h. Representative images of 3 biological replicates are shown.

Extended Data Fig. 15 Separation of MS phosphoproteomic patterns of TrkA and TrkB cell states and the STV projection into the PCA space.

Following GF stimulation, TrkA (blue) and TrkB (red) states were separated by a SVM. Projections of data points, the separating hyperplane (grey) and the STV (dark red) are shown in the space of the first three principal components. The text in red indicates the kinases that phosphorylate the top STV components.

Extended Data Fig. 16 Separation of apoptotic and proliferation states of SKMEL-133 cells and a projection into the PCA space.

SVM separation of phosphoproteomic patterns of proliferation states in growing SKMEL-133 cells and apoptotic states after treatment with a combination of PI3K/AKT/mTOR and MEK inhibitors. The data are taken from Korkut et al.29. Projections of the separated data points, the separating hyperplane (black) and the STV (dark red) are shown in the space of the first two principal components.

Extended Data Fig. 17 Model calculated and experimentally determined DPD responses of SKMEL-133 cells to different inhibitors.

(A) The experimentally measured DPD values (dots) are calculated based on the data from the reference Korkut et al.29. Model-predicted (blue curves) DPD responses to many inhibitors exhibit abrupt DPD decreases at certain inhibitor doses caused by the loss of stability of a proliferation state and the induction of apoptosis in a threshold manner. Mathematically, an abrupt DPD decrease relates to a saddle-node bifurcation63 (a fold catastrophe) that occurs when a stable steady-state solution corresponding to a proliferation state disappears. Data are presented as mean values +/− SEM for n = 3 biologically independent experiments. (B) Synergy between MEK/ERK and PI3K/AKT inhibitors is demonstrated by concave Loewe isoboles.

Extended Data Fig. 18 Separation of scRNAseq transcriptomic patterns of epithelial and mesenchymal states and projections into the PCA states.

Single cell RNAseq data31 were separated by SVM in untreated (blue) and treated with TGFβ (red) A549 (A), DU145 (B), MCF7 (C) and OVCA420 (D) cells. Projections of data points, the separating hyperplane (grey) and the STV (dark red) are shown in the space of the first three principal components.

Extended Data Fig. 19 DPD responses of Py2T, A549, DU145, MCF7 and OVCA420 cell lines to specific inhibitors of different signaling modules.

The cell lines, ligands and inhibited modules are indicated on the horizontal and vertical axes. The normalized DPD value 1 corresponds to fully mesenchymal state, and normalized DPD value 0 corresponds to fully epithelial state.

Extended Data Fig. 20 Single-cell DPD distributions for A549, DU145, MCF7, OVCA420 cells.

Left panels show single-cell DPD distributions for cells stimulated with TGFβ, EGF or TNF for 7 days. Right panels show single-cell DPD distributions for TGFβ-stimulated cells treated with RIPK1 inhibitor for 7 days.

Extended Data Fig. 21

Single-cell DPD distribution for Py2T cells treated with TGFβ for 7 days.

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Rukhlenko, O.S., Halasz, M., Rauch, N. et al. Control of cell state transitions. Nature 609, 975–985 (2022). https://doi.org/10.1038/s41586-022-05194-y

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