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Spatially resolved subcellular protein–protein interactomics in drug-perturbed lung-cancer cultures and tissues

Nature Biomedical Engineering volume 9, pages 1039–1061 (2025)Cite this article

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Abstract

Protein–protein interactions (PPIs) regulate signalling pathways and cell phenotypes, and the visualization of spatially resolved dynamics of PPIs would thus shed light on the activation and crosstalk of signalling networks. Here we report a method that leverages a sequential proximity ligation assay for the multiplexed profiling of PPIs with up to 47 proteins involved in multisignalling crosstalk pathways. We applied the method, followed by conventional immunofluorescence, to cell cultures and tissues of non-small-cell lung cancers with a mutated epidermal growth-factor receptor to determine the co-localization of PPIs in subcellular volumes and to reconstruct changes in the subcellular distributions of PPIs in response to perturbations by the tyrosine kinase inhibitor osimertinib. We also show that a graph convolutional network encoding spatially resolved PPIs can accurately predict the cell-treatment status of single cells. Multiplexed proximity ligation assays aided by graph-based deep learning can provide insights into the subcellular organization of PPIs towards the design of drugs for targeting the protein interactome.

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Fig. 1: Schematic illustration of iseqPLA for subcellular spatial-signalling networks.
Fig. 2: PPI networks, co-expression analysis and predictive models of 16-plex profiling for 5 PPIs and 6 signalling and organelle markers in HCC827 cells.
Fig. 3: Quantification, co-expression and modelling of 34-plex profiling for 13 PPIs and 8 signalling and organelle markers in HCC827.
Fig. 4: Predictive models in 2D and 3D of 5, 9 and 13 PPIs in HCC827 cells.
Fig. 5: Evaluation of drug perturbing PPIs using VP drug in HCC827 cells.
Fig. 6: Multiple PLA assays generated 47-plex protein profiles in HCC827 cells.
Fig. 7: Quantification and modelling of 16-plex profiling for 5 PPIs and 6 organelle signalling markers in HCC827-cell-derived mouse xenografts.
Fig. 8: Quantification of 17-plex profiling for 5 PPIs and 7 organelle signalling markers in patients with EGFRm NSCLC.

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Data availability

The main data supporting the results of this study are available within the paper and its Supplementary Information. The statistics needed to recreate the figures are provided as Source Data. The raw data are available in figshare105. Source data are provided with this paper.

Code availability

The custom codes used in the study are available in GitHub106.

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Acknowledgements

A.F.C. acknowledges a Career Award from the Scientific Interface of Burroughs Wellcome Fund and a Bernie-Marcus Early-Career Professorship. A.F.C. was supported by start-up funds from the Georgia Institute of Technology and Emory University. Research reported in this publication was supported by Lung Spore and the National Cancer Institute of the National Institutes of Health under Award Number P50CA217691 from the Career Enhancement Program, R33CA291197, NSF CAREER and R35GM151028. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Research reported in this publication was supported in part by the Cancer Tissue and Pathology Shared Resource and the Data and Technology Applications Shared Resource of Winship Cancer Institute of Emory University and NIH/NCI under award number P30CA138292.

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Author notes

  1. These authors contributed equally: Shuangyi Cai, Thomas Hu.

Authors and Affiliations

  1. Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA

    Shuangyi Cai, Thomas Hu, Abhijeet Venkataraman, Efe Ozturk, Nicholas Zhang, Mingshuang Wang, Tatenda Zvidzai, Sandip Das, Adithya Pillai & Ahmet F. Coskun

  2. School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA

    Thomas Hu & Efe Ozturk

  3. Parker H. Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, GA, USA

    Abhijeet Venkataraman, Efe Ozturk & Mingshuang Wang

  4. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, USA

    Felix G. Rivera Moctezuma

  5. Interdisciplinary Bioengineering Graduate Program, Georgia Institute of Technology, Atlanta, GA, USA

    Felix G. Rivera Moctezuma, Nicholas Zhang & Ahmet F. Coskun

  6. Department of Pathology and Laboratory Medicine, Emory University School of Medicine, Atlanta, GA, USA

    Frank Schneider

  7. Winship Cancer Institute of Emory University, Atlanta, GA, USA

    Frank Schneider, Suresh S. Ramalingam, You-Take Oh, Shi-Yong Sun & Ahmet F. Coskun

  8. Department of Hematology and Medical Oncology, Emory University School of Medicine, Atlanta, GA, USA

    Suresh S. Ramalingam, You-Take Oh & Shi-Yong Sun

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Contributions

S.C., T.H., F.G.R.M., E.O. and A.F.C. designed experiments, analysed data and wrote the manuscript. M.W. analysed data. Y.-T.O. designed experiments and analysed data. S.C., A.V., F.G.R.M., N.Z., T.Z., S.D., A.P. and Y.-T.O. conducted experiments. S.C., T.H., A.F.C., F.S., S.S.R. and S.-Y.S. contributed materials.

Corresponding author

Correspondence to Ahmet F. Coskun.

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Competing interests

A.F.C., S.C. and T.H. declare a patent application related to the spatial-signalling interactomics assay (US Provisional 63/399,427 and US Application No. 18/452,178). The other authors declare no competing interests.

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Nature Biomedical Engineering thanks Feixiong Cheng, Xiangxiang Zeng and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Evaluation and quantification of iseqPLA properties in HCC827 cells.

a, Visualization of the workflow evaluating PLA sensitivity, specificity, baseline vs PPI, and batch consistency in HCC827 cells. Following different treatments, the cells were stained with PLA or IF. The detailed experimental designs are in Supplementary Fig. 6a, 7a, 8a, and 9a, Created with BioRender.com. b, The comparison of PPI counts in HCC827 cells between those treated with a range of Osimertinib for 12 hours. The detailed results are in Supplementary Fig. 6. c, The comparison of PPI counts in two HCC827AR cells. The detailed results are in Supplementary Fig. 7. d, The comparison of PPI counts in HCC827 cells between those treated with and without Osimertinib. The baseline levels of 4 proteins in HCC827 cells with and without treatment were quantified on the right panel. The detailed results are in Supplementary Fig. 8. e, The comparison of PPI counts from 4 pairs in HCC827 cells from two batches treated with and without 12-hour Osimertinib. The detailed results are in Supplementary Fig. 9. Statistical testing was performed using Mann Whitney Wilcoxon Test two-sided (***: 0.0001 < p <= 0.001, ****: p < =0.0001). Box plots and violin plots: median (horizontal line inside box), 25th and 75th percentiles (box), 25th and 75th percentiles ±1.5 times the interquartile range (whiskers).

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Extended Data Fig. 2 Schematics showing the graphical implementation of spatial neighbouring information.

a, Schematic showing the PPI events spatial information incorporated in the graph representation of PPI spatial neighbourhood. During each step of the spPPI-GNN, from the spatial graph, each PPI neighbour’s embedding is incorporated until a global cell-level embedding is extracted and used for prediction. Created with BioRender.com. b, Example of cell PPI events spatial graph showing similar PPI event type density with different PPI event neighbours’ distribution. This shows a spatial distribution heterogeneity of PPI events at the subcellular level. c, Line plot showing the variation of PPI type neighbouring count across cells (x-axis: cell ID) showed in b.

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Extended Data Fig. 3 Orthogonal validation of p-ERK/c-Myc interaction and expression in HCC827 cells.

a, Schematic illustration of measuring p-ERK/c-Myc interaction using co-IP and PLA, Created with BioRender.com. b, Top Panel of western blots depicted results of co-IP of p-ERK and c-Myc in c-Myc pull-down samples. The cells were treated with 100 nM Osimertinib for 0, 8 12 hours. N-IgG served as a negative control for co-IP. The bottom panel demonstrated the results of p-ERK, c-Myc, and GAPDH expression run on different gels from the same HCC827 cell lysate. GAPDH was used as a negative control. P-ERK expressions at short and long exposure were shown in the gel. c, Workflow of measuring 9 phosphorylated proteins in HCC827 cells using Luminex. Created with BioRender.com. d, Quantification of 9 phosphorylated proteins in HCC827 cells treated with and without 12-hour 100 nM Osimertinib was shown in the heatmap and bar graph. Bar graphs are shown as mean ± 1 SD. e, Quantification of p-ERK/c-Myc PPI counts in different ROIs with a high, low, and medium expression of p-ERK/c-Myc in HCC827 cells. The right graph shows the cumulative density which measures the percentage of cells expressing different numbers of p-ERK/c-Myc events per cell across three ROIs. The detailed statistics for each ROI are shown in Supplementary Fig. 25. Box plots: median (horizontal line inside box), 25th and 75th percentiles (box), 25th and 75th percentiles ±1.5 times the interquartile range (whiskers).

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Extended Data Fig. 4 Quantification of 26-plex profiling for 9 PPIs and 8 signalling and organelle markers in HCC827.

a, The comparison of PPI counts in HCC827 whole cells, nuclei, and cytosol between those treated with and without Osimertinib. Statistical testing was performed using Mann Whitney Wilcoxon Test two-sided (***: 0.0001 < p <= 0.001, ****: p < =0.0001). The total cell numbers are 836 and 655 for untreated and Osimertinib-treated cells in the comparison of PPI count per cell. b, UMAP visualizes the similarity of p-ERK/c-Myc counts in the 5PPI and 9PPI datasets. Box plots: median (horizontal line inside box), 25th and 75th percentiles (box), 25th and 75th percentiles ±1.5 times the interquartile range (whiskers).

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Extended Data Fig. 5 Quantification of PPIs and Ki67 in HCC827 cells.

a, The PPI count comparison in cytosol and nuclei is separately shown in the figure. The HCC827 cells were treated with VP at 0, 1, and 10 µM for 24 hours. b, The comparison of Ki67 density in HCC827 cells treated with and without VP. Ki67 density was calculated by dividing the Ki67 positive regions by the nuclear size. c, The comparison of Ki67 density in HCC827 cells treated with and without 100 nM Osimertinib for 12 hours. Statistical testing was performed using Mann Whitney Wilcoxon Test two-sided (***: 0.0001 < p <= 0.001, ****: p < =0.0001). Box plots and violin plots: median (horizontal line inside box), 25th and 75th percentiles (box), 25th and 75th percentiles ±1.5 times the interquartile range (whiskers).

Source data

Extended Data Fig. 6 PPI counts per cell and PPI density in patient tissues.

a, Quantification of PPI counts per stromal, per immune cell with immune neighbours, and PPI counts per tumour cell with tumour neighbours at the single cell level in the responder tissue. Statistical testing was performed using t-test independent samples with Bonferroni correction (***: 0.0001 < p <= 0.001, ****: p < =0.0001). b, Comparison of the density of PPI counts in lymphocyte-enriched regions between responders and non-responders. A plot with a wider y-axis range is in Supplementary Fig. 40d to show the complete individual data points. Example images of 5 PPIs expression in lymphocyte-enriched regions are shown on the right. The first column is the visualization of 5 PPIs in lymphocyte-enriched regions. The second column displays the distributions of Sox2/Oct4 PPI in red. The third column exhibits the distributions of NF-kB/p-P90RSK PPIs in green. Statistical testing was performed using t-test independent samples (***: 0.0001 < p <= 0.001, ****: p < =0.0001). Box plots and Violin plots: median (horizontal line inside box), 25th and 75th percentiles (box), 25th and 75th percentiles ±1.5 times the interquartile range (whiskers).

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Cai, S., Hu, T., Venkataraman, A. et al. Spatially resolved subcellular protein–protein interactomics in drug-perturbed lung-cancer cultures and tissues. Nat. Biomed. Eng 9, 1039–1061 (2025). https://doi.org/10.1038/s41551-024-01271-x

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