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Graph-Based Spatial Proximity of Super-Resolved Protein–Protein Interactions Predicts Cancer Drug Responses in Single Cells

  • SI: 2024 CMBE Young Innovators
  • Published:
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Abstract

Purpose

Current bulk molecular assays fail to capture spatial signaling activities in cancers, limiting our understanding of drug resistance mechanisms. We developed a graph-based super-resolution protein-protein interaction (GSR-PPI) technique to spatially resolve single-cell signaling networks and evaluate whether higher resolution microscopy enhances the biological study of PPIs using deep learning classification models.

Methods

Single-cell spatial proximity ligation assays (PLA, ≤ 9 PPI pairs) were conducted on EGFR mutant (EGFRm) PC9 and HCC827 cells (>10,000 cells) treated with 100 nM Osimertinib. Multiplexed PPI images were obtained using wide-field and super-resolution microscopy (Zeiss Airyscan, SRRF). Graph-based deep learning models analyzed subcellular protein interactions to classify drug treatment states and test GSR-PPI on clinical tissue samples. GSR-PPI triangulated PPI nodes into 3D relationships, predicting drug treatment labels. Biological discriminative ability (BDA) was evaluated using accuracy, AUC, and F1 scores. The method was also applied to 3D spatial proteomic molecular pixelation (PixelGen) data from T cells.

Results

GSR-PPI outperformed baseline models in predicting drug responses from multiplexed PPI imaging in EGFRm cells. Super-resolution data significantly improved accuracy over localized wide-field imaging. GSR-PPI classified drug treatment states in cancer cells and human lung tissues, with performance improving as imaging resolution increased. It differentiated single and combination drug therapies in HCC827 cells and human tissues. Additionally, GSR-PPI accurately distinguished T-cell stimulation states, identifying key nodes such as CD44, CD45, and CD54.

Conclusion

The GSR-PPI framework provides valuable insights into spatial protein interactions and drug responses, enhancing the study of signaling biology and drug resistance.

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Data Availability

The data that support this published work are available at https://figshare.com/projects/Signaling_Project_PLA/195958. Relevant code can be found at https://github.com/coskunlab/GSR-PPI/tree/main.

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Acknowledgments

AFC holds a Career Award at the Scientific Interface from 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 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. The research was also supported by 1R33CA291197.

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Authors and Affiliations

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

    Nicholas Zhang Ph.D, Shuangyi Cai BS, PhD, Mingshuang Wang MD, Thomas Hu MS, PhD & Ahmet F. Coskun Ph.D

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

    Nicholas Zhang Ph.D & Ahmet F. Coskun Ph.D

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

    Nicholas Zhang Ph.D, Shuangyi Cai BS, PhD, Mingshuang Wang MD, Thomas Hu MS, PhD & Ahmet F. Coskun Ph.D

  4. School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA

    Thomas Hu MS, PhD

  5. Winship Cancer Institute of Emory University, Atlanta, GA, 30322, USA

    Frank Schneider MD & Shi-Yong Sun

  6. Department of Pathology and Laboratory Medicine, Emory University School of Medicine, Atlanta, GA, 30322, USA

    Frank Schneider MD

  7. Department of Hematology and Medical Oncology, Emory University School of Medicine, Atlanta, GA, 30322, USA

    Shi-Yong Sun

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Correspondence to Ahmet F. Coskun Ph.D.

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Conflict of interest

Coskun, Cai, and Hu declare a patent application related to the spatial signaling interactomics assay (US Provisional 63/399,427 and US Application No 18/452,178). Nicholas Zhang, Mingshuang Wang, Frank Schneider, and Shi-Yong Sun declare no competing interests.

Ethical Approval

The study did not directly involve animal or human subjects. The use of human specimens was approved by the Institutional Review Board of Emory University (IRB00098377).

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Zhang, N., Cai, S., Wang, M. et al. Graph-Based Spatial Proximity of Super-Resolved Protein–Protein Interactions Predicts Cancer Drug Responses in Single Cells. Cel. Mol. Bioeng. 17, 467–490 (2024). https://doi.org/10.1007/s12195-024-00822-1

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