AI Article Synopsis

  • Current bulk molecular assays miss important details about how cancer cells signal, making it hard to understand drug resistance, so a new technique called GSR-PPI was developed to analyze these interactions at a single-cell level with advanced imaging and deep learning.
  • The study utilized an experimental method that involved tagging proteins and imaging them in EGFR mutant cancer cells after drug treatment, generating high-resolution images and applying deep learning models to interpret the data.
  • GSR-PPI showed superior performance over traditional methods by accurately classifying drug responses and detecting specific protein interactions, ultimately providing better insights into cancer signaling and potential therapies.

Article 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.

Supplementary Information: The online version contains supplementary material available at 10.1007/s12195-024-00822-1.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11538221PMC
http://dx.doi.org/10.1007/s12195-024-00822-1DOI Listing

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