Shuangyi Cai
BME PhD Defense Presentation

Date: 2024-06-05
Time: 1 pm -3 pm
Location / Meeting Link: IBB Suddath Seminar Room 1128, https://gatech.zoom.us/j/92316101603

Committee Members:
Ahmet F. Coskun (Advisor); Shi-Yong Sun; Levi Wood; Melissa Kemp; Costas Arvanitis


Title: Deciphering Spatial Signaling Networks Using Image-Based Multiplexed Approaches

Abstract:
Non-small cell lung cancer (NSCLC), accounting for 80% to 85% of lung cancer cases includes a subgroup of patients with EGFR mutations who can benefit from EGFR tyrosine kinase inhibitors (TKIs). However, patients can still develop acquired drug resistance due to the activation and crosstalk among signaling pathways. Protein-protein interactions (PPI) significantly regulate signaling pathways and cell phenotyping. Visualizing the dynamics of proteins sheds light on the crosstalk of spatially resolved signaling networks. Current approaches have been limited to bulk-level molecular assays or non-spatial measurements. To overcome these limitations, we first presented rapid multiplexed immunofluorescence (RapMIF) to explore the signaling proteins involved in the WNT/β-catenin and AKT/mTOR pathways. RapMIF automated iterative staining, bleaching, and imaging, and achieved measuring up to 25-plex spatial protein maps across 33 multiplexed pixel-lever clusters, revealing intricate signaling states, translocation patterns, and subcellular signaling clusters within single cells. Furthermore, we developed a new multiplex image-based assay to detect the PPIs at the subcellular level, termed rapid multiplexed protein interaction (RapMPI). RapMPI enables multiplexed profiling of 47-plex proteins including 22 pairs of proteins involved in the AKT/mTOR, MEK/ERK, and YAP/TEAD pathways NSCLC EGFR mutant cell cultures. The capability of performing RapMPI on tissues was further validated. The multiplexed single-cell data reconstructs the subcellular distributions of signaling proteins and PPIs under drug perturbations and uncovers the dynamic changes in signaling networks. Integrating RapMIF and RapMPI data could help predict cell status, providing invaluable insights into the intricate subcellular organization of PPIs toward precision therapy design and signaling discovery.