BioE PhD Defense Presentation- Nicholas Zhang

Advisor:

Ahmet F. Coskun, Ph.D. (Biomedical Engineering, Georgia Institute of Technology)

Committee:

Peng Qiu, Ph.D. (Department of Biomedical Engineering, Georgia Institute of Technology)

Saurabh Sinha, Ph.D. (Department of Biomedical Engineering, Georgia Institute of Technology)

Rabindra Tirouvanziam, Ph.D. (Department of Biomedical Engineering, Georgia Institute of Technology)

Marcus Cicerone, Ph.D. (Department of Chemistry & Biochemistry, Georgia Institute of Technology)

Timeseries Spatial Omics of Immune Cell Signaling in Inflammation

Cystic fibrosis (CF) affects over 162,000 people worldwide. Although FDA-approved therapies have shown promising results, chronic inflammation and bacterial infections persist, causing tissue damage and worsening quality of life despite a large immune cell presence. This thesis investigates the spatiotemporal dynamics of NF-κB signaling during inflammation by developing and applying spatial omics tools at the single-cell level across multiple biological contexts.

We first introduce PRISMS, an open-sourced, automated multiplexing pipeline for spatial transcriptomic and proteomic imaging compatible with Nikon widefield and Cephla spinning disk confocal microscopy. Using PRISMS, we apply pSigOmics static fixation to profile over 100,000 mouse fibroblasts stimulated with TNFα and IL-1β, revealing a novel asymmetric protein-RNA (APR) relationship between p65 RNA and protein. Graph neural network classification and PHATe trajectory analysis further delineate distinct APR subpopulations among stimulated fibroblasts.

We next extend pSigOmics with programmable fixation via helical perfusion to create a continuous gradient of cell response, confirming the APR phenomenon with finer temporal resolution through cross-correlation, generalized additive model smoothing, and pseudotime analyses. Together, static and programmable fixation establish a comprehensive spatiotemporal framework for studying oscillatory translational regulation in single cells.

We then develop graph-based super-resolution protein-protein interaction (GSR-PPI) analysis to predict cancer drug responses from spatial PPI networks in lung adenocarcinoma cells and patient-derived NSCLC tissues, and introduce multiplexed iterative sequential PLA (iseqPLA) to visualize 3D NF-κB supercomplex dynamics. Upstream supercomplexes containing TRAF-5_TRADD and TRAF-5_TRAF-2 undergo rapid dissociation upon cytokine stimulation, inversely correlated with p65 nuclear translocation. Macrophage-fibroblast coculture experiments reveal that CF airway supernatant-exposed macrophages impart a hyperinflammatory phenotype to neighboring fibroblasts through paracrine NF-κB activation. We additionally validate our NF-κB gene panel using scGPT foundation models trained on multi-disease transcriptomic datasets, establishing a mechanistic link between protein-level supercomplex dynamics and transcriptional outputs.

These contributions demonstrate the power of combining spatial multiplexing, temporal reconstruction, and computational modeling to decode immune signaling at single-cell and subcellular resolution, with implications for understanding chronic inflammation in CF and identifying opportune windows for therapeutic intervention.