Proximity Sequencing for Multi-Omics Profiling of Protein Interactions and Post-Translational Modifications in Spatial and Single Cell Contexts

A central question in biology is how to define and understand the functional state of a cell. While single-cell RNA sequencing has enabled high-resolution characterization of cellular heterogeneity, it primarily measures transcriptional abundance and provides only an indirect view of cellular function. Proteins are the direct executors of cellular behavior, and their activity is governed not only by abundance but also by protein–protein interactions (PPIs) and post-translational modifications (PTMs). These layers of regulation more closely determine cell state and phenotype, yet remain difficult to measure at scale in single-cell and spatial contexts. To address this gap, proximity sequencing (Prox-seq) was developed to integrate proximity ligation assays with single- cell sequencing, enabling simultaneous measurement of transcripts, proteins, and protein proximity in individual cells. However, the initial work lacks spatial resolution, has limited capacity to capture PTM-dependent states, and presents challenges in interpreting pairwise proximity signals into higher-order protein complexes. In this dissertation, I extend Prox-seq into a more comprehensive platform for characterizing functional protein organizations in single-cell and spatial contexts. First, I develop spatial Prox-seq (Sprox), which integrates proximity measurements with spatial transcriptomics to map protein interactions within intact tissue architecture. Sprox reveals spatiotemporal dynamics of protein interactions during B cell progression in the germinal center. Second, I introduce glycan Prox-seq (GPS), enabling the measurement of protein-adjacent glycosylation alongside transcriptomic profiles in single cells. GPS reveals glycan remodeling during T cell differentiation, and identifies poly-LacNAc enrichment in the CD45-associated glycan microenvironment being linked to an exhaustion state in terminally differentiated CD8+ T cells. Third, I establish computational frameworks to infer protein dimers and higher-order multimers from proximity data, bridging the gap between observed signals and molecular assemblies. Predictions from these methods align well with controlled ground truth from simulation models. Together, my doctoral research establishes Prox- seq as a flexible platform for integrating protein interactions, PTMs, and transcriptomic information across single-cell and spatial contexts, enabling a more direct and functionally relevant understanding of cellular systems and tissue architectures.