Mapping protein signatures to single allele chromatin topologies at genomic resolution

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Interphase chromatin is hierarchically organized in chromosome territories, active and inactive compartments, and topologically associating domains (TADs). Gene expression is controlled by regulatory chromatin elements (enhancers and promoters) that bind transcription factors and interact within, but not across TAD boundaries. Recent advances in single cell biology have revealed a tremendous amount of cell-to-cell variability in both chromatin topology and protein coverage. Even though TADs appear to delineate functional units at the population level, their boundaries only emerge as average properties from large ensembles of cells. TAD-like structures persist even in the absence of boundaries at the ensemble level. Similarly, single cell ChIP-seq has revealed sub-states with distinct epigenetic profiles at enhancers in a population of stem cells. But no assay exists to link topological and functional variability by reading out protein coverage and epigenetic signatures simultaneously from single cell chromatin traces. Here, we will leverage recent advances in multiplexed chromatin imaging and single molecule super-resolution microscopy to fill in this gap. In Aim I, we will characterize the tradeoff between sequence resolution and spatial precision in multiplexed chromatin imaging. We will then use optimized conditions to map super-resolved protein signal to chromatin topologies at genomic resolution. In Aim II, we will extend the capabilities of the assay to detect multiple protein signatures simultaneously. Such combinatorial data on protein signatures of regulatory elements at genomic resolution is not available through any other single cell assay. We will further characterize the performance of the assay under challenging conditions by mapping both stably integrated, widespread histone modifications and transiently binding sequence-specific transcription factors with a sizable unbound fraction to chromatin. Using computational clustering strategies, we will stratify the data by chromatin topology to determine if structural variation is driven by specific protein factors such as transcription factors that orchestrate long- range enhancer interactions. Finally, we will establish protocols and technological solutions to accelerate acquisition, processing, and visualization of statistically meaningful datasets comprising 100s-1000s of single alleles. The resulting datasets will provide unprecedented insight into the molecular mechanisms underlying cell-to-cell variability in chromatin topology and serve as a powerful hypothesis generator for investigating single cell genome structure-function relationships.