Mapping Cellular Signals at Scale: A Reporter Platform for Multiplexed Detection of Transcription Factor Activity

Cellular signaling is far more complex than the simple linear pathways often depicted in textbooks. Rather than moving through a straightforward chain from receptor to transcription factor (TF), signals flow through interconnected networks with extensive cross-talk, feedback, and regulation. TFs, which translate signals into gene expression by binding specific DNA sequences, add another layer of complexity. Although several TFs are well studied, we still lack a comprehensive view of how they respond to signals, interact with one another, and control gene expression. Understanding TF activity is crucial both for basic biology and for uncovering mechanisms underlying disease, where signaling is frequently dysregulated. This thesis focuses on developing and applying tools to measure the activity of many TFs in parallel using massively parallel reporter assays (MPRAs). MPRAs quantify how active DNA sequences are in driving transcription by linking each sequence to a unique barcode, enabling simultaneous measurement of thousands of sequences. Chapter 2 summarizes the strengths and limitations of this approach for studying transcriptional control. In Chapter 3, we applied MPRAs to study TP53, a key tumor suppressor TF that induces cell cycle arrest or apoptosis in response to stress. Mutations in TP53, common in cancer, disrupt these protective functions. By designing thousands of synthetic sequences containing TP53 binding sites, we systematically analyzed how TP53 drives transcription. This approach yielded optimized reporter constructs with higher sensitivity than existing TP53 sensors, offering improved tools to detect aberrant TP53 activity in cancer. Chapter 4 describes the design and optimization of reporters for 86 different TFs. For each TF, we constructed synthetic DNA reporters containing multiple copies of its binding motif and systematically tested sequence variants to determine optimal configurations. Using hundreds of signaling and TF perturbation conditions, we evaluated reporter specificity and robustness, identifying reliable reporters for 62 TFs that previously lacked accurate measurement tools. These optimized reporters, termed prime TF reporters, were combined into a compact mini-MPRA library containing the best-performing construct for each TF. This streamlined library enables sensitive, multiplexed TF activity profiling from small cell numbers—ideal for high-throughput applications such as drug or genetic screens. To support this tool, we developed a computational analysis pipeline, primetime, detailed in Chapter 5. In Chapter 6, we benchmarked the prime TF reporter library against ATAC-seq, a method that infers TF activity from chromatin accessibility. While both methods showed concordant patterns, the reporter-based approach often provided higher sensitivity, revealing that combining complementary assays yields the most complete view of TF activity. Finally, Chapter 7 applies MPRAs to study CRISPR-Cas9–induced DNA breaks. By integrating reporter sequences across the genome and exposing them to a library of 160 epigenetic drugs, we identified compounds that enhance CRISPR cutting or repair efficiency, revealing how chromatin context influences genome editing outcomes.