Transcription factors, acting as primary interpreters of the genome, regulate gene expression by binding to cis-regulatory elements. The mechanisms of cis-regulation by transcription factors are more complex and dynamic than simply binding to motif sequences within cis-regulatory elements. The molecular complexes formed by transcription factors interact with open and closed chromatin to delicately regulate transcription at appropriate levels, with variations in a cell-type-specific manner.My dissertation focuses on understanding cis-regulatory elements and transcription factors in plants using advanced methods, including single-cell genomics and machine learning. In the first project, I uncovered changes in chromatin accessibility in the maize genome of mutants overexpressing the transcription factor WUSCHEL. WUSCHEL is a key transcription factor governing self-renewal and differentiation of stem cells in the maize inflorescence meristem. Using single-cell ATAC-seq, I discovered that WUSCHEL in maize is associated with changes in chromatin accessibility in specific cells, particularly in the central zone cells harboring stem cells. Notably, the motif usage of WUSCHEL in maize varies depending on the cellular context, suggesting that it forms unique molecular complexes in a cell-type-specific manner. These findings advance our understanding of plant stem cell regulation and elucidate how transcription factors use different molecular complexes and motif usages to exhibit distinct properties and perform specific functions. In the next project, I used machine learning, specifically natural language processing applied to DNA sequences, to predict transcription factor binding sites. To build a prediction model, I used unmethylated regions and AUXIN RESPONSE FACTOR DAP-seq data in maize and soybean. While cross-species predictions showed lower performance, within-species predictions were more successful, demonstrating the potential of machine learning to identify transcription factor binding sites. Together, my research showcases the application of single-cell genomics and machine learning in uncovering the regulatory mechanisms of transcription factors.