Adult multicellular organisms consist of many distinct cell types with different morphologies, functions, and gene-expression programs, all derived from a single zygotic cell. A central question in developmental biology is how reproducible cell-fate decisions emerge despite the inherently stochastic nature of transcription. Gene expression occurs in bursts, and differences in burst frequency and burst size can generate substantial variation in RNA output between otherwise similar cells. How these transcriptional kinetics change across cell types and developmental stages in an intact organism remains poorly understood. In this project, we will use Smart-seq3xpress to generate allele-specific, transcriptome-wide measurements of transcriptional bursting in Drosophila melanogaster embryos. By crossing genetically distinct isogenic lines, we will exploit naturally occurring sequence variants to distinguish maternal and paternal alleles and infer allele-specific burst frequency, burst size, and transcriptional output. Embryos will be profiled across key developmental stages, from zygotic genome activation through gastrulation, cell specification, differentiation, and late embryogenesis. This will allow us to determine how transcriptional kinetics are modulated during development, identify genes with intrinsically noisy expression, and distinguish cis-regulatory effects from shared cellular influences on transcription. The project builds on our previous single-cell studies of Drosophila embryogenesis, in which we profiled the active and repressive histone marks H3K27ac and H3K27me3 to reconstruct developmental epigenetic landscapes. The same computational allocation will also be used for additional analyses required for revision and resubmission of this work, including reprocessing of existing single-cell datasets, parameter testing, integration of chromatin and transcriptomic data, and generation of revised analyses and figures. Together, these approaches will connect transcriptional dynamics with the developmental and epigenetic context in which they occur, providing a quantitative framework for understanding how stochastic gene expression is controlled during reproducible cell-fate specification.