Ribosome biogenesis (RiBi) is one of the most energy-demanding cellular processes and is frequently dysregulated in cancer. While elevated RiBi supports rapid proliferation, excessive nucleolar activity also creates unique vulnerabilities that can be exploited therapeutically. Our research aims to understand how perturbation of ribosome biogenesis influences cell fate decisions, including cell cycle arrest, senescence, and cell death, and to identify molecular mechanisms that can be translated into novel cancer treatment strategies. To achieve this, we combine multiple high-throughput experimental approaches, including bulk RNA sequencing, single-cell RNA sequencing, proteomics, ribosome profiling (Ribo-Seq), ChIP-Seq, and high-content fluorescence microscopy. These complementary datasets will be integrated to characterize transcriptional, translational, epigenetic, and proteomic responses following perturbation of ribosome biogenesis and related stress pathways. Particular emphasis will be placed on identifying molecular signatures associated with treatment response, resistance mechanisms, and cellular heterogeneity across different cancer models. Single-cell transcriptomics will be used to resolve heterogeneous cell populations and reconstruct cellular state transitions following therapeutic intervention, whereas bulk RNA-Seq and complementary omics datasets will identify global molecular pathways underlying these responses. High-content microscopy datasets comprising thousands of multichannel images will be analysed using automated image analysis pipelines to quantify phenotypic changes, including senescence, apoptosis, DNA damage, and alterations in nuclear and nucleolar architecture. Integration of imaging-derived phenotypes with molecular profiling will provide a comprehensive systems-level understanding of cancer cell responses to ribosome biogenesis stress. The proposed work requires substantial computational resources due to the large volume of sequencing and imaging data, the computational complexity of modern single-cell analyses, and the integration of multiple omics modalities. The resulting analyses will generate comprehensive molecular maps of cancer cell responses and facilitate the identification of biomarkers and therapeutic targets for cancers with significant unmet clinical needs.