Single-cell transcriptomics encompasses transformative approaches that enable gene expression profiling at single-cell resolution, revealing cellular heterogeneity, rare subpopulations, lineage relationships, and context-specific programs. This chapter focuses on scRNA-seq, recognized as Science’s Breakthrough of the Year in 2018, and highlights recent technological developments, major platforms, and applications in cancer, particularly aggressive and heterogeneous contexts such as triple-negative breast cancer (TNBC). Integrating scRNA-seq with spatial transcriptomics allows precise mapping of gene expression within native tissue environments, providing insights into dynamic processes such as epithelial–mesenchymal transition (EMT), a developmental program often reactivated in disease, including cancer, where it drives tumor progression, metastasis, and therapy resistance. Combining single-cell data with multiomics including chromatin accessibility, surface protein expression, and epigenetic modifications illuminates regulatory networks, cellular interactions, and context-specific signaling pathways. Furthermore, advanced computational pipelines facilitate quality control, clustering, trajectory inference, RNA velocity, and spatial niche reconstruction, revealing rare populations, signaling gradients, and dynamic processes such as EMT. Together, these approaches provide a multidimensional framework for understanding tissue complexity, guiding biomarker discovery, and informing precision therapeutic strategies, underscoring the transformative impact of single-cell technologies in cancer research and beyond.