In the rapidly advancing field of cancer research, next-generation sequencing (NGS) has become a cornerstone technology for unraveling the genetic and transcriptomic complexity of tumors. At the foundation of every successful sequencing experiment lies a crucial step: DNA and RNA library preparation. These techniques determine not only the quality of the data generated but also the depth of biological insight that can be achieved. Building the Foundation: DNA and RNA Library Preparation DNA library preparation involves fragmenting genomic DNA and attaching sequencing adapters, making the material compatible with NGS platforms. The quality of these libraries directly impacts downstream applications such as whole-genome sequencing (WGS) and targeted sequencing, which are essential for identifying mutations, copy number variations, and structural rearrangements driving tumor development. In parallel, RNA library preparation converts fragile RNA molecules from tumor samples into stable complementary DNA (cDNA) for sequencing. This process enables the comprehensive profiling of gene expression, alternative splicing events, and the activity of RNA species including messenger RNA (mRNA), microRNA (miRNA), and long non-coding RNA (lncRNA). Because clinical samples—particularly formalin-fixed, paraffin-embedded (FFPE) tissues—often yield degraded or low-input RNA, precise library construction is critical to ensure accurate transcriptomic measurements. Single-Cell Sequencing: Revealing Tumor Heterogeneity Building upon these preparatory foundations, single-cell RNA sequencing (scRNA-seq) has redefined how scientists study cancer biology. Unlike traditional bulk RNA-seq, which captures an averaged signal from millions of cells, scRNA-seq analyzes gene expression at the level of individual cells. This high-resolution view exposes the heterogeneity within tumors—revealing diverse cell populations such as malignant cells, stromal components, and infiltrating immune cells. By dissecting these cellular subtypes, researchers can identify rare but clinically significant populations, such as drug-resistant or metastasis-prone cells, that might otherwise remain undetected. Moreover, single-cell transcriptomics provides valuable insights into lineage trajectories and dynamic cellular states, illuminating how tumors evolve and adapt under selective pressures like therapy or immune attack. Profiling the Immune Landscape: Single-Cell Immune Repertoire Sequencing An emerging extension of single-cell technology is immune repertoire sequencing at the single-cell level. This approach decodes the diversity of T-cell and B-cell receptors (TCRs and BCRs), offering a detailed snapshot of the adaptive immune landscape within tumors. Understanding immune receptor repertoires helps researchers determine how immune cells recognize tumor antigens, respond to immunotherapies, or become exhausted in the tumor microenvironment. When immune repertoire sequencing is integrated with scRNA-seq data, it creates a powerful multidimensional dataset linking receptor sequences to specific cellular phenotypes and functions. Such combined analyses allow scientists to identify tumor-reactive lymphocytes and uncover immune escape mechanisms—findings that directly inform the design of next-generation immunotherapies, including checkpoint inhibitors and CAR-T cell treatments. Toward Precision Oncology Together, advanced library preparation, single-cell transcriptomics, and immune repertoire sequencing form a cohesive toolkit that enables an unprecedented exploration of tumor biology. These technologies offer a holistic view—from the genomic mutations that initiate cancer to the immune dynamics that influence treatment response. As protocols become more robust and accessible, they are accelerating biomarker discovery, guiding patient stratification, and shaping the future of precision oncology. Conclusion The synergy between refined library preparation and single-cell sequencing technologies represents a transformative leap in cancer research. By combining genomic, transcriptomic, and immunologic perspectives, researchers can now map tumor ecosystems with extraordinary detail. This integration not only deepens our understanding of cancer heterogeneity but also paves the way for more predictive, personalized, and effective therapeutic strategies.