Single-cell RNA sequencing (scRNA-seq) has transformed precision oncology by allowing for high-resolution transcriptome investigation at the individual cell level. Unlike bulk RNA sequencing, which yields averaged gene expression data, scRNA-seq exposes cellular heterogeneity inside tumors, detecting rare cancer subpopulations, stem-like cells, and drug-resistant clones. This skill has major implications for tumor drug discovery, enabling researchers to identify new therapeutic targets, anticipate patient-specific medication responses, and devise more accurate treatment plans. Furthermore, scRNA-seq allows for a better knowledge of tumor microenvironment interactions, revealing information on the roles of immune and stromal cells in cancer growth and therapeutic resistance. Recent advances in scRNA-seq technologies, including as droplet-based sequencing systems and spatial transcriptomics, have increased their usefulness in oncology research. Droplet-based technologies allow scientists to study tumor differences in much greater detail than ever before, utilizing platforms created by 10× Genomics and Drop-seq, which allow the high-throughput collection and barcoding of thousands of individual cells. These systems enable researchers to find rare cell groups that are frequently undetectable with bulk RNA-seq, specifically, cancer stem cells or drug-resistant clones. Spatial transcriptomics, on the other hand, maps different cellular subpopulations directly within the tumor microenvironment by fusing tissue architecture and gene expression profiling. Understanding cancer growth and treatment resistance requires knowledge of immune infiltration patterns, cell-cell interactions, and tumor evolution dynamics, all of which are crucially revealed by this technique. All of these developments have combined to make scRNA-seq an effective tool for identifying new biomarkers, predicting treatment outcomes, and directing the creation of precision oncology plans. Furthermore, the combination of artificial intelligence (AI) and machine learning has improved the interpretation of scRNA-seq datasets, allowing for the discovery of major oncogenic pathways and possible therapeutic candidates. Despite its transformative promise, scRNA-seq faces significant barriers to widespread use in clinical cancer, including high sequencing costs, technical constraints in single-cell isolation, and the complexity of bioinformatics analysis. This study investigates the present applications of scRNA-seq in tumor drug discovery, focusing on recent advances in discovering druggable targets, tracking tumor progression, and overcoming therapeutic resistance. We also highlight new tactics for overcoming current difficulties, including as advancements in multi-omics integration and computational modeling. As scRNA-seq advances, it is predicted to play a critical role in bringing precision oncology into clinical practice, ultimately improving cancer treatment outcomes through more effective and tailored therapeutic interventions.
Md Atiq Ashhab (Fri,) studied this question.