Single-cell analysis has revolutionized biology by revealing the enriched cellular heterogeneity that is masked by traditional bulk measurements. It provides a detailed view of individual genomes, epigenomes, transcriptomes, proteomes, and more. Over the course of its development from simple microscopic approaches and flow-cytometry in the 1970s–1990s to the pivotal development in 2009, scRNA-seq demonstration was rapidly developed. Platforms such as 10× chromium and Drop seq, using microfluidics and combinatorial barcoding, allow profiling of millions of cells that are also cost effective. Methodological workflows include controlled tissue dissociation, isolation of single cells with the help of fluorescence-activated cell sorting (FACS), magnetic-activated cell sorting (MACS), or nano-wells. These advances are supported by sophisticated library preparation techniques such as Smart-seq2 for full-length transcripts or scATAC-seq for chromatin accessibility and robust computational pipelines that features normalization (e.g., scran), doublet detection (e.g., Scrublet), and trajectory inference (e.g., UMAP). These advances serve as basis for transformative applications in oncology including identification of tumor subpopulations, progression of circulating tumor cells, immunotherapy biomarkers; in neuroscience: brain cell types mapping system, lineage relationship and functional and pathological states with molecular identity. Despite its high resolution, single-cell analysis is limited by technical noise, data sparsity, and loss of spatial context during tissue dissociation, which complicate biological interpretation.