Single-cell technologies are revolutionizing our understanding of cellular diversity by examining individual cell genomes, epigenomes, transcriptomes, and proteomes. These methods vary in accuracy and usability, with well-established techniques like single-cell RNA sequencing (scRNA-seq) and emerging multiomics approaches that combine different molecular layers. High-throughput cell isolation techniques, such as microfluidics, contrast with more labor-intensive methods like micromanipulation. Effective cell barcoding, especially combinatorial indexing, is key for sequencing efficiency. Single-cell genome amplification methods like MDA and LIANTI are being refined for better accuracy and coverage, while epigenome sequencing techniques face challenges due to limited DNA copies per cell. Transcriptome sequencing methods, including Smart-seq and Drop-seq, offer varying sensitivity and accuracy. Integrative approaches like scTrio-seq, which combine genomic, epigenetic, and transcriptional data, are enhancing our understanding of cellular behavior. Future advancements aim for "omni-omics," capturing a complete molecular and spatial picture of cells to fully understand their properties and lineage histories.