Single-Cell analysis: uses, techniques, steps and more

Single-cell analysis encompasses experimental and computational methods to study molecular characteristics such as gene expression and protein abundance at the level of individual cells, offering insights that bulk sequencing cannot provide. This approach is crucial for understanding cellular diversity and dynamics in areas like cancer research, immunology, and developmental biology, where it helps identify rare cell types, map cell states, and uncover disease mechanisms. Techniques such as scRNA-seq, scATAC-seq, spatial transcriptomics, and multimodal assays enable researchers to capture various cellular modalities, providing a comprehensive view of cell identity and function. The process involves isolating cells, extracting and amplifying molecular material, preparing sequencing-ready libraries, and performing computational analyses to generate interpretable insights. Databases like the CZ CELLxGENE Census, Human Cell Atlas, and Gene Expression Omnibus serve as repositories for single-cell data, while software tools like Seurat, Bioconductor, and Scanpy aid in data analysis and visualization, with solutions like TileDB offering efficient data management and integration.