Single-Cell RNA Sequencing

Single-cell RNA sequencing (scRNA-seq) is a high-throughput genomics technique that measures gene expression at the individual cell level, enabling detailed analysis of cellular heterogeneity, developmental trajectories, and rare cell populations. It involves isolating single cells, capturing their RNA, converting it to cDNA, and sequencing it to generate transcriptomic profiles. This technology is widely used in biology, immunology, and medicine to study complex tissues, diseases like cancer, and cellular responses.

Also known as: scRNA-seq, Single Cell RNA-seq, Single-Cell Transcriptomics, scRNAseq, Single Cell Sequencing

Why learn Single-Cell RNA Sequencing?

Developers should learn scRNA-seq when working in bioinformatics, computational biology, or healthcare data science, as it is essential for analyzing large-scale genomic datasets to uncover insights into disease mechanisms, drug discovery, and personalized medicine. Use cases include identifying cell types in tumors, tracking cell differentiation in development, and analyzing immune cell diversity in autoimmune disorders. Proficiency in scRNA-seq tools is valuable for roles involving data preprocessing, statistical modeling, and visualization in research or clinical settings.

Learning Resources

• docs: Single Cell Best Practices
• tutorial: Orchestrating Single-Cell Analysis with Bioconductor
• course: Single Cell RNA-Seq Analysis Course on Coursera
• docs: Introduction to Single-Cell RNA-seq by 10x Genomics
• video: Single-Cell RNA Sequencing Explained (YouTube Video)