Single-Cell sequencing: Definition, process, uses, cost and more

Single-cell sequencing isolates and profiles the DNA, RNA, or epigenetic features of individual cells, providing a higher-resolution alternative to bulk sequencing, which averages signals across cell populations. Its main modalities include single-cell genome sequencing, scRNA-seq for gene expression, epigenomic methods such as scATAC-seq and methylation sequencing, and multi-omics approaches that combine measurements. The technology helps researchers identify cellular heterogeneity, rare cell populations, developmental lineages, tumor subclones, and potential therapeutic targets, with applications in cancer, rare disease, developmental biology, microbial genomics, and immunology. Typical workflows involve isolating cells through methods such as fluorescence-activated sorting or microfluidics, extracting and amplifying genetic material, preparing barcoded libraries, sequencing them with next-generation platforms, and analyzing the resulting high-dimensional data with tools such as Seurat or Scanpy. Despite its scientific value, single-cell sequencing can be costly and technically demanding, with limitations including incomplete genome or transcript coverage, amplification bias, sparse data, transcript dropout, and substantial computational storage and analysis requirements; library preparation and sequencing may cost several thousand dollars per sample. Public repositories and initiatives including the Human Cell Atlas, PanglaoDB, GEO, and CellxGene support the sharing, comparison, and reuse of single-cell datasets.

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