In a similar vein, analyses based on single-cell DNA sequencing (scDNA-seq) can highlight somatic clonal structures (e.g., in cancer, see ), thus helping to track the formation of cell lineages and provide insight into evolutionary processes acting on somatic mutations. This can lead to a much clearer view of the dynamics of tissue and organism development, and on structures within cell populations that had so far been perceived as homogeneous.
Single-cell RNA sequencing (scRNA-seq) enables transcriptome-wide gene expression measurement at single-cell resolution, allowing for cell type clusters to be distinguished (for an early example, see ), the arrangement of populations of cells according to novel hierarchies, and the identification of cells transitioning between states. Single-cell measurements of both RNA and DNA, and more recently also of epigenetic marks and protein levels, can stratify cells at the finest resolution possible. Since being highlighted as “Method of the Year” in 2013, sequencing of the genetic material of individual cells has become routine when investigating cell-to-cell heterogeneity. Genome Biology volume 21, Article number: 31 ( 2020) Eleven grand challenges in single-cell data science
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