![]() ![]() In particular, the emergence of microfluidics techniques and combinatorial indexing strategies has led to hundreds of thousands of cells routinely being sequenced in one experiment. A prominent example is the Human Cell Atlas, an initiative aiming to map the numerous cell types and states comprising a human being.Įncouraged by the great potential of investigating DNA and RNA at the single-cell level, the development of the corresponding experimental technologies has experienced considerable growth. It is therefore no surprise that enthusiasm about the possibility to screen the genetic material of the basic units of life has continued to grow. The opportunities arising from single-cell sequencing (sc-seq) are enormous: only now is it possible to re-evaluate hypotheses about differences between pre-defined sample groups at the single-cell level-no matter if such sample groups are disease subtypes, treatment groups, or simply morphologically distinct cell types. 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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