Single-Cell Analysis in Cancer Genomics

Abstract

Genetic changes and environmental differences result in cellular heterogeneity among cancer cells within the same tumor, thereby complicating treatment outcomes. Recent advances in single-cell technologies have opened new avenues to characterize the intra-tumor cellular heterogeneity, identify rare cell types, measure mutation rates, and, ultimately, guide diagnosis and treatment. In this paper we review the recent single-cell technological and computational advances at the genomic, transcriptomic, and proteomic levels, and discuss their applications in cancer research.

Figure 1. An overview of single-cell cancer genomics

Single-cell technologies are used to generate genomic, transcriptomic, and proteomic data from cancer cells. These data are analyzed by computational methods to identify clusters, lineages, and networks, which in turn generate new biological hypotheses. Biological discoveries in turn guide development of new technologies and computational approaches. The figure also shows a toy example with a heterogeneous cancer sample containing three cell types (orange, blue, and purple). An integrated single-cell analysis is used to identify the cell-types, lineages, and network profiles.

Figure 2. A typical flow chart for single-cell data analysis

Representative methods are mentioned. See the main text for detailed description.