Cancer genomics: one cell at a time

Abstract

The study of single cancer cells has transformed from qualitative microscopic images to quantitative genomic datasets. This paradigm shift has been fueled by the development of single-cell sequencing technologies, which provide a powerful new approach to study complex biological processes in human cancers.

Figure 1

Single-cell processes in cancer. Although single cancer cells interact with their neighbors and the adjacent stromal cells, there are many biological processes that occur through the actions of individual cancer cells, shown in this illustration. These complex biological processes in human cancers include: (a) transformation from a single normal somatic cell into a tumor cell; (b) clonal evolution that occurs through a series of selective sweeps when single cells acquire driver mutations and diversify, leading to intratumor heterogeneity; (c) single cells from the primary tumor intravasate into the circulatory system and extravasate at distant organ sites to form metastatic tumors; and (d) the evolution of chemoresistance that occurs when the tumor is eradicated but survived by single tumor cells that harbor resistance mutations and expand to reconstitute the tumor mass.

Figure 2

Methods for isolating single cancer cells from abundant and rare populations. (a) Methods for isolating single cells from abundant cellular populations include: micromanipulation by robotics or mouth pipetting, serial dilutions, flow-sorting, microfluidics platforms and laser-capture microdissection (LCM; 63X objective). (b) Methods for isolating single cells from rare cellular populations include: CellSearch (Johnson & Johnson), DEP-Array (Silicon Biosciences), CellCelector (Automated Lab Solutions), MagSweeper (Illumina) and nano-fabricated filters (Creatv MicroTech).

Figure 3

Technical errors and coverage in single-cell sequencing data. (a) Technical errors that occur in single-cell sequencing (SCS) data include: false-positive errors, allelic dropout events and false-negative errors due to insufficient coverage. ‘Pop’ indicates a population of cells. (b) Coverage metrics in SCS data include coverage depth and total physical coverage, or breadth. (c) Coverage uniformity, or ‘eveness’ in SCS data can vary from cell to cell, but is often more uniform in standard genomic DNA sequencing experiments using populations of cells.