High-dimension single-cell analysis applied to cancer

Abstract

High-dimension single-cell technology is transforming our ability to study and understand cancer. Numerous studies and reviews have reported advances in technology development. The biological insights gleaned from single-cell technology about cancer biology are less reviewed. Here we focus on research studies that illustrate novel aspects of cancer biology that bulk analysis could not achieve, and discuss the fresh insights gained from the application of single-cell technology across basic and clinical cancer studies.

Keywords: Cell identity; Correlation analysis; Single-cell analysis; Subclone phylogeny; Tumor ecosystem; Tumor heterogeneity.

Figure 1

Key uses of high-dimension single-cell analysis in studying cancer.

Figure 2. Workflow for high-dimension single-cell experiments

(A) The predominant methods currently used for isolation of single cells are flow sorting and microfluidics. Examples of microfluidic methods include inDrop (Klein et al., 2015), Drop-seq (Macosko et al., 2015), and Seq-Well (Gierahn et al., 2017). (B) The single-cell analysis of cancer proceeds from discovery of heterogeneity using unbiased, high-dimension methods on a relatively small number of patients to validation in a greater number of patients using targeted techniques.

Figure 3. Single-cell multiomic analysis of cancer

The diagram indicates the different layers of information that could be obtained for the same set of single cells.

Figure 4. Potential impact of single-cell analysis on cancer diagnostics

An example of how single-cell analysis (in this case, using high throughput droplet approaches) could inform clinical diagnostics for early disease detection, early detection of relapse and evaluation of the functional state of the relapsed cells.