Defining cell types and states with single-cell genomics

Abstract

A revolution in cellular measurement technology is under way: For the first time, we have the ability to monitor global gene regulation in thousands of individual cells in a single experiment. Such experiments will allow us to discover new cell types and states and trace their developmental origins. They overcome fundamental limitations inherent in measurements of bulk cell population that have frustrated efforts to resolve cellular states. Single-cell genomics and proteomics enable not only precise characterization of cell state, but also provide a stunningly high-resolution view of transitions between states. These measurements may finally make explicit the metaphor that C.H. Waddington posed nearly 60 years ago to explain cellular plasticity: Cells are residents of a vast "landscape" of possible states, over which they travel during development and in disease. Single-cell technology helps not only locate cells on this landscape, but illuminates the molecular mechanisms that shape the landscape itself. However, single-cell genomics is a field in its infancy, with many experimental and computational advances needed to fully realize its full potential.

Figure 1.

Single-cell measurements preserve crucial information that is lost by bulk genomics assays. (A) Simpson's Paradox describes the misleading effects that arise when averaging signals from multiple individuals. (B) Bulk measurements cannot distinguish changes due to gene regulation from those that arise due to shifts in the ratio of different cell types in a mixed sample. (C) Time series experiments are affected by averaging when cells proceed through a biological process in an unsynchronized manner. A single time point may contain cells from different stages in the process, obscuring the dynamics of relevant genes. Reordering the cells in “pseudotime” according to biological progress eliminates averaging and recovers the true signal in expression (Trapnell et al. 2014).

Figure 2.

Single-cell “trajectories” shed light on gene regulation. (A) An idealized regulatory network consisting of two genes can have three distinct stable states. If the ratio of A to B is sufficiently high, the system will fall into a state in which only A is expressed (green region). Likewise, cells expressing predominantly B will eventually express only B. However, cells with roughly equal expression of A and B will remain in a “poised” state (blue) region. The shaded areas are referred to as “basins of attraction,” which determine where cells at different initial positions (white circles) will ultimately rest at equilibrium. (B) Gene expression profiles for individual differentiating cells can be informatically organized into trajectories, potentially revealing regulatory network structure and cell fate dynamics.