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Review
. 2019 Apr 11:10:755.
doi: 10.3389/fimmu.2019.00755. eCollection 2019.

Techniques for Studying Decoding of Single Cell Dynamics

Stevan Jeknić  1   2 , Takamasa Kudo  2   3 , Markus W Covert  1   2   3
Affiliations
Review

Techniques for Studying Decoding of Single Cell Dynamics

Stevan Jeknić et al. Front Immunol. .

Abstract

Cells must be able to interpret signals they encounter and reliably generate an appropriate response. It has long been known that the dynamics of transcription factor and kinase activation can play a crucial role in selecting an individual cell's response. The study of cellular dynamics has expanded dramatically in the last few years, with dynamics being discovered in novel pathways, new insights being revealed about the importance of dynamics, and technological improvements increasing the throughput and capabilities of single cell measurements. In this review, we highlight the important developments in this field, with a focus on the methods used to make new discoveries. We also include a discussion on improvements in methods for engineering and measuring single cell dynamics and responses. Finally, we will briefly highlight some of the many challenges and avenues of research that are still open.

Keywords: decoding; dynamics; encoding; live cell microscopy; signaling; single cell.

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Figures

Figure 1
Figure 1
Fundamentals of dynamic encoding and decoding. (A) Cells can encode information about the signals they encounter as dynamic patterns of signaling pathway activation. These patterns can then be decoded to produce a specific response. For example, NGF creates sustained ERK activation, which leads to differentiation, while EGF creates transient ERK activation, which leads to proliferation. (B) Population-level measurements, such as a western blot, can hide the behavior of single cells in the underlying system. For example, an analog or digital response could produce similar western blots, despite having different amounts of active cells and activity per cell. (C) Examples of some features of dynamic traces that can be used to encode information.
Figure 2
Figure 2
Examples of decoding dynamic signaling patterns. (A) Apoptosis due to p53 signaling is not determined by a static threshold, but by a dynamic, increasing threshold. Some cells do not undergo apoptosis, even though they have higher p53 levels than some cells that do undergo apoptosis. Figure adapted from Paek et al. (47). (B) Subpopulations with distinct patterns of NF-κB activity exist in single cells stimulated with LPS. These patterns are correlated with different gene expression patterns for known NF-κB targets. Figure adapted from Lane et al. (32). (C) Basal rates of adipocyte differentiation are low in vivo, despite large pulses of glucocorticoid production daily. However, continuous glucocorticoid inputs of similar total magnitude induce more stabilization of PPARG, indicated in green, and higher differentiation rates. Figure adapted from Bahrami-Nejad et al. (73).
Figure 3
Figure 3
Engineering approaches for manipulating dynamic signaling patterns. (A) Optogenetic tools can dynamically and selectively activate a pathway in isolation from endogenous receptor signaling contexts. (B) Blue light induces dimerization between the N-terminal CIB1 and Cry2 domain fused to cRaf, leading to the recruitment of cRaf to the membrane. Ras activates cRaf at the membrane, and thus it activates the downstream ERK pathway. (C) Microfluidic devices were used to control the flux of small-molecule inhibitors of the Notch and Wnt signaling pathway. In-phase oscillations of these two pathways led to proper mesoderm segmentation, whereas out-of-phase oscillation impaired segmentation.
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