Single-Molecule Kinetics in Living Cells

Abstract

In the past decades, advances in microscopy have made it possible to study the dynamics of individual biomolecules in vitro and resolve intramolecular kinetics that would otherwise be hidden in ensemble averages. More recently, single-molecule methods have been used to image, localize and track individually labeled macromolecules in the cytoplasm of living cells, allowing investigations of intermolecular kinetics under physiologically relevant conditions. In this review, we illuminate the particular advantages of singlemolecule techniques when studying kinetics in living cells and discuss solutions to specific challenges associated with these methods. Counting and cell-to-cell heterogeneitySingle-molecule sensitive methods are needed to determine the number of a particular molecular species and how this number varies over time, in space or between cells. The use of single-molecule counting falls into a few different types of experiments. Inference of kinetics from steady-state distributionsAn obvious strength of single-molecule counting in cells is the possibility to characterize the cell-to-cell variation in molecule numbers and determine the steadystate distribution from snapshots over different cells. These distributions arise from stochasticity in the underlying processes, such as gene expression, fluorophore maturation, and protein partitioning in cell division, and depend on the mechanisms and kinetic rates specific for each process(13). By assuming a dynamic model, it might be possible to fit the model parameters based on the steady-state distribution(14-18) and thus estimate the underlying kinetic rates (Fig. 1A). When using such methods, it is important to keep in mind that different stochastic models can give rise to very similar steady-state distributions (19) and that deterministic differences between individual cells can also be mistaken for stochastic effects(20, 21). Following actual low copy dynamicsFollowing individual cells over time makes it possible to study dynamic correlations that cannot be seen in steady-state distributions. Some classical examples are the early single-molecule studies of bursts in protein (22,23) or RNA(24) expression from the lac operon. Looking at numbers of molecules in single cells over time, it is also possible to study how low copy number dynamics give rise to phenotypic copy number transitions. For example, Choi et al.(25) monitored fluctuations in the expression of fluorescently labeled lacY permeases to deduce how many LacY that are needed to switch the bistability observed for the Lac operon in the presence of Methyl-β-D-thiogalactoside (TMG). Similar in nature is the work by Uphoff et al., which shows that the DNA methylation repair by Ada is turned on by a single expression event which then activates further expression of the protein by positive feedback(26).

Chromosome-wide co-fluctuation of stochastic gene expression in mammalian cells

“…Third, our study highlights the importance of the impact of sub-nucleus spatial heterogeneity in gene expression. This can be studied more thoroughly via real-time imaging and spatial modeling of chemical reactions [38,75]. The lack of knowledge about the details of transcription reactions prevents us from constructing an accurate quantitative model of gene expression, which can be achieved only by more accurate measurement and more advanced computational modeling.…”

Single particle diffusion characterization by deep learning

“…those produced by single-molecule fluorescence microscopy experiments using genetically-encoded labels. While these probes are seemingly ideal for reporting on the biological and biomimetic environments (19), their limited stability and brightness typically produces many short trajectories. The key challenge presented by these experiments is how to infer material properties of biological systems, and transport properties of single biomolecules from very short trajectories in which the asymptotic behavior is experimentally inaccessible.…”

Identification of multiple kinetic populations of DNA-binding proteins in live cells

“…Singlemolecule live-cell imaging commonly relies on fluorescent proteins that are genetically fused to the protein of interest ( Fig. 1A) (17)(18)(19)(20). Tracking the fluorescence signal of thousands of molecules, one molecule at a time, enables the building of physical models, from which physical parameters such as diffusion constants and detachment rates from DNA can be determined.…”