Paul J. Choi

Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells.

Devil in the Detail Genetically identical cells in the same environment can show variation in gene expression that may cause phenotypic variation at the single-cell level. But how noisy are most genes? Taniguchi et al. (p. 533; see the Perspective by Tyagi) now report single-cell global profiling of both messenger RNA (mRNA) and proteins in Escherichia coli using a yellow fluorescent protein fusion library. As well as a common extrinsic noise in high-abundance proteins, large fluctuations were observed in low-abundance proteins. Remarkably, in single-cell experiments, mRNA and protein levels for the same gene were uncorrelated. Measurement of protein and messenger RNA copy numbers in single Escherichia coli cells gives a system-wide view of stochastic gene expression. Protein and messenger RNA (mRNA) copy numbers vary from cell to cell in isogenic bacterial populations. However, these molecules often exist in low copy numbers and are difficult to detect in single cells. We carried out quantitative system-wide analyses of protein and mRNA expression in individual cells with single-molecule sensitivity using a newly constructed yellow fluorescent protein fusion library for Escherichia coli. We found that almost all protein number distributions can be described by the gamma distribution with two fitting parameters which, at low expression levels, have clear physical interpretations as the transcription rate and protein burst size. At high expression levels, the distributions are dominated by extrinsic noise. We found that a single cell’s protein and mRNA copy numbers for any given gene are uncorrelated.

A stochastic single-molecule event triggers phenotype switching of a bacterial cell.

By monitoring fluorescently labeled lactose permease with single-molecule sensitivity, we investigated the molecular mechanism of how an Escherichia coli cell with the lac operon switches from one phenotype to another. At intermediate inducer concentrations, a population of genetically identical cells exhibits two phenotypes: induced cells with highly fluorescent membranes and uninduced cells with a small number of membrane-bound permeases. We found that this basal-level expression results from partial dissociation of the tetrameric lactose repressor from one of its operators on looped DNA. In contrast, infrequent events of complete dissociation of the repressor from DNA result in large bursts of permease expression that trigger induction of the lac operon. Hence, a stochastic single-molecule event determines a cells phenotype.

Single-Molecule Approach to Molecular Biology in Living Bacterial Cells

Recent developments on fluorescent proteins and microscopy techniques have allowed the probing of single molecules in a living bacterial cell with high specificity, millisecond time resolution, and nanometer spatial precision. Recording movies and analyzing dynamics of individual macromolecules have brought new insights into the mechanisms of many processes in molecular biology, such as DNA-protein interactions, gene regulation, transcription, translation, and replication, among others. Here we review the key methods of single-molecule detection and highlight numerous examples to illustrate how these experiments are contributing to the quantitative understanding of the fundamental processes in a living cell.

Entropic Stabilization of Proteins and Its Proteomic Consequences

Evolutionary traces of thermophilic adaptation are manifest, on the whole-genome level, in compositional biases toward certain types of amino acids. However, it is sometimes difficult to discern their causes without a clear understanding of underlying physical mechanisms of thermal stabilization of proteins. For example, it is well-known that hyperthermophiles feature a greater proportion of charged residues, but, surprisingly, the excess of positively charged residues is almost entirely due to lysines but not arginines in the majority of hyperthermophilic genomes. All-atom simulations show that lysines have a much greater number of accessible rotamers than arginines of similar degree of burial in folded states of proteins. This finding suggests that lysines would preferentially entropically stabilize the native state. Indeed, we show in computational experiments that arginine-to-lysine amino acid substitutions result in noticeable stabilization of proteins. We then hypothesize that if evolution uses this physical mechanism as a complement to electrostatic stabilization in its strategies of thermophilic adaptation, then hyperthermostable organisms would have much greater content of lysines in their proteomes than comparably sized and similarly charged arginines. Consistent with that, high-throughput comparative analysis of complete proteomes shows extremely strong bias toward arginine-to-lysine replacement in hyperthermophilic organisms and overall much greater content of lysines than arginines in hyperthermophiles. This finding cannot be explained by genomic GC compositional biases or by the universal trend of amino acid gain and loss in protein evolution. We discovered here a novel entropic mechanism of protein thermostability due to residual dynamics of rotamer isomerization in native state and demonstrated its immediate proteomic implications. Our study provides an example of how analysis of a fundamental physical mechanism of thermostability helps to resolve a puzzle in comparative genomics as to why amino acid compositions of hyperthermophilic proteomes are significantly biased toward lysines but not similarly charged arginines.

Imaging burst kinetics and spatial coordination during serial killing by single natural killer cells

Cytotoxic lymphocytes eliminate virus-infected and cancerous cells by immune recognition and killing through the perforin-granzyme pathway. Traditional killing assays measure average target cell lysis at fixed times and high effector:target ratios. Such assays obscure kinetic details that might reveal novel physiology. We engineered target cells to report on granzyme activity, used very low effector:target ratios to observe potential serial killing, and performed low magnification time-lapse imaging to reveal time-dependent statistics of natural killer (NK) killing at the single-cell level. Most kills occurred during serial killing, and a single NK cell killed up to 10 targets over a 6-h assay. The first kill was slower than subsequent kills, especially on poor targets, or when NK signaling pathways were partially inhibited. Spatial analysis showed that sequential kills were usually adjacent. We propose that NK cells integrate signals from the previous and current target, possibly by simultaneous contact. The resulting burst kinetics and spatial coordination may control the activity of NK cells in tissues.

Stochastic switching in gene networks can occur by a single-molecule event or many molecular steps.

Due to regulatory feedback, biological networks can exist stably in multiple states, leading to heterogeneous phenotypes among genetically identical cells. Random fluctuations in protein numbers, tuned by specific molecular mechanisms, have been hypothesized to drive transitions between these different states. We develop a minimal theoretical framework to analyze the limits of switching in terms of simple experimental parameters. Our model identifies and distinguishes between two distinct molecular mechanisms for generating stochastic switches. In one class of switches, the stochasticity of a single-molecule event, a specific and rare molecular reaction, directly controls the macroscopic change in a cells state. In the second class, no individual molecular event is significant, and stochasticity arises from the propagation of biochemical noise through many molecular pathways and steps. As an example, we explore switches based on protein-DNA binding fluctuations and predict relations between transcription factor kinetics, absolute switching rate, robustness, and efficiency that differentiate between switching by single-molecule events or many molecular steps. Finally, we apply our methods to recent experimental data on switching in Escherichia coli lactose metabolism, providing quantitative interpretations of a single-molecule switching mechanism.