Following the messenger: Recent innovations in live cell single molecule fluorescence imaging

Messenger RNAs (mRNAs) convey genetic information from the DNA genome to proteins and thus lie at the heart of gene expression and regulation of all cellular activities. Live cell single molecule tracking tools enable the investigation of mRNA trafficking, translation and degradation within the complex environment of the cell and in real time. Over the last 5 years, nearly all tools within the mRNA tracking toolbox have been improved to achieve high‐quality multi‐color tracking in live cells. For example, the bacteriophage‐derived MS2‐MCP system has been improved to facilitate cloning and achieve better signal‐to‐noise ratio, while the newer PP7‐PCP system now allows for orthogonal tracking of a second mRNA or mRNA region. The coming of age of epitope‐tagging technologies, such as the SunTag, MoonTag and Frankenbody, enables monitoring the translation of single mRNA molecules. Furthermore, the portfolio of fluorogenic RNA aptamers has been expanded to improve cellular stability and achieve a higher fluorescence “turn‐on” signal upon fluorogen binding. Finally, microinjection‐based tools have been shown to be able to track multiple RNAs with only small fluorescent appendages and to track mRNAs together with their interacting partners. We systematically review and compare the advantages, disadvantages and demonstrated applications in discovering new RNA biology of this refined, expanding toolbox. Finally, we discuss developments expected in the near future based on the limitations of the current methods.This article is categorized under:RNA Export and Localization > RNA LocalizationRNA Structure and Dynamics > RNA Structure, Dynamics, and ChemistryRNA Interactions with Proteins and Other Molecules > RNA–Protein ComplexesTools for the intracellular visualization of mRNA metabolism and function.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/155943/1/wrna1587_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155943/2/wrna1587.pd

Protection from antibiotics in growth-arrest-prone media.

<p>(A) Survival curves of bacteria grown at indicated lactose concentrations in ampicillin-treated cultures (100 μg/ml). (B) Culture conditions favoring growth arrest enhance the presence of antibiotic tolerant cells after 20 h treatment of 32 μg/ml doxycycline (blue bars; ANOVA <i>p</i> = 0.003) or 100 μg/ml ampicillin (yellow bars; ANOVA <i>p</i> = 0.004). Survival ratios are normalized by cell densities of untreated cultures in corresponding conditions. <i>N</i> = 3 for each condition and the survival curve points, reporting mean ± SEM. In the final timepoint of the survival curve for 0.1 mg/ml lactose, two of the replicates were below the level of detection; the remaining replicate value is plotted.</p

Three discrete growth phases in metabolic pathways.

<p>(A) A metabolic pathway consists of enzymes <i>A</i> and <i>B</i> that produce and consume the intracellular metabolite <i>M</i>, respectively. (B) Mathematical models predict three growth phases for combinations of metabolite production rate and demand <i>δ</i>. Color bar indicates normalized growth rate. Colored spots correspond to the colored lines in panel C. The black line represents the effect of experimental conditions changing extracellular lactose concentrations in <i>E</i>. <i>coli</i> causing intracellular lactose concentration changes because of LacY activity. Results are for two models of toxicity: metabolite buildup (left) and permease proton symport (right). (C) Mathematical models predict that growth is maximized below a threshold production rate (dashed line between cyan and yellow) past which no steady state exists. Increasing the rate of metabolite production (<i>V</i>⁺) translates the rate curves upward. When the rate is beyond the dashed line, there is a runaway buildup of metabolite and consequent toxic effects. Results are for two models of toxicity: metabolite buildup (left) and permease proton symport (toxic byproduct; right). The toxic byproduct model has two variables; we plot only the rate of toxic byproduct buildup for simplicity because it crosses the threshold at a lower <i>V</i>⁺.</p

A framework for population growth dynamics in the presence of metabolic risk.

<p>(A). Growth conditions, gene expression, replication, and regulatory factors determine timescales for switching between different types of growth. On a characteristic timescale <i>τ</i>_<i>1</i>, cells stochastically switch from balanced growth (<i>g</i>) to a condition of rapidly changing growth rate (<i>ĝ</i>) and escape on a timescale of <i>τ</i>_<i>-1</i>. Growth arrest arises from the growth shift state on a timescale of <i>τ</i>_<i>2</i>. Escape from growth arrest permits cells to resume growth on a timescale of <i>τ</i>_<i>-2</i>, or die on a timescale of <i>τ</i>_<i>3</i>. (B) In the limit of large <i>τ</i>_<i>1</i> or small <i>τ</i>_<i>-1</i>, populations have classical balanced growth. (C) In the limit of small <i>τ</i>_<i>1</i> and <i>τ</i>_<i>2</i> with large <i>τ</i>_<i>-1</i> and <i>τ</i>_<i>-2</i>, the metastable population model holds.</p

Cellular Growth Arrest and Persistence from Enzyme Saturation

<div><p>Metabolic efficiency depends on the balance between supply and demand of metabolites, which is sensitive to environmental and physiological fluctuations, or noise, causing shortages or surpluses in the metabolic pipeline. How cells can reliably optimize biomass production in the presence of metabolic fluctuations is a fundamental question that has not been fully answered. Here we use mathematical models to predict that enzyme saturation creates distinct regimes of cellular growth, including a phase of growth arrest resulting from toxicity of the metabolic process. Noise can drive entry of single cells into growth arrest while a fast-growing majority sustains the population. We confirmed these predictions by measuring the growth dynamics of <i>Escherichia coli</i> utilizing lactose as a sole carbon source. The predicted heterogeneous growth emerged at high lactose concentrations, and was associated with cell death and production of antibiotic-tolerant persister cells. These results suggest how metabolic networks may balance costs and benefits, with important implications for drug tolerance.</p></div

Reactions, propensities and parameter values used for stochastic simulations.

<p>Reactions, propensities and parameter values used for stochastic simulations.</p

Population and individual cell fitness of <i>E</i>. <i>coli</i> (<i>lacI</i>⁻ B REL606) in varying growth conditions.

<p>(A) Mean ± SEM (<i>N</i> = 3) growth rate (<i>p</i> < 10⁻⁶ for no trend in lactose concentrations > 1 mg/ml) and fraction of propidium iodide-stained (PI+) cells (<i>p</i> = 0.0051 for no PI+ trend in lactose concentrations > 1 mg/ml) at various lactose concentrations. Dashed lines indicate quadratic regression models, which fit significantly better than do linear models (see text for details). PI+ fractions at low lactose concentrations are shown in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004825#pcbi.1004825.s005" target="_blank">S4c Fig</a>. (B) Mean ± SEM (<i>N</i> = 3) expression of GFP at various lactose concentrations (<i>p</i> = 0.00016 for no trend). CV, coefficient of variation (<i>p</i> < 10⁻⁶ for no trend). Dashed lines indicate fits of statistical models used as a guide to the eye (fitted model is: ). (C) PI-stained <i>E</i>. <i>coli</i> grown in a microfluidic device perfused with the indicated concentration of lactose (mg/ml). Note the patchy distribution of fast growing (low GFP) and slow- or non-growing (high GFP) cells at 50 mg/ml lactose. Brightfield alone is shown below. PI staining identifies dead cells and appears red or yellow, depending on the amount of GFP in the same cell. Dark spots are silicone support structures.</p

From "cellular" RNA to "smart" RNA: multiple roles of RNA in genome stability and beyond

Coding for proteins has been considered the main function of RNA since the "central dogma" of biology was proposed. The discovery of noncoding transcripts shed light on additional roles of RNA, ranging from the support of polypeptide synthesis, to the assembly of subnuclear structures, to gene expression modulation. Cellular RNA has therefore been recognized as a central player in often unanticipated biological processes, including genomic stability. This ever-expanding list of functions inspired us to think of RNA as a "smart" phone, which has replaced the older obsolete "cellular" phone. In this review, we summarize the last two decades of advances in research on the interface between RNA biology and genome stability. We start with an account of the emergence of noncoding RNA, and then we discuss the involvement of RNA in DNA damage signaling and repair, telomere maintenance, and genomic rearrangements. We continue with the depiction of single-molecule RNA detection techniques, and we conclude by illustrating the possibilities of RNA modulation in hopes of creating or improving new therapies. The widespread biological functions of RNA have made this molecule a reoccurring theme in basic and translational research, warranting it the transcendence from classically studied "cellular" RNA to "smart" RNA