Dissecting non-coding RNA mechanisms in cellulo by Single-molecule High-Resolution Localization and Counting

BNP-Track: a framework for superresolved tracking

Superresolution tools, such as PALM and STORM, provide nanoscale localization accuracy by relying on rare photophysical events, limiting these methods to static samples. By contrast, here, we extend superresolution to dynamics without relying on photodynamics by simultaneously determining emitter numbers and their tracks (localization and linking) with the same localization accuracy per frame as widefield superresolution on immobilized emitters under similar imaging conditions (≈50 nm). We demonstrate our Bayesian nonparametric track (BNP-Track) framework on both in cellulo and synthetic data. BNP-Track develops a joint (posterior) distribution that learns and quantifies uncertainty over emitter numbers and their associated tracks propagated from shot noise, camera artifacts, pixelation, background and out-of-focus motion. In doing so, we integrate spatiotemporal information into our distribution, which is otherwise compromised by modularly determining emitter numbers and localizing and linking emitter positions across frames. For this reason, BNP-Track remains accurate in crowding regimens beyond those accessible to other single-particle tracking tools.

MaxCal can infer models from coupled stochastic trajectories of gene expression and cell division

Gene expression is inherently noisy due to small numbers of proteins and nucleic acids inside a cell. Likewise, cell division is stochastic, particularly when tracking at the level of a single cell. The two can be coupled when gene expression affects the rate of cell division. Single-cell time-lapse experiments can measure both fluctuations by simultaneously recording protein levels inside a cell and its stochastic division. These information-rich noisy trajectory data sets can be harnessed to learn about the underlying molecular and cellular details that are often not known a priori. A critical question is: How can we infer a model given data where fluctuations at two levels-gene expression and cell division-are intricately convoluted? We show the principle of maximum caliber (MaxCal)-integrated within a Bayesian framework-can be used to infer several cellular and molecular details (division rates, protein production, and degradation rates) from these coupled stochastic trajectories (CSTs). We demonstrate this proof of concept using synthetic data generated from a known model. An additional challenge in data analysis is that trajectories are often not in protein numbers, but in noisy fluorescence that depends on protein number in a probabilistic manner. We again show that MaxCal can infer important molecular and cellular rates even when data are in fluorescence, another example of CST with three confounding factors-gene expression noise, cell division noise, and fluorescence distortion-all coupled. Our approach will provide guidance to build models in synthetic biology experiments as well as general biological systems where examples of CSTs are abundant.

BNP-Track: A framework for superresolved tracking

Assessing dynamic processes at single molecule scales is key toward capturing life at the level of its molecular actors. Widefield superresolution methods, such as STORM, PALM, and PAINT, provide nanoscale localization accuracy, even when distances between fluorescently labeled single molecules ("emitters") fall below light's diffraction limit. However, as these superresolution methods rely on rare photophysical events to distinguish emitters from both each other and background, they are largely limited to static samples. In contrast, here we leverage spatiotemporal correlations of dynamic widefield imaging data to extend superresolution to simultaneous multiple emitter tracking without relying on photodynamics even as emitter distances from one another fall below the diffraction limit. We simultaneously determine emitter numbers and their tracks (localization and linking) with the same localization accuracy per frame as widefield superresolution does for immobilized emitters under similar imaging conditions (≈50nm). We demonstrate our results for both in cellulo data and, for benchmarking purposes, on synthetic data. To this end, we avoid the existing tracking paradigm relying on completely or partially separating the tasks of emitter number determination, localization of each emitter, and linking emitter positions across frames. Instead, we develop a fully joint posterior distribution over the quantities of interest, including emitter tracks and their total, otherwise unknown, number within the Bayesian nonparametric paradigm. Our posterior quantifies the full uncertainty over emitter numbers and their associated tracks propagated from origins including shot noise and camera artefacts, pixelation, stochastic background, and out-of-focus motion. Finally, it remains accurate in more crowded regimes where alternative tracking tools cannot be applied.

Tracking the Message: Applying Single Molecule Localization Microscopy to Cellular RNA Imaging

RNA function is increasingly appreciated to be more complex than merely communicating between DNA sequence and protein structure. RNA localization has emerged as a key contributor to the intricate roles RNA plays in the cell, and the link between dysregulated spatiotemporal localization and disease warrants an exploration beyond sequence and structure. However, the tools needed to visualize RNA with precise resolution are lacking in comparison to methods available for studying proteins. In the past decade, many techniques have been developed for imaging RNA, and in parallel super resolution and single-molecule techniques have enabled imaging of single molecules in cells. Of these methods, single molecule localization microscopy (SMLM) has shown significant promise for probing RNA localization. In this review, we highlight current approaches that allow super resolution imaging of specific RNA transcripts and summarize challenges and future opportunities for developing innovative RNA labeling methods that leverage the power of SMLM.

Modeling Non-additive Effects in Neighboring Chemically Identical Fluorophores

Quantitative fluorescence analysis is often used to derive chemical properties, including stoichiometries, of biomolecular complexes. One fundamental underlying assumption in the analysis of fluorescence data─whether it be the determination of protein complex stoichiometry by super-resolution, or step-counting by photobleaching, or the determination of RNA counts in diffraction-limited spots in RNA fluorescence in situ hybridization (RNA-FISH) experiments─is that fluorophores behave identically and do not interact. However, recent experiments on fluorophore-labeled DNA origami structures such as fluorocubes have shed light on the nature of the interactions between identical fluorophores as these are brought closer together, thereby raising questions on the validity of the modeling assumption that fluorophores do not interact. Here, we analyze photon arrival data under pulsed illumination from fluorocubes where distances between dyes range from 2 to 10 nm. We discuss the implications of non-additivity of brightness on quantitative fluorescence analysis.

Illuminating RNA biology through imaging

RNA processing plays a central role in accurately transmitting genetic information into functional RNA and protein regulators. To fully appreciate the RNA life-cycle, tools to observe RNA with high spatial and temporal resolution are critical. Here we review recent advances in RNA imaging and highlight how they will propel the field of RNA biology. We discuss current trends in RNA imaging and their potential to elucidate unanswered questions in RNA biology.

RNA and liquid-liquid phase separation

Liquid-Liquid Phase Separation (LLPS) is a biological phenomenon that refers to the components of similar properties form droplets condensate in cells. These droplets play an important role in maintaining the stability of order in cells. In the studies of phase separation, weak multivalent interactions between proteins have always been the focus of attentions. With the deepening research of phase separation, more and more evidences show that RNA, especially long noncoding RNA (lncRNA), also plays an important regulatory role in the phase separation. We summarized recent researches between phase separation and RNA, and focused on the function of non-coding RNA (ncRNA) in the process of phase separation. In fact, phase separation and RNA have a two-way regulation relationship. Noncoding RNA usually recruits proteins as molecular scaffolds to drive phase separation. On the other hand, phase separation is also involved in RNA transcription, transport, metabolism and other processes.

Multivalent Proteins Rapidly and Reversibly Phase-Separate upon Osmotic Cell Volume Change

Processing bodies (PBs) and stress granules (SGs) are prominent examples of subcellular, membraneless compartments that are observed under physiological and stress conditions, respectively. We observe that the trimeric PB protein DCP1A rapidly (within ∼10 s) phase-separates in mammalian cells during hyperosmotic stress and dissolves upon isosmotic rescue (over ∼100 s) with minimal effect on cell viability even after multiple cycles of osmotic perturbation. Strikingly, this rapid intracellular hyperosmotic phase separation (HOPS) correlates with the degree of cell volume compression, distinct from SG assembly, and is exhibited broadly by homo-multimeric (valency ≥ 2) proteins across several cell types. Notably, HOPS sequesters pre-mRNA cleavage factor components from actively transcribing genomic loci, providing a mechanism for hyperosmolarity-induced global impairment of transcription termination. Our data suggest that the multimeric proteome rapidly responds to changes in hydration and molecular crowding, revealing an unexpected mode of globally programmed phase separation and sequestration.

Insights into mRNA degradation from single-molecule imaging in living cells

Single-molecule fluorescence microscopy techniques have enabled the lifecycle of individual RNA transcripts to be quantitatively measured in living cells. The application of these approaches to monitor mRNA degradation, however, has presented a challenge to unequivocally detect these events due to the inherent loss-of-signal resulting from decay of a transcript. Here, we highlight the recent technological developments that have enabled the spatial and temporal dynamics of mRNA degradation of individual transcripts to be visualized within living cells.

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 Localization RNA Structure and Dynamics > RNA Structure, Dynamics, and Chemistry RNA Interactions with Proteins and Other Molecules > RNA-Protein Complexes.

Dynamic Recruitment of Single RNAs to Processing Bodies Depends on RNA Functionality

Cellular RNAs often colocalize with cytoplasmic, membrane-less ribonucleoprotein (RNP) granules enriched for RNA-processing enzymes, termed processing bodies (PBs). Here we track the dynamic localization of individual miRNAs, mRNAs, and long non-coding RNAs (lncRNAs) to PBs using intracellular single-molecule fluorescence microscopy. We find that unused miRNAs stably bind to PBs, whereas functional miRNAs, repressed mRNAs, and lncRNAs both transiently and stably localize within either the core or periphery of PBs, albeit to different extents. Consequently, translation potential and 3' versus 5' placement of miRNA target sites significantly affect the PB localization dynamics of mRNAs. Using computational modeling and supporting experimental approaches, we show that partitioning in the PB phase attenuates mRNA silencing, suggesting that physiological mRNA turnover occurs predominantly outside of PBs. Instead, our data support a PB role in sequestering unused miRNAs for surveillance and provide a framework for investigating the dynamic assembly of RNP granules by phase separation at single-molecule resolution.

Coming Together: RNAs and Proteins Assemble under the Single-Molecule Fluorescence Microscope

RNAs, across their numerous classes, often work in concert with proteins in RNA-protein complexes (RNPs) to execute critical cellular functions. Ensemble-averaging methods have been instrumental in revealing many important aspects of these RNA-protein interactions, yet are insufficiently sensitive to much of the dynamics at the heart of RNP function. Single-molecule fluorescence microscopy (SMFM) offers complementary, versatile tools to probe RNP conformational and compositional changes in detail. In this review, we first outline the basic principles of SMFM as applied to RNPs, describing key considerations for labeling, imaging, and quantitative analysis. We then sample applications of in vitro and in vivo single-molecule visualization using the case studies of pre-messenger RNA (mRNA) splicing and RNA silencing, respectively. After discussing specific insights single-molecule fluorescence methods have yielded, we briefly review recent developments in the field and highlight areas of anticipated growth.

Maximum Caliber Can Build and Infer Models of Oscillation in a Three-Gene Feedback Network

Single-cell protein expression time trajectories provide rich temporal data quantifying cellular variability and its role in dictating fitness. However, theoretical models to analyze and fully extract information from these measurements remain limited for three reasons: (i) gene expression profiles are noisy, rendering models of averages inapplicable, (ii) experiments typically measure only a few protein species while leaving other molecular actors-necessary to build traditional bottom-up models-unnoticed, and (iii) measured data are in fluorescence, not particle number. We recently addressed these challenges in an alternate top-down approach using the principle of Maximum Caliber (MaxCal) to model genetic switches with one and two protein species. In the present work we address scalability and broader applicability of MaxCal by extending to a three-gene (A, B, C) feedback network that exhibits oscillation, commonly known as the repressilator. We test MaxCal's inferential power by using synthetic data of noisy protein number time traces-serving as a proxy for experimental data-generated from a known underlying model. We notice that the minimal MaxCal model-accounting for production, degradation, and only one type of symmetric coupling between all three species-reasonably infers several underlying features of the circuit such as the effective production rate, degradation rate, frequency of oscillation, and protein number distribution. Next, we build models of higher complexity including different levels of coupling between A, B, and C and rigorously assess their relative performance. While the minimal model (with four parameters) performs remarkably well, we note that the most complex model (with six parameters) allowing all possible forms of crosstalk between A, B, and C slightly improves prediction of rates, but avoids ad hoc assumption of all the other models. It is also the model of choice based on Bayesian information criteria. We further analyzed time trajectories in arbitrary fluorescence (using synthetic trajectories) to mimic realistic data. We conclude that even with a three-protein system including both fluorescence noise and intrinsic gene expression fluctuations, MaxCal can faithfully infer underlying details of the network, opening future directions to model other network motifs with many species.

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.

Maximum Caliber Can Characterize Genetic Switches with Multiple Hidden Species

Gene networks with feedback often involve interactions between multiple species of biomolecules, much more than experiments can actually monitor. Coupled with this is the challenge that experiments often measure gene expression in noisy fluorescence instead of protein numbers. How do we infer biophysical information and characterize the underlying circuits from this limited and convoluted data? We address this by building stochastic models using the principle of Maximum Caliber (MaxCal). MaxCal uses the basic information on synthesis, degradation, and feedback-without invoking any other auxiliary species and ad hoc reactions-to generate stochastic trajectories similar to those typically measured in experiments. MaxCal in conjunction with Maximum Likelihood (ML) can infer parameters of the model using fluctuating trajectories of protein expression over time. We demonstrate the success of the MaxCal + ML methodology using synthetic data generated from known circuits of different genetic switches: (i) a single-gene autoactivating circuit involving five species (including mRNA), (ii) a mutually repressing two-gene circuit (toggle switch) with seven species (including mRNA) considering stochastic time traces of two proteins, and (iii) the same toggle switch circuit considering stochastic time traces of only one of the two proteins. To further challenge the MaxCal + ML inference scheme, we repeat our analysis for the second and third scenario with traces expressed in noisy fluorescence instead of protein number to closely mimic typical experiments. We show that, for all of these models with increasing complexity and obfuscation, the minimal model of MaxCal is still able to capture the fluctuations of the trajectory and infer basic underlying rate parameters when benchmarked against the known values used to generate the synthetic data. Importantly, the model also yields an effective feedback parameter that can be used to quantify interactions within these circuits. These applications show the promise of MaxCal's ability to characterize circuits with limited data, and its utility to better understand evolution and advance design strategies for specific functions.

Building Predictive Models of Genetic Circuits Using the Principle of Maximum Caliber

Learning the underlying details of a gene network is a major challenge in cellular and synthetic biology. We address this challenge by building a chemical kinetic model that utilizes information encoded in the stochastic protein expression trajectories typically measured in experiments. The applicability of the proposed method is demonstrated in an auto-activating genetic circuit, a common motif in natural and synthetic gene networks. Our approach is based on the principle of maximum caliber (MaxCal)-a dynamical analog of the principle of maximum entropy-and builds a minimal model using only three constraints: 1) protein synthesis, 2) protein degradation, and 3) positive feedback. The MaxCal-generated model (described with four parameters) was benchmarked against synthetic data generated using a Gillespie algorithm on a known reaction network (with seven parameters). MaxCal accurately predicts underlying rate parameters of protein synthesis and degradation as well as experimental observables such as protein number and dwell-time distributions. Furthermore, MaxCal yields an effective feedback parameter that can be useful for circuit design. We also extend our methodology and demonstrate how to analyze trajectories that are not in protein numbers but in arbitrary fluorescence units, a more typical condition in experiments. This "top-down" methodology based on minimal information-in contrast to traditional "bottom-up" approaches that require ad hoc knowledge of circuit details-provides a powerful tool to accurately infer underlying details of feedback circuits that are not otherwise visible in experiments and to help guide circuit design.

Damage-induced lncRNAs control the DNA damage response through interaction with DDRNAs at individual double-strand breaks

The DNA damage response (DDR) preserves genomic integrity. Small non-coding RNAs termed DDRNAs are generated at DNA double-strand breaks (DSBs) and are critical for DDR activation. Here we show that active DDRNAs specifically localize to their damaged homologous genomic sites in a transcription-dependent manner. Following DNA damage, RNA polymerase II (RNAPII) binds to the MRE11-RAD50-NBS1 complex, is recruited to DSBs and synthesizes damage-induced long non-coding RNAs (dilncRNAs) from and towards DNA ends. DilncRNAs act both as DDRNA precursors and by recruiting DDRNAs through RNA-RNA pairing. Together, dilncRNAs and DDRNAs fuel DDR focus formation and associate with 53BP1. Accordingly, inhibition of RNAPII prevents DDRNA recruitment, DDR activation and DNA repair. Antisense oligonucleotides matching dilncRNAs and DDRNAs impair site-specific DDR focus formation and DNA repair. We propose that DDR signalling sites, in addition to sharing a common pool of proteins, individually host a unique set of site-specific RNAs necessary for DDR activation.

Resolving Subcellular miRNA Trafficking and Turnover at Single-Molecule Resolution

Regulation of microRNA (miRNA) localization and stability is critical for their extensive cytoplasmic RNA silencing activity and emerging nuclear functions. Here, we have developed single-molecule fluorescence-based tools to assess the subcellular trafficking, integrity, and activity of miRNAs. We find that seed-matched RNA targets protect miRNAs against degradation and enhance their nuclear retention. While target-stabilized, functional, cytoplasmic miRNAs reside in high-molecular-weight complexes, nuclear miRNAs, as well as cytoplasmic miRNAs targeted by complementary anti-miRNAs, are sequestered stably within significantly lower-molecular-weight complexes and rendered repression incompetent. miRNA stability and activity depend on Argonaute protein abundance, whereas miRNA strand selection, unwinding, and nuclear retention depend on Argonaute identity. Taken together, our results show that miRNA degradation competes with Argonaute loading and target binding to control subcellular miRNA abundance for gene silencing surveillance. Probing single cells for miRNA activity, trafficking, and metabolism promises to facilitate screening for effective miRNA mimics and anti-miRNA drugs.

In vitro labeling strategies for in cellulo fluorescence microscopy of single ribonucleoprotein machines

RNA plays a fundamental, ubiquitous role as either substrate or functional component of many large cellular complexes-"molecular machines"-used to maintain and control the readout of genetic information, a functional landscape that we are only beginning to understand. The cellular mechanisms for the spatiotemporal organization of the plethora of RNAs involved in gene expression are particularly poorly understood. Intracellular single-molecule fluorescence microscopy provides a powerful emerging tool for probing the pertinent mechanistic parameters that govern cellular RNA functions, including those of protein coding messenger RNAs (mRNAs). Progress has been hampered, however, by the scarcity of efficient high-yield methods to fluorescently label RNA molecules without the need to drastically increase their molecular weight through artificial appendages that may result in altered behavior. Herein, we employ T7 RNA polymerase to body label an RNA with a cyanine dye, as well as yeast poly(A) polymerase to strategically place multiple 2'-azido-modifications for subsequent fluorophore labeling either between the body and tail or randomly throughout the tail. Using a combination of biochemical and single-molecule fluorescence microscopy approaches, we demonstrate that both yeast poly(A) polymerase labeling strategies result in fully functional mRNA, whereas protein coding is severely diminished in the case of body labeling.

From mobility to crosstalk. A model of intracellular miRNAs motion may explain the RNAs interaction mechanism on the basis of target subcellular localization

MicroRNAs (miRNAs), 22 nucleotides long molecules with the function to reduce gene expression by inhibiting mRNA translation through partial complementary to one or more messenger RNA (mRNA) molecules. A single miRNA can reduce the expression levels of hundreds of genes and one mRNA can be a target for many miRNAs. Despite the study models used so far, miRNAs and mRNAs cannot be seen as acting in an isolated manner or even "in pairs". They most likely exert their complex actions through numerous overlapping interrelations. One of the models depicting interdependence of intracytoplasmic RNAs is the crosstalk model. It is based on a competition between several target mRNAs which are regulated by the same miRNA. In this paper, we will discuss the mobility mechanism of miRNAs, recently suggested by data from "single particle tracking" experiments. These data suggests that miRNA intracellular mobility may be of "intermittent active transport"(IAT) type. IAT is a mobility model composed by alternation of active transport (AT) and Brownian motion (BM). Based on a mathematical model, we concluded that, AT phase may explain the efficiency in reaching far targets and the BM phase may explain the competition. Furthermore, we suggest that the interaction between miRNAs and their targets depends on the concentration of the molecules, the affinity between the molecules and also on the intracellular localization of the molecules. Hence, the probability that a miRNA interacts with its target depends also on the distance to the target and the macromolecular crowding. Taken together, our data proposes an intracytoplasmic mobility mechanism for miRNA and shows that this model can partially explain the RNA crosstalk.

A novel method to accurately locate and count large numbers of steps by photobleaching

Photobleaching event counting is a single-molecule fluorescence technique that is increasingly being used to determine the stoichiometry of protein and RNA complexes composed of many subunits in vivo as well as in vitro. By tagging protein or RNA subunits with fluorophores, activating them, and subsequently observing as the fluorophores photobleach, one obtains information on the number of subunits in a complex. The noise properties in a photobleaching time trace depend on the number of active fluorescent subunits. Thus, as fluorophores stochastically photobleach, noise properties of the time trace change stochastically, and these varying noise properties have created a challenge in identifying photobleaching steps in a time trace. Although photobleaching steps are often detected by eye, this method only works for high individual fluorophore emission signal-to-noise ratios and small numbers of fluorophores. With filtering methods or currently available algorithms, it is possible to reliably identify photobleaching steps for up to 20-30 fluorophores and signal-to-noise ratios down to ∼1. Here we present a new Bayesian method of counting steps in photobleaching time traces that takes into account stochastic noise variation in addition to complications such as overlapping photobleaching events that may arise from fluorophore interactions, as well as on-off blinking. Our method is capable of detecting ≥50 photobleaching steps even for signal-to-noise ratios as low as 0.1, can find up to ≥500 steps for more favorable noise profiles, and is computationally inexpensive.

Lights, camera, action! Capturing the spliceosome and pre-mRNA splicing with single-molecule fluorescence microscopy

The process of removing intronic sequences from a precursor to messenger RNA (pre-mRNA) to yield a mature mRNA transcript via splicing is an integral step in eukaryotic gene expression. Splicing is carried out by a cellular nanomachine called the spliceosome that is composed of RNA components and dozens of proteins. Despite decades of study, many fundamentals of spliceosome function have remained elusive. Recent developments in single-molecule fluorescence microscopy have afforded new tools to better probe the spliceosome and the complex, dynamic process of splicing by direct observation of single molecules. These cutting-edge technologies enable investigators to monitor the dynamics of specific splicing components, whole spliceosomes, and even cotranscriptional splicing within living cells. WIREs RNA 2016, 7:683-701. doi: 10.1002/wrna.1358 For further resources related to this article, please visit the WIREs website.

KRAS Engages AGO2 to Enhance Cellular Transformation

Oncogenic mutations in RAS provide a compelling yet intractable therapeutic target. Using co-immunoprecipitation mass spectrometry, we uncovered an interaction between RAS and Argonaute 2 (AGO2). Endogenously, RAS and AGO2 co-sediment and co-localize in the endoplasmic reticulum. The AGO2 N-terminal domain directly binds the Switch II region of KRAS, agnostic of nucleotide (GDP/GTP) binding. Functionally, AGO2 knockdown attenuates cell proliferation in mutant KRAS-dependent cells and AGO2 overexpression enhances KRAS(G12V)-mediated transformation. Using AGO2-/- cells, we demonstrate that the RAS-AGO2 interaction is required for maximal mutant KRAS expression and cellular transformation. Mechanistically, oncogenic KRAS attenuates AGO2-mediated gene silencing. Overall, the functional interaction with AGO2 extends KRAS function beyond its canonical role in signaling.

Meeting report: SMART timing--principles of single molecule techniques course at the University of Michigan 2014

Four days after the announcement of the 2014 Nobel Prize in Chemistry for "the development of super-resolved fluorescence microscopy" based on single molecule detection, the Single Molecule Analysis in Real-Time (SMART) Center at the University of Michigan hosted a "Principles of Single Molecule Techniques 2014" course. Through a combination of plenary lectures and an Open House at the SMART Center, the course took a snapshot of a technology with an especially broad and rapidly expanding range of applications in the biomedical and materials sciences. Highlighting the continued rapid emergence of technical and scientific advances, the course underscored just how brightly the future of the single molecule field shines.

Single-molecule tools for enzymology, structural biology, systems biology and nanotechnology: an update

Toxicology is the highly interdisciplinary field studying the adverse effects of chemicals on living organisms. It requires sensitive tools to detect such effects. After their initial implementation during the 1990s, single-molecule fluorescence detection tools were quickly recognized for their potential to contribute greatly to many different areas of scientific inquiry. In the intervening time, technical advances in the field have generated ever-improving spatial and temporal resolution and have enabled the application of single-molecule fluorescence to increasingly complex systems, such as live cells. In this review, we give an overview of the optical components necessary to implement the most common versions of single-molecule fluorescence detection. We then discuss current applications to enzymology and structural studies, systems biology, and nanotechnology, presenting the technical considerations that are unique to each area of study, along with noteworthy recent results. We also highlight future directions that have the potential to revolutionize these areas of study by further exploiting the capabilities of single-molecule fluorescence microscopy.

Essentially all excess fibroblast cholesterol moves from plasma membranes to intracellular compartments

It has been shown that modestly increasing plasma membrane cholesterol beyond its physiological set point greatly increases the endoplasmic reticulum and mitochondrial pools, thereby eliciting manifold feedback responses that return cell cholesterol to its resting state. The question arises whether this homeostatic mechanism reflects the targeting of cell surface cholesterol to specific intracellular sites or its general equilibration among the organelles. We now show that human fibroblast cholesterol can be increased as much as two-fold from 2-hydroxypropyl-β-cyclodextrin without changing the size of the cell surface pool. Rather, essentially all of the added cholesterol disperses rapidly among cytoplasmic membranes, increasing their overall cholesterol content by as much as five-fold. We conclude that the level of plasma membrane cholesterol is normally at capacity and that even small increments above this physiological set point redistribute essentially entirely to intracellular membranes, perhaps down their chemical activity gradients.