Crossing boundaries of light microscopy resolution discerns novel assemblies in the nucleolus.

The nucleolus is the largest membraneless organelle and nuclear body in mammalian cells. It is primarily involved in the biogenesis of ribosomes, essential macromolecular machines responsible for synthesizing all proteins required by the cell. The assembly of ribosomes is evolutionarily conserved and accounts for the most energy-consuming cellular process needed for cell growth, proliferation, and homeostasis. Despite the significance of this process, the substructural mechanistic principles of the nucleolar function in preribosome biogenesis have only recently begun to emerge. Here, we provide a new perspective using advanced super-resolution microscopy and single-molecule MINFLUX nanoscopy on the mechanistic principles governing ribosomal RNA-seeded nucleolar formation and the resulting tripartite suborganization of the nucleolus driven, in part, by liquid-liquid phase separation. With recent advances in the cryogenic electron microscopy (cryoEM) structural analysis of ribosome biogenesis intermediates, we highlight the current understanding of the step-wise assembly of preribosomal subunits in the nucleolus. Finally, we address how novel anticancer drug candidates target early steps in ribosome biogenesis to exploit these essential dependencies for growth arrest and tumor control.

Bayesian analysis dissects kinetic modulation during non-stationary gene expression.

Labelling of nascent stem loops with fluorescent proteins has fostered the visualization of transcription in living cells. Quantitative analysis of recorded fluorescence traces can shed light on kinetic transcription parameters and regulatory mechanisms. However, existing methods typically focus on steady state dynamics. Here, we combine a stochastic process transcription model with a hierarchical Bayesian method to infer global as well locally shared parameters for groups of cells and recover unobserved quantities such as initiation times and polymerase loading of the gene. We apply our approach to the cyclic response of the yeast CUP1 locus to heavy metal stress. Within the previously described slow cycle of transcriptional activity on the scale of minutes, we discover fast time-modulated bursting on the scale of seconds. Model comparison suggests that slow oscillations of transcriptional output are regulated by the amplitude of the bursts. Several polymerases may initiate during a burst.

Social cognitive processes explain bias in juror decisions.

Jury decisions are among the most consequential social decisions in which bias plays a notable role. While courts take measures to reduce the influence of non-evidentiary factors, jurors may still incorporate biases into their decisions. One common bias, crime-type bias, is the extent to which the perceived strength of a prosecutor's case depends on the severity of the crime. Moral judgment, affect and social cognition have been proposed as core processes underlying this and other biases. Behavioral evidence alone has been insufficient to distinguish these explanations. To identify the mechanism underlying crime-type bias, we collected functional magnetic resonance imaging patterns of brain activation from mock jurors reading criminal scenarios. Brain patterns from crime-type bias were most similar to those associated with social cognition (mentalizing and racial bias) but not affect or moral judgment. Our results support a central role for social cognition in juror decisions and suggest that crime-type bias and cultural bias may arise from similar mechanisms.

Impact of Saccharomyces cerevisiae on the Field of Single-Molecule Biophysics.

Cellular functions depend on the dynamic assembly of protein regulator complexes at specific cellular locations. Single Molecule Tracking (SMT) is a method of choice for the biochemical characterization of protein dynamics in vitro and in vivo. SMT follows individual molecules in live cells and provides direct information about their behavior. SMT was successfully applied to mammalian models. However, mammalian cells provide a complex environment where protein mobility depends on numerous factors that are difficult to control experimentally. Therefore, yeast cells, which are unicellular and well-studied with a small and completely sequenced genome, provide an attractive alternative for SMT. The simplicity of organization, ease of genetic manipulation, and tolerance to gene fusions all make yeast a great model for quantifying the kinetics of major enzymes, membrane proteins, and nuclear and cellular bodies. However, very few researchers apply SMT techniques to yeast. Our goal is to promote SMT in yeast to a wider research community. Our review serves a dual purpose. We explain how SMT is conducted in yeast cells, and we discuss the latest insights from yeast SMT while putting them in perspective with SMT of higher eukaryotes.

MYC amplifies gene expression through global changes in transcription factor dynamics.

The MYC oncogene has been studied for decades, yet there is still intense debate over how this transcription factor controls gene expression. Here, we seek to answer these questions with an in vivo readout of discrete events of gene expression in single cells. We engineered an optogenetic variant of MYC (Pi-MYC) and combined this tool with single-molecule RNA and protein imaging techniques to investigate the role of MYC in modulating transcriptional bursting and transcription factor binding dynamics in human cells. We find that the immediate consequence of MYC overexpression is an increase in the duration rather than in the frequency of bursts, a functional role that is different from the majority of human transcription factors. We further propose that the mechanism by which MYC exerts global effects on the active period of genes is by altering the binding dynamics of transcription factors involved in RNA polymerase II complex assembly and productive elongation.

Neural Support for Contributions of Utility and Narrative Processing of Evidence in Juror Decision Making.

Efforts to explain complex human decisions have focused on competing theories emphasizing utility and narrative mechanisms. These are difficult to distinguish using behavior alone. Both narrative and utility theories have been proposed to explain juror decisions, which are among the most consequential complex decisions made in a modern society. Here, we asked jury-eligible male and female subjects to rate the strength of a series of criminal cases while recording the resulting patterns of brain activation. We compared patterns of brain activation associated with evidence accumulation to patterns of brain activation derived from a large neuroimaging database to look for signatures of the cognitive processes associated with different models of juror decision-making. Evidence accumulation correlated with multiple narrative processes, including reading and recall. Of the cognitive processes traditionally viewed as components of utility, activation patterns associated with uncertainty, but not value, were more active with stronger evidence. Independent of utility and narrative, activations linked to reasoning and relational logic also correlated with increasing evidence. Hierarchical modeling of cognitive processes associated with evidence accumulation supported a more prominent role for narrative in weighing evidence in complex decisions. However, utility processes were also associated with evidence accumulation. These complementary findings support an emerging view that integrates utility and narrative processes in complex decisions.SIGNIFICANCE STATEMENT The last decade has seen a sharply increased interest in narrative as a central cognitive process in human decision-making and as an important factor in the evolution of human societies. However, the roles of narrative versus utility models of decision-making remain hotly debated. While available models frequently produce similar behavioral predictions, they rely on different cognitive processes and so their roles can be separated using the right neural tests. Here, we use brain imaging during mock juror decisions to show that cognitive processes associated with narrative, and to a lesser extent utility, were engaged while subjects evaluated evidence. These results are consistent with interactions between narrative and utility processes during complex decision-making.

Towards a 'Spot On' Understanding of Transcription in the Nucleus.

Regulation of transcription by RNA Polymerase II (RNAPII) is a rapidly evolving area of research. Technological developments in microscopy have revealed insight into the dynamics, structure, and localization of transcription components within single cells. A frequent observation in many studies is the appearance of 'spots' in cell nuclei associated with the transcription process. In this review we highlight studies that characterize the temporal and spatial characteristics of these spots, examine possible pitfalls in interpreting these kind of imaging data, and outline directions where single-cell imaging may advance in ways to further our understanding of transcription regulation.

Live-cell imaging reveals the interplay between transcription factors, nucleosomes, and bursting.

Transcription factors show rapid and reversible binding to chromatin in living cells, and transcription occurs in sporadic bursts, but how these phenomena are related is unknown. Using a combination of in vitro and in vivo single-molecule imaging approaches, we directly correlated binding of the Gal4 transcription factor with the transcriptional bursting kinetics of the Gal4 target genes GAL3 and GAL10 in living yeast cells. We find that Gal4 dwell time sets the transcriptional burst size. Gal4 dwell time depends on the affinity of the binding site and is reduced by orders of magnitude by nucleosomes. Using a novel imaging platform called orbital tracking, we simultaneously tracked transcription factor binding and transcription at one locus, revealing the timing and correlation between Gal4 binding and transcription. Collectively, our data support a model in which multiple RNA polymerases initiate transcription during one burst as long as the transcription factor is bound to DNA, and bursts terminate upon transcription factor dissociation.

Single molecule analysis of lamin dynamics.

The nuclear envelope (NE) is an essential cellular structure that contributes to nuclear stability, organization, and function. Mutations in NE-associated proteins result in a myriad of pathologies with widely diverse clinical manifestations, ages of onsets, and affected tissues. Notably, several hundred disease-causing mutations have been mapped to the LMNA gene, which encodes the intermediate filament proteins lamin A and C, two of the major architectural components of the nuclear envelope. However, how NE dysfunction leads to the highly variable pathologies observed in patient cells and tissues remains poorly understood. One model suggests alterations in the dynamic properties of the nuclear lamina and its associated proteins contribute to disease phenotype. Here, we describe the application of single molecule tracking (SMT) methodology to characterize the behavior of nuclear envelope transmembrane proteins and nuclear lamins in their native cellular environment at the single molecule level. As proof-of-concept, we demonstrate by SMT that Halo-tagged lamin B1, Samp1, lamin A, and lamin AΔ50 have distinct binding and kinetic properties, and we identify several disease-relevant mutants which exhibit altered binding dynamics. SMT is also able to separately probe the dynamics of the peripheral and the nucleoplasmic populations of lamin A mutants. We suggest that SMT is a robust and sensitive method to investigate the relationship between pathogenic mutations or cellular processes and protein dynamics at the NE.

Single-Molecule Analysis Reveals Linked Cycles of RSC Chromatin Remodeling and Ace1p Transcription Factor Binding in Yeast.

It is unknown how the dynamic binding of transcription factors (TFs) is molecularly linked to chromatin remodeling and transcription. Using single-molecule tracking (SMT), we show that the chromatin remodeler RSC speeds up the search process of the TF Ace1p for its response elements (REs) at the CUP1 promoter. We quantified smFISH mRNA data using a gene bursting model and demonstrated that RSC regulates transcription bursts of CUP1 only by modulating TF occupancy but does not affect initiation and elongation rates. We show by SMT that RSC binds to activated promoters transiently, and based on MNase-seq data, that RSC does not affect the nucleosomal occupancy at CUP1. Therefore, transient binding of Ace1p and rapid bursts of transcription at CUP1 may be dependent on short repetitive cycles of nucleosome mobilization. This type of regulation reduces the transcriptional noise and ensures a homogeneous response of the cell population to heavy metal stress.

Associations of Simian Immunodeficiency Virus (SIV)-Specific Follicular CD8+ T Cells with Other Follicular T Cells Suggest Complex Contributions to SIV Viremia Control.

Follicular CD8+ T (fCD8) cells reside within B cell follicles and are thought to be immune-privileged sites of HIV/SIV infection. We have observed comparable levels of fCD8 cells between chronically SIV-infected rhesus macaques with low viral loads (LVL) and high viral loads (HVL), raising the question concerning their contribution to viremia control. In this study, we sought to clarify the role of SIV-specific fCD8 cells in lymph nodes during the course of SIV infection in rhesus macaques. We observed that fCD8 cells, T follicular helper (Tfh) cells, and T follicular regulatory cells (Tfreg) were all elevated in chronic SIV infection. fCD8 cells of LVL animals tended to express more Gag-specific granzyme B and exhibited significantly greater killing than did HVL animals, and their cell frequencies were negatively correlated with viremia, suggesting a role in viremia control. Env- and Gag-specific IL-21+ Tfh of LVL but not HVL macaques negatively correlated with viral load, suggesting better provision of T cell help to fCD8 cells. Tfreg positively correlated with fCD8 cells in LVL animals and negatively correlated with viremia, suggesting a potential benefit of Tfreg via suppression of chronic inflammation. In contrast, in HVL macaques, Tfreg and fCD8 cell frequencies tended to be negatively correlated, and a positive correlation was seen between Tfreg number and viremia, suggesting possible dysfunction and suppression of an effective fCD8 cell immune response. Our data suggest that control of virus-infected cells in B cell follicles not only depends on fCD8 cell cytotoxicity but also on complex fCD8 cell associations with Tfh cells and Tfreg.

Modelling the effects of crime type and evidence on judgments about guilt.

Concerns over wrongful convictions have spurred an increased focus on understanding criminal justice decision-making. This study describes an experimental approach that complements conventional mock-juror experiments and case studies by providing a rapid, high-throughput screen for identifying preconceptions and biases that can influence how jurors and lawyers evaluate evidence in criminal cases. The approach combines an experimental decision task derived from marketing research with statistical modeling to explore how subjects evaluate the strength of the case against a defendant. The results show that, in the absence of explicit information about potential error rates or objective reliability, subjects tend to overweight widely used types of forensic evidence, but give much less weight than expected to a defendant's criminal history. Notably, for mock jurors, the type of crime also biases their confidence in guilt independent of the evidence. This bias is positively correlated with the seriousness of the crime. For practicing prosecutors and other lawyers, the crime-type bias is much smaller, yet still correlates with the seriousness of the crime.

Single-molecule analysis of steroid receptor and cofactor action in living cells.

Population-based assays have been employed extensively to investigate the interactions of transcription factors (TFs) with chromatin and are often interpreted in terms of static and sequential binding. However, fluorescence microscopy techniques reveal a more dynamic binding behaviour of TFs in live cells. Here we analyse the strengths and limitations of in vivo single-molecule tracking and performed a comprehensive analysis on the intranuclear dwell times of four steroid receptors and a number of known cofactors. While the absolute residence times estimates can depend on imaging acquisition parameters due to sampling bias, our results indicate that only a small proportion of factors are specifically bound to chromatin at any given time. Interestingly, the glucocorticoid receptor and its cofactors affect each other's dwell times in an asymmetric manner. Overall, our data indicate transient rather than stable TF-cofactors chromatin interactions at response elements at the single-molecule level.

Quantifying transcription factor binding dynamics at the single-molecule level in live cells.

Progressive, technological achievements in the quantitative fluorescence microscopy field are allowing researches from many different areas to start unraveling the dynamic intricacies of biological processes inside living cells. From super-resolution microscopy techniques to tracking of individual proteins, fluorescence microscopy is changing our perspective on how the cell works. Fortunately, a growing number of research groups are exploring single-molecule studies in living cells. However, no clear consensus exists on several key aspects of the technique such as image acquisition conditions, or analysis of the obtained data. Here, we describe a detailed approach to perform single-molecule tracking (SMT) of transcription factors in living cells to obtain key binding characteristics, namely their residence time and bound fractions. We discuss different types of fluorophores, labeling density, microscope, cameras, data acquisition, and data analysis. Using the glucocorticoid receptor as a model transcription factor, we compared alternate tags (GFP, mEOS, HaloTag, SNAP-tag, CLIP-tag) for potential multicolor applications. We also examine different methods to extract the dissociation rates and compare them with simulated data. Finally, we discuss several challenges that this exciting technique still faces.

A Stochastic Model of the Yeast Cell Cycle Reveals Roles for Feedback Regulation in Limiting Cellular Variability.

The cell division cycle of eukaryotes is governed by a complex network of cyclin-dependent protein kinases (CDKs) and auxiliary proteins that govern CDK activities. The control system must function reliably in the context of molecular noise that is inevitable in tiny yeast cells, because mistakes in sequencing cell cycle events are detrimental or fatal to the cell or its progeny. To assess the effects of noise on cell cycle progression requires not only extensive, quantitative, experimental measurements of cellular heterogeneity but also comprehensive, accurate, mathematical models of stochastic fluctuations in the CDK control system. In this paper we provide a stochastic model of the budding yeast cell cycle that accurately accounts for the variable phenotypes of wild-type cells and more than 20 mutant yeast strains simulated in different growth conditions. We specifically tested the role of feedback regulations mediated by G1- and SG2M-phase cyclins to minimize the noise in cell cycle progression. Details of the model are informed and tested by quantitative measurements (by fluorescence in situ hybridization) of the joint distributions of mRNA populations in yeast cells. We use the model to predict the phenotypes of ~30 mutant yeast strains that have not yet been characterized experimentally.

Single molecule tracking of Ace1p in Saccharomyces cerevisiae defines a characteristic residence time for non-specific interactions of transcription factors with chromatin.

In vivo single molecule tracking has recently developed into a powerful technique for measuring and understanding the transient interactions of transcription factors (TF) with their chromatin response elements. However, this method still lacks a solid foundation for distinguishing between specific and non-specific interactions. To address this issue, we took advantage of the power of molecular genetics of yeast. Yeast TF Ace1p has only five specific sites in the genome and thus serves as a benchmark to distinguish specific from non-specific binding. Here, we show that the estimated residence time of the short-residence molecules is essentially the same for Hht1p, Ace1p and Hsf1p, equaling 0.12-0.32 s. These three DNA-binding proteins are very different in their structure, function and intracellular concentration. This suggests that (i) short-residence molecules are bound to DNA non-specifically, and (ii) that non-specific binding shares common characteristics between vastly different DNA-bound proteins and thus may have a common underlying mechanism. We develop new and robust procedure for evaluation of adverse effects of labeling, and new quantitative analysis procedures that significantly improve residence time measurements by accounting for fluorophore blinking. Our results provide a framework for the reliable performance and analysis of single molecule TF experiments in yeast.

Adaptive imaging cytometry to estimate parameters of gene networks models in systems and synthetic biology.

The use of microfluidics in live cell imaging allows the acquisition of dense time-series from individual cells that can be perturbed through computer-controlled changes of growth medium. Systems and synthetic biologists frequently perform gene expression studies that require changes in growth conditions to characterize the stability of switches, the transfer function of a genetic device, or the oscillations of gene networks. It is rarely possible to know a priori at what times the various changes should be made, and the success of the experiment is unknown until all of the image processing is completed well after the completion of the experiment. This results in wasted time and resources, due to the need to repeat the experiment to fine-tune the imaging parameters. To overcome this limitation, we have developed an adaptive imaging platform called GenoSIGHT that processes images as they are recorded, and uses the resulting data to make real-time adjustments to experimental conditions. We have validated this closed-loop control of the experiment using galactose-inducible expression of the yellow fluorescent protein Venus in Saccharomyces cerevisiae. We show that adaptive imaging improves the reproducibility of gene expression data resulting in more accurate estimates of gene network parameters while increasing productivity ten-fold.

Cognitive functioning and driving simulator performance in middle-aged and older adults with HIV.

Nearly half of people living with HIV experience cognitive deficits that may impact instrumental activities of daily living. As the number of people aging with HIV increases, concerns mount that disease-related cognitive deficits may be compounded by age-related deficits, which may further compromise everyday functions such as driving. In this cross-sectional pilot study, during a 2.5-hour visit, 26 middle-aged and older adults (40 + years) were administered demographic, health, psychosocial, and driving habits questionnaires; cognitive assessments; and driving simulator tests. Although CD4+ T lymphocyte count and viral load were unrelated to driving performance, older age was related to poorer driving. Furthermore, poorer visual speed of processing performance (i.e., useful field of view) was related to poorer driving performance (e.g., average gross reaction time). Mixed findings were observed between driving performance and cognitive function on self-reported driving habits of participants. Implications for these findings on nursing practice and research are posited.

Measurement and modeling of transcriptional noise in the cell cycle regulatory network.

Fifty years of genetic and molecular experiments have revealed a wealth of molecular interactions involved in the control of cell division. In light of the complexity of this control system, mathematical modeling has proved useful in analyzing biochemical hypotheses that can be tested experimentally. Stochastic modeling has been especially useful in understanding the intrinsic variability of cell cycle events, but stochastic modeling has been hampered by a lack of reliable data on the absolute numbers of mRNA molecules per cell for cell cycle control genes. To fill this void, we used fluorescence in situ hybridization (FISH) to collect single molecule mRNA data for 16 cell cycle regulators in budding yeast, Saccharomyces cerevisiae. From statistical distributions of single-cell mRNA counts, we are able to extract the periodicity, timing, and magnitude of transcript abundance during the cell cycle. We used these parameters to improve a stochastic model of the cell cycle to better reflect the variability of molecular and phenotypic data on cell cycle progression in budding yeast.