A pairwise distance distribution correction (DDC) algorithm to eliminate blinking-caused artifacts in SMLM.

Single-molecule localization microscopy (SMLM) relies on the blinking behavior of a fluorophore, which is the stochastic switching between fluorescent and dark states. Blinking creates multiple localizations belonging to the same fluorophore, confounding quantitative analyses and interpretations. Here we present a method, termed distance distribution correction (DDC), to eliminate blinking-caused repeat localizations without any additional calibrations. The approach relies on obtaining the true pairwise distance distribution of different fluorophores naturally from the imaging sequence by using distances between localizations separated by a time much longer than the average fluorescence survival time. We show that, using the true pairwise distribution, we can define and maximize the likelihood, obtaining a set of localizations void of blinking artifacts. DDC results in drastic improvements in obtaining the closest estimate of the true spatial organization and number of fluorescent emitters in a wide range of applications, enabling accurate reconstruction and quantification of SMLM images.

Tau forms oligomeric complexes on microtubules that are distinct from tau aggregates.

Tau is a microtubule-associated protein, which promotes neuronal microtubule assembly and stability. Accumulation of tau into insoluble aggregates known as neurofibrillary tangles (NFTs) is a pathological hallmark of several neurodegenerative diseases. The current hypothesis is that small, soluble oligomeric tau species preceding NFT formation cause toxicity. However, thus far, visualizing the spatial distribution of tau monomers and oligomers inside cells under physiological or pathological conditions has not been possible. Here, using single-molecule localization microscopy, we show that tau forms small oligomers on microtubules ex vivo. These oligomers are distinct from those found in cells exhibiting tau aggregation and could be precursors of aggregated tau in pathology. Furthermore, using an unsupervised shape classification algorithm that we developed, we show that different tau phosphorylation states are associated with distinct tau aggregate species. Our work elucidates tau's nanoscale composition under nonaggregated and aggregated conditions ex vivo.

Spatial organization of RNA polymerase and its relationship with transcription in Escherichia coli.

Recent studies have shown that RNA polymerase (RNAP) is organized into distinct clusters in Escherichia coli and Bacillus subtilis cells. Spatially organized molecular components in prokaryotic systems imply compartmentalization without the use of membranes, which may offer insights into unique functions and regulations. It has been proposed that the formation of RNAP clusters is driven by active ribosomal RNA (rRNA) transcription and that RNAP clusters function as factories for highly efficient transcription. In this work, we examined these hypotheses by investigating the spatial organization and transcription activity of RNAP in E. coli cells using quantitative superresolution imaging coupled with genetic and biochemical assays. We observed that RNAP formed distinct clusters that were engaged in active rRNA synthesis under a rich medium growth condition. Surprisingly, a large fraction of RNAP clusters persisted in the absence of high rRNA transcription activities or when the housekeeping σ70 was sequestered, and was only significantly diminished when all RNA transcription was inhibited globally. In contrast, the cellular distribution of RNAP closely followed the morphology of the underlying nucleoid under all conditions tested irrespective of the corresponding transcription activity, and RNAP redistributed into dispersed, smaller clusters when the supercoiling state of the nucleoid was perturbed. These results suggest that RNAP was organized into active transcription centers under the rich medium growth condition; its spatial arrangement at the cellular level, however, was not dependent on rRNA synthesis activity and was likely organized by the underlying nucleoid.

Complex Diffusion in Bacteria.

Diffusion within bacteria is often thought of as a "simple" random process by which molecules collide and interact with each other. New research however shows that this is far from the truth. Here we shed light on the complexity and importance of diffusion in bacteria, illustrating the similarities and differences of diffusive behaviors of molecules within different compartments of bacterial cells. We first describe common methodologies used to probe diffusion and the associated models and analyses. We then discuss distinct diffusive behaviors of molecules within different bacterial cellular compartments, highlighting the influence of metabolism, size, crowding, charge, binding, and more. We also explicitly discuss where further research and a united understanding of what dictates diffusive behaviors across the different compartments of the cell are required, pointing out new research avenues to pursue.

Reduction of Confinement Error in Single-Molecule Tracking in Live Bacterial Cells Using SPICER.

Single-molecule tracking can extract quantitative kinetic information and identify possible state transitions of diffusing molecules (such as switching between binding and unbinding) in the in vivo environment of living cells. Confined diffusion, caused by the encompassing membrane boundary of the cell, results in increased uncertainties in identifying state-associated diffusion coefficients and transition probabilities. This problem is particularly acute in bacterial cells because of their small sizes. A standard approach to eliminating confinement errors in bacterial cells is to analyze molecule displacements only along the long axis of the cell, where molecules experience the least confinement, and hence turn three-dimensional tracking into a one-dimensional problem. However, this approach dramatically decreases the amount of data usable for statistical analysis and leads to increased uncertainties in identifying different states. Here, we present a simple algorithm, termed single-particle tracking improvement with confinement error reduction (SPICER), which significantly decreases confinement errors by selectively incorporating data not only from the long axis but also from the short axes of the cell. We validate SPICER using both reaction-diffusion simulations and experimental single-molecule tracking (SMT) data of RNA polymerase in live Escherichia coli cells. SPICER is easy to implement with existing SMT analysis routines and should find broad applications in SMT analysis.

Enhancers: A Focus on Synthetic Biology and Correlated Gene Expression.

Enhancers are central for the regulation of metazoan transcription but have proven difficult to study, primarily due to a myriad of interdependent variables shaping their activity. Consequently, synthetic biology has emerged as the main approach for dissecting mechanisms of enhancer function. We start by reviewing simple but highly parallel reporter assays, which have been successful in quantifying the complexity of the activator/coactivator mechanisms at enhancers. We then describe studies that examine how enhancers function in the genomic context and in combination with other enhancers, revealing that they activate genes through a variety of different mechanisms, working together as a system. Here, we primarily focus on synthetic reporter genes that can quantify the dynamics of enhancer biology through time. We end by considering the consequences of having many genes and enhancers within a 'local environment', which we believe leads to correlated gene expression and likely reports on the general principles of enhancer biology.

High-throughput image processing software for the study of nuclear architecture and gene expression.

High-throughput imaging (HTI) generates complex imaging datasets from a large number of experimental perturbations. Commercial HTI software programs for image analysis workflows typically do not allow full customization and adoption of new image processing algorithms in the analysis modules. While open-source HTI analysis platforms provide individual modules in the workflow, like nuclei segmentation, spot detection, or cell tracking, they are often limited in integrating novel analysis modules or algorithms. Here, we introduce the High-Throughput Image Processing Software (HiTIPS) to expand the range and customization of existing HTI analysis capabilities. HiTIPS incorporates advanced image processing and machine learning algorithms for automated cell and nuclei segmentation, spot signal detection, nucleus tracking, nucleus registration, spot tracking, and quantification of spot signal intensity. Furthermore, HiTIPS features a graphical user interface that is open to integration of new analysis modules for existing analysis pipelines and to adding new analysis modules. To demonstrate the utility of HiTIPS, we present three examples of image analysis workflows for high-throughput DNA FISH, immunofluorescence (IF), and live-cell imaging of transcription in single cells. Altogether, we demonstrate that HiTIPS is a user-friendly, flexible, and open-source HTI software platform for a variety of cell biology applications.

3D chromatin architecture, BRD4, and Mediator have distinct roles in regulating genome-wide transcriptional bursting and gene network.

Discontinuous transcription is evolutionarily conserved and a fundamental feature of gene regulation; yet, the exact mechanisms underlying transcriptional bursting are unresolved. Analyses of bursting transcriptome-wide have focused on the role of cis-regulatory elements, but other factors that regulate this process remain elusive. We applied mathematical modeling to single-cell RNA sequencing data to infer bursting dynamics transcriptome-wide under multiple conditions to identify possible molecular mechanisms. We found that Mediator complex subunit 26 (MED26) primarily regulates frequency, MYC regulates burst size, while cohesin and Bromodomain-containing protein 4 (BRD4) can modulate both. Despite comparable effects on RNA levels among these perturbations, acute depletion of MED26 had the most profound impact on the entire gene regulatory network, acting downstream of chromatin spatial architecture and without affecting TATA box-binding protein (TBP) recruitment. These results indicate that later steps in the initiation of transcriptional bursts are primary nodes for integrating gene networks in single cells.

Synthetic analysis of chromatin tracing and live-cell imaging indicates pervasive spatial coupling between genes.

The role of the spatial organization of chromosomes in directing transcription remains an outstanding question in gene regulation. Here, we analyze two recent single-cell imaging methodologies applied across hundreds of genes to systematically analyze the contribution of chromosome conformation to transcriptional regulation. Those methodologies are (1) single-cell chromatin tracing with super-resolution imaging in fixed cells; and (2) high-throughput labeling and imaging of nascent RNA in living cells. Specifically, we determine the contribution of physical distance to the coordination of transcriptional bursts. We find that individual genes adopt a constrained conformation and reposition toward the centroid of the surrounding chromatin upon activation. Leveraging the variability in distance inherent in single-cell imaging, we show that physical distance - but not genomic distance - between genes on individual chromosomes is the major factor driving co-bursting. By combining this analysis with live-cell imaging, we arrive at a corrected transcriptional correlation of [Formula: see text] for genes separated by < 400 nm. We propose that this surprisingly large correlation represents a physical property of human chromosomes and establishes a benchmark for future experimental studies.

HiTIPS: High-Throughput Image Processing Software for the Study of Nuclear Architecture and Gene Expression.

High-throughput imaging (HTI) generates complex imaging datasets from a large number of experimental perturbations. Commercial HTI software for image analysis workflows does not allow full customization and adoption of new image processing algorithms in the analysis modules. While open-source HTI analysis platforms provide individual modules in the workflow, like nuclei segmentation, spot detection, or cell tracking, they are often limited in integrating novel analysis modules or algorithms. Here, we introduce the High-Throughput Image Processing Software (HiTIPS) to expand the range and customization of existing HTI analysis capabilities. HiTIPS incorporates advanced image processing and machine learning algorithms for automated cell and nuclei segmentation, spot signal detection, nucleus tracking, spot tracking, and quantification of spot signal intensity. Furthermore, HiTIPS features a graphical user interface that is open to integration of new algorithms for existing analysis pipelines and to adding new analysis pipelines through separate plugins. To demonstrate the utility of HiTIPS, we present three examples of image analysis workflows for high-throughput DNA FISH, immunofluorescence (IF), and live-cell imaging of transcription in single cells. Altogether, we demonstrate that HiTIPS is a user-friendly, flexible, and open-source HTI analysis platform for a variety of cell biology applications.

The Stochastic Genome and Its Role in Gene Expression.

Mammalian genomes have distinct levels of spatial organization and structure that have been hypothesized to play important roles in transcription regulation. Although much has been learned about these architectural features with ensemble techniques, single-cell studies are showing a new universal trend: Genomes are stochastic and dynamic at every level of organization. Stochastic gene expression, on the other hand, has been studied for years. In this review, we probe whether there is a causative link between the two phenomena. We specifically discuss the functionality of chromatin state, topologically associating domains (TADs), and enhancer biology in light of their stochastic nature and their specific roles in stochastic gene expression. We highlight persistent fundamental questions in this area of research.

Spatially compartmentalized phase regulation of a Ca2+-cAMP-PKA oscillatory circuit.

Signaling networks are spatiotemporally organized to sense diverse inputs, process information, and carry out specific cellular tasks. In ß cells, Ca2+, cyclic adenosine monophosphate (cAMP), and Protein Kinase A (PKA) exist in an oscillatory circuit characterized by a high degree of feedback. Here, we describe a mode of regulation within this circuit involving a spatial dependence of the relative phase between cAMP, PKA, and Ca2+. We show that in mouse MIN6 ß cells, nanodomain clustering of Ca2+-sensitive adenylyl cyclases (ACs) drives oscillations of local cAMP levels to be precisely in-phase with Ca2+ oscillations, whereas Ca2+-sensitive phosphodiesterases maintain out-of-phase oscillations outside of the nanodomain. Disruption of this precise phase relationship perturbs Ca2+ oscillations, suggesting the relative phase within an oscillatory circuit can encode specific functional information. This work unveils a novel mechanism of cAMP compartmentation utilized for localized tuning of an oscillatory circuit and has broad implications for the spatiotemporal regulation of signaling networks.

A biophysical model of supercoiling dependent transcription predicts a structural aspect to gene regulation.

BACKGROUND: Transcription in Escherichia coli generates positive supercoiling in the DNA, which is relieved by the enzymatic activity of gyrase. Recently published experimental evidence suggests that transcription initiation and elongation are inhibited by the buildup of positive supercoiling. It has therefore been proposed that intermittent binding of gyrase plays a role in transcriptional bursting. Considering that transcription is one of the most fundamental cellular processes, it is desirable to be able to account for the buildup and release of positive supercoiling in models of transcription. RESULTS: Here we present a detailed biophysical model of gene expression that incorporates the effects of supercoiling due to transcription. By directly linking the amount of positive supercoiling to the rate of transcription, the model predicts that highly transcribed genes' mRNA distributions should substantially deviate from Poisson distributions, with enhanced density at low mRNA copy numbers. Additionally, the model predicts a high degree of correlation between expression levels of genes inside the same supercoiling domain. CONCLUSIONS: Our model, incorporating the supercoiling state of the gene, makes specific predictions that differ from previous models of gene expression. Genes in the same supercoiling domain influence the expression level of neighboring genes. Such structurally dependent regulation predicts correlations between genes in the same supercoiling domain. The topology of the chromosome therefore creates a higher level of gene regulation, which has broad implications for understanding the evolution and organization of bacterial genomes.