A DNA repair pathway can regulate transcriptional noise to promote cell fate transitions

Synthetic gene circuit evolution: Insights and opportunities at the mid-scale

Directed evolution focuses on optimizing single genetic components for predefined engineering goals by artificial mutagenesis and selection. In contrast, experimental evolution studies the adaptation of entire genomes in serially propagated cell populations, to provide an experimental basis for evolutionary theory. There is a relatively unexplored gap at the middle ground between these two techniques, to evolve in vivo entire synthetic gene circuits with nontrivial dynamic function instead of single parts or whole genomes. We discuss the requirements for such mid-scale evolution, with hypothetical examples for evolving synthetic gene circuits by appropriate selection and targeted shuffling of a seed set of genetic components in vivo. Implementing similar methods should aid the rapid generation, functionalization, and optimization of synthetic gene circuits in various organisms and environments, accelerating both the development of biomedical and technological applications and the understanding of principles guiding regulatory network evolution.

Programmable promoter editing for precise control of transgene expression

Subtle changes in gene expression direct cells to distinct cellular states. Identifying and controlling dose-dependent transgenes requires tools for precisely titrating expression. To this end, we developed a framework called DIAL for building editable promoters that allows for fine-scale, heritable changes in transgene expression. Using DIAL, we increase expression by recombinase-mediated excision of spacers between the binding sites of a synthetic zinc-finger transcription factor and the core promoter. By nesting varying numbers and lengths of spacers, DIAL generates a tunable range of unimodal setpoints from a single promoter construct. Through small-molecule control of transcription factors and recombinases, DIAL supports temporally defined, user-guided control of transgene expression. Integration of DIAL promoters into lentivirus allows for efficient delivery to primary cells. As promoter editing generates stable states, DIAL setpoints are heritable, facilitating mapping of transgene levels to phenotypes. The highly modular and extensible DIAL framework opens up new opportunities for screening and tailoring transgene expression to regulate gene and cell-based therapies.

Highlights

Promoter editing generates a range of unimodal setpoints from DIAL, a synthetic promoter systemLength of the excisable spacer and identity of the zinc-finger activator tune setpointsNested, excisable spacers expand the number of unimodal setpointsDIAL generates stable setpoints that are robust to varying transactivator levelsDIAL transmits transient inputs into heritable statesThe TET-DIAL system enables small molecule activation of defined setpointsDIAL controls expression in primary cells and iPSCs; regulates physiologically-relevant transgenes.

One Sentence Summary

DIAL offers an extensible framework for designing synthetic promoters that generate heritable setpoints of gene expression and performs across a range of cell types and delivery systems.

Decoupled degradation and translation enables noise modulation by poly(A) tails

Poly(A) tails are crucial for mRNA translation and degradation, but the exact relationship between tail length and mRNA kinetics remains unclear. Here, we employ a small library of identical mRNAs that differ only in their poly(A)-tail length to examine their behavior in human embryonic kidney cells. We find that tail length strongly correlates with mRNA degradation rates but is decoupled from translation. Interestingly, an optimal tail length of ∼100 nt displays the highest translation rate, which is identical to the average endogenous tail length measured by nanopore sequencing. Furthermore, poly(A)-tail length variability-a feature of endogenous mRNAs-impacts translation efficiency but not mRNA degradation rates. Stochastic modeling combined with single-cell tracking reveals that poly(A) tails provide cells with an independent handle to tune gene expression fluctuations by decoupling mRNA degradation and translation. Together, this work contributes to the basic understanding of gene expression regulation and has potential applications in nucleic acid therapeutics.

Effect of genomic and cellular environments on gene expression noise

BACKGROUND

Individual cells from isogenic populations often display large cell-to-cell differences in gene expression. This "noise" in expression derives from several sources, including the genomic and cellular environment in which a gene resides. Large-scale maps of genomic environments have revealed the effects of epigenetic modifications and transcription factor occupancy on mean expression levels, but leveraging such maps to explain expression noise will require new methods to assay how expression noise changes at locations across the genome.

RESULTS

To address this gap, we present Single-cell Analysis of Reporter Gene Expression Noise and Transcriptome (SARGENT), a method that simultaneously measures the noisiness of reporter genes integrated throughout the genome and the global mRNA profiles of individual reporter-gene-containing cells. Using SARGENT, we perform the first comprehensive genome-wide survey of how genomic locations impact gene expression noise. We find that the mean and noise of expression correlate with different histone modifications. We quantify the intrinsic and extrinsic components of reporter gene noise and, using the associated mRNA profiles, assign the extrinsic component to differences between the CD24+ "stem-like" substate and the more "differentiated" substate. SARGENT also reveals the effects of transgene integrations on endogenous gene expression, which will help guide the search for "safe-harbor" loci.

CONCLUSIONS

Taken together, we show that SARGENT is a powerful tool to measure both the mean and noise of gene expression at locations across the genome and that the data generatd by SARGENT reveals important insights into the regulation of gene expression noise genome-wide.

ARTseq-FISH reveals position-dependent differences in gene expression of micropatterned mESCs

Differences in gene-expression profiles between individual cells can give rise to distinct cell fate decisions. Yet how localisation on a micropattern impacts initial changes in mRNA, protein, and phosphoprotein abundance remains unclear. To identify the effect of cellular position on gene expression, we developed a scalable antibody and mRNA targeting sequential fluorescence in situ hybridisation (ARTseq-FISH) method capable of simultaneously profiling mRNAs, proteins, and phosphoproteins in single cells. We studied 67 (phospho-)protein and mRNA targets in individual mouse embryonic stem cells (mESCs) cultured on circular micropatterns. ARTseq-FISH reveals relative changes in both abundance and localisation of mRNAs and (phospho-)proteins during the first 48 hours of exit from pluripotency. We confirm these changes by conventional immunofluorescence and time-lapse microscopy. Chemical labelling, immunofluorescence, and single-cell time-lapse microscopy further show that cells closer to the edge of the micropattern exhibit increased proliferation compared to cells at the centre. Together these data suggest that while gene expression is still highly heterogeneous position-dependent differences in mRNA and protein levels emerge as early as 12 hours after LIF withdrawal.

Cis-regulatory control of transcriptional timing and noise in response to estrogen

Cis-regulatory elements control transcription levels, temporal dynamics, and cell-cell variation or transcriptional noise. However, the combination of regulatory features that control these different attributes is not fully understood. Here, we used single-cell RNA-seq during an estrogen treatment time course and machine learning to identify predictors of expression timing and noise. We found that genes with multiple active enhancers exhibit faster temporal responses. We verified this finding by showing that manipulation of enhancer activity changes the temporal response of estrogen target genes. Analysis of transcriptional noise uncovered a relationship between promoter and enhancer activity, with active promoters associated with low noise and active enhancers linked to high noise. Finally, we observed that co-expression across single cells is an emergent property associated with chromatin looping, timing, and noise. Overall, our results indicate a fundamental tradeoff between a gene's ability to quickly respond to incoming signals and maintain low variation across cells.

Effect of chitin-architected spatiotemporal three-dimensional culture microenvironments on human umbilical cord-derived mesenchymal stem cells

Mesenchymal stem cell (MSC) transplantation has been explored for the clinical treatment of various diseases. However, the current two-dimensional (2D) culture method lacks a natural spatial microenvironment in vitro. This limitation restricts the stable establishment and adaptive maintenance of MSC stemness. Using natural polymers with biocompatibility for constructing stereoscopic MSC microenvironments may have significant application potential. This study used chitin-based nanoscaffolds to establish a novel MSC three-dimensional (3D) culture. We compared 2D and 3D cultured human umbilical cord-derived MSCs (UCMSCs), including differentiation assays, cell markers, proliferation, and angiogenesis. When UCMSCs are in 3D culture, they can differentiate into bone, cartilage, and fat. In 3D culture condition, cell proliferation is enhanced, accompanied by an elevation in the secretion of paracrine factors, including vascular endothelial growth factor (VEGF), hepatocyte growth factor (HGF), Interleukin-6 (IL-6), and Interleukin-8 (IL-8) by UCMSCs. Additionally, a 3D culture environment promotes angiogenesis and duct formation with HUVECs (Human Umbilical Vein Endothelial Cells), showing greater luminal area, total length, and branching points of tubule formation than a 2D culture. MSCs cultured in a 3D environment exhibit enhanced undifferentiated, as well as higher cell activity, making them a promising candidate for regenerative medicine and therapeutic applications.

A logic-incorporated gene regulatory network deciphers principles in cell fate decisions

Organisms utilize gene regulatory networks (GRN) to make fate decisions, but the regulatory mechanisms of transcription factors (TF) in GRNs are exceedingly intricate. A longstanding question in this field is how these tangled interactions synergistically contribute to decision-making procedures. To comprehensively understand the role of regulatory logic in cell fate decisions, we constructed a logic-incorporated GRN model and examined its behavior under two distinct driving forces (noise-driven and signal-driven). Under the noise-driven mode, we distilled the relationship among fate bias, regulatory logic, and noise profile. Under the signal-driven mode, we bridged regulatory logic and progression-accuracy trade-off, and uncovered distinctive trajectories of reprogramming influenced by logic motifs. In differentiation, we characterized a special logic-dependent priming stage by the solution landscape. Finally, we applied our findings to decipher three biological instances: hematopoiesis, embryogenesis, and trans-differentiation. Orthogonal to the classical analysis of expression profile, we harnessed noise patterns to construct the GRN corresponding to fate transition. Our work presents a generalizable framework for top-down fate-decision studies and a practical approach to the taxonomy of cell fate decisions.

Cis-regulatory control of transcriptional timing and noise in response to estrogen

Cis-regulatory elements control transcription levels, temporal dynamics, and cell-cell variation or transcriptional noise. However, the combination of regulatory features that control these different attributes is not fully understood. Here, we used single cell RNA-seq during an estrogen treatment time course and machine learning to identify predictors of expression timing and noise. We find that genes with multiple active enhancers exhibit faster temporal responses. We verified this finding by showing that manipulation of enhancer activity changes the temporal response of estrogen target genes. Analysis of transcriptional noise uncovered a relationship between promoter and enhancer activity, with active promoters associated with low noise and active enhancers linked to high noise. Finally, we observed that co-expression across single cells is an emergent property associated with chromatin looping, timing, and noise. Overall, our results indicate a fundamental tradeoff between a gene's ability to quickly respond to incoming signals and maintain low variation across cells.

Time will tell: comparing timescales to gain insight into transcriptional bursting

Recent imaging studies have captured the dynamics of regulatory events of transcription inside living cells. These events include transcription factor (TF) DNA binding, chromatin remodeling and modification, enhancer-promoter (E-P) proximity, cluster formation, and preinitiation complex (PIC) assembly. Together, these molecular events culminate in stochastic bursts of RNA synthesis, but their kinetic relationship remains largely unclear. In this review, we compare the timescales of upstream regulatory steps (input) with the kinetics of transcriptional bursting (output) to generate mechanistic models of transcription dynamics in single cells. We highlight open questions and potential technical advances to guide future endeavors toward a quantitative and kinetic understanding of transcription regulation.

Epigenetic reshaping through damage: promoting cell fate transition by BrdU and IdU incorporation

BACKGROUND

Thymidine analogs have long been recognized for their ability to randomly incorporate into DNA. However, the precise mechanisms through which thymidine analogs facilitate cell fate transition remains unclear.

RESULTS

Here, we discovered a strong correlation between the dosage dependence of thymidine analogs and their ability to overcome reprogramming barrier. The extraembryonic endoderm (XEN) state seems to be a cell's selective response to DNA damage repair (DDR), offering a shortcut to overcome reprogramming barriers. Meanwhile, we found that homologous recombination repair (HRR) pathway causes an overall epigenetic reshaping of cells and enabling them to overcome greater barriers. This response leads to the creation of a hypomethylated environment, which facilitates the transition of cell fate in various reprogramming systems. We term this mechanism as Epigenetic Reshaping through Damage (ERD).

CONCLUSION

Overall, our study finds that BrdU/IdU can activate the DNA damage repair pathway (HRR), leading to increased histone acetylation and genome-wide DNA demethylation, regulating somatic cell reprogramming. This offers valuable insights into mechanisms underlying cell fate transition.

The APE1/REF-1 and the hallmarks of cancer

APE1/REF-1 (apurinic/apyrimidinic endonuclease 1 / redox factor-1) is a protein with two domains, with endonuclease function and redox activity. Its main activity described is acting in DNA repair by base excision repair (BER) pathway, which restores DNA damage caused by oxidation, alkylation, and single-strand breaks. In contrast, the APE1 redox domain is responsible for regulating transcription factors, such as AP-1 (activating protein-1), NF-κB (Nuclear Factor kappa B), HIF-1α (Hypoxia-inducible factor 1-alpha), and STAT3 (Signal Transducers and Activators of Transcription 3). These factors are involved in physiological cellular processes, such as cell growth, inflammation, and angiogenesis, as well as in cancer. In human malignant tumors, APE1 overexpression is associated with lung, colon, ovaries, prostate, and breast cancer progression, more aggressive tumor phenotypes, and worse prognosis. In this review, we explore APE1 and its domain's role in cancer development processes, highlighting the role of APE1 in the hallmarks of cancer. We reviewed original articles and reviews from Pubmed related to APE1 and cancer and found that both domains of APE1/REF-1, but mainly its redox activity, are essential to cancer cells. This protein is often overexpressed in cancer, and its expression and activity are correlated to processes such as proliferation, invasion, inflammation, angiogenesis, and resistance to cell death. Therefore, APE1 participates in essential processes of cancer development. Then, the activity of APE1/REF-1 in these hallmarks suggests that targeting this protein could be a good therapeutic approach.

Evolution of hierarchy and irreversibility in theoretical cell differentiation model

The process of cell differentiation in multicellular organisms is characterized by hierarchy and irreversibility in many cases. However, the conditions and selection pressures that give rise to these characteristics remain poorly understood. By using a mathematical model, here we show that the network of differentiation potency (differentiation diagram) becomes necessarily hierarchical and irreversible by increasing the number of terminally differentiated states under certain conditions. The mechanisms generating these characteristics are clarified using geometry in the cell state space. The results demonstrate that the hierarchical organization and irreversibility can manifest independently of direct selection pressures associated with these characteristics, instead they appear to evolve as byproducts of selective forces favoring a diversity of differentiated cell types. The study also provides a new perspective on the structure of gene regulatory networks that produce hierarchical and irreversible differentiation diagrams. These results indicate some constraints on cell differentiation, which are expected to provide a starting point for theoretical discussion of the implicit limits and directions of evolution in multicellular organisms.

FOXO transcription factors as mediators of stress adaptation

The forkhead box protein O (FOXO, consisting of FOXO1, FOXO3, FOXO4 and FOXO6) transcription factors are the mammalian orthologues of Caenorhabditis elegans DAF-16, which gained notoriety for its capability to double lifespan in the absence of daf-2 (the gene encoding the worm insulin receptor homologue). Since then, research has provided many mechanistic details on FOXO regulation and FOXO activity. Furthermore, conditional knockout experiments have provided a wealth of data as to how FOXOs control development and homeostasis at the organ and organism levels. The lifespan-extending capabilities of DAF-16/FOXO are highly correlated with their ability to induce stress response pathways. Exogenous and endogenous stress, such as cellular redox stress, are considered the main drivers of the functional decline that characterizes ageing. Functional decline often manifests as disease, and decrease in FOXO activity indeed negatively impacts on major age-related diseases such as cancer and diabetes. In this context, the main function of FOXOs is considered to preserve cellular and organismal homeostasis, through regulation of stress response pathways. Paradoxically, the same FOXO-mediated responses can also aid the survival of dysfunctional cells once these eventually emerge. This general property to control stress responses may underlie the complex and less-evident roles of FOXOs in human lifespan as opposed to model organisms such as C. elegans.

Synthetic genetic circuits to uncover the OCT4 trajectories of successful reprogramming of human fibroblasts

Reprogramming human fibroblasts to induced pluripotent stem cells (iPSCs) is inefficient, with heterogeneity among transcription factor (TF) trajectories driving divergent cell states. Nevertheless, the impact of TF dynamics on reprogramming efficiency remains uncharted. We develop a system that accurately reports OCT4 protein levels in live cells and use it to reveal the trajectories of OCT4 in successful reprogramming. Our system comprises a synthetic genetic circuit that leverages noise to generate a wide range of OCT4 trajectories and a microRNA targeting endogenous OCT4 to set total cellular OCT4 protein levels. By fusing OCT4 to a fluorescent protein, we are able to track OCT4 trajectories with clonal resolution via live-cell imaging. We discover that a supraphysiological, stable OCT4 level is required, but not sufficient, for efficient iPSC colony formation. Our synthetic genetic circuit design and high-throughput live-imaging pipeline are generalizable for investigating TF dynamics for other cell fate programming applications.

The multiple activations in budding yeast S-phase checkpoint are Poisson processes

Eukaryotic cells activate the S-phase checkpoint signal transduction pathway in response to DNA replication stress. Affected by the noise in biochemical reactions, such activation process demonstrates cell-to-cell variability. Here, through the analysis of microfluidics-integrated time-lapse imaging, we found multiple S-phase checkpoint activations in a certain budding yeast cell cycle. Yeast cells not only varied in their activation moments but also differed in the number of activations within the cell cycle, resulting in a stochastic multiple activation process. By investigating dynamics at the single-cell level, we showed that stochastic waiting times between consecutive activations are exponentially distributed and independent from each other. Finite DNA replication time provides a robust upper time limit to the duration of multiple activations. The mathematical model, together with further experimental evidence from the mutant strain, revealed that the number of activations under different levels of replication stress agreed well with Poisson distribution. Therefore, the activation events of S-phase checkpoint meet the criterion of Poisson process during DNA replication. In sum, the observed Poisson activation process may provide new insights into the complex stochastic dynamics of signal transduction pathways.

Studying stochastic systems biology of the cell with single-cell genomics data

Recent experimental developments in genome-wide RNA quantification hold considerable promise for systems biology. However, rigorously probing the biology of living cells requires a unified mathematical framework that accounts for single-molecule biological stochasticity in the context of technical variation associated with genomics assays. We review models for a variety of RNA transcription processes, as well as the encapsulation and library construction steps of microfluidics-based single-cell RNA sequencing, and present a framework to integrate these phenomena by the manipulation of generating functions. Finally, we use simulated scenarios and biological data to illustrate the implications and applications of the approach.

Transcriptional repression by a secondary DNA binding surface of DNA topoisomerase I safeguards against hypertranscription

Regulation of global transcription output is important for normal development and disease, but little is known about the mechanisms involved. DNA topoisomerase I (TOP1) is an enzyme well-known for its role in relieving DNA supercoils for enabling transcription. Here, we report a non-enzymatic function of TOP1 that downregulates RNA synthesis. This function is dependent on specific DNA-interacting residues located on a conserved protein surface. A loss-of-function knock-in mutation on this surface, R548Q, is sufficient to cause hypertranscription and alter differentiation outcomes in mouse embryonic stem cells (mESCs). Hypertranscription in mESCs is accompanied by reduced TOP1 chromatin binding and change in genomic supercoiling. Notably, the mutation does not impact TOP1 enzymatic activity; rather, it diminishes TOP1-DNA binding and formation of compact protein-DNA structures. Thus, TOP1 exhibits opposing influences on transcription through distinct activities which are likely to be coordinated. This highlights TOP1 as a safeguard of appropriate total transcription levels in cells.

Serum Response Factor Reduces Gene Expression Noise and Confers Cell State Stability

The role of serum response factor (Srf), a central mediator of actin dynamics and mechanical signaling, in cell identity regulation is debated to be either a stabilizer or a destabilizer. We investigated the role of Srf in cell fate stability using mouse pluripotent stem cells. Despite the fact that serum-containing cultures yield heterogeneous gene expression, deletion of Srf in mouse pluripotent stem cells leads to further exacerbated cell state heterogeneity. The exaggerated heterogeneity is detectible not only as increased lineage priming but also as the developmentally earlier 2C-like cell state. Thus, pluripotent cells explore more variety of cellular states in both directions of development surrounding naïve pluripotency, a behavior that is constrained by Srf. These results support that Srf functions as a cell state stabilizer, providing rationale for its functional modulation in cell fate intervention and engineering.

Transcriptional Stochasticity as a Key Aspect of HIV-1 Latency

This review summarizes current advances in the role of transcriptional stochasticity in HIV-1 latency, which were possible in a large part due to the development of single-cell approaches. HIV-1 transcription proceeds in bursts of RNA production, which stem from the stochastic switching of the viral promoter between ON and OFF states. This switching is caused by random binding dynamics of transcription factors and nucleosomes to the viral promoter and occurs at several time scales from minutes to hours. Transcriptional bursts are mainly controlled by the core transcription factors TBP, SP1 and NF-κb, the chromatin status of the viral promoter and RNA polymerase II pausing. In particular, spontaneous variability in the promoter chromatin creates heterogeneity in the response to activators such as TNF-α, which is then amplified by the Tat feedback loop to generate high and low viral transcriptional states. This phenomenon is likely at the basis of the partial and stochastic response of latent T cells from HIV-1 patients to latency-reversing agents, which is a barrier for the development of shock-and-kill strategies of viral eradication. A detailed understanding of the transcriptional stochasticity of HIV-1 and the possibility to precisely model this phenomenon will be important assets to develop more effective therapeutic strategies.

The epithelial-mesenchymal plasticity landscape: principles of design and mechanisms of regulation

Epithelial-mesenchymal plasticity (EMP) enables cells to interconvert between several states across the epithelial-mesenchymal landscape, thereby acquiring hybrid epithelial/mesenchymal phenotypic features. This plasticity is crucial for embryonic development and wound healing, but also underlies the acquisition of several malignant traits during cancer progression. Recent research using systems biology and single-cell profiling methods has provided novel insights into the main forces that shape EMP, which include the microenvironment, lineage specification and cell identity, and the genome. Additionally, key roles have emerged for hysteresis (cell memory) and cellular noise, which can drive stochastic transitions between cell states. Here, we review these forces and the distinct but interwoven layers of regulatory control that stabilize EMP states or facilitate epithelial-mesenchymal transitions (EMTs) and discuss the therapeutic potential of manipulating the EMP landscape.

The specious art of single-cell genomics

Dimensionality reduction is standard practice for filtering noise and identifying relevant features in large-scale data analyses. In biology, single-cell genomics studies typically begin with reduction to 2 or 3 dimensions to produce "all-in-one" visuals of the data that are amenable to the human eye, and these are subsequently used for qualitative and quantitative exploratory analysis. However, there is little theoretical support for this practice, and we show that extreme dimension reduction, from hundreds or thousands of dimensions to 2, inevitably induces significant distortion of high-dimensional datasets. We therefore examine the practical implications of low-dimensional embedding of single-cell data and find that extensive distortions and inconsistent practices make such embeddings counter-productive for exploratory, biological analyses. In lieu of this, we discuss alternative approaches for conducting targeted embedding and feature exploration to enable hypothesis-driven biological discovery.

Increased gene expression variability hinders the formation of regional mechanical conflicts leading to reduced organ shape robustness

To relate gene networks and organ shape, one needs to address two wicked problems: i) Gene expression is often variable locally, and shape is reproducible globally; ii) gene expression can have cascading effects on tissue mechanics, with possibly counterintuitive consequences for the final organ shape. Here, we address such wicked problems, taking advantage of simpler plant organ development where shape only emerges from cell division and elongation. We confirm that mutation in VERNALIZATION INDEPENDENCE 3 (VIP3), a subunit of the conserved polymerase-associated factor 1 complex (Paf1C), increases gene expression variability in Arabidopsis. Then, we focused on the Arabidopsis sepal, which exhibits a reproducible shape and stereotypical regional growth patterns. In vip3 sepals, we measured higher growth heterogeneity between adjacent cells. This even culminated in the presence of negatively growing cells in specific growth conditions. Interestingly, such increased local noise interfered with the stereotypical regional pattern of growth. We previously showed that regional differential growth at the wild-type sepal tip triggers a mechanical conflict, to which cells resist by reinforcing their walls, leading to growth arrest. In vip3, the disturbed regional growth pattern delayed organ growth arrest and increased final organ shape variability. Altogether, we propose that gene expression variability is managed by Paf1C to ensure organ robustness by building up mechanical conflicts at the regional scale, instead of the local scale.

Studying stochastic systems biology of the cell with single-cell genomics data

Recent experimental developments in genome-wide RNA quantification hold considerable promise for systems biology. However, rigorously probing the biology of living cells requires a unified mathematical framework that accounts for single-molecule biological stochasticity in the context of technical variation associated with genomics assays. We review models for a variety of RNA transcription processes, as well as the encapsulation and library construction steps of microfluidics-based single-cell RNA sequencing, and present a framework to integrate these phenomena by the manipulation of generating functions. Finally, we use simulated scenarios and biological data to illustrate the implications and applications of the approach.

Functional coordination between transcription factor clustering and gene activity

The prevailing view of metazoan gene regulation is that transcription is facilitated through the formation of static activator complexes at distal regulatory regions. Here, we employed quantitative single-cell live-imaging and computational analysis to provide evidence that the dynamic assembly and disassembly process of transcription factor (TF) clusters at enhancers is a major source of transcriptional bursting in developing Drosophila embryos. We further show that the regulatory connectivity between TF clustering and burst induction is highly regulated through intrinsically disordered regions (IDRs). Addition of a poly-glutamine tract to the maternal morphogen Bicoid demonstrated that extended IDR length leads to ectopic TF clustering and burst induction from its endogenous target genes, resulting in defects in body segmentation during embryogenesis. Moreover, we successfully visualized the presence of "shared" TF clusters during the co-activation of two distant genes, which provides a concrete molecular explanation for the newly proposed "topological operon" hypothesis in metazoan gene regulation.

DNA supercoiling restricts the transcriptional bursting of neighboring eukaryotic genes

DNA supercoiling has emerged as a major contributor to gene regulation in bacteria, but how DNA supercoiling impacts transcription dynamics in eukaryotes is unclear. Here, using single-molecule dual-color nascent transcription imaging in budding yeast, we show that transcriptional bursting of divergent and tandem GAL genes is coupled. Temporal coupling of neighboring genes requires rapid release of DNA supercoils by topoisomerases. When DNA supercoils accumulate, transcription of one gene inhibits transcription at its adjacent genes. Transcription inhibition of the GAL genes results from destabilized binding of the transcription factor Gal4. Moreover, wild-type yeast minimizes supercoiling-mediated inhibition by maintaining sufficient levels of topoisomerases. Overall, we discover fundamental differences in transcriptional control by DNA supercoiling between bacteria and yeast and show that rapid supercoiling release in eukaryotes ensures proper gene expression of neighboring genes.

Dynamic fluctuations in a bacterial metabolic network

The operation of the central metabolism is typically assumed to be deterministic, but dynamics and high connectivity of the metabolic network make it potentially prone to generating fluctuations. However, time-resolved measurements of metabolite levels in individual cells that are required to characterize such fluctuations remained a challenge, particularly in small bacterial cells. Here we use single-cell metabolite measurements based on Förster resonance energy transfer, combined with computer simulations, to explore the real-time dynamics of the metabolic network of Escherichia coli. We observe that steplike exposure of starved E. coli to glycolytic carbon sources elicits large periodic fluctuations in the intracellular concentration of pyruvate in individual cells. These fluctuations are consistent with predicted oscillatory dynamics of E. coli metabolic network, and they are primarily controlled by biochemical reactions around the pyruvate node. Our results further indicate that fluctuations in glycolysis propagate to other cellular processes, possibly leading to temporal heterogeneity of cellular states within a population.

Inferring transcriptional bursting kinetics from single-cell snapshot data using a generalized telegraph model

Gene expression has inherent stochasticity resulting from transcription's burst manners. Single-cell snapshot data can be exploited to rigorously infer transcriptional burst kinetics, using mathematical models as blueprints. The classical telegraph model (CTM) has been widely used to explain transcriptional bursting with Markovian assumptions. However, growing evidence suggests that the gene-state dwell times are generally non-exponential, as gene-state switching is a multi-step process in organisms. Therefore, interpretable non-Markovian mathematical models and efficient statistical inference methods are urgently required in investigating transcriptional burst kinetics. We develop an interpretable and tractable model, the generalized telegraph model (GTM), to characterize transcriptional bursting that allows arbitrary dwell-time distributions, rather than exponential distributions, to be incorporated into the ON and OFF switching process. Based on the GTM, we propose an inference method for transcriptional bursting kinetics using an approximate Bayesian computation framework. This method demonstrates an efficient and scalable estimation of burst frequency and burst size on synthetic data. Further, the application of inference to genome-wide data from mouse embryonic fibroblasts reveals that GTM would estimate lower burst frequency and higher burst size than those estimated by CTM. In conclusion, the GTM and the corresponding inference method are effective tools to infer dynamic transcriptional bursting from static single-cell snapshot data.

Comparative analysis between single-cell RNA-seq and single-molecule RNA FISH indicates that the pyrimidine nucleobase idoxuridine (IdU) globally amplifies transcriptional noise

Stochastic fluctuations (noise) in transcription generate substantial cell-to-cell variability, but the physiological roles of noise have remained difficult to determine in the absence of generalized noise-modulation approaches. Previous single-cell RNA-sequencing (scRNA-seq) suggested that the pyrimidine-base analog (5'-iodo-2'-deoxyuridine, IdU) could generally amplify noise without substantially altering mean-expression levels but scRNA-seq technical drawbacks potentially obscured the penetrance of IdU-induced transcriptional noise amplification. Here we quantify global-vs.-partial penetrance of IdU-induced noise amplification by assessing scRNA-seq data using numerous normalization algorithms and directly quantifying noise using single-molecule RNA FISH (smFISH) for a panel of genes from across the transcriptome. Alternate scRNA-seq analyses indicate IdU-induced noise amplification for ~90% of genes, and smFISH data verified noise amplification for ~90% of tested genes. Collectively, this analysis indicates which scRNA-seq algorithms are appropriate for quantifying noise and argues that IdU is a globally penetrant noise-enhancer molecule that could enable investigations of the physiological impacts of transcriptional noise.

Leveraging transcriptomics for precision diagnosis: Lessons learned from cancer and sepsis

Diagnostics require precision and predictive ability to be clinically useful. Integration of multi-omic with clinical data is crucial to our understanding of disease pathogenesis and diagnosis. However, interpretation of overwhelming amounts of information at the individual level requires sophisticated computational tools for extraction of clinically meaningful outputs. Moreover, evolution of technical and analytical methods often outpaces standardisation strategies. RNA is the most dynamic component of all -omics technologies carrying an abundance of regulatory information that is least harnessed for use in clinical diagnostics. Gene expression-based tests capture genetic and non-genetic heterogeneity and have been implemented in certain diseases. For example patients with early breast cancer are spared toxic unnecessary treatments with scores based on the expression of a set of genes (e.g., Oncotype DX). The ability of transcriptomics to portray the transcriptional status at a moment in time has also been used in diagnosis of dynamic diseases such as sepsis. Gene expression profiles identify endotypes in sepsis patients with prognostic value and a potential to discriminate between viral and bacterial infection. The application of transcriptomics for patient stratification in clinical environments and clinical trials thus holds promise. In this review, we discuss the current clinical application in the fields of cancer and infection. We use these paradigms to highlight the impediments in identifying useful diagnostic and prognostic biomarkers and propose approaches to overcome them and aid efforts towards clinical implementation.

Diagnostic classification of childhood cancer using multiscale transcriptomics

The causes of pediatric cancers' distinctiveness compared to adult-onset tumors of the same type are not completely clear and not fully explained by their genomes. In this study, we used an optimized multilevel RNA clustering approach to derive molecular definitions for most childhood cancers. Applying this method to 13,313 transcriptomes, we constructed a pediatric cancer atlas to explore age-associated changes. Tumor entities were sometimes unexpectedly grouped due to common lineages, drivers or stemness profiles. Some established entities were divided into subgroups that predicted outcome better than current diagnostic approaches. These definitions account for inter-tumoral and intra-tumoral heterogeneity and have the potential of enabling reproducible, quantifiable diagnostics. As a whole, childhood tumors had more transcriptional diversity than adult tumors, maintaining greater expression flexibility. To apply these insights, we designed an ensemble convolutional neural network classifier. We show that this tool was able to match or clarify the diagnosis for 85% of childhood tumors in a prospective cohort. If further validated, this framework could be extended to derive molecular definitions for all cancer types.

Characterizing inhibitors of human AP endonuclease 1

AP endonuclease 1 (APE1) processes DNA lesions including apurinic/apyrimidinic sites and 3´-blocking groups, mediating base excision repair and single strand break repair. Much effort has focused on developing specific inhibitors of APE1, which could have important applications in basic research and potentially lead to clinical anticancer agents. We used structural, biophysical, and biochemical methods to characterize several reported inhibitors, including 7-nitroindole-2-carboxylic acid (CRT0044876), given its small size, reported potency, and widespread use for studying APE1. Intriguingly, NMR chemical shift perturbation (CSP) experiments show that CRT0044876 and three similar indole-2-carboxylic acids bind a pocket distal from the APE1 active site. A crystal structure confirms these findings and defines the pose for 5-nitroindole-2-carboxylic acid. However, dynamic light scattering experiments show the indole compounds form colloidal aggregates that could bind (sequester) APE1, causing nonspecific inhibition. Endonuclease assays show the compounds lack significant APE1 inhibition under conditions (detergent) that disrupt aggregation. Thus, binding of the indole-2-carboxylic acids at the remote pocket does not inhibit APE1 repair activity. Myricetin also forms aggregates and lacks APE1 inhibition under aggregate-disrupting conditions. Two other reported compounds (MLS000552981, MLS000419194) inhibit APE1 in vitro with low micromolar IC50 and do not appear to aggregate in this concentration range. However, NMR CSP experiments indicate the compounds do not bind specifically to apo- or Mg2+-bound APE1, pointing to a non-specific mode of inhibition, possibly DNA binding. Our results highlight methods for rigorous interrogation of putative APE1 inhibitors and should facilitate future efforts to discover compounds that specifically inhibit this important repair enzyme.

Length biases in single-cell RNA sequencing of pre-mRNA

Single-cell RNA sequencing data can be modeled using Markov chains to yield genome-wide insights into transcriptional physics. However, quantitative inference with such data requires careful assessment of noise sources. We find that long pre-mRNA transcripts are over-represented in sequencing data. To explain this trend, we propose a length-based model of capture bias, which may produce false-positive observations. We solve this model and use it to find concordant parameter trends as well as systematic, mechanistically interpretable technical and biological differences in paired data sets.

Allelic correlation is a marker of trade-offs between barriers to transmission of expression variability and signal responsiveness in genetic networks

Genetic networks should respond to signals but prevent the transmission of spontaneous fluctuations. Limited data from mammalian cells suggest that noise transmission is uncommon, but systematic claims about noise transmission have been limited by the inability to directly measure it. Here, we build a mathematical framework modeling allelic correlation and noise transmission, showing that allelic correlation and noise transmission correspond across model parameters and network architectures. Limiting noise transmission comes with the trade-off of being unresponsive to signals, and within responsive regimes, there is a further trade-off between response time and basal noise transmission. Analysis of allele-specific single-cell RNA-sequencing data revealed that genes encoding upstream factors in signaling pathways and cell-type-specific factors have higher allelic correlation than downstream factors, suggesting they are more subject to regulation. Overall, our findings suggest that some noise transmission must result from signal responsiveness, but it can be minimized by trading off for a slower response. A record of this paper's transparent peer review process is included in the supplemental information.

GLH-1/Vasa represses neuropeptide expression and drives spermiogenesis in the C. elegans germline

Germ granules harbor processes that maintain germline integrity and germline stem cell capacity. Depleting core germ granule components in C. elegans leads to the reprogramming of germ cells, causing them to express markers of somatic differentiation in day-two adults. Somatic reprogramming is associated with complete sterility at this stage. The resulting germ cell atrophy and other pleiotropic defects complicate our understanding of the initiation of reprogramming and how processes within germ granules safeguard the totipotency and immortal potential of germline stem cells. To better understand the initial events of somatic reprogramming, we examined total mRNA (transcriptome) and polysome-associated mRNA (translatome) changes in a precision full-length deletion of glh-1, which encodes a homolog of the germline-specific Vasa/DDX4 DEAD-box RNA helicase. Fertile animals at a permissive temperature were analyzed as young adults, a stage that precedes by 24 ​h the previously determined onset of somatic reporter-gene expression in the germline. Two significant changes are observed at this early stage. First, the majority of neuropeptide-encoding transcripts increase in both the total and polysomal mRNA fractions, suggesting that GLH-1 or its effectors suppress this expression. Second, there is a significant decrease in Major Sperm Protein (MSP)-domain mRNAs when glh-1 is deleted. We find that the presence of GLH-1 helps repress spermatogenic expression during oogenesis, but boosts MSP expression to drive spermiogenesis and sperm motility. These insights define an early role for GLH-1 in repressing somatic reprogramming to maintain germline integrity.

Supercoiling-mediated feedback rapidly couples and tunes transcription

Transcription induces a wave of DNA supercoiling, altering the binding affinity of RNA polymerases and reshaping the biochemical landscape of gene regulation. As supercoiling rapidly diffuses, transcription dynamically reshapes the regulation of proximal genes, forming a complex feedback loop. However, a theoretical framework is needed to integrate biophysical regulation with biochemical transcriptional regulation. To investigate the role of supercoiling-mediated feedback within multi-gene systems, we model transcriptional regulation under the influence of supercoiling-mediated polymerase dynamics, allowing us to identify patterns of expression that result from physical inter-gene coupling. We find that gene syntax—the relative ordering and orientation of genes—defines the expression profiles, variance, burst dynamics, and inter-gene correlation of two-gene systems. Furthermore, supercoiling can enhance or weaken biochemical regulation. Our results suggest that supercoiling couples behavior between neighboring genes, providing a regulatory mechanism that tunes transcriptional variance in engineered gene networks and explains the behavior of co-localized native circuits.

Supercoiling-mediated feedback couples the transcription of proximal genes. Here, Johnstone and Galloway provide a framework for integrating biochemical gene regulation with the biophysical effects of DNA supercoiling. This unified model provides design principles for improving the performance of gene networks, developing novel regulatory functions, and accessing previously inaccessible regulatory dynamics.

RNA velocity unraveled

We perform a thorough analysis of RNA velocity methods, with a view towards understanding the suitability of the various assumptions underlying popular implementations. In addition to providing a self-contained exposition of the underlying mathematics, we undertake simulations and perform controlled experiments on biological datasets to assess workflow sensitivity to parameter choices and underlying biology. Finally, we argue for a more rigorous approach to RNA velocity, and present a framework for Markovian analysis that points to directions for improvement and mitigation of current problems.

Single-cell sequencing data are snapshots of biological processes, making it challenging to infer dynamic relationships between cell types. RNA velocity attempts to bypass this challenge by treating the unspliced RNA content as a proxy for spliced RNA content in the near future, and using this “extrapolation” to build directional relationships. However, the method, as implemented in several software packages, is not yet reliable enough to be actionable, in part due to the large number of arbitrary, user-set hyperparameters, as well as fundamental incompatibilities between the biophysics of transcription in the living cell and the models used throughout the velocity workflows. In this study, we review these issues, and use existing results from the fields of stochastic modeling and fluorescence transcriptomics to develop an alternative theoretical framework. We show that our framework can facilitate the development and inference of physically consistent models for sequencing data, as well as the unification of single-cell analyses to self-consistently treat variation due to cell type dynamics and identities, the stochasticity inherent to single-molecule processes, and the uncertainty introduced by sequencing experiments.

Tipping the Balance: A Criticality Perspective

Cell populations are often characterised by phenotypic heterogeneity in the form of two distinct subpopulations. We consider a model of tumour cells consisting of two subpopulations: non-cancer promoting (NCP) and cancer-promoting (CP). Under steady state conditions, the model has similarities with a well-known model of population genetics which exhibits a purely noise-induced transition from unimodality to bimodality at a critical value of the noise intensity σ2. The noise is associated with the parameter λ representing the system-environment coupling. In the case of the tumour model, λ has a natural interpretation in terms of the tissue microenvironment which has considerable influence on the phenotypic composition of the tumour. Oncogenic transformations give rise to considerable fluctuations in the parameter. We compute the λ−σ2 phase diagram in a stochastic setting, drawing analogies between bifurcations and phase transitions. In the region of bimodality, a transition from a state of balance to a state of dominance, in terms of the competing subpopulations, occurs at λ = 0. Away from this point, the NCP (CP) subpopulation becomes dominant as λ changes towards positive (negative) values. The variance of the steady state probability density function as well as two entropic measures provide characteristic signatures at the transition point.

Ferrocene as a potential electrochemical reporting surrogate of abasic sites in DNA

We have evaluated the possibility of replacing abasic sites with ferrocene for enzymatic synthesis of canonical and modified DNA.

Epigenetics of Ribosomal RNA Genes

This review is focused on biology of genes encoding ribosomal RNA (rRNA) in mammals. rRNA is a structural component of the most abundant cellular molecule, the ribosome. There are many copies of rRNA genes per genome that are under tight transcriptional control by epigenetic mechanisms serving to meet cellular needs in protein synthesis. Curiously, only a fraction of rRNA genes is used even in the fast-growing cells, raising a question why unused copies of these genes have not been lost during evolution. Two plausible explanations are discussed. First, there is evidence that besides their direct function in production of rRNA, ribosomal RNA genes are involved in regulation of many other genes in the nucleus by forming either temporary or persistent complexes with these genes. Second, it seems that rRNA genes also play a role in the maintenance of genome stability, where lower copy number of rRNA genes destabilizes the genome. These "additional" functions of rRNA genes make them recurrent candidate drivers of chronic human diseases and aging. Experimental support for the involvement of these genes in human diseases and potential mechanisms are also discussed.

Synthetic biology applications of the yeast mating signal pathway

Cell fusion is a fundamental biological process that is involved in the development of most eukaryotic organisms. During the fusion process in Saccharomyces cerevisiae, cells respond to pheromones to trigger the MAPK (mitogen-activated protein kinase) cascade to initiate mating, followed by polarization, cell-wall remodeling, membrane fusion, and karyogamy. We highlight the applications of the yeast mating signal pathway in promoter engineering for tuning the expression of output genes, as well as in metabolic engineering for decoupling growth and metabolism, biosensors for sensitive detection and signal amplification, genetic circuits for programmable biological functionalities, and artificial consortia for cell-cell communication. Strategies such as exploiting rational engineering of modular circuits and optimizing the reproductive pathway to precisely maneuver physiological events have implications for scientific research and industrial development.

Single cell biology-a Keystone Symposia report

Single cell biology has the potential to elucidate many critical biological processes and diseases, from development and regeneration to cancer. Single cell analyses are uncovering the molecular diversity of cells, revealing a clearer picture of the variation among and between different cell types. New techniques are beginning to unravel how differences in cell state-transcriptional, epigenetic, and other characteristics-can lead to different cell fates among genetically identical cells, which underlies complex processes such as embryonic development, drug resistance, response to injury, and cellular reprogramming. Single cell technologies also pose significant challenges relating to processing and analyzing vast amounts of data collected. To realize the potential of single cell technologies, new computational approaches are needed. On March 17-19, 2021, experts in single cell biology met virtually for the Keystone eSymposium "Single Cell Biology" to discuss advances both in single cell applications and technologies.