Coherence and Timing of Cell Cycle Start Examined at Single-Cell Resolution

Molecular mechanisms of precise timing in cell lysis

Many biological systems exhibit precise timing of events, and one of the most known examples is cell lysis, which is a process of breaking bacterial host cells in the virus infection cycle. However, the underlying microscopic picture of precise timing remains not well understood. We present a novel theoretical approach to explain the molecular mechanisms of effectively deterministic dynamics in biological systems. Our hypothesis is based on the idea of stochastic coupling between relevant underlying biophysical and biochemical processes that lead to noise cancellation. To test this hypothesis, we introduced a minimal discrete-state stochastic model to investigate how holin proteins produced by bacteriophages break the inner membranes of gram-negative bacteria. By explicitly solving this model, the dynamic properties of cell lysis are fully evaluated, and theoretical predictions quantitatively agree with available experimental data for both wild-type and holin mutants. It is found that the observed threshold-like behavior is a result of the balance between holin proteins entering the membrane and leaving the membrane during the lysis. Theoretical analysis suggests that the cell lysis achieves precise timing for wild-type species by maximizing the number of holins in the membrane and narrowing their spatial distribution. In contrast, for mutated species, these conditions are not satisfied. Our theoretical approach presents a possible molecular picture of precise dynamic regulation in intrinsically random biological processes.

A stochastic vs deterministic perspective on the timing of cellular events

Cells are the fundamental units of life, and like all life forms, they change over time. Changes in cell state are driven by molecular processes; of these many are initiated when molecule numbers reach and exceed specific thresholds, a characteristic that can be described as "digital cellular logic". Here we show how molecular and cellular noise profoundly influence the time to cross a critical threshold-the first-passage time-and map out scenarios in which stochastic dynamics result in shorter or longer average first-passage times compared to noise-less dynamics. We illustrate the dependence of the mean first-passage time on noise for a set of exemplar models of gene expression, auto-regulatory feedback control, and enzyme-mediated catalysis. Our theory provides intuitive insight into the origin of these effects and underscores two important insights: (i) deterministic predictions for cellular event timing can be highly inaccurate when molecule numbers are within the range known for many cells; (ii) molecular noise can significantly shift mean first-passage times, particularly within auto-regulatory genetic feedback circuits.

Whi5 hypo- and hyper-phosphorylation dynamics control cell-cycle entry and progression

Progression through the cell cycle depends on the phosphorylation of key substrates by cyclin-dependent kinases. In budding yeast, these substrates include the transcriptional inhibitor Whi5 that regulates G1/S transition. In early G1 phase, Whi5 is hypo-phosphorylated and inhibits the Swi4/Swi6 (SBF) complex that promotes transcription of the cyclins CLN1 and CLN2. In late G1, Whi5 is rapidly hyper-phosphorylated by Cln1 and Cln2 in complex with the cyclin-dependent kinase Cdk1. This hyper-phosphorylation inactivates Whi5 and excludes it from the nucleus. Here, we set out to determine the molecular mechanisms responsible for Whi5's multi-site phosphorylation and how they regulate the cell cycle. To do this, we first identified the 19 Whi5 sites that are appreciably phosphorylated and then determined which of these sites are responsible for G1 hypo-phosphorylation. Mutation of 7 sites removed G1 hypo-phosphorylation, increased cell size, and delayed the G1/S transition. Moreover, the rapidity of Whi5 hyper-phosphorylation in late G1 depends on "priming" sites that dock the Cks1 subunit of Cln1,2-Cdk1 complexes. Hyper-phosphorylation is crucial for Whi5 nuclear export, normal cell size, full expression of SBF target genes, and timely progression through both the G1/S transition and S/G2/M phases. Thus, our work shows how Whi5 phosphorylation regulates the G1/S transition and how it is required for timely progression through S/G2/M phases and not only G1 as previously thought.

Whi5 hypo- and hyper-phosphorylation dynamics control cell cycle entry and progression

Progression through the cell cycle depends on the phosphorylation of key substrates by cyclin-dependent kinases. In budding yeast, these substrates include the transcriptional inhibitor Whi5 that regulates the G1/S transition. In early G1 phase, Whi5 is hypo-phosphorylated and inhibits the SBF complex that promotes transcription of the cyclins CLN1 and CLN2 . In late-G1, Whi5 is rapidly hyper-phosphorylated by Cln1,2 in complex with the cyclin-dependent kinase Cdk1. This hyper-phosphorylation inactivates Whi5 and excludes it from the nucleus. Here, we set out to determine the molecular mechanisms responsible for Whi5's multi-site phosphorylation and how they regulate the cell cycle. To do this, we first identified the 19 Whi5 sites that are appreciably phosphorylated and then determined which of these sites are responsible for G1 hypo-phosphorylation. Mutation of 7 sites removed G1 hypo-phosphorylation, increased cell size, and delayed the G1/S transition. Moreover, the rapidity of Whi5 hyper-phosphorylation in late G1 depends on 'priming' sites that dock the Cks1 subunit of Cln1,2-Cdk1 complexes. Hyper-phosphorylation is crucial for Whi5 nuclear export, normal cell size, full expression of SBF target genes, and timely progression through both the G1/S transition and S/G2/M phases. Thus, our work shows how Whi5 phosphorylation regulates the G1/S transition and how it is required for timely progression through S/G2/M phases and not only G1 as previously thought.

Development and CT image-domain validation of a computational lung lesion model for use in virtual imaging trials

PURPOSE

Computational abnormalities (e.g., lesion models) for use in medical imaging simulation studies are frequently generated using data collected from clinical images. Although this approach allows for highly-customizable lesion detectability studies on clinical computed tomography (CT) data, the ground-truth lesion models produced with this method do not provide a sufficiently realistic lesion morphology for use with current anthropomorphic simulation studies. This work is intended to demonstrate that the new anatomically-informed lesion model presented here is not inferior to the previous lesion model under CT imaging, and can therefore provide a more biologically-informed model for use with simulated CT imaging studies.

METHODS

The lesion model was simulated initially from a seed cell with 10 μm diameter placed in an anatomical location within segmented lung CT and was allowed to reproduce locally within the available solid angle in discrete time-intervals (corresponding to synchronous cell cycles) up to a size of ∼200 μm in diameter. Daughter cells of generation G were allowed also to reproduce on the next available time-step given sufficient space. At lesion sizes beyond 200 μm in diameter, the health of subregions of cells were tracked with a Markov chain technique, indicating which regions were likely to continue growing, which were likely stable, and which were likely to develop necrosis given their proximity to anatomical features and other lesion cells. For lesion sizes beyond 500 μm, the lesion was represented with three nested, triangulated surfaces (corresponding to proliferating, dormant, and necrotic regions), indicating how discrete volumes of the lesion were behaving at a particular time. Lesions were then assigned smoothly-varying material properties based on their cellular level health in each region, resulting in a multi-material lesion model. The lesions produced with this model were then voxelized and placed into lung CT images for comparison with both prior work and clinical data. This model was subject to an observer study in which cardiothoracic imaging radiologists assessed the realism of both clinical and synthetic lesions in CT images.

RESULTS

The useable outputs of this work were voxel- or surface-based, validated, computational lesions, at a scale clearly visible on clinical CT (3-4 cm). Analysis of the observer study results indicated that the computationally-generated lesions were indistinguishable from clinical lesions (AUC = 0.49, 95% CI = [0.36, 0.61]) and non-inferior to an earlier image-based lesion model-indicating the advantage of the model for use in both hybrid CT images and in simulated CT imaging of the lungs.

CONCLUSIONS

Results indicated the non-inferiority of this model as compared to previous methods, indicating the utility of the model for use in both hybrid CT images and in simulated CT imaging.

Segmentation, tracking and cell cycle analysis of live-cell imaging data with Cell-ACDC

BACKGROUND

High-throughput live-cell imaging is a powerful tool to study dynamic cellular processes in single cells but creates a bottleneck at the stage of data analysis, due to the large amount of data generated and limitations of analytical pipelines. Recent progress on deep learning dramatically improved cell segmentation and tracking. Nevertheless, manual data validation and correction is typically still required and tools spanning the complete range of image analysis are still needed.

RESULTS

We present Cell-ACDC, an open-source user-friendly GUI-based framework written in Python, for segmentation, tracking and cell cycle annotations. We included state-of-the-art deep learning models for single-cell segmentation of mammalian and yeast cells alongside cell tracking methods and an intuitive, semi-automated workflow for cell cycle annotation of single cells. Using Cell-ACDC, we found that mTOR activity in hematopoietic stem cells is largely independent of cell volume. By contrast, smaller cells exhibit higher p38 activity, consistent with a role of p38 in regulation of cell size. Additionally, we show that, in S. cerevisiae, histone Htb1 concentrations decrease with replicative age.

CONCLUSIONS

Cell-ACDC provides a framework for the application of state-of-the-art deep learning models to the analysis of live cell imaging data without programming knowledge. Furthermore, it allows for visualization and correction of segmentation and tracking errors as well as annotation of cell cycle stages. We embedded several smart algorithms that make the correction and annotation process fast and intuitive. Finally, the open-source and modularized nature of Cell-ACDC will enable simple and fast integration of new deep learning-based and traditional methods for cell segmentation, tracking, and downstream image analysis. Source code: https://github.com/SchmollerLab/Cell_ACDC.

Cell and extracellular matrix growth theory and its implications for tumorigenesis

Cells associated with an abnormal (cancerous) growth exchange flows, morph freely and grow hand-in-glove with their immediate environment, the extracellular matrix (ECM). The cell structure experiences two mass flows in counterflow. Flowing into the structure are nutrients and flowing out is refuse from the metabolically active biomass within. The physical effect of the evolution of the cell and extracellular structure is more flow and mixing in that space, that is, more mixing than in the absence of a biological growth in that space. The objective of the present theory is to predict the increase in the size of the cell cluster as a function of its structure, and also to predict the critical cluster sizes that mark the transitions from one distinct cluster configuration to the next. This amounts to predicting the timing and the main features of the transitions from single cell to clusters with two, four, eight and more cells, including larger clusters with cells organized on its outer surface. The predicted evolution of the size and configuration of the cell cluster is validated successfully by comparison with measurements from several independent studies of cancerous and non-cancerous growth patterns.

Temporal precision of molecular events with regulation and feedback

Cellular behaviors such as migration, division, and differentiation rely on precise timing, and yet the molecular events that govern these behaviors are highly stochastic. We investigate regulatory strategies that decrease the timing noise of molecular events. Autoregulatory feedback increases noise. Yet we find that in the presence of regulation by a second species, autoregulatory feedback decreases noise. To explain this finding, we develop a method to calculate the optimal regulation function that minimizes the timing noise. The method reveals that the combination of feedback and regulation minimizes noise by maximizing the number of molecular events that must happen in sequence before a threshold is crossed. We compute the optimal timing precision for all two-node networks with regulation and feedback, derive a generic lower bound on timing noise, and discuss our results in the context of neuroblast migration during Caenorhabditis elegans development.

Growth-Dependent Activation of Protein Kinases Suggests a Mechanism for Measuring Cell Growth

In all cells, progression through the cell cycle occurs only when sufficient growth has occurred. Thus, cells must translate growth into a proportional signal that can be used to measure and transmit information about growth. Previous genetic studies in budding yeast suggested that related kinases called Gin4 and Hsl1 could function in mechanisms that measure bud growth; however, interpretation of the data was complicated by the use of gene deletions that cause complex terminal phenotypes. Here, we used the first conditional alleles of Gin4 and Hsl1 to more precisely define their functions. We show that excessive bud growth during a prolonged mitotic delay is an immediate consequence of inactivating Gin4 and Hsl1 Thus, acute loss of Gin4 and Hsl1 causes cells to behave as though they cannot detect that bud growth has occurred. We further show that Gin4 and Hsl1 undergo gradual hyperphosphorylation during bud growth that is dependent upon growth and correlated with the extent of growth. Moreover, gradual hyperphosphorylation of Gin4 during bud growth requires binding to anionic phospholipids that are delivered to the growing bud. While alternative models are possible, the data suggest that signaling lipids delivered to the growing bud generate a growth-dependent signal that could be used to measure bud growth.

Critical slowing down and attractive manifold: A mechanism for dynamic robustness in the yeast cell-cycle process

Biological processes that execute complex multiple functions, such as the cell cycle, must ensure the order of sequential events and maintain dynamic robustness against various fluctuations. Here, we examine the mechanisms and fundamental structure that achieve these properties in the cell cycle of the budding yeast Saccharomyces cerevisiae. We show that this process behaves like an excitable system containing three well-decoupled saddle-node bifurcations to execute DNA replication and mitosis events. The yeast cell-cycle regulatory network can be divided into three modules-the G1/S phase, early M phase, and late M phase-wherein both positive feedback loops in each module and interactions among modules play important roles. Specifically, when the cell-cycle process operates near the critical points of the saddle-node bifurcations, a critical slowing down effect takes place. Such interregnum then allows for an attractive manifold and sufficient duration for cell-cycle events, within which to assess the completion of DNA replication and mitosis, e.g., spindle assembly. Moreover, such arrangement ensures that any fluctuation in an early module or event will not transmit to a later module or event. Thus, our results suggest a possible dynamical mechanism of the cell-cycle process to ensure event order and dynamic robustness and give insight into the evolution of eukaryotic cell-cycle processes.

Differential Scaling of Gene Expression with Cell Size May Explain Size Control in Budding Yeast

Yeast cells must grow to a critical size before committing to division. It is unknown how size is measured. We find that as cells grow, mRNAs for some cell-cycle activators scale faster than size, increasing in concentration, while mRNAs for some inhibitors scale slower than size, decreasing in concentration. Size-scaled gene expression could cause an increasing ratio of activators to inhibitors with size, triggering cell-cycle entry. Consistent with this, expression of the CLN2 activator from the promoter of the WHI5 inhibitor, or vice versa, interfered with cell size homeostasis, yielding a broader distribution of cell sizes. We suggest that size homeostasis comes from differential scaling of gene expression with size. Differential regulation of gene expression as a function of cell size could affect many cellular processes.

Stochastic fluctuations in apoptotic threshold of tumour cells can enhance apoptosis and combat fractional killing

Fractional killing, which is a significant impediment to successful chemotherapy, is observed even in a population of genetically identical cancer cells exposed to apoptosis-inducing agents. This phenomenon arises not from genetic mutation but from cell-to-cell variation in the activation timing and level of the proteins that regulates apoptosis. To understand the mechanism behind the phenomenon, we formulate complex fractional killing processes as a first-passage time (FPT) problem with a stochastically fluctuating boundary. Analytical calculations are performed for the FPT distribution in a toy model of stochastic p53 gene expression, where the cancer cell is killed only when the p53 expression level crosses an active apoptotic threshold. Counterintuitively, we find that threshold fluctuations can effectively enhance cellular killing by significantly decreasing the mean time that the p53 protein reaches the threshold level for the first time. Moreover, faster fluctuations lead to the killing of more cells. These qualitative results imply that fluctuations in threshold are a non-negligible stochastic source, and can be taken as a strategy for combating fractional killing of cancer cells.

Control strategies for the timing of intracellular events

While the timing of intracellular events is essential for many cellular processes, gene expression inside a single cell can exhibit substantial cell-to-cell variability, raising the question of how cells ensure precision in event timing despite such stochasticity. We address this question by analyzing a biologically reasonable model of gene expression in the context of first passage time (FPT), focusing on two experimentally measurable statistics: mean FPT (MFPT) and timing variability (TV). We show that (1) transcriptional burst size (BS) and burst frequency (BF) can minimize the TV; (2) translational BS monotonically reduces the MFPT to a nonzero low bound; (3) the timescale of promoter kinetics can minimize both the MFPT and the TV, depending on the ratio of the on-switching rate over the off-switching rate; and (4) positive feedback regulation of any form can all minimize the TV, whereas negative feedback regulation of transcriptional BF or BS always enhances the TV. These control strategies can have broad implications for diverse cellular processes relying on precise temporal triggering of events.

A Visual Framework for Classifying Determinants of Cell Size

Cells control their size by coordinating cell cycle progression with volume growth. Size control is typically studied at specific cell cycle transitions that are delayed or accelerated depending on size. This focus is well suited for revealing mechanisms acting at these transitions, but neglects the dynamics in other cell cycle phases, and is therefore inherently limited for studying how the characteristic cell size is determined. We address this limitation through a formalism that intuitively visualizes the characteristic size emerging from integrated cell cycle dynamics of individual cells. Applying this formalism to budding yeast, we describe the contributions of the un-budded (G1) and budded (S-G2-M) phase to size adjustments following environmental or genetic perturbations. We show that although the budded phase can be perturbed with little consequences for G1 dynamics, perturbations in G1 propagate to the budded phase. Our study provides an integrated view on cell size determinants in budding yeast.

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An intuitive visualization framework for cell size control is described

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Cell size control in different environments or mutant backgrounds can be compared

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Mutual dependencies between size control at different cell cycle phases are described

Cell Cycle Inhibitor Whi5 Records Environmental Information to Coordinate Growth and Division in Yeast

Proliferating cells need to evaluate the environment to determine the optimal timing for cell cycle entry. However, how this is achieved is not well understood. Here, we show that, in budding yeast, the G1 inhibitor Whi5 is a key environmental indicator and plays a crucial role in coordinating cell growth and division. We found that, under a variety of nutrient and stress conditions, Whi5 amount in G1 is proportional to the cell's doubling time in the environment, which in turn influences the timing for the next cell cycle entry. In addition, the coordination between division and environment is further fine-tuned in G1 by environmentally dependent growth rate, G1 cyclin-Cdk1 contribution, and Whi5 threshold at the start. Our results show that the cell stores the past environmental information in Whi5, which works together with other mechanisms sensing the current environmental condition to achieve an adaptive cellular decision making process.

Characterization of the APSES-family transcriptional regulators of Histoplasma capsulatum

The fungal APSES protein family of transcription factors is characterized by a conserved DNA-binding motif facilitating regulation of gene expression in fungal development and other biological processes. However, their functions in the thermally dimorphic fungal pathogen Histoplasma capsulatum are unexplored. Histoplasma capsulatum switches between avirulent hyphae in the environment and virulent yeasts in mammalian hosts. We identified five APSES domain-containing proteins in H. capsulatum homologous to Swi6, Mbp1, Stu1 and Xbp1 proteins and one protein found in related Ascomycetes (APSES-family protein 1; Afp1). Through transcriptional analyses and RNA interference-based functional tests we explored their roles in fungal biology and virulence. Mbp1 serves an essential role and Swi6 contributes to full yeast cell growth. Stu1 is primarily expressed in mycelia and is necessary for aerial hyphae development and conidiation. Xbp1 is the only factor enriched specifically in yeast cells. The APSES proteins do not regulate conversion of conidia into yeast and hyphal morphologies. The APSES-family transcription factors are not individually required for H. capsulatum infection of cultured macrophages or murine infection, nor do any contribute significantly to resistance to cellular stresses including cell wall perturbation, osmotic stress, oxidative stress or antifungal treatment. Further studies of the downstream genes regulated by the individual APSES factors will be helpful in revealing their functional roles in H. capsulatum biology.

Using time-lapse fluorescence microscopy to study gene regulation

Time-lapse fluorescence microscopy is a powerful tool to study gene regulation. By probing fluorescent signals in single cells over extended period of time, this method can be used to study the dynamics, noise, movement, memory, inheritance, and coordination, of gene expression during cell growth, development, and differentiation. In combination with a flow-cell device, it can also measure gene regulation by external stimuli. Due to the single cell nature and the spatial/temporal capacity, this method can often provide information that is hard to get using other methods. Here, we review the standard experimental procedures and new technical developments in this field.

Temporal precision of regulated gene expression

Important cellular processes such as migration, differentiation, and development often rely on precise timing. Yet, the molecular machinery that regulates timing is inherently noisy. How do cells achieve precise timing with noisy components? We investigate this question using a first-passage-time approach, for an event triggered by a molecule that crosses an abundance threshold and that is regulated by either an accumulating activator or a diminishing repressor. We find that either activation or repression outperforms an unregulated strategy. The optimal regulation corresponds to a nonlinear increase in the amount of the target molecule over time, arises from a tradeoff between minimizing the timing noise of the regulator and that of the target molecule itself, and is robust to additional effects such as bursts and cell division. Our results are in quantitative agreement with the nonlinear increase and low noise of mig-1 gene expression in migrating neuroblast cells during Caenorhabditis elegans development. These findings suggest that dynamic regulation may be a simple and powerful strategy for precise cellular timing.

Cells control important processes with precise timing, even though their underlying molecular machinery is inherently imprecise. In the case of Caenorhabditis elegans development, migrating neuroblast cells produce a molecule until a certain abundance is reached, at which time the cells stop moving. Precise timing of this event is critical to C. elegans development, and here we investigate how it can be achieved. Specifically, we investigate regulation of the molecule production by either an accumulating activator or a diminishing repressor. Our results are consistent with the nonlinear increase and low noise of gene expression observed in the C. elegans cells.

Daughter-cell-specific modulation of nuclear pore complexes controls cell cycle entry during asymmetric division

The acquisition of cellular identity is coupled to changes in the nuclear periphery and nuclear pore complexes (NPCs). Whether and how these changes determine cell fate remains unclear. We have uncovered a mechanism regulating NPC acetylation to direct cell fate after asymmetric division in budding yeast. The lysine deacetylase Hos3 associates specifically with daughter cell NPCs during mitosis to delay cell cycle entry (Start). Hos3-dependent deacetylation of nuclear basket and central channel nucleoporins establishes daughter cell-specific nuclear accumulation of the transcriptional repressor Whi5 during anaphase and perinuclear silencing of the CLN2 gene in the following G1 phase. Hos3-dependent coordination of both events restrains Start in daughter but not in mother cells. We propose that deacetylation modulates transport-dependent and -independent functions of NPCs, leading to differential cell cycle progression in mother and daughter cells. Similar mechanisms might regulate NPC functions in specific cell types and/or cell cycle stages in multicellular organisms.

Stochastic timing in gene expression for simple regulatory strategies

Timing is essential for many cellular processes, from cellular responses to external stimuli to the cell cycle and circadian clocks. Many of these processes are based on gene expression. For example, an activated gene may be required to reach in a precise time a threshold level of expression that triggers a specific downstream process. However, gene expression is subject to stochastic fluctuations, naturally inducing an uncertainty in this threshold-crossing time with potential consequences on biological functions and phenotypes. Here, we consider such ‘timing fluctuations’ and we ask how they can be controlled. Our analytical estimates and simulations show that, for an induced gene, timing variability is minimal if the threshold level of expression is approximately half of the steady-state level. Timing fluctuations can be reduced by increasing the transcription rate, while they are insensitive to the translation rate. In presence of self-regulatory strategies, we show that self-repression reduces timing noise for threshold levels that have to be reached quickly, while self-activation is optimal at long times. These results lay a framework for understanding stochasticity of endogenous systems such as the cell cycle, as well as for the design of synthetic trigger circuits.

Heterosis as a consequence of regulatory incompatibility

BACKGROUND

The merging of genomes in inter-specific hybrids can result in novel phenotypes, including increased growth rate and biomass yield, a phenomenon known as heterosis. Heterosis is typically viewed as the opposite of hybrid incompatibility. In this view, the superior performance of the hybrid is attributed to heterozygote combinations that compensate for deleterious mutations accumulating in each individual genome, or lead to new, over-dominating interactions with improved performance. Still, only fragmented knowledge is available on genes and processes contributing to heterosis.

RESULTS

We describe a budding yeast hybrid that grows faster than both its parents under different environments. Phenotypically, the hybrid progresses more rapidly through cell cycle checkpoints, relieves the repression of respiration in fast growing conditions, does not slow down its growth when presented with ethanol stress, and shows increased signs of DNA damage. A systematic genetic screen identified hundreds of S. cerevisiae alleles whose deletion reduced growth of the hybrid. These growth-affecting alleles were condition-dependent, and differed greatly from alleles that reduced the growth of the S. cerevisiae parent.

CONCLUSIONS

Our results define a budding yeast hybrid that is perturbed in multiple regulatory processes but still shows a clear growth heterosis. We propose that heterosis results from incompatibilities that perturb regulatory mechanisms, which evolved to protect cells against damage or prepare them for future challenges by limiting cell growth.

First-passage time approach to controlling noise in the timing of intracellular events

In the noisy cellular environment, gene products are subject to inherent random fluctuations in copy numbers over time. How cells ensure precision in the timing of key intracellular events despite such stochasticity is an intriguing fundamental problem. We formulate event timing as a first-passage time problem, where an event is triggered when the level of a protein crosses a critical threshold for the first time. Analytical calculations are performed for the first-passage time distribution in stochastic models of gene expression. Derivation of these formulas motivates an interesting question: Is there an optimal feedback strategy to regulate the synthesis of a protein to ensure that an event will occur at a precise time, while minimizing deviations or noise about the mean? Counterintuitively, results show that for a stable long-lived protein, the optimal strategy is to express the protein at a constant rate without any feedback regulation, and any form of feedback (positive, negative, or any combination of them) will always amplify noise in event timing. In contrast, a positive feedback mechanism provides the highest precision in timing for an unstable protein. These theoretical results explain recent experimental observations of single-cell lysis times in bacteriophage [Formula: see text] Here, lysis of an infected bacterial cell is orchestrated by the expression and accumulation of a stable [Formula: see text] protein up to a threshold, and precision in timing is achieved via feedforward rather than feedback control. Our results have broad implications for diverse cellular processes that rely on precise temporal triggering of events.

Inference of gene regulation functions from dynamic transcriptome data

To quantify gene regulation, a function is required that relates transcription factor binding to DNA (input) to the rate of mRNA synthesis from a target gene (output). Such a ‘gene regulation function’ (GRF) generally cannot be measured because the experimental titration of inputs and simultaneous readout of outputs is difficult. Here we show that GRFs may instead be inferred from natural changes in cellular gene expression, as exemplified for the cell cycle in the yeast S. cerevisiae. We develop this inference approach based on a time series of mRNA synthesis rates from a synchronized population of cells observed over three cell cycles. We first estimate the functional form of how input transcription factors determine mRNA output and then derive GRFs for target genes in the CLB2 gene cluster that are expressed during G2/M phase. Systematic analysis of additional GRFs suggests a network architecture that rationalizes transcriptional cell cycle oscillations. We find that a transcription factor network alone can produce oscillations in mRNA expression, but that additional input from cyclin oscillations is required to arrive at the native behaviour of the cell cycle oscillator.

DOI:http://dx.doi.org/10.7554/eLife.12188.001

Living cells rely on networks of genes to control their behavior, including how they grow, develop and respond to stress. Genes encode instructions needed to make proteins and other molecules, and much of the control is exerted at the first stage of protein production, known as transcription. During this process, a gene is copied to make molecules known as transcripts that may later be used as templates to make proteins.

Many genes encode proteins that act to regulate transcription. Therefore, an individual gene may receive inputs from other genes, and these inputs affect how much transcript the gene produces, which can be considered as the gene’s output. While these inputs and outputs can often be wired together to form a network, it is less clear exactly how all the different inputs at a gene interact to determine its output. These interactions are known as “gene regulation functions”, and knowing them would be an important step towards understanding gene networks, which would help us to predict how cells will behave in different situations.

Gene regulation functions are difficult to measure directly, so researchers would like to find other ways to assess them indirectly. A recently developed experimental technique called “dynamic transcriptome analysis” seemed promising as it measures both the inputs and outputs of all genes in a cell over time. Hillenbrand et al. used this technique to infer gene regulation functions with one or two inputs in yeast cells.

Comparing these estimates with experimental data from previous studies showed that these inferred gene regulation functions could successfully predict the output of a gene based on its inputs. Hillenbrand et al. then used these estimates to search and model a well-known genetic network that is thought to be part of the molecular clockwork that controls the timing of events that cause a cell to divide.

Currently, the approach used by Hillenbrand et al. treats gene regulation functions like “black boxes”. This means that, while an output can be predicted if the inputs are known, it cannot reveal all of the detailed mechanisms behind it. Gaining insights into the inner workings of these black boxes will require taking more data into account, such as how abundant the proteins that regulate transcription are, where they are located within cells or whether they are active or not. Therefore, the next challenge is to incorporate these kinds of data to gain a fuller picture of how gene networks operate within cells.

DOI:http://dx.doi.org/10.7554/eLife.12188.002

An APC/C-Cdh1 Biosensor Reveals the Dynamics of Cdh1 Inactivation at the G1/S Transition

B-type cyclin-dependent kinase activity must be turned off for mitotic exit and G1 stabilization. B-type cyclin degradation is mediated by the anaphase-promoting complex/cyclosome (APC/C); during and after mitotic exit, APC/C is dependent on Cdh1. Cdh1 is in turn phosphorylated and inactivated by cyclin-CDK at the Start transition of the new cell cycle. We developed a biosensor to assess the cell cycle dynamics of APC/C-Cdh1. Nuclear exit of the G1 transcriptional repressor Whi5 is a known marker of Start; APC/C-Cdh1 is inactivated 12 min after Whi5 nuclear exit with little measurable cell-to-cell timing variability. Multiple phosphorylation sites on Cdh1 act in a redundant manner to repress its activity. Reducing the number of phosphorylation sites on Cdh1 can to some extent be tolerated for cell viability, but it increases variability in timing of APC/C-Cdh1 inactivation. Mutants with minimal subsets of phosphorylation sites required for viability exhibit striking stochasticity in multiple responses including budding, nuclear division, and APC/C-Cdh1 activity itself. Multiple cyclin-CDK complexes, as well as the stoichiometric inhibitor Acm1, contribute to APC/C-Cdh1 inactivation; this redundant control is likely to promote rapid and reliable APC/C-Cdh1 inactivation immediately following the Start transition.

A Model of Yeast Cell-Cycle Regulation Based on a Standard Component Modeling Strategy for Protein Regulatory Networks

To understand the molecular mechanisms that regulate cell cycle progression in eukaryotes, a variety of mathematical modeling approaches have been employed, ranging from Boolean networks and differential equations to stochastic simulations. Each approach has its own characteristic strengths and weaknesses. In this paper, we propose a “standard component” modeling strategy that combines advantageous features of Boolean networks, differential equations and stochastic simulations in a framework that acknowledges the typical sorts of reactions found in protein regulatory networks. Applying this strategy to a comprehensive mechanism of the budding yeast cell cycle, we illustrate the potential value of standard component modeling. The deterministic version of our model reproduces the phenotypic properties of wild-type cells and of 125 mutant strains. The stochastic version of our model reproduces the cell-to-cell variability of wild-type cells and the partial viability of the CLB2-dbΔ clb5Δ mutant strain. Our simulations show that mathematical modeling with “standard components” can capture in quantitative detail many essential properties of cell cycle control in budding yeast.

Punctuated evolution and transitional hybrid network in an ancestral cell cycle of fungi

Although cell cycle control is an ancient, conserved, and essential process, some core animal and fungal cell cycle regulators share no more sequence identity than non-homologous proteins. Here, we show that evolution along the fungal lineage was punctuated by the early acquisition and entrainment of the SBF transcription factor through horizontal gene transfer. Cell cycle evolution in the fungal ancestor then proceeded through a hybrid network containing both SBF and its ancestral animal counterpart E2F, which is still maintained in many basal fungi. We hypothesize that a virally-derived SBF may have initially hijacked cell cycle control by activating transcription via the cis-regulatory elements targeted by the ancestral cell cycle regulator E2F, much like extant viral oncogenes. Consistent with this hypothesis, we show that SBF can regulate promoters with E2F binding sites in budding yeast.

DOI:http://dx.doi.org/10.7554/eLife.09492.001

Living cells grow and divide with remarkable precision to ensure that their genetic material is faithfully duplicated and distributed equally to the newly formed daughter cells. This precision is achieved through a series of steps known as the cell cycle. The cell cycle is ancient and conserved across all Eukaryotes, including plants, animals and fungi. However, some of the core proteins present in animals and fungi are unrelated. This raises the question as to how a drastic change could have occurred and been tolerated over evolution.

In animals and plants, a protein called E2F controls the expression of genes that are needed to begin the cell cycle. In most fungi, an equivalent protein called SBF performs the same role as E2F, but the two proteins are very different and do not appear to share a common ancestor. This is unexpected given that fungi and animals are more closely related to one another than either is to plants.

Medina et al. searched the genomes of many animals, fungi, plants, algae, and their closest relatives for genes that encoded proteins like E2F and SBF. SBF-like proteins were only found in fungi, yet some fungal groups had cell cycle regulators like those found in animals. Zoosporic fungi, which diverged early from the fungal ancestor, had both SBF- and E2F-like proteins, while many fungi later lost E2F during evolution.

So how did fungi acquire SBF? Medina et al. observed that part of the SBF protein is similar to proteins found in many viruses. The broad distribution of these viral SBF-like proteins suggests that they arose first in viruses, and a fungal ancestor acquired one such protein during a viral infection. As SBF and E2F bind similar DNA sequences, Medina et al. hypothesized that this viral SBF hijacked control of the cell cycle in the fungal ancestor by controlling expression of genes that were originally controlled only by E2F. In support of this idea, experiments showed that many E2F binding sites in modern genes are also SBF binding sites, and that E2F sites can substitute for SBF sites in SBF-controlled genes. Future experiments in zoosporic fungi, which have animal-like and fungal-like features, would provide a glimpse of how a fungal ancestor may have used both SBF and E2F. These experiments may also reveal why most fungi have retained the newer SBF but lost the ancestral and widely conserved E2F protein.

DOI:http://dx.doi.org/10.7554/eLife.09492.002

Regulation of cell-to-cell variability in divergent gene expression

Cell-to-cell variability (noise) is an important feature of gene expression that impacts cell fitness and development. The regulatory mechanism of this variability is not fully understood. Here we investigate the effect on gene expression noise in divergent gene pairs (DGPs). We generated reporters driven by divergent promoters, rearranged their gene order, and probed their expressions using time-lapse fluorescence microscopy and single-molecule fluorescence in situ hybridization (smFISH). We show that two genes in a co-regulated DGP have higher expression covariance compared with the separate, tandem and convergent configurations, and this higher covariance is caused by more synchronized firing of the divergent transcriptions. For differentially regulated DGPs, the regulatory signal of one gene can stochastically ‘leak' to the other, causing increased gene expression noise. We propose that the DGPs' function in limiting or promoting gene expression noise may enhance or compromise cell fitness, providing an explanation for the conservation pattern of DGPs.

Gene expression noise affects cell fitness and development. Here, Yan et al. show that co-regulated divergent gene pairs (DGPs) suppress uncorrelated gene expression noise due to more synchronized transcription firing, and differentially regulated DGPs enhance gene expression noise due to transcription leakage.

Single-Cell Analysis of Growth in Budding Yeast and Bacteria Reveals a Common Size Regulation Strategy

To maintain a constant cell size, dividing cells have to coordinate cell-cycle events with cell growth. This coordination has long been supposed to rely on the existence of size thresholds determining cell-cycle progression [1]. In budding yeast, size is controlled at the G1/S transition [2]. In agreement with this hypothesis, the size at birth influences the time spent in G1: smaller cells have a longer G1 period [3]. Nevertheless, even though cells born smaller have a longer G1, the compensation is imperfect and they still bud at smaller cell sizes. In bacteria, several recent studies have shown that the incremental model of size control, in which size is controlled by addition of a constant volume (in contrast to a size threshold), is able to quantitatively explain the experimental data on four different bacterial species [4-7]. Here, we report on experimental results for the budding yeast Saccharomyces cerevisiae, finding, surprisingly, that cell size control in this organism is very well described by the incremental model, suggesting a common strategy for cell size control with bacteria. Additionally, we argue that for S. cerevisiae the "volume increment" is not added from birth to division, but rather between two budding events.

Model-Based Analysis of Cell Cycle Responses to Dynamically Changing Environments

Cell cycle progression is carefully coordinated with a cell's intra- and extracellular environment. While some pathways have been identified that communicate information from the environment to the cell cycle, a systematic understanding of how this information is dynamically processed is lacking. We address this by performing dynamic sensitivity analysis of three mathematical models of the cell cycle in Saccharomyces cerevisiae. We demonstrate that these models make broadly consistent qualitative predictions about cell cycle progression under dynamically changing conditions. For example, it is shown that the models predict anticorrelated changes in cell size and cell cycle duration under different environments independently of the growth rate. This prediction is validated by comparison to available literature data. Other consistent patterns emerge, such as widespread nonmonotonic changes in cell size down generations in response to parameter changes. We extend our analysis by investigating glucose signalling to the cell cycle, showing that known regulation of Cln3 translation and Cln1,2 transcription by glucose is sufficient to explain the experimentally observed changes in cell cycle dynamics at different glucose concentrations. Together, these results provide a framework for understanding the complex responses the cell cycle is capable of producing in response to dynamic environments.

The Stationary-Phase Cells of Saccharomyces cerevisiae Display Dynamic Actin Filaments Required for Processes Extending Chronological Life Span

Stationary-growth-phase Saccharomyces cerevisiae yeast cultures consist of nondividing cells that undergo chronological aging. For their successful survival, the turnover of proteins and organelles, ensured by autophagy and the activation of mitochondria, is performed. Some of these processes are engaged in by the actin cytoskeleton. In S. cerevisiae stationary-phase cells, F actin has been shown to form static aggregates named actin bodies, subsequently cited to be markers of quiescence. Our in vivo analyses revealed that stationary-phase cultures contain cells with dynamic actin filaments, besides the cells with static actin bodies. The cells with dynamic actin displayed active endocytosis and autophagy and well-developed mitochondrial networks. Even more, stationary-phase cell cultures grown under calorie restriction predominantly contained cells with actin cables, confirming that the presence of actin cables is linked to successful adaptation to stationary phase. Cells with actin bodies were inactive in endocytosis and autophagy and displayed aberrations in mitochondrial networks. Notably, cells of the respiratory activity-deficient cox4Δ strain displayed the same mitochondrial aberrations and actin bodies only. Additionally, our results indicate that mitochondrial dysfunction precedes the formation of actin bodies and the appearance of actin bodies corresponds to decreased cell fitness. We conclude that the F-actin status reflects the extent of damage that arises from exponential growth.

Experimental testing of a new integrated model of the budding yeast Start transition

Mathematical modeling of the cell cycle has unveiled recurrent features and emergent behaviors of cellular networks. Constructing new mutants and performing experimental tests during development of a new model of the budding yeast cell cycle yields a more efficient modeling process and results in several testable hypotheses.

The cell cycle is composed of bistable molecular switches that govern the transitions between gap phases (G1 and G2) and the phases in which DNA is replicated (S) and partitioned between daughter cells (M). Many molecular details of the budding yeast G1–S transition (Start) have been elucidated in recent years, especially with regard to its switch-like behavior due to positive feedback mechanisms. These results led us to reevaluate and expand a previous mathematical model of the yeast cell cycle. The new model incorporates Whi3 inhibition of Cln3 activity, Whi5 inhibition of SBF and MBF transcription factors, and feedback inhibition of Whi5 by G1–S cyclins. We tested the accuracy of the model by simulating various mutants not described in the literature. We then constructed these novel mutant strains and compared their observed phenotypes to the model’s simulations. The experimental results reported here led to further changes of the model, which will be fully described in a later article. Our study demonstrates the advantages of combining model design, simulation, and testing in a coordinated effort to better understand a complex biological network.

Dynamic single cell measurements of kinase activity by synthetic kinase activity relocation sensors

BACKGROUND

Mitogen activated protein kinases (MAPK) play an essential role in integrating extra-cellular signals and intra-cellular cues to allow cells to grow, adapt to stresses, or undergo apoptosis. Budding yeast serves as a powerful system to understand the fundamental regulatory mechanisms that allow these pathways to combine multiple signals and deliver an appropriate response. To fully comprehend the variability and dynamics of these signaling cascades, dynamic and quantitative single cell measurements are required. Microscopy is an ideal technique to obtain these data; however, novel assays have to be developed to measure the activity of these cascades.

RESULTS

We have generated fluorescent biosensors that allow the real-time measurement of kinase activity at the single cell level. Here, synthetic MAPK substrates were engineered to undergo nuclear-to-cytoplasmic relocation upon phosphorylation of a nuclear localization sequence. Combination of fluorescence microscopy and automated image analysis allows the quantification of the dynamics of kinase activity in hundreds of single cells. A large heterogeneity in the dynamics of MAPK activity between individual cells was measured. The variability in the mating pathway can be accounted for by differences in cell cycle stage, while, in the cell wall integrity pathway, the response to cell wall stress is independent of cell cycle stage.

CONCLUSIONS

These synthetic kinase activity relocation sensors allow the quantification of kinase activity in live single cells. The modularity of the architecture of these reporters will allow their application in many other signaling cascades. These measurements will allow to uncover new dynamic behaviour that previously could not be observed in population level measurements.

Decoupling of divergent gene regulation by sequence-specific DNA binding factors

Divergent gene pairs (DGPs) are abundant in eukaryotic genomes. Since two genes in a DGP potentially share the same regulatory sequence, one might expect that they should be co-regulated. However, an inspection of yeast DGPs containing cell-cycle or stress response genes revealed that most DGPs are differentially-regulated. The mechanism underlying DGP differential regulation is not understood. Here, we showed that co- versus differential regulation cannot be explained by genetic features including promoter length, binding site orientation, TATA elements, nucleosome distribution, or presence of non-coding RNAs. Using time-lapse fluorescence microscopy, we carried out an in-depth study of a differentially regulated DGP, PFK26-MOB1. We found that their differential regulation is mainly achieved through two DNA-binding factors, Tbf1 and Mcm1. Similar to 'enhancer-blocking insulators' in higher eukaryotes, these factors shield the proximal promoter from the action of more distant transcription regulators. We confirmed the blockage function of Tbf1 using synthetic promoters. We further presented evidence that the blockage mechanism is widely used among genome-wide DGPs. Besides elucidating the DGP regulatory mechanism, our work revealed a novel class of insulators in yeast.

Systematic identification of cell size regulators in budding yeast

Cell size is determined by a complex interplay between growth and division, involving multiple cellular pathways. To identify systematically processes affecting size control in G1 in budding yeast, we imaged and analyzed the cell cycle of millions of individual cells representing 591 mutants implicated in size control. Quantitative metric distinguished mutants affecting the mechanism of size control from the majority of mutants that have a perturbed size due to indirect effects modulating cell growth. Overall, we identified 17 negative and dozens positive size control regulators, with the negative regulators forming a small network centered on elements of mitotic exit network. Some elements of the translation machinery affected size control with a notable distinction between the deletions of parts of small and large ribosomal subunit: parts of small ribosomal subunit tended to regulate size control, while parts of the large subunit affected cell growth. Analysis of small cells revealed additional size control mechanism that functions in G2/M, complementing the primary size control in G1. Our study provides new insights about size control mechanisms in budding 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.

Topology and control of the cell-cycle-regulated transcriptional circuitry

Nearly 20% of the budding yeast genome is transcribed periodically during the cell division cycle. The precise temporal execution of this large transcriptional program is controlled by a large interacting network of transcriptional regulators, kinases, and ubiquitin ligases. Historically, this network has been viewed as a collection of four coregulated gene clusters that are associated with each phase of the cell cycle. Although the broad outlines of these gene clusters were described nearly 20 years ago, new technologies have enabled major advances in our understanding of the genes comprising those clusters, their regulation, and the complex regulatory interplay between clusters. More recently, advances are being made in understanding the roles of chromatin in the control of the transcriptional program. We are also beginning to discover important regulatory interactions between the cell-cycle transcriptional program and other cell-cycle regulatory mechanisms such as checkpoints and metabolic networks. Here we review recent advances and contemporary models of the transcriptional network and consider these models in the context of eukaryotic cell-cycle controls.

Long-term single cell analysis of S. pombe on a microfluidic microchemostat array

Although Schyzosaccharomyces pombe is one of the principal model organisms for studying the cell cycle, surprisingly few methods have characterized S. pombe growth on the single cell level, and no methods exist capable of analyzing thousands of cells and tens of thousands of cell division events. We developed an automated microfluidic platform permitting S. pombe to be grown on-chip for several days under defined and changeable conditions. We developed an image processing pipeline to extract and quantitate several physiological parameters including cell length, time to division, and elongation rate without requiring synchronization of the culture. Over a period of 50 hours our platform analyzed over 100000 cell division events and reconstructed single cell lineages up to 10 generations in length. We characterized cell lengths and division times in a temperature shift experiment in which cells were initially grown at 30°C and transitioned to 25°C. Although cell length was identical at both temperatures at steady-state, we observed transient changes in cell length if the temperature shift took place during a critical phase of the cell cycle. We further show that cells born with normal length do divide over a wide range of cell lengths and that cell length appears to be controlled in the second generation, were large newly born cells have a tendency to divide more rapidly and thus at a normalized cell size. The platform is thus applicable to measure fine-details in cell cycle dynamics, should be a useful tool to decipher the molecular mechanism underlying size homeostasis, and will be generally applicable to study processes on the single cell level that require large numbers of precision measurements and single cell lineages.

Fluctuation analysis: can estimates be trusted?

The estimation of mutation rates and relative fitnesses in fluctuation analysis is based on the unrealistic hypothesis that the single-cell times to division are exponentially distributed. Using the classical Luria-Delbrück distribution outside its modelling hypotheses induces an important bias on the estimation of the relative fitness. The model is extended here to any division time distribution. Mutant counts follow a generalization of the Luria-Delbrück distribution, which depends on the mean number of mutations, the relative fitness of normal cells compared to mutants, and the division time distribution of mutant cells. Empirical probability generating function techniques yield precise estimates both of the mean number of mutations and the relative fitness of normal cells compared to mutants. In the case where no information is available on the division time distribution, it is shown that the estimation procedure using constant division times yields more reliable results. Numerical results both on observed and simulated data are reported.

Nuclear repulsion enables division autonomy in a single cytoplasm

BACKGROUND

Current models of cell-cycle control, based on classic studies of fused cells, predict that nuclei in a shared cytoplasm respond to the same CDK activities to undergo synchronous cycling. However, synchrony is rarely observed in naturally occurring syncytia, such as the multinucleate fungus Ashbya gossypii. In this system, nuclei divide asynchronously, raising the question of how nuclear timing differences are maintained despite sharing a common milieu.

RESULTS

We observe that neighboring nuclei are highly variable in division-cycle duration and that neighbors repel one another to space apart and demarcate their own cytoplasmic territories. The size of these territories increases as a nucleus approaches mitosis and can influence cycling rates. This nonrandom nuclear spacing is regulated by microtubules and is required for nuclear asynchrony, as nuclei that transiently come in very close proximity will partially synchronize. Sister nuclei born of the same mitosis are generally not persistent neighbors over their lifetimes yet remarkably retain similar division cycle times. This indicates that nuclei carry a memory of their birth state that influences their division timing and supports that nuclei subdivide a common cytosol into functionally distinct yet mobile compartments.

CONCLUSIONS

These findings support that nuclei use cytoplasmic microtubules to establish "cells within cells." Individual compartments appear to push against one another to compete for cytoplasmic territory and insulate the division cycle. This provides a mechanism by which syncytial nuclei can spatially organize cell-cycle signaling and suggests size control can act in a system without physical boundaries.

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.

Cell-cycle dependence of transcription dominates noise in gene expression

The large variability in mRNA and protein levels found from both static and dynamic measurements in single cells has been largely attributed to random periods of transcription, often occurring in bursts. The cell cycle has a pronounced global role in affecting transcriptional and translational output, but how this influences transcriptional statistics from noisy promoters is unknown and generally ignored by current stochastic models. Here we show that variable transcription from the synthetic tetO promoter in S. cerevisiae is dominated by its dependence on the cell cycle. Real-time measurements of fluorescent protein at high expression levels indicate tetO promoters increase transcription rate ∼2-fold in S/G2/M similar to constitutive genes. At low expression levels, where tetO promoters are thought to generate infrequent bursts of transcription, we observe random pulses of expression restricted to S/G2/M, which are correlated between homologous promoters present in the same cell. The analysis of static, single-cell mRNA measurements at different points along the cell cycle corroborates these findings. Our results demonstrate that highly variable mRNA distributions in yeast are not solely the result of randomly switching between periods of active and inactive gene expression, but instead largely driven by differences in transcriptional activity between G1 and S/G2/M.

There is an astonishing amount of variation in the number of mRNA and protein molecules generated from particular genes between genetically identical single cells grown in the same environment. Particularly for mRNA, the large variation seen from these “noisy” genes is consistent with the idea of transcriptional bursting where transcription occurs in random, intermittent periods of high activity. There is considerable experimental support for transcriptional bursting, and it is a primary feature of stochastic models of gene expression that account for variation. Still, it has long been recognized that variation, especially in protein levels, can occur because of global differences between genetically identical cells. We show that in budding yeast, mRNA variation is driven to a large extent by differences in the transcriptional activity of a noisy gene between different phases of the cell cycle. These differences are not because of specific cell-cycle regulation, and in some cases transcription appears restricted to certain phases, leading to pulses of mRNA production. These results raise new questions about the origins of transcriptional bursting and how the statistics of gene expression are regulated in a global way by the cell cycle.

Stochastic expression and epigenetic memory at the yeast HO promoter

Eukaryotic gene regulation usually involves sequence-specific transcription factors and sequence-nonspecific cofactors. A large effort has been made to understand how these factors affect the average gene expression level among a population. However, little is known about how they regulate gene expression in individual cells. In this work, we address this question by mutating multiple factors in the regulatory pathway of the yeast HO promoter (HOpr) and probing the corresponding promoter activity in single cells using time-lapse fluorescence microscopy. We show that the HOpr fires in an "on/off" fashion in WT cells as well as in different genetic backgrounds. Many chromatin-related cofactors that affect the average level of HO expression do not actually affect the firing amplitude of the HOpr; instead, they affect the firing frequency among individual cell cycles. With certain mutations, the bimodal expression exhibits short-term epigenetic memory across the mitotic boundary. This memory is propagated in "cis" and reflects enhanced activator binding after a previous "on" cycle. We present evidence that the memory results from slow turnover of the histone acetylation marks.

Increasing population growth by asymmetric segregation of a limiting resource during cell division

We report that when budding yeast are transferred to low-metal environment, they adopt a proliferation pattern in which division is restricted to the subpopulation of mother cells which were born in rich conditions, before the shift. Mother cells continue to divide multiple times following the shift, generating at each division a single daughter cell, which arrests in G1. The transition to a mother-restricted proliferation pattern is characterized by asymmetric segregation of the vacuole to the mother cell and requires the transcription repressor Whi5. Notably, while deletion of WHI5 alleviates daughter cell division arrest in low-zinc conditions, it results in a lower final population size, as cell division rate becomes progressively slower. Our data suggest a new stress-response strategy, in which the dilution of a limiting cellular resource is prevented by maintaining it within a subset of dividing cells, thereby increasing population growth.

Branching process deconvolution algorithm reveals a detailed cell-cycle transcription program

Due to cell-to-cell variability and asymmetric cell division, cells in a synchronized population lose synchrony over time. As a result, time-series measurements from synchronized cell populations do not reflect the underlying dynamics of cell-cycle processes. Here, we present a branching process deconvolution algorithm that learns a more accurate view of dynamic cell-cycle processes, free from the convolution effects associated with imperfect cell synchronization. Through wavelet-basis regularization, our method sharpens signal without sharpening noise and can remarkably increase both the dynamic range and the temporal resolution of time-series data. Although applicable to any such data, we demonstrate the utility of our method by applying it to a recent cell-cycle transcription time course in the eukaryote Saccharomyces cerevisiae. Our method more sensitively detects cell-cycle-regulated transcription and reveals subtle timing differences that are masked in the original population measurements. Our algorithm also explicitly learns distinct transcription programs for mother and daughter cells, enabling us to identify 82 genes transcribed almost entirely in early G1 in a daughter-specific manner.

Event timing at the single-cell level

The timing of a cellular event often hides critical information on the process leading to the event. Our ability to measure event times in single cells along with other quantities allow us to learn about the drivers of the timed process and its downstream effects. In this review, we cover different types of events that have been timed in single cells, methods to time such events and types of analysis that have been applied to event timings. We show how different timing distributions suggest different natures for the process. The statistical relations between the timing of different events may reveal how their respective processes are related biologically: Do they occur in sequence or in parallel? Are they independent or inter-dependent? Finally, quantifying morphological and molecular variables may help assess their contribution to the timing of an event and its related process.

Single-molecule mRNA decay measurements reveal promoter- regulated mRNA stability in yeast

Messenger RNA decay measurements are typically performed on a population of cells. However, this approach cannot reveal sufficient complexity to provide information on mechanisms that may regulate mRNA degradation, possibly on short timescales. To address this deficiency, we measured cell cycle-regulated decay in single yeast cells using single-molecule FISH. We found that two genes responsible for mitotic progression, SWI5 and CLB2, exhibit a mitosis-dependent mRNA stability switch. Their transcripts are stable until mitosis, when a precipitous decay eliminates the mRNA complement, preventing carryover into the next cycle. Remarkably, the specificity and timing of decay is entirely regulated by their promoter, independent of specific cis mRNA sequences. The mitotic exit network protein Dbf2p binds to SWI5 and CLB2 mRNAs cotranscriptionally and regulates their decay. This work reveals the promoter-dependent control of mRNA stability, a regulatory mechanism that could be employed by a variety of mRNAs and organisms.

Commitment to a cellular transition precedes genome-wide transcriptional change

In budding yeast, commitment to cell division corresponds to activating the positive feedback loop of G1 cyclins controlled by the transcription factors SBF and MBF. This pair of transcription factors has over 200 targets, implying that cell-cycle commitment coincides with genome-wide changes in transcription. Here, we find that genes within this regulon have a well-defined distribution of transcriptional activation times. Combinatorial use of SBF and MBF results in a logical OR function for gene expression and partially explains activation timing. Activation of G1 cyclin expression precedes the activation of the bulk of the G1/S regulon, ensuring that commitment to cell division occurs before large-scale changes in transcription. Furthermore, we find similar positive feedback-first regulation in the yeasts S. bayanus and S. cerevisiae, as well as human cells. The widespread use of the feedback-first motif in eukaryotic cell-cycle control, implemented by nonorthologous proteins, suggests its frequent deployment at cellular transitions.