Integration of Multiple Metabolic Signals Determines Cell Fate Prior to Commitment

Multi-signal regulation of the GSK-3β homolog Rim11 controls meiosis entry in budding yeast

Starvation in diploid budding yeast cells triggers a cell-fate program culminating in meiosis and spore formation. Transcriptional activation of early meiotic genes (EMGs) hinges on the master regulator Ime1, its DNA-binding partner Ume6, and GSK-3β kinase Rim11. Phosphorylation of Ume6 by Rim11 is required for EMG activation. We report here that Rim11 functions as the central signal integrator for controlling Ume6 phosphorylation and EMG transcription. In nutrient-rich conditions, PKA suppresses Rim11 levels, while TORC1 retains Rim11 in the cytoplasm. Inhibition of PKA and TORC1 induces Rim11 expression and nuclear localization. Remarkably, nuclear Rim11 is required, but not sufficient, for Rim11-dependent Ume6 phosphorylation. In addition, Ime1 is an anchor protein enabling Ume6 phosphorylation by Rim11. Subsequently, Ume6-Ime1 coactivator complexes form and induce EMG transcription. Our results demonstrate how various signaling inputs (PKA/TORC1/Ime1) converge through Rim11 to regulate EMG expression and meiosis initiation. We posit that the signaling-regulatory network elucidated here generates robustness in cell-fate control.

Deep learning-driven imaging of cell division and cell growth across an entire eukaryotic life cycle

The life cycle of biomedical and agriculturally relevant eukaryotic microorganisms involves complex transitions between proliferative and non-proliferative states such as dormancy, mating, meiosis, and cell division. New drugs, pesticides, and vaccines can be created by targeting specific life cycle stages of parasites and pathogens. However, defining the structure of a microbial life cycle often relies on partial observations that are theoretically assembled in an ideal life cycle path. To create a more quantitative approach to studying complete eukaryotic life cycles, we generated a deep learning-driven imaging framework to track microorganisms across sexually reproducing generations. Our approach combines microfluidic culturing, life cycle stage-specific segmentation of microscopy images using convolutional neural networks, and a novel cell tracking algorithm, FIEST, based on enhancing the overlap of single cell masks in consecutive images through deep learning video frame interpolation. As proof of principle, we used this approach to quantitatively image and compare cell growth and cell cycle regulation across the sexual life cycle of Saccharomyces cerevisiae. We developed a fluorescent reporter system based on a fluorescently labeled Whi5 protein, the yeast analog of mammalian Rb, and a new High-Cdk1 activity sensor, LiCHI, designed to report during DNA replication, mitosis, meiotic homologous recombination, meiosis I, and meiosis II. We found that cell growth preceded the exit from non-proliferative states such as mitotic G1, pre-meiotic G1, and the G0 spore state during germination. A decrease in the total cell concentration of Whi5 characterized the exit from non-proliferative states, which is consistent with a Whi5 dilution model. The nuclear accumulation of Whi5 was developmentally regulated, being at its highest during meiotic exit and spore formation. The temporal coordination of cell division and growth was not significantly different across three sexually reproducing generations. Our framework could be used to quantitatively characterize other single-cell eukaryotic life cycles that remain incompletely described. An off-the-shelf user interface Yeastvision provides free access to our image processing and single-cell tracking algorithms.

Control of meiotic entry by dual inhibition of a key mitotic transcription factor

The mitosis to meiosis transition requires dynamic changes in gene expression, but whether and how the mitotic transcriptional machinery is regulated during this transition is unknown. In budding yeast, SBF and MBF transcription factors initiate the mitotic gene expression program. Here, we report two mechanisms that work together to restrict SBF activity during meiotic entry: repression of the SBF-specific Swi4 subunit through LUTI-based regulation and inhibition of SBF by Whi5, a functional homolog of the Rb tumor suppressor. We find that untimely SBF activation causes downregulation of early meiotic genes and delays meiotic entry. These defects are largely driven by the SBF-target G1 cyclins, which block the interaction between the central meiotic regulator Ime1 and its cofactor Ume6. Our study provides insight into the role of SWI4LUTI in establishing the meiotic transcriptional program and demonstrates how the LUTI-based regulation is integrated into a larger regulatory network to ensure timely SBF activity.

Ferroptosis-protective membrane domains in quiescence

Quiescence is a common cellular state, required for stem cell maintenance and microorganismal survival under stress conditions or starvation. However, the mechanisms promoting quiescence maintenance remain poorly known. Plasma membrane components segregate into distinct microdomains, yet the role of this compartmentalization in quiescence remains unexplored. Here, we show that flavodoxin-like proteins (FLPs), ubiquinone reductases of the yeast eisosome membrane compartment, protect quiescent cells from lipid peroxidation and ferroptosis. Eisosomes and FLPs expand specifically in respiratory-active quiescent cells, and mutants lacking either show accelerated aging and defective quiescence maintenance and accumulate peroxidized phospholipids with monounsaturated or polyunsaturated fatty acids (PUFAs). FLPs are essential for the extramitochondrial regeneration of the lipophilic antioxidant ubiquinol. FLPs, alongside the Gpx1/2/3 glutathione peroxidases, prevent iron-driven, PUFA-dependent ferroptotic cell death. Our work describes ferroptosis-protective mechanisms in yeast and introduces plasma membrane compartmentalization as an important factor in the long-term survival of quiescent cells.

Control of meiotic entry by dual inhibition of a key mitotic transcription factor

The mitosis to meiosis transition requires dynamic changes in gene expression, but whether and how the mitotic transcriptional machinery is regulated during this transition is unknown. In budding yeast, SBF and MBF transcription factors initiate the mitotic gene expression program. Here, we report two mechanisms that work together to restrict SBF activity during meiotic entry: repression of the SBF-specific Swi4 subunit through LUTI-based regulation and inhibition of SBF by Whi5, a homolog of the Rb tumor suppressor. We find that untimely SBF activation causes downregulation of early meiotic genes and delays meiotic entry. These defects are largely driven by the SBF-target G1 cyclins, which block the interaction between the central meiotic regulator Ime1 and its cofactor Ume6. Our study provides insight into the role of SWI4LUTI in establishing the meiotic transcriptional program and demonstrates how the LUTI-based regulation is integrated into a larger regulatory network to ensure timely SBF activity.

Single-cell transcriptional uncertainty landscape of cell differentiation

Background: Single-cell studies have demonstrated the presence of significant cell-to-cell heterogeneity in gene expression. Whether such heterogeneity is only a bystander or has a functional role in the cell differentiation process is still hotly debated. Methods: In this study, we quantified and followed single-cell transcriptional uncertainty - a measure of gene transcriptional stochasticity in single cells - in 10 cell differentiation systems of varying cell lineage progressions, from single to multi-branching trajectories, using the stochastic two-state gene transcription model. Results: By visualizing the transcriptional uncertainty as a landscape over a two-dimensional representation of the single-cell gene expression data, we observed universal features in the cell differentiation trajectories that include: (i) a peak in single-cell uncertainty during transition states, and in systems with bifurcating differentiation trajectories, each branching point represents a state of high transcriptional uncertainty; (ii) a positive correlation of transcriptional uncertainty with transcriptional burst size and frequency; (iii) an increase in RNA velocity preceding the increase in the cell transcriptional uncertainty. Conclusions: Our findings suggest a possible universal mechanism during the cell differentiation process, in which stem cells engage stochastic exploratory dynamics of gene expression at the start of the cell differentiation by increasing gene transcriptional bursts, and disengage such dynamics once cells have decided on a particular terminal cell identity. Notably, the peak of single-cell transcriptional uncertainty signifies the decision-making point in the cell differentiation process.

A Systematic Review on Quiescent State Research Approaches in S. cerevisiae

Quiescence, the temporary and reversible arrest of cell growth, is a fundamental biological process. However, the lack of standardization in terms of reporting the experimental details of quiescent cells and populations can cause confusion and hinder knowledge transfer. We employ the systematic review methodology to comprehensively analyze the diversity of approaches used to study the quiescent state, focusing on all published research addressing the budding yeast Saccharomyces cerevisiae. We group research articles into those that consider all cells comprising the stationary-phase (SP) population as quiescent and those that recognize heterogeneity within the SP by distinguishing phenotypically distinct subpopulations. Furthermore, we investigate the chronological age of the quiescent populations under study and the methods used to induce the quiescent state, such as gradual starvation or abrupt environmental change. We also assess whether the strains used in research are prototrophic or auxotrophic. By combining the above features, we identify 48 possible experimental setups that can be used to study quiescence, which can be misleading when drawing general conclusions. We therefore summarize our review by proposing guidelines and recommendations pertaining to the information included in research articles. We believe that more rigorous reporting on the features of quiescent populations will facilitate knowledge transfer within and between disciplines, thereby stimulating valuable scientific discussion.

Quiescence in Saccharomyces cerevisiae

Most cells live in environments that are permissive for proliferation only a small fraction of the time. Entering quiescence enables cells to survive long periods of nondivision and reenter the cell cycle when signaled to do so. Here, we describe what is known about the molecular basis for quiescence in Saccharomyces cerevisiae, with emphasis on the progress made in the last decade. Quiescence is triggered by depletion of an essential nutrient. It begins well before nutrient exhaustion, and there is extensive crosstalk between signaling pathways to ensure that all proliferation-specific activities are stopped when any one essential nutrient is limiting. Every aspect of gene expression is modified to redirect and conserve resources. Chromatin structure and composition change on a global scale, from histone modifications to three-dimensional chromatin structure. Thousands of proteins and RNAs aggregate, forming unique structures with unique fates, and the cytoplasm transitions to a glass-like state.

N6-methyladenosine (m6A) reader Pho92 is recruited co-transcriptionally and couples translation to mRNA decay to promote meiotic fitness in yeast

N6- methyladenosine (m6A) RNA modification impacts mRNA fate primarily via reader proteins, which dictate processes in development, stress, and disease. Yet little is known about m6A function in Saccharomyces cerevisiae, which occurs solely during early meiosis. Here, we perform a multifaceted analysis of the m6A reader protein Pho92/Mrb1. Cross-linking immunoprecipitation analysis reveals that Pho92 associates with the 3'end of meiotic mRNAs in both an m6A-dependent and independent manner. Within cells, Pho92 transitions from the nucleus to the cytoplasm, and associates with translating ribosomes. In the nucleus Pho92 associates with target loci through its interaction with transcriptional elongator Paf1C. Functionally, we show that Pho92 promotes and links protein synthesis to mRNA decay. As such, the Pho92-mediated m6A-mRNA decay is contingent on active translation and the CCR4-NOT complex. We propose that the m6A reader Pho92 is loaded co-transcriptionally to facilitate protein synthesis and subsequent decay of m6A modified transcripts, and thereby promotes meiosis.

Live cell microscopy: From image to insight

Live-cell microscopy is a powerful tool that can reveal cellular behavior as well as the underlying molecular processes. A key advantage of microscopy is that by visualizing biological processes, it can provide direct insights. Nevertheless, live-cell imaging can be technically challenging and prone to artifacts. For a successful experiment, many careful decisions are required at all steps from hardware selection to downstream image analysis. Facing these questions can be particularly intimidating due to the requirement for expertise in multiple disciplines, ranging from optics, biophysics, and programming to cell biology. In this review, we aim to summarize the key points that need to be considered when setting up and analyzing a live-cell imaging experiment. While we put a particular focus on yeast, many of the concepts discussed are applicable also to other organisms. In addition, we discuss reporting and data sharing strategies that we think are critical to improve reproducibility in the field.

Enabling reactive microscopy with MicroMator

Microscopy image analysis has recently made enormous progress both in terms of accuracy and speed thanks to machine learning methods and improved computational resources. This greatly facilitates the online adaptation of microscopy experimental plans using real-time information of the observed systems and their environments. Applications in which reactiveness is needed are multifarious. Here we report MicroMator, an open and flexible software for defining and driving reactive microscopy experiments. It provides a Python software environment and an extensible set of modules that greatly facilitate the definition of events with triggers and effects interacting with the experiment. We provide a pedagogic example performing dynamic adaptation of fluorescence illumination on bacteria, and demonstrate MicroMator’s potential via two challenging case studies in yeast to single-cell control and single-cell recombination, both requiring real-time tracking and light targeting at the single-cell level.

In microscopy, applications in which reactiveness is needed are multifarious. Here the authors report MicroMator, a Python software package for reactive experiments, which they use for applications requiring real-time tracking and light-targeting at the single-cell level.

Live-cell fluorescence spectral imaging as a data science challenge

Live-cell fluorescence spectral imaging is an evolving modality of microscopy that uses specific properties of fluorophores, such as excitation or emission spectra, to detect multiple molecules and structures in intact cells. The main challenge of analyzing live-cell fluorescence spectral imaging data is the precise quantification of fluorescent molecules despite the weak signals and high noise found when imaging living cells under non-phototoxic conditions. Beyond the optimization of fluorophores and microscopy setups, quantifying multiple fluorophores requires algorithms that separate or unmix the contributions of the numerous fluorescent signals recorded at the single pixel level. This review aims to provide both the experimental scientist and the data analyst with a straightforward description of the evolution of spectral unmixing algorithms for fluorescence live-cell imaging. We show how the initial systems of linear equations used to determine the concentration of fluorophores in a pixel progressively evolved into matrix factorization, clustering, and deep learning approaches. We outline potential future trends on combining fluorescence spectral imaging with label-free detection methods, fluorescence lifetime imaging, and deep learning image analysis.

Cdc14 spatiotemporally dephosphorylates Atg13 to activate autophagy during meiotic divisions

Autophagy is a conserved eukaryotic lysosomal degradation pathway that responds to environmental and cellular cues. Autophagy is essential for the meiotic exit and sporulation in budding yeast, but the underlying molecular mechanisms remain unknown. Here, we show that autophagy is maintained during meiosis and stimulated in anaphase I and II. Cells with higher levels of autophagy complete meiosis faster, and genetically enhanced autophagy increases meiotic kinetics and sporulation efficiency. Strikingly, our data reveal that the conserved phosphatase Cdc14 regulates meiosis-specific autophagy. Cdc14 is activated in anaphase I and II, accompanying its subcellular relocation from the nucleolus to the cytoplasm, where it dephosphorylates Atg13 to stimulate Atg1 kinase activity and thus autophagy. Together, our findings reveal a meiosis-tailored mechanism that spatiotemporally controls meiotic autophagy activity to ensure meiosis progression, exit, and sporulation.

Cell cycle-independent integration of stress signals by Xbp1 promotes Non-G1/G0 quiescence entry

Cellular quiescence is a nonproliferative state required for cell survival under stress and during development. In most quiescent cells, proliferation is stopped in a reversible state of low Cdk1 kinase activity; in many organisms, however, quiescent states with high-Cdk1 activity can also be established through still uncharacterized stress or developmental mechanisms. Here, we used a microfluidics approach coupled to phenotypic classification by machine learning to identify stress pathways associated with starvation-triggered high-Cdk1 quiescent states in Saccharomyces cerevisiae. We found that low- and high-Cdk1 quiescent states shared a core of stress-associated processes, such as autophagy, protein aggregation, and mitochondrial up-regulation, but differed in the nuclear accumulation of the stress transcription factors Xbp1, Gln3, and Sfp1. The decision between low- or high-Cdk1 quiescence was controlled by cell cycle-independent accumulation of Xbp1, which acted as a time-delayed integrator of the duration of stress stimuli. Our results show how cell cycle-independent stress-activated factors promote cellular quiescence outside G1/G0.

ER-localized phosphatidylethanolamine synthase plays a conserved role in lipid droplet formation

The asymmetric distribution of phospholipids in membranes is a fundamental principle of cellular compartmentalization and organization. Phosphatidylethanolamine (PE), a nonbilayer phospholipid that contributes to organelle shape and function, is synthesized at several subcellular localizations via semiredundant pathways. Previously, we demonstrated in budding yeast that the PE synthase Psd1, which primarily operates on the mitochondrial inner membrane, is additionally targeted to the ER. While ER-localized Psd1 is required to support cellular growth in the absence of redundant pathways, its physiological function is unclear. We now demonstrate that ER-localized Psd1 sublocalizes on the ER to lipid droplet (LD) attachment sites and show it is specifically required for normal LD formation. We also find that the role of phosphatidylserine decarboxylase (PSD) enzymes in LD formation is conserved in other organisms. Thus we have identified PSD enzymes as novel regulators of LDs and demonstrate that both mitochondria and LDs in yeast are organized and shaped by the spatial positioning of a single PE synthesis enzyme.

Single-cell image analysis to explore cell-to-cell heterogeneity in isogenic populations

Single-cell image analysis provides a powerful approach for studying cell-to-cell heterogeneity, which is an important attribute of isogenic cell populations, from microbial cultures to individual cells in multicellular organisms. This phenotypic variability must be explained at a mechanistic level if biologists are to fully understand cellular function and address the genotype-to-phenotype relationship. Variability in single-cell phenotypes is obscured by bulk readouts or averaging of phenotypes from individual cells in a sample; thus, single-cell image analysis enables a higher resolution view of cellular function. Here, we consider examples of both small- and large-scale studies carried out with isogenic cell populations assessed by fluorescence microscopy, and we illustrate the advantages, challenges, and the promise of quantitative single-cell image analysis.

Saccharomyces cerevisiae Gene Expression during Fermentation of Pinot Noir Wines at an Industrially Relevant Scale

This study characterized Saccharomyces cerevisiae RC212 gene expression during Pinot noir fermentation at pilot scale (150 liters) using industry-relevant conditions. The reported gene expression patterns of RC212 are generally similar to those observed under laboratory fermentation conditions but also contain gene expression signatures related to yeast-environment interactions found in a production setting (e.g., the presence of non-Saccharomyces microorganisms).

Saccharomyces cerevisiae metabolism produces ethanol and other compounds during the fermentation of grape must into wine. Thousands of genes change expression over the course of a wine fermentation, allowing S. cerevisiae to adapt to and dominate the fermentation environment. Investigations into these gene expression patterns previously revealed genes that underlie cellular adaptation to the grape must and wine environments, involving metabolic specialization and ethanol tolerance. However, the majority of studies detailing gene expression patterns have occurred in controlled environments that may not recapitulate the biological and chemical complexity of fermentations performed at production scale. Here, an analysis of the S. cerevisiae RC212 gene expression program is presented, drawing from 40 pilot-scale fermentations (150 liters) using Pinot noir grapes from 10 California vineyards across two vintages. A core gene expression program was observed across all fermentations irrespective of vintage, similar to that of laboratory fermentations, in addition to novel gene expression patterns likely related to the presence of non-Saccharomyces microorganisms and oxygen availability during fermentation. These gene expression patterns, both common and diverse, provide insight into Saccharomyces cerevisiae biology critical to fermentation outcomes under industry-relevant conditions.

IMPORTANCE This study characterized Saccharomyces cerevisiae RC212 gene expression during Pinot noir fermentation at pilot scale (150 liters) using industry-relevant conditions. The reported gene expression patterns of RC212 are generally similar to those observed under laboratory fermentation conditions but also contain gene expression signatures related to yeast-environment interactions found in a production setting (e.g., the presence of non-Saccharomyces microorganisms). Key genes and pathways highlighted by this work remain undercharacterized, indicating the need for further research to understand the roles of these genes and their impact on industrial wine fermentation outcomes.

Cellular quiescence in budding yeast

Cellular quiescence, the temporary and reversible exit from proliferative growth, is the predominant state of all cells. However, our understanding of the biological processes and molecular mechanisms that underlie cell quiescence remains incomplete. As with the mitotic cell cycle, budding and fission yeast are preeminent model systems for studying cellular quiescence owing to their rich experimental toolboxes and the evolutionary conservation across eukaryotes of pathways and processes that control quiescence. Here, we review current knowledge of cell quiescence in budding yeast and how it pertains to cellular quiescence in other organisms, including multicellular animals. Quiescence entails large-scale remodeling of virtually every cellular process, organelle, gene expression, and metabolic state that is executed dynamically as cells undergo the initiation, maintenance, and exit from quiescence. We review these major transitions, our current understanding of their molecular bases, and highlight unresolved questions. We summarize the primary methods employed for quiescence studies in yeast and discuss their relative merits. Understanding cell quiescence has important consequences for human disease as quiescent single-celled microbes are notoriously difficult to kill and quiescent human cells play important roles in diseases such as cancer. We argue that research on cellular quiescence will be accelerated through the adoption of common criteria, and methods, for defining cell quiescence. An integrated approach to studying cell quiescence, and a focus on the behavior of individual cells, will yield new insights into the pathways and processes that underlie cell quiescence leading to a more complete understanding of the life cycle of cells. TAKE AWAY: Quiescent cells are viable cells that have reversibly exited the cell cycle Quiescence is induced in response to a variety of nutrient starvation signals Quiescence is executed dynamically through three phases: initiation, maintenance, and exit Quiescence entails large-scale remodeling of gene expression, organelles, and metabolism Single-cell approaches are required to address heterogeneity among quiescent cells.

Regulation of trehalase activity by multi-site phosphorylation and 14-3-3 interaction

Protein phosphorylation enables a rapid adjustment of cellular activities to diverse intracellular and environmental stimuli. Many phosphoproteins are targeted on more than one site, which allows the integration of multiple signals and the implementation of complex responses. However, the hierarchy and interplay between multiple phospho-sites are often unknown. Here, we study multi‐site phosphorylation using the yeast trehalase Nth1 and its activator, the 14-3-3 protein Bmh1, as a model. Nth1 is known to be phosphorylated by the metabolic kinase PKA on four serine residues and by the cell cycle kinase CDK on one residue. However, how these five phospho-sites adjust Nth1 activity remains unclear. Using a novel reporter construct, we investigated the contribution of the individual sites for the regulation of the trehalase and its 14-3-3 interactor. In contrast to the constitutively phosphorylated S20 and S83, the weaker sites S21 and S60 are only phosphorylated by increased PKA activity. For binding Bmh1, S83 functions as the high‐affinity “gatekeeper” site, but successful binding of the Bmh1 dimer and thus Nth1 activation requires S60 as a secondary site. Under nutrient-poor conditions with low PKA activity, S60 is not efficiently phosphorylated and the cell cycle dependent phosphorylation of S66 by Cdk1 contributes to Nth1 activity, likely by providing an alternative Bmh1 binding site. Additionally, the PKA sites S20 and S21 modulate the dephosphorylation of Nth1 on downstream Bmh1 sites. In summary, our results expand our molecular understanding of Nth1 regulation and provide a new aspect of the interaction of 14-3-3 proteins with their targets.

Nutrient Signaling, Stress Response, and Inter-organelle Communication Are Non-canonical Determinants of Cell Fate

Isogenic cells manifest distinct cellular fates for a single stress; however, the nongenetic mechanisms driving such fates remain poorly understood. Here, we implement a robust multi-channel live-cell imaging approach to uncover noncanonical factors governing cell fate. We show that in response to acute glucose removal (AGR), budding yeast undergoes distinct fates, becoming either quiescent or senescent. Senescent cells fail to resume mitotic cycles following glucose replenishment but remain responsive to nutrient stimuli. Whereas quiescent cells manifest starvation-induced adaptation, senescent cells display perturbed endomembrane trafficking and defective nucleus-vacuole junction (NVJ) expansion. Surprisingly, senescence occurs even in the absence of lipid droplets. Importantly, we identify the nutrient-sensing kinase Rim15 as a key biomarker predicting cell fates before AGR stress. We propose that isogenic yeast challenged with acute nutrient shortage contains determinants influencing post-stress fate and demonstrate that specific nutrient signaling, stress response, trafficking, and inter-organelle biomarkers are early indicators for long-term fate outcomes.

Winter is coming: Regulation of cellular metabolism by enzyme polymerization in dormancy and disease

Metabolism feeds growth. Accordingly, metabolism is regulated by nutrient-sensing pathways that converge growth promoting signals into biosynthesis by regulating the activity of metabolic enzymes. When the environment does not support growth, organisms invest in survival. For cells, this entails transitioning into a dormant, quiescent state (G0). In dormancy, the activity of biosynthetic pathways is dampened, and catabolic metabolism and stress tolerance pathways are activated. Recent work in yeast has demonstrated that dormancy is associated with alterations in the physicochemical properties of the cytoplasm, including changes in pH, viscosity and macromolecular crowding. Accompanying these changes, numerous metabolic enzymes transition from soluble to polymerized assemblies. These large-scale self-assemblies are dynamic and depolymerize when cells resume growth. Here we review how enzyme polymerization enables metabolic plasticity by tuning carbohydrate, nucleic acid, amino acid and lipid metabolic pathways, with particular focus on its potential adaptive value in cellular dormancy.

Regulated repression governs the cell fate promoter controlling yeast meiosis

Intrinsic signals and external cues from the environment drive cell fate decisions. In budding yeast, the decision to enter meiosis is controlled by nutrient and mating-type signals that regulate expression of the master transcription factor for meiotic entry, IME1. How nutrient signals control IME1 expression remains poorly understood. Here, we show that IME1 transcription is regulated by multiple sequence-specific transcription factors (TFs) that mediate association of Tup1-Cyc8 co-repressor to its promoter. We find that at least eight TFs bind the IME1 promoter when nutrients are ample. Remarkably, association of these TFs is highly regulated by different nutrient cues. Mutant cells lacking three TFs (Sok2/Phd1/Yap6) displayed reduced Tup1-Cyc8 association, increased IME1 expression, and earlier onset of meiosis. Our data demonstrate that the promoter of a master regulator is primed for rapid activation while repression by multiple TFs mediating Tup1-Cyc8 recruitment dictates the fate decision to enter meiosis.

Functional interrelationships between carbohydrate and lipid storage, and mitochondrial activity during sporulation in Saccharomyces cerevisiae

In Saccharomyces cerevisiae under conditions of nutrient stress, meiosis precedes the formation of spores. Although the molecular mechanisms that regulate meiosis, such as meiotic recombination and nuclear divisions, have been extensively studied, the metabolic factors that determine the efficiency of sporulation are less understood. Here, we have directly assessed the relationship between metabolic stores and sporulation in S. cerevisiae by genetically disrupting the synthetic pathways for the carbohydrate stores, glycogen (gsy1/2Δ cells), trehalose (tps1Δ cells), or both (gsy1/2Δ and tps1Δ cells). We show that storage carbohydrate-deficient strains are highly inefficient in sporulation. Although glycogen and trehalose stores can partially compensate for each other, they have differential effects on sporulation rate and spore number. Interestingly, deletion of the G₁ cyclin, CLN3, which resulted in an increase in cell size, mitochondria and lipid stores, partially rescued meiosis progression and spore ascus formation but not spore number in storage carbohydrate-deficient strains. Sporulation efficiency in the carbohydrate-deficient strain exhibited a greater dependency on mitochondrial activity and lipid stores than wild-type yeast. Taken together, our results provide new insights into the complex crosstalk between metabolic factors that support gametogenesis.

Forecasting cell fate during antibiotic exposure using stochastic gene expression

Antibiotic killing does not occur at a single, precise time for all cells within a population. Variability in time to death can be caused by stochastic expression of genes, resulting in differences in endogenous stress-resistance levels between individual cells in a population. Here we investigate whether single-cell differences in gene expression prior to antibiotic exposure are related to cell survival times after antibiotic exposure for a range of genes of diverse function. We quantified the time to death of single cells under antibiotic exposure in combination with expression of reporters. For some reporters, including genes involved in stress response and cellular processes like metabolism, the time to cell death had a strong relationship with the initial expression level of the genes. Our results highlight the single-cell level non-uniformity of antibiotic killing and also provide examples of key genes where cell-to-cell variation in expression is strongly linked to extended durations of antibiotic survival.

A fully-automated, robust, and versatile algorithm for long-term budding yeast segmentation and tracking

Live cell time-lapse microscopy, a widely-used technique to study gene expression and protein dynamics in single cells, relies on segmentation and tracking of individual cells for data generation. The potential of the data that can be extracted from this technique is limited by the inability to accurately segment a large number of cells from such microscopy images and track them over long periods of time. Existing segmentation and tracking algorithms either require additional dyes or markers specific to segmentation or they are highly specific to one imaging condition and cell morphology and/or necessitate manual correction. Here we introduce a fully automated, fast and robust segmentation and tracking algorithm for budding yeast that overcomes these limitations. Full automatization is achieved through a novel automated seeding method, which first generates coarse seeds, then automatically fine-tunes cell boundaries using these seeds and automatically corrects segmentation mistakes. Our algorithm can accurately segment and track individual yeast cells without any specific dye or biomarker. Moreover, we show how existing channels devoted to a biological process of interest can be used to improve the segmentation. The algorithm is versatile in that it accurately segments not only cycling cells with smooth elliptical shapes, but also cells with arbitrary morphologies (e.g. sporulating and pheromone treated cells). In addition, the algorithm is independent of the specific imaging method (bright-field/phase) and objective used (40X/63X/100X). We validate our algorithm's performance on 9 cases each entailing a different imaging condition, objective magnification and/or cell morphology. Taken together, our algorithm presents a powerful segmentation and tracking tool that can be adapted to numerous budding yeast single-cell studies.

Quiescence, an individual journey

Quiescence is operationally characterized as a temporary and reversible proliferation arrest. There are many preconceived ideas about quiescence, quiescent cells being generally viewed as insignificant sleeping G1 cells. In fact, quiescence is central for organism physiology and its dysregulation involved in many pathologies. The quiescent state encompasses very diverse cellular situations depending on the cell type and its environment. This diversity challenges not only quiescence uniformity but also the universality of the molecular mechanisms beyond quiescence regulation. In this mini-perspective, we discuss recent advances in the concept of quiescence, and illustrate that this multifaceted cellular state is gaining increasing attention in many fields of biology.

The cell biology of quiescent yeast – a diversity of individual scenarios

Most cells, from unicellular to complex organisms, spend part of their life in quiescence, a temporary non-proliferating state. Although central for a variety of essential processes including tissue homeostasis, development and aging, quiescence is poorly understood. In fact, quiescence encompasses various cellular situations depending on the cell type and the environmental niche. Quiescent cell properties also evolve with time, adding another layer of complexity. Studying quiescence is, above all, limited by the fact that a quiescent cell can be recognized as such only after having proved that it is capable of re-proliferating. Recent cellular biology studies in yeast have reported the relocalization of hundreds of proteins and the reorganization of several cellular machineries upon proliferation cessation. These works have revealed that quiescent cells can display various properties, shedding light on a plethora of individual behaviors. The deciphering of the molecular mechanisms beyond these reorganizations, together with the understanding of their cellular functions, have begun to provide insights into the physiology of quiescent cells. In this Review, we discuss recent findings and emerging concepts in Saccharomyces cerevisiae quiescent cell biology.