An algorithm to automate yeast segmentation and tracking

Automated quantification of vacuole fusion and lipophagy in Saccharomyces cerevisiae from fluorescence and cryo-soft X-ray microscopy data using deep learning

During starvation in the yeast Saccharomyces cerevisiae vacuolar vesicles fuse and lipid droplets (LDs) can become internalized into the vacuole in an autophagic process named lipophagy. There is a lack of tools to quantitatively assess starvation-induced vacuole fusion and lipophagy in intact cells with high resolution and throughput. Here, we combine soft X-ray tomography (SXT) with fluorescence microscopy and use a deep-learning computational approach to visualize and quantify these processes in yeast. We focus on yeast homologs of mammalian NPC1 (NPC intracellular cholesterol transporter 1; Ncr1 in yeast) and NPC2 proteins, whose dysfunction leads to Niemann Pick type C (NPC) disease in humans. We developed a convolutional neural network (CNN) model which classifies fully fused versus partially fused vacuoles based on fluorescence images of stained cells. This CNN, named Deep Yeast Fusion Network (DYFNet), revealed that cells lacking Ncr1 (ncr1∆ cells) or Npc2 (npc2∆ cells) have a reduced capacity for vacuole fusion. Using a second CNN model, we implemented a pipeline named LipoSeg to perform automated instance segmentation of LDs and vacuoles from high-resolution reconstructions of X-ray tomograms. From that, we obtained 3D renderings of LDs inside and outside of the vacuole in a fully automated manner and additionally measured droplet volume, number, and distribution. We find that ncr1∆ and npc2∆ cells could ingest LDs into vacuoles normally but showed compromised degradation of LDs and accumulation of lipid vesicles inside vacuoles. Our new method is versatile and allows for analysis of vacuole fusion, droplet size and lipophagy in intact cells.Abbreviations: BODIPY493/503: 4,4-difluoro-1,3,5,7,8-pentamethyl-4-bora-3a,4a-diaza-s-Indacene; BPS: bathophenanthrolinedisulfonic acid disodium salt hydrate; CNN: convolutional neural network; DHE; dehydroergosterol; npc2∆, yeast deficient in Npc2; DSC, Dice similarity coefficient; EM, electron microscopy; EVs, extracellular vesicles; FIB-SEM, focused ion beam milling-scanning electron microscopy; FM 4-64, N-(3-triethylammoniumpropyl)-4-(6-[4-{diethylamino} phenyl] hexatrienyl)-pyridinium dibromide; LDs, lipid droplets; Ncr1, yeast homolog of human NPC1 protein; ncr1∆, yeast deficient in Ncr1; NPC, Niemann Pick type C; NPC2, Niemann Pick type C homolog; OD600, optical density at 600 nm; ReLU, rectifier linear unit; PPV, positive predictive value; NPV, negative predictive value; MCC, Matthews correlation coefficient; SXT, soft X-ray tomography; UV, ultraviolet; YPD, yeast extract peptone dextrose.

When yeast cells change their mind: cell cycle "Start" is reversible under starvation

Eukaryotic cells decide in late G1 phase of the cell cycle whether to commit to another round of division. This point of cell cycle commitment is termed "Restriction Point" in mammals and "Start" in the budding yeast Saccharomyces cerevisiae. At Start, yeast cells integrate multiple signals such as pheromones and nutrients, and will not pass Start if nutrients are lacking. However, how cells respond to nutrient depletion after the Start decision remains poorly understood. Here, we analyze how post-Start cells respond to nutrient depletion, by monitoring Whi5, the cell cycle inhibitor whose export from the nucleus determines Start. Surprisingly, we find that cells that have passed Start can re-import Whi5 into the nucleus. In these cells, the positive feedback loop activating G1/S transcription is interrupted, and the Whi5 repressor re-binds DNA. Cells which re-import Whi5 become again sensitive to mating pheromone, like pre-Start cells, and CDK activation can occur a second time upon replenishment of nutrients. These results demonstrate that upon starvation, the commitment decision at Start can be reversed. We therefore propose that cell cycle commitment in yeast is a multi-step process, similar to what has been suggested for mammalian cells.

DarT-mediated mtDNA damage induces dynamic reorganization and selective segregation of mitochondria

Mitochondria are dynamic organelles that play essential roles in cell growth and survival. Processes of fission and fusion are critical for the distribution, segregation, and maintenance of mitochondria and their genomes (mtDNA). While recent work has revealed the significance of mitochondrial organization for mtDNA maintenance, the impact of mtDNA perturbations on mitochondrial dynamics remains less understood. Here, we develop a tool to induce mitochondria-specific DNA damage using a mitochondrial-targeted base modifying bacterial toxin, DarT. Following damage, we observe dynamic reorganization of mitochondrial networks, likely driven by mitochondrial dysfunction. Changes in the organization are associated with the loss of mtDNA, independent of mitophagy. Unexpectedly, perturbation to exonuclease function of mtDNA replicative polymerase, Mip1, results in rapid loss of mtDNA. Our data suggest that, under damage, partitioning of defective mtDNA and organelle are de-coupled, with emphasis on mitochondrial segregation independent of its DNA. Together, our work underscores the importance of genome maintenance on mitochondrial function, which can act as a modulator of organelle organization and segregation.

A synthetic biology approach reveals diverse and dynamic CDK response profiles via multisite phosphorylation of NLS-NES modules

The complexity of multisite phosphorylation mechanisms in regulating nuclear localization signals (NLSs) and nuclear export signals (NESs) is not understood, and its potential has not been used in synthetic biology. The nucleocytoplasmic shuttling of many proteins is regulated by cyclin-dependent kinases (CDKs) that rely on multisite phosphorylation patterns and short linear motifs (SLiMs) to dynamically control proteins in the cell cycle. We studied the role of motif patterns in nucleocytoplasmic shuttling using sensors based on the CDK targets Dna2, Psy4, and Mcm2/3 of Saccharomyces cerevisiae. We designed multisite phosphorylation modules by rearranging phosphorylation sites, cyclin-specific SLiMs, phospho-priming, phosphatase specificity, and NLS/NES phospho-regulation and obtained very different substrate localization dynamics. These included ultrasensitive responses with and without a delay, graded responses, and different homeostatic plateaus. Thus, CDK can do much more than trigger sequential switches during the cell cycle as it can drive complex patterns of protein localization and activity by using multisite phosphorylation networks.

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

Supplementary Information

The online version contains supplementary material available at 10.1186/s12915-022-01372-6.

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.

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.

Cdc6 is sequentially regulated by PP2A-Cdc55, Cdc14, and Sic1 for origin licensing in S. cerevisiae

Cdc6, a subunit of the pre-replicative complex (pre-RC), contains multiple regulatory cyclin-dependent kinase (Cdk1) consensus sites, SP or TP motifs. In Saccharomyces cerevisiae, Cdk1 phosphorylates Cdc6-T7 to recruit Cks1, the Cdk1 phospho-adaptor in S phase, for subsequent multisite phosphorylation and protein degradation. Cdc6 accumulates in mitosis and is tightly bound by Clb2 through N-terminal phosphorylation in order to prevent premature origin licensing and degradation. It has been extensively studied how Cdc6 phosphorylation is regulated by the cyclin-Cdk1 complex. However, a detailed mechanism on how Cdc6 phosphorylation is reversed by phosphatases has not been elucidated. Here, we show that PP2ACdc55 dephosphorylates Cdc6 N-terminal sites to release Clb2. Cdc14 dephosphorylates the C-terminal phospho-degron, leading to Cdc6 stabilization in mitosis. In addition, Cdk1 inhibitor Sic1 releases Clb2·Cdk1·Cks1 from Cdc6 to load Mcm2-7 on the chromatin upon mitotic exit. Thus, pre-RC assembly and origin licensing are promoted by phosphatases through the attenuation of distinct Cdk1-dependent Cdc6 inhibitory mechanisms.

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.

Transcriptional and chromatin-based partitioning mechanisms uncouple protein scaling from cell size

Biosynthesis scales with cell size such that protein concentrations generally remain constant as cells grow. As an exception, synthesis of the cell-cycle inhibitor Whi5 "sub-scales" with cell size so that its concentration is lower in larger cells to promote cell-cycle entry. Here, we find that transcriptional control uncouples Whi5 synthesis from cell size, and we identify histones as the major class of sub-scaling transcripts besides WHI5 by screening for similar genes. Histone synthesis is thereby matched to genome content rather than cell size. Such sub-scaling proteins are challenged by asymmetric cell division because proteins are typically partitioned in proportion to newborn cell volume. To avoid this fate, Whi5 uses chromatin-binding to partition similar protein amounts to each newborn cell regardless of cell size. Disrupting both Whi5 synthesis and chromatin-based partitioning weakens G1 size control. Thus, specific transcriptional and partitioning mechanisms determine protein sub-scaling to control cell size.

Docking to a Basic Helix Promotes Specific Phosphorylation by G1-Cdk1

Cyclins are the activators of cyclin-dependent kinase (CDK) complex, but they also act as docking scaffolds for different short linear motifs (SLiMs) in CDK substrates and inhibitors. According to the unified model of CDK function, the cell cycle is coordinated by CDK both via general CDK activity thresholds and cyclin-specific substrate docking. Recently, it was found that the G1-cyclins of S. cerevisiae have a specific function in promoting polarization and growth of the buds, making the G1 cyclins essential for cell survival. Thus, while a uniform CDK specificity of a single cyclin can be sufficient to drive the cell cycle in some cells, such as in fission yeast, cyclin specificity can be essential in other organisms. However, the known G1-CDK specific LP docking motif, was not responsible for this essential function, indicating that G1-CDKs use yet other unknown docking mechanisms. Here we report a discovery of a G1 cyclin-specific (Cln1,2) lysine-arginine-rich helical docking motif (the K/R motif) in G1-CDK targets involved in the mating pathway (Ste7), transcription (Xbp1), bud morphogenesis (Bud2) and spindle pole body (Spc29, Spc42, Spc110, Sli15) function of S. cerevisiae. We also show that the docking efficiency of K/R motif can be regulated by basophilic kinases such as protein kinase A. Our results further widen the list of cyclin specificity mechanisms and may explain the recently demonstrated unique essential function of G1 cyclins in budding yeast.

Transcription coordinates histone amounts and genome content

Biochemical reactions typically depend on the concentrations of the molecules involved, and cell survival therefore critically depends on the concentration of proteins. To maintain constant protein concentrations during cell growth, global mRNA and protein synthesis rates are tightly linked to cell volume. While such regulation is appropriate for most proteins, certain cellular structures do not scale with cell volume. The most striking example of this is the genomic DNA, which doubles during the cell cycle and increases with ploidy, but is independent of cell volume. Here, we show that the amount of histone proteins is coupled to the DNA content, even though mRNA and protein synthesis globally increase with cell volume. As a consequence, and in contrast to the global trend, histone concentrations decrease with cell volume but increase with ploidy. We find that this distinct coordination of histone homeostasis and genome content is already achieved at the transcript level, and is an intrinsic property of histone promoters that does not require direct feedback mechanisms. Mathematical modeling and histone promoter truncations reveal a simple and generalizable mechanism to control the cell volume- and ploidy-dependence of a given gene through the balance of the initiation and elongation rates.

Proline-Rich Motifs Control G2-CDK Target Phosphorylation and Priming an Anchoring Protein for Polo Kinase Localization

The hydrophobic patch (hp), a docking pocket on cyclins of CDKs (cyclin-dependent kinases), has been thought to accommodate a single short linear motif (SLiM), the "RxL or Cy" docking motif. Here we show that hp can bind different motifs with high specificity. We identify a PxxPxF motif that is necessary for G2-cyclin Clb3 function in S. cerevisiae, and that mediates Clb3-Cdk1 phosphorylation of Ypr174c (proposed name: Cdc5 SPB anchor-Csa1) to regulate the localization of Polo kinase Cdc5. Similar motifs exist in other Clb3-Cdk1 targets. Our work completes the set of docking specificities for the four major cyclins: LP, RxL, PxxPxF, and LxF motifs for G1-, S-, G2-, and M-phase cyclins, respectively. Further, we show that variations in motifs can change their specificity for human cyclins. This diversity could provide complexity for the encoding of CDK thresholds to achieve ordered cell-cycle phosphorylation.

Glucose restriction drives spatial reorganization of mevalonate metabolism

Eukaryotes compartmentalize metabolic pathways into sub-cellular domains, but the role of inter-organelle contacts in organizing metabolic reactions remains poorly understood. Here, we show that in response to acute glucose restriction (AGR) yeast undergo metabolic remodeling of their mevalonate pathway that is spatially coordinated at nucleus-vacuole junctions (NVJs). The NVJ serves as a metabolic platform by selectively retaining HMG-CoA Reductases (HMGCRs), driving mevalonate pathway flux in an Upc2-dependent manner. Both spatial retention of HMGCRs and increased mevalonate pathway flux during AGR is dependent on NVJ tether Nvj1. Furthermore, we demonstrate that HMGCRs associate into high-molecular-weight assemblies during AGR in an Nvj1-dependent manner. Loss of Nvj1-mediated HMGCR partitioning can be bypassed by artificially multimerizing HMGCRs, indicating NVJ compartmentalization enhances mevalonate pathway flux by promoting the association of HMGCRs in high molecular weight assemblies. Loss of HMGCR compartmentalization perturbs yeast growth following glucose starvation, indicating it promotes adaptive metabolic remodeling. Collectively, we propose a non-canonical mechanism regulating mevalonate metabolism via the spatial compartmentalization of rate-limiting HMGCR enzymes at an inter-organelle contact site.

YeastNet: Deep-Learning-Enabled Accurate Segmentation of Budding Yeast Cells in Bright-Field Microscopy

Accurate and efficient segmentation of live-cell images is critical in maximizing data extraction and knowledge generation from high-throughput biology experiments. Despite recent development of deep-learning tools for biomedical imaging applications, great demand for automated segmentation tools for high-resolution live-cell microscopy images remains in order to accelerate the analysis. YeastNet dramatically improves the performance of the non-trainable classic algorithm, and performs considerably better than the current state-of-the-art yeast-cell segmentation tools. We have designed and trained a U-Net convolutional network (named YeastNet) to conduct semantic segmentation on bright-field microscopy images and generate segmentation masks for cell labeling and tracking. YeastNet enables accurate automatic segmentation and tracking of yeast cells in biomedical applications. YeastNet is freely provided with model weights as a Python package on GitHub.

Stress-induced growth rate reduction restricts metabolic resource utilization to modulate osmo-adaptation time

A near-constant feature of stress responses is a downregulation or arrest of the cell cycle, resulting in transient growth slowdown. To investigate the role of growth slowdown in the hyperosmotic shock response of S. cerevisiae, we perturbed the G1/S checkpoint protein Sic1 to enable osmo-stress response activation with diminished growth slowdown. We document that in this mutant, adaptation to stress is accelerated rather than delayed. This accelerated recovery of the mutant proceeds by liquidation of internal glycogen stores, which are then shunted into the osmo-shock response. Therefore, osmo-adaptation in wild-type cells is delayed because growth slowdown prevents full accessibility to cellular glycogen stores. However, faster adaptation comes at the cost of acute sensitivity to subsequent osmo-stresses. We suggest that stress-induced growth slowdown acts as an arbiter to regulate the resources devoted to osmo-shock, balancing short-term adaptation with long-term robustness.

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.

A new linear cyclin docking motif that mediates exclusively S-phase CDK-specific signaling

Cyclin‐dependent kinases (CDKs), the master regulators of cell division, are activated by different cyclins at different cell cycle stages. In addition to being activators of CDKs, cyclins recognize various linear motifs to target CDK activity to specific proteins. We uncovered a cyclin docking motif, NLxxxL, that contributes to phosphorylation‐dependent degradation of the CDK inhibitor Far1 at the G1/S stage in the yeast Saccharomyces cerevisiae. This motif is recognized exclusively by S‐phase CDK (S‐CDK) Clb5/6‐Cdc28 and is considerably more potent than the conventional RxL docking motif. The NLxxxL and RxL motifs were found to overlap in some target proteins, suggesting that cyclin docking motifs can evolve to switch from one to another for fine‐tuning of cell cycle events. Using time‐lapse fluorescence microscopy, we show how different docking connections temporally control phosphorylation‐driven target degradation. This also revealed a differential function of the phosphoadaptor protein Cks1, as Cks1 docking potentiated degron phosphorylation of RxL‐containing but not of NLxxxL‐containing substrates. The NLxxxL motif was found to govern S‐cyclin‐specificity in multiple yeast CDK targets including Fin1, Lif1, and Slx4, suggesting its wider importance.

A convolutional neural network segments yeast microscopy images with high accuracy

The identification of cell borders ('segmentation') in microscopy images constitutes a bottleneck for large-scale experiments. For the model organism Saccharomyces cerevisiae, current segmentation methods face challenges when cells bud, crowd, or exhibit irregular features. We present a convolutional neural network (CNN) named YeaZ, the underlying training set of high-quality segmented yeast images (>10 000 cells) including mutants, stressed cells, and time courses, as well as a graphical user interface and a web application ( www.quantsysbio.com/data-and-software ) to efficiently employ, test, and expand the system. A key feature is a cell-cell boundary test which avoids the need for fluorescent markers. Our CNN is highly accurate, including for buds, and outperforms existing methods on benchmark images, indicating it transfers well to other conditions. To demonstrate how efficient large-scale image processing uncovers new biology, we analyze the geometries of ≈2200 wild-type and cyclin mutant cells and find that morphogenesis control occurs unexpectedly early and gradually.

Optogenetic Control Reveals Differential Promoter Interpretation of Transcription Factor Nuclear Translocation Dynamics

Gene expression is thought to be affected not only by the concentration of transcription factors (TFs) but also the dynamics of their nuclear translocation. Testing this hypothesis requires direct control of TF dynamics. Here, we engineer CLASP, an optogenetic tool for rapid and tunable translocation of a TF of interest. Using CLASP fused to Crz1, we observe that, for the same integrated concentration of nuclear TF over time, changing input dynamics changes target gene expression: pulsatile inputs yield higher expression than continuous inputs, or vice versa, depending on the target gene. Computational modeling reveals that a dose-response saturating at low TF input can yield higher gene expression for pulsatile versus continuous input, and that multi-state promoter activation can yield the opposite behavior. Our integrated tool development and modeling approach characterize promoter responses to Crz1 nuclear translocation dynamics, extracting quantitative features that may help explain the differential expression of target genes.

CLASP is a modular optogenetic strategy to control the nuclear localization of transcription factors (TFs) and elicit gene expression from their cognate promoters. CLASP control of Crz1 nuclear localization, coupled with computational modeling, revealed how promoters can differentially decode dynamic transcription factor signals. The integrated strategy of CLASP development and modeling presents a generalized approach to causally investigate the transcriptional consequences of dynamic TF nuclear shuttling.

Cell size sets the diameter of the budding yeast contractile ring

The formation and maintenance of subcellular structures and organelles with a well-defined size is a key requirement for cell function, yet our understanding of the underlying size control mechanisms is limited. While budding yeast cell polarization and subsequent assembly of a septin ring at the site of bud formation has been successfully used as a model for biological self-assembly processes, the mechanisms that set the size of the septin ring at the bud neck are unknown. Here, we use live-cell imaging and genetic manipulation of cell volume to show that the septin ring diameter increases with cell volume. This cell-volume-dependence largely accounts for modulations of ring size due to changes in ploidy and genetic manipulation of cell polarization. Our findings suggest that the ring diameter is set through the dynamic interplay of septin recruitment and Cdc42 polarization, establishing it as a model for size homeostasis of self-assembling organelles.

Budding yeast cell polarization is known to self-assemble, but it is still not clear what controls the size of the resulting septin ring. Here the authors show that the septin ring diameter is set by cell volume, ensuring that larger cells have larger rings.

A processive phosphorylation circuit with multiple kinase inputs and mutually diversional routes controls G1/S decision

Studies on multisite phosphorylation networks of cyclin-dependent kinase (CDK) targets have opened a new level of signaling complexity by revealing signal processing routes encoded into disordered proteins. A model target, the CDK inhibitor Sic1, contains linear phosphorylation motifs, docking sites, and phosphodegrons to empower an N-to-C terminally directed phosphorylation process. Here, we uncover a signal processing mechanism involving multi-step competition between mutually diversional phosphorylation routes within the S-CDK-Sic1 inhibitory complex. Intracomplex phosphorylation plays a direct role in controlling Sic1 degradation, and provides a mechanism to sequentially integrate both the G1- and S-CDK activities while keeping S-CDK inhibited towards other targets. The competing phosphorylation routes prevent premature Sic1 degradation and demonstrate how integration of MAPK from the pheromone pathway allows one to tune the competition of alternative phosphorylation paths. The mutually diversional phosphorylation circuits may be a general way for processing multiple kinase signals to coordinate cellular decisions in eukaryotes.

The decision of whether and when a cell divides is tightly controlled. Here, the authors show in yeast that there is a multi-step competition between different phosphorylation states and sites in the S phase CDK-Sic1 complex, which controls Sic1 degradation and coordinates the precise timing of the G1/S transition.

Multisite phosphorylation code of CDK

The quantitative model of cyclin-dependent kinase (CDK) function states that cyclins temporally order cell cycle events at different CDK activity levels, or thresholds. The model lacks a mechanistic explanation, as it is not understood how different thresholds are encoded into substrates. We show that a multisite phosphorylation code governs the phosphorylation of CDK targets and that phosphorylation clusters act as timing tags that trigger specific events at different CDK thresholds. Using phospho-degradable CDK threshold sensors with rationally encoded phosphorylation patterns, we were able to predictably program thresholds over the entire range of the Saccharomyces cerevisiae cell cycle. We defined three levels of CDK multisite phosphorylation encoding: (i) Ser-Thr swapping in phosphorylation sites, (ii) patterning of phosphorylation sites, and (iii) cyclin-specific docking combined with modulation of CDK activity. Thus, CDK can signal via hundreds of differentially encoded targets at precise times to provide a temporally ordered phosphorylation pattern required for cell division.

Cyclin-Specific Docking Mechanisms Reveal the Complexity of M-CDK Function in the Cell Cycle

Cyclin-dependent kinases (CDKs) coordinate hundreds of molecular events during the cell cycle. Multiple cyclins are involved, but the global role of cyclin-specific phosphorylation has remained unsolved. We uncovered a cyclin docking motif, LxF, that mediates binding of replication factor Cdc6 to mitotic cyclin. This interaction leads to phospho-adaptor Cks1-mediated inhibition of M-CDK to facilitate Cdc6 accumulation and sequestration in mitosis. The LxF motif and Cks1 also mediate the mutual inhibition between M-CDK and the tyrosine kinase Swe1. Additionally, the LxF motif is critical for targeting M-CDK to phosphorylate several mitotic regulators; for example, Spo12 is targeted via LxF to release the phosphatase Cdc14. The results complete the full set of G1, S, and M-CDK docking mechanisms and outline the unified role of cyclin specificity and CDK activity thresholds. Cooperation of cyclin and Cks1 docking creates a variety of CDK thresholds and switching orders, including combinations of last in, first out (LIFO) and first in, first out (FIFO) ordering.

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Mitotic cyclin Clb2 binds a specific linear motif, LxF, in targets or inhibitors

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LxF interaction enhances mitotic CDK substrate phosphorylation

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Phospho-adaptor Cks1 and the LxF docking mediate CDK inhibition by Cdc6 and Swe1

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Cyclin-specific targeting enables finetuning of CDK function

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.

TAMMiCol: Tool for analysis of the morphology of microbial colonies

Many microbes are studied by examining colony morphology via two-dimensional top-down images. The quantification of such images typically requires each pixel to be labelled as belonging to either the colony or background, producing a binary image. While this may be achieved manually for a single colony, this process is infeasible for large datasets containing thousands of images. The software Tool for Analysis of the Morphology of Microbial Colonies (TAMMiCol) has been developed to efficiently and automatically convert colony images to binary. TAMMiCol exploits the structure of the images to choose a thresholding tolerance and produce a binary image of the colony. The images produced are shown to compare favourably with images processed manually, while TAMMiCol is shown to outperform standard segmentation methods. Multiple images may be imported together for batch processing, while the binary data may be exported as a CSV or MATLAB MAT file for quantification, or analysed using statistics built into the software. Using the in-built statistics, it is found that images produced by TAMMiCol yield values close to those computed from binary images processed manually. Analysis of a new large dataset using TAMMiCol shows that colonies of Saccharomyces cerevisiae reach a maximum level of filamentous growth once the concentration of ammonium sulfate is reduced to 200 μM. TAMMiCol is accessed through a graphical user interface, making it easy to use for those without specialist knowledge of image processing, statistical methods or coding.

Many microbes are studied by examining the colony morphology via a two-dimensional top-down image. In order to quantify such images, we typically need to label each pixel as belonging either to the colony or the background, creating a binary image. This task is laborious when performed manually and proves infeasible for large datasets. To overcome this, we have developed the software Tool for Analysis of the Morphology of Microbial Colonies (TAMMiCol), which automatically and efficiently converts colony images to binary. Multiple images may be imported and processed simultaneously, and TAMMiCol exploits the structure of the images to identify an appropriate threshold for the binary conversion of each image. The images produced by TAMMiCol, which take around 20 seconds each to process, compare favourably with images processed manually, which take anywhere up to 15 minutes, while TAMMiCol outperforms several standard image segmentation methods. After processing, the images may be exported as a CSV or MATLAB MAT file for further analysis, or may be quantified by TAMMiCol using the in-built statistics. Using TAMMiCol, we have found that colonies of S. cerevisiae reach a maximum level of filamentous growth once the concentration of ammonium sulfate is reduced to 200 μM.

Morphologically constrained and data informed cell segmentation of budding yeast

Motivation

Although high-content image cytometry is becoming increasingly routine, processing the large amount of data acquired during time-lapse experiments remains a challenge. The majority of approaches for automated single-cell segmentation focus on flat, uniform fields of view covered with a single layer of cells. In the increasingly popular microfluidic devices that trap individual cells for long term imaging, these conditions are not met. Consequently, most techniques for segmentation perform poorly. Although potentially constraining the generalizability of software, incorporating information about the microfluidic features, flow of media and the morphology of the cells can substantially improve performance.

Results

Here we present DISCO (Data Informed Segmentation of Cell Objects), a framework for using the physical constraints imposed by microfluidic traps, the shape based morphological constraints of budding yeast and temporal information about cell growth and motion to allow tracking and segmentation of cells in microfluidic devices. Using manually curated datasets, we demonstrate substantial improvements in both tracking and segmentation when compared with existing software.

Availability and implementation

The MATLAB code for the algorithm and for measuring performance is available at https://github.com/pswain/segmentation-software and the test images and the curated ground-truth results used for comparing the algorithms are available at http://datashare.is.ed.ac.uk/handle/10283/2002.

Contact

mcrane2@uw.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

Integration of Multiple Metabolic Signals Determines Cell Fate Prior to Commitment

Cell-fate decisions are central to the survival and development of both uni- and multicellular organisms. It remains unclear when and to what degree cells can decide on future fates prior to commitment. This uncertainty stems from experimental and theoretical limitations in measuring and integrating multiple signals at the single-cell level during a decision process. Here, we combine six-color live-cell imaging with the Bayesian method of statistical evidence to study the meiosis/quiescence decision in budding yeast. Integration of multiple upstream metabolic signals predicts individual cell fates with high probability well before commitment. Cells "decide" their fates before birth, well before the activation of pathways characteristic of downstream cell fates. This decision, which remains stable through several cell cycles, occurs when multiple metabolic parameters simultaneously cross cell-fate-specific thresholds. Taken together, our results show that cells can decide their future fates long before commitment mechanisms are activated.

A Kinect-Based Segmentation of Touching-Pigs for Real-Time Monitoring

Segmenting touching-pigs in real-time is an important issue for surveillance cameras intended for the 24-h tracking of individual pigs. However, methods to do so have not yet been reported. We particularly focus on the segmentation of touching-pigs in a crowded pig room with low-contrast images obtained using a Kinect depth sensor. We reduce the execution time by combining object detection techniques based on a convolutional neural network (CNN) with image processing techniques instead of applying time-consuming operations, such as optimization-based segmentation. We first apply the fastest CNN-based object detection technique (i.e., You Only Look Once, YOLO) to solve the separation problem for touching-pigs. If the quality of the YOLO output is not satisfied, then we try to find the possible boundary line between the touching-pigs by analyzing the shape. Our experimental results show that this method is effective to separate touching-pigs in terms of both accuracy (i.e., 91.96%) and execution time (i.e., real-time execution), even with low-contrast images obtained using a Kinect depth sensor.

A regulatory circuit of two lncRNAs and a master regulator directs cell fate in yeast

Transcription of long noncoding RNAs (lncRNAs) regulates local gene expression in eukaryotes. Many examples of how a single lncRNA controls the expression of an adjacent or nearby protein-coding gene have been described. Here we examine the regulation of a locus consisting of two contiguous lncRNAs and the master regulator for entry into yeast meiosis, IME1. We find that the cluster of two lncRNAs together with several transcription factors form a regulatory circuit by which IME1 controls its own promoter and thereby promotes its own expression. Inhibition or stimulation of this unusual feedback circuit affects timing and rate of IME1 accumulation, and hence the ability for cells to enter meiosis. Our data demonstrate that orchestrated transcription through two contiguous lncRNAs promotes local gene expression and determines a critical cell fate decision.

A Precise Cdk Activity Threshold Determines Passage through the Restriction Point

At the restriction point (R), mammalian cells irreversibly commit to divide. R has been viewed as a point in G1 that is passed when growth factor signaling initiates a positive feedback loop of Cdk activity. However, recent studies have cast doubt on this model by claiming R occurs prior to positive feedback activation in G1 or even before completion of the previous cell cycle. Here we reconcile these results and show that whereas many commonly used cell lines do not exhibit a G1 R, primary fibroblasts have a G1 R that is defined by a precise Cdk activity threshold and the activation of cell-cycle-dependent transcription. A simple threshold model, based solely on Cdk activity, predicted with more than 95% accuracy whether individual cells had passed R. That a single measurement accurately predicted cell fate shows that the state of complex regulatory networks can be assessed using a few critical protein activities.

The Adder Phenomenon Emerges from Independent Control of Pre- and Post-Start Phases of the Budding Yeast Cell Cycle

Although it has long been clear that cells actively regulate their size, the molecular mechanisms underlying this regulation have remained poorly understood. In budding yeast, cell size primarily modulates the duration of the cell-division cycle by controlling the G1/S transition known as Start. We have recently shown that the rate of progression through Start increases with cell size, because cell growth dilutes the cell-cycle inhibitor Whi5 in G1. Recent phenomenological studies in yeast and bacteria have shown that these cells add an approximately constant volume during each complete cell cycle, independent of their size at birth. These results seem to be in conflict, as the phenomenological studies suggest that cells measure the amount they grow, rather than their size, and that size control acts over the whole cell cycle, rather than specifically in G1. Here, we propose an integrated model that unifies the adder phenomenology with the molecular mechanism of G1/S cell-size control. We use single-cell microscopy to parameterize a full cell-cycle model based on independent control of pre- and post-Start cell-cycle periods. We find that our model predicts the size-independent amount of cell growth during the full cell cycle. This suggests that the adder phenomenon is an emergent property of the independent regulation of pre- and post-Start cell-cycle periods rather than the consequence of an underlying molecular mechanism measuring a fixed amount of growth.

The Yeast Cyclin-Dependent Kinase Routes Carbon Fluxes to Fuel Cell Cycle Progression

Cell division entails a sequence of processes whose specific demands for biosynthetic precursors and energy place dynamic requirements on metabolism. However, little is known about how metabolic fluxes are coordinated with the cell division cycle. Here, we examine budding yeast to show that more than half of all measured metabolites change significantly through the cell division cycle. Cell cycle-dependent changes in central carbon metabolism are controlled by the cyclin-dependent kinase (Cdk1), a major cell cycle regulator, and the metabolic regulator protein kinase A. At the G1/S transition, Cdk1 phosphorylates and activates the enzyme Nth1, which funnels the storage carbohydrate trehalose into central carbon metabolism. Trehalose utilization fuels anabolic processes required to reliably complete cell division. Thus, the cell cycle entrains carbon metabolism to fuel biosynthesis. Because the oscillation of Cdk activity is a conserved feature of the eukaryotic cell cycle, we anticipate its frequent use in dynamically regulating metabolism for efficient proliferation.

Robust microbial cell segmentation by optical-phase thresholding with minimal processing requirements

High-throughput imaging with single-cell resolution has enabled remarkable discoveries in cell physiology and Systems Biology investigations. A common, and often the most challenging step in all such imaging implementations, is the ability to segment multiple images to regions that correspond to individual cells. Here, a robust segmentation strategy for microbial cells using Quantitative Phase Imaging is reported. The proposed method enables a greater than 99% yeast cell segmentation success rate, without any computationally-intensive, post-acquisition processing. We also detail how the method can be expanded to bacterial cell segmentation with 98% success rates with substantially reduced processing requirements in comparison to existing methods. We attribute this improved performance to the remarkably uniform background, elimination of cell-to-cell and intracellular optical artifacts, and enhanced signal-to-background ratio-all innate properties of imaging in the optical-phase domain. © 2017 International Society for Advancement of Cytometry.

Form and function of topologically associating genomic domains in budding yeast

The genome of metazoan cells is organized into topologically associating domains (TADs) that have similar histone modifications, transcription level, and DNA replication timing. Although similar structures appear to be conserved in fission yeast, computational modeling and analysis of high-throughput chromosome conformation capture (Hi-C) data have been used to argue that the small, highly constrained budding yeast chromosomes could not have these structures. In contrast, herein we analyze Hi-C data for budding yeast and identify 200-kb scale TADs, whose boundaries are enriched for transcriptional activity. Furthermore, these boundaries separate regions of similarly timed replication origins connecting the long-known effect of genomic context on replication timing to genome architecture. To investigate the molecular basis of TAD formation, we performed Hi-C experiments on cells depleted for the Forkhead transcription factors, Fkh1 and Fkh2, previously associated with replication timing. Forkhead factors do not regulate TAD formation, but do promote longer-range genomic interactions and control interactions between origins near the centromere. Thus, our work defines spatial organization within the budding yeast nucleus, demonstrates the conserved role of genome architecture in regulating DNA replication, and identifies a molecular mechanism specifically regulating interactions between pericentric origins.

Long-term tracking of budding yeast cells in brightfield microscopy: CellStar and the Evaluation Platform

With the continuous expansion of single cell biology, the observation of the behaviour of individual cells over extended durations and with high accuracy has become a problem of central importance. Surprisingly, even for yeast cells that have relatively regular shapes, no solution has been proposed that reaches the high quality required for long-term experiments for segmentation and tracking (S&T) based on brightfield images. Here, we present CellStar, a tool chain designed to achieve good performance in long-term experiments. The key features are the use of a new variant of parametrized active rays for segmentation, a neighbourhood-preserving criterion for tracking, and the use of an iterative approach that incrementally improves S&T quality. A graphical user interface enables manual corrections of S&T errors and their use for the automated correction of other, related errors and for parameter learning. We created a benchmark dataset with manually analysed images and compared CellStar with six other tools, showing its high performance, notably in long-term tracking. As a community effort, we set up a website, the Yeast Image Toolkit, with the benchmark and the Evaluation Platform to gather this and additional information provided by others.

Spatial and temporal signal processing and decision making by MAPK pathways

Recent studies show that MAPK pathways perform exquisite spatial and temporal signal processing. This review discusses the mechanisms that process dynamic inputs into graded output responses, the role of positive and negative feedbacks, and feedforward regulation.

Mitogen-activated protein kinase (MAPK) pathways are conserved from yeast to man and regulate a variety of cellular processes, including proliferation and differentiation. Recent developments show how MAPK pathways perform exquisite spatial and temporal signal processing and underscores the importance of studying the dynamics of signaling pathways to understand their physiological response. The importance of dynamic mechanisms that process input signals into graded downstream responses has been demonstrated in the pheromone-induced and osmotic stress–induced MAPK pathways in yeast and in the mammalian extracellular signal-regulated kinase MAPK pathway. Particularly, recent studies in the yeast pheromone response have shown how positive feedback generates switches, negative feedback enables gradient detection, and coherent feedforward regulation underlies cellular memory. More generally, a new wave of quantitative single-cell studies has begun to elucidate how signaling dynamics determine cell physiology and represents a paradigm shift from descriptive to predictive biology.

Switch-like Transitions Insulate Network Motifs to Modularize Biological Networks

Cellular decisions are made by complex networks that are difficult to analyze. Although it is common to analyze smaller sub-networks known as network motifs, it is unclear whether this is valid, because these motifs are embedded in complex larger networks. Here, we address the general question of modularity by examining the S. cerevisiae pheromone response. We demonstrate that the feedforward motif controlling the cell-cycle inhibitor Far1 is insulated from cell-cycle dynamics by the positive feedback switch that drives reentry to the cell cycle. Before cells switch on positive feedback, the feedforward motif model predicts the behavior of the larger network. Conversely, after the switch, the feedforward motif is dismantled and has no discernable effect on the cell cycle. When insulation is broken, the feedforward motif no longer predicts network behavior. This work illustrates how, despite the interconnectivity of networks, the activity of motifs can be insulated by switches that generate well-defined cellular states.

The Biosynthetic Basis of Cell Size Control

Cell size is an important physiological trait that sets the scale of all biosynthetic processes. Although physiological studies have revealed that cells actively regulate their size, the molecular mechanisms underlying this regulation have remained unclear. Here we review recent progress in identifying the molecular mechanisms of cell size control. We focus on budding yeast, where cell growth dilutes a cell cycle inhibitor to couple growth and division. We discuss a new model for size control based on the titration of activator and inhibitor molecules whose synthesis rates are differentially dependent on cell size.

Dilution of the cell cycle inhibitor Whi5 controls budding-yeast cell size

Cell size fundamentally affects all biosynthetic processes by determining the scale of organelles and influencing surface transport^1,2. Although extensive studies have identified many mutations affecting cell size, the molecular mechanisms underlying size control have remained elusive³. In budding yeast, size control occurs in G1 phase prior to Start, the point of irreversible commitment to cell division^4,5. It was previously thought that activity of the G1 cyclin Cln3 increased with cell size to trigger Start by initiating the inhibition of the transcriptional inhibitor Whi5^6-8. However, while Cln3 concentration does modulate the rate at which cells pass Start, we found that its synthesis increases in proportion to cell size so that its total concentration is nearly constant during pre-Start G1. Rather than increasing Cln3 activity, we identify decreasing Whi5 activity — due to the dilution of Whi5 by cell growth — as a molecular mechanism through which cell size controls proliferation. Whi5 is synthesized in S/G2/M phases of the cell cycle in a largely size-independent manner. This results in smaller daughter cells being born with higher Whi5 concentrations that extend their pre-Start G1 phase. Thus, at its most fundamental level, budding yeast size control results from the differential scaling of Cln3 and Whi5 synthesis rates with cell size. More generally, our work shows that differential size-dependency of protein synthesis can provide an elegant mechanism to coordinate cellular functions with growth.

High-throughput microfluidics to control and measure signaling dynamics in single yeast cells

Microfluidics coupled to quantitative time-lapse fluorescence microscopy is transforming our ability to control, measure and understand signaling dynamics in single living cells. Here we describe a pipeline that incorporates multiplexed microfluidic cell culture, automated programmable fluid handling for cell perturbation, quantitative time-lapse microscopy and computational analysis of time-lapse movies. We illustrate how this setup can be used to control the nuclear localization of the budding yeast transcription factor Msn2. By using this protocol, we generate oscillations of Msn2 localization and measure the dynamic gene expression response of individual genes in single cells. The protocol allows a single researcher to perform up to 20 different experiments in a single day, while collecting data for thousands of single cells. Compared with other protocols, the present protocol is relatively easy to adopt and of higher throughput. The protocol can be widely used to control and monitor single-cell signaling dynamics in other signal transduction systems in microorganisms.

Compartmentalization of a bistable switch enables memory to cross a feedback-driven transition

Cells make accurate decisions in the face of molecular noise and environmental fluctuations by relying not only on present pathway activity, but also on their memory of past signaling dynamics. Once a decision is made, cellular transitions are often rapid and switch-like due to positive feedback loops in the regulatory network. While positive feedback loops are good at promoting switch-like transitions, they are not expected to retain information to inform subsequent decisions. However, this expectation is based on our current understanding of network motifs that accounts for temporal, but not spatial, dynamics. Here, we show how spatial organization of the feedback-driven yeast G1/S switch enables the transmission of memory of past pheromone exposure across this transition. We expect this to be one of many examples where the exquisite spatial organization of the eukaryotic cell enables previously well-characterized network motifs to perform new and unexpected signal processing functions.

A docking interface in the cyclin Cln2 promotes multi-site phosphorylation of substrates and timely cell-cycle entry

BACKGROUND

Eukaryotic cell division is driven by cyclin-dependent kinases (CDKs). Distinct cyclin-CDK complexes are specialized to drive different cell-cycle events, though the molecular foundations for these specializations are only partly understood. In budding yeast, the decision to begin a new cell cycle is regulated by three G1 cyclins (Cln1-Cln3). Recent studies revealed that some CDK substrates contain a novel docking motif that is specifically recognized by Cln1 and Cln2, and not by Cln3 or later S- or M-phase cyclins, but the responsible cyclin interface was unknown.

RESULTS

Here, to explore the role of this new docking mechanism in the cell cycle, we first show that it is conserved in a distinct cyclin subtype (Ccn1). Then, we exploit phylogenetic variation to identify cyclin mutations that disrupt docking. These mutations disrupt binding to multiple substrates as well as the ability to use docking sites to promote efficient, multi-site phosphorylation of substrates in vitro. In cells where the Cln2 docking function is blocked, we observed reductions in the polarized morphogenesis of daughter buds and reduced ability to fully phosphorylate the G1/S transcriptional repressor Whi5. Furthermore, disruption of Cln2 docking perturbs the coordination between cell size and division, such that the G1/S transition is delayed.

CONCLUSIONS

The findings point to a novel substrate interaction interface on cyclins, with patterns of conservation and divergence that relate to functional distinctions among cyclin subtypes. Furthermore, this docking function helps ensure full phosphorylation of substrates with multiple phosphorylation sites, and this contributes to punctual cell-cycle entry.

A genetically encoded Förster resonance energy transfer sensor for monitoring in vivo trehalose-6-phosphate dynamics

Trehalose-6-phosphate is a pivotal regulator of sugar metabolism, growth, and osmotic equilibrium in bacteria, yeasts, and plants. To directly visualize the intracellular levels of intracellular trehalose-6-phosphate, we developed a series of specific Förster resonance energy transfer (FRET) sensors for in vivo microscopy. We demonstrated real-time monitoring of regulation in the trehalose pathway of Escherichia coli. In Saccharomyces cerevisiae, we could show that the concentration of free trehalose-6-phosphate during growth on glucose is in a range sufficient for inhibition of hexokinase. These findings support the hypothesis of trehalose-6-phosphate as the effector of a negative feedback system, similar to the inhibition of hexokinase by glucose-6-phosphate in mammalian cells and controlling glycolytic flux.

User-friendly tools for quantifying the dynamics of cellular morphology and intracellular protein clusters

Understanding the heterogeneous dynamics of cellular processes requires not only tools to visualize molecular behavior but also versatile approaches to extract and analyze the information contained in live-cell movies of many cells. Automated identification and tracking of cellular features enable thorough and consistent comparative analyses in a high-throughput manner. Here, we present tools for two challenging problems in computational image analysis: (1) classification of motion for cells with complex shapes and dynamics and (2) segmentation of clustered cells and quantification of intracellular protein distributions based on a single fluorescence channel. We describe these methods and user-friendly software(1) (MATLAB applications with graphical user interfaces) so these tools can be readily applied without an extensive knowledge of computational techniques.

Feedforward regulation ensures stability and rapid reversibility of a cellular state

Cellular transitions are important for all life. Such transitions, including cell fate decisions, often employ positive feedback regulation to establish and stabilize new cellular states. However, positive feedback is unlikely to underlie stable cell-cycle arrest in yeast exposed to mating pheromone because the signaling pathway is linear, rather than bistable, over a broad range of extracellular pheromone concentration. We show that the stability of the pheromone-arrested state results from coherent feedforward regulation of the cell-cycle inhibitor Far1. This network motif is effectively isolated from the more complex regulatory network in which it is embedded. Fast regulation of Far1 by phosphorylation allows rapid cell-cycle arrest and reentry, whereas slow Far1 synthesis reinforces arrest. We expect coherent feedforward regulation to be frequently implemented at reversible cellular transitions because this network motif can achieve the ostensibly conflicting aims of arrest stability and rapid reversibility without loss of signaling information.