Single-cell quantification of molecules and rates using open-source microscope-based cytometry

Assembly and function of the amyloid-like translational repressor Rim4 is coupled with nutrient conditions

Amyloid-like protein assemblies have been associated with toxic phenotypes because of their repetitive and stable structure. However, evidence that cells exploit these structures to control function and activity of some proteins in response to stimuli has questioned this paradigm. How amyloid-like assembly can confer emergent functions and how cells couple assembly with environmental conditions remains unclear. Here, we study Rim4, an RNA-binding protein that forms translation-repressing assemblies during yeast meiosis. We demonstrate that in its assembled and repressive state, Rim4 binds RNA more efficiently than in its monomeric and idle state, revealing a causal connection between assembly and function. The Rim4-binding site location within the transcript dictates whether the assemblies can repress translation, underscoring the importance of the architecture of this RNA-protein structure for function. Rim4 assembly depends exclusively on its intrinsically disordered region and is prevented by the Ras/protein kinase A signaling pathway, which promotes growth and suppresses meiotic entry in yeast. Our results suggest a mechanism whereby cells couple a functional protein assembly with a stimulus to enforce a cell fate decision.

New discoveries in the field of metabolism by applying single-cell and spatial omics

Single-cell multi-Omics (SCM-Omics) and spatial multi-Omics (SM-Omics) technologies provide state-of-the-art methods for exploring the composition and function of cell types in tissues/organs. Since its emergence in 2009, single-cell RNA sequencing (scRNA-seq) has yielded many groundbreaking new discoveries. The combination of this method with the emergence and development of SM-Omics techniques has been a pioneering strategy in neuroscience, developmental biology, and cancer research, especially for assessing tumor heterogeneity and T-cell infiltration. In recent years, the application of these methods in the study of metabolic diseases has also increased. The emerging SCM-Omics and SM-Omics approaches allow the molecular and spatial analysis of cells to explore regulatory states and determine cell fate, and thus provide promising tools for unraveling heterogeneous metabolic processes and making them amenable to intervention. Here, we review the evolution of SCM-Omics and SM-Omics technologies, and describe the progress in the application of SCM-Omics and SM-Omics in metabolism-related diseases, including obesity, diabetes, nonalcoholic fatty liver disease (NAFLD) and cardiovascular disease (CVD). We also conclude that the application of SCM-Omics and SM-Omics approaches can help resolve the molecular mechanisms underlying the pathogenesis of metabolic diseases in the body and facilitate therapeutic measures for metabolism-related diseases. This review concludes with an overview of the current status of this emerging field and the outlook for its future.

Multidimensional characterization of inducible promoters and a highly light-sensitive LOV-transcription factor

The ability to independently control the expression of different genes is important for quantitative biology. Using budding yeast, we characterize GAL1pr, GALL, MET3pr, CUP1pr, PHO5pr, tetOpr, terminator-tetOpr, Z₃EV, blue-light inducible optogenetic systems El222-LIP, El222-GLIP, and red-light inducible PhyB-PIF3. We report kinetic parameters, noise scaling, impact on growth, and the fundamental leakiness of each system using an intuitive unit, maxGAL1. We uncover disadvantages of widely used tools, e.g., nonmonotonic activity of MET3pr and GALL, slow off kinetics of the doxycycline- and estradiol-inducible systems tetOpr and Z₃EV, and high variability of PHO5pr and red-light activated PhyB-PIF3 system. We introduce two previously uncharacterized systems: strongLOV, a more light-sensitive El222 mutant, and ARG3pr, which is induced in the absence of arginine or presence of methionine. To demonstrate fine control over gene circuits, we experimentally tune the time between cell cycle Start and mitosis, artificially simulating near-wild-type timing. All strains, constructs, code, and data ( https://promoter-benchmark.epfl.ch/ ) are made available.

Characterization of cell-to-cell variation in nuclear transport rates and identification of its sources

Nuclear transport is an essential part of eukaryotic cell function. Here, we present scFRAP, a model-assisted fluorescent recovery after photobleaching (FRAP)- based method to determine nuclear import and export rates independently in individual live cells. To overcome the inherent noise of single-cell measurements, we performed sequential FRAPs on the same cell. We found large cell-to-cell variation in transport rates within isogenic yeast populations. For passive transport, the variability in NPC number might explain most of the variability. Using this approach, we studied mother-daughter cell asymmetry in the active nuclear shuttling of the transcription factor Ace2, which is specifically concentrated in daughter cell nuclei in early G1. Rather than reduced export in the daughter cell, as previously hypothesized, we found that this asymmetry is mainly due to an increased import in daughters. These results shed light on cell-to-cell variation in cellular dynamics and its sources.

Cell region fingerprints enable highly precise single-cell tracking and lineage reconstruction

Experimental studies of cell growth, inheritance and their associated processes by microscopy require accurate single-cell observations of sufficient duration to reconstruct the genealogy. However, cell tracking-assigning identical cells on consecutive images to a track-is often challenging, resulting in laborious manual verification. Here, we propose fingerprints to identify problematic assignments rapidly. A fingerprint distance compares the structural information contained in the low frequencies of a Fourier transform to measure the similarity between cells in two consecutive images. We show that fingerprints are broadly applicable across cell types and image modalities, provided the image has sufficient structural information. Our tracker (Trac^X) uses fingerprints to reject unlikely assignments, thereby increasing tracking performance on published and newly generated long-term data sets. For Saccharomyces cerevisiae, we propose a comprehensive model for cell size control at the single-cell and population level centered on the Whi5 regulator, demonstrating how precise tracking can help uncover previously undescribed single-cell biology.

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.

Systematic In Vivo Characterization of Fluorescent Protein Maturation in Budding Yeast

Fluorescent protein (FP) maturation can limit the accuracy with which dynamic intracellular processes are captured and reduce the in vivo brightness of a given FP in fast-dividing cells. The knowledge of maturation timescales can therefore help users determine the appropriate FP for each application. However, in vivo maturation rates can greatly deviate from in vitro estimates that are mostly available. In this work, we present the first systematic study of in vivo maturation for 12 FPs in budding yeast. To overcome the technical limitations of translation inhibitors commonly used to study FP maturation, we implemented a new approach based on the optogenetic stimulations of FP expression in cells grown under constant nutrient conditions. Combining the rapid and orthogonal induction of FP transcription with a mathematical model of expression and maturation allowed us to accurately estimate maturation rates from microscopy data in a minimally invasive manner. Besides providing a useful resource for the budding yeast community, we present a new joint experimental and computational approach for characterizing FP maturation, which is applicable to a wide range of organisms.

A convolutional neural network for segmentation of yeast cells without manual training annotations

MOTIVATION

Single-cell time-lapse microscopy is a ubiquitous tool for studying the dynamics of complex cellular processes. While imaging can be automated to generate very large volumes of data, the processing of the resulting movies to extract high-quality single-cell information remains a challenging task. The development of software tools that automatically identify and track cells is essential for realizing the full potential of time-lapse microscopy data. Convolutional neural networks (CNNs) are ideally suited for such applications, but require great amounts of manually annotated data for training, a time-consuming and tedious process.

RESULTS

We developed a new approach to CNN training for yeast cell segmentation based on synthetic data and present (i) a software tool for the generation of synthetic images mimicking brightfield images of budding yeast cells and (ii) a convolutional neural network (Mask R-CNN) for yeast segmentation that was trained on a fully synthetic dataset. The Mask R-CNN performed excellently on segmenting actual microscopy images of budding yeast cells, and a density-based spatial clustering algorithm (DBSCAN) was able to track the detected cells across the frames of microscopy movies. Our synthetic data creation tool completely bypassed the laborious generation of manually annotated training datasets, and can be easily adjusted to produce images with many different features. The incorporation of synthetic data creation into the development pipeline of CNN-based tools for budding yeast microscopy is a critical step toward the generation of more powerful, widely applicable and user-friendly image processing tools for this microorganism.

AVAILABILITY AND IMPLEMENTATION

The synthetic data generation code can be found at https://github.com/prhbrt/synthetic-yeast-cells. The Mask R-CNN as well as the tuning and benchmarking scripts can be found at https://github.com/ymzayek/yeastcells-detection-maskrcnn. We also provide Google Colab scripts that reproduce all the results of this work.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A precisely adjustable, variation-suppressed eukaryotic transcriptional controller to enable genetic discovery

Conditional expression of genes and observation of phenotype remain central to biological discovery. Current methods enable either on/off or imprecisely controlled graded gene expression. We developed a 'well-tempered' controller, WTC₈₄₆, for precisely adjustable, graded, growth condition independent expression of genes in Saccharomyces cerevisiae. Controlled genes are expressed from a strong semisynthetic promoter repressed by the prokaryotic TetR, which also represses its own synthesis; with basal expression abolished by a second, 'zeroing' repressor. The autorepression loop lowers cell-to-cell variation while enabling precise adjustment of protein expression by a chemical inducer. WTC₈₄₆ allelic strains in which the controller replaced the native promoters recapitulated known null phenotypes (CDC42, TPI1), exhibited novel overexpression phenotypes (IPL1), showed protein dosage-dependent growth rates and morphological phenotypes (CDC28, TOR2, PMA1 and the hitherto uncharacterized PBR1), and enabled cell cycle synchronization (CDC20). WTC₈₄₆ defines an 'expression clamp' allowing protein dosage to be adjusted by the experimenter across the range of cellular protein abundances, with limited variation around the setpoint.

Introducing Parameter Clustering to the OED Procedure for Model Calibration of a Synthetic Inducible Promoter in S. cerevisiae

In recent years, synthetic gene circuits for adding new cell features have become one of the most powerful tools in biological and pharmaceutical research and development. However, because of the inherent non-linearity and noisy experimental data, the experiment-based model calibration of these synthetic parts is perceived as a laborious and time-consuming procedure. Although the optimal experimental design (OED) based on the Fisher information matrix (FIM) has been proved to be an effective means to improve the calibration efficiency, the required calculation increases dramatically with the model size (parameter number). To reduce the OED complexity without losing the calibration accuracy, this paper proposes two OED approaches with different parameter clustering methods and validates the accuracy of calibrated models with in-silico experiments. A model of an inducible synthetic promoter in S. cerevisiae is adopted for bench-marking. The comparison with the traditional off-line OED approach suggests that the OED approaches with both of the clustering methods significantly reduce the complexity of OED problems (for at least 49.0%), while slightly improving the calibration accuracy (11.8% and 19.6% lower estimation error in average for FIM-based and sensitivity-based approaches). This study implicates that for calibrating non-linear models of biological pathways, cluster-based OED could be a beneficial approach to improve the efficiency of optimal experimental design.

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.

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.

GPCR receptor phosphorylation and endocytosis are not necessary to switch polarized growth between internal cues during pheromone response in S. cerevisiae

Chemotactic/chemotropic cells follow accurately the direction of gradients of regulatory molecules. Many G-protein-coupled receptors (GPCR) function as chemoattractant receptors to guide polarized responses. In "a" mating type yeast, the GPCR Ste2 senses the α-cell's pheromone. Previously, phosphorylation and trafficking of this receptor have been implicated in the process of gradient sensing, where cells dynamically correct growth. Correction is often necessary since yeast have intrinsic polarity sites that interfere with a correct initial gradient decoding. We have recently showed that when actively dividing (not in G1) yeast are exposed to a uniform pheromone concentration, they initiate a pheromone-induced polarization next to the mother-daughter cytokinesis site. Then, they reorient their growth to the intrinsic polarity site. Here, to study if Ste2 phosphorylation and internalization are involved in this process, we generated receptor variants combining three types of mutated signals for the first time: phosphorylation, ubiquitylation and the NPFX1,2D Sla1-binding motif. We first characterized their effect on endocytosis and found that these processes regulate internalization in a more complex manner than previously shown. Interestingly, we showed that receptor phosphorylation can drive internalization independently of ubiquitylation and the NPFX1,2D motif. When tested in our assays, cells expressing either phosphorylation or endocytosis-deficient receptors were able to switch away from the cytokinesis site to find the intrinsic polarity site as efficiently as their WT counterparts. Thus, we conclude that these processes are not necessary for the reorientation of polarization.

Fine-Tuning of Energy Levels Regulates SUC2 via a SNF1-Dependent Feedback Loop

Nutrient sensing pathways are playing an important role in cellular response to different energy levels. In budding yeast, Saccharomyces cerevisiae, the sucrose non-fermenting protein kinase complex SNF1 is a master regulator of energy homeostasis. It is affected by multiple inputs, among which energy levels is the most prominent. Cells which are exposed to a switch in carbon source availability display a change in the gene expression machinery. It has been shown that the magnitude of the change varies from cell to cell. In a glucose rich environment Snf1/Mig1 pathway represses the expression of its downstream target, such as SUC2. However, upon glucose depletion SNF1 is activated which leads to an increase in SUC2 expression. Our single cell experiments indicate that upon starvation, gene expression pattern of SUC2 shows rapid increase followed by a decrease to initial state with high cell-to-cell variability. The mechanism behind this behavior is currently unknown. In this work we study the long-term behavior of the Snf1/Mig1 pathway upon glucose starvation with a microfluidics and non-linear mixed effect modeling approach. We show a negative feedback mechanism, involving Snf1 and Reg1, which reduces SUC2 expression after the initial strong activation. Snf1 kinase activity plays a key role in this feedback mechanism. Our systems biology approach proposes a negative feedback mechanism that works through the SNF1 complex and is controlled by energy levels. We further show that Reg1 likely is involved in the negative feedback mechanism.

YeastRGB: comparing the abundance and localization of yeast proteins across cells and libraries

The ability to measure the abundance and visualize the localization of proteins across the yeast proteome has stimulated hypotheses on gene function and fueled discoveries. While the classic C’ tagged GFP yeast library has been the only resource for over a decade, the recent development of the SWAT technology has led to the creation of multiple novel yeast libraries where new-generation fluorescent reporters are fused at the N’ and C’ of open reading frames. Efficient access to these data requires a user interface to visualize and compare protein abundance, localization and co-localization across cells, strains, and libraries. YeastRGB (www.yeastRGB.org) was designed to address such a need, through a user-friendly interface that maximizes informative content. It employs a compact display where cells are cropped and tiled together into a ‘cell-grid.’ This representation enables viewing dozens of cells for a particular strain within a display unit, and up to 30 display units can be arrayed on a standard high-definition screen. Additionally, the display unit allows users to control zoom-level and overlay of images acquired using different color channels. Thus, YeastRGB makes comparing abundance and localization efficient, across thousands of cells from different strains and libraries.

Evidence for rate-dependent filtering of global extrinsic noise by biochemical reactions in mammalian cells

Recent studies have revealed that global extrinsic noise arising from stochasticity in the intracellular biochemical environment plays a critical role in heterogeneous cell physiologies. However, it remains largely unclear how such extrinsic noise dynamically influences downstream reactions and whether it could be neutralized by cellular reactions. Here, using fluorescent protein (FP) maturation as a model biochemical reaction, we explored how cellular reactions might combat global extrinsic noise in mammalian cells. We developed a novel single-cell assay to systematically quantify the maturation rate and the associated noise for over a dozen FPs. By exploiting the variation in the maturation rate for different FPs, we inferred that global extrinsic noise could be temporally filtered by maturation reactions, and as a result, the noise levels for slow-maturing FPs are lower compared to fast-maturing FPs. This mechanism is validated by directly perturbing the maturation rates of specific FPs and measuring the resulting noise levels. Together, our results revealed a potentially general principle governing extrinsic noise propagation, where timescale separation allows cellular reactions to cope with dynamic global extrinsic noise.

Harnessing the Power of Optical Microscopic and Macroscopic Imaging for Natural Products as Cancer Therapeutics

Natural products (NPs) are an important source for new drug discovery over the past decades, which have been demonstrated to be effectively used in cancer prevention, treatment, and adjuvant therapy. Many methods, such as the genomic and metabolomic approaches, immunochemistry, mass spectrometry, and chromatography, have been used to study the effects of NPs on cancer as well as themselves. Because of the advantages in specificity, sensitivity, high throughput, and cost-effectiveness, optical imaging (OI) approaches, including optical microscopic imaging and macroscopic imaging techniques have also been applied in the studies of NPs. Optical microscopic imaging can observe NPs as cancer therapeutics at the cellular level and analyze its cytotoxicity and mechanism of action. Optical macroscopic imaging observes the distribution, metabolic pathway, and target lesions of NPs in vivo, and evaluates NPs as cancer therapeutics at the whole-body level in small living animals. This review focuses on the recent advances in NPs as cancer therapeutics, with particular emphasis on the powerful use of optical microscopic and macroscopic imaging techniques, including the studies of observation of ingestion by cells, anticancer mechanism, and in vivo delivery. Finally, we prospect the wider application and future potential of OI approaches in NPs as cancer therapeutics.

Ratiometric GPCR signaling enables directional sensing in yeast

Accurate detection of extracellular chemical gradients is essential for many cellular behaviors. Gradient sensing is challenging for small cells, which can experience little difference in ligand concentrations on the up-gradient and down-gradient sides of the cell. Nevertheless, the tiny cells of the yeast Saccharomyces cerevisiae reliably decode gradients of extracellular pheromones to find their mates. By imaging the behavior of polarity factors and pheromone receptors, we quantified the accuracy of initial polarization during mating encounters. We found that cells bias the orientation of initial polarity up-gradient, even though they have unevenly distributed receptors. Uneven receptor density means that the gradient of ligand-bound receptors does not accurately reflect the external pheromone gradient. Nevertheless, yeast cells appear to avoid being misled by responding to the fraction of occupied receptors rather than simply the concentration of ligand-bound receptors. Such ratiometric sensing also serves to amplify the gradient of active G protein. However, this process is quite error-prone, and initial errors are corrected during a subsequent indecisive phase in which polarity clusters exhibit erratic mobile behavior.

Cells use surface receptors to decode spatial information from chemical gradients, but accurate decoding is hampered by small cell size and the presence of molecular noise. This study shows that yeast cells decode pheromone gradients by measuring the local ratio of bound to unbound receptors. This mechanism corrects for uneven receptor density at the surface and amplifies the gradient transmitted to downstream components.

Integrating after CEN Excision (ICE) Plasmids: Combining the ease of yeast recombination cloning with the stability of genomic integration

Yeast recombination cloning is a straightforward and powerful method for recombining a plasmid backbone with a specific DNA fragment. However, the utility of yeast recombination cloning is limited by the requirement for the backbone to contain an CEN/ARS element, which allows for the recombined plasmids to propagate. Although yeast CEN/ARS plasmids are often suitable for further studies, we demonstrate here that they can vary considerably in copy number from cell to cell and from colony to colony. Variation in plasmid copy number can pose an unacceptable and often unacknowledged source of phenotypic variation. If expression levels are critical to experimentation, then constructs generated with yeast recombination cloning must be subcloned into integrating plasmids, a step that often abrogates the utility of recombination cloning. Accordingly, we have designed a vector that can be used for yeast recombination cloning but can be converted into the integrating version of the resulting vector without an additional subcloning. We call these "ICE" vectors, for "Integrating after CEN Excision." The ICE series was created by introducing a "rare-cutter" NotI-flanked CEN/ARS element into the multiple cloning sites of the pRS series yeast integration plasmids. Upon recovery from yeast, the CEN/ARS is excised by NotI digest and subsequently religated without need for purification or transfer to new conditions. Excision by this approach takes ~3 hr, allowing this refinement in the same time frame as standard recombination cloning.

In vivo study of gene expression with an enhanced dual-color fluorescent transcriptional timer

Fluorescent transcriptional reporters are widely used as signaling reporters and biomarkers to monitor pathway activities and determine cell type identities. However, a large amount of dynamic information is lost due to the long half-life of the fluorescent proteins. To better detect dynamics, fluorescent transcriptional reporters can be destabilized to shorten their half-lives. However, applications of this approach in vivo are limited due to significant reduction of signal intensities. To overcome this limitation, we enhanced translation of a destabilized fluorescent protein and demonstrate the advantages of this approach by characterizing spatio-temporal changes of transcriptional activities in Drosophila. In addition, by combining a fast-folding destabilized fluorescent protein and a slow-folding long-lived fluorescent protein, we generated a dual-color transcriptional timer that provides spatio-temporal information about signaling pathway activities. Finally, we demonstrate the use of this transcriptional timer to identify new genes with dynamic expression patterns.

High-throughput quantification of microbial birth and death dynamics using fluorescence microscopy

BACKGROUND

Microbes live in dynamic environments where nutrient concentrations fluctuate. Quantifying fitness in terms of birth rate and death rate in a wide range of environments is critical for understanding microbial evolution and ecology.

METHODS

Here, using high-throughput time-lapse microscopy, we have quantified how Saccharomyces cerevisiae mutants incapable of synthesizing an essential metabolite (auxotrophs) grow or die in various concentrations of the required metabolite. We establish that cells normally expressing fluorescent proteins lose fluorescence upon death and that the total fluorescence in an imaging frame is proportional to the number of live cells even when cells form multiple layers. We validate our microscopy approach of measuring birth and death rates using flow cytometry, cell counting, and chemostat culturing.

RESULTS

For lysine-requiring cells, very low concentrations of lysine are not detectably consumed and do not support cell birth, but delay the onset of death phase and reduce the death rate compared to no lysine. In contrast, in low hypoxanthine, hypoxanthine-requiring cells can produce new cells, yet also die faster than in the absence of hypoxanthine. For both strains, birth rates under various metabolite concentrations are better described by the sigmoidal-shaped Moser model than the well-known Monod model, while death rates can vary with metabolite concentration and time.

CONCLUSIONS

Our work reveals how time-lapse microscopy can be used to discover non-intuitive microbial birth and death dynamics and to quantify growth rates in many environments.

CDK and MAPK Synergistically Regulate Signaling Dynamics via a Shared Multi-site Phosphorylation Region on the Scaffold Protein Ste5

We report an unanticipated system of joint regulation by cyclin-dependent kinase (CDK) and mitogen-activated protein kinase (MAPK), involving collaborative multi-site phosphorylation of a single substrate. In budding yeast, the protein Ste5 controls signaling through a G1 arrest pathway. Upon cell-cycle entry, CDK inhibits Ste5 via multiple phosphorylation sites, disrupting its membrane association. Using quantitative time-lapse microscopy, we examined Ste5 membrane recruitment dynamics at different cell-cycle stages. Surprisingly, in S phase, where Ste5 recruitment should be blocked, we observed an initial recruitment followed by a steep drop-off. This delayed inhibition revealed a requirement for both CDK activity and negative feedback from the pathway MAPK Fus3. Mutagenesis, mass spectrometry, and electrophoretic analyses suggest that the CDK and MAPK modify shared sites, which are most extensively phosphorylated when both kinases are active and able to bind their docking sites on Ste5. Such collaborative phosphorylation can broaden regulatory inputs and diversify output dynamics of signaling pathways.

Prediction and characterization of promoters and ribosomal binding sites of Zymomonas mobilis in system biology era

BACKGROUND

Zymomonas mobilis is a model bacterial ethanologen with many systems biology studies reported. Besides lignocellulosic ethanol production, Z. mobilis has been developed as a platform for biochemical production through metabolic engineering. However, identification and rigorous understanding of the genetic origins of cellular function, especially those based in non-coding region of DNA, such as promoters and ribosomal binding sites (RBSs), are still in its infancy. This knowledge is crucial for the effective application of Z. mobilis to new industrial applications of biotechnology for fuels and chemicals production.

RESULTS

In this study, we explored the possibility to systematically predict the strength of promoters based on systems biology datasets. The promoter strength was clustered based on the expression values of downstream genes (or proteins) from systems biology studies including microarray, RNA-Seq and proteomics. Candidate promoters with different strengths were selected for further characterization, which include 19 strong, nine medium, and ten weak ones. A dual reporter-gene system was developed which included appropriate reporter genes. These are the opmCherry reporter gene driven by the constitutive PlacUV5 promoter for calibration, and EGFP reporter gene driven by candidate promoters for quantification. This dual reporter-gene system was confirmed using the inducible promoter, Ptet, which was used to determine the strength of these predicted promoters with different strengths. In addition, the dual reporter-gene system was applied to determine four synthetic RBSs with different translation initiation rates based on the prediction from bioinformatics server RBS calculator. Our results showed that the correlations between the prediction and experimental results for the promoter and RBS strength are relatively high, with R ² values more than 0.7 and 0.9, respectively.

CONCLUSIONS

This study not only identified and characterized 38 promoters and four RBSs with different strengths for future metabolic engineering in Z. mobilis, but also established a flow cytometry-based dual reporter-gene system to characterize genetic elements including, but not limited to the promoters and RBSs studied in this work. This study also suggested the feasibility of predicting and selecting candidate genetic elements based on omics datasets and bioinformatics tools. Moreover, the dual reporter-gene system developed in this study can be utilized to characterize other genetic elements of Z. mobilis, which can also be applied to other microorganisms.

Expression and regulation of the Pseudomonas aeruginosa hibernation promoting factor

Bacterial biofilms contain subpopulations of cells that are dormant and highly tolerant to antibiotics. While dormant, the bacteria must maintain the integrity of macromolecules required for resuscitation. Previously, we showed that hibernation promoting factor (HPF) is essential for protecting Pseudomonas aeruginosa from ribosomal loss during dormancy. In this study, we mapped the genetic components required for hpf expression. Using 5'-RACE and fluorescent protein reporter fusions, we show that hpf is expressed as part of the rpoN operon, but that hpf also has a second promoter (P_hpf ) within the rpoN gene. P_hpf is active when the cells enter stationary phase, and expression from P_hpf is modulated, but not eliminated, in mutant strains impaired in stationary phase transition (ΔdksA2, ΔrpoS and ΔrelA/ΔspoT mutants). The results of reporter gene studies and mRNA folding predictions indicated that the 5' end of the hpf mRNA may also influence hpf expression. Mutations that opened or that stabilized the mRNA hairpin loop structures strongly influenced the amount of HPF produced. The results demonstrate that hpf is expressed independently of rpoN, and that hpf regulation includes both transcriptional and post-transcriptional processes, allowing the cells to produce sufficient HPF during stationary phase to maintain viability while dormant.

Heat-stress triggers MAPK crosstalk to turn on the hyperosmotic response pathway

Cells make decisions based on a combination of external and internal signals. In yeast, the high osmolarity response (HOG) is a mitogen-activated protein kinase (MAPK) pathway that responds to a variety of stimuli, and it is central to the general stress response. Here we studied the effect of heat-stress (HS) on HOG. Using live-cell reporters and genetics, we show that HS promotes Hog1 phosphorylation and Hog1-dependent gene expression, exclusively via the Sln1 phosphorelay branch, and that the strength of the activation is larger in yeast adapted to high external osmolarity. HS stimulation of HOG is indirect. First, we show that HS causes glycerol loss, necessary for HOG activation. Preventing glycerol efflux by deleting the glyceroporin FPS1 or its regulators RGC1 and ASK10/RGC2, or by increasing external glycerol, greatly reduced HOG activation. Second, we found that HOG stimulation by HS depended on the operation of a second MAPK pathway, the cell-wall integrity (CWI), a well-known mediator of HS, since inactivating Pkc1 or deleting the MAPK SLT2 greatly reduced HOG activation. Our data suggest that the main role of the CWI in this process is to stimulate glycerol loss. We found that in yeast expressing the constitutively open channel mutant (Fps1-Δ11), HOG activity was independent of Slt2. In summary, we suggest that HS causes a reduction in turgor due to the loss of glycerol and the accompanying water, and that this is what actually stimulates HOG. Thus, taken together, our findings highlight a central role for Fps1, and the metabolism of glycerol, in the communication between the yeast MAPK pathways, essential for survival and reproduction in changing environments.

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.

Pulsatile inputs achieve tunable attenuation of gene expression variability and graded multi-gene regulation

Many natural transcription factors are regulated in a pulsatile fashion, but it remains unknown whether synthetic gene expression systems can benefit from such dynamic regulation. Here we find, using a fast-acting, optogenetic transcription factor in Saccharomyces cerevisiae, that dynamic pulsatile signals reduce cell-to-cell variability in gene expression. We then show that by encoding such signals into a single input, expression mean and variability can be independently tuned. Further, we construct a light-responsive promoter library and demonstrate how pulsatile signaling also enables graded multi-gene regulation at fixed expression ratios, despite differences in promoter dose-response characteristics. Pulsatile regulation can thus lead to beneficial functional behaviors in synthetic biological systems, which previously required laborious optimization of genetic parts or the construction of synthetic gene networks.

New approaches in bioprocess-control: Consortium guidance by synthetic cell-cell communication based on fungal pheromones

Bioconversions in industrial processes are currently dominated by single-strain approaches. With the growing complexity of tasks to be carried out, microbial consortia become increasingly advantageous and eventually may outperform single-strain fermentations. Consortium approaches benefit from the combined metabolic capabilities of highly specialized strains and species, and the inherent division of labor reduces the metabolic burden for each strain while increasing product yields and reaction specificities. However, consortium-based designs still suffer from a lack of available tools to control the behavior and performance of the individual subpopulations and of the entire consortium. Here, we propose to implement novel control elements for microbial consortia based on artificial cell-cell communication via fungal mating pheromones. Coupling to the desired output is mediated by pheromone-responsive gene expression, thereby creating pheromone-dependent communication channels between different subpopulations of the consortia. We highlight the benefits of artificial communication to specifically target individual subpopulations of microbial consortia and to control e.g. their metabolic profile or proliferation rate in a predefined and customized manner. Due to the steadily increasing knowledge of sexual cycles of industrially relevant fungi, a growing number of strains and species can be integrated into pheromone-controlled sensor-actor systems, exploiting their unique metabolic properties for microbial consortia approaches.

Single-cell profiling screen identifies microtubule-dependent reduction of variability in signaling

Populations of isogenic cells often respond coherently to signals, despite differences in protein abundance and cell state. Previously, we uncovered processes in the Saccharomyces cerevisiae pheromone response system (PRS) that reduced cell-to-cell variability in signal strength and cellular response. Here, we screened 1,141 non-essential genes to identify 50 "variability genes". Most had distinct, separable effects on strength and variability of the PRS, defining these quantities as genetically distinct "axes" of system behavior. Three genes affected cytoplasmic microtubule function: BIM1, GIM2, and GIM4 We used genetic and chemical perturbations to show that, without microtubules, PRS output is reduced but variability is unaffected, while, when microtubules are present but their function is perturbed, output is sometimes lowered, but its variability is always high. The increased variability caused by microtubule perturbations required the PRS MAP kinase Fus3 and a process at or upstream of Ste5, the membrane-localized scaffold to which Fus3 must bind to be activated. Visualization of Ste5 localization dynamics demonstrated that perturbing microtubules destabilized Ste5 at the membrane signaling site. The fact that such microtubule perturbations cause aberrant fate and polarity decisions in mammals suggests that microtubule-dependent signal stabilization might also operate throughout metazoans.

Quantification of sterol-specific response in human macrophages using automated imaged-based analysis

BACKGROUND

The transformation of normal macrophage cells into lipid-laden foam cells is an important step in the progression of atherosclerosis. One major contributor to foam cell formation in vivo is the intracellular accumulation of cholesterol.

METHODS

Here, we report the effects of various combinations of low-density lipoprotein, sterols, lipids and other factors on human macrophages, using an automated image analysis program to quantitatively compare single cell properties, such as cell size and lipid content, in different conditions.

RESULTS

We observed that the addition of cholesterol caused an increase in average cell lipid content across a range of conditions. All of the sterol-lipid mixtures examined were capable of inducing increases in average cell lipid content, with variations in the distribution of the response, in cytotoxicity and in how the sterol-lipid combination interacted with other activating factors. For example, cholesterol and lipopolysaccharide acted synergistically to increase cell lipid content while also increasing cell survival compared with the addition of lipopolysaccharide alone. Additionally, ergosterol and cholesteryl hemisuccinate caused similar increases in lipid content but also exhibited considerably greater cytotoxicity than cholesterol.

CONCLUSIONS

The use of automated image analysis enables us to assess not only changes in average cell size and content, but also to rapidly and automatically compare population distributions based on simple fluorescence images. Our observations add to increasing understanding of the complex and multifactorial nature of foam-cell formation and provide a novel approach to assessing the heterogeneity of macrophage response to a variety of factors.

Distributing tasks via multiple input pathways increases cellular survival in stress

Improving in one aspect of a task can undermine performance in another, but how such opposing demands play out in single cells and impact on fitness is mostly unknown. Here we study budding yeast in dynamic environments of hyperosmotic stress and show how the corresponding signalling network increases cellular survival both by assigning the requirements of high response speed and high response accuracy to two separate input pathways and by having these pathways interact to converge on Hog1, a p38 MAP kinase. Cells with only the less accurate, reflex-like pathway are fitter in sudden stress, whereas cells with only the slow, more accurate pathway are fitter in increasing but fluctuating stress. Our results demonstrate that cellular signalling is vulnerable to trade-offs in performance, but that these trade-offs can be mitigated by assigning the opposing tasks to different signalling subnetworks. Such division of labour could function broadly within cellular signal transduction.

DOI:http://dx.doi.org/10.7554/eLife.21415.001

The faster we do tasks the harder it is to do them well. For example, when we wish to judge if, say, a cup, is too hot, we first quickly withdraw our hand after touching it: we know that the cup is hot but not how much. Next we hold a finger against the cup to accurately judge its temperature. Such speed-accuracy trade-offs are studied widely in fields ranging from neuroscience to engineering, but their consequences for single cells are unknown. This is despite the fact that when cells are exposed to stress they must respond both quickly (to survive) and accurately (to reduce how many resources they consume).

One way of stressing yeast cells is to place them in a syrupy substance called sorbitol. This causes the cells to lose water, shrink in size, and launch a stress response to regain volume. If the cells respond inappropriately to the situation, they may die.

The signalling network that produces the stress response is unusual in that it has a Y-shaped structure, where the two ‘arms’ of the Y are the input pathways. Although it was known that one input pathway responds to stress faster than the other, the advantages of having two inputs in the signalling network were not understood. Granados, Crane et al. thought that the differences in speed and the Y-shaped structure could allow the cell to respond to stress with both speed and accuracy.

To investigate this theory, Granados, Crane et al. used a microscope to study individual yeast cells that had been exposed to sorbitol. Combining these results with a mathematical model of the cell signalling network revealed that a mutant yeast cell that only has one of the input pathways specializes in speed but is inaccurate, similar to a reflex-like response. In contrast, a mutant with only the other pathway specializes in accuracy, being slower but matching the level of the cell’s response to the level of stress placed on it. This trade-off is reflected in rates of cell survival: the first mutant survives best in sudden shocks of stress; the second mutant survives best in gradually increasing stress. Normal yeast cells that have both input pathways survive more often than either mutant.

Overall, the results presented by Granados, Crane et al. reveal principles behind cellular decision-making that should hold true in more complex organisms and could be exploited by synthetic biologists to programme cells with new behaviours.

DOI:http://dx.doi.org/10.7554/eLife.21415.002

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.

MitoGen: A Framework for Generating 3D Synthetic Time-Lapse Sequences of Cell Populations in Fluorescence Microscopy

The proper analysis of biological microscopy images is an important and complex task. Therefore, it requires verification of all steps involved in the process, including image segmentation and tracking algorithms. It is generally better to verify algorithms with computer-generated ground truth datasets, which, compared to manually annotated data, nowadays have reached high quality and can be produced in large quantities even for 3D time-lapse image sequences. Here, we propose a novel framework, called MitoGen, which is capable of generating ground truth datasets with fully 3D time-lapse sequences of synthetic fluorescence-stained cell populations. MitoGen shows biologically justified cell motility, shape and texture changes as well as cell divisions. Standard fluorescence microscopy phenomena such as photobleaching, blur with real point spread function (PSF), and several types of noise, are simulated to obtain realistic images. The MitoGen framework is scalable in both space and time. MitoGen generates visually plausible data that shows good agreement with real data in terms of image descriptors and mean square displacement (MSD) trajectory analysis. Additionally, it is also shown in this paper that four publicly available segmentation and tracking algorithms exhibit similar performance on both real and MitoGen-generated data. The implementation of MitoGen is freely available.

Optogenetic Regulation of Tunable Gene Expression in Yeast Using Photo-Labile Caged Methionine

Light-mediated gene expression enables the noninvasive regulation of cellular functions. Apart from their classical application of regulating single cells with high spatiotemporal resolution, we highlight the potential of light-mediated gene expression for biotechnological issues. Here, we demonstrate the first light-mediated gene regulation in Saccharomyces cerevisiae using the repressible pMET17 promoter and the photolabile NVOC methionine that releases methionine upon irradiation with UVA light. In this system, the expression can be repressed upon irradiation and is reactivated due to consumption of methionine. The photolytic release allows precise control over the methionine concentration and therefore over the repression duration. Using this light regulation mechanism, we were able to apply an in-house constructed 48-well cultivation system which allows parallelized and automated irradiation programs as well as online detection of fluorescence and growth. This system enables screening of multiple combinations of several repression/derepression intervals to realize complex expression programs (e.g., a stepwise increase of temporally constant expression levels, linear expression rates with variable slopes, and accurate control over the expression induction, although we used a repressible promoter.) Thus, we were able to control all general parameters of a gene expression experiment precisely, namely start, pause, and stop at desired time points, as well as the ongoing expression rate. Furthermore, we gained detailed insights into single-cell expression dynamics with spatiotemporal resolution by applying microfluidics cultivation technology combined with fluorescence time-lapse microscopy.

Digital Quantification of Proteins and mRNA in Single Mammalian Cells

Absolute quantification of macromolecules in single cells is critical for understanding and modeling biological systems that feature cellular heterogeneity. Here we show extremely sensitive and absolute quantification of both proteins and mRNA in single mammalian cells by a very practical workflow that combines proximity ligation assay (PLA) and digital PCR. This digital PLA method has femtomolar sensitivity, which enables the quantification of very small protein concentration changes over its entire 3-log dynamic range, a quality necessary for accounting for single-cell heterogeneity. We counted both endogenous (CD147) and exogenously expressed (GFP-p65) proteins from hundreds of single cells and determined the correlation between CD147 mRNA and the protein it encodes. Using our data, a stochastic two-state model of the central dogma was constructed and verified using joint mRNA/protein distributions, allowing us to estimate transcription burst sizes and extrinsic noise strength and calculate the transcription and translation rate constants in single mammalian cells.

Using measures of single-cell physiology and physiological state to understand organismic aging

Genetically identical organisms in homogeneous environments have different lifespans and healthspans. These differences are often attributed to stochastic events, such as mutations and 'epimutations', changes in DNA methylation and chromatin that change gene function and expression. But work in the last 10 years has revealed differences in lifespan- and health-related phenotypes that are not caused by lasting changes in DNA or identified by modifications to DNA or chromatin. This work has demonstrated persistent differences in single-cell and whole-organism physiological states operationally defined by values of reporter gene signals in living cells. While some single-cell states, for example, responses to oxygen deprivation, were defined previously, others, such as a generally heightened ability to make proteins, were, revealed by direct experiment only recently, and are not well understood. Here, we review technical progress that promises to greatly increase the number of these measurable single-cell physiological variables and measureable states. We discuss concepts that facilitate use of single-cell measurements to provide insight into physiological states and state transitions. We assert that researchers will use this information to relate cell level physiological readouts to whole-organism outcomes, to stratify aging populations into groups based on different physiologies, to define biomarkers predictive of outcomes, and to shed light on the molecular processes that bring about different individual physiologies. For these reasons, quantitative study of single-cell physiological variables and state transitions should provide a valuable complement to genetic and molecular explanations of how organisms age.

Mapping regions in Ste5 that support Msn5-dependent and -independent nuclear export

Careful control of the available pool of the MAPK scaffold Ste5 is important for mating-pathway activation and the prevention of inappropriate mating differentiation in haploid Saccharomyces cerevisiae. Ste5 shuttles constitutively through the nucleus, where it is degraded by a ubiquitin-dependent mechanism triggered by G1 CDK phosphorylation. Here we narrow-down regions of Ste5 that mediate nuclear export. Four regions in Ste5 relocalize SV40-TAgNLS-GFP-GFP from nucleus to cytoplasm. One region is N-terminal, dependent on exportin Msn5/Ste21/Kap142, and interacts with Msn5 in 2 hybrid assays independently of mating pheromone, Fus3, Kss1, Ptc1, the NLS/PM, and RING-H2. A second region overlaps the PH domain and Ste11 binding site and 2 others are on the vWA domain and include residues essential for MAPK activation. We find no evidence for dependence on Crm1/Xpo1, despite numerous potential nuclear export sequences (NESs) detected by LocNES and NetNES1.1 predictors. Thus, Msn5 (homolog of human Exportin-5) and one or more exportins or adaptor molecules besides Crm1/Xpo1 may regulate Ste5 through multiple recognition sites.

Protein turnover analysis in Salmonella Typhimurium during infection by dynamic SILAC, Topograph, and quantitative proteomics

Protein turnover affects protein abundance and phenotypes. Comprehensive investigation of protein turnover dynamics has the potential to provide substantial information about gene expression. Here we report a large-scale protein turnover study in Salmonella Typhimurium during infection by quantitative proteomics. Murine macrophage-like RAW 264.7 cells were infected with SILAC labeled Salmonella. Bacterial cells were extracted after 0, 30, 60, 120, and 240 min. Mass spectrometry analyses yielded information about Salmonella protein turnover dynamics and a software program named Topograph was used for the calculation of protein half lives. The half lives of 311 proteins from intracellular Salmonella were obtained. For bacteria cultured in control medium (DMEM), the half lives for 870 proteins were obtained. The calculated median of protein half lives was 69.13 and 99.30 min for the infection group and the DMEM group, respectively, indicating an elevated protein turnover at the initial stage of infection. Gene ontology analyses revealed that a number of protein functional groups were significantly regulated by infection, including proteins involved in ribosome, periplasmic space, cellular amino acid metabolic process, ion binding, and catalytic activity. The half lives of proteins involved in purine metabolism pathway were found to be significantly shortened during infection.

Characterization of Intrinsic Properties of Promoters

Accurate characterization of promoter behavior is essential for the rational design of functional synthetic transcription networks such as logic gates and oscillators. However, transcription rates observed from promoters can vary significantly depending on the growth rate of host cells and the experimental and genetic contexts of the measurement. Furthermore, in vivo measurement methods must accommodate variation in translation, protein folding, and maturation rates of reporter proteins, as well as metabolic load. The external factors affecting transcription activity may be considered to be extrinsic, and the goal of characterization should be to obtain quantitative measures of the intrinsic characteristics of promoters. We have developed a promoter characterization method that is based on a mathematical model for cell growth and reporter gene expression and exploits multiple in vivo measurements to compensate for variation due to extrinsic factors. First, we used optical density and fluorescent reporter gene measurements to account for the effect of differing cell growth rates. Second, we compared the output of reporter genes to that of a control promoter using concurrent dual-channel fluorescence measurements. This allowed us to derive a quantitative promoter characteristic (ρ) that provides a robust measure of the intrinsic properties of a promoter, relative to the control. We imposed different extrinsic factors on growing cells, altering carbon source and adding bacteriostatic agents, and demonstrated that the use of ρ values reduced the fraction of variance due to extrinsic factors from 78% to less than 4%. This is a simple and reliable method to quantitatively describe promoter properties.

Survey statistics of automated segmentations applied to optical imaging of mammalian cells

BACKGROUND

The goal of this survey paper is to overview cellular measurements using optical microscopy imaging followed by automated image segmentation. The cellular measurements of primary interest are taken from mammalian cells and their components. They are denoted as two- or three-dimensional (2D or 3D) image objects of biological interest. In our applications, such cellular measurements are important for understanding cell phenomena, such as cell counts, cell-scaffold interactions, cell colony growth rates, or cell pluripotency stability, as well as for establishing quality metrics for stem cell therapies. In this context, this survey paper is focused on automated segmentation as a software-based measurement leading to quantitative cellular measurements.

METHODS

We define the scope of this survey and a classification schema first. Next, all found and manually filteredpublications are classified according to the main categories: (1) objects of interests (or objects to be segmented), (2) imaging modalities, (3) digital data axes, (4) segmentation algorithms, (5) segmentation evaluations, (6) computational hardware platforms used for segmentation acceleration, and (7) object (cellular) measurements. Finally, all classified papers are converted programmatically into a set of hyperlinked web pages with occurrence and co-occurrence statistics of assigned categories.

RESULTS

The survey paper presents to a reader: (a) the state-of-the-art overview of published papers about automated segmentation applied to optical microscopy imaging of mammalian cells, (b) a classification of segmentation aspects in the context of cell optical imaging, (c) histogram and co-occurrence summary statistics about cellular measurements, segmentations, segmented objects, segmentation evaluations, and the use of computational platforms for accelerating segmentation execution, and (d) open research problems to pursue.

CONCLUSIONS

The novel contributions of this survey paper are: (1) a new type of classification of cellular measurements and automated segmentation, (2) statistics about the published literature, and (3) a web hyperlinked interface to classification statistics of the surveyed papers at https://isg.nist.gov/deepzoomweb/resources/survey/index.html.

Development of an image-based screening system for inhibitors of the plastidial MEP pathway and of protein geranylgeranylation

In a preceding study we have recently established an in vivo visualization system for the geranylgeranylation of proteins in a stably transformed tobacco BY-2 cell line, which involves expressing a dexamethasone-inducible GFP fused to the prenylable, carboxy-terminal basic domain of the rice calmodulin CaM61, which naturally bears a CaaL geranylgeranylation motif (GFP-BD-CVIL). By using pathway-specific inhibitors it was there demonstrated that inhibition of the methylerythritol phosphate (MEP) pathway with oxoclomazone and fosmidomycin, as well as inhibition of protein geranylgeranyl transferase type 1 (PGGT-1), shifted the localization of the GFP-BD-CVIL protein from the membrane to the nucleus. In contrast, the inhibition of the mevalonate (MVA) pathway with mevinolin did not affect this localization. Furthermore, in this initial study complementation assays with pathway-specific intermediates confirmed that the precursors for the cytosolic isoprenylation of this fusion protein are predominantly provided by the MEP pathway. In order to optimize this visualization system from a more qualitative assay to a statistically trustable medium or a high-throughput screening system, we established now new conditions that permit culture and analysis in 96-well microtiter plates, followed by fluorescence microscopy. For further refinement, the existing GFP-BD-CVIL cell line was transformed with an estradiol-inducible vector driving the expression of a RFP protein, C-terminally fused to a nuclear localization signal (NLS-RFP). We are thus able to quantify the total number of viable cells versus the number of inhibited cells after various treatments. This approach also includes a semi-automatic counting system, based on the freely available image processing software. As a result, the time of image analysis as well as the risk of user-generated bias is reduced to a minimum. Moreover, there is no cross-induction of gene expression by dexamethasone and estradiol, which is an important prerequisite for this test system.

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.

Temporal hierarchy of gene expression mediated by transcription factor binding affinity and activation dynamics

Understanding cellular responses to environmental stimuli requires not only the knowledge of specific regulatory components but also the quantitative characterization of the magnitude and timing of regulatory events. The two-component system is one of the major prokaryotic signaling schemes and is the focus of extensive interest in quantitative modeling and investigation of signaling dynamics. Here we report how the binding affinity of the PhoB two-component response regulator (RR) to target promoters impacts the level and timing of expression of PhoB-regulated genes. Information content has often been used to assess the degree of conservation for transcription factor (TF)-binding sites. We show that increasing the information content of PhoB-binding sites in designed phoA promoters increased the binding affinity and that the binding affinity and concentration of phosphorylated PhoB (PhoB~P) together dictate the level and timing of expression of phoA promoter variants. For various PhoB-regulated promoters with distinct promoter architectures, expression levels appear not to be correlated with TF-binding affinities, in contrast to the intuitive and oversimplified assumption that promoters with higher affinity for a TF tend to have higher expression levels. However, the expression timing of the core set of PhoB-regulated genes correlates well with the binding affinity of PhoB~P to individual promoters and the temporal hierarchy of gene expression appears to be related to the function of gene products during the phosphate starvation response. Modulation of the information content and binding affinity of TF-binding sites may be a common strategy for temporal programming of the expression profile of RR-regulated genes.

IMPORTANCE

A single TF often orchestrates the expression of multiple genes in response to environmental stimuli. It is not clear how different TF-binding sites within the regulon dictate the expression profile. Our studies of Escherichia coli PhoB, a response regulator that controls expression of a core set of phosphate assimilation genes in response to phosphate starvation, showed that expression levels of PhoB-regulated genes are under sophisticated control and do not follow a simple correlation with the binding affinity of PhoB~P to individual promoters. However, the expression timing correlates with the PhoB-binding affinity and gene functions. Genes involved in direct Pi uptake contain high-affinity sites and are transcribed earlier than genes involved in phosphorus scavenging. This illustrates an elaborate mechanism of temporally programmed gene expression, even for nondevelopmental pathways.

Single Cell Quantification of Reporter Gene Expression in Live Adult Caenorhabditis elegans Reveals Reproducible Cell-Specific Expression Patterns and Underlying Biological Variation

In multicellular organisms such as Caenorhabditis elegans, differences in complex phenotypes such as lifespan correlate with the level of expression of particular engineered reporter genes. In single celled organisms, quantitative understanding of responses to extracellular signals and of cell-to-cell variation in responses has depended on precise measurement of reporter gene expression. Here, we developed microscope-based methods to quantify reporter gene expression in cells of Caenorhabditis elegans with low measurement error. We then quantified expression in strains that carried different configurations of P_hsp-16.2-fluorescent-protein reporters, in whole animals, and in all 20 cells of the intestine tissue, which is responsible for most of the fluorescent signal. Some animals bore more recently developed single copy P_hsp-16.2 reporters integrated at defined chromosomal sites, others, “classical” multicopy reporter gene arrays integrated at random sites. At the level of whole animals, variation in gene expression was similar: strains with single copy reporters showed the same amount of animal-to-animal variation as strains with multicopy reporters. At the level of cells, in animals with single copy reporters, the pattern of expression in cells within the tissue was highly stereotyped. In animals with multicopy reporters, the cell-specific expression pattern was also stereotyped, but distinct, and somewhat more variable. Our methods are rapid and gentle enough to allow quantification of expression in the same cells of an animal at different times during adult life. They should allow investigators to use changes in reporter expression in single cells in tissues as quantitative phenotypes, and link those to molecular differences. Moreover, by diminishing measurement error, they should make possible dissection of the causes of the remaining, real, variation in expression. Understanding such variation should help reveal its contribution to differences in complex phenotypic outcomes in multicellular organisms.