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

Ageing-dependent thiol oxidation reveals early oxidation of proteins with core proteostasis functions

Oxidative post-translational modifications of protein thiols are well recognized as a readily occurring alteration of proteins, which can modify their function and thus control cellular processes. The development of techniques enabling the site-specific assessment of protein thiol oxidation on a proteome-wide scale significantly expanded the number of known oxidation-sensitive protein thiols. However, lacking behind are large-scale data on the redox state of proteins during ageing, a physiological process accompanied by increased levels of endogenous oxidants. Here, we present the landscape of protein thiol oxidation in chronologically aged wild-type Saccharomyces cerevisiae in a time-dependent manner. Our data determine early-oxidation targets in key biological processes governing the de novo production of proteins, protein folding, and degradation, and indicate a hierarchy of cellular responses affected by a reversible redox modification. Comparison with existing datasets in yeast, nematode, fruit fly, and mouse reveals the evolutionary conservation of these oxidation targets. To facilitate accessibility, we integrated the cross-species comparison into the newly developed OxiAge Database.

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

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

Crucial aspects of metabolism and cell biology relating to industrial production and processing of Saccharomyces biomass

The multitude of applications to which Saccharomyces spp. are put makes these yeasts the most prolific of industrial microorganisms. This review considers biological aspects pertaining to the manufacture of industrial yeast biomass. It is proposed that the production of yeast biomass can be considered in two distinct but interdependent phases. Firstly, there is a cell replication phase that involves reproduction of cells by their transitions through multiple budding and metabolic cycles. Secondly, there needs to be a cell conditioning phase that enables the accrued biomass to withstand the physicochemical challenges associated with downstream processing and storage. The production of yeast biomass is not simply a case of providing sugar, nutrients, and other growth conditions to enable multiple budding cycles to occur. In the latter stages of culturing, it is important that all cells are induced to complete their current budding cycle and subsequently enter into a quiescent state engendering robustness. Both the cell replication and conditioning phases need to be optimized and considered in concert to ensure good biomass production economics, and optimum performance of industrial yeasts in food and fermentation applications. Key features of metabolism and cell biology affecting replication and conditioning of industrial Saccharomyces are presented. Alternatives for growth substrates are discussed, along with the challenges and prospects associated with defining the genetic bases of industrially important phenotypes, and the generation of new yeast strains."I must be cruel only to be kind: Thus bad begins, and worse remains behind." William Shakespeare: Hamlet, Act 3, Scene 4.

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.

Quiescence in Saccharomyces cerevisiae

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

Explaining Redundancy in CDK-Mediated Control of the Cell Cycle: Unifying the Continuum and Quantitative Models

In eukaryotes, cyclin-dependent kinases (CDKs) are required for the onset of DNA replication and mitosis, and distinct CDK–cyclin complexes are activated sequentially throughout the cell cycle. It is widely thought that specific complexes are required to traverse a point of commitment to the cell cycle in G1, and to promote S-phase and mitosis, respectively. Thus, according to a popular model that has dominated the field for decades, the inherent specificity of distinct CDK–cyclin complexes for different substrates at each phase of the cell cycle generates the correct order and timing of events. However, the results from the knockouts of genes encoding cyclins and CDKs do not support this model. An alternative “quantitative” model, validated by much recent work, suggests that it is the overall level of CDK activity (with the opposing input of phosphatases) that determines the timing and order of S-phase and mitosis. We take this model further by suggesting that the subdivision of the cell cycle into discrete phases (G0, G1, S, G2, and M) is outdated and problematic. Instead, we revive the “continuum” model of the cell cycle and propose that a combination with the quantitative model better defines a conceptual framework for understanding cell cycle control.

Live-cell fluorescence spectral imaging as a data science challenge

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

Application of Microfluidic Systems for Breast Cancer Research

Cancer is a disease in which cells in the body grow out of control; breast cancer is the most common cancer in women in the United States. Due to early screening and advancements in therapeutic interventions, deaths from breast cancer have declined over time, although breast cancer remains the second leading cause of cancer death among women. Most deaths are due to metastasis, as cancer cells from the primary tumor in the breast form secondary tumors in remote sites in distant organs. Over many years, the basic biological mechanisms of breast cancer initiation and progression, as well as the subsequent metastatic cascade, have been studied using cell cultures and animal models. These models, although extremely useful for delineating cellular mechanisms, are poor predictors of physiological responses, primarily due to lack of proper microenvironments. In the last decade, microfluidics has emerged as a technology that could lead to a paradigm shift in breast cancer research. With the introduction of the organ-on-a-chip concept, microfluidic-based systems have been developed to reconstitute the dominant functions of several organs. These systems enable the construction of 3D cellular co-cultures mimicking in vivo tissue-level microenvironments, including that of breast cancer. Several reviews have been presented focusing on breast cancer formation, growth and metastasis, including invasion, intravasation, and extravasation. In this review, realizing that breast cancer can recur decades following post-treatment disease-free survival, we expand the discussion to account for microfluidic applications in the important areas of breast cancer detection, dormancy, and therapeutic development. It appears that, in the future, the role of microfluidics will only increase in the effort to eradicate breast cancer.