Cellular organization in lab-evolved and extant multicellular species obeys a maximum entropy law.

The prevalence of multicellular organisms is due in part to their ability to form complex structures. How cells pack in these structures is a fundamental biophysical issue, underlying their functional properties. However, much remains unknown about how cell packing geometries arise, and how they are affected by random noise during growth - especially absent developmental programs. Here, we quantify the statistics of cellular neighborhoods of two different multicellular eukaryotes: lab-evolved 'snowflake' yeast and the green alga Volvox carteri. We find that despite large differences in cellular organization, the free space associated with individual cells in both organisms closely fits a modified gamma distribution, consistent with maximum entropy predictions originally developed for granular materials. This 'entropic' cellular packing ensures a degree of predictability despite noise, facilitating parent-offspring fidelity even in the absence of developmental regulation. Together with simulations of diverse growth morphologies, these results suggest that gamma-distributed cell neighborhood sizes are a general feature of multicellularity, arising from conserved statistics of cellular packing.

Dependence of Graphene Oxide (GO) Toxicity on Oxidation Level, Elemental Composition, and Size.

The mass production of graphene oxide (GO) unavoidably elevates the chance of human exposure, as well as the possibility of release into the environment with high stability, raising public concern as to its potential toxicological risks and the implications for humans and ecosystems. Therefore, a thorough assessment of GO toxicity, including its potential reliance on key physicochemical factors, which is lacking in the literature, is of high significance and importance. In this study, GO toxicity, and its dependence on oxidation level, elemental composition, and size, were comprehensively assessed. A newly established quantitative toxicogenomic-based toxicity testing approach, combined with conventional phenotypic bioassays, were employed. The toxicogenomic assay utilized a GFP-fused yeast reporter library covering key cellular toxicity pathways. The results reveal that, indeed, the elemental composition and size do exert impacts on GO toxicity, while the oxidation level exhibits no significant effects. The UV-treated GO, with significantly higher carbon-carbon groups and carboxyl groups, showed a higher toxicity level, especially in the protein and chemical stress categories. With the decrease in size, the toxicity level of the sonicated GOs tended to increase. It is proposed that the covering and subsequent internalization of GO sheets might be the main mode of action in yeast cells.

In-vivo Single-Molecule Imaging in Yeast: Applications and Challenges.

Single-molecule imaging has gained momentum to quantify the dynamics of biomolecules in live cells, as it provides direct real-time measurements of various cellular activities under their physiological environment. Yeast, a simple and widely used eukaryote, serves as a good model system to quantify single-molecule dynamics of various cellular processes because of its low genomic and cellular complexities, as well as its facile ability to be genetically manipulated. In the past decade, significant developments have been made regarding the intracellular labeling of biomolecules (proteins, mRNA, fatty acids), the microscopy setups to visualize single-molecules and capture their fast dynamics, and the data analysis pipelines to interpret such dynamics. In this review, we summarize the current state of knowledge for the single-molecule imaging in live yeast cells to provide a ready reference for beginners. We provide a comprehensive table to demonstrate how various labs tailored the imaging regimes and data analysis pipelines to estimate various biophysical parameters for a variety of biological processes. Lastly, we present current challenges and future directions for developing better tools and resources for single-molecule imaging in live yeast cells.

Yeast Cells in Microencapsulation. General Features and Controlling Factors of the Encapsulation Process.

Besides their best-known uses in the food and fermentation industry, yeasts have also found application as microcapsules. In the encapsulation process, exogenous and most typically hydrophobic compounds diffuse and end up being passively entrapped in the cell body, and can be released upon application of appropriate stimuli. Yeast cells can be employed either living or dead, intact, permeabilized, or even emptied of all their original cytoplasmic contents. The main selling points of this set of encapsulation technologies, which to date has predominantly targeted food and-to a lesser extent-pharmaceutical applications, are the low cost, biodegradability and biocompatibility of the capsules, coupled to their sustainable origin (e.g., spent yeast from brewing). This review aims to provide a broad overview of the different kinds of yeast-based microcapsules and of the main physico-chemical characteristics that control the encapsulation process and its efficiency.

Complementary performances of convolutional and capsule neural networks on classifying microfluidic images of dividing yeast cells.

Microfluidic-based assays have become effective high-throughput approaches to examining replicative aging of budding yeast cells. Deep learning may offer an efficient way to analyze a large number of images collected from microfluidic experiments. Here, we compare three deep learning architectures to classify microfluidic time-lapse images of dividing yeast cells into categories that represent different stages in the yeast replicative aging process. We found that convolutional neural networks outperformed capsule networks in terms of accuracy, precision, and recall. The capsule networks had the most robust performance in detecting one specific category of cell images. An ensemble of three best-fitted single-architecture models achieves the highest overall accuracy, precision, and recall due to complementary performances. In addition, extending classification classes and data augmentation of the training dataset can improve the predictions of the biological categories in our study. This work lays a useful framework for sophisticated deep-learning processing of microfluidic-based assays of yeast replicative aging.

Long-Term Imaging and Dynamic Analysis of Cytoophidia in Yeast.

Live-cell imaging is widely used by researchers to study cellular dynamics and obtain a deep understanding of cell biological processes. Keeping cells in the proper growing environment and immobilizing the cells are essential for the imaging of live yeast cells. Here we describe a protocol for monitoring cytoophidia in Saccharomyces cerevisiae and Schizosaccharomyces pombe using inverted confocal fluorescence microscopy. This protocol includes yeast culture, sample preparation, fluorescence imaging, and data analysis.

A Rab escort protein regulates the MAPK pathway that controls filamentous growth in yeast.

MAPK pathways regulate different responses yet can share common components. Although core regulators of MAPK pathways are well known, new pathway regulators continue to be identified. Overexpression screens can uncover new roles for genes in biological processes and are well suited to identify essential genes that cannot be evaluated by gene deletion analysis. In this study, a genome-wide screen was performed to identify genes that, when overexpressed, induce a reporter (FUS1-HIS3) that responds to ERK-type pathways (Mating and filamentous growth or fMAPK) but not p38-type pathways (HOG) in yeast. Approximately 4500 plasmids overexpressing individual yeast genes were introduced into strains containing the reporter by high-throughput transformation. Candidate genes were identified by measuring growth as a readout of reporter activity. Fourteen genes were identified and validated by re-testing: two were metabolic controls (HIS3, ATR1), five had established roles in regulating ERK-type pathways (STE4, STE7, BMH1, BMH2, MIG2) and seven represent potentially new regulators of MAPK signaling (RRN6, CIN5, MRS6, KAR2, TFA1, RSC3, RGT2). MRS6 encodes a Rab escort protein and effector of the TOR pathway that plays a role in nutrient signaling. MRS6 overexpression stimulated invasive growth and phosphorylation of the ERK-type fMAPK, Kss1. Overexpression of MRS6 reduced the osmotolerance of cells and phosphorylation of the p38/HOG MAPK, Hog1. Mrs6 interacted with the PAK kinase Ste20 and MAPKK Ste7 by two-hybrid analysis. Based on these results, Mrs6 may selectively propagate an ERK-dependent signal. Identifying new regulators of MAPK pathways may provide new insights into signal integration among core cellular processes and the execution of pathway-specific responses.

Evolutionary Overview of Molecular Interactions and Enzymatic Activities in the Yeast Cell Walls.

Fungal cell walls are composed of a polysaccharide network that serves as a scaffold in which different glycoproteins are embedded. Investigation of fungal cell walls, besides simple identification and characterization of the main cell wall building blocks, covers the pathways and regulations of synthesis of each individual component of the wall and biochemical reactions by which they are cross-linked and remodeled in response to different growth phase and environmental signals. In this review, a survey of composition and organization of so far identified and characterized cell wall components of different yeast genera including Saccharomyces, Candida, Kluyveromyces, Yarrowia, and Schizosaccharomyces are presented with the focus on their cell wall proteomes.

Lysing Yeast Cells for Immunoprecipitation Using a Coffee Grinder.

This protocol describes the freezing of yeast in liquid nitrogen (LN2) to form small "beans" that can be ground using a simple propeller-blade coffee grinder. The method is ideally suited for lysate preparations from larger yeast cultures ranging from 50 mL to 5 L and displays the advantage that samples remain cold during the preparative steps. Cells are cultured and collected by centrifugation while in log phase, and the resultant cell pellets are mixed with deionized distilled water and dropped into LN2 to form small frozen beans. Before the freezing process, it is imperative to keep all cell pellets at 4°C on ice. The frozen yeast beans are ground by using a simple kitchen coffee grinder, and the yeast powder is collected for immediate lysis or storage at -80°C for subsequent use. Protective clothing and safety glasses should be worn at all times when working with liquid nitrogen. Plasticware may shatter upon repeated cooling in liquid nitrogen, and appropriate care should be taken.

Conventional BODIPY Conjugates for Live-Cell Super-Resolution Microscopy and Single-Molecule Tracking.

Single molecule localization microscopy (SMLM) techniques overcome the optical diffraction limit of conventional fluorescence microscopy and can resolve intracellular structures and the dynamics of biomolecules with ~20 nm precision. A prerequisite for SMLM are fluorophores that transition from a dark to a fluorescent state in order to avoid spatio-temporal overlap of their point spread functions in each of the thousands of data acquisition frames. BODIPYs are well-established dyes with numerous conjugates used in conventional microscopy. The transient formation of red-shifted BODIPY ground-state dimers (DII) results in bright single molecule emission enabling single molecule localization microscopy (SMLM). Here we present a simple but versatile protocol for SMLM with conventional BODIPY conjugates in living yeast and mammalian cells. This procedure can be used to acquire super-resolution images and to track single BODIPY-DII states to extract spatio-temporal information of BODIPY conjugates. We apply this procedure to resolve lipid droplets (LDs), fatty acids, and lysosomes in living yeast and mammalian cells at the nanoscopic length scale. Furthermore, we demonstrate the multi-color imaging capability with BODIPY dyes when used in conjunction with other fluorescent probes. Our representative results show the differential spatial distribution and mobility of BODIPY-fatty acids and neutral lipids in yeast under fed and fasted conditions. This optimized protocol for SMLM can be used with hundreds of commercially available BODIPY conjugates and is a useful resource to study biological processes at the nanoscale far beyond the applications of this work.

Application of dielectric spectroscopy to unravel the physiological state of microorganisms: current state, prospects and limits.

Microbial physiology is an essential characteristic to be considered in the research and industrial use of microorganisms. Conventionally, the study of microbial physiology has been limited to carrying out qualitative and quantitative analysis of the role of individual components in global cell behaviour at a specific time and under certain growth conditions. In this framework, groups of observable cell physiological variables that remain over time define the physiological states. Recently, with advances in omics techniques, it has been possible to demonstrate that microbial physiology is a dynamic process and that, even with low variations in environmental culture conditions, physiological changes in the cell are provoked. However, the changes cannot be detected at a macroscopic level, and it is not possible to observe these changes in real time. As an alternative to solve this inconvenience, dielectric spectroscopy has been used as a complementary technique to monitor on-line cell physiology variations to avoid long waiting times during measurements. In this review, we discuss the state-of-the-art application of dielectric spectroscopy to unravel the physiological state of microorganisms, its current state, prospects and limitations during fermentation processes. Key points • Summary of the state of the art of several issues of dielectric spectroscopy. • Discussion of correlation among dielectric properties and cell physiological states. • View of the potential use of dielectric spectroscopy in monitoring bioprocesses.

An appreciation of the prescience of Don Gilbert (1930-2011): master of the theory and experimental unravelling of biochemical and cellular oscillatory dynamics.

We review Don Gilbert's pioneering seminal contributions that both detailed the mathematical principles and the experimental demonstration of several of the key dynamic characteristics of life. Long before it became evident to the wider biochemical community, Gilbert proposed that cellular growth and replication necessitate autodynamic occurrence of cycles of oscillations that initiate, coordinate and terminate the processes of growth, during which all components are duplicated and become spatially re-organised in the progeny. Initiation and suppression of replication exhibit switch-like characteristics, that is, bifurcations in the values of parameters that separate static and autodynamic behaviour. His limit cycle solutions present models developed in a series of papers reported between 1974 and 1984, and these showed that most or even all of the major facets of the cell division cycle could be accommodated. That the cell division cycle may be timed by a multiple of shorter period (ultradian) rhythms, gave further credence to the central importance of oscillatory phenomena and homeodynamics as evident on multiple time scales (seconds to hours). Further application of the concepts inherent in limit cycle operation as hypothesised by Gilbert more than 50 years ago are now validated as being applicable to oscillatory transcript, metabolite and enzyme levels, cellular differentiation, senescence, cancerous states and cell death. Now, we reiterate especially for students and young colleagues, that these early achievements were even more exceptional, as his own lifetime's work on modelling was continued with experimental work in parallel with his predictions of the major current enterprises of biological research.

Analyzing chromosome condensation in yeast by second-harmonic generation microscopy.

Condensation is a fundamental property of mitotic chromosomes in eukaryotic cells. However, analyzing chromosome condensation in yeast is a challenging task while existing methods have notable weaknesses. Second-harmonic generation (SHG) microscopy is a label-free, advanced imaging technique for measuring the surface curve of isotropic molecules such as chromatin in live cells. We applied this method to detect changes in chromatin organization throughout the cell cycle in live yeast cells. We showed that SHG microscopy can be used to identify changes in chromatin organization throughout the cell cycle and in response to inactivation of the SMC complexes, cohesin and condensin. Implementation of this method will improve our ability to analyze chromatin structure in protozoa and will enhance our understanding of chromatin organization in eukaryotic cells.

NNVA: Neural Network Assisted Visual Analysis of Yeast Cell Polarization Simulation.

Complex computational models are often designed to simulate real-world physical phenomena in many scientific disciplines. However, these simulation models tend to be computationally very expensive and involve a large number of simulation input parameters, which need to be analyzed and properly calibrated before the models can be applied for real scientific studies. We propose a visual analysis system to facilitate interactive exploratory analysis of high-dimensional input parameter space for a complex yeast cell polarization simulation. The proposed system can assist the computational biologists, who designed the simulation model, to visually calibrate the input parameters by modifying the parameter values and immediately visualizing the predicted simulation outcome without having the need to run the original expensive simulation for every instance. Our proposed visual analysis system is driven by a trained neural network-based surrogate model as the backend analysis framework. In this work, we demonstrate the advantage of using neural networks as surrogate models for visual analysis by incorporating some of the recent advances in the field of uncertainty quantification, interpretability and explainability of neural network-based models. We utilize the trained network to perform interactive parameter sensitivity analysis of the original simulation as well as recommend optimal parameter configurations using the activation maximization framework of neural networks. We also facilitate detail analysis of the trained network to extract useful insights about the simulation model, learned by the network, during the training process. We performed two case studies, and discovered multiple new parameter configurations, which can trigger high cell polarization results in the original simulation model. We evaluated our results by comparing with the original simulation model outcomes as well as the findings from previous parameter analysis performed by our experts.

On-Chip Concentration and Patterning of Biological Cells Using Interplay of Electrical and Thermal Fields.

We demonstrate a method of concentrating and patterning of biological cells on a chip, exploiting the confluence of electric and thermal fields, without necessitating the use of any external heating or illuminating sources. The technique simply employs two parallel plate electrodes and an insulating layer over the bottom electrode, with a drilled insulating layer for inducing localized variations in the thermal field. A strong induced electric field, in the process, penetrates through the narrow hole and generates highly nonuniform heating, which in turn, results in gradients in electrical properties and induces mobile charges to impose directional fluid flow. The toroidal vortices, induced by secondary electrokinetic forces originating out of temperature-dependent electrical property variations, transport the suspended cells toward a hot-spot site of the chip, for rapid concentrating and patterning into different shaped clusters based on predesigned conditions, without exceeding safe temperature limits that do not result in damage of thermally labile biological samples. We characterize the efficacy of the cell trapping process for two different biological entities, namely, Escherichia coli bacteria and yeast cells. These results have importance toward developing biomedical microdevices for drug discovery, antibiotic resistance assessment, and medical diagnostics.

Pulling-force generation by ensembles of polymerizing actin filaments.

The process by which actin polymerization generates pulling forces in cellular processes such as endocytosis is less well understood than pushing-force generation. To clarify the basic mechanisms of pulling-force generation, we perform stochastic polymerization simulations for a square array of polymerizing semiflexible actin filaments, having different interactions with the membrane. The filaments near the array center have a strong attractive component. Filament bending and actin-network elasticity are treated explicitly. We find that the outer filaments push on the membrane and the inner filaments pull, with a net balance of forces. The total calculated pulling force is maximized when the central filaments have a very deep potential well, and the outer filaments have no well. The steady-state force is unaffected by the gel rigidity, but equilibration takes longer for softer gels. The force distributions are flat over the pulling and pushing regions. Actin polymerization is enhanced by softening the gel or reducing the filament binding to the membrane. Filament-membrane detachment can occur for softer gels, even if the total binding energy of the filaments to the membrane is 100 [Formula: see text] or more. It propagates via a stress-concentration mechanism similar to that of a brittle crack in a solid, and the breaking stress is determined by a criterion similar to that of the 'Griffith' theory of crack propagation.

Learning unsupervised feature representations for single cell microscopy images with paired cell inpainting.

Cellular microscopy images contain rich insights about biology. To extract this information, researchers use features, or measurements of the patterns of interest in the images. Here, we introduce a convolutional neural network (CNN) to automatically design features for fluorescence microscopy. We use a self-supervised method to learn feature representations of single cells in microscopy images without labelled training data. We train CNNs on a simple task that leverages the inherent structure of microscopy images and controls for variation in cell morphology and imaging: given one cell from an image, the CNN is asked to predict the fluorescence pattern in a second different cell from the same image. We show that our method learns high-quality features that describe protein expression patterns in single cells both yeast and human microscopy datasets. Moreover, we demonstrate that our features are useful for exploratory biological analysis, by capturing high-resolution cellular components in a proteome-wide cluster analysis of human proteins, and by quantifying multi-localized proteins and single-cell variability. We believe paired cell inpainting is a generalizable method to obtain feature representations of single cells in multichannel microscopy images.

Cell Size Control in Plants.

The genetic control of the characteristic cell sizes of different species and tissues is a long-standing enigma. Plants are convenient for studying this question in a multicellular context, as their cells do not move and are easily tracked and measured from organ initiation in the meristems to subsequent morphogenesis and differentiation. In this article, we discuss cell size control in plants compared with other organisms. As seen from yeast cells to mammalian cells, size homeostasis is maintained cell autonomously in the shoot meristem. In developing organs, vacuolization contributes to cell size heterogeneity and may resolve conflicts between growth control at the cellular and organ levels. Molecular mechanisms for cell size control have implications for how cell size responds to changes in ploidy, which are particularly important in plant development and evolution. We also discuss comparatively the functional consequences of cell size and their potential repercussions at higher scales, including genome evolution.

Microscopy examination of red blood and yeast cell agglutination induced by bacterial lectins.

Lectins are a group of ubiquitous proteins which specifically recognize and reversibly bind sugar moieties of glycoprotein and glycolipid constituents on cell surfaces. The mutagenesis approach is often employed to characterize lectin binding properties. As lectins are not enzymes, it is not easy to perform a rapid specificity screening of mutants using chromogenic substrates. It is necessary to use different binding assays such as isothermal titration calorimetry (ITC), surface plasmon resonance (SPR), microscale thermophoresis (MST), enzyme-linked lectin assays (ELLA), or glycan arrays for their characterization. These methods often require fluorescently labeled proteins (MST), highly purified proteins (SPR) or high protein concentrations (ITC). Mutant proteins may often exhibit problematic behaviour, such as poor solubility or low stability. Lectin-based cell agglutination is a simple and low-cost technique which can overcome most of these problems. In this work, a modified method of the agglutination of human erythrocytes and yeast cells with microscopy detection was successfully used for a specificity study of the newly prepared mutant lectin RS-IIL_A22S, which experimentally completed studies on sugar preferences of lectins in the PA-IIL family. Results showed that the sensitivity of this method is comparable with ITC, is able to determine subtle differences in lectin specificity, and works directly in cell lysates. The agglutination method with microscopy detection was validated by comparison of the results with results obtained by agglutination assay in standard 96-well microtiter plate format. In contrast to this assay, the microscopic method can clearly distinguish between hemagglutination and hemolysis. Therefore, this method is suitable for examination of lectins with known hemolytic activity as well as mutant or uncharacterized lectins, which could damage red blood cells. This is due to the experimental arrangement, which includes very short sample incubation time in combination with microscopic detection of agglutinates, that are easily observed by a small portable microscope.

Randomness in microtubule dynamics: an error that requires correction or an inherent plasticity required for normal cellular function?

Microtubules (MTs) play roles in regulating the mechanical structure and dynamics of cells. While MTs appear to be highly ordered structures, recent data suggest some randomness in their structure and dynamics. Part of this inherent randomness is attributed to errors and correction mechanisms are being investigated to overcome these 'mistakes.' However, this randomness may also be part of the normal intracellular function of MTs. It is possible that random events in MT structure and dynamics may contribute to their normal function and may even be part of an improved efficacy mechanism. An alternative view, wherein MT and kinetochore errors are part of required cell plasticity, is also discussed. These data may further support the concept of randomness in biological pathways as part of self-organization or accurate and enhanced function.