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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., Competing Interests: TD, SH, SZ, DY, AB, JP, AH, HW, HS, WR, PY No competing interests declared, RG Reviewing editor, eLife, (© 2022, Day et al.)

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., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

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.

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.

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., Competing Interests: The authors have declared that no competing interests exist.

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.

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.

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.

This protocol describes the freezing of yeast in liquid nitrogen (LN 2 ) 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 LN 2 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., (© 2020 Cold Spring Harbor Laboratory Press.)

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.

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.

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., (© 2020 The Authors. Cell Biology International published by John Wiley & Sons Ltd on behalf of International Federation of Cell Biology.)

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.

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.

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.

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.

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.

The dynamic properties of subcellular organism are important biomarkers of the health. Imaging subcellular level dynamics provides effective solutions for evaluating cell metabolism and testing the responses of cells to pathogens and drugs in pharmaceutical engineering. In this paper, we demonstrate an innovative approach to contrast the subcellular motion by using eigen decomposition (ED)-based variance analysis of time-dependent complex optical coherence tomography signals. This method reveals a superior advantage of contrast to noise ratio when compared with the approach that employs intensity decorrelation. Furthermore, the eigen values derived from ED processing are calculated and applied to assess the power ratios of complex signal invariance that decreases exponentially along time dimension. The validation experiments are performed on the patterned samples of yeast powder mixed with gelatin/TiO2 water solution. Additionally, the proposed method is used to image mouse cerebral cortex in normal and pathological conditions, suggesting the practicality of variance power mapping in analyzing cortical neural activities. The technique promises efficient measurement of subcellular motions with high sensitivity and high throughput for in vivo and in situ applications., (© 2019 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

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., Competing Interests: Oren Z Kraus and Sam Cooper are employees of Phenomic AI.

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., Competing Interests: The authors have declared that no competing interests exist.

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., (© 2019 International Federation for Cell Biology.)

Cohesin, one of structural maintenance of chromosomes (SMC) complexes, forms a ring-shaped protein complex, and mediates sister chromatid cohesion for accurate chromosome segregation and precise genome inheritance. The cohesin ring entraps one or two DNA molecules to achieve cohesion, which is further regulated by cohesin-binding proteins and modification enzymes in a cell cycle-dependent manner. Recent significant advancements in Hi-C technologies have revealed numerous cohesin-dependent higher-order chromatin structures. Simultaneously, single-molecule imaging has also unveiled the detailed dynamics of cohesin on DNA and/or chromatin. Thus, those studies are providing novel visions for the authentic chromatin structure regulated by cohesin., (Copyright © 2018. Published by Elsevier Ltd.)

Mahewu is a fermented cereal beverage produced in Zimbabwe. This study determined the composition and origin of mahewu microbiota. The microbiota of mahewu samples consisted of 3 to 7 dominant strains of lactobacilli and two strains of yeasts. Enterobacteriaceae were not detected. Candida glabrata was present in high cell counts from samples collected in summer but not from samples collected in winter. Millet malt is the only raw ingredient used in the production of mahewu and is a likely source of fermentation microbiota; therefore, malt microbiota was also analyzed by culture-dependent and high-throughput 16S rRNA gene sequencing methodologies. Millet malt contained 8 to 19 strains of Enterobacteriaceae , lactobacilli, bacilli, and very few yeasts. Strain-specific quantitative PCR assays were established on the basis of the genome sequences of Lactobacillus fermentum FUA3588 and FUA3589 and Lactobacillus plantarum FUA3590 to obtain a direct assessment of the identity of strains from malt and mahewu. L. fermentum FUA3588 and FUA3589 were detected in millet malt, demonstrating that millet malt is a main source of mahewu microbiota. Strains which were detected in summer were not detected in samples produced at the same site in winter. Model mahewu fermentations conducted with a 5-strain inoculum consisting of lactobacilli, Klebsiella pneumoniae, and Cronobacter sakazakii demonstrated that lactobacilli outcompete Enterobacteriaceae , which sharply decreased in the first 24 h. In conclusion, mahewu microbiota is mainly derived from millet malt microbiota, but minor components of malt microbiota rapidly outcompete Enterobacteriaceae and Bacillus species during fermentation. IMPORTANCE This study provides insight into the composition and origin of the microbiota of mahewu and the composition of millet malt microbiota. Fermentation microbiota are often hypothesized to be derived from the environment, but the evidence remains inconclusive. Our findings confirm that millet malt is the major source of mahewu microbiota. By complementing culture methods with high-throughput sequencing of 16S rRNA amplicons and strain-specific quantitative PCR, this study provides evidence about the source of mahewu microbiota, which can inform the development of starter cultures for mahewu production. The study also documents the fate of Enterobacteriaceae during the fermentation of mahewu. There are concerns regarding the safety of traditionally prepared mahewu, and this requires in-depth knowledge of the fermentation process. Therefore, this study elucidated millet malt microbiota and identified cultures that are able to control the high numbers of Enterobacteriaceae that are initially present in mahewu fermentations., (Copyright © 2019 American Society for Microbiology.)

A passive microfluidic device is reported for continuous microparticle enrichment. The microparticle is enriched based on the inertial effect in a microchannel with contracting-expanding structures on one side where microparticles/cells are subjected to the inertial lift force and the momentum-change-induced inertial force induced by highly curved streamlines. Under the combined effect of the two forces, yeast cells and microparticles of different sizes were continuously focused in the present device over a range of Reynolds numbers from 16.7 to 125. ∼68% of the particle-free liquid was separated from the sample at Re = 66.7, and ∼18 μL particle-free liquid was fast obtained within 10 s. Results also showed that the geometry of the contracting-expanding structure significantly influenced the lateral migration of the particle. Structures with a large angle induced strong inertial effect and weak disturbance effect of vortex on the particle, both of which enhanced the microparticle enrichment in microchannel. With simple structure, small footprint (18 × 0.35 mm), easy operation and cell-friendly property, the present device has great potential in biomedical applications, such as the enrichment of cells and the fast extraction of plasma from blood for disease diagnose and therapy., (© 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Insulator-based dielectrophoresis (iDEP), also known as electrodeless DEP, has become a well-known dielectrophoretic technique, no longer viewed as a new methodology. Significant advances on iDEP have been reported during the last 15 years. This review article aims to summarize some of the most important findings on iDEP organized by the type of dielectrophoretic mode: streaming and trapping iDEP. The former is primarily used for particle sorting, while the latter has great capability for particle enrichment. The characteristics of a wide array of devices are discussed for each type of dielectrophoretic mode in order to present an overview of the distinct designs and applications developed with iDEP. A short section on Joule heating effects and electrothermal flow is also included to highlight some of the challenges in the utilization of iDEP systems. The significant progress on iDEP illustrates its potential for a large number of applications, ranging from bioanalysis to clinical and biomedical assessments. The present article discusses the work on iDEP by numerous research groups around the world, with the aim of proving the reader with an overview of the state-of-the-art in iDEP microfluidic systems., (© 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Yeasts are efficient cell factories and are commonly used for the production of recombinant proteins for biopharmaceutical and industrial purposes. For such products high levels of correctly folded proteins are needed, which sometimes requires improvement and engineering of the expression system. The article summarizes major breakthroughs that led to the efficient use of yeasts as production platforms and reviews bottlenecks occurring during protein production. Special focus is given to the metabolic impact of protein production. Furthermore, strategies that were shown to enhance secretion of recombinant proteins in different yeast species are presented.

Cell contact formation, which is the process by which cells are brought into close proximity is an important biotechnological process in cell and molecular biology. Such manipulation is achieved by various means, among which dielectrophoresis (DEP) is widely used due to its simplicity. Here, we show the advantages in the judicious choice of the DEP microelectrode configuration in terms of limiting undesirable effects of dielectric heating on the cells, which could lead to their inactivation or death, as well as the possibility for cell clustering, which is particularly advantageous over the linear cell chain arrangement typically achieved to date with DEP. This study comprises of experimental work as well as mathematical modeling using COMSOL. In particular, we establish the parameters in a capillary-based microfluidic system giving rise to these optimum cell-cell contact configurations, together with the possibility for facilitating other cell manipulations such as spinning and rotation, thus providing useful protocols for application into microfluidic bioparticle manipulation systems for diagnostics, therapeutics or for furthering research in cellular bioelectricity and intercellular interactions.

During macroautophagy, the phagophore-mediated formation of autophagosomes and their subsequent fusion with lysosomes requires extensive transformation of the endomembrane system. Membrane dynamics in eukaryotic cells is regulated by small GTPase proteins including Arfs and Rabs. The small GTPase proteins that regulate autophagic membrane traffic are mostly conserved in yeast and metazoans, but there are also several differences. In this mini-review, we compare the small GTPase network of yeast and metazoan cells that regulates autophagy, and point out the similarities and differences in these organisms.

Background: According to the two-factor theory cryodamage arises from intracellular crystallization and solution effects due to freeze concentration., Objective: The study aims to evaluate the contribution of two types of cryodamages that are related to extra- and intracellular crystallization., Methods: The probability of intracellular crystal formation during cooling of cell suspension in cryoprotective solution has been determined based on general thermodynamics theory., Results: According to the obtained correlations and taking into account of the individual characteristics of yeast cells and murine enterocytes, the optimal cooling rates during freezing of these cells in cryoprotectant solutions were determined., Conclusion: The proposed algorithm for the estimation of the optimal cooling rates at linear freezing mode of a particular cellular suspension can be utilized to develope methods for cryopreservation of different cell suspensions.

Selective autophagy represents the major quality control mechanism that ensures proper turnover of exhausted or harmful organelles, among them the endoplasmic reticulum (ER), which is fragmented and delivered to the lysosome for degradation via a specific type of autophagy called ER-phagy. The recent discovery of ER-resident proteins that bind to mammalian Atg8 proteins has revealed that the selective elimination of ER involves different receptors that are specific for different ER subdomains or ER stresses. FAM134B (also known as RETREG1) and RTN3 are reticulon-type proteins that are able to remodel the ER network and ensure the basal membrane turnover. SEC62 and CCPG1 are transmembrane ER receptors that function in response to ER stress signals. This task sharing reflects the complexity of the ER in terms of biological functions and morphology. In this Cell Science at a Glance article and the accompanying poster, we summarize the most recent findings about ER-phagy in yeast and in mammalian cells., Competing Interests: Competing interestsThe authors declare no competing or financial interests., (© 2018. Published by The Company of Biologists Ltd.)

Optical fiber tweezers have been gaining prominence in several applications in Biology and Medicine. Due to their outstanding focusing abilities, they are able to trap and manipulate microparticles, including cells, needing any physical contact and with a low degree of invasiveness to the trapped cell. Recently, we proposed a fiber tweezer configuration based on a polymeric micro-lens on the top of a single mode fiber, obtained by a self-guided photopolymerization process. This configuration is able to both trap and identify the target through the analysis of short-term portions of the back-scattered signal. In this paper, we propose a variant of this fabrication method, capable of producing more robust fiber tips, which produce stronger trapping effects on targets by as much as two to ten fold. These novel lenses maintain the capability of distinguish the different classes of trapped particles based on the back-scattered signal. This novel fabrication method consists in the introduction of a multi mode fiber section on the tip of a single mode (SM) fiber. A detailed description of how relevant fabrication parameters such as the length of the multi mode section and the photopolymerization laser power can be tuned for different purposes (e.g., microparticles trapping only, simultaneous trapping and sensing) is also provided, based on both experimental and theoretical evidences.

Cell organelles and the plasma membrane have unique lipid compositions. Consequently, there is a gradient of lipid-dependent properties along the secretory pathway. We focus here on the steep sterol gradients that exist between the endoplasmic reticulum and plasma membrane, as well as across the plasma membrane where sterols are found principally in the cytoplasmic leaflet. Recent progress in these areas has been remarkable, with new concepts and molecules to account for non-vesicular intracellular sterol transport, and an exciting new mechanism to explain the unexpected transbilayer distribution of sterols., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Background: The inference of genetic regulatory networks (GRNs) provides insight into the cellular responses to signals. A class of recurrent neural networks (RNNs) capturing the dynamics of GRN has been used as a basis for inferring small-scale GRNs from gene expression time series. The Bayesian framework facilitates incorporating the hypothesis of GRN into the model estimation to improve the accuracy of GRN inference., Results: We present new methods for inferring small-scale GRNs based on RNNs. The weights of wires of RNN represent the strengths of gene-to-gene regulatory interactions. We use a class of automatic relevance determination (ARD) priors to enforce the sparsity in the maximum a posteriori (MAP) estimates of wire weights of RNN. A particle swarm optimization (PSO) is integrated as an optimization engine into the MAP estimation process. Likely networks of genes generated based on estimated wire weights are combined using the majority rule to determine a final estimated GRN. As an alternative, a class of [Formula: see text] priors is used for attaining the sparse MAP estimates of wire weights of RNN. We also infer the GRN using the maximum likelihood (ML) estimates of wire weights of RNN. The RNN-based GRN inference algorithms, ARD-RNN, [Formula: see text] -RNN, and ML-RNN are tested on simulated and experimental E. coli and yeast time series containing 6-11 genes and 7-19 data points. Published GRN inference algorithms based on regressions and mutual information networks are performed on the benchmark datasets to compare performances., Conclusion: ARD and [Formula: see text] -norm priors are used for the estimation of wire weights of RNN. Results of GRN inference experiments show that ARD-RNN, [Formula: see text] -RNN have similar best accuracies on the simulated time series. The ARD-RNN is more accurate than [Formula: see text] -RNN, ML-RNN, and mostly more accurate than the reference algorithms on the experimental time series. The effectiveness of ARD-RNN for inferring small-scale GRNs using gene expression time series of limited length is empirically verified.

During clathrin-mediated endocytosis in yeast cells, short actin filaments (< 200nm) and crosslinking protein fimbrin assemble to drive the internalization of the plasma membrane. However, the organization of the actin meshwork during endocytosis remains largely unknown. In addition, only a small fraction of the force necessary to elongate and pinch off vesicles can be accounted for by actin polymerization alone. In this paper, we used mathematical modeling to study the self-organization of rigid actin filaments in the presence of elastic crosslinkers in conditions relevant to endocytosis. We found that actin filaments condense into either a disordered meshwork or an ordered bundle depending on filament length and the mechanical and kinetic properties of the crosslinkers. Our simulations also demonstrated that these nanometer-scale actin structures can store a large amount of elastic energy within the crosslinkers (up to 10kBT per crosslinker). This conversion of binding energy into elastic energy is the consequence of geometric constraints created by the helical pitch of the actin filaments, which results in frustrated configurations of crosslinkers attached to filaments. We propose that this stored elastic energy can be used at a later time in the endocytic process. As a proof of principle, we presented a simple mechanism for sustained torque production by ordered detachment of crosslinkers from a pair of parallel filaments., Competing Interests: The authors have declared that no competing interests exist.

In quantitative analyses of biological processes, one may use many different scales of models (e.g. spatial or non-spatial, deterministic or stochastic, time-varying or at steady-state) or many different approaches to match models to experimental data (e.g. model fitting or parameter uncertainty/sloppiness quantification with different experiment designs). These different analyses can lead to surprisingly different results, even when applied to the same data and the same model. We use a simplified gene regulation model to illustrate many of these concerns, especially for ODE analyses of deterministic processes, chemical master equation and finite state projection analyses of heterogeneous processes, and stochastic simulations. For each analysis, we employ Matlab and Python software to consider a time-dependent input signal (e.g. a kinase nuclear translocation) and several model hypotheses, along with simulated single-cell data. We illustrate different approaches (e.g. deterministic and stochastic) to identify the mechanisms and parameters of the same model from the same simulated data. For each approach, we explore how uncertainty in parameter space varies with respect to the chosen analysis approach or specific experiment design. We conclude with a discussion of how our simulated results relate to the integration of experimental and computational investigations to explore signal-activated gene expression models in yeast (Neuert et al 2013 Science 339 584-7) and human cells (Senecal et al 2014 Cell Rep. 8 75-83) 5 .

Live-cell microscopy is quickly becoming an indispensable technique for studying the dynamics of cellular processes. Maintaining the specimen in focus during image acquisition is crucial for high-throughput applications, especially for long experiments or when a large sample is being continuously scanned. Automated focus control methods are often expensive, imperfect, or ill-adapted to a specific application and are a bottleneck for widespread adoption of high-throughput, live-cell imaging. Here, we demonstrate a neural network approach for automatically maintaining focus during bright-field microscopy. Z-stacks of yeast cells growing in a microfluidic device were collected and used to train a convolutional neural network to classify images according to their z-position. We studied the effect on prediction accuracy of the various hyperparameters of the neural network, including downsampling, batch size, and z-bin resolution. The network was able to predict the z-position of an image with ±1 μm accuracy, outperforming human annotators. Finally, we used our neural network to control microscope focus in real-time during a 24 hour growth experiment. The method robustly maintained the correct focal position compensating for 40 μm of focal drift and was insensitive to changes in the field of view. About ~100 annotated z-stacks were required to train the network making our method quite practical for custom autofocus applications.

Clonal populations of cells exhibit cell-to-cell variation in the transcription of individual genes. In addition to this noise in gene expression, heterogeneity in the proteome and the proteostasis network expands the phenotypic diversity of a population. Heat shock factor 1 (Hsf1) regulates chaperone gene expression, thereby coupling transcriptional noise to proteostasis. Here we show that cell-to-cell variation in Hsf1 activity is an important determinant of phenotypic plasticity. Budding yeast cells with high Hsf1 activity were enriched for the ability to acquire resistance to an antifungal drug, and this enrichment depended on Hsp90, a known phenotypic capacitor and canonical Hsf1 target. We show that Hsf1 phosphorylation promotes cell-to-cell variation, and this variation, rather than absolute Hsf1 activity, promotes antifungal resistance. We propose that Hsf1 phosphorylation enables differential tuning of the proteostasis network in individual cells, allowing populations to access a range of phenotypic states., (Copyright © 2018 The Author(s). Published by Elsevier Inc. All rights reserved.)

The [URE3] yeast prion is the self-propagating amyloid form of the Ure2 protein. [URE3] is cured by overexpression of several yeast proteins, including Ydj1, Btn2, Cur1, Hsp42, and human DnaJB6. To better understand [URE3] curing, we used real-time imaging with a yeast strain expressing a GFP-labeled full-length Ure2 construct to monitor the curing of [URE3] over time. [URE3] yeast cells exhibited numerous fluorescent foci, and expression of the GFP-labeled Ure2 affected neither mitotic stability of [URE3] nor the rate of [URE3] curing by the curing proteins. Using guanidine to cure [URE3] via Hsp104 inactivation, we found that the fluorescent foci are progressively lost as the cells divide until they are cured; the fraction of cells that retained the foci was equivalent to the [URE3] cell fraction measured by a plating assay, indicating that the foci were the prion seeds. During the curing of [URE3] by Btn2, Cur1, Hsp42, or Ydj1 overexpression, the foci formed aggregates, many of which were 0.5 μm or greater in size, and [URE3] was cured by asymmetric segregation of the aggregated seeds. In contrast, DnaJB6 overexpression first caused a loss of detectable foci in cells that were still [URE3] before there was complete dissolution of the seeds, and the cells were cured. We conclude that GFP labeling of full-length Ure2 enables differentiation among the different [URE3]-curing mechanisms, including inhibition of severing followed by seed dilution, seed clumping followed by asymmetric segregation between mother and daughter cells, and seed dissolution.

Bulk tissue samples examined by gene expression studies are usually heterogeneous. The data gained from these samples display the confounding patterns of mixtures consisting of multiple cell types or similar cell types in various functional states, which hinders the elucidation of the molecular mechanisms underlying complex biological phenomena. A realistic approach to compensate for the limitations of experimentally separating homogenous cell populations from mixed tissues is to computationally identify cell-type specific patterns from bulk, heterogeneous measurements. We designed the CellDistinguisher algorithm to analyze the gene expression data of mixed samples, identifying genes that best distinguish biological processes and cell types. Coupled with a deconvolution algorithm that takes cell type specific gene lists as input, we show that CellDistinguisher performs as well as partial deconvolution algorithms in predicting cell type composition without the need for prior knowledge of cell type signatures. This approach is also better in predicting cell type signatures than the one-step traditional complete deconvolution methods. To illustrate its wide applicability, the algorithm was tested on multiple publicly available data sets. In each case, CellDistinguisher identified genes reflecting biological processes typical for the tissues and development stages of interest and estimated the sample compositions accurately.

The process of detection and separation of yeast cells based on their morphological characteristics is critical to the understanding of cell division cycles, which is of vital importance to the understanding of some diseases such as cancer. The traditional process of manual detection is usually tedious and inconsistent. This paper presents a microfluidic device integrated with microvalves for fluid control for the sorting of yeast cells using image processing algorithms and confirmation based on their fluorescent tag. The proposed device is completely automated, low cost and easy to implement in an academic research setting. Design details of the integrated microfluidic system are highlighted in this paper, along with experimental validation. Real time cell sorting was demonstrated with a cell detection rate of 12 cells per minute.

Cryogenic fluorescent light microscopy of flash-frozen cells stands out by artifact-free fixation and very little photobleaching of the fluorophores used. To attain the highest level of resolution, aberration-free immersion objectives with accurately matched immersion media are required, but both do not exist for imaging below the glass-transition temperature of water. Here, we resolve this challenge by combining a cryoimmersion medium, HFE-7200, which matches the refractive index of room-temperature water, with a technological concept in which the body of the objective and the front lens are not in thermal equilibrium. We implemented this concept by replacing the metallic front-lens mount of a standard bioimaging water immersion objective with an insulating ceramic mount heated around its perimeter. In this way, the objective metal housing can be maintained at room temperature, while creating a thermally shielded cold microenvironment around the sample and front lens. To demonstrate the range of potential applications, we show that our method can provide superior contrast in Escherichia coli and yeast cells expressing fluorescent proteins and resolve submicrometer structures in multicolor immunolabeled human bone osteosarcoma epithelial (U2OS) cells at [Formula: see text]C., Competing Interests: The authors declare no conflict of interest., (Copyright © 2018 the Author(s). Published by PNAS.)

Optical tweezing is a non-invasive technique that can enable a variety of single cell experiments; however, it tends to be based on a high numerical aperture (NA) microscope objective to both deliver the tweezing laser light and image the sample. This introduces restrictions in system flexibility when both trapping and imaging. Here, we demonstrate a novel, high NA tweezing system based on micro-machined multicore optical fibers. Using the machined, multicore fiber tweezer, cells are optically manipulated under a variety of microscopes, without requiring a high NA objective lens. The maximum NA of the fiber-based tweezer demonstrated is 1.039. A stable trap with a maximum total power 30 mW has been characterized to exert a maximum optical force of 26.4 pN, on a trapped, 7 μm diameter yeast cell. Single cells are held 15-35 μm from the fiber end and can be manipulated in the x, y and z directions throughout the sample. In this way, single cells are controllably trapped under a Raman microscope to categorize the yeast cells as live or dead, demonstrating trapping by the machined multicore fiber-based tweezer decoupled from the imaging or excitation objective lens.

Actin polymerization provides driving force to aid several types of processes that involve pulling the plasma membrane into the cell, including phagocytosis, cellular entry of large viruses, and endocytosis. In endocytosis, actin polymerization is especially important under conditions of high membrane tension or high turgor pressure. Recent modeling efforts have shown how actin polymerization can give rise to a distribution of forces around the endocytic site, and explored how these forces affect the shape dynamics; experiments have revealed the structure of the endocytic machinery in increasing detail, and demonstrated key feedback interactions between actin assembly and membrane curvature. Here we provide a perspective on these findings and suggest avenues for future research., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

We report herein a novel microfluidic particle concentrator that utilizes constriction microchannels to enhance the flow-focusing performance of induced-charge electroosmosis (ICEO), where viscous hemi-spherical oil droplets are embedded within the mainchannel to form deformable converging-diverging constriction structures. The constriction region between symmetric oil droplets partially coated on the electrode strips can improve the focusing performance by inducing a granular wake flow area at the diverging channel, which makes almost all of the scattered sample particles trapped within a narrow stream on the floating electrode. Another asymmetric droplet pair arranged near the outlets can further direct the trajectory of focused particle stream to one specified outlet port depending on the symmetry breaking in the shape of opposing phase interfaces. By fully exploiting rectification properties of induced-charge electrokinetic phenomena at immiscible water/oil interfaces of tunable geometry, the expected function of continuous and switchable flow-focusing is demonstrated by preconcentrating both inorganic silica particles and biological yeast cells. Physical mechanisms responsible for particle focusing and locus deflection in the droplet-assisted concentrentor are analyzed in detail, and simulation results are in good accordance with experimental observations. Our work provides new routes to construct flexible electrokinetic framework for preprocessing on-chip biological samples before performing subsequent analysis., (© 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

A series of optimized protocols to isolate vacuoles from both yeast and plant cells, and to characterize the purified organelles at a functional and structural level, are described. For this purpose, we took advantage of the combined use of cell fractionation techniques with different fluorescence-based approaches namely flow cytometry, fluorescence microscopy and spectrofluorimetry. These protocols altogether constitute valuable tools for the study of vacuole structure and function, as well as for the high-throughput screening of drug libraries to identify new molecules that target the vacuole.

Protein-conjugated magnetic nanoparticles (mNPs) are promising tools for a variety of biomedical applications, from immunoassays and biosensors to theranostics and drug-delivery. In such applications, conjugation of affinity proteins (e.g., antibodies) to the nanoparticle surface many times compromises biological activity and specificity, leading to increased reagent consumption and decreased assay performance. To address this problem, we engineered a biomolecular magnetic separation system that eliminates the need to chemically modify nanoparticles with the capture biomolecules or synthetic polymers of any kind. The system consists of (i) thermoresponsive magnetic iron oxide nanoparticles displaying poly(N-isopropylacrylamide) (pNIPAm), and (ii) an elastin-like polypeptide (ELP) fused with the affinity protein Cohesin (Coh). Proper design of pNIPAm-mNPs and ELP-Coh allowed for efficient cross-aggregation of the two distinct nanoparticle types under collapsing stimuli, which enabled magnetic separation of ELP-Coh aggregates bound to target Dockerin (Doc) molecules. Selective resolubilization of the ELP-Coh/Doc complexes was achieved under intermediate conditions under which only the pNIPAm-mNPs remained aggregated. We show that ELP-Coh is capable of magnetically separating and purifying nanomolar quantities of Doc as well as eukaryotic whole cells displaying the complementary Doc domain from diluted human plasma. This modular system provides magnetic enrichment and purification of captured molecular targets and eliminates the requirement of biofunctionalization of magnetic nanoparticles to achieve bioseparations. Our streamlined and simplified approach is amenable for point-of-use applications and brings the advantages of ELP-fusion proteins to the realm of magnetic particle separation systems.

Controversy has long surrounded the question of whether spontaneous lateral demixing of membranes into coexisting liquid phases can organize proteins and lipids on micron scales within unperturbed, living cells. A clear answer hinges on observation of hallmarks of a reversible phase transition. Here, by directly imaging micron-scale membrane domains of yeast vacuoles both in vivo and cell free, we demonstrate that the domains arise through a phase separation mechanism. The domains are large, have smooth boundaries, and can merge quickly, consistent with fluid phases. Moreover, the domains disappear above a distinct miscibility transition temperature (T mix ) and reappear below T mix , over multiple heating and cooling cycles. Hence, large-scale membrane organization in living cells under physiologically relevant conditions can be controlled by tuning a single thermodynamic parameter., (Copyright © 2017 Biophysical Society. Published by Elsevier Inc. All rights reserved.)

In this study, we propose an electrodeposition method of fabricating shape-controlled calcium alginate-poly-L-lysine hydrogel microcapsules. The micro-patterned electrodes, which are produced by coating a patterned photoresist layer onto fluorine-doped tin oxide glass slide based on photolithography technique, are used for making different shapes of microcapsules. By the electrolysis of water in alginate gelation on micro-patterned anode electrode, the 2D alginate hydrogel structures embedded with yeast cells are formed on fluorine-doped tin oxide glass slide. Then, the 2D structures would be detached from the microelectrode surface and treated with given reagent to be transformed into 3D microcapsules while maintaining the ring and hexagon shapes. Finally, the yeast cells within the microcapsules are further promoted into compact tissues by cultivation. The experimental results indicate the method can successfully fabricate tissues which can maintain certain cells bioactivity after 24 h cultivation. The recommended method can lead to fabricating cell-laden scaffold for tissue engineering, biological studies and drug discovery with higher accuracy and efficiency.