Application of unimodal probability distribution models for morphological phenotyping of budding yeast.

Morphological phenotyping of the budding yeast Saccharomyces cerevisiae has helped to greatly clarify the functions of genes and increase our understanding of cellular functional networks. It is necessary to understand cell morphology and perform quantitative morphological analysis (QMA) but assigning precise values to morphological phenotypes has been challenging. We recently developed the Unimodal Morphological Data image analysis pipeline for this purpose. All true values can be estimated theoretically by applying an appropriate probability distribution if the distribution of experimental values follows a unimodal pattern. This reliable pipeline allows several downstream analyses, including detection of subtle morphological differences, selection of mutant strains with similar morphology, clustering based on morphology, and study of morphological diversity. In addition to basic research, morphological analyses of yeast cells can also be used in applied research to monitor breeding and fermentation processes and control the fermentation activity of yeast cells.

Is AI essential? Examining the need for deep learning in image-activated sorting of Saccharomyces cerevisiae.

Artificial intelligence (AI) has become a focal point across a multitude of societal sectors, with science not being an exception. Particularly in the life sciences, imaging flow cytometry has increasingly integrated AI for automated management and categorization of extensive cell image data. However, the necessity of AI over traditional classification methods when extending imaging flow cytometry to include cell sorting remains uncertain, primarily due to the time constraints between image acquisition and sorting actuation. AI-enabled image-activated cell sorting (IACS) methods remain substantially limited, even as recent advancements in IACS have found success while largely relying on traditional feature gating strategies. Here we assess the necessity of AI for image classification in IACS by contrasting the performance of feature gating, classical machine learning (ML), and deep learning (DL) with convolutional neural networks (CNNs) in the differentiation of Saccharomyces cerevisiae mutant images. We show that classical ML could only yield a 2.8-fold enhancement in target enrichment capability, albeit at the cost of a 13.7-fold increase in processing time. Conversely, a CNN could offer an 11.0-fold improvement in enrichment capability at an 11.5-fold increase in processing time. We further executed IACS on mixed mutant populations and quantified target strain enrichment via downstream DNA sequencing to substantiate the applicability of DL for the proposed study. Our findings validate the feasibility and value of employing DL in IACS for morphology-based genetic screening of S. cerevisiae, encouraging its incorporation in future advancements of similar technologies.

Targeted Mutations Produce Divergent Characteristics in Pedigreed Sake Yeast Strains.

Modification of the genetic background and, in some cases, the introduction of targeted mutations can play a critical role in producing trait characteristics during the breeding of crops, livestock, and microorganisms. However, the question of how similar trait characteristics emerge when the same target mutation is introduced into different genetic backgrounds is unclear. In a previous study, we performed genome editing of AWA1, CAR1, MDE1, and FAS2 on the standard sake yeast strain Kyokai No. 7 to breed a sake yeast with multiple excellent brewing characteristics. By introducing the same targeted mutations into other pedigreed sake yeast strains, such as Kyokai strains No. 6, No. 9, and No. 10, we were able to create sake yeasts with the same excellent brewing characteristics. However, we found that other components of sake made by the genome-edited yeast strains did not change in the exact same way. For example, amino acid and isobutanol contents differed among the strain backgrounds. We also showed that changes in yeast cell morphology induced by the targeted mutations also differed depending on the strain backgrounds. The number of commonly changed morphological parameters was limited. Thus, divergent characteristics were produced by the targeted mutations in pedigreed sake yeast strains, suggesting a breeding strategy to generate a variety of sake yeasts with excellent brewing characteristics.

Prediction of ethanol fermentation under stressed conditions using yeast morphological data.

A high sugar concentration is used as a starting condition in alcoholic fermentation by budding yeast, which shows changes in intracellular state and cell morphology under conditions of high-sugar stress. In this study, we developed artificial intelligence (AI) models to predict ethanol yields in yeast fermentation cultures under conditions of high-sugar stress using cell morphological data. Our method involves the extraction of high-dimensional morphological data from phase contrast images using image processing software, and predicting ethanol yields by supervised machine learning. The neural network algorithm produced the best performance, with a coefficient of determination (R2) of 0.95, and could predict ethanol yields well even 60 min in the future. Morphological data from cells cultured in low-glucose medium could not be used for accurate prediction under conditions of high-glucose stress. Cells cultured in high-concentration glucose medium were similar in terms of morphology to cells cultured under high osmotic pressure. Feeding experiments revealed that morphological changes differed depending on the fermentation phase. By monitoring the morphology of yeast under stress, it was possible to understand the intracellular physiological conditions, suggesting that analysis of cell morphology can aid the management and stable production of desired biocommodities.

Intelligent sort-timing prediction for image-activated cell sorting.

Intelligent image-activated cell sorting (iIACS) has enabled high-throughput image-based sorting of single cells with artificial intelligence (AI) algorithms. This AI-on-a-chip technology combines fluorescence microscopy, AI-based image processing, sort-timing prediction, and cell sorting. Sort-timing prediction is particularly essential due to the latency on the order of milliseconds between image acquisition and sort actuation, during which image processing is performed. The long latency amplifies the effects of the fluctuations in the flow speed of cells, leading to fluctuation and uncertainty in the arrival time of cells at the sort point on the microfluidic chip. To compensate for this fluctuation, iIACS measures the flow speed of each cell upstream, predicts the arrival timing of the cell at the sort point, and activates the actuation of the cell sorter appropriately. Here, we propose and demonstrate a machine learning technique to increase the accuracy of the sort-timing prediction that would allow for the improvement of sort event rate, yield, and purity. Specifically, we trained an algorithm to predict the sort timing for morphologically heterogeneous budding yeast cells. The algorithm we developed used cell morphology, position, and flow speed as inputs for prediction and achieved 41.5% lower prediction error compared to the previously employed method based solely on flow speed. As a result, our technique would allow for an increase in the sort event rate of iIACS by a factor of ~2.

BIONIC: biological network integration using convolutions.

Biological networks constructed from varied data can be used to map cellular function, but each data type has limitations. Network integration promises to address these limitations by combining and automatically weighting input information to obtain a more accurate and comprehensive representation of the underlying biology. We developed a deep learning-based network integration algorithm that incorporates a graph convolutional network framework. Our method, BIONIC (Biological Network Integration using Convolutions), learns features that contain substantially more functional information compared to existing approaches. BIONIC has unsupervised and semisupervised learning modes, making use of available gene function annotations. BIONIC is scalable in both size and quantity of the input networks, making it feasible to integrate numerous networks on the scale of the human genome. To demonstrate the use of BIONIC in identifying new biology, we predicted and experimentally validated essential gene chemical-genetic interactions from nonessential gene profiles in yeast.

Multifarious Translational Regulation during Replicative Aging in Yeast.

Protein synthesis is strictly regulated during replicative aging in yeast, but global translational regulation during replicative aging is poorly characterized. To conduct ribosome profiling during replicative aging, we collected a large number of dividing aged cells using a miniature chemostat aging device. Translational efficiency, defined as the number of ribosome footprints normalized to transcript abundance, was compared between young and aged cells for each gene. We identified more than 700 genes with changes greater than twofold during replicative aging. Increased translational efficiency was observed in genes involved in DNA repair and chromosome organization. Decreased translational efficiency was observed in genes encoding ribosome components, transposon Ty1 and Ty2 genes, transcription factor HAC1 gene associated with the unfolded protein response, genes involved in cell wall synthesis and assembly, and ammonium permease genes. Our results provide a global view of translational regulation during replicative aging, in which the pathways involved in various cell functions are translationally regulated and cause diverse phenotypic changes.

Assignment of unimodal probability distribution models for quantitative morphological phenotyping.

BACKGROUND: Cell morphology is a complex and integrative readout, and therefore, an attractive measurement for assessing the effects of genetic and chemical perturbations to cells. Microscopic images provide rich information on cell morphology; therefore, subjective morphological features are frequently extracted from digital images. However, measured datasets are fundamentally noisy; thus, estimation of the true values is an ultimate goal in quantitative morphological phenotyping. Ideal image analyses require precision, such as proper probability distribution analyses to detect subtle morphological changes, recall to minimize artifacts due to experimental error, and reproducibility to confirm the results. RESULTS: Here, we present UNIMO (UNImodal MOrphological data), a reliable pipeline for precise detection of subtle morphological changes by assigning unimodal probability distributions to morphological features of the budding yeast cells. By defining the data type, followed by validation using the model selection method, examination of 33 probability distributions revealed nine best-fitting probability distributions. The modality of the distribution was then clarified for each morphological feature using a probabilistic mixture model. Using a reliable and detailed set of experimental log data of wild-type morphological replicates, we considered the effects of confounding factors. As a result, most of the yeast morphological parameters exhibited unimodal distributions that can be used as basic tools for powerful downstream parametric analyses. The power of the proposed pipeline was confirmed by reanalyzing morphological changes in non-essential yeast mutants and detecting 1284 more mutants with morphological defects compared with a conventional approach (Box-Cox transformation). Furthermore, the combined use of canonical correlation analysis permitted global views on the cellular network as well as new insights into possible gene functions. CONCLUSIONS: Based on statistical principles, we showed that UNIMO offers better predictions of the true values of morphological measurements. We also demonstrated how these concepts can provide biologically important information. This study draws attention to the necessity of employing a proper approach to do more with less.

S-adenosyl-L-homocysteine extends lifespan through methionine restriction effects.

Methionine restriction (MetR) can extend lifespan and delay the onset of aging-associated pathologies in most model organisms. Previously, we showed that supplementation with the metabolite S-adenosyl-L-homocysteine (SAH) extends lifespan and activates the energy sensor AMP-activated protein kinase (AMPK) in the budding yeast Saccharomyces cerevisiae. However, the mechanism involved and whether SAH can extend metazoan lifespan have remained unknown. Here, we show that SAH supplementation reduces Met levels and recapitulates many physiological and molecular effects of MetR. In yeast, SAH supplementation leads to inhibition of the target of rapamycin complex 1 (TORC1) and activation of autophagy. Furthermore, in Caenorhabditis elegans SAH treatment extends lifespan by activating AMPK and providing benefits of MetR. Therefore, we propose that SAH can be used as an intervention to lower intracellular Met and confer benefits of MetR.

Deep imaging flow cytometry.

Imaging flow cytometry (IFC) has become a powerful tool for diverse biomedical applications by virtue of its ability to image single cells in a high-throughput manner. However, there remains a challenge posed by the fundamental trade-off between throughput, sensitivity, and spatial resolution. Here we present deep-learning-enhanced imaging flow cytometry (dIFC) that circumvents this trade-off by implementing an image restoration algorithm on a virtual-freezing fluorescence imaging (VIFFI) flow cytometry platform, enabling higher throughput without sacrificing sensitivity and spatial resolution. A key component of dIFC is a high-resolution (HR) image generator that synthesizes "virtual" HR images from the corresponding low-resolution (LR) images acquired with a low-magnification lens (10×/0.4-NA). For IFC, a low-magnification lens is favorable because of reduced image blur of cells flowing at a higher speed, which allows higher throughput. We trained and developed the HR image generator with an architecture containing two generative adversarial networks (GANs). Furthermore, we developed dIFC as a method by combining the trained generator and IFC. We characterized dIFC using Chlamydomonas reinhardtii cell images, fluorescence in situ hybridization (FISH) images of Jurkat cells, and Saccharomyces cerevisiae (budding yeast) cell images, showing high similarities of dIFC images to images obtained with a high-magnification lens (40×/0.95-NA), at a high flow speed of 2 m s-1. We lastly employed dIFC to show enhancements in the accuracy of FISH-spot counting and neck-width measurement of budding yeast cells. These results pave the way for statistical analysis of cells with high-dimensional spatial information.

Jerveratrum-Type Steroidal Alkaloids Inhibit ß-1,6-Glucan Biosynthesis in Fungal Cell Walls.

The limited number of available effective agents necessitates the development of new antifungals. We report that jervine, a jerveratrum-type steroidal alkaloid isolated from Veratrum californicum, has antifungal activity. Phenotypic comparisons of cell wall mutants, K1 killer toxin susceptibility testing, and quantification of cell wall components revealed that ß-1,6-glucan biosynthesis was significantly inhibited by jervine. Temperature-sensitive mutants defective in essential genes involved in ß-1,6-glucan biosynthesis, including BIG1, KEG1, KRE5, KRE9, and ROT1, were hypersensitive to jervine. In contrast, point mutations in KRE6 or its paralog SKN1 produced jervine resistance, suggesting that jervine targets Kre6 and Skn1. Jervine exhibited broad-spectrum antifungal activity and was effective against human-pathogenic fungi, including Candida parapsilosis and Candida krusei. It was also effective against phytopathogenic fungi, including Botrytis cinerea and Puccinia recondita. Jervine exerted a synergistic effect with fluconazole. Therefore, jervine, a jerveratrum-type steroidal alkaloid used in pharmaceutical products, represents a new class of antifungals active against mycoses and plant-pathogenic fungi. IMPORTANCE Non-Candida albicans Candida species (NCAC) are on the rise as a cause of mycosis. Many antifungal drugs are less effective against NCAC, limiting the available therapeutic agents. Here, we report that jervine, a jerveratrum-type steroidal alkaloid, is effective against NCAC and phytopathogenic fungi. Jervine acts on Kre6 and Skn1, which are involved in ß-1,6-glucan biosynthesis. The skeleton of jerveratrum-type steroidal alkaloids has been well studied, and more recently, their anticancer properties have been investigated. Therefore, jerveratrum-type alkaloids could potentially be applied as treatments for fungal infections and cancer.

High-throughput platform for yeast morphological profiling predicts the targets of bioactive compounds.

Morphological profiling is an omics-based approach for predicting intracellular targets of chemical compounds in which the dose-dependent morphological changes induced by the compound are systematically compared to the morphological changes in gene-deleted cells. In this study, we developed a reliable high-throughput (HT) platform for yeast morphological profiling using drug-hypersensitive strains to minimize compound use, HT microscopy to speed up data generation and analysis, and a generalized linear model to predict targets with high reliability. We first conducted a proof-of-concept study using six compounds with known targets: bortezomib, hydroxyurea, methyl methanesulfonate, benomyl, tunicamycin, and echinocandin B. Then we applied our platform to predict the mechanism of action of a novel diferulate-derived compound, poacidiene. Morphological profiling of poacidiene implied that it affects the DNA damage response, which genetic analysis confirmed. Furthermore, we found that poacidiene inhibits the growth of phytopathogenic fungi, implying applications as an effective antifungal agent. Thus, our platform is a new whole-cell target prediction tool for drug discovery.

AI-based forecasting of ethanol fermentation using yeast morphological data.

Several industries require getting information of products as soon as possible during fermentation. However, the trade-off between sensing speed and data quantity presents challenges for forecasting fermentation product yields. In this study, we tried to develop AI models to forecast ethanol yields in yeast fermentation cultures, using cell morphological data. Our platform involves the quick acquisition of yeast morphological images using a nonstaining protocol, extraction of high-dimensional morphological data using image processing software, and forecasting of ethanol yields via supervised machine learning. We found that the neural network algorithm produced the best performance, which had a coefficient of determination of >0.9 even at 30 and 60 min in the future. The model was validated using test data collected using the CalMorph-PC(10) system, which enables rapid image acquisition within 10 min. AI-based forecasting of product yields based on cell morphology will facilitate the management and stable production of desired biocommodities.

Defining Functions of Mannoproteins in Saccharomyces cerevisiae by High-Dimensional Morphological Phenotyping.

Mannoproteins are non-filamentous glycoproteins localized to the outermost layer of the yeast cell wall. The physiological roles of these structural components have not been completely elucidated due to the limited availability of appropriate tools. As the perturbation of mannoproteins may affect cell morphology, we investigated mannoprotein mutants in Saccharomyces cerevisiae via high-dimensional morphological phenotyping. The mannoprotein mutants were morphologically classified into seven groups using clustering analysis with Gaussian mixture modeling. The pleiotropic phenotypes of cluster I mutant cells (ccw12Δ) indicated that CCW12 plays major roles in cell wall organization. Cluster II (ccw14Δ, flo11Δ, srl1Δ, and tir3Δ) mutants exhibited altered mother cell size and shape. Mutants of cluster III and IV exhibited no or very small morphological defects. Cluster V (dse2Δ, egt2Δ, and sun4Δ) consisted of endoglucanase mutants with cell separation defects due to incomplete septum digestion. The cluster VI mutant cells (ecm33Δ) exhibited perturbation of apical bud growth. Cluster VII mutant cells (sag1Δ) exhibited differences in cell size and actin organization. Biochemical assays further confirmed the observed morphological defects. Further investigations based on various omics data indicated that morphological phenotyping is a complementary tool that can help with gaining a deeper understanding of the functions of mannoproteins.

Are droplets really suitable for single-cell analysis? A case study on yeast in droplets.

Single-cell analysis has become one of the main cornerstones of biotechnology, inspiring the advent of various microfluidic compartments for cell cultivation such as microwells, microtrappers, microcapillaries, and droplets. A fundamental assumption for using such microfluidic compartments is that unintended stress or harm to cells derived from the microenvironments is insignificant, which is a crucial condition for carrying out unbiased single-cell studies. Despite the significance of this assumption, simple viability or growth tests have overwhelmingly been the assay of choice for evaluating culture conditions while empirical studies on the sub-lethal effect on cellular functions have been insufficient in many cases. In this work, we assessed the effect of culturing cells in droplets on the cellular function using yeast morphology as an indicator. Quantitative morphological analysis using CalMorph, an image-analysis program, demonstrated that cells cultured in flasks, large droplets, and small droplets significantly differed morphologically. From these differences, we identified that the cell cycle was delayed in droplets during the G1 phase and during the process of bud growth likely due to the checkpoint mechanism and impaired mitochondrial function, respectively. Furthermore, comparing small and large droplets, cells cultured in large droplets were morphologically more similar to those cultured in a flask, highlighting the advantage of increasing the droplet size. These results highlight a potential source of bias in cell analysis using droplets and reinforce the significance of assessing culture conditions of microfluidic cultivation methods for specific study cases.

Poacic acid, a ß-1,3-glucan-binding antifungal agent, inhibits cell-wall remodeling and activates transcriptional responses regulated by the cell-wall integrity and high-osmolarity glycerol pathways in yeast.

As a result of the relatively few available antifungals and the increasing frequency of resistance to them, the development of novel antifungals is increasingly important. The plant natural product poacic acid (PA) inhibits ß-1,3-glucan synthesis in Saccharomyces cerevisiae and has antifungal activity against a wide range of plant pathogens. However, the mode of action of PA is unclear. Here, we reveal that PA specifically binds to ß-1,3-glucan, its affinity for which is ~30-fold that for chitin. Besides its effect on ß-1,3-glucan synthase activity, PA inhibited the yeast glucan-elongating activity of Gas1 and Gas2 and the chitin-glucan transglycosylase activity of Crh1. Regarding the cellular response to PA, transcriptional co-regulation was mediated by parallel activation of the cell-wall integrity (CWI) and high-osmolarity glycerol signaling pathways. Despite targeting ß-1,3-glucan remodeling, the transcriptional profiles and regulatory circuits activated by caspofungin, zymolyase, and PA differed, indicating that their effects on CWI have different mechanisms. The effects of PA on the growth of yeast strains indicated that it has a mode of action distinct from that of echinocandins, suggesting it is a unique antifungal agent.

Genome Editing to Generate Sake Yeast Strains with Eight Mutations That Confer Excellent Brewing Characteristics.

Sake yeast is mostly diploid, so the introduction of recessive mutations to improve brewing characteristics requires considerable effort. To construct sake yeast with multiple excellent brewing characteristics, we used an evidence-based approach that exploits genome editing technology. Our breeding targeted the AWA1, CAR1, MDE1, and FAS2 genes. We introduced eight mutations into standard sake yeast to construct a non-foam-forming strain that makes sake without producing carcinogens or an unpleasant odor, while producing a sweet ginjo aroma. Small-scale fermentation tests showed that the desired sake could be brewed with our genome-edited strains. The existence of a few unexpected genetic perturbations introduced during breeding proved that genome editing technology is extremely effective for the serial breeding of sake yeast.

The kinetic landscape and interplay of protein networks in cytokinesis.

Cytokinesis is executed by protein networks organized into functional modules. Individual proteins within each module have been characterized to various degrees. However, the collective behavior and interplay of the modules remain poorly understood. In this study, we conducted quantitative time-lapse imaging to analyze the accumulation kinetics of more than 20 proteins from different modules of cytokinesis in budding yeast. This analysis has led to a comprehensive picture of the kinetic landscape of cytokinesis, from actomyosin ring (AMR) assembly to cell separation. It revealed that the AMR undergoes biphasic constriction and that the switch between the constriction phases is likely triggered by AMR maturation and primary septum formation. This analysis also provided further insights into the functions of actin filaments and the transglutaminase-like protein Cyk3 in cytokinesis and, in addition, defined Kre6 as the likely enzyme that catalyzes ß-1,6-glucan synthesis to drive cell wall maturation during cell growth and division.

Genetic profiling of protein burden and nuclear export overload.

Overproduction (op) of proteins triggers cellular defects. One of the consequences of overproduction is the protein burden/cost, which is produced by an overloading of the protein synthesis process. However, the physiology of cells under a protein burden is not well characterized. We performed genetic profiling of protein burden by systematic analysis of genetic interactions between GFP-op, surveying both deletion and temperature-sensitive mutants in budding yeast. We also performed genetic profiling in cells with overproduction of triple-GFP (tGFP), and the nuclear export signal-containing tGFP (NES-tGFP). The mutants specifically interacted with GFP-op were suggestive of unexpected connections between actin-related processes like polarization and the protein burden, which was supported by morphological analysis. The tGFP-op interactions suggested that this protein probe overloads the proteasome, whereas those that interacted with NES-tGFP involved genes encoding components of the nuclear export process, providing a resource for further analysis of the protein burden and nuclear export overload.

Intelligent image-activated cell sorting 2.0.

The advent of intelligent image-activated cell sorting (iIACS) has enabled high-throughput intelligent image-based sorting of single live cells from heterogeneous populations. iIACS is an on-chip microfluidic technology that builds on a seamless integration of a high-throughput fluorescence microscope, cell focuser, cell sorter, and deep neural network on a hybrid software-hardware data management architecture, thereby providing the combined merits of optical microscopy, fluorescence-activated cell sorting (FACS), and deep learning. Here we report an iIACS machine that far surpasses the state-of-the-art iIACS machine in system performance in order to expand the range of applications and discoveries enabled by the technology. Specifically, it provides a high throughput of ∼2000 events per second and a high sensitivity of ∼50 molecules of equivalent soluble fluorophores (MESFs), both of which are 20 times superior to those achieved in previous reports. This is made possible by employing (i) an image-sensor-based optomechanical flow imaging method known as virtual-freezing fluorescence imaging and (ii) a real-time intelligent image processor on an 8-PC server equipped with 8 multi-core CPUs and GPUs for intelligent decision-making, in order to significantly boost the imaging performance and computational power of the iIACS machine. We characterize the iIACS machine with fluorescent particles and various cell types and show that the performance of the iIACS machine is close to its achievable design specification. Equipped with the improved capabilities, this new generation of the iIACS technology holds promise for diverse applications in immunology, microbiology, stem cell biology, cancer biology, pathology, and synthetic biology.