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.

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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12915-022-01283-6.

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.

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 non-staining 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.

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.

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.

Poacic acid, a β-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 β-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 β-1,3-glucan, its affinity for which is ~30-fold that for chitin. Besides its effect on β-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 β-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.

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.

Sequentially addressable dielectrophoretic array for high-throughput sorting of large-volume biological compartments

Droplet microfluidics has become a powerful tool in precision medicine, green biotechnology, and cell therapy for single-cell analysis and selection by virtue of its ability to effectively confine cells. However, there remains a fundamental trade-off between droplet volume and sorting throughput, limiting the advantages of droplet microfluidics to small droplets (<10 pl) that are incompatible with long-term maintenance and growth of most cells. We present a sequentially addressable dielectrophoretic array (SADA) sorter to overcome this problem. The SADA sorter uses an on-chip array of electrodes activated and deactivated in a sequence synchronized to the speed and position of a passing target droplet to deliver an accumulated dielectrophoretic force and gently pull it in the direction of sorting in a high-speed flow. We use it to demonstrate large-droplet sorting with ~20-fold higher throughputs than conventional techniques and apply it to long-term single-cell analysis of Saccharomyces cerevisiae based on their growth rate.

Virtual-freezing fluorescence imaging flow cytometry

By virtue of the combined merits of flow cytometry and fluorescence microscopy, imaging flow cytometry (IFC) has become an established tool for cell analysis in diverse biomedical fields such as cancer biology, microbiology, immunology, hematology, and stem cell biology. However, the performance and utility of IFC are severely limited by the fundamental trade-off between throughput, sensitivity, and spatial resolution. Here we present an optomechanical imaging method that overcomes the trade-off by virtually freezing the motion of flowing cells on the image sensor to effectively achieve 1000 times longer exposure time for microscopy-grade fluorescence image acquisition. Consequently, it enables high-throughput IFC of single cells at >10,000 cells s⁻¹ without sacrificing sensitivity and spatial resolution. The availability of numerous information-rich fluorescence cell images allows high-dimensional statistical analysis and accurate classification with deep learning, as evidenced by our demonstration of unique applications in hematology and microbiology.

High throughput imaging flow cytometry suffers from trade-offs between throughput, sensitivity and spatial resolution. Here the authors introduce a method to virtually freeze cells in the image acquisition window to enable 1000 times longer signal integration time and improve signal-to-noise ratio.

Genome editing to generate nonfoam-forming sake yeast strains

Mutations frequently occur during breeding of sake yeasts and result in unexpected phenotypes. Here, genome editing tools were applied to develop an ideal nonfoam-forming sake yeast strain, K7GE01, which had homozygous awa1∆/awa1∆ deletion alleles that were responsible for nonfoam formation and few off-target mutations. High-dimensional morphological phenotyping revealed no detectable morphological differences between the genome-edited strain and its parent, while the canonical nonfoam-forming strain, K701, showed obvious morphological changes. Small-scale fermentation tests also showed differences between components of sake produced by K7GE01 and K701. The K7GE01 strain produced sake with significant differences in the concentrations of ethyl acetate, malic acid, lactic acid, and acetic acid, while K701 produced sake with more differences. Our results indicated genuine phenotypes of awa1∆/awa1∆ in sake yeast isolates and showed the usefulness of genome editing tools for sake yeast breeding.

Implications of maintenance of mother-bud neck size in diverse vital processes of Saccharomyces cerevisiae

The mother-bud neck is defined as the boundary between the mother cell and bud in budding microorganisms, wherein sequential morphological events occur throughout the cell cycle. This study was designed to quantitatively investigate the morphology of the mother-bud neck in budding yeast Saccharomyces cerevisiae. Observation of yeast cells with time-lapse microscopy revealed an increase of mother-bud neck size through the cell cycle. After screening of yeast non-essential gene-deletion mutants with the image processing software CalMorph, we comprehensively identified 274 mutants with broader necks during S/G2 phase. Among these yeasts, we extensively analyzed 19 representative deletion mutants with defects in genes annotated to six gene ontology terms (polarisome, actin reorganization, endosomal tethering complex, carboxy-terminal domain protein kinase complex, DNA replication, and maintenance of DNA trinucleotide repeats). The representative broad-necked mutants exhibited calcofluor white sensitivity, suggesting defects in their cell walls. Correlation analysis indicated that maintenance of mother-bud neck size is important for cellular processes such as cell growth, system robustness, and replicative lifespan. We conclude that neck-size maintenance in budding yeast is regulated by numerous genes and has several aspects that are physiologically significant.

Simulated microgravity triggers characteristic morphology and stress response in Saccharomyces cerevisiae

Reduction of gravity results in changes in gene expression and morphology in the budding yeast Saccharomyces cerevisiae. We studied the genes responsible for the morphological changes induced by simulated microgravity (SMG) using the yeast morphology data. We comprehensively captured the features of the morphological changes in yeast cells cultured in SMG with CalMorph, a high-throughput image-processing system. Statistical analysis revealed that 95 of 501 morphological traits were significantly affected, which included changes in bud direction, the ratio of daughter to mother cell size, the random daughter cell shape, the large mother cell size, bright nuclei in the M phase, and the decrease in angle between two nuclei. We identified downregulated genes that impacted the morphological changes in conditions of SMG by focusing on each of the morphological features individually. Gene Ontology (GO)-enrichment analysis indicated that morphological changes under conditions of SMG were caused by cooperative downregulation of 103 genes annotated to six GO terms, which included cytoplasmic ribonucleoprotein granule, RNA elongation, mitotic cell cycle phase transition, nucleocytoplasmic transport, protein-DNA complex subunit organization, and RNA localization. P-body formation was also promoted under conditions of SMG. These results suggest that cooperative downregulation of multiple genes occurs in conditions of SMG.

The budding yeast RSC complex maintains ploidy by promoting spindle pole body insertion

Sing et al. characterize an unanticipated role for the Saccharomyces cerevisiae RSC complex in ploidy maintenance. They show that RSC promotes the distribution of Nbp1 and Ndc1 to the spindle pole body (SPB) to facilitate SPB maturation and accurate chromosome segregation.

Ploidy is tightly regulated in eukaryotic cells and is critical for cell function and survival. Cells coordinate multiple pathways to ensure replicated DNA is segregated accurately to prevent abnormal changes in chromosome number. In this study, we characterize an unanticipated role for the Saccharomyces cerevisiae “remodels the structure of chromatin” (RSC) complex in ploidy maintenance. We show that deletion of any of six nonessential RSC genes causes a rapid transition from haploid to diploid DNA content because of nondisjunction events. Diploidization is accompanied by diagnostic changes in cell morphology and is stably maintained without further ploidy increases. We find that RSC promotes chromosome segregation by facilitating spindle pole body (SPB) duplication. More specifically, RSC plays a role in distributing two SPB insertion factors, Nbp1 and Ndc1, to the new SPB. Thus, we provide insight into a role for a SWI/SNF family complex in SPB duplication and ploidy maintenance.

High-dimensional single-cell phenotyping reveals extensive haploinsufficiency

Haploinsufficiency, a dominant phenotype caused by a heterozygous loss-of-function mutation, has been rarely observed. However, high-dimensional single-cell phenotyping of yeast morphological characteristics revealed haploinsufficiency phenotypes for more than half of 1,112 essential genes under optimal growth conditions. Additionally, 40% of the essential genes with no obvious phenotype under optimal growth conditions displayed haploinsufficiency under severe growth conditions. Haploinsufficiency was detected more frequently in essential genes than in nonessential genes. Similar haploinsufficiency phenotypes were observed mostly in mutants with heterozygous deletion of functionally related genes, suggesting that haploinsufficiency phenotypes were caused by functional defects of the genes. A global view of the gene network was presented based on the similarities of the haploinsufficiency phenotypes. Our dataset contains rich information regarding essential gene functions, providing evidence that single-cell phenotyping is a powerful approach, even in the heterozygous condition, for analyzing complex biological systems.

Diploid organisms harboring a wild-type gene and a loss-of-function mutation are called heterozygotes. They are expected to have weak or no individual phenotypes because the mutation is compensated for by the intact allele. The dominant inheritance of phenotypes in heterozygotes is an exceptional phenomenon called haploinsufficiency. Haploinsufficiency was thought to be a rare occurrence; however, a sensitive technique called high-dimensional single-cell phenotyping challenges this perspective. Investigations of single-cell phenotypes revealed that a large extent of the essential genes in yeast exhibit haploinsufficiency. Our analyses also provided crucial information on gene functional networks based on haploinsufficiency phenotypes. This work shows that high-dimensional single-cell phenotyping is a useful tool that can be used to better understand complex biological systems.

Global study of holistic morphological effectors in the budding yeast Saccharomyces cerevisiae

Background

The size of the phenotypic effect of a gene has been thoroughly investigated in terms of fitness and specific morphological traits in the budding yeast Saccharomyces cerevisiae, but little is known about gross morphological abnormalities.

Results

We identified 1126 holistic morphological effectors that cause severe gross morphological abnormality when deleted, and 2241 specific morphological effectors with weak holistic effects but distinctive effects on yeast morphology. Holistic effectors fell into many gene function categories and acted as network hubs, affecting a large number of morphological traits, interacting with a large number of genes, and facilitating high protein expression. Holistic morphological abnormality was useful for estimating the importance of a gene to morphology. The contribution of gene importance to fitness and morphology could be used to efficiently classify genes into functional groups.

Conclusion

Holistic morphological abnormality can be used as a reproducible and reliable gene feature for high-dimensional morphological phenotyping. It can be used in many functional genomic applications.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-4526-z) contains supplementary material, which is available to authorized users.

Morphometric analysis of autophagy-related structures in Saccharomyces cerevisiae

When macroautophagy (autophagy) is induced by nutrient starvation or rapamycin treatment, Atg (autophagy-related) proteins are assembled at a restricted region close to the vacuole. Subsequently, the phagophore expands to form a closed autophagosome. In Saccharomyces cerevisiae cells overexpressing precursor Ape1 (prApe1), a specific autophagosome cargo protein, the phagophore can be visualized as a cup-shaped structure labeled with green fluorescent protein (GFP)-tagged Atg8. Previously, our group has shown that the maximum length of GFP-Atg8-labeled structures reflects the magnitude of bulk autophagy. In that study, the morphological parameters of the autophagy-related structures were extracted manually, requiring a great deal of time. Moreover, only well-expanded phagophores were subjected to further analysis. Here we report Qautas (Quantitative autophagy-related structure analysis system), a high-throughput and comprehensive system for morphological analysis of autophagy-related structures using a combination of image processing and machine learning. We describe both the manual method and Qautas in detail.

Systematic analysis of Ca2+ homeostasis in Saccharomyces cerevisiae based on chemical-genetic interaction profiles

We investigated the global landscape of Ca2+ homeostasis in budding yeast based on high-dimensional chemical-genetic interaction profiles. The morphological responses of 62 Ca2+-sensitive (cls) mutants were quantitatively analyzed with the image processing program CalMorph after exposure to a high concentration of Ca2+ After a generalized linear model was applied, an analysis of covariance model was used to detect significant Ca2+-cls interactions. We found that high-dimensional, morphological Ca2+-cls interactions were mixed with positive (86%) and negative (14%) chemical-genetic interactions, whereas one-dimensional fitness Ca2+-cls interactions were all negative in principle. Clustering analysis with the interaction profiles revealed nine distinct gene groups, six of which were functionally associated. In addition, characterization of Ca2+-cls interactions revealed that morphology-based negative interactions are unique signatures of sensitized cellular processes and pathways. Principal component analysis was used to discriminate between suppression and enhancement of the Ca2+-sensitive phenotypes triggered by inactivation of calcineurin, a Ca2+-dependent phosphatase. Finally, similarity of the interaction profiles was used to reveal a connected network among the Ca2+ homeostasis units acting in different cellular compartments. Our analyses of high-dimensional chemical-genetic interaction profiles provide novel insights into the intracellular network of yeast Ca2+ homeostasis.

Single-cell phenomics in budding yeast

The demand for phenomics, a high-dimensional and high-throughput phenotyping method, has been increasing in many fields of biology. The budding yeast Saccharomyces cerevisiae, a unicellular model organism, provides an invaluable system for dissecting complex cellular processes using high-resolution phenotyping. Moreover, the addition of spatial and temporal attributes to subcellular structures based on microscopic images has rendered this cell phenotyping system more reliable and amenable to analysis. A well-designed experiment followed by appropriate multivariate analysis can yield a wealth of biological knowledge. Here we review recent advances in cell imaging and illustrate their broad applicability to eukaryotic cells by showing how these techniques have advanced our understanding of budding yeast.

Quantification of cell, actin, and nuclear DNA morphology with high-throughput microscopy and CalMorph

Automated image acquisition and processing systems have been developed to quantitatively describe yeast cell morphology. These systems are superior to the preceding qualitative methods in terms of reproducibility, as they completely avoid subjective recognition of images. Because high-throughput microscopy has enabled rapid production of numerous cellular images, reinforcement of high-performance and high-throughput automated image-processing techniques has been in increasing demand in the field of biology. This protocol describes how to use a high-throughput microscope in conjunction with the image-processing software CalMorph, which outputs more than 500 morphological parameters, for quantification of cell, actin, and nuclear DNA morphology.

Image-based prediction of drug target in yeast

Discovering the intracellular target of drugs is a fundamental challenge in biomedical research. We developed an image-based technique with which we were able to identify intracellular target of the compounds in the yeast Saccharomyces cerevisiae. Here, we describe the rationale of the technique, staining of yeast cells, image acquisition, data processing, and statistical analysis required for prediction of drug targets.

Hyperspectral imaging techniques for the characterization of Haematococcus pluvialis (Chlorophyceae)

A hyperspectral imaging camera was combined with a bright-field microscope to investigate the intracellular distribution of pigments in cells of the green microalga Haematococcus pluvialis, a synonym for H. lacustris (Chlorophyceae). We applied multivariate curve resolution to the hyperspectral image data to estimate the pigment contents in culture and revealed that the predicted values were consistent with actual measurements obtained from extracted pigments. Because it was possible to estimate pigment contents in every pixel, the intracellular distribution of the pigments was investigated during various life-cycle stages. Astaxanthin was localized specifically at the eyespot of zoospores in early culture stages. Then, it became widely distributed in cells, but subsequently localized differently than the chl. Integrated with our recently developed image-processing program "HaematoCalMorph," the hyperspectral imaging system was useful for monitoring intracellular distributions of pigments during culture as well as for studying cellular responses under various conditions.

Distinct roles of cell wall biogenesis in yeast morphogenesis as revealed by multivariate analysis of high-dimensional morphometric data

To better define how cell wall structure affects morphogenesis, the morphology of yeast cells was analyzed quantitatively after treatment with the three drugs that inhibit different aspects of cell wall synthesis. These drugs induced both similar effects, including broader necks and increased morphological variation, and distinct effects.

The cell wall of budding yeast is a rigid structure composed of multiple components. To thoroughly understand its involvement in morphogenesis, we used the image analysis software CalMorph to quantitatively analyze cell morphology after treatment with drugs that inhibit different processes during cell wall synthesis. Cells treated with cell wall–affecting drugs exhibited broader necks and increased morphological variation. Tunicamycin, which inhibits the initial step of N-glycosylation of cell wall mannoproteins, induced morphologies similar to those of strains defective in α-mannosylation. The chitin synthase inhibitor nikkomycin Z induced morphological changes similar to those of mutants defective in chitin transglycosylase, possibly due to the critical role of chitin in anchoring the β-glucan network. To define the mode of action of echinocandin B, a 1,3-β-glucan synthase inhibitor, we compared the morphology it induced with mutants of Fks1 that contains the catalytic domain for 1,3-β-glucan synthesis. Echinocandin B exerted morphological effects similar to those observed in some fks1 mutants, with defects in cell polarity and reduced glucan synthesis activity, suggesting that echinocandin B affects not only 1,3-β-glucan synthesis, but also another functional domain. Thus our multivariate analyses reveal discrete functions of cell wall components and increase our understanding of the pharmacology of antifungal drugs.

Dynamic changes in brewing yeast cells in culture revealed by statistical analyses of yeast morphological data

The vitality of brewing yeasts has been used to monitor their physiological state during fermentation. To investigate the fermentation process, we used the image processing software, CalMorph, which generates morphological data on yeast mother cells and bud shape, nuclear shape and location, and actin distribution. We found that 248 parameters changed significantly during fermentation. Successive use of principal component analysis (PCA) revealed several important features of yeast, providing insight into the dynamic changes in the yeast population. First, PCA indicated that much of the observed variability in the experiment was summarized in just two components: a change with a peak and a change over time. Second, PCA indicated the independent and important morphological features responsible for dynamic changes: budding ratio, nucleus position, neck position, and actin organization. Thus, the large amount of data provided by imaging analysis can be used to monitor the fermentation processes involved in beer and bioethanol production.

Single-cell phenomics reveals intra-species variation of phenotypic noise in yeast

Background

Most quantitative measures of phenotypic traits represent macroscopic contributions of large numbers of cells. Yet, cells of a tissue do not behave similarly, and molecular studies on several organisms have shown that regulations can be highly stochastic, sometimes generating diversified cellular phenotypes within tissues. Phenotypic noise, defined here as trait variability among isogenic cells of the same type and sharing a common environment, has therefore received a lot of attention. Given the potential fitness advantage provided by phenotypic noise in fluctuating environments, the possibility that it is directly subjected to evolutionary selection is being considered. For selection to act, phenotypic noise must differ between contemporary genotypes. Whether this is the case or not remains, however, unclear because phenotypic noise has very rarely been quantified in natural populations.

Results

Using automated image analysis, we describe here the phenotypic diversity of S. cerevisiae morphology at single-cell resolution. We profiled hundreds of quantitative traits in more than 1,000 cells of 37 natural strains, which represent various geographical and ecological origins of the species. We observed abundant trait variation between strains, with no correlation with their ecological origin or population history. Phenotypic noise strongly depended on the strain background. Noise variation was largely trait-specific (specific strains showing elevated noise for subset of traits) but also global (a few strains displaying elevated noise for many unrelated traits).

Conclusions

Our results demonstrate that phenotypic noise does differ quantitatively between natural populations. This supports the possibility that, if noise is adaptive, microevolution may tune it in the wild. This tuning may happen on specific traits or by varying the degree of global phenotypic buffering.

Profilin is required for Ca2+ homeostasis and Ca2+-modulated bud formation in yeast

A cls5-1 mutant of Saccharomyces cerevisiae is specifically sensitive to high concentrations of Ca2+, with elevated intracellular calcium content and altered cell morphology in the presence of 100 mM Ca2+. To reveal the mechanisms of the Ca2+-sensitive phenotype, we investigated the gene responsible and its interacting network. We demonstrated that CLS5 is identical to PFY1, encoding profilin. Involvement of profilin in the maintenance of intracellular Ca2+ homeostasis was supported by the fact that both exchangeable and non-exchangeable intracellular Ca2+ pools in the cls5-1 mutant are higher than those of the wild-type strain. Several mutations of the genes whose proteins physically interact with profilin resulted in the Ca2+-sensitive phenotype. Examination of the intracellular Ca2+ pools indicated that Bni1p, Bem1p, Rho1p, and Cla4p are also required for the maintenance of Ca2+ homeostasis. Quantitative morphological analysis revealed that the Ca2+-induced morphological changes in cls5-1 cells are similar to bem1 and cls4-1 cells. Common Ca2+-induced morphological changes were an increase in cell size and a decrease of the ratio of budded cells in the population. Since a mutation allele of cls4-1 is located in the CDC24 gene, we suggest that profilin, Bem1p, and Cdc24p are required for Ca2+-modulated bud formation. Thus, profilin is involved in Ca2+ regulation in two ways: the first is Ca2+ homeostasis by coordination with Bni1p, Bem1p, Rho1p, and Cla4p, and the second is the requirement of Ca2+ for bud formation by coordination with Bem1p and Cdc24p.

Vanillin inhibits translation and induces messenger ribonucleoprotein (mRNP) granule formation in saccharomyces cerevisiae: application and validation of high-content, image-based profiling

Vanillin, generated by acid hydrolysis of lignocellulose, acts as a potent inhibitor of the growth of the yeast Saccharomyces cerevisiae. Here, we investigated the cellular processes affected by vanillin using high-content, image-based profiling. Among 4,718 non-essential yeast deletion mutants, the morphology of those defective in the large ribosomal subunit showed significant similarity to that of vanillin-treated cells. The defects in these mutants were clustered in three domains of the ribosome: the mRNA tunnel entrance, exit and backbone required for small subunit attachment. To confirm that vanillin inhibited ribosomal function, we assessed polysome and messenger ribonucleoprotein granule formation after treatment with vanillin. Analysis of polysome profiles showed disassembly of the polysomes in the presence of vanillin. Processing bodies and stress granules, which are composed of non-translating mRNAs and various proteins, were formed after treatment with vanillin. These results suggest that vanillin represses translation in yeast cells.

Analysis of the biological activity of a novel 24-membered macrolide JBIR-19 in Saccharomyces cerevisiae by the morphological imaging program CalMorph

To investigate the biological activity of a novel 24-membered macrolide compound, JBIR-19, isolated from the culture broth of the entomopathogenic fungus Metarhizium sp. fE61, morphological changes in yeast cells were examined using the automated image-processing program CalMorph. Principal components analysis was used to elucidate dynamic changes in the phenotypes, revealing two independent effects of JBIR-19 in yeast cells: bud elongation and increased size of the actin region. Using a fitness assay, we identified the genes required for robust growth in the presence of JBIR-19. Among these were CCW12, YLR111W, and DHH1, which are also involved in abnormal bud morphology. Based on these results and others, we predict intracellular targets of JBIR-19 and its functional interactions.

Effect of Minor Alloying Element on Dispersing Nano-particles in ODS Steel

From the irradiation resistance and high-temperature strength, oxide dispersion strengthened (ODS) ferritic steels are candidate materials for advanced and fusion reactors. For the development of advanced steels the key issue is to homogenize nano-particles into matrix. Recent studies have indicated that Ti addition can homogenize Y-Ti complex particles into ferrite matrix, but the reason of the effect of additional elements has not been clarified. In this model study, we focus on the effect of additional elements, such as IV and V families and other oxide formers, which can control potentially the distribution of the oxide particles. The materials used in this study were based on Fe-9Cr-Y2O3 alloys which were mechanical alloyed (MA) from the powder of Fe, Cr and Y2O3, which was added systematically with the element of Ti, Zr, Ta, V, Nb, Hf, Al, Si and others. Usually ODS fabrication process is required for hot extrusion, but we annealed up to 1150 C for simplify the microstructure. To evaluate the distribution of ODS particles; we used TEM equipped with EDS after electro-polishing or FIB techniques. (1) In the case of Si or Al addition, oxides were disappeared after MA process, which means Y2O3 and other elements should be in solution at non-equilibrium condition. Two types of oxides of Y2O3 and Al2O3 or SiO2 developed after the annealing at 850 C, but only complex oxides were developed after the annealing at 1150 C. This result suggests that the oxide formation is independent process for Y and Si or Al. (2) In the case of Ti addition, oxides also were disappeared after MA process, but developed after annealing at 1150 C. This means that Ti can stabilize complex oxides of Y and Ti, and enhance the fine distribution of the oxides comparing with simple Fe-9Cr-Y2O3 alloy.

High-content, image-based screening for drug targets in yeast

Background

Drug discovery and development are predicated on elucidation of the potential mechanisms of action and cellular targets of candidate chemical compounds. Recent advances in high-content imaging techniques allow simultaneous analysis of a range of cellular events. In this study, we propose a novel strategy to identify drug targets by combining genetic screening and high-content imaging in yeast.

Methodology

In this approach, we infer the cellular functions affected by candidate drugs by comparing morphologic changes induced by the compounds with the phenotypes of yeast mutants.

Conclusions

Using this method and four well-characterized reagents, we successfully identified previously known target genes of the compounds as well as other genes involved with functionally related cellular pathways. This is the first demonstration of a genetic high-content assay that can be used to identify drug targets based on morphologic phenotypes of a reference mutant panel.

A microfluidic device to acquire high-magnification microphotographs of yeast cells

BACKGROUND

Yeast cell morphology was investigated to reveal the molecular mechanisms of cell morphogenesis and to identify key factors of other processes such as cell cycle progression. We recently developed a semi-automatic image processing program called CalMorph, which allows us to quantitatively analyze yeast cell morphology with the 501 parameters as biological traits and uncover statistical relationships between cell morphological phenotypes and genotypes. However, the current semi-automatic method is not suitable for morphological analysis of large-scale yeast mutants for the reliable prediction of gene functions because of its low-throughput especially at the manual image-acquiring process.

RESULTS

In this study, we developed a microfluidic chip designed to acquire successive microscopic images of yeast cells suitable for CalMorph image analysis. With the microfluidic chip, the morphology of living cells and morphological changes that occur during the cell cycle were successfully characterized.

CONCLUSION

The microfluidic chip enabled us to acquire the images faster than the conventional method. We speculate that the use of microfluidic chip is effective in acquiring images of large-scale for automated analysis of yeast strains.

Diversity of Ca2+-induced morphology revealed by morphological phenotyping of Ca2+-sensitive mutants of Saccharomyces cerevisiae

Yeast cell morphology can be treated as a quantitative trait using the image processing software CalMorph. In the present study, we investigated Ca(2+)-induced morphological changes in Ca(2+)-sensitive (cls) mutants of Saccharomyces cerevisiae, based on the discovery that the characteristic Ca(2+)-induced morphological changes in the Ca(2+)-sensitive mutant zds1 reflect changes in the Ca(2+) signaling-mediated cell cycle control pathway. By applying hierarchical cluster analysis to the quantitative morphological data of 58 cls mutants, 31 of these mutants were classified into seven classes based on morphological similarities. The patterns of morphological change induced by Ca(2+) in one class differed from those of another class. Based on the results obtained using versatile methods for phenotypic analysis, we conclude that a high concentration of Ca(2+) exerts a wide variety of effects on yeast and that there are multiple Ca(2+)-regulatory pathways that are distinct from the Zds1p-related pathway.