Multidimensional quantification of subcellular morphology of Saccharomyces cerevisiae using CalMorph, the high-throughput image-processing program

Phenomics approaches to understand genetic networks and gene function in yeast

Over the past decade, major efforts have been made to systematically survey the characteristics or phenotypes associated with genetic variation in a variety of model systems. These so-called phenomics projects involve the measurement of 'phenomes', or the set of phenotypic information that describes an organism or cell, in various genetic contexts or states, and in response to external factors, such as environmental signals. Our understanding of the phenome of an organism depends on the availability of reagents that enable systematic evaluation of the spectrum of possible phenotypic variation and the types of measurements that can be taken. Here, we highlight phenomics studies that use the budding yeast, a pioneer model organism for functional genomics research. We focus on genetic perturbation screens designed to explore genetic interactions, using a variety of phenotypic read-outs, from cell growth to subcellular morphology.

Computerized fluorescence microscopy of microbial cells

The upgrading of fluorescence microscopy by the introduction of computer technologies has led to the creation of a new methodology, computerized fluorescence microscopy (CFM). CFM improves subjective visualization and combines it with objective quantitative analysis of the microscopic data. CFM has opened up two fundamentally new opportunities for studying microorganisms. The first is the quantitative measurement of the fluorescence parameters of the targeted fluorophores in association with certain structures of individual cells. The second is the expansion of the boundaries of visualization/resolution of intracellular components beyond the "diffraction limit" of light microscopy into the nanometer range. This enables to obtain unique information about the localization and dynamics of intracellular processes at the molecular level. The purpose of this review is to demonstrate the potential of CFM in the study of fundamental aspects of the structural and functional organization of microbial cells. The basics of computer processing and analysis of digital images are briefly described. The fluorescent molecules used in CFM with an emphasis on fluorescent proteins are characterized. The main methods of super-resolution microscopy (nanoscopy) are presented. The capabilities of various CFM methods for exploring microbial cells at the subcellular level are illustrated by the examples of various studies on yeast and bacteria.

Extent and context dependence of pleiotropy revealed by high-throughput single-cell phenotyping

Pleiotropy-when a single mutation affects multiple traits-is a controversial topic with far-reaching implications. Pleiotropy plays a central role in debates about how complex traits evolve and whether biological systems are modular or are organized such that every gene has the potential to affect many traits. Pleiotropy is also critical to initiatives in evolutionary medicine that seek to trap infectious microbes or tumors by selecting for mutations that encourage growth in some conditions at the expense of others. Research in these fields, and others, would benefit from understanding the extent to which pleiotropy reflects inherent relationships among phenotypes that correlate no matter the perturbation (vertical pleiotropy). Alternatively, pleiotropy may result from genetic changes that impose correlations between otherwise independent traits (horizontal pleiotropy). We distinguish these possibilities by using clonal populations of yeast cells to quantify the inherent relationships between single-cell morphological features. Then, we demonstrate how often these relationships underlie vertical pleiotropy and how often these relationships are modified by genetic variants (quantitative trait loci [QTL]) acting via horizontal pleiotropy. Our comprehensive screen measures thousands of pairwise trait correlations across hundreds of thousands of yeast cells and reveals ample evidence of both vertical and horizontal pleiotropy. Additionally, we observe that the correlations between traits can change with the environment, genetic background, and cell-cycle position. These changing dependencies suggest a nuanced view of pleiotropy: biological systems demonstrate limited pleiotropy in any given context, but across contexts (e.g., across diverse environments and genetic backgrounds) each genetic change has the potential to influence a larger number of traits. Our method suggests that exploiting pleiotropy for applications in evolutionary medicine would benefit from focusing on traits with correlations that are less dependent on context.

Machine learning and computer vision approaches for phenotypic profiling

Grys et al. review computer vision and machine-learning methods that have been applied to phenotypic profiling of image-based data. Descriptions are provided for segmentation, feature extraction, selection, and dimensionality reduction, as well as clustering, outlier detection, and classification of data.

With recent advances in high-throughput, automated microscopy, there has been an increased demand for effective computational strategies to analyze large-scale, image-based data. To this end, computer vision approaches have been applied to cell segmentation and feature extraction, whereas machine-learning approaches have been developed to aid in phenotypic classification and clustering of data acquired from biological images. Here, we provide an overview of the commonly used computer vision and machine-learning methods for generating and categorizing phenotypic profiles, highlighting the general biological utility of each approach.

Selection Transforms the Landscape of Genetic Variation Interacting with Hsp90

The protein-folding chaperone Hsp90 has been proposed to buffer the phenotypic effects of mutations. The potential for Hsp90 and other putative buffers to increase robustness to mutation has had major impact on disease models, quantitative genetics, and evolutionary theory. But Hsp90 sometimes contradicts expectations for a buffer by potentiating rapid phenotypic changes that would otherwise not occur. Here, we quantify Hsp90’s ability to buffer or potentiate (i.e., diminish or enhance) the effects of genetic variation on single-cell morphological features in budding yeast. We corroborate reports that Hsp90 tends to buffer the effects of standing genetic variation in natural populations. However, we demonstrate that Hsp90 tends to have the opposite effect on genetic variation that has experienced reduced selection pressure. Specifically, Hsp90 tends to enhance, rather than diminish, the effects of spontaneous mutations and recombinations. This result implies that Hsp90 does not make phenotypes more robust to the effects of genetic perturbation. Instead, natural selection preferentially allows buffered alleles to persist and thereby creates the false impression that Hsp90 confers greater robustness.

Most biologists appreciate that natural selection filters new mutations (e.g., by eliminating deleterious ones), such that genetic variation in nature is biased. The idea that selection also skews the types of genetic interactions that exist in nature is less appreciated. For example, studies spanning diverse species have shown that the protein Hsp90, which helps other proteins to fold properly, tends to diminish the observable effects of genetic variation. This observation has led to the assumption that Hsp90 also buffers the effects of new mutations. This untested assumption has served as a rationale for cancer-treatment strategies and shaped our understanding of variation in complex traits. We measured the effects of new mutations on the shapes and sizes of individual yeast cells and found that Hsp90 does not tend to buffer these effects. Instead, Hsp90 interacts with new mutations in diverse ways, sometimes buffering, but more often enhancing mutational effects on cell shape and size. We conclude that selection preferentially allows buffered mutations to persist in natural populations. This result alters common perceptions about why cryptic (i.e., buffered) genetic variation exists and casts doubt on cancer-treatment strategies aiming to target presumed buffers of mutational effects.

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.

Organelle acidification is important for localisation of vacuolar proteins in Saccharomyces cerevisiae

The acidic environments in the vacuole and other acidic organelles are important for many cellular processes in eukaryotic cells. In this study, we comprehensively investigated the roles of organelle acidification in vacuolar protein localisation in Saccharomyces cerevisiae. After repressing the acidification of acidic compartments by treatment with concanamycin A, a specific inhibitor of vacuolar H(+)-ATPase (V-ATPase), we examined the localisation of GFP-fused proteins that were predicted to localise in the vacuolar lumen or on the vacuolar membrane. Of the 73 proteins examined, 19 changed their localisation to the cytoplasmic region. Localisation changes were evaluated quantitatively using the image processing programme CalMorph. The delocalised proteins included vacuolar hydrolases, V-ATPase subunits, transporters and enzymes for membrane biogenesis, as well as proteins required for protein transport. These results suggest that many alterations in the localisation of vacuolar proteins occur after loss of the acidification of acidic compartments.

Target identification of small molecules based on chemical biology approaches

Recently, a phenotypic approach-screens that assess the effects of compounds on cells, tissues, or whole organisms-has been reconsidered and reintroduced as a complementary strategy of a target-based approach for drug discovery. Although the finding of novel bioactive compounds from large chemical libraries has become routine, the identification of their molecular targets is still a time-consuming and difficult process, making this step rate-limiting in drug development. In the last decade, we and other researchers have amassed a large amount of phenotypic data through progress in omics research and advances in instrumentation. Accordingly, the profiling methodologies using these datasets expertly have emerged to identify and validate specific molecular targets of drug candidates, attaining some progress in current drug discovery (e.g., eribulin). In the case of a compound that shows an unprecedented phenotype likely by inhibiting a first-in-class target, however, such phenotypic profiling is invalid. Under the circumstances, a photo-crosslinking affinity approach should be beneficial. In this review, we describe and summarize recent progress in both affinity-based (direct) and phenotypic profiling (indirect) approaches for chemical biology target identification.

The details in the distributions: why and how to study phenotypic variability

Phenotypic variability is present even when genetic and environmental differences between cells are reduced to the greatest possible extent. For example, genetically identical bacteria display differing levels of resistance to antibiotics, clonal yeast populations demonstrate morphological and growth-rate heterogeneity, and mouse blastomeres from the same embryo have stochastic differences in gene expression. However, the distributions of phenotypes present among isogenic organisms are often overlooked; instead, many studies focus on population aggregates such as the mean. The details of these distributions are relevant to major questions in diverse fields, including the evolution of antimicrobial-drug and chemotherapy resistance. We review emerging experimental and statistical techniques that allow rigorous analysis of phenotypic variability and thereby may lead to advances across the biological sciences.

Identification of vacuole defects in fungi

Fungal vacuoles are involved in a diverse range of cellular functions, participating in cellular homeostasis, degradation of intracellular components, and storage of ions and molecules. In recent years there has been a significant increase in the number of studies linking these organelles with the regulation of growth and control of cellular morphology, particularly in those fungal species able to undergo yeast-hypha morphogenetic transitions. This has contributed to the refinement of previously published protocols and the development of new techniques, particularly in the area of live-cell imaging of membrane trafficking events and vacuolar dynamics. The current review outlines recent advances in the imaging of fungal vacuoles and assays for characterization of trafficking pathways, and other physiological activities of this important cell organelle.

Getting the whole picture: combining throughput with content in microscopy

The increasing availability and performance of automated scientific equipment in the past decades have brought about a revolution in the biological sciences. The ease with which data can now be generated has led to a new culture of high-throughput science, in which new types of biological questions can be asked and tackled in a systematic and unbiased manner. High-throughput microscopy, also often referred to as high-content screening (HCS), allows acquisition of systematic data at the single-cell level. Moreover, it allows the visualization of an enormous array of cellular features and provides tools to quantify a large number of parameters for each cell. These features make HCS a powerful method to create data that is rich and biologically meaningful without compromising systematic capabilities. In this Commentary, we will discuss recent work, which has used HCS, to demonstrate the diversity of applications and technological solutions that are evolving in this field. Such advances are placing HCS methodologies at the frontier of high-throughput science and enable scientists to combine throughput with content to address a variety of cell biological questions.

High-throughput confocal imaging of intact live tissue enables quantification of membrane trafficking in Arabidopsis

Membrane compartmentalization and trafficking within and between cells is considered an essential cellular property of higher eukaryotes. We established a high-throughput imaging method suitable for the quantitative detection of membrane compartments at subcellular resolution in intact epidermal tissue. Whole Arabidopsis (Arabidopsis thaliana) cotyledon leaves were subjected to quantitative confocal laser microscopy using automated image acquisition, computational pattern recognition, and quantification of membrane compartments. This revealed that our method is sensitive and reliable to detect distinct endomembrane compartments. We applied quantitative confocal laser microscopy to a transgenic line expressing GFP-2xFYVE as a marker for endosomal compartments during biotic or abiotic stresses, and detected markedly quantitative adaptations in response to changing environments. Using a transgenic line expressing the plasma membrane-resident syntaxin GFP-PEN1, we quantified the pathogen-inducible extracellular accumulation of this fusion protein at fungal entry sites. Our protocol provides a platform to study the quantitative and dynamic changes of endomembrane trafficking, and potential adaptations of this machinery to physiological stress.

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.

Scaling properties of cell and organelle size

How size is controlled is a fundamental question in biology. In this review, we discuss the use of scaling relationships-for example, power-laws of the form y∝x(α)-to provide a framework for comparison and interpretation of size measurements. Such analysis can illustrate the biological and physical principles underlying observed trends, as has been proposed for the allometric dependence of metabolic rate or limb structure on organism mass. Techniques for measuring size at smaller length-scales continue to improve, leading to more data on the control of size in cells and organelles. Size scaling of these structures is expected to influence growth patterns, functional capacity and intracellular transport. Furthermore, organelles such as the nucleus, mitochondria and endoplasmic reticulum show widely varying morphologies that affect their scaling properties. We provide brief summaries of these issues for individual organelles, and conclude with a discussion on how to apply this concept to better understand the mechanisms of size control in the cellular environment.

Light microscopic analysis of mitochondrial heterogeneity in cell populations and within single cells

Heterogeneity in the shapes of individual multicellular organisms is a daily experience. Likewise, even a quick glance through the ocular of a light microscope reveals the morphological heterogeneities in genetically identical cultured cells, whereas heterogeneities on the level of the organelles are much less obvious. This short review focuses on intracellular heterogeneities at the example of the mitochondria and their analysis by fluorescence microscopy. The overall mitochondrial shape as well as mitochondrial dynamics can be studied by classical (fluorescence) light microscopy. However, with an organelle diameter generally close to the resolution limit of light, the heterogeneities within mitochondria cannot be resolved with conventional light microscopy. Therefore, we briefly discuss here the potential of subdiffraction light microscopy (nanoscopy) to study inner-mitochondrial heterogeneities.