Systematic profiling of cellular phenotypes with spotted cell microarrays reveals mating-pheromone response genes

N-terminal acetylation and replicative age affect proteasome localization and cell fitness during aging

Specific degradation of proteins is essential for virtually all cellular processes and is carried out predominantly by the proteasome. The proteasome is important for clearance of damaged cellular proteins. Damaged proteins accumulate over time and excess damaged proteins can aggregate and induce the death of old cells. In yeast, the localization of the proteasome changes dramatically during aging, possibly in response to altered proteasome activity requirements. We followed two key parameters of this process: the distribution of proteasomes in nuclear and cytosolic compartments, and the formation of cytoplasmic aggregate-like structures called proteasome storage granules (PSGs). Whereas replicative young cells efficiently relocalized proteasomes from the nucleus to the cytoplasm and formed PSGs, replicative old cells were less efficient in relocalizing the proteasome and had less PSGs. By using a microscopy-based genome-wide screen, we identified genetic factors involved in these processes. Both relocalization of the proteasome and PSG formation were affected by two of the three N-acetylation complexes. These N-acetylation complexes also had different effects on the longevity of cells, indicating that each N-acetylation complex has different roles in proteasome location and aging.

Functional genomics in the study of yeast cell polarity: moving in the right direction

The budding yeast Saccharomyces cerevisiae has been used extensively for the study of cell polarity, owing to both its experimental tractability and the high conservation of cell polarity and other basic biological processes among eukaryotes. The budding yeast has also served as a pioneer model organism for virtually all genome-scale approaches, including functional genomics, which aims to define gene function and biological pathways systematically through the analysis of high-throughput experimental data. Here, we outline the contributions of functional genomics and high-throughput methodologies to the study of cell polarity in the budding yeast. We integrate data from published genetic screens that use a variety of functional genomics approaches to query different aspects of polarity. Our integrated dataset is enriched for polarity processes, as well as some processes that are not intrinsically linked to cell polarity, and may provide new areas for future study.

Proteome-wide screens in Saccharomyces cerevisiae using the yeast GFP collection

The budding yeast is a simple and genetically tractable eukaryotic organism. It remains a leading system for functional genomic work and has been the focus of many pioneering efforts, including the systematic construction and analysis of gene deletion mutants. Over the past decade, many large-scale studies have made use of the deletion and other mutant collections to assay genetic interactions, chemical sensitivities, and other phenotypes, contributing enormously to our understanding of gene function. The deletion mutant collection has also been used in cell biological surveys to identify genes that control cell and organelle morphology. One valuable approach for systematic definition of gene function and biological pathways involves global assessment of the localization patterns of the proteins they encode and how these patterns are altered in response to environmental or genetic perturbation. However, proteome-wide, cell biological screens are extremely challenging, from both a technical and computational perspective. The yeast GFP collection, an elegant and unique strain set, is ideal for studying both protein localization and abundance across the proteome ( http://yeastgfp.yeastgenome.org/ ). In this chapter, we outline how the yeast GFP collection has been used to date and discuss approaches for conducting future surveys of the proteome.

Swimming upstream: identifying proteomic signals that drive transcriptional changes using the interactome and multiple "-omics" datasets

Signaling and transcription are tightly integrated processes that underlie many cellular responses to the environment. A network of signaling events, often mediated by post-translational modification on proteins, can lead to long-term changes in cellular behavior by altering the activity of specific transcriptional regulators and consequently the expression level of their downstream targets. As many high-throughput, "-omics" methods are now available that can simultaneously measure changes in hundreds of proteins and thousands of transcripts, it should be possible to systematically reconstruct cellular responses to perturbations in order to discover previously unrecognized signaling pathways. This chapter describes a computational method for discovering such pathways that aims to compensate for the varying levels of noise present in these diverse data sources. Based on the concept of constraint optimization on networks, the method seeks to achieve two conflicting aims: (1) to link together many of the signaling proteins and differentially expressed transcripts identified in the experiments "constraints" using previously reported protein-protein and protein-DNA interactions, while (2) keeping the resulting network small and ensuring it is composed of the highest confidence interactions "optimization". A further distinctive feature of this approach is the use of transcriptional data as evidence of upstream signaling events that drive changes in gene expression, rather than as proxies for downstream changes in the levels of the encoded proteins. We recently demonstrated that by applying this method to phosphoproteomic and transcriptional data from the pheromone response in yeast, we were able to recover functionally coherent pathways and to reveal many components of the cellular response that are not readily apparent in the original data. Here, we provide a more detailed description of the method, explore the robustness of the solution to the noise level of input data and discuss the effect of parameter values.

Development of a high-throughput Candida albicans biofilm chip

We have developed a high-density microarray platform consisting of nano-biofilms of Candida albicans. A robotic microarrayer was used to print yeast cells of C. albicans encapsulated in a collagen matrix at a volume as low as 50 nL onto surface-modified microscope slides. Upon incubation, the cells grow into fully formed “nano-biofilms”. The morphological and architectural complexity of these biofilms were evaluated by scanning electron and confocal scanning laser microscopy. The extent of biofilm formation was determined using a microarray scanner from changes in fluorescence intensities due to FUN 1 metabolic processing. This staining technique was also adapted for antifungal susceptibility testing, which demonstrated that, similar to regular biofilms, cells within the on-chip biofilms displayed elevated levels of resistance against antifungal agents (fluconazole and amphotericin B). Thus, results from structural analyses and antifungal susceptibility testing indicated that despite miniaturization, these biofilms display the typical phenotypic properties associated with the biofilm mode of growth. In its final format, the C. albicans biofilm chip (CaBChip) is composed of 768 equivalent and spatially distinct nano-biofilms on a single slide; multiple chips can be printed and processed simultaneously. Compared to current methods for the formation of microbial biofilms, namely the 96-well microtiter plate model, this fungal biofilm chip has advantages in terms of miniaturization and automation, which combine to cut reagent use and analysis time, minimize labor intensive steps, and dramatically reduce assay costs. Such a chip should accelerate the antifungal drug discovery process by enabling rapid, convenient and inexpensive screening of hundreds-to-thousands of compounds simultaneously.

Cell-based microarrays: overview

Cell-based microarrays were first described by Ziauddin and Sabatini in 2001 as a novel method for performing high-throughput screens of gene function. They reported a technique whereby expression vectors containing the open reading frame (ORF) of human genes were printed onto glass microscope slides to form a microarray. Transfection reagents were added pre- or post-spotting and cells grown over the surface of the array. They demonstrated that cells growing in the immediate vicinity of the expression vectors underwent 'reverse transfection' and that subsequent alterations in cell function could then be detected by secondary assays performed on the array. Subsequent publications have adapted the technique to a variety of applications and have also shown that the approach works when arrays are fabricated using siRNAs and compounds. The potential of this method for performing analyses of gene function and identification of novel therapeutic agents has now been clearly demonstrated. Current efforts are focused on improving and harnessing this technology for high-throughput screening applications.

High-throughput immunofluorescence microscopy using yeast spheroplast cell-based microarrays

We have described a protocol for performing high-throughput immunofluorescence microscopy on microarrays of yeast cells. This approach employs immunostaining of spheroplasted yeast cells printed as high-density cell microarrays, followed by imaging using automated microscopy. A yeast spheroplast microarray can contain more than 5,000 printed spots, each containing cells from a given yeast strain, and is thus suitable for genome-wide screens focusing on single cell phenotypes, such as systematic localization or co-localization studies or genetic assays for genes affecting probed targets. We demonstrate the use of yeast spheroplast microarrays to probe microtubule and spindle defects across a collection of yeast strains harboring tetracycline-down-regulatable alleles of essential genes.

Enteropathogenic and enterohemorrhagic Escherichia coli type III secretion effector EspV induces radical morphological changes in eukaryotic cells

Enteropathogenic Escherichia coli (EPEC) and enterohemorrhagic Escherichia coli (EHEC) are important human pathogens that rely on translocation of type III secretion system (T3SS) effectors for subversion of signal transduction pathways and colonization of the mammalian gut mucosa. While a core set of effectors is conserved between EPEC and EHEC strains, a growing number of accessory effectors that were found at various frequencies in clinical and environmental isolates have been recently identified. Recent genome projects identified espV as a pseudogene in EHEC but a putative functional gene in EPEC strains E110019 and E22 and the closely related mouse pathogen Citrobacter rodentium. The aim of this study was to determine the distribution of espV among clinical EPEC and EHEC strains and to investigate its function and role in pathogenesis. espV was found in 16% of the tested strains. While deletion of espV from C. rodentium did not affect colonization dynamics or fitness in mixed infections, expression of EspV in mammalian cells led to drastic morphological alterations, which were characterized by nuclear condensation, cell rounding, and formation of dendrite-like projections. Expression of EspV in yeast resulted in a dramatic increase in cell size and irreversible growth arrest. Although the role of EspV in infection and its target host cell protein(s) require further investigation, the data point to a novel mechanism by which the T3SS subverts cell signaling.

Synthetic genetic array (SGA) analysis in Saccharomyces cerevisiae and Schizosaccharomyces pombe

A genetic interaction occurs when the combination of two mutations leads to an unexpected phenotype. Screens for synthetic genetic interactions have been used extensively to identify genes whose products are functionally related. In particular, synthetic lethal genetic interactions often identify genes that buffer one another or impinge on the same essential pathway. For the yeast Saccharomyces cerevisiae, we developed a method termed synthetic genetic array (SGA) analysis, which offers an efficient approach for the systematic construction of double mutants and enables a global analysis of synthetic genetic interactions. In a typical SGA screen, a query mutation is crossed to an ordered array of ~5000 viable gene deletion mutants (representing ~80% of all yeast genes) such that meiotic progeny harboring both mutations can be scored for fitness defects. This approach can be extended to all ~6000 genes through the use of yeast arrays containing mutants carrying conditional or hypomorphic alleles of essential genes. Estimating the fitness for the two single mutants and their corresponding double mutant enables a quantitative measurement of genetic interactions, distinguishing negative (synthetic lethal) and positive (within pathway and suppression) interactions. The profile of genetic interactions represents a rich phenotypic signature for each gene and clustering genetic interaction profiles group genes into functionally relevant pathways and complexes. This array-based approach automates yeast genetic analysis in general and can be easily adapted for a number of different genetic screens or combined with high-content screening systems to quantify the activity of specific reporters in genome-wide sets of single or more complex multiple mutant backgrounds. Comparison of genetic and chemical-genetic interaction profiles offers the potential to link bioactive compounds to their targets. Finally, we also developed an SGA system for the fission yeast Schizosaccharomyces pombe, providing another model system for comparative analysis of genetic networks and testing the conservation of genetic networks over millions of years of evolution.

Human cell chips: adapting DNA microarray spotting technology to cell-based imaging assays

Here we describe human spotted cell chips, a technology for determining cellular state across arrays of cells subjected to chemical or genetic perturbation. Cells are grown and treated under standard tissue culture conditions before being fixed and printed onto replicate glass slides, effectively decoupling the experimental conditions from the assay technique. Each slide is then probed using immunofluorescence or other optical reporter and assayed by automated microscopy. We show potential applications of the cell chip by assaying HeLa and A549 samples for changes in target protein abundance (of the dsRNA-activated protein kinase PKR), subcellular localization (nuclear translocation of NFκB) and activation state (phosphorylation of STAT1 and of the p38 and JNK stress kinases) in response to treatment by several chemical effectors (anisomycin, TNFα, and interferon), and we demonstrate scalability by printing a chip with ∼4,700 discrete samples of HeLa cells. Coupling this technology to high-throughput methods for culturing and treating cell lines could enable researchers to examine the impact of exogenous effectors on the same population of experimentally treated cells across multiple reporter targets potentially representing a variety of molecular systems, thus producing a highly multiplexed dataset with minimized experimental variance and at reduced reagent cost compared to alternative techniques. The ability to prepare and store chips also allows researchers to follow up on observations gleaned from initial screens with maximal repeatability.

Integrating proteomic, transcriptional, and interactome data reveals hidden components of signaling and regulatory networks

Cellular signaling and regulatory networks underlie fundamental biological processes such as growth, differentiation, and response to the environment. Although there are now various high-throughput methods for studying these processes, knowledge of them remains fragmentary. Typically, the majority of hits identified by transcriptional, proteomic, and genetic assays lie outside of the expected pathways. These unexpected components of the cellular response are often the most interesting, because they can provide new insights into biological processes and potentially reveal new therapeutic approaches. However, they are also the most difficult to interpret. We present a technique, based on the Steiner tree problem, that uses previously reported protein-protein and protein-DNA interactions to determine how these hits are organized into functionally coherent pathways, revealing many components of the cellular response that are not readily apparent in the original data. Applied simultaneously to phosphoproteomic and transcriptional data for the yeast pheromone response, it identifies changes in diverse cellular processes that extend far beyond the expected pathways.

Widespread reorganization of metabolic enzymes into reversible assemblies upon nutrient starvation

Proteins are likely to organize into complexes that assemble and disassemble depending on cellular needs. When approximately 800 yeast strains expressing GFP-tagged proteins were grown to stationary phase, a surprising number of proteins involved in intermediary metabolism and stress response were observed to form punctate cytoplasmic foci. The formation of these discrete physical structures was confirmed by immunofluorescence and mass spectrometry of untagged proteins. The purine biosynthetic enzyme Ade4-GFP formed foci in the absence of adenine, and cycling between punctate and diffuse phenotypes could be controlled by adenine subtraction and addition. Similarly, glutamine synthetase (Gln1-GFP) foci cycled reversibly in the absence and presence of glucose. The structures were neither targeted for vacuolar or autophagosome degradation nor colocalized with P bodies or major organelles. Thus, upon nutrient depletion we observe widespread protein assemblies displaying nutrient-specific formation and dissolution.

Systematic definition of protein constituents along the major polarization axis reveals an adaptive reuse of the polarization machinery in pheromone-treated budding yeast

Polarizing cells extensively restructure cellular components in a spatially and temporally coupled manner along the major axis of cellular extension. Budding yeast are a useful model of polarized growth, helping to define many molecular components of this conserved process. Besides budding, yeast cells also differentiate upon treatment with pheromone from the opposite mating type, forming a mating projection (the ‘shmoo’) by directional restructuring of the cytoskeleton, localized vesicular transport and overall reorganization of the cytosol. To characterize the proteomic localization changes accompanying polarized growth, we developed and implemented a novel cell microarray-based imaging assay for measuring the spatial redistribution of a large fraction of the yeast proteome, and applied this assay to identify proteins localized along the mating projection following pheromone treatment. We further trained a machine learning algorithm to refine the cell imaging screen, identifying additional shmoo-localized proteins. In all, we identified 74 proteins that specifically localize to the mating projection, including previously uncharacterized proteins (Ycr043c, Ydr348c, Yer071c, Ymr295c, and Yor304c-a) and known polarization complexes such as the exocyst. Functional analysis of these proteins, coupled with quantitative analysis of individual organelle movements during shmoo formation, suggests a model in which the basic machinery for cell polarization is generally conserved between processes forming the bud and the shmoo, with a distinct subset of proteins used only for shmoo formation. The net effect is a defined ordering of major organelles along the polarization axis, with specific proteins implicated at the proximal growth tip.

Upon sensing mating pheromone, budding yeast cells form a mating projection (the ‘shmoo’) that serves as a model for polarized cell growth, involving cytoskeletal/cytosolic restructuring and directed vesicular transport. We developed a cell microarray-based imaging assay for measuring localization of the yeast proteome during polarized growth. We find major organelles ordered along the polarization axis, localize 74 proteins to the growth tip, and observe adaptive reuse of general polarization machinery.

Dynamic analysis of MAPK signaling using a high-throughput microfluidic single-cell imaging platform

Cells have evolved biomolecular networks that process and respond to changing chemical environments. Understanding how complex protein interactions give rise to emergent network properties requires time-resolved analysis of cellular response under a large number of genetic perturbations and chemical environments. To date, the lack of technologies for scalable cell analysis under well-controlled and time-varying conditions has made such global studies either impossible or impractical. To address this need, we have developed a high-throughput microfluidic imaging platform for single-cell studies of network response under hundreds of combined genetic perturbations and time-varying stimulant sequences. Our platform combines programmable on-chip mixing and perfusion with high-throughput image acquisition and processing to perform 256 simultaneous time-lapse live-cell imaging experiments. Nonadherent cells are captured in an array of 2,048 microfluidic cell traps to allow for the imaging of eight different genotypes over 12 h and in response to 32 unique sequences of stimulation, generating a total of 49,000 images per run. Using 12 devices, we carried out >3,000 live-cell imaging experiments to investigate the mating pheromone response in Saccharomyces cerevisiae under combined genetic perturbations and changing environmental conditions. Comprehensive analysis of 11 deletion mutants reveals both distinct thresholds for morphological switching and new dynamic phenotypes that are not observed in static conditions. For example, kss1Delta, fus3Delta, msg5Delta, and ptp2Delta mutants exhibit distinctive stimulus-frequency-dependent signaling phenotypes, implicating their role in filtering and network memory. The combination of parallel microfluidic control with high-throughput imaging provides a powerful tool for systems-level studies of single-cell decision making.

Advancing microarray assembly with acoustic dispensing technology

In the assembly of microarrays and microarray-based chemical assays and enzymatic bioassays, most approaches use pins for contact spotting. Acoustic dispensing is a technology capable of nanoliter transfers by using acoustic energy to eject liquid sample from an open source well. Although typically used for well plate transfers, when applied to microarraying, it avoids the drawbacks of undesired physical contact with the sample; difficulty in assembling multicomponent reactions on a chip by readdressing, a rigid mode of printing that lacks patterning capabilities; and time-consuming wash steps. We demonstrated the utility of acoustic dispensing by delivering human cathepsin L in a drop-on-drop fashion into individual 50-nanoliter, prespotted reaction volumes to activate enzyme reactions at targeted positions on a microarray. We generated variable-sized spots ranging from 200 to 750 microm (and higher) and handled the transfer of fluorescent bead suspensions with increasing source well concentrations of 0.1 to 10 x 10(8) beads/mL in a linear fashion. There are no tips that can clog, and liquid dispensing CVs are generally below 5%. This platform expands the toolbox for generating analytical arrays and meets needs associated with spatially addressed assembly of multicomponent microarrays on the nanoliter scale.

Group II intron protein localization and insertion sites are affected by polyphosphate

Mobile group II introns consist of a catalytic intron RNA and an intron-encoded protein with reverse transcriptase activity, which act together in a ribonucleoprotein particle to promote DNA integration during intron mobility. Previously, we found that the Lactococcus lactis Ll.LtrB intron-encoded protein (LtrA) expressed alone or with the intron RNA to form ribonucleoprotein particles localizes to bacterial cellular poles, potentially accounting for the intron's preferential insertion in the oriC and ter regions of the Escherichia coli chromosome. Here, by using cell microarrays and automated fluorescence microscopy to screen a transposon-insertion library, we identified five E. coli genes (gppA, uhpT, wcaK, ynbC, and zntR) whose disruption results in both an increased proportion of cells with more diffuse LtrA localization and a more uniform genomic distribution of Ll.LtrB-insertion sites. Surprisingly, we find that a common factor affecting LtrA localization in these and other disruptants is the accumulation of intracellular polyphosphate, which appears to bind LtrA and other basic proteins and delocalize them away from the poles. Our findings show that the intracellular localization of a group II intron-encoded protein is a major determinant of insertion-site preference. More generally, our results suggest that polyphosphate accumulation may provide a means of localizing proteins to different sites of action during cellular stress or entry into stationary phase, with potentially wide physiological consequences.

Group II introns are bacterial mobile elements thought to be ancestors of introns—genetic material that is discarded from messenger RNA transcripts—and retroelements—genetic elements and viruses that replicate via reverse transcription—in higher organisms. They propagate by forming a complex consisting of the catalytically active intron RNA and an intron-encoded reverse transcriptase (which converts the RNA to DNA, which can then be reinserted in the host genome). The Ll.LtrB group II intron-encoded protein (LtrA) was found previously to localize to bacterial cellular poles, potentially accounting for the preferential insertion of Ll.LtrB in the replication origin (oriC) and terminus (ter) regions of the Escherichia coli chromosome, which are located near the poles during much of the cell cycle. Here, we identify E. coli genes whose disruption leads both to more diffuse LtrA localization and a more uniform chromosomal distribution of Ll.LtrB-insertion sites, proving that the location of the LtrA protein contributes to insertion-site preference. Surprisingly, we find that LtrA localization in the disruptants is affected by the accumulation of intracellular polyphosphate, which appears to bind basic proteins and delocalize them away from the cellular poles. Thus, polyphosphate, a ubiquitous but enigmatic molecule in prokaryotes and eukaryotes, can localize proteins to different sites of action, with potentially wide physiological consequences.

A novel cell microarray method uncovers connections between group II intron mobility, cell stress, and polyphosphate metabolism, including the finding that polyphosphate can influence intracellular protein localization.

Translation of ASH1 mRNA is repressed by Puf6p-Fun12p/eIF5B interaction and released by CK2 phosphorylation

Translational repression during mRNA transport is essential for spatial restriction of protein production. In the yeast Saccharomyces cerevisae, silencing of ASH1 mRNA before it is localized to the bud cortex in late anaphase is critical for asymmetric segregation of Ash1p to the daughter cell nucleus. Puf6p, an ASH1 mRNA-binding protein, has been implicated in this process as a translational repressor, but the underlying mechanism is unknown. Here, we used yeast extract-based in vitro translation assays, which recapitulate translation and phosphorylation, to characterize the mechanism of Puf6p-mediated translational regulation. We report that Puf6p interferes with the conversion of the 48S complex to the 80S complex during initiation, and this repression by Puf6p is mediated through the general translation factor eIF5B (Fun12p in S. cerevisiae). Puf6p interacts with Fun12p via the PUF domain, and this interaction is RNA-dependent and essential for translational repression by Puf6p. This repression is relieved by phosphorylation of the N-terminal region of Puf6p mediated by protein kinase CK2 (casein kinase II). Inhibition of phosphorylation at Ser31, Ser34, and Ser35 of Puf6p increases its translational repression and results in ASH1 mRNA delocalization. Our results indicate that Puf6p suppresses the translation initiation of ASH1 mRNA via interaction with Fun12p during its transport, and this repression can be released by CK2 phosphorylation in the N-terminal region of Puf6p when the mRNA reaches the bud tip.

Translation of ASH1 mRNA is repressed by Puf6p-Fun12p/eIF5B interaction and released by CK2 phosphorylation

Translational repression during mRNA transport is essential for spatial restriction of protein production. In the yeast Saccharomyces cerevisae, silencing of ASH1 mRNA before it is localized to the bud cortex in late anaphase is critical for asymmetric segregation of Ash1p to the daughter cell nucleus. Puf6p, an ASH1 mRNA-binding protein, has been implicated in this process as a translational repressor, but the underlying mechanism is unknown. Here, we used yeast extract-based in vitro translation assays, which recapitulate translation and phosphorylation, to characterize the mechanism of Puf6p-mediated translational regulation. We report that Puf6p interferes with the conversion of the 48S complex to the 80S complex during initiation, and this repression by Puf6p is mediated through the general translation factor eIF5B (Fun12p in S. cerevisiae). Puf6p interacts with Fun12p via the PUF domain, and this interaction is RNA-dependent and essential for translational repression by Puf6p. This repression is relieved by phosphorylation of the N-terminal region of Puf6p mediated by protein kinase CK2 (casein kinase II). Inhibition of phosphorylation at Ser31, Ser34, and Ser35 of Puf6p increases its translational repression and results in ASH1 mRNA delocalization. Our results indicate that Puf6p suppresses the translation initiation of ASH1 mRNA via interaction with Fun12p during its transport, and this repression can be released by CK2 phosphorylation in the N-terminal region of Puf6p when the mRNA reaches the bud tip.

A K-means segmentation method for finding 2-D object areas based on 3-D image stacks obtained by confocal microscopy

A segmentation method for three-dimensional image stacks obtained by confocal microscopy is proposed. The method can be used to find two-dimensional object areas based on an image stack. The segmentation method is based on K-means clustering, global thresholding, and mathematical morphology. As a case study, the proposed method is applied to 244 image stacks of the yeast Saccharomyces cerevisiae. Quantitative comparisons with manually obtained results as well as with results obtained by a two-dimensional segmentation method are used to illustrate how the additional information provided by three-dimensional image stacks can improve segmentation results.

Extraction of the number of peroxisomes in yeast cells by automated image analysis

An automated image analysis method for extracting the number of peroxisomes in yeast cells is presented. Two images of the cell population are required for the method: a bright field microscope image from which the yeast cells are detected and the respective fluorescent image from which the number of peroxisomes in each cell is found. The segmentation of the cells is based on clustering the local mean-variance space. The watershed transformation is thereafter employed to separate cells that are clustered together. The peroxisomes are detected by thresholding the fluorescent image. The method is tested with several images of a budding yeast Saccharomyces cerevisiae population, and the results are compared with manually obtained results.

Broad network-based predictability of Saccharomyces cerevisiae gene loss-of-function phenotypes

Loss-of-function phenotypes of yeast genes can be predicted from the loss-of-function phenotypes of their neighbours in functional gene networks. This could potentially be applied to the prediction of human disease genes.

We demonstrate that loss-of-function yeast phenotypes are predictable by guilt-by-association in functional gene networks. Testing 1,102 loss-of-function phenotypes from genome-wide assays of yeast reveals predictability of diverse phenotypes, spanning cellular morphology, growth, metabolism, and quantitative cell shape features. We apply the method to extend a genome-wide screen by predicting, then verifying, genes whose disruption elongates yeast cells, and to predict human disease genes. To facilitate network-guided screens, a web server is available .

Mammalian cell-seeded hydrogel microarrays printed via dip-pin technology

Although significant advances have been made in the development of DNA and protein microarrays, less effort has been put toward developing mammalian cell microarrays. Such cellular microarrays may be useful in examining the effects of biological or chemical agents on cells, particularly in drug development and toxicological applications. Here, mammalian cell-seeded hydrogel microarrays were created using two different commercial microarrayers, with four different pin types. Human dermal fibroblasts were used here as a model cell type, seeded within polyethylene glycol-based hydrogels similar to those under investigation as tissue engineering scaffolds, which serve as synthetic extracellular matrices for the cells. Spot sizes of the hydrogels were found to vary with pin type. Multiple touches on a slide following a single dip in the reservoir print solution led to decreasing spot size with each touch; therefore, subsequent microarrays were printed with single touches after a dip. Individual pins of the same type and tip diameter had significantly different spot sizes, likely due to wear of the pins at the tip. However, there was high run-to-run reproducibility between subsequent microarrays. Cell viability varied with pin type, and the number of cells per spot varied with cell density in the print solution, as expected.

Beyond toothpicks: new methods for isolating mutant bacteria

Over the past 50 years genetic analysis in microbiology has relied predominantly on selections and plate assays using chromogenic enzyme substrates - for example, X-gal assays for the detection of beta-galactosidase activity. Recent advances in fluorescent assays and high throughput screening technologies have paved the way for the rapid isolation of mutants that confer complex phenotypes and for the quantitative analysis of the evolution of new traits in bacterial populations. This Review highlights the power of novel single-cell screening technologies and their applications to genetics, evolution and the biotechnological uses of bacteria.

Automated image analysis of protein localization in budding yeast

MOTIVATION

The yeast Saccharomyces cerevisiae is the first eukaryotic organism to have its genome completely sequenced. Since then, several large-scale analyses of the yeast genome have provided extensive functional annotations of individual genes and proteins. One fundamental property of a protein is its subcellular localization, which provides critical information about how this protein works in a cell. An important project therefore was the creation of the yeast GFP fusion localization database by the University of California, San Francisco, USA (UCSF). This database provides localization data for 75% of the proteins believed to be encoded by the yeast genome. These proteins were classified into 22 distinct subcellular location categories by visual examination. Based on our past success at building automated systems to classify subcellular location patterns in mammalian cells, we sought to create a similar system for yeast.

RESULTS

We developed computational methods to automatically analyze the images created by the UCSF yeast GFP fusion localization project. The system was trained to recognize the same location categories that were used in that study. We applied the system to 2640 images, and the system gave the same label as the previous assignments to 2139 images (81%). When only the highest confidence assignments were considered, 94.7% agreement was observed. Visual examination of the proteins for which the two approaches disagree suggests that at least some of the automated assignments may be more accurate. The automated method provides an objective, quantitative and repeatable assignment of protein locations that can be applied to new collections of yeast images (e.g. for different strains or the same strain under different conditions). It is also important to note that this performance could be achieved without requiring colocalization with any marker proteins.

AVAILABILITY

The original images analyzed in this article are available at http://yeastgfp.ucsf.edu, and source code and results are available at http://murphylab.web.cmu.edu/software.

Functional genomics of wine yeast Saccharomyces cerevisiae

The application of genomic technologies to the analysis of wine strains of Saccharomyces cerevisiae has greatly enhanced our understanding of both native and laboratory strains of this important model eukaryote. Not only are differences in transcript, protein, and metabolite profiles being uncovered, but the heritable basis of these differences is also being elucidated. Although some challenges remain in the application of functional genomic technologies to commercial and native strains of S. cerevisiae, recent improvements, particularly in data analysis, have greatly extended the utility of these tools. Comparative analysis of laboratory and wine isolates is refining our understanding of the mechanisms of genome evolution. Genomic analysis of Saccharomyces in native environments is providing evidence of gene function to previously uncharacterized open reading frames and delineating the physiological parameters of ecological niche specialization and stress adaptation. The wealth of information being generated will soon be utilized to construct commercial stains with more desirable phenotypes, traits that will be designed to be genetically stable under commercial production conditions.