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Fluorescence microscopy data describe protein localization patterns at single-cell resolution and have the potential to reveal whole-proteome functional information with remarkable precision. Yet, extracting biologically meaningful representations from cell micrographs remains a major challenge. Existing approaches often fail to learn robust and noise-invariant features or rely on supervised labels for accurate annotations. We developed PIFiA (Protein Image-based Functional Annotation), a self-supervised approach for protein functional annotation from single-cell imaging data. We imaged the global yeast ORF-GFP collection and applied PIFiA to generate protein feature profiles from single-cell images of fluorescently tagged proteins. We show that PIFiA outperforms existing approaches for molecular representation learning and describe a range of downstream analysis tasks to explore the information content of the feature profiles. Specifically, we cluster extracted features into a hierarchy of functional organization, study cell population heterogeneity, and develop techniques to distinguish multi-localizing proteins and identify functional modules. Finally, we confirm new PIFiA predictions using a colocalization assay, suggesting previously unappreciated biological roles for several proteins. Paired with a fully interactive website ( https://thecellvision.org/pifia/ ), PIFiA is a resource for the quantitative analysis of protein organization within the cell., (© 2024. The Author(s).)

We describe a protocol for live-cell high-throughput (HTP) screening of yeast mutant strains carrying fluorescent protein markers for subcellular compartments of choice using automated confocal microscopy. This procedure, which combines HTP genetics and microscopy, results in the acquisition of thousands of images that can be analyzed in a systematic and quantitative way to identify morphology defects in the tagged subcellular compartments. This HTP protocol is readily adapted for screening any combination of markers and can be expanded to different growth conditions or higher order mutant genetic backgrounds.

We describe a protocol for high-content screening in budding yeast that can be used to study genetic interactions from a cell biological perspective. This approach can be used to map genetic interactions by monitoring one or more subcellular fluorescent markers of interest. In this case, changes in the morphology or abundance of a subcellular compartment, pathway or bioprocess are monitored in the background of a systematic array of yeast double mutants. Alternatively, the protocol can be used to monitor proteome-wide abundance and localization changes in a double mutant of interest by screening the yeast ORF-GFP collection. The protocol can be readily adapted for high-content screening of triple mutants, other large-scale yeast collections or expanded to screening of multiple growth conditions., (© 2021. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Our ability to understand the genotype-to-phenotype relationship is hindered by the lack of detailed understanding of phenotypes at a single-cell level. To systematically assess cell-to-cell phenotypic variability, we combined automated yeast genetics, high-content screening and neural network-based image analysis of single cells, focussing on genes that influence the architecture of four subcellular compartments of the endocytic pathway as a model system. Our unbiased assessment of the morphology of these compartments-endocytic patch, actin patch, late endosome and vacuole-identified 17 distinct mutant phenotypes associated with ~1,600 genes (~30% of all yeast genes). Approximately half of these mutants exhibited multiple phenotypes, highlighting the extent of morphological pleiotropy. Quantitative analysis also revealed that incomplete penetrance was prevalent, with the majority of mutants exhibiting substantial variability in phenotype at the single-cell level. Our single-cell analysis enabled exploration of factors that contribute to incomplete penetrance and cellular heterogeneity, including replicative age, organelle inheritance and response to stress., (© 2020 The Authors. Published under the terms of the CC BY 4.0 license.)

To systematically explore complex genetic interactions, we constructed ~200,000 yeast triple mutants and scored negative trigenic interactions. We selected double-mutant query genes across a broad spectrum of biological processes, spanning a range of quantitative features of the global digenic interaction network and tested for a genetic interaction with a third mutation. Trigenic interactions often occurred among functionally related genes, and essential genes were hubs on the trigenic network. Despite their functional enrichment, trigenic interactions tended to link genes in distant bioprocesses and displayed a weaker magnitude than digenic interactions. We estimate that the global trigenic interaction network is ~100 times as large as the global digenic network, highlighting the potential for complex genetic interactions to affect the biology of inheritance, including the genotype-to-phenotype relationship., (Copyright © 2018 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.)

Fluorescent reporter genes have long been used to quantify various cell features such as transcript and protein abundance. Here, we describe a method, reporter synthetic genetic array (R-SGA) analysis, which allows for the simultaneous quantification of any fluorescent protein readout in thousands of yeast strains using an automated pipeline. R-SGA combines a fluorescent reporter system with standard SGA analysis and can be used to examine any array-based strain collection available to the yeast community. This protocol describes the R-SGA methodology for screening different arrays of yeast mutants including the deletion collection, a collection of temperature-sensitive strains for the assessment of essential yeast genes and a collection of inducible overexpression strains. We also present an alternative pipeline for the analysis of R-SGA output strains using flow cytometry of cells in liquid culture. Data normalization for both pipelines is discussed.

Neonicotinoid insecticides are an important contribution to plant protection products. At the same time, their environmental impact on non-target organisms is often problematic. It has been shown that the toxicity of formulations of neonicotinoid insecticides can originate from non-neonicotinoid additives. In the present study we used chemogenomics to analyse side effects of purified neonicotinoids, additives and formulations on the genome-wide scale. We show that the additives in formulations have more pronounced effects than the active components, and that these effects could explain previously observed negative effects of neonicotinoid insecticides on spermatogenesis in animals. We also demonstrate that cell wall organization and biogenesis in yeast is negatively affected by neonicotinoid substances., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

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.

Yeast Saccharomyces cerevisiae has proven to be a relevant and convenient model organism for the study of diverse biological phenomena, due to its straightforward genetics, cost-effectiveness and rapid growth, combined with the typical characteristics of a eukaryotic cell. More than 40% of yeast proteins share at least part of their primary amino acid sequence with the corresponding human protein, making yeast a valuable model in biomedical research. In the last decade, high-throughput and genome-wide experimental approaches developed in yeast have paved the way to functional genomics that aims at a global understanding of the relationship between genotype and phenotype. In this review we first present the yeast strain and plasmid collections for genome-wide experimental approaches to study complex interactions between genes, proteins and endo- or exogenous small molecules. We describe methods for protein-protein, protein-DNA, genetic and chemo-genetic interactions, as well as localization studies, focussing on their application in research on small pharmacologically active molecules. Next we review the use of yeast as a model organism in neurobiology, emphasizing work done towards elucidating the pathogenesis of neurodegenerative diseases and the mechanism of action of neurotoxic phospholipases A(2)., (Copyright © 2012 Elsevier Ltd. All rights reserved.)

Modulating composition and shape of biological membranes is an emerging mode of regulation of cellular processes. We investigated the global effects that such perturbations have on a model eukaryotic cell. Phospholipases A(2) (PLA(2)s), enzymes that cleave one fatty acid molecule from membrane phospholipids, exert their biological activities through affecting both membrane composition and shape. We have conducted a genome-wide analysis of cellular effects of a PLA(2) in the yeast Saccharomyces cerevisiae as a model system. We demonstrate functional genetic and biochemical interactions between PLA(2) activity and the Rim101 signaling pathway in S. cerevisiae. Our results suggest that the composition and/or the shape of the endosomal membrane affect the Rim101 pathway. We describe a genetically and functionally related network, consisting of components of the Rim101 pathway and the prefoldin, retromer and SWR1 complexes, and predict its functional relation to PLA(2) activity in a model eukaryotic cell. This study provides a list of the players involved in the global response to changes in membrane composition and shape in a model eukaryotic cell, and further studies are needed to understand the precise molecular mechanisms connecting them.