PIFiA: self-supervised approach for protein functional annotation from single-cell imaging data.

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.

Proteome-scale movements and compartment connectivity during the eukaryotic cell cycle.

Cell cycle progression relies on coordinated changes in the composition and subcellular localization of the proteome. By applying two distinct convolutional neural networks on images of millions of live yeast cells, we resolved proteome-level dynamics in both concentration and localization during the cell cycle, with resolution of ∼20 subcellular localization classes. We show that a quarter of the proteome displays cell cycle periodicity, with proteins tending to be controlled either at the level of localization or concentration, but not both. Distinct levels of protein regulation are preferentially utilized for different aspects of the cell cycle, with changes in protein concentration being mostly involved in cell cycle control and changes in protein localization in the biophysical implementation of the cell cycle program. We present a resource for exploring global proteome dynamics during the cell cycle, which will aid in understanding a fundamental biological process at a systems level.

Single-cell imaging of protein dynamics of paralogs reveals mechanisms of gene retention.

Gene duplication is common across the tree of life, including yeast and humans, and contributes to genomic robustness. In this study, we examined changes in the subcellular localization and abundance of proteins in response to the deletion of their paralogs originating from the whole-genome duplication event, which is a largely unexplored mechanism of functional divergence. We performed a systematic single-cell imaging analysis of protein dynamics and screened subcellular redistribution of proteins, capturing their localization and abundance changes, providing insight into forces determining paralog retention. Paralogs showed dependency, whereby proteins required their paralog to maintain their native abundance or localization, more often than compensation. Network feature analysis suggested the importance of functional redundancy and rewiring of protein and genetic interactions underlying redistribution response of paralogs. Translation of non-canonical protein isoform emerged as a novel compensatory mechanism. This study provides new insights into paralog retention and evolutionary forces that shape genomes.

PIFiA: Self-supervised Approach for Protein Functional Annotation from Single-Cell Imaging Data.

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.

Single-cell image analysis to explore cell-to-cell heterogeneity in isogenic populations.

Single-cell image analysis provides a powerful approach for studying cell-to-cell heterogeneity, which is an important attribute of isogenic cell populations, from microbial cultures to individual cells in multicellular organisms. This phenotypic variability must be explained at a mechanistic level if biologists are to fully understand cellular function and address the genotype-to-phenotype relationship. Variability in single-cell phenotypes is obscured by bulk readouts or averaging of phenotypes from individual cells in a sample; thus, single-cell image analysis enables a higher resolution view of cellular function. Here, we consider examples of both small- and large-scale studies carried out with isogenic cell populations assessed by fluorescence microscopy, and we illustrate the advantages, challenges, and the promise of quantitative single-cell image analysis.

A genome-scale yeast library with inducible expression of individual genes.

The ability to switch a gene from off to on and monitor dynamic changes provides a powerful approach for probing gene function and elucidating causal regulatory relationships. Here, we developed and characterized YETI (Yeast Estradiol strains with Titratable Induction), a collection in which > 5,600 yeast genes are engineered for transcriptional inducibility with single-gene precision at their native loci and without plasmids. Each strain contains SGA screening markers and a unique barcode, enabling high-throughput genetics. We characterized YETI using growth phenotyping and BAR-seq screens, and we used a YETI allele to identify the regulon of Rof1, showing that it acts to repress transcription. We observed that strains with inducible essential genes that have low native expression can often grow without inducer. Analysis of data from eukaryotic and prokaryotic systems shows that native expression is a variable that can bias promoter-perturbing screens, including CRISPRi. We engineered a second expression system, Z3 EB42, that gives lower expression than Z3 EV, a feature enabling conditional activation and repression of lowly expressed essential genes that grow without inducer in the YETI library.

Systematic genetics and single-cell imaging reveal widespread morphological pleiotropy and cell-to-cell variability.

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.

Ubiquitin orchestrates proteasome dynamics between proliferation and quiescence in yeast.

Proteasomes are essential for protein degradation in proliferating cells. Little is known about proteasome functions in quiescent cells. In nondividing yeast, a eukaryotic model of quiescence, proteasomes are depleted from the nucleus and accumulate in motile cytosolic granules termed proteasome storage granules (PSGs). PSGs enhance resistance to genotoxic stress and confer fitness during aging. Upon exit from quiescence PSGs dissolve, and proteasomes are rapidly delivered into the nucleus. To identify key players in PSG organization, we performed high-throughput imaging of green fluorescent protein (GFP)-labeled proteasomes in the yeast null-mutant collection. Mutants with reduced levels of ubiquitin are impaired in PSG formation. Colocalization studies of PSGs with proteins of the yeast GFP collection, mass spectrometry, and direct stochastic optical reconstitution microscopy of cross-linked PSGs revealed that PSGs are densely packed with proteasomes and contain ubiquitin but no polyubiquitin chains. Our results provide insight into proteasome dynamics between proliferating and quiescent yeast in response to cellular requirements for ubiquitin-dependent degradation.

Functional Analysis of Kinases and Transcription Factors in Saccharomyces cerevisiae Using an Integrated Overexpression Library.

Kinases and transcription factors (TFs) are key modulators of important signaling pathways and their activities underlie the proper function of many basic cellular processes such as cell division, differentiation, and development. Changes in kinase and TF dosage are often associated with disease, yet a systematic assessment of the cellular phenotypes caused by the combined perturbation of kinases and TFs has not been undertaken. We used a reverse-genetics approach to study the phenotypic consequences of kinase and TF overexpression (OE) in the budding yeast, Saccharomyces cerevisiae We constructed a collection of strains expressing stably integrated inducible alleles of kinases and TFs and used a variety of assays to characterize the phenotypes caused by TF and kinase OE. We used the Synthetic Genetic Array (SGA) method to examine dosage-dependent genetic interactions (GIs) between 239 gain-of-function (OE) alleles of TFs and six loss-of-function (LOF) and seven OE kinase alleles, the former identifying Synthetic Dosage Lethal (SDL) interactions and the latter testing a GI we call Double Dosage Lethality (DDL). We identified and confirmed 94 GIs between 65 OE alleles of TFs and 9 kinase alleles. Follow-up experiments validated regulatory relationships between genetically interacting pairs (Cdc28-Stb1 and Pho85-Pdr1), suggesting that GI studies involving OE alleles of regulatory proteins will be a rich source of new functional information.

Exploring genetic suppression interactions on a global scale.

Genetic suppression occurs when the phenotypic defects caused by a mutation in a particular gene are rescued by a mutation in a second gene. To explore the principles of genetic suppression, we examined both literature-curated and unbiased experimental data, involving systematic genetic mapping and whole-genome sequencing, to generate a large-scale suppression network among yeast genes. Most suppression pairs identified novel relationships among functionally related genes, providing new insights into the functional wiring diagram of the cell. In addition to suppressor mutations, we identified frequent secondary mutations,in a subset of genes, that likely cause a delay in the onset of stationary phase, which appears to promote their enrichment within a propagating population. These findings allow us to formulate and quantify general mechanisms of genetic suppression.

High-Throughput Microscopy-Based Screening in Saccharomyces cerevisiae.

The budding yeastSaccharomyces cerevisiaehas served as the pioneer model organism for virtually all genome-scale methods, including genome sequencing, DNA microarrays, gene deletion collections, and a variety of proteomic platforms. Yeast has also provided a test-bed for the development of systematic fluorescence-based imaging screens to enable the analysis of protein localization and abundance in vivo. Especially important has been the integration of high-throughput microscopy with automated image-processing methods, which has allowed researchers to overcome issues associated with manual image analysis and acquire unbiased, quantitative data. Here we provide an introduction to automated imaging in budding yeast.

High-Content Screening for Quantitative Cell Biology.

High-content screening (HCS), which combines automated fluorescence microscopy with quantitative image analysis, allows the acquisition of unbiased multiparametric data at the single cell level. This approach has been used to address diverse biological questions and identify a plethora of quantitative phenotypes of varying complexity in numerous different model systems. Here, we describe some recent applications of HCS, ranging from the identification of genes required for specific biological processes to the characterization of genetic interactions. We review the steps involved in the design of useful biological assays and automated image analysis, and describe major challenges associated with each. Additionally, we highlight emerging technologies and future challenges, and discuss how the field of HCS might be enhanced in the future.

Yeast Proteome Dynamics from Single Cell Imaging and Automated Analysis.

Proteomics has proved invaluable in generating large-scale quantitative data; however, the development of systems approaches for examining the proteome in vivo has lagged behind. To evaluate protein abundance and localization on a proteome scale, we exploited the yeast GFP-fusion collection in a pipeline combining automated genetics, high-throughput microscopy, and computational feature analysis. We developed an ensemble of binary classifiers to generate localization data from single-cell measurements and constructed maps of ∼3,000 proteins connected to 16 localization classes. To survey proteome dynamics in response to different chemical and genetic stimuli, we measure proteome-wide abundance and localization and identified changes over time. We analyzed >20 million cells to identify dynamic proteins that redistribute among multiple localizations in hydroxyurea, rapamycin, and in an rpd3Δ background. Because our localization and abundance data are quantitative, they provide the opportunity for many types of comparative studies, single cell analyses, modeling, and prediction. VIDEO ABSTRACT.

CYCLoPs: A Comprehensive Database Constructed from Automated Analysis of Protein Abundance and Subcellular Localization Patterns in Saccharomyces cerevisiae.

Changes in protein subcellular localization and abundance are central to biological regulation in eukaryotic cells. Quantitative measures of protein dynamics in vivo are therefore highly useful for elucidating specific regulatory pathways. Using a combinatorial approach of yeast synthetic genetic array technology, high-content screening, and machine learning classifiers, we developed an automated platform to characterize protein localization and abundance patterns from images of log phase cells from the open-reading frame-green fluorescent protein collection in the budding yeast, Saccharomyces cerevisiae. For each protein, we produced quantitative profiles of localization scores for 16 subcellular compartments at single-cell resolution to trace proteome-wide relocalization in conditions over time. We generated a collection of ∼300,000 micrographs, comprising more than 20 million cells and ∼9 billion quantitative measurements. The images depict the localization and abundance dynamics of more than 4000 proteins under two chemical treatments and in a selected mutant background. Here, we describe CYCLoPs (Collection of Yeast Cells Localization Patterns), a web database resource that provides a central platform for housing and analyzing our yeast proteome dynamics datasets at the single cell level. CYCLoPs version 1.0 is available at http://cyclops.ccbr.utoronto.ca. CYCLoPs will provide a valuable resource for the yeast and eukaryotic cell biology communities and will be updated as new experiments become available.

Cell cycle-regulated oscillator coordinates core histone gene transcription through histone acetylation.

DNA replication occurs during the synthetic (S) phase of the eukaryotic cell cycle and features a dramatic induction of histone gene expression for concomitant chromatin assembly. Ectopic production of core histones outside of S phase is toxic, underscoring the critical importance of regulatory pathways that ensure proper expression of histone genes. Several regulators of histone gene expression in the budding yeast Saccharomyces cerevisiae are known, yet the key oscillator responsible for restricting gene expression to S phase has remained elusive. Here, we show that suppressor of Ty (Spt)10, a putative histone acetyltransferase, and its binding partner Spt21 are key determinants of S-phase-specific histone gene expression. We show that Spt21 abundance is restricted to S phase in part by anaphase promoting complex Cdc20-homologue 1 (APC(Cdh1)) and that it is recruited to histone gene promoters in S phase by Spt10. There, Spt21-Spt10 enables the recruitment of a cascade of regulators, including histone chaperones and the histone-acetyltransferase general control nonderepressible (Gcn) 5, which we hypothesize lead to histone acetylation and consequent transcription activation.

Bck2 acts through the MADS box protein Mcm1 to activate cell-cycle-regulated genes in budding yeast.

The Bck2 protein is a potent genetic regulator of cell-cycle-dependent gene expression in budding yeast. To date, most experiments have focused on assessing a potential role for Bck2 in activation of the G1/S-specific transcription factors SBF (Swi4, Swi6) and MBF (Mbp1, Swi6), yet the mechanism of gene activation by Bck2 has remained obscure. We performed a yeast two-hybrid screen using a truncated version of Bck2 and discovered six novel Bck2-binding partners including Mcm1, an essential protein that binds to and activates M/G1 promoters through Early Cell cycle Box (ECB) elements as well as to G2/M promoters. At M/G1 promoters Mcm1 is inhibited by association with two repressors, Yox1 or Yhp1, and gene activation ensues once repression is relieved by an unknown activating signal. Here, we show that Bck2 interacts physically with Mcm1 to activate genes during G1 phase. We used chromatin immunoprecipitation (ChIP) experiments to show that Bck2 localizes to the promoters of M/G1-specific genes, in a manner dependent on functional ECB elements, as well as to the promoters of G1/S and G2/M genes. The Bck2-Mcm1 interaction requires valine 69 on Mcm1, a residue known to be required for interaction with Yox1. Overexpression of BCK2 decreases Yox1 localization to the early G1-specific CLN3 promoter and rescues the lethality caused by overexpression of YOX1. Our data suggest that Yox1 and Bck2 may compete for access to the Mcm1-ECB scaffold to ensure appropriate activation of the initial suite of genes required for cell cycle commitment.

Exploring the yeast acetylome using functional genomics.

Lysine acetylation is a dynamic posttranslational modification with a well-defined role in regulating histones. The impact of acetylation on other cellular functions remains relatively uncharacterized. We explored the budding yeast acetylome with a functional genomics approach, assessing the effects of gene overexpression in the absence of lysine deacetylases (KDACs). We generated a network of 463 synthetic dosage lethal (SDL) interactions involving class I and II KDACs, revealing many cellular pathways regulated by different KDACs. A biochemical survey of genes interacting with the KDAC RPD3 identified 72 proteins acetylated in vivo. In-depth analysis of one of these proteins, Swi4, revealed a role for acetylation in G1-specific gene expression. Acetylation of Swi4 regulates interaction with its partner Swi6, both components of the SBF transcription factor. This study expands our view of the yeast acetylome, demonstrates the utility of functional genomic screens for exploring enzymatic pathways, and provides functional information that can be mined for future studies.

Functional wiring of the yeast kinome revealed by global analysis of genetic network motifs.

A combinatorial genetic perturbation strategy was applied to interrogate the yeast kinome on a genome-wide scale. We assessed the global effects of gene overexpression or gene deletion to map an integrated genetic interaction network of synthetic dosage lethal (SDL) and loss-of-function genetic interactions (GIs) for 92 kinases, producing a meta-network of 8700 GIs enriched for pathways known to be regulated by cognate kinases. Kinases most sensitive to dosage perturbations had constitutive cell cycle or cell polarity functions under standard growth conditions. Condition-specific screens confirmed that the spectrum of kinase dosage interactions can be expanded substantially in activating conditions. An integrated network composed of systematic SDL, negative and positive loss-of-function GIs, and literature-curated kinase-substrate interactions revealed kinase-dependent regulatory motifs predictive of novel gene-specific phenotypes. Our study provides a valuable resource to unravel novel functional relationships and pathways regulated by kinases and outlines a general strategy for deciphering mutant phenotypes from large-scale GI networks.

Restriction of histone gene transcription to S phase by phosphorylation of a chromatin boundary protein.

The cell cycle-regulated expression of core histone genes is required for DNA replication and proper cell cycle progression in eukaryotic cells. Although some factors involved in histone gene transcription are known, the molecular mechanisms that ensure proper induction of histone gene expression during S phase remain enigmatic. Here we demonstrate that S-phase transcription of the model histone gene HTA1 in yeast is regulated by a novel attach-release mechanism involving phosphorylation of the conserved chromatin boundary protein Yta7 by both cyclin-dependent kinase 1 (Cdk1) and casein kinase 2 (CK2). Outside S phase, integrity of the AAA-ATPase domain is required for Yta7 boundary function, as defined by correct positioning of the histone chaperone Rtt106 and the chromatin remodeling complex RSC. Conversely, in S phase, Yta7 is hyperphosphorylated, causing its release from HTA1 chromatin and productive transcription. Most importantly, abrogation of Yta7 phosphorylation results in constitutive attachment of Yta7 to HTA1 chromatin, preventing efficient transcription post-recruitment of RNA polymerase II (RNAPII). Our study identified the chromatin boundary protein Yta7 as a key regulator that links S-phase kinases with RNAPII function at cell cycle-regulated histone gene promoters.

Clathrin light chain directs endocytosis by influencing the binding of the yeast Hip1R homologue, Sla2, to F-actin.

The role of clathrin light chain (CLC) in clathrin-mediated endocytosis is not completely understood. Previous studies showed that the CLC N-terminus (CLC-NT) binds the Hip1/Hip1R/Sla2 family of membrane/actin-binding factors and that overexpression of the CLC-NT in yeast suppresses endocytic defects of clathrin heavy-chain mutants. To elucidate the mechanistic basis for this suppression, we performed synthetic genetic array analysis with a clathrin CLC-NT deletion mutation (clc1-Δ19-76). clc1-Δ19-76 suppressed the internalization defects of null mutations in three late endocytic factors: amphiphysins (rvs161 and rvs167) and verprolin (vrp1). In actin sedimentation assays, CLC binding to Sla2 inhibited Sla2 interaction with F-actin. Furthermore, clc1-Δ19-76 suppression of the rvs and vrp phenotypes required the Sla2 actin-binding talin-Hip1/R/Sla2 actin-tethering C-terminal homology domain, suggesting that clc1-Δ19-76 promotes internalization by prolonging actin engagement by Sla2. We propose that CLC directs endocytic progression by pruning the Sla2-actin attachments in the clathrin lattice, providing direction for membrane internalization.