Yeast Proteome Dynamics from Single Cell Imaging and Automated Analysis

Chong, Yolanda T.; Koh, Judice L.Y.; Friesen, Helena; Duffy, Supipi; Cox, Michael J.; Moses, Alan M.; Moffat, Jason; Boone, Charles; Andrews, Brenda

doi:10.1016/j.cell.2015.04.051

Cited by 269 publications

(405 citation statements)

References 55 publications

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“…An important assumption underlying our model is equal stoichiometry of the triad with other core subunits. Such an assumption appears to be warranted, in that a recent single-cell proteomic analysis revealed that most core subunits are present in similar numbers (ϳ75 to 150 molecules) in wild-type haploid cells (70). A second assumption is that the anchor away technique per se does not trigger dissociation of labile subunits from core Mediator.…”

Section: Discussionmentioning

confidence: 99%

Evidence for Multiple Mediator Complexes in Yeast Independently Recruited by Activated Heat Shock Factor

Anandhakumar

Moustafa

Chowdhary

et al. 2016

Molecular and Cellular Biology

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Mediator is an evolutionarily conserved coactivator complex essential for RNA polymerase II transcription. Although it has been generally assumed that in Saccharomyces cerevisiae, Mediator is a stable trimodular complex, its structural state in vivo remains unclear. Using the "anchor away" (AA) technique to conditionally deplete select subunits within Mediator and its reversibly associated Cdk8 kinase module (CKM), we provide evidence that Mediator's tail module is highly dynamic and that a subcomplex consisting of Med2, Med3, and Med15 can be independently recruited to the regulatory regions of heat shock factor 1 (Hsf1)-activated genes. Fluorescence microscopy of a scaffold subunit (Med14)-anchored strain confirmed parallel cytoplasmic sequestration of core subunits located outside the tail triad. In addition, and contrary to current models, we provide evidence that Hsf1 can recruit the CKM independently of core Mediator and that core Mediator has a role in regulating postinitiation events. Collectively, our results suggest that yeast Mediator is not monolithic but potentially has a dynamic complexity heretofore unappreciated. Multiple species, including CKM-Mediator, the 21-subunit core complex, the Med2-Med3-Med15 tail triad, and the foursubunit CKM, can be independently recruited by activated Hsf1 to its target genes in AA strains.

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Section: Discussionmentioning

confidence: 99%

Evidence for Multiple Mediator Complexes in Yeast Independently Recruited by Activated Heat Shock Factor

Anandhakumar

Moustafa

Chowdhary

et al. 2016

Molecular and Cellular Biology

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“…As a result, functional tags can be easily inserted in a gene locus of interest, preserving endogenous expression levels and minimizing genomic disruption. Together, these genome-wide tagged libraries helped provide a comprehensive snapshot of the yeast protein landscape under near-native conditions (4,5,(9)(10)(11).…”

mentioning

confidence: 99%

A scalable strategy for high-throughput GFP tagging of endogenous human proteins

Leonetti

Sekine

Kamiyama

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

222

201

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A central challenge of the postgenomic era is to comprehensively characterize the cellular role of the ∼20,000 proteins encoded in the human genome. To systematically study protein function in a native cellular background, libraries of human cell lines expressing proteins tagged with a functional sequence at their endogenous loci would be very valuable. Here, using electroporation of Cas9 nuclease/single-guide RNA ribonucleoproteins and taking advantage of a split-GFP system, we describe a scalable method for the robust, scarless, and specific tagging of endogenous human genes with GFP. Our approach requires no molecular cloning and allows a large number of cell lines to be processed in parallel. We demonstrate the scalability of our method by targeting 48 human genes and show that the resulting GFP fluorescence correlates with protein expression levels. We next present how our protocols can be easily adapted for the tagging of a given target with GFP repeats, critically enabling the study of low-abundance proteins. Finally, we show that our GFP tagging approach allows the biochemical isolation of native protein complexes for proteomic studies. Taken together, our results pave the way for the large-scale generation of endogenously tagged human cell lines for the proteome-wide analysis of protein localization and interaction networks in a native cellular context. CRISPR/Cas9 | GFP library | genome engineering M ore than a decade after the completion of the Human Genome Project (1), over 30% of human genes still lack clear functional annotation (2, 3). Functional tagging is a powerful strategy to characterize the cellular role of proteins. In particular, tags allow access to two key features of protein function: localization (using fluorescent tags) and interaction partners (using epitope tags and immunoprecipitation). Hence, by tagging proteins in a systematic manner, a comprehensive functional description of an organism's proteome can be achieved. The power of systematic tagging approaches is best illustrated by studies conducted in the budding yeast Saccharomyces cerevisiae (4). In particular, a genome-wide collection of GFP-tagged yeast strains enabled the systematic study of protein localization in live cells (5), whereas libraries of strains expressing TAP epitope-fusion proteins paved the way for the large-scale isolation and proteomic analysis of protein complexes (6, 7). One of the great advantages of yeast genetics (especially in S. cerevisiae) is the efficiency and relative simplicity of PCR-based homologous recombination (8). As a result, functional tags can be easily inserted in a gene locus of interest, preserving endogenous expression levels and minimizing genomic disruption. Together, these genome-wide tagged libraries helped provide a comprehensive snapshot of the yeast protein landscape under near-native conditions (4, 5, 9-11).The development of clustered regularly interspersed short palindromic repeat associated protein 9 (CRISPR/Cas9)-based methods has profoundly transformed our ability to dir...

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“…One desired end product is a cell phenotype, which captures cell state either qualitatively or quantitatively [54]. Previous methods for obtaining phenotypes have ranged from low-level image processing transforms that can be applied to any image (Gabor or Zernicke filters, Haralick features, a range of signal processing tools, [55]), to bespoke crafting of features that precisely capture the desired image characteristic in a given dataset [56,57] and unsupervised clustering of full images [58]. An important intermediate approach is to learn informative features from a given dataset de novo, a task that deep neural networks excel at.…”

Section: Cell and Image Phenotypingmentioning

confidence: 99%

“…Pärnamaa and Parts used convolutional neural networks with a popular design (e.g. also applied for plant phenotyping, [59]) to solve this task with high accuracy for images of single yeast cells [60] obtained in a highcontent screen [56]. They employed eight convolutional layers of 3 × 3 filters interspersed with pooling steps, which were followed by three fully connected layers that learn the feature combinations that discriminate organelles.…”

Section: Cell and Image Phenotypingmentioning

confidence: 99%

Computational biology: deep learning

Jones

Alasoo

Fishman

et al. 2017

Emerging Topics in Life Sciences

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Deep learning is the trendiest tool in a computational biologist's toolbox. This exciting class of methods, based on artificial neural networks, quickly became popular due to its competitive performance in prediction problems. In pioneering early work, applying simple network architectures to abundant data already provided gains over traditional counterparts in functional genomics, image analysis, and medical diagnostics. Now, ideas for constructing and training networks and even off-the-shelf models have been adapted from the rapidly developing machine learning subfield to improve performance in a range of computational biology tasks. Here, we review some of these advances in the last 2 years.

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Yeast Proteome Dynamics from Single Cell Imaging and Automated Analysis

Cited by 269 publications

References 55 publications

Evidence for Multiple Mediator Complexes in Yeast Independently Recruited by Activated Heat Shock Factor

Evidence for Multiple Mediator Complexes in Yeast Independently Recruited by Activated Heat Shock Factor

A scalable strategy for high-throughput GFP tagging of endogenous human proteins

Computational biology: deep learning

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