Generation of a fluorescently labeled endogenous protein library in living human cells

SARS-CoV-2 evolves increased infection elicited cell death and fusion in an immunosuppressed individual

The milder clinical manifestations of Omicron infection relative to pre-Omicron SARS CoV-2 raises the possibility that extensive evolution results in reduced pathogenicity. To test this hypothesis, we quantified induction of cell fusion and cell death in SARS CoV-2 evolved from ancestral virus during long-term infection. Both cell fusion and death were reduced in Omicron BA.1 infection relative to ancestral virus. Evolved virus was isolated at different times during a 6-month infection in an immunosuppressed individual with advanced HIV disease. The virus isolated 16 days post-reported symptom onset induced fusogenicity and cell death at levels similar to BA.1. However, fusogenicity was increased in virus isolated at 6 months post-symptoms to levels intermediate between BA.1 and ancestral SARS-CoV-2. Similarly, infected cell death showed a graded increase from earlier to later isolates. These results may indicate that, at least by the cellular measures used here, evolution in long-term infection does not necessarily attenuate the virus.

OpenCell: Endogenous tagging for the cartography of human cellular organization

Elucidating the wiring diagram of the human cell is a central goal of the postgenomic era. We combined genome engineering, confocal live-cell imaging, mass spectrometry, and data science to systematically map the localization and interactions of human proteins. Our approach provides a data-driven description of the molecular and spatial networks that organize the proteome. Unsupervised clustering of these networks delineates functional communities that facilitate biological discovery. We found that remarkably precise functional information can be derived from protein localization patterns, which often contain enough information to identify molecular interactions, and that RNA binding proteins form a specific subgroup defined by unique interaction and localization properties. Paired with a fully interactive website (opencell.czbiohub.org), our work constitutes a resource for the quantitative cartography of human cellular organization.

SUBCELLULAR TRANSCRIPTOMICS & PROTEOMICS: A COMPARATIVE METHODS REVIEW

The internal environment of cells is molecularly crowded, which requires spatial organization via subcellular compartmentalization. These compartments harbor specific conditions for molecules to perform their biological functions, such as coordination of the cell cycle, cell survival, and growth. This compartmentalization is also not static, with molecules trafficking between these subcellular neighborhoods to carry out their functions. For example, some biomolecules are multifunctional, requiring an environment with differing conditions or interacting partners, and others traffic to export such molecules. Aberrant localization of proteins or RNA species has been linked to many pathological conditions, such as neurological, cancer, and pulmonary diseases. Differential expression studies in transcriptomics and proteomics are relatively common, but the majority have overlooked the importance of subcellular information. In addition, subcellular transcriptomics and proteomics data do not always colocate because of the biochemical processes that occur during and after translation, highlighting the complementary nature of these fields. In this review, we discuss and directly compare the current methods in spatial proteomics and transcriptomics, which include sequencing- and imaging-based strategies, to give the reader an overview of the current tools available. We also discuss current limitations of these strategies as well as future developments in the field of spatial -omics.

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Subcellular information of protein and RNA give insights into molecular function.

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This review discusses strategies available to measure subcellular information.

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Hybridization of methods shows promise for exploring the composition of organelles.

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Advances are aiding understanding of the organisation and dynamics of cells.

Evaluation of methods for generative modeling of cell and nuclear shape

MOTIVATION

Cell shape provides both geometry for, and a reflection of, cell function. Numerous methods for describing and modeling cell shape have been described, but previous evaluation of these methods in terms of the accuracy of generative models has been limited.

RESULTS

Here we compare traditional methods and deep autoencoders to build generative models for cell shapes in terms of the accuracy with which shapes can be reconstructed from models. We evaluated the methods on different collections of 2D and 3D cell images, and found that none of the methods gave accurate reconstructions using low dimensional encodings. As expected, much higher accuracies were observed using high dimensional encodings, with outline-based methods significantly outperforming image-based autoencoders. The latter tended to encode all cells as having smooth shapes, even for high dimensions. For complex 3D cell shapes, we developed a significant improvement of a method based on the spherical harmonic transform that performs significantly better than other methods. We obtained similar results for the joint modeling of cell and nuclear shape. Finally, we evaluated the modeling of shape dynamics by interpolation in the shape space. We found that our modified method provided lower deformation energies along linear interpolation paths than other methods. This allows practical shape evolution in high dimensional shape spaces. We conclude that our improved spherical harmonic based methods are preferable for cell and nuclear shape modeling, providing better representations, higher computational efficiency and requiring fewer training images than deep learning methods.

AVAILABILITY AND IMPLEMENTATION

All software and data is available at http://murphylab.cbd.cmu.edu/software.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Spatial proteomics: a powerful discovery tool for cell biology

Protein subcellular localization is tightly controlled and intimately linked to protein function in health and disease. Capturing the spatial proteome - that is, the localizations of proteins and their dynamics at the subcellular level - is therefore essential for a complete understanding of cell biology. Owing to substantial advances in microscopy, mass spectrometry and machine learning applications for data analysis, the field is now mature for proteome-wide investigations of spatial cellular regulation. Studies of the human proteome have begun to reveal a complex architecture, including single-cell variations, dynamic protein translocations, changing interaction networks and proteins localizing to multiple compartments. Furthermore, several studies have successfully harnessed the power of comparative spatial proteomics as a discovery tool to unravel disease mechanisms. We are at the beginning of an era in which spatial proteomics finally integrates with cell biology and medical research, thereby paving the way for unbiased systems-level insights into cellular processes. Here, we discuss current methods for spatial proteomics using imaging or mass spectrometry and specifically highlight global comparative applications. The aim of this Review is to survey the state of the field and also to encourage more cell biologists to apply spatial proteomics approaches.

A molecular toolbox for studying protein degradation in mammalian cells

Protein degradation is a crucial regulatory process in maintaining cellular proteostasis. The selective degradation of intracellular proteins controls diverse cellular and biochemical processes in all kingdoms of life. Targeted protein degradation is implicated in controlling the levels of regulatory proteins as well as eliminating misfolded and any otherwise abnormal proteins. Deregulation of protein degradation is concomitant with the progression of various neurodegenerative disorders such as Parkinson's and Alzheimer's diseases. Thus, methods of measuring metabolic half-lives of proteins greatly influence our understanding of the diverse functions of proteins in mammalian cells including neuronal cells. Historically, protein degradation rates have been studied via exploiting methods that estimate overall protein degradation or focus on few individual proteins. Notably, with the recent technical advances and developments in proteomic and imaging techniques, it is now possible to measure degradation rates of a large repertoire of defined proteins and analyze the degradation profile in a detailed spatio-temporal manner, with the aim of determining proteome-wide protein stabilities upon different physiological conditions. Herein, we discuss some of the classical and novel methods for determining protein degradation rates highlighting the crucial role of some state of art approaches in deciphering the global impact of dynamic nature of targeted degradation of cellular proteins. This article is part of the Special Issue "Proteomics".

Efficient and flexible tagging of endogenous genes by homology-independent intron targeting

Genome editing tools have simplified the generation of knock-in gene fusions, yet the prevalent use of gene-specific homology-directed repair (HDR) templates still hinders scalability. Consequently, realization of large-scale gene tagging requires further development of approaches to generate knock-in protein fusions via generic donors that do not require locus-specific homology sequences. Here, we combine intron-based protein trapping with homology-independent repair-based integration of a generic donor and demonstrate precise, scalable, and efficient gene tagging. Because editing is performed in introns using a synthetic exon, this approach tolerates mutations in the unedited allele, indels at the integration site, and the addition of resistance genes that do not disrupt the target gene coding sequence, resulting in easy and flexible gene tagging.

Dynamics of protein synthesis and degradation through the cell cycle

Protein expression levels depend on the balance between their synthesis and degradation rates. Even quiescent (G0) cells display a continuous turnover of proteins, despite protein levels remaining largely constant over time. In cycling cells, global protein levels need to be precisely doubled at each cell division in order to maintain cellular homeostasis, but we still lack a quantitative understanding of how this is achieved. Recent studies have shed light on cell cycle-dependent changes in protein synthesis and degradation rates. Here we discuss current population-based and single cell approaches used to assess protein synthesis and degradation, and review the insights they have provided into the dynamics of protein turnover in different cell cycle phases.

Revealing the alternative promoter usage of SAF/MAZ gene by bichromatic fluorescent reporter construct

The large-scale identification of putative alternative promoters study shows more than 52% of human genes are regulated by alternative promoters. The human myc-associated zinc finger protein (SAF/MAZ) gene have SAF-1 and SAF-3 variants transcripted from two transcription start sites (TSSs). By using SAF/MAZ promoter as a model, we set up an approach to probe how the alternative promoters are regulated in real time. We have constructed the bichromatic fluorescent reporter driven by SAF/MAZ 5'-proximal promoter plasmids from which transactivation status of SAF-1 and SAF-3 alternative promoter could be monitored by EGFP and DsRed expression respectively. The results showed that the SAF-3 expression is regulated by alternative promoters. When the bichromatic fluorescent reporter was driven by -1692/+277 or -1401/+277 SAF/MAZ promoter the dominant expression of SAF-3 would be observed in comparison with SAF-1 expression. We also identified that Elk-1 is an inhibitory transcription factor for SAF-3 expression. The temporal diversity of SAF-1 and SAF-3 expressions can be observed via bichromatic fluorescent reporters. These imply that the bichromatic fluorescent reporter driven by alternative promoter construct might be a useful tool for decoding the temporal regulatory repertoire of alternative promoter in human genes.

Dynamic Proteomics of Herpes Simplex Virus Infection

The cellular response to viral infection is usually studied at the level of cell populations. Currently, it remains an open question whether and to what extent cell-to-cell variability impacts the course of infection. Here we address this by dynamic proteomics-imaging and tracking 400 yellow fluorescent protein (YFP)-tagged host proteins in individual cells infected by herpes simplex virus 1. By quantifying time-lapse fluorescence imaging, we analyze how cell-to-cell variability impacts gene expression from the viral genome. We identify two proteins, RFX7 and geminin, whose levels at the time of infection correlate with successful initiation of gene expression. These proteins are cell cycle markers, and we find that the position in the cell cycle at the time of infection (along with the cell motility and local cell density) can reasonably predict in which individual cells gene expression from the viral genome will commence. We find that the onset of cell division dramatically impacts the progress of infection, with 70% of dividing cells showing no additional gene expression after mitosis. Last, we identify four host proteins that are specifically modulated in infected cells, of which only one has been previously recognized. SUMO2 and RPAP3 levels are rapidly reduced, while SLTM and YTHDC1 are redistributed to form nuclear foci. These modulations are dependent on the expression of ICP0, as shown by infection with two mutant viruses that lack ICP0. Taken together, our results provide experimental validation for the long-held notion that the success of infection is dependent on the state of the host cell at the time of infection.IMPORTANCE High-throughput assays have revolutionized many fields in biology, both by allowing a more global understanding of biological processes and by deciphering rare events in subpopulations. Here we use such an assay, dynamic proteomics, to study viral infection at the single-cell level. We follow tens of thousands of individual cells infected by herpes simplex virus using fluorescence live imaging. Our results link the state of a cell at the time of virus infection with its probability to successfully initiate gene expression from the viral genome. Further, we identified three cellular proteins that were previously unknown to respond to viral infection. We conclude that dynamic proteomics provides a powerful tool to study single-cell differences during viral infection.

An Endogenously Tagged Fluorescent Fusion Protein Library in Mouse Embryonic Stem Cells

Embryonic stem cells (ESCs), with their dual capacity to self-renew and differentiate, are commonly used to study differentiation, epigenetic regulation, lineage choices, and more. Using non-directed retroviral integration of a YFP/Cherry exon into mouse ESCs, we generated a library of over 200 endogenously tagged fluorescent fusion proteins and present several proof-of-concept applications of this library. We show the utility of this library to track proteins in living cells; screen for pluripotency-related factors; identify heterogeneously expressing proteins; measure the dynamics of endogenously labeled proteins; track proteins recruited to sites of DNA damage; pull down tagged fluorescent fusion proteins using anti-Cherry antibodies; and test for interaction partners. Thus, this library can be used in a variety of different directions, either exploiting the fluorescent tag for imaging-based techniques or utilizing the fluorescent fusion protein for biochemical pull-down assays, including immunoprecipitation, co-immunoprecipitation, chromatin immunoprecipitation, and more.

Novel tools for primary immunodeficiency diagnosis: making a case for deep profiling

PURPOSE OF REVIEW

This review gives an overview of the systems-immunology single-cell proteomic and transcriptomic approaches that can be applied to study primary immunodeficiency. It also introduces recent advances in multiparameter tissue imaging, which allows extensive immune phenotyping in disease-affected tissue.

RECENT FINDINGS

Mass cytometry is a variation of flow cytometry that uses rare earth metal isotopes instead of fluorophores as tags bound to antibodies, allowing simultaneous measurement of over 40 parameters per single-cell. Mass cytomety enables comprehensive single-cell immunophenotyping and functional assessments, capturing the complexity of the immune system, and the molecularly heterogeneous consequences of primary immunodeficiency defects. Protein epitopes and transcripts can be simultaneously detected allowing immunophenotype and gene expression evaluation in mixed cell populations. Multiplexed epitope imaging has the potential to provide extensive phenotypic characterization at the subcellular level, in the context of 3D tissue microenvironment.

SUMMARY

Mass cytometry and multiplexed epitope imaging can complement genetic methods in diagnosis and study of the pathogenesis of primary immunodeficiencies. The ability to understand the effect of a specific defect across multiple immune cell types and pathways, and in affected tissues, may provide new insight into tissue-specific disease pathogenesis and evaluate effects of therapeutic interventions.

CD-tagging-MS2: detecting allelic expression of endogenous mRNAs and their protein products in single cells

Discriminating between the mRNA and protein outputs of each of the alleles of an endogenous gene in intact cells, is a difficult task. To examine endogenous transcripts originating from a specific allele, we applied Central Dogma tagging (CD-tagging), which is based on a tag insertion into an endogenous gene by creation of a new exon. Previously, CD-tagging was used to tag endogenous proteins. Here we developed a CD-tagging-MS2 approach in which two tags were inserted in tandem; a fluorescent protein tag in conjunction with the mRNA MS2 tag used for tagging mRNAs in cells. A cell clone library of CD-tagged-MS2 genes was generated, and protein and mRNA distributions were examined and characterized in single cells. Taking advantage of having one allele tagged, we demonstrate how the transcriptional activity of all alleles, tagged and untagged, can be identified using single molecule RNA fluorescence in situ hybridization (smFISH). Allele-specific mRNA expression and localization were quantified under normal and stress conditions. The latter generate cytoplasmic stress granules (SGs) that can store mRNAs, and the distribution of the mRNAs within and outside of the SGs was measured. Altogether, CD-tagging-MS2 is a robust and inexpensive approach for direct simultaneous detection of an endogenous mRNA and its translated protein product in the same cell.

Dynamic proteomics reveals bimodal protein dynamics of cancer cells in response to HSP90 inhibitor

BACKGROUND

Drugs often kill some cancer cells while others survive. This stochastic outcome is seen even in clonal cells grown under the same conditions. Understanding the molecular reasons for this stochastic outcome is a current challenge, which requires studying the proteome at the single cell level over time. In a previous study we used dynamic proteomics to study the response of cancer cells to a DNA damaging drug, camptothecin. Several proteins showed bimodal dynamics: they rose in some cells and decreased in others, in a way that correlated with eventual cell fate: death or survival. Here we ask whether bimodality is a special case for camptothecin, or whether it occurs for other drugs as well. To address this, we tested a second drug with a different mechanism of action, an HSP90 inhibitor. We used dynamic proteomics to follow 100 proteins in space and time, endogenously tagged in their native chromosomal location in individual living human lung-cancer cells, following drug administration.

RESULTS

We find bimodal dynamics for a quarter of the proteins. In some cells these proteins strongly rise in level about 12 h after treatment, but in other cells their level drops or remains constant. The proteins which rise in surviving cells included anti-apoptotic factors such as DDX5, and cell cycle regulators such as RFC1. The proteins that rise in cells that eventually die include pro-apoptotic factors such as APAF1. The two drugs shared some aspects in their single-cell response, including 7 of the bimodal proteins and translocation of oxidative response proteins to the nucleus, but differed in other aspects, with HSP90i showing more bimodal proteins. Moreover, the cell cycle phase at drug administration impacted the probability to die from HSP90i but not camptothecin.

CONCLUSIONS

Single-cell dynamic proteomics reveals sub-populations of cells within a clonal cell line with different protein dynamics in response to a drug. These different dynamics correlate with cell survival or death. Bimodal proteins which correlate with cell fate may be potential drug targets to enhance the effects of therapy.

The neXtProt knowledgebase on human proteins: 2017 update

The neXtProt human protein knowledgebase (https://www.nextprot.org) continues to add new content and tools, with a focus on proteomics and genetic variation data. neXtProt now has proteomics data for over 85% of the human proteins, as well as new tools tailored to the proteomics community.Moreover, the neXtProt release 2016-08-25 includes over 8000 phenotypic observations for over 4000 variations in a number of genes involved in hereditary cancers and channelopathies. These changes are presented in the current neXtProt update. All of the neXtProt data are available via our user interface and FTP site. We also provide an API access and a SPARQL endpoint for more technical applications.

Enhanced genome editing in mammalian cells with a modified dual-fluorescent surrogate system

Programmable DNA nucleases such as TALENs and CRISPR/Cas9 are emerging as powerful tools for genome editing. Dual-fluorescent surrogate systems have been demonstrated by several studies to recapitulate DNA nuclease activity and enrich for genetically edited cells. In this study, we created a single-strand annealing-directed, dual-fluorescent surrogate reporter system, referred to as C-Check. We opted for the Golden Gate Cloning strategy to simplify C-Check construction. To demonstrate the utility of the C-Check system, we used the C-Check in combination with TALENs or CRISPR/Cas9 in different scenarios of gene editing experiments. First, we disrupted the endogenous pIAPP gene (3.0 % efficiency) by C-Check-validated TALENs in primary porcine fibroblasts (PPFs). Next, we achieved gene-editing efficiencies of 9.0-20.3 and 4.9 % when performing single- and double-gene targeting (MAPT and SORL1), respectively, in PPFs using C-Check-validated CRISPR/Cas9 vectors. Third, fluorescent tagging of endogenous genes (MYH6 and COL2A1, up to 10.0 % frequency) was achieved in human fibroblasts with C-Check-validated CRISPR/Cas9 vectors. We further demonstrated that the C-Check system could be applied to enrich for IGF1R null HEK293T cells and CBX5 null MCF-7 cells with frequencies of nearly 100.0 and 86.9 %, respectively. Most importantly, we further showed that the C-Check system is compatible with multiplexing and for studying CRISPR/Cas9 sgRNA specificity. The C-Check system may serve as an alternative dual-fluorescent surrogate tool for measuring DNA nuclease activity and enrichment of gene-edited cells, and may thereby aid in streamlining programmable DNA nuclease-mediated genome editing and biological research.

Joint modeling of cell and nuclear shape variation

Modeling cell shape variation is critical to our understanding of cell biology. Previous work has demonstrated the utility of nonrigid image registration methods for the construction of nonparametric nuclear shape models in which pairwise deformation distances are measured between all shapes and are embedded into a low-dimensional shape space. Using these methods, we explore the relationship between cell shape and nuclear shape. We find that these are frequently dependent on each other and use this as the motivation for the development of combined cell and nuclear shape space models, extending nonparametric cell representations to multiple-component three-dimensional cellular shapes and identifying modes of joint shape variation. We learn a first-order dynamics model to predict cell and nuclear shapes, given shapes at a previous time point. We use this to determine the effects of endogenous protein tags or drugs on the shape dynamics of cell lines and show that tagged C1QBP reduces the correlation between cell and nuclear shape. To reduce the computational cost of learning these models, we demonstrate the ability to reconstruct shape spaces using a fraction of computed pairwise distances. The open-source tools provide a powerful basis for future studies of the molecular basis of cell organization.

Target discovery from protein databases: challenges for curation

Protein databases are a gold mine of potential new drug targets. The ready access to a complete overview of all aspects of protein biology provides the most benefit at the outset of drug discovery pipelines. Ideally, curation strategies used to move from the raw data to the validated knowledge should contain the checks and balances necessary for accuracy. The neXtProt human protein knowledgebase is used here as an example to give insight into these methods.

The neXtProt knowledgebase on human proteins: current status

neXtProt (http://www.nextprot.org) is a human protein-centric knowledgebase developed at the SIB Swiss Institute of Bioinformatics. Focused solely on human proteins, neXtProt aims to provide a state of the art resource for the representation of human biology by capturing a wide range of data, precise annotations, fully traceable data provenance and a web interface which enables researchers to find and view information in a comprehensive manner. Since the introductory neXtProt publication, significant advances have been made on three main aspects: the representation of proteomics data, an extended representation of human variants and the development of an advanced search capability built around semantic technologies. These changes are presented in the current neXtProt update.

NetworkPainter: dynamic intracellular pathway animation in Cytobank

BACKGROUND

High-throughput technologies such as flow and mass cytometry have the potential to illuminate cellular networks. However, analyzing the data produced by these technologies is challenging. Visualization is needed to help researchers explore this data.

RESULTS

We developed a web-based software program, NetworkPainter, to enable researchers to analyze dynamic cytometry data in the context of pathway diagrams. NetworkPainter provides researchers a graphical interface to draw and "paint" pathway diagrams with experimental data, producing animated diagrams which display the activity of each network node at each time point.

CONCLUSION

NetworkPainter enables researchers to more fully explore multi-parameter, dynamical cytometry data.

Assemblages: functional units formed by cellular phase separation

The partitioning of intracellular space beyond membrane-bound organelles can be achieved with collections of proteins that are multivalent or contain low-complexity, intrinsically disordered regions. These proteins can undergo a physical phase change to form functional granules or other entities within the cytoplasm or nucleoplasm that collectively we term “assemblage.” Intrinsically disordered proteins (IDPs) play an important role in forming a subset of cellular assemblages by promoting phase separation. Recent work points to an involvement of assemblages in disease states, indicating that intrinsic disorder and phase transitions should be considered in the development of therapeutics.

Protein expression analyses at the single cell level

The central dogma of molecular biology explains how genetic information is converted into its end product, proteins, which are responsible for the phenotypic state of the cell. Along with the protein type, the phenotypic state depends on the protein copy number. Therefore, quantification of the protein expression in a single cell is critical for quantitative characterization of the phenotypic states. Protein expression is typically a dynamic and stochastic phenomenon that cannot be well described by standard experimental methods. As an alternative, fluorescence imaging is being explored for the study of protein expression, because of its high sensitivity and high throughput. Here we review key recent progresses in fluorescence imaging-based methods and discuss their application to proteome analysis at the single cell level.

The emergence of proteome-wide technologies: systematic analysis of proteins comes of age

During the lifetime of a cell proteins can change their localization, alter their abundance and undergo modifications, all of which cannot be assayed by tracking mRNAs alone. Methods to study proteomes directly are coming of age, thereby opening new perspectives on the role of post-translational regulation in stabilizing the cellular milieu. Proteomics has undergone a revolution, and novel technologies for the systematic analysis of proteins have emerged. These methods can expand our ability to acquire information from single proteins to proteomes, from static to dynamic measures and from the population level to the level of single cells. Such approaches promise that proteomes will soon be studied at a similar level of dynamic resolution as has been the norm for transcriptomes.

Noise genetics: inferring protein function by correlating phenotype with protein levels and localization in individual human cells

To understand gene function, genetic analysis uses large perturbations such as gene deletion, knockdown or over-expression. Large perturbations have drawbacks: they move the cell far from its normal working point, and can thus be masked by off-target effects or compensation by other genes. Here, we offer a complementary approach, called noise genetics. We use natural cell-cell variations in protein level and localization, and correlate them to the natural variations of the phenotype of the same cells. Observing these variations is made possible by recent advances in dynamic proteomics that allow measuring proteins over time in individual living cells. Using motility of human cancer cells as a model system, and time-lapse microscopy on 566 fluorescently tagged proteins, we found 74 candidate motility genes whose level or localization strongly correlate with motility in individual cells. We recovered 30 known motility genes, and validated several novel ones by mild knockdown experiments. Noise genetics can complement standard genetics for a variety of phenotypes.

Ouabain-induced internalization and lysosomal degradation of the Na+/K+-ATPase

Internalization of the Na(+)/K(+)-ATPase (the Na(+) pump) has been studied in the human lung carcinoma cell line H1299 that expresses YFP-tagged α1 from its normal genomic localization. Both real-time imaging and surface biotinylation have demonstrated internalization of α1 induced by ≥100 nm ouabain which occurs in a time scale of hours. Unlike previous studies in other systems, the ouabain-induced internalization was insensitive to Src or PI3K inhibitors. Accumulation of α1 in the cells could be augmented by inhibition of lysosomal degradation but not by proteosomal inhibitors. In agreement, the internalized α1 could be colocalized with the lysosomal marker LAMP1 but not with Golgi or nuclear markers. In principle, internalization could be triggered by a conformational change of the ouabain-bound Na(+)/K(+)-ATPase molecule or more generally by the disruption of cation homeostasis (Na(+), K(+), Ca(2+)) due to the partial inhibition of active Na(+) and K(+) transport. Overexpression of ouabain-insensitive rat α1 failed to inhibit internalization of human α1 expressed in the same cells. In addition, incubating cells in a K(+)-free medium did not induce internalization of the pump or affect the response to ouabain. Thus, internalization is not the result of changes in the cellular cation balance but is likely to be triggered by a conformational change of the protein itself. In physiological conditions, internalization may serve to eliminate pumps that have been blocked by endogenous ouabain or other cardiac glycosides. This mechanism may be required due to the very slow dissociation of the ouabain·Na(+)/K(+)-ATPase complex.

Single-cell mass cytometry for analysis of immune system functional states

Mass cytometry facilitates high-dimensional, quantitative analysis of the effects of bioactive molecules on cell populations at single-cell resolution. Datasets are generated with panels of up to 45 antibodies. Each antibody is conjugated to a polymer chelated with a stable metal isotope, usually in the lanthanide series of the periodic table. Antibody panels recognize surface markers to delineate cell types simultaneously with intracellular signaling molecules to measure biological functions, such as metabolism, survival, DNA damage, cell cycle and apoptosis, to provide an overall determination of the network state of an individual cell. This review will cover the basics of mass cytometry as well as outline assays developed for the platform that enhance the immunologist's analytical arsenal.

A novel single-cell screening platform reveals proteome plasticity during yeast stress responses

Unprecedented proteome plasticity in response to stress in yeast is revealed using a novel screening platform that allows tracking of protein localization and abundance at single-cell resolution.

Uncovering the mechanisms underlying robust responses of cells to stress is crucial for our understanding of cellular physiology. Indeed, vast amounts of data have been collected on transcriptional responses in Saccharomyces cerevisiae. However, only a handful of pioneering studies describe the dynamics of proteins in response to external stimuli, despite the fact that regulation of protein levels and localization is an essential part of such responses. Here we characterized unprecedented proteome plasticity by systematically tracking the localization and abundance of 5,330 yeast proteins at single-cell resolution under three different stress conditions (DTT, H₂O₂, and nitrogen starvation) using the GFP-tagged yeast library. We uncovered a unique “fingerprint” of changes for each stress and elucidated a new response arsenal for adapting to radical environments. These include bet-hedging strategies, organelle rearrangement, and redistribution of protein localizations. All data are available for download through our online database, LOQATE (localization and quantitation atlas of yeast proteome).

Measuring the dynamics of chromatin proteins during differentiation

Chromatin-protein interactions are important in determining chromosome structure and function, thereby regulating gene expression patterns. Most chromatin associated proteins bind chromatin in a transient manner, with residence times on the order of a few seconds to minutes. This is especially pertinent in mouse embryonic stem cells (ESCs), where hyperdynamic binding of chromatin associated proteins to chromatin is thought to regulate genome plasticity. In order to quantitatively measure binding dynamics of such chromatin proteins in living cells, a combination of GFP-fusion proteins and photobleaching-based assays such as fluorescence recovery after photobleaching (FRAP) and fluorescence loss in photobleaching (FLIP) are advantageous over other existing biochemical assays, because they are applied in living cells at a single cell level. In this chapter we describe a detailed protocol for performing FRAP and FLIP assays for measuring structural chromatin protein dynamics such as Heterochromatin Protein 1 (HP1) and linker histone H1 in mouse ESCs and during ESC differentiation.

neXtProt: organizing protein knowledge in the context of human proteome projects

About 5000 (25%) of the ~20400 human protein-coding genes currently lack any experimental evidence at the protein level. For many others, there is only little information relative to their abundance, distribution, subcellular localization, interactions, or cellular functions. The aim of the HUPO Human Proteome Project (HPP, www.thehpp.org ) is to collect this information for every human protein. HPP is based on three major pillars: mass spectrometry (MS), antibody/affinity capture reagents (Ab), and bioinformatics-driven knowledge base (KB). To meet this objective, the Chromosome-Centric Human Proteome Project (C-HPP) proposes to build this catalog chromosome-by-chromosome ( www.c-hpp.org ) by focusing primarily on proteins that currently lack MS evidence or Ab detection. These are termed "missing proteins" by the HPP consortium. The lack of observation of a protein can be due to various factors including incorrect and incomplete gene annotation, low or restricted expression, or instability. neXtProt ( www.nextprot.org ) is a new web-based knowledge platform specific for human proteins that aims to complement UniProtKB/Swiss-Prot ( www.uniprot.org ) with detailed information obtained from carefully selected high-throughput experiments on genomic variation, post-translational modifications, as well as protein expression in tissues and cells. This article describes how neXtProt contributes to prioritize C-HPP efforts and integrates C-HPP results with other research efforts to create a complete human proteome catalog.

Dynamic proteomics of human protein level and localization across the cell cycle

Regulation of proteins across the cell cycle is a basic process in cell biology. It has been difficult to study this globally in human cells due to lack of methods to accurately follow protein levels and localizations over time. Estimates based on global mRNA measurements suggest that only a few percent of human genes have cell-cycle dependent mRNA levels. Here, we used dynamic proteomics to study the cell-cycle dependence of proteins. We used 495 clones of a human cell line, each with a different protein tagged fluorescently at its endogenous locus. Protein level and localization was quantified in individual cells over 24h of growth using time-lapse microscopy. Instead of standard chemical or mechanical methods for cell synchronization, we employed in-silico synchronization to place protein levels and localization on a time axis between two cell divisions. This non-perturbative synchronization approach, together with the high accuracy of the measurements, allowed a sensitive assay of cell-cycle dependence. We further developed a computational approach that uses texture features to evaluate changes in protein localizations. We find that 40% of the proteins showed cell cycle dependence, of which 11% showed changes in protein level and 35% in localization. This suggests that a broader range of cell-cycle dependent proteins exists in human cells than was previously appreciated. Most of the cell-cycle dependent proteins exhibit changes in cellular localization. Such changes can be a useful tool in the regulation of the cell-cycle being fast and efficient.

Mass spectrometry-based proteomics and network biology

In the life sciences, a new paradigm is emerging that places networks of interacting molecules between genotype and phenotype. These networks are dynamically modulated by a multitude of factors, and the properties emerging from the network as a whole determine observable phenotypes. This paradigm is usually referred to as systems biology, network biology, or integrative biology. Mass spectrometry (MS)-based proteomics is a central life science technology that has realized great progress toward the identification, quantification, and characterization of the proteins that constitute a proteome. Here, we review how MS-based proteomics has been applied to network biology to identify the nodes and edges of biological networks, to detect and quantify perturbation-induced network changes, and to correlate dynamic network rewiring with the cellular phenotype. We discuss future directions for MS-based proteomics within the network biology paradigm.

Studying and modelling dynamic biological processes using time-series gene expression data

Biological processes are often dynamic, thus researchers must monitor their activity at multiple time points. The most abundant source of information regarding such dynamic activity is time-series gene expression data. These data are used to identify the complete set of activated genes in a biological process, to infer their rates of change, their order and their causal effects and to model dynamic systems in the cell. In this Review we discuss the basic patterns that have been observed in time-series experiments, how these patterns are combined to form expression programs, and the computational analysis, visualization and integration of these data to infer models of dynamic biological systems.

Intracellular trafficking of FXYD1 (phospholemman) and FXYD7 proteins in Xenopus oocytes and mammalian cells

FXYD proteins are a group of short single-span transmembrane proteins that interact with the Na(+)/K(+) ATPase and modulate its kinetic properties. This study characterizes intracellular trafficking of two FXYD family members, FXYD1 (phospholemman (PLM)) and FXYD7. Surface expression of PLM in Xenopus oocytes requires coexpression with the Na(+)/K(+) ATPase. On the other hand, the Na(+)/Ca(2+) exchanger, another PLM-interacting protein could not drive it to the cell surface. The Na(+)/K(+) ATPase-dependent surface expression of PLM could be facilitated by either a phosphorylation-mimicking mutation at Thr-69 or a truncation of three terminal arginine residues. Unlike PLM, FXYD7 could translocate to the cell surface of Xenopus oocytes independently of the coexpression of α1β1 Na(+)/K(+) ATPase. The Na(+)/K(+) ATPase-independent membrane translocation of FXYD7 requires O-glycosylation of at least two of three conserved threonines in its ectodomain. Subsequent experiments in mammalian cells confirmed the role of conserved extracellular threonine residues and demonstrated that FXYD7 protein, in which these have been mutated to alanine, is trapped in the endoplasmic reticulum and Golgi apparatus.

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.

Using bleach-chase to measure protein half-lives in living cells

Protein removal has a central role in numerous cellular processes. Obtaining systematic measurements of multiple protein removal rates is necessary to understand the principles that govern these processes, but it is currently a major technical challenge. To address this, we developed 'bleach-chase', a noninvasive method for measuring the half-lives of multiple proteins at high temporal resolution in living cells. The method uses a library of annotated human reporter cell clones, each with a unique fluorescently tagged protein expressed from its native chromosomal location. In this protocol, we detail a simple procedure that bleaches the cells and uses time-lapse fluorescence microscopy and automated image analysis to systematically measure the half-life dynamics of multiple proteins. The duration of the protocol is 4-5 d. The method may be applicable to a wide range of fluorescently tagged proteins and cell lines.

Genes adopt non-optimal codon usage to generate cell cycle-dependent oscillations in protein levels

Most cell cycle-regulated genes adopt non-optimal codon usage, namely, their translation involves wobbly matching codons. Here, the authors show that tRNA expression is cyclic and that codon usage, therefore, can give rise to cell-cycle regulation of proteins.

Most cell cycle-regulated genes adopt non-optimal codon usage.

Non-optimal codon usage can give rise to cell-cycle dynamics at the protein level.

The high expression of transfer RNAs (tRNAs) observed in G2 phase enables cell cycle-regulated genes to adopt non-optimal codon usage, and conversely the lower expression of tRNAs at the end of G1 phase is associated with optimal codon usage.

The protein levels of aminoacyl-tRNA synthetases oscillate, peaking in G2/M phase, consistent with the observed cyclic expression of tRNAs.

The cell cycle is a temporal program that regulates DNA synthesis and cell division. When we compared the codon usage of cell cycle-regulated genes with that of other genes, we discovered that there is a significant preference for non-optimal codons. Moreover, genes encoding proteins that cycle at the protein level exhibit non-optimal codon preferences. Remarkably, cell cycle-regulated genes expressed in different phases display different codon preferences. Here, we show empirically that transfer RNA (tRNA) expression is indeed highest in the G2 phase of the cell cycle, consistent with the non-optimal codon usage of genes expressed at this time, and lowest toward the end of G1, reflecting the optimal codon usage of G1 genes. Accordingly, protein levels of human glycyl-, threonyl-, and glutamyl-prolyl tRNA synthetases were found to oscillate, peaking in G2/M phase. In light of our findings, we propose that non-optimal (wobbly) matching codons influence protein synthesis during the cell cycle. We describe a new mathematical model that shows how codon usage can give rise to cell-cycle regulation. In summary, our data indicate that cells exploit wobbling to generate cell cycle-dependent dynamics of proteins.

Importin 7 and exportin 1 link c-Myc and p53 to regulation of ribosomal biogenesis

Members of the β-karyopherin family mediate nuclear import of ribosomal proteins and export of ribosomal subunits, both required for ribosome biogenesis. We report that transcription of the β-karyopherin genes importin 7 (IPO7) and exportin 1 (XPO1), and several additional nuclear import receptors, is regulated positively by c-Myc and negatively by p53. Partial IPO7 depletion triggers p53 activation and p53-dependent growth arrest. Activation of p53 by IPO7 knockdown has distinct features of ribosomal biogenesis stress, with increased binding of Mdm2 to ribosomal proteins L5 and L11 (RPL5 and RPL11). Furthermore, p53 activation is dependent on RPL5 and RPL11. Of note, IPO7 and XPO1 are frequently overexpressed in cancer. Altogether, we propose that c-Myc and p53 counter each other in the regulation of elements within the nuclear transport machinery, thereby exerting opposing effects on the rate of ribosome biogenesis. Perturbation of this balance may play a significant role in promoting cancer.

neXtProt: a knowledge platform for human proteins

neXtProt (http://www.nextprot.org/) is a new human protein-centric knowledge platform. Developed at the Swiss Institute of Bioinformatics (SIB), it aims to help researchers answer questions relevant to human proteins. To achieve this goal, neXtProt is built on a corpus containing both curated knowledge originating from the UniProtKB/Swiss-Prot knowledgebase and carefully selected and filtered high-throughput data pertinent to human proteins. This article presents an overview of the database and the data integration process. We also lay out the key future directions of neXtProt that we consider the necessary steps to make neXtProt the one-stop-shop for all research projects focusing on human proteins.

Genomic analysis at the single-cell level

Studying complex biological systems such as a developing embryo, a tumor, or a microbial ecosystem often involves understanding the behavior and heterogeneity of the individual cells that constitute the system and their interactions. In this review, we discuss a variety of approaches to single-cell genomic analysis.

Photobleaching assays (FRAP & FLIP) to measure chromatin protein dynamics in living embryonic stem cells

Fluorescence Recovery After Photobleaching (FRAP) and Fluorescence Loss In Photobleaching (FLIP) enable the study of protein dynamics in living cells with good spatial and temporal resolution. Here we describe how to perform FRAP and FLIP assays of chromatin proteins, including H1 and HP1, in mouse embryonic stem (ES) cells. In a FRAP experiment, cells are transfected, either transiently or stably, with a protein of interest fused with the green fluorescent protein (GFP) or derivatives thereof (YFP, CFP, Cherry, etc.). In the transfected, fluorescing cells, an intense focused laser beam bleaches a relatively small region of interest (ROI). The laser wavelength is selected according to the fluorescent protein used for fusion. The laser light irreversibly bleaches the fluorescent signal of molecules in the ROI and, immediately following bleaching, the recovery of the fluorescent signal in the bleached area - mediated by the replacement of the bleached molecules with the unbleached molecules - is monitored using time lapse imaging. The generated fluorescence recovery curves provide information on the protein's mobility. If the fluorescent molecules are immobile, no fluorescence recovery will be observed. In a complementary approach, Fluorescence Loss in Photobleaching (FLIP), the laser beam bleaches the same spot repeatedly and the signal intensity is measured elsewhere in the fluorescing cell. FLIP experiments therefore measure signal decay rather than fluorescence recovery and are useful to determine protein mobility as well as protein shuttling between cellular compartments. Transient binding is a common property of chromatin-associated proteins. Although the major fraction of each chromatin protein is bound to chromatin at any given moment at steady state, the binding is transient and most chromatin proteins have a high turnover on chromatin, with a residence time in the order of seconds. These properties are crucial for generating high plasticity in genome expression¹. Photobleaching experiments are therefore particularly useful to determine chromatin plasticity using GFP-fusion versions of chromatin structural proteins, especially in ES cells, where the dynamic exchange of chromatin proteins (including heterochromatin protein 1 (HP1), linker histone H1 and core histones) is higher than in differentiated cells. ² (,)³

Proteome half-life dynamics in living human cells

Cells remove proteins by two processes: degradation and dilution due to cell growth. The balance between these basic processes is poorly understood. We addressed this by developing an accurate and noninvasive method for measuring protein half-lives, called "bleach-chase," that is applicable to fluorescently tagged proteins. Assaying 100 proteins in living human cancer cells showed half-lives that ranged between 45 minutes and 22.5 hours. A variety of stresses that stop cell division showed the same general effect: Long-lived proteins became longer-lived, whereas short-lived proteins remained largely unaffected. This effect is due to the relative strengths of degradation and dilution and suggests a mechanism for differential killing of rapidly growing cells by growth-arresting drugs. This approach opens a way to understand proteome half-life dynamics in living cells.

Generation of double-labeled reporter cell lines for studying co-dynamics of endogenous proteins in individual human cells

Understanding the dynamic relationship between components of a system or pathway at the individual cell level is a current challenge. To address this, we developed an approach that allows simultaneous tracking of several endogenous proteins of choice within individual living human cells. The approach is based on fluorescent tagging of proteins at their native locus by directed gene targeting. A fluorescent tag-encoding DNA is introduced as a new exon into the intronic region of the gene of interest, resulting in expression of a full-length fluorescently tagged protein. We used this approach to establish human cell lines simultaneously expressing two components of a major antioxidant defense system, thioredoxin 1 (Trx) and thioredoxin reductase 1 (TrxR1), labeled with CFP and YFP, respectively. We find that the distributions of both proteins between nuclear and cytoplasmic compartments were highly variable between cells. However, the two proteins did not vary independently of each other: protein levels of Trx and TrxR1 in both the whole cell and the nucleus were substantially correlated. We further find that in response to a stress-inducing drug (CPT), both Trx and TrxR1 accumulated in the nuclei in a manner that was highly temporally correlated. This accumulation considerably reduced cell-to-cell variability in nuclear content of both proteins, suggesting a uniform response of the thioredoxin system to stress. These results indicate that Trx and TrxR1 act in concert in response to stress in regard to both time course and variability. Thus, our approach provides an efficient tool for studying dynamic relationship between components of systems of interest at a single-cell level.

Cell death goes LIVE: technological advances in real-time tracking of cell death

Cell population can be viewed as a quantum system, which like Schrödinger's cat exists as a combination of survival- and death-allowing states. Tracking and understanding cell-to-cell variability in processes of high spatio-temporal complexity such as cell death is at the core of current systems biology approaches. As probabilistic modeling tools attempt to impute information inaccessible by current experimental approaches, advances in technologies for single-cell imaging and omics (proteomics, genomics, metabolomics) should go hand in hand with the computational efforts. Over the last few years we have made exciting technological advances that allow studies of cell death dynamically in real-time and with the unprecedented accuracy. These approaches are based on innovative fluorescent assays and recombinant proteins, bioelectrical properties of cells, and more recently also on state-of-the-art optical spectroscopy. Here, we review current status of the most innovative analytical technologies for dynamic tracking of cell death, and address the interdisciplinary promises and future challenges of these methods.

Protein dynamics in drug combinations: a linear superposition of individual-drug responses

Drugs and drug combinations have complex biological effects on cells and organisms. Little is known about how drugs affect protein dynamics that determine these effects. Here, we use a dynamic proteomics approach to accurately follow 15 protein levels in human cells in response to 13 different drugs. We find that protein dynamics in response to combinations of drugs are described accurately by a linear superposition (weighted sum) of their response to individual drugs. The weights in this superposition describe the relative impact of each drug on each protein. Using these weights, we show that one can predict the dynamics in a three-drug or four-drug combination on the basis of the dynamics in drug pairs. Our approach might eliminate the need to increase the number of experiments exponentially with the number of drugs and suggests that it might be possible to rationally control protein dynamics with specific drug combinations.

Signaling motifs and Weber's law

New experimental and theoretical studies reported by Uri Alon, Marc Kirschner, and colleagues in this issue of Molecular Cell suggest that Weber's law of sensory perception may apply to a number of cell signaling processes.