Quantitative proteomic approach to study subcellular localization of membrane proteins

SOS1 tonoplast neo-localization and the RGG protein SALTY are important in the extreme salinity tolerance of Salicornia bigelovii

The identification of genes involved in salinity tolerance has primarily focused on model plants and crops. However, plants naturally adapted to highly saline environments offer valuable insights into tolerance to extreme salinity. Salicornia plants grow in coastal salt marshes, stimulated by NaCl. To understand this tolerance, we generated genome sequences of two Salicornia species and analyzed the transcriptomic and proteomic responses of Salicornia bigelovii to NaCl. Subcellular membrane proteomes reveal that SbiSOS1, a homolog of the well-known SALT-OVERLY-SENSITIVE 1 (SOS1) protein, appears to localize to the tonoplast, consistent with subcellular localization assays in tobacco. This neo-localized protein can pump Na+ into the vacuole, preventing toxicity in the cytosol. We further identify 11 proteins of interest, of which SbiSALTY, substantially improves yeast growth on saline media. Structural characterization using NMR identified it as an intrinsically disordered protein, localizing to the endoplasmic reticulum in planta, where it can interact with ribosomes and RNA, stabilizing or protecting them during salt stress.

Metal organic layers enabled cell surface engineering coupling biomembrane fusion for dynamic membrane proteome profiling

Systematically dissecting the highly dynamic and tightly communicating membrane proteome of living cells is essential for the system-level understanding of fundamental cellular processes and intricate relationship between membrane-bound organelles constructed through membrane traffic. While extensive efforts have been made to enrich membrane proteins, their comprehensive analysis with high selectivity and deep coverage remains a challenge, especially at the living cell state. To address this problem, we developed the cell surface engineering coupling biomembrane fusion method to map the whole membrane proteome from the plasma membrane to various organelle membranes taking advantage of the exquisite interaction between two-dimensional metal-organic layers and phospholipid bilayers on the membrane. This approach, which bypassed conventional biochemical fractionation and ultracentrifugation, facilitated the enrichment of membrane proteins in their native phospholipid bilayer environment, helping to map the membrane proteome with a specificity of 77% and realizing the deep coverage of the HeLa membrane proteome (5087 membrane proteins). Furthermore, membrane N-phosphoproteome was profiled by integrating the N-phosphoproteome analysis strategy, and the dynamic membrane proteome during apoptosis was deciphered in combination with quantitative proteomics. The features of membrane protein N-phosphorylation modifications and many differential proteins during apoptosis associated with mitochondrial dynamics and ER homeostasis were found. The method provided a simple and robust strategy for efficient analysis of membrane proteome, offered a reliable platform for research on membrane-related cell dynamic events and expanded the application of metal-organic layers.

Microbial Pathogenesis in the Era of Spatial Omics

The biology of a cell, whether it is a unicellular organism or part of a multicellular network, is influenced by cell type, temporal changes in cell state, and the cell's environment. Spatial cues play a critical role in the regulation of microbial pathogenesis strategies. Information about where the pathogen is-in a tissue or in proximity to a host cell-regulates gene expression and the compartmentalization of gene products in the microbe and the host. Our understanding of host and pathogen identity has bloomed with the accessibility of transcriptomics and proteomics techniques. A missing piece of the puzzle has been our ability to evaluate global transcript and protein expression in the context of the subcellular niche, primary cell, or native tissue environment during infection. This barrier is now lower with the advent of new spatial omics techniques to understand how location regulates cellular functions. This review will discuss how recent advances in spatial proteomics and transcriptomics approaches can address outstanding questions in microbial pathogenesis.

Application of Machine Learning in Spatial Proteomics

Spatial proteomics is an interdisciplinary field that investigates the localization and dynamics of proteins, and it has gained extensive attention in recent years, especially the subcellular proteomics. Numerous evidence indicate that the subcellular localization of proteins is associated with various cellular processes and disease progression. Mass spectrometry (MS)-based and imaging-based experimental approaches have been developed to acquire large-scale spatial proteomic data. To allow the reliable analysis of increasingly complex spatial proteomics data, machine learning (ML) methods have been widely used in both MS-based and imaging-based spatial proteomic data analysis pipelines. Here, we comprehensively survey the applications of ML in spatial proteomics from following aspects: (1) data resources for spatial proteome are comprehensively introduced; (2) the roles of different ML algorithms in data analysis pipelines are elaborated; (3) successful applications of spatial proteomics and several analytical tools integrating ML methods are presented; (4) challenges existing in modern ML-based spatial proteomics studies are discussed. This review provides guidelines for researchers seeking to apply ML methods to analyze spatial proteomic data and can facilitate insightful understanding of cell biology as well as the future research in medical and drug discovery communities.

Reduced mitochondria provide an essential function for the cytosolic methionine cycle

The loss of mitochondria in oxymonad protists has been associated with the redirection of the essential Fe-S cluster assembly to the cytosol. Yet as our knowledge of diverse free-living protists broadens, the list of functions of their mitochondrial-related organelles (MROs) expands. We revealed another such function in the closest oxymonad relative, Paratrimastix pyriformis, after we solved the proteome of its MRO with high accuracy, using localization of organelle proteins by isotope tagging (LOPIT). The newly assigned enzymes connect to the glycine cleavage system (GCS) and produce folate derivatives with one-carbon units and formate. These are likely to be used by the cytosolic methionine cycle involved in S-adenosyl methionine recycling. The data provide consistency with the presence of the GCS in MROs of free-living species and its absence in most endobionts, which typically lose the methionine cycle and, in the case of oxymonads, the mitochondria.

Semi-Supervised Non-Parametric Bayesian Modelling of Spatial Proteomics

Understanding sub-cellular protein localisation is an essential component in the analysis of context specific protein function. Recent advances in quantitative mass-spectrometry (MS) have led to high resolution mapping of thousands of proteins to sub-cellular locations within the cell. Novel modelling considerations to capture the complex nature of these data are thus necessary. We approach analysis of spatial proteomics data in a non-parametric Bayesian framework, using K-component mixtures of Gaussian process regression models. The Gaussian process regression model accounts for correlation structure within a sub-cellular niche, with each mixture component capturing the distinct correlation structure observed within each niche. The availability of marker proteins (i.e. proteins with a priori known labelled locations) motivates a semi-supervised learning approach to inform the Gaussian process hyperparameters. We moreover provide an efficient Hamiltonian-within-Gibbs sampler for our model. Furthermore, we reduce the computational burden associated with inversion of covariance matrices by exploiting the structure in the covariance matrix. A tensor decomposition of our covariance matrices allows extended Trench and Durbin algorithms to be applied to reduce the computational complexity of inversion and hence accelerate computation. We provide detailed case-studies on Drosophila embryos and mouse pluripotent embryonic stem cells to illustrate the benefit of semi-supervised functional Bayesian modelling of the data.

Methodologies for Measuring Protein Trafficking across Cellular Membranes

Nearly all proteins are synthesized in the cytosol. The majority of this proteome must be trafficked elsewhere, such as to membranes, to subcellular compartments, or outside of the cell. Proper trafficking of nascent protein is necessary for protein folding, maturation, quality control and cellular and organismal health. To better understand cellular biology, molecular and chemical technologies to properly characterize protein trafficking (and mistrafficking) have been developed and applied. Herein, we take a biochemical perspective to review technologies that enable spatial and temporal measurement of protein distribution, focusing on both the most widely adopted methodologies and exciting emerging approaches.

Proximity-dependent biotinylation approaches to study apicomplexan biology

In the last 10 years, proximity-dependent biotinylation (PDB) techniques greatly expanded the ability to study protein environments in the living cell that range from specific protein complexes to entire compartments. This is achieved by using enzymes such as BirA* and APEX that are fused to proteins of interest and biotinylate proteins in their proximity. PDB techniques are now also increasingly used in apicomplexan parasites. In this review, we first give an overview of the main PDB approaches and how they compare with other techniques that address similar questions. PDB is particularly valuable to detect weak or transient protein associations under physiological conditions and to study cellular structures that are difficult to purify or have a poorly understood protein composition. We also highlight new developments such as novel smaller or faster-acting enzyme variants and conditional PDB approaches, providing improvements in both temporal and spatial resolution which may offer broader application possibilities useful in apicomplexan research. In the second part, we review work using PDB techniques in apicomplexan parasites and how this expanded our knowledge about these medically important parasites.

Closing the protein gap in plant chronobiology

Our modern understanding of diel cell regulation in plants stems from foundational work in the late 1990s that analysed the dynamics of selected genes and mutants in Arabidopsis thaliana. The subsequent rise of transcriptomics technologies such as microarrays and RNA sequencing has substantially increased our understanding of anticipatory (circadian) and reactive (light- or dark-triggered) diel events in plants. However, it is also becoming clear that gene expression data fail to capture critical events in diel regulation that can only be explained by studying protein-level dynamics. Over the past decade, mass spectrometry technologies and quantitative proteomic workflows have significantly advanced, finally allowing scientists to characterise diel protein regulation at high throughput. Initial proteomic investigations suggest that the diel transcriptome and proteome generally lack synchrony and that the timing of daily regulatory events in plants is impacted by multiple levels of protein regulation (e.g., post-translational modifications [PTMs] and protein-protein interactions [PPIs]). Here, we highlight and summarise how the use of quantitative proteomics to elucidate diel plant cell regulation has advanced our understanding of these processes. We argue that this new understanding, coupled with the extraordinary developments in mass spectrometry technologies, demands greater focus on protein-level regulation of, and by, the circadian clock. This includes hitherto unexplored diel dynamics of protein turnover, PTMs, protein subcellular localisation and PPIs that can be masked by simple transcript- and protein-level changes. Finally, we propose new directions for how the latest advancements in quantitative proteomics can be utilised to answer outstanding questions in plant chronobiology.

Cell Envelope Proteomics of Mycobacteria

The World Health Organization (WHO) estimates that Mycobacterium tuberculosis, the most pathogenic mycobacterium species to humans, has infected up to a quarter of the world's population, with the occurrence of multidrug-resistant strains on the rise. Research into the detailed composition of the cell envelope proteome in mycobacteria over the last 20 years has formed a key part of the efforts to understand host-pathogen interactions and to control the current tuberculosis epidemic. This is due to the great importance of the cell envelope proteome during infection and during the development of antibiotic resistance as well as the search of surface-exposed proteins that could be targeted by therapeutics and vaccines. A variety of experimental approaches and mycobacterial species have been used in proteomic studies thus far. Here we provide for the first time an extensive summary of the different approaches to isolate the mycobacterial cell envelope, highlight some of the limitations of the studies performed thus far, and comment on how the recent advances in membrane proteomics in other fields might be translated into the field of mycobacteria to provide deeper coverage.

Proteomics and metabolomics - Current and future perspectives in clinical andrology

Proteomics and metabolomics are emerging as promising tools to investigate the molecular mechanisms associated with male infertility. Proteins and metabolites play a pivotal role in regulating the molecular pathways associated with physiological functions of spermatozoa. Semen analysis, physical examination and laboratory work up cannot identify the etiology of infertility in 30%-40% of cases, which are classified as idiopathic. Therefore, the application of proteomics and metabolomics in the field of andrology will aid to overcome the limitations of the standard semen analysis. Understanding the molecular pathways associated with male infertility will help in planning ad hoc treatments, contributing to the clinical management of infertile patients. In this review, proteomics and metabolomics studies on spermatozoa and seminal plasma are discussed with a focus on molecular biomarkers associated with male infertility-related conditions.

Progress and pitfalls of using isobaric mass tags for proteome profiling

Introduction: Quantitative proteomics using mass spectrometry is performed via label-free or label-based approaches. Labeling strategies rely on the incorporation of stable heavy isotopes by metabolic, enzymatic, or chemical routes. Isobaric labeling uses chemical labels of identical masses but of different fragmentation behaviors to allow the relative quantitative comparison of peptide/protein abundances between biological samples.Areas covered: We have carried out a systematic review on the use of isobaric mass tags in proteomic research since their inception in 2003. We focused on their quantitative performances, their multiplexing evolution, as well as their broad use for relative quantification of proteins in pre-clinical models and clinical studies. Current limitations, primarily linked to the quantitative ratio distortion, as well as state-of-the-art and emerging solutions to improve their quantitative readouts are discussed.Expert opinion: The isobaric mass tag technology offers a unique opportunity to compare multiple protein samples simultaneously, allowing higher sample throughput and internal relative quantification for improved trueness and precision. Large studies can be performed when shared reference samples are introduced in multiple experiments. The technology is well suited for proteome profiling in the context of proteomic discovery studies.

A Protocol to Map the Spatial Proteome Using HyperLOPIT in Saccharomyces cerevisiae

The correct subcellular localization of proteins is vital for cellular function and the study of this process at the systems level will therefore enrich our understanding of the roles of proteins within the cell. Multiple methods are available for the study of protein subcellular localization, including fluorescence microscopy, organelle cataloging, proximity labeling methods, and whole-cell protein correlation profiling methods. We provide here a protocol for the systems-level study of the subcellular localization of the yeast proteome, using a version of hyperplexed Localization of Organelle Proteins by Isotope Tagging (hyperLOPIT) that has been optimized for use with Saccharomyces cerevisiae. The entire protocol encompasses cell culture, cell lysis by nitrogen cavitation, subcellular fractionation, monitoring of the fractionation using Western blotting, labeling of samples with TMT isobaric tags and mass spectrometric analysis. Also included is a brief explanation of downstream processing of the mass spectrometry data to produce a map of the spatial proteome. If required, the nitrogen cavitation lysis and Western blotting portions of the protocol may be performed independently of the mass spectrometry analysis. The protocol in its entirety, however, enables the unbiased, systems-level and high-resolution analysis of the localizations of thousands of proteins in parallel within a single experiment.

Combining LOPIT with differential ultracentrifugation for high-resolution spatial proteomics

The study of protein localisation has greatly benefited from high-throughput methods utilising cellular fractionation and proteomic profiling. Hyperplexed Localisation of Organelle Proteins by Isotope Tagging (hyperLOPIT) is a well-established method in this area. It achieves high-resolution separation of organelles and subcellular compartments but is relatively time- and resource-intensive. As a simpler alternative, we here develop Localisation of Organelle Proteins by Isotope Tagging after Differential ultraCentrifugation (LOPIT-DC) and compare this method to the density gradient-based hyperLOPIT approach. We confirm that high-resolution maps can be obtained using differential centrifugation down to the suborganellar and protein complex level. HyperLOPIT and LOPIT-DC yield highly similar results, facilitating the identification of isoform-specific localisations and high-confidence localisation assignment for proteins in suborganellar structures, protein complexes and signalling pathways. By combining both approaches, we present a comprehensive high-resolution dataset of human protein localisations and deliver a flexible set of protocols for subcellular proteomics.

Mapping the Saccharomyces cerevisiae Spatial Proteome with High Resolution Using hyperLOPIT

The subcellular localization of proteins is a posttranslational modification of paramount importance. The ability to study subcellular and organelle proteomes improves our understanding of cellular homeostasis and cellular dynamics. In this chapter, we describe a protocol for the unbiased and high-throughput study of protein subcellular localization in the yeast Saccharomyces cerevisiae: hyperplexed localization of organelle proteins by isotope tagging (hyperLOPIT), which involves biochemical fractionation of Saccharomyces cerevisiae and high resolution mass spectrometry-based protein quantitation using TMT 10-plex isobaric tags. This protocol enables the determination of the subcellular localizations of thousands of proteins in parallel in a single experiment and thereby deep sampling and high-resolution mapping of the spatial proteome.

A Bayesian mixture modelling approach for spatial proteomics

Analysis of the spatial sub-cellular distribution of proteins is of vital importance to fully understand context specific protein function. Some proteins can be found with a single location within a cell, but up to half of proteins may reside in multiple locations, can dynamically re-localise, or reside within an unknown functional compartment. These considerations lead to uncertainty in associating a protein to a single location. Currently, mass spectrometry (MS) based spatial proteomics relies on supervised machine learning algorithms to assign proteins to sub-cellular locations based on common gradient profiles. However, such methods fail to quantify uncertainty associated with sub-cellular class assignment. Here we reformulate the framework on which we perform statistical analysis. We propose a Bayesian generative classifier based on Gaussian mixture models to assign proteins probabilistically to sub-cellular niches, thus proteins have a probability distribution over sub-cellular locations, with Bayesian computation performed using the expectation-maximisation (EM) algorithm, as well as Markov-chain Monte-Carlo (MCMC). Our methodology allows proteome-wide uncertainty quantification, thus adding a further layer to the analysis of spatial proteomics. Our framework is flexible, allowing many different systems to be analysed and reveals new modelling opportunities for spatial proteomics. We find our methods perform competitively with current state-of-the art machine learning methods, whilst simultaneously providing more information. We highlight several examples where classification based on the support vector machine is unable to make any conclusions, while uncertainty quantification using our approach provides biologically intriguing results. To our knowledge this is the first Bayesian model of MS-based spatial proteomics data.

Proteomics of nucleocytoplasmic partitioning

The partitioning of the proteome between nucleus and cytoplasm affects nearly every aspect of eukaryotic biology. Despite this central role, we still have a poor understanding of which proteins localize in the nucleus and how this varies in different cell types and conditions. Recent advances in quantitative proteomics and high-throughput imaging are starting to close this knowledge gap. Studies on protein interaction are beginning to reveal the spectrum of cargos of nuclear import and export receptors. We anticipate that it will soon be possible to predict each protein's nucleocytoplasmic localization based on its importin/exportin interactions and its estimated diffusion rate through the nuclear pore. This insight is likely to provide us with a fundamental understanding of how cells use nucleocytoplasmic partitioning to encode and relay information.

Early Cold-Induced Peroxidases and Aquaporins Are Associated With High Cold Tolerance in Dajiao (Musa spp. 'Dajiao')

Banana is an important tropical fruit with high economic value. One of the main cultivars ('Cavendish') is susceptible to low temperatures, while another closely related specie ('Dajiao') has considerably higher cold tolerance. We previously reported that some membrane proteins appear to be involved in the cold tolerance of Dajiao bananas via an antioxidation mechanism. To investigate the early cold stress response of Dajiao, here we applied comparative membrane proteomics analysis for both cold-sensitive Cavendish and cold-tolerant Dajiao bananas subjected to cold stress at 10°C for 0, 3, and 6 h. A total of 2,333 and 1,834 proteins were identified in Cavendish and Dajiao, respectively. Subsequent bioinformatics analyses showed that 692 Cavendish proteins and 524 Dajiao proteins were predicted to be membrane proteins, of which 82 and 137 differentially abundant membrane proteins (DAMPs) were found in Cavendish and Dajiao, respectively. Interestingly, the number of DAMPs with increased abundance following 3 h of cold treatment in Dajiao (80) was seven times more than that in Cavendish (11). Gene ontology molecular function analysis of DAMPs for Cavendish and Dajiao indicated that they belong to eight categories including hydrolase activity, binding, transporter activity, antioxidant activity, etc., but the number in Dajiao is twice that in Cavendish. Strikingly, we found peroxidases (PODs) and aquaporins among the protein groups whose abundance was significantly increased after 3 h of cold treatment in Dajiao. Some of the PODs and aquaporins were verified by reverse-transcription PCR, multiple reaction monitoring, and green fluorescent protein-based subcellular localization analysis, demonstrating that the global membrane proteomics data are reliable. By combining the physiological and biochemical data, we found that membrane-bound Peroxidase 52 and Peroxidase P7, and aquaporins (MaPIP1;1, MaPIP1;2, MaPIP2;4, MaPIP2;6, MaTIP1;3) are mainly involved in decreased lipid peroxidation and maintaining leaf cell water potential, which appear to be the key cellular adaptations contributing to the cold tolerance of Dajiao. This membrane proteomics study provides new insights into cold stress tolerance mechanisms of banana, toward potential applications for ultimate genetic improvement of cold tolerance in banana.

Mass spectrometry approaches to study plant endomembrane trafficking

Intracellular proteins reside in highly controlled microenvironments in which they perform context specific functions. Trafficking pathways have evolved that enable proteins to be precisely delivered to the correct location but also to re-locate in response to environmental perturbation. Trafficking of membrane proteins to their correct endomembrane location is especially important to enable them to carry out their function. Although a considerable amount of knowledge about membrane protein trafficking in plants has been delivered by years of dedicated research, there are still significant gaps in our understanding of this process. Further knowledge of endomembrane trafficking is dependent on thorough characterization of the subcellular components that constitute the endomembrane system. Such studies are challenging for a number of reasons including the complexity of the plant endomembrane system, inability to purify individual constituents, discrimination protein cargo for full time residents of compartments, and the fact that many proteins function at more than one location. In this review, we describe the components of the secretory pathway and focus on how mass spectrometry based proteomics methods have helped elucidation of this pathway. We demonstrate that the combination of targeted and untargeted approaches is allowing research into new areas of the secretory pathway investigation. Finally we describe new enabling technologies that will impact future studies in this area.

Using hyperLOPIT to perform high-resolution mapping of the spatial proteome

The organization of eukaryotic cells into distinct subcompartments is vital for all functional processes, and aberrant protein localization is a hallmark of many diseases. Microscopy methods, although powerful, are usually low-throughput and dependent on the availability of fluorescent fusion proteins or highly specific and sensitive antibodies. One method that provides a global picture of the cell is localization of organelle proteins by isotope tagging (LOPIT), which combines biochemical cell fractionation using density gradient ultracentrifugation with multiplexed quantitative proteomics mass spectrometry, allowing simultaneous determination of the steady-state distribution of hundreds of proteins within organelles. Proteins are assigned to organelles based on the similarity of their gradient distribution to those of well-annotated organelle marker proteins. We have substantially re-developed our original LOPIT protocol (published by Nature Protocols in 2006) to enable the subcellular localization of thousands of proteins per experiment (hyperLOPIT), including spatial resolution at the suborganelle and large protein complex level. This Protocol Extension article integrates all elements of the hyperLOPIT pipeline, including an additional enrichment strategy for chromatin, extended multiplexing capacity of isobaric mass tags, state-of-the-art mass spectrometry methods and multivariate machine-learning approaches for analysis of spatial proteomics data. We have also created an open-source infrastructure to support analysis of quantitative mass-spectrometry-based spatial proteomics data (http://bioconductor.org/packages/pRoloc) and an accompanying interactive visualization framework (http://www. bioconductor.org/packages/pRolocGUI). The procedure we outline here is applicable to any cell culture system and requires ∼1 week to complete sample preparation steps, ∼2 d for mass spectrometry data acquisition and 1-2 d for data analysis and downstream informatics.

Learning from Heterogeneous Data Sources: An Application in Spatial Proteomics

Sub-cellular localisation of proteins is an essential post-translational regulatory mechanism that can be assayed using high-throughput mass spectrometry (MS). These MS-based spatial proteomics experiments enable us to pinpoint the sub-cellular distribution of thousands of proteins in a specific system under controlled conditions. Recent advances in high-throughput MS methods have yielded a plethora of experimental spatial proteomics data for the cell biology community. Yet, there are many third-party data sources, such as immunofluorescence microscopy or protein annotations and sequences, which represent a rich and vast source of complementary information. We present a unique transfer learning classification framework that utilises a nearest-neighbour or support vector machine system, to integrate heterogeneous data sources to considerably improve on the quantity and quality of sub-cellular protein assignment. We demonstrate the utility of our algorithms through evaluation of five experimental datasets, from four different species in conjunction with four different auxiliary data sources to classify proteins to tens of sub-cellular compartments with high generalisation accuracy. We further apply the method to an experiment on pluripotent mouse embryonic stem cells to classify a set of previously unknown proteins, and validate our findings against a recent high resolution map of the mouse stem cell proteome. The methodology is distributed as part of the open-source Bioconductor pRoloc suite for spatial proteomics data analysis.

Sub-cellular localisation of proteins is critical to their function in all cellular processes; proteins localising to their intended micro-environment, e.g organelles, vesicles or macro-molecular complexes, will meet the interaction partners and biochemical conditions suitable to pursue their molecular function. Therefore, sound data and methods to reliably and systematically study protein localisation, and hence their mis-localisation and the disruption of protein trafficking, that are relied upon by the cell biology community, are essential. Here we present a method to infer protein localisation relying on the optimal integration of experimental mass spectrometry-based data and auxiliary sources, such as GO annotation, outputs from third-party software, protein-protein interactions or immunocytochemistry data. We found that the application of transfer learning algorithms across these diverse data sources considerably improves on the quantity and reliability of sub-cellular protein assignment, compared to single data classifiers previously applied to infer sub-cellular localisation using experimental data only. We show how our method does not compromise biologically relevant experimental-specific signal after integration with heterogeneous freely available third-party resources. The integration of different data sources is an important challenge in the data intensive world of biology and we anticipate the transfer learning methods presented here will prove useful to many areas of biology, to unify data obtained from different but complimentary sources.

Integrative subcellular proteomic analysis allows accurate prediction of human disease-causing genes

Proteomic profiling on subcellular fractions provides invaluable information regarding both protein abundance and subcellular localization. When integrated with other data sets, it can greatly enhance our ability to predict gene function genome-wide. In this study, we performed a comprehensive proteomic analysis on the light-sensing compartment of photoreceptors called the outer segment (OS). By comparing with the protein profile obtained from the retina tissue depleted of OS, an enrichment score for each protein is calculated to quantify protein subcellular localization, and 84% accuracy is achieved compared with experimental data. By integrating the protein OS enrichment score, the protein abundance, and the retina transcriptome, the probability of a gene playing an essential function in photoreceptor cells is derived with high specificity and sensitivity. As a result, a list of genes that will likely result in human retinal disease when mutated was identified and validated by previous literature and/or animal model studies. Therefore, this new methodology demonstrates the synergy of combining subcellular fractionation proteomics with other omics data sets and is generally applicable to other tissues and diseases.

Purification of Early and Late Endosomes

Proteomic analysis of early and late endosomes has been constrained by the limited purity of the endosomal fractions that can be achieved by biochemical methods. Here we briefly review endocytic pathways, and then introduce fractionation strategies that have been used to improve the purity of isolated endosomes. In addition, we describe innovative proteomics analysis methods that have been shown to partially circumvent the limitations found in the enrichment steps.

Protein Neighbors and Proximity Proteomics

Within cells, proteins can co-assemble into functionally integrated and spatially restricted multicomponent complexes. Often, the affinities between individual proteins are relatively weak, and proteins within such clusters may interact only indirectly with many of their other protein neighbors. This makes proteomic characterization difficult using methods such as immunoprecipitation or cross-linking. Recently, several groups have described the use of enzyme-catalyzed proximity labeling reagents that covalently tag the neighbors of a targeted protein with a small molecule such as fluorescein or biotin. The modified proteins can then be isolated by standard pulldown methods and identified by mass spectrometry. Here we will describe the techniques as well as their similarities and differences. We discuss their applications both to study protein assemblies and to provide a new way for characterizing organelle proteomes. We stress the importance of proteomic quantitation and independent target validation in such experiments. Furthermore, we suggest that there are biophysical and cell-biological principles that dictate the appropriateness of enzyme-catalyzed proximity labeling methods to address particular biological questions of interest.

Proteome profile of salt gland-rich epidermis extracted from a salt-tolerant tree species

Preparation of proteins from salt-gland-rich tissues of mangrove plant is necessary for a systematic study of proteins involved in the plant's unique desalination mechanism. Extraction of high-quality proteins from the leaves of mangrove tree species, however, is difficult due to the presence of high levels of endogenous phenolic compounds. In our study, preparation of proteins from only a part of the leaf tissues (i.e. salt gland-rich epidermal layers) was required, rendering extraction even more challenging. By comparing several extraction methods, we developed a reliable procedure for obtaining proteins from salt gland-rich tissues of the mangrove species Avicennia officinalis. Protein extraction was markedly improved using a phenol-based extraction method. Greater resolution 1D protein gel profiles could be obtained. More promising proteome profiles could be obtained through 1D-LC-MS/MS. The number of proteins detected was twice as much as compared to TUTS extraction method. Focusing on proteins that were solely present in each extraction method, phenol-based extracts contained nearly ten times more proteins than those in the extracts without using phenol. The approach could thus be applied for downstream high-throughput proteomic analyses involving LC-MS/MS or equivalent. The proteomics data presented herein are available via ProteomeXchange with identifier PXD001691.

Proteomic Characterisation of the Salt Gland-Enriched Tissues of the Mangrove Tree Species Avicennia officinalis

Plant salt glands are nature’s desalination devices that harbour potentially useful information pertaining to salt and water transport during secretion. As part of the program toward deciphering secretion mechanisms in salt glands, we used shotgun proteomics to compare the protein profiles of salt gland-enriched (isolated epidermal peels) and salt gland-deprived (mesophyll) tissues of the mangrove species Avicennia officinalis. The purpose of the work is to identify proteins that are present in the salt gland-enriched tissues. An average of 2189 and 977 proteins were identified from the epidermal peel and mesophyll tissues, respectively. Among these, 2188 proteins were identified in salt gland-enriched tissues and a total of 1032 selected proteins were categorized by Gene Ontology (GO) analysis. This paper reports for the first time the proteomic analysis of salt gland-enriched tissues of a mangrove tree species. Candidate proteins that may play a role in the desalination process of the mangrove salt glands and their potential localization were identified. Information obtained from this study paves the way for future proteomic research aiming at elucidating the molecular mechanism underlying secretion in plant salt glands. The data have been deposited to the ProteomeXchange with identifier PXD000771.

Dramatic improvement of proteomic analysis of zebrafish liver tumor by effective protein extraction with sodium deoxycholate and heat denaturation

Majority of the proteomic studies on tissue samples involve the use of gel-based approach for profiling and digestion. The laborious gel-based approach is slowly being replaced by the advancing in-solution digestion approach. However, there are still several difficulties such as difficult-to-solubilize proteins, poor proteomic analysis in complex tissue samples, and the presence of sample impurities. Henceforth, there is a great demand to formulate a highly efficient protein extraction buffer with high protein extraction efficiency from tissue samples, high compatibility with in-solution digestion, reduced number of sample handling steps to reduce sample loss, low time consumption, low cost, and ease of usage. Here, we evaluated various existing protein extraction buffers with zebrafish liver tumor samples and found that sodium deoxycholate- (DOC-) based extraction buffer with heat denaturation was the most effective approach for highly efficient extraction of proteins from complex tissues such as the zebrafish liver tumor. A total of 4,790 proteins have been identified using shotgun proteomics approach with 2D LC, which to our knowledge is the most comprehensive study for zebrafish liver tumor proteome.

iTRAQ-based proteomic identification of proteins involved in anti-angiogenic effects of Panduratin A on HUVECs

Panduratin A (PA), a cyclohexanyl chalcone from Boesenbergia rotunda (L.) Mansf. was shown to possess anti-angiogenic effects in our previous study. In the present study, the molecular targets and anti-angiogenic mechanisms of PA on human umbilical vein endothelial cells (HUVECs) were identified using an iTRAQ-based quantitative proteomics approach. A total of 263 proteins were found to be differentially regulated in response to treatment with PA. Ingenuity Pathway Analysis revealed that cellular growth and proliferation, protein synthesis, RNA post-transcriptional modification, cellular assembly and organization and cell-to-cell signaling and interaction were the most significantly deregulated molecular and cellular functions in PA-treated HUVECs. PA inhibited the expressions of ARPC2 and CTNND1 that are associated with the formation of actin cytoskeleton, focal adhesion and cellular protrusions. In addition, PA down-regulated CD63, GRB-2, ICAM-2 and STAB-1 that are implicated in adhesion, migration and tube formation of endothelial cells. The differential expressions of three targets, namely, ARPC2, CDK4, and GRB-2 were validated by western blot analyses. Furthermore, PA inhibited G1-S progression, and resulted in G0/G1 arrest in HUVECs. The blockage in cell cycle progression was accompanied with the suppression of mTOR signaling. Treatment of HUVECs with PA resulted in decreased phosphorylation of ribosomal S6 and 4EBP1 proteins, the two downstream effectors of mTOR signaling. We further showed that PA is able to inhibit mTOR signaling induced by VEGF, a potent inducer of angiogenesis. Taken together, by integrating quantitative proteomic approach, we identified protein targets in which PA mediates its anti-angiogenic effects. The present study thus provides mechanistic evidence to the previously reported multifaceted anti-angiogenic effects of PA. Our study further identified mTOR signaling as an important target of PA, and therefore highlights the potential of PA for therapeutic intervention against angiogenesis-related pathogenesis, particularly, metastatic malignancy.

Proteomic analyses of a bi-lobed structure in Trypanosoma brucei

The position and function of the Golgi apparatus are tightly coupled with the microtubule-organizing centers (MTOCs) that play important roles in cell growth and polarity. In the unicellular parasite Trypanosoma brucei, the single Golgi apparatus is adjacent to a novel, bilobed structure located at the proximal base of the flagellum, near to the basal body that nucleates the flagellar axoneme. The duplication and segregation of the bilobed structure are tightly coupled to the duplication and segregation of Golgi, ER exit site, basal bodies, and flagellum, suggesting a role of this unique structure in the precise positioning, biogenesis, and inheritance of these single-copied structures during the cell cycle of procyclic T. brucei. Here, we describe in details two different isolation and proteomic methods to characterize the protein components present on or associated with the bilobed structure, which allow further understanding of its function and association with other membranous and cytoskeletal organelles in the parasite.

The proteome under translational control

A single eukaryotic gene can give rise to a variety of protein forms (proteoforms) as a result of genetic variation and multilevel regulation of gene expression. In addition to alternative splicing, an increasing line of evidence shows that alternative translation contributes to the overall complexity of proteomes. Identifying the repertoire of proteins and micropeptides expressed by alternative selection of (near-)cognate translation initiation sites and different reading frames however remains challenging with contemporary proteomics. MS-enabled identification of proteoforms is expected to benefit from transcriptome and translatome data by the creation of customized and sample-specific protein sequence databases. Here, we focus on contemporary integrative omics approaches that complement proteomics with DNA- and/or RNA-oriented technologies to elucidate the mechanisms of translational control. Together, these technologies enable to map the translation (initiation) landscape and more comprehensively define the inventory of proteoforms raised upon alternative translation, thus assisting in the (re-)annotation of genomes.

Stable isotope dilution mass spectrometry for membrane transporter quantitation

This review provides an introduction to stable isotope dilution mass spectrometry (MS) and its emerging applications in the analysis of membrane transporter proteins. Various approaches and application examples, for the generation and use of quantitation reference standards—either stable isotope-labeled peptides or proteins—are discussed as they apply to the MS quantitation of membrane proteins. Technological considerations for the sample preparation of membrane transporter proteins are also presented.

Enhancing gold recovery from electronic waste via lixiviant metabolic engineering in Chromobacterium violaceum

Conventional leaching (extraction) methods for gold recovery from electronic waste involve the use of strong acids and pose considerable threat to the environment. The alternative use of bioleaching microbes for gold recovery is non-pollutive and relies on the secretion of a lixiviant or (bio)chemical such as cyanide for extraction of gold from electronic waste. However, widespread industrial use of bioleaching microbes has been constrained by the limited cyanogenic capabilities of lixiviant-producing microorganisms such as Chromobacterium violaceum. Here we show the construction of a metabolically-engineered strain of Chromobacterium violaceum that produces more (70%) cyanide lixiviant and recovers more than twice as much gold from electronic waste compared to wild-type bacteria. Comparative proteome analyses suggested the possibility of further enhancement in cyanogenesis through subsequent metabolic engineering. Our results demonstrated the utility of lixiviant metabolic engineering in the construction of enhanced bioleaching microbes for the bioleaching of precious metals from electronic waste.

Proteome-wide identification of predominant subcellular protein localizations in a bacterial model organism

Proteomics data provide unique insights into biological systems, including the predominant subcellular localization (SCL) of proteins, which can reveal important clues about their functions. Here we analyzed data of a complete prokaryotic proteome expressed under two conditions mimicking interaction of the emerging pathogen Bartonella henselae with its mammalian host. Normalized spectral count data from cytoplasmic, total membrane, inner and outer membrane fractions allowed us to identify the predominant SCL for 82% of the identified proteins. The spectral count proportion of total membrane versus cytoplasmic fractions indicated the propensity of cytoplasmic proteins to co-fractionate with the inner membrane, and enabled us to distinguish cytoplasmic, peripheral inner membrane and bona fide inner membrane proteins. Principal component analysis and k-nearest neighbor classification training on selected marker proteins or predominantly localized proteins, allowed us to determine an extensive catalog of at least 74 expressed outer membrane proteins, and to extend the SCL assignment to 94% of the identified proteins, including 18% where in silico methods gave no prediction. Suitable experimental proteomics data combined with straightforward computational approaches can thus identify the predominant SCL on a proteome-wide scale. Finally, we present a conceptual approach to identify proteins potentially changing their SCL in a condition-dependent fashion.

BIOLOGICAL SIGNIFICANCE

The work presented here describes the first prokaryotic proteome-wide subcellular localization (SCL) dataset for the emerging pathogen B. henselae (Bhen). The study indicates that suitable subcellular fractionation experiments combined with straight-forward computational analysis approaches assessing the proportion of spectral counts observed in different subcellular fractions are powerful for determining the predominant SCL of a large percentage of the experimentally observed proteins. This includes numerous cases where in silico prediction methods do not provide any prediction. Avoiding a treatment with harsh conditions, cytoplasmic proteins tend to co-fractionate with proteins of the inner membrane fraction, indicative of close functional interactions. The spectral count proportion (SCP) of total membrane versus cytoplasmic fractions allowed us to obtain a good indication about the relative proximity of individual protein complex members to the inner membrane. Using principal component analysis and k-nearest neighbor approaches, we were able to extend the percentage of proteins with a predominant experimental localization to over 90% of all expressed proteins and identified a set of at least 74 outer membrane (OM) proteins. In general, OM proteins represent a rich source of candidates for the development of urgently needed new therapeutics in combat of resurgence of infectious disease and multi-drug resistant bacteria. Finally, by comparing the data from two infection biology relevant conditions, we conceptually explore methods to identify and visualize potential candidates that may partially change their SCL in these different conditions. The data are made available to researchers as a SCL compendium for Bhen and as an assistance in further improving in silico SCL prediction algorithms.

Identification of trans-golgi network proteins in Arabidopsis thaliana root tissue

Knowledge of protein subcellular localization assists in the elucidation of protein function and understanding of different biological mechanisms that occur at discrete subcellular niches. Organelle-centric proteomics enables localization of thousands of proteins simultaneously. Although such techniques have successfully allowed organelle protein catalogues to be achieved, they rely on the purification or significant enrichment of the organelle of interest, which is not achievable for many organelles. Incomplete separation of organelles leads to false discoveries, with erroneous assignments. Proteomics methods that measure the distribution patterns of specific organelle markers along density gradients are able to assign proteins of unknown localization based on comigration with known organelle markers, without the need for organelle purification. These methods are greatly enhanced when coupled to sophisticated computational tools. Here we apply and compare multiple approaches to establish a high-confidence data set of Arabidopsis root tissue trans-Golgi network (TGN) proteins. The method employed involves immunoisolations of the TGN, coupled to probability-based organelle proteomics techniques. Specifically, the technique known as LOPIT (localization of organelle protein by isotope tagging), couples density centrifugation with quantitative mass-spectometry-based proteomics using isobaric labeling and targeted methods with semisupervised machine learning methods. We demonstrate that while the immunoisolation method gives rise to a significant data set, the approach is unable to distinguish cargo proteins and persistent contaminants from full-time residents of the TGN. The LOPIT approach, however, returns information about many subcellular niches simultaneously and the steady-state location of proteins. Importantly, therefore, it is able to dissect proteins present in more than one organelle and cargo proteins en route to other cellular destinations from proteins whose steady-state location favors the TGN. Using this approach, we present a robust list of Arabidopsis TGN proteins.

Determining protein subcellular localization in mammalian cell culture with biochemical fractionation and iTRAQ 8-plex quantification

Protein subcellular localization is a fundamental feature of posttranslational functional regulation. Traditional microscopy based approaches to study protein localization are typically of limited throughput, and dependent on the availability of antibodies with high specificity and sensitivity, or fluorescent fusion proteins. In this chapter we describe how Localization of Organelle Proteins by Isotope Tagging (LOPIT), a mass spectrometry based workflow coupling biochemical fractionation and iTRAQ™ 8-plex quantification, can be applied for the high-throughput characterization of protein localization in a mammalian cell culture line.

Proteomics methods for subcellular proteome analysis

The elucidation of the subcellular distribution of proteins under different conditions is a major challenge in cell biology. This challenge is further complicated by the multicompartmental and dynamic nature of protein localization. To address this issue, quantitative proteomics workflows have been developed to reliably identify the protein complement of whole organelles, as well as for protein assignment to subcellular location and relative protein quantification based on different cell culture conditions. Here, we review quantitative MS-based approaches that combine cellular fractionation with proteomic analysis. The application of these methods to the characterization of organellar composition and to the determination of the dynamic nature of protein complexes is improving our understanding of protein functions and dynamics.

The effect of organelle discovery upon sub-cellular protein localisation

Prediction of protein sub-cellular localisation by employing quantitative mass spectrometry experiments is an expanding field. Several methods have led to the assignment of proteins to specific subcellular localisations by partial separation of organelles across a fractionation scheme coupled with computational analysis. Methods developed to analyse organelle data have largely employed supervised machine learning algorithms to map unannotated abundance profiles to known protein-organelle associations. Such approaches are likely to make association errors if organelle-related groupings present in experimental output are not included in data used to create a protein-organelle classifier. Currently, there is no automated way to detect organelle-specific clusters within such datasets. In order to address the above issues we adapted a phenotype discovery algorithm, originally created to filter image-based output for RNAi screens, to identify putative subcellular groupings in organelle proteomics experiments. We were able to mine datasets to a deeper level and extract interesting phenotype clusters for more comprehensive evaluation in an unbiased fashion upon application of this approach. Organelle-related protein clusters were identified beyond those sufficiently annotated for use as training data. Furthermore, we propose avenues for the incorporation of observations made into general practice for the classification of protein-organelle membership from quantitative MS experiments.

BIOLOGICAL SIGNIFICANCE

Protein sub-cellular localisation plays an important role in molecular interactions, signalling and transport mechanisms. The prediction of protein localisation by quantitative mass-spectrometry (MS) proteomics is a growing field and an important endeavour in improving protein annotation. Several such approaches use gradient-based separation of cellular organelle content to measure relative protein abundance across distinct gradient fractions. The distribution profiles are commonly mapped in silico to known protein-organelle associations via supervised machine learning algorithms, to create classifiers that associate unannotated proteins to specific organelles. These strategies are prone to error, however, if organelle-related groupings present in experimental output are not represented, for example owing to the lack of existing annotation, when creating the protein-organelle mapping. Here, the application of a phenotype discovery approach to LOPIT gradient-based MS data identifies candidate organelle phenotypes for further evaluation in an unbiased fashion. Software implementation and usage guidelines are provided for application to wider protein-organelle association experiments. In the wider context, semi-supervised organelle discovery is discussed as a paradigm with which to generate new protein annotations from MS-based organelle proteomics experiments.

Deep proteome coverage based on ribosome profiling aids mass spectrometry-based protein and peptide discovery and provides evidence of alternative translation products and near-cognate translation initiation events

An increasing number of studies involve integrative analysis of gene and protein expression data, taking advantage of new technologies such as next-generation transcriptome sequencing and highly sensitive mass spectrometry (MS) instrumentation. Recently, a strategy, termed ribosome profiling (or RIBO-seq), based on deep sequencing of ribosome-protected mRNA fragments, indirectly monitoring protein synthesis, has been described. We devised a proteogenomic approach constructing a custom protein sequence search space, built from both Swiss-Prot- and RIBO-seq-derived translation products, applicable for MS/MS spectrum identification. To record the impact of using the constructed deep proteome database, we performed two alternative MS-based proteomic strategies as follows: (i) a regular shotgun proteomic and (ii) an N-terminal combined fractional diagonal chromatography (COFRADIC) approach. Although the former technique gives an overall assessment on the protein and peptide level, the latter technique, specifically enabling the isolation of N-terminal peptides, is very appropriate in validating the RIBO-seq-derived (alternative) translation initiation site profile. We demonstrate that this proteogenomic approach increases the overall protein identification rate 2.5% (e.g. new protein products, new protein splice variants, single nucleotide polymorphism variant proteins, and N-terminally extended forms of known proteins) as compared with only searching UniProtKB-SwissProt. Furthermore, using this custom database, identification of N-terminal COFRADIC data resulted in detection of 16 alternative start sites giving rise to N-terminally extended protein variants besides the identification of four translated upstream ORFs. Notably, the characterization of these new translation products revealed the use of multiple near-cognate (non-AUG) start codons. As deep sequencing techniques are becoming more standard, less expensive, and widespread, we anticipate that mRNA sequencing and especially custom-tailored RIBO-seq will become indispensable in the MS-based protein or peptide identification process. The underlying mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium with the dataset identifier PXD000124.

Identification and functional validation of caldesmon as a potential gastric cancer metastasis-associated protein

In this study, we aim to identify biomarkers for gastric cancer metastasis using a quantitative proteomics approach. The proteins extracted from a panel of 4 gastric cancer cell lines, two derived from primary cancer (AGS, FU97) and two from lymph node metastasis (AZ521, MKN7), were labeled with iTRAQ (8-plex) reagents and analyzed by 2D-LC-MALDI-TOF/TOF MS. In total, 641 proteins were identified with at least a 95% confidence. Using cutoff values of >1.5 and <0.67, 19 proteins were found to be up-regulated and 34 were down-regulated in the metastatic versus primary gastric cancer cell lines respectively. Several of these dysregulated proteins, including caldesmon, were verified using Western blotting. It was found that caldesmon expression was decreased in the two metastasis-derived cell lines, and this was confirmed by further analysis of 7 gastric cancer cell lines. Furthermore, immunohistochemical staining of 9 pairs of primary gastric cancer and the matched lymph node metastasis tissue also corroborated this observation. Finally, knockdown of caldesmon using siRNA in AGS and FU97 gastric cancer cells resulted in an increase in cell migration and invasion, while the overexpression of caldesmon in AZ521 cells led to a decrease in cell migration and invasion. This study has thus established the potential role of caldesmon in gastric cancer metastasis, and further functional studies are underway to delineate the underlying mechanism of action of this protein.

Putative glycosyltransferases and other plant Golgi apparatus proteins are revealed by LOPIT proteomics

The Golgi apparatus is the central organelle in the secretory pathway and plays key roles in glycosylation, protein sorting, and secretion in plants. Enzymes involved in the biosynthesis of complex polysaccharides, glycoproteins, and glycolipids are located in this organelle, but the majority of them remain uncharacterized. Here, we studied the Arabidopsis (Arabidopsis thaliana) membrane proteome with a focus on the Golgi apparatus using localization of organelle proteins by isotope tagging. By applying multivariate data analysis to a combined data set of two new and two previously published localization of organelle proteins by isotope tagging experiments, we identified the subcellular localization of 1,110 proteins with high confidence. These include 197 Golgi apparatus proteins, 79 of which have not been localized previously by a high-confidence method, as well as the localization of 304 endoplasmic reticulum and 208 plasma membrane proteins. Comparison of the hydrophobic domains of the localized proteins showed that the single-span transmembrane domains have unique properties in each organelle. Many of the novel Golgi-localized proteins belong to uncharacterized protein families. Structure-based homology analysis identified 12 putative Golgi glycosyltransferase (GT) families that have no functionally characterized members and, therefore, are not yet assigned to a Carbohydrate-Active Enzymes database GT family. The substantial numbers of these putative GTs lead us to estimate that the true number of plant Golgi GTs might be one-third above those currently annotated. Other newly identified proteins are likely to be involved in the transport and interconversion of nucleotide sugar substrates as well as polysaccharide and protein modification.

Analysis, occurrence, and function of 9-cis-retinoic acid

Metabolic conversion of vitamin A (retinol) into retinoic acid (RA) controls numerous physiological processes. 9-cis-retinoic acid (9cRA), an active metabolite of vitamin A, is a high affinity ligand for retinoid X receptor (RXR) and also activates retinoic acid receptor (RAR). Despite the identification of candidate enzymes that produce 9cRA and the importance of RXRs as established by knockout experiments, in vivo detection of 9cRA in tissue was elusive until recently when 9cRA was identified as an endogenous pancreas retinoid by validated liquid chromatography-tandem mass spectrometry (LC-MS/MS) methodology. This review will discuss the current status of the analysis, occurrence, and function of 9cRA. Understanding both the nuclear receptor-mediated and non-genomic mechanisms of 9cRA will aid in the elucidation of disease physiology and possibly lead to the development of new retinoid-based therapeutics. This article is part of a Special Issue entitled Retinoid and Lipid Metabolism.

Differential protein expression throughout the life cycle of Trypanosoma congolense, a major parasite of cattle in Africa

Trypanosoma congolense is an important pathogen of livestock in Africa. To study protein expression throughout the T. congolense life cycle, we used culture-derived parasites of each of the three main insect stages and bloodstream stage parasites isolated from infected mice, to perform differential protein expression analysis. Three complete biological replicates of all four life cycle stages were produced from T. congolense IL3000, a cloned parasite that is amenable to culture of major life cycle stages in vitro. Cellular proteins from each life cycle stage were trypsin digested and the resulting peptides were labeled with isobaric tags for relative and absolute quantification (iTRAQ). The peptides were then analyzed by tandem mass spectrometry (MS/MS). This method was used to identify and relatively quantify proteins from the different life cycle stages in the same experiment. A search of the Wellcome Trust's Sanger Institute's semi-annotated T. congolense database was performed using the MS/MS fragmentation data to identify the corresponding source proteins. A total of 2088 unique protein sequences were identified, representing 23% of the ∼9000 proteins predicted for the T. congolense proteome. The 1291 most confidently identified proteins were prioritized for further study. Of these, 784 yielded annotated hits while 501 were described as "hypothetical proteins". Six proteins showed no significant sequence similarity to any known proteins (from any species) and thus represent new, previously uncharacterized T. congolense proteins. Of particular interest among the remainder are several membrane molecules that showed drastic differential expression, including, not surprisingly, the well-studied variant surface glycoproteins (VSGs), invariant surface glycoproteins (ISGs) 65 and 75, congolense epimastigote specific protein (CESP), the surface protease GP63, an amino acid transporter, a pteridine transporter and a haptoglobin-hemoglobin receptor. Several of these surface disposed proteins are of functional interest as they are necessary for survival of the parasites.

Proteomics of total membranes and subcellular membranes

Membrane proteins are key molecules in the cell and are important targets for drug development. Much effort has, therefore, been directed towards research of this group of proteins, but their hydrophobic nature can make working with them challenging. Here we discuss methodologies used in the study of the membrane proteome, specifically discussing approaches that circumvent technical issues specific to the membrane. In addition, we review several techniques used for visualization, qualification, quantitation and localization of membrane proteins. The combination of the techniques we describe holds great promise to allow full characterization of the membrane proteome and to map the dynamic changes within it essential for cellular function.

Novel asymmetrically localizing components of human centrosomes identified by complementary proteomics methods

Centrosomes in animal cells are dynamic organelles with a proteinaceous matrix of pericentriolar material assembled around a pair of centrioles. They organize the microtubule cytoskeleton and the mitotic spindle apparatus. Mature centrioles are essential for biogenesis of primary cilia that mediate key signalling events. Despite recent advances, the molecular basis for the plethora of processes coordinated by centrosomes is not fully understood. We have combined protein identification and localization, using PCP-SILAC mass spectrometry, BAC transgeneOmics, and antibodies to define the constituents of human centrosomes. From a background of non-specific proteins, we distinguished 126 known and 40 candidate centrosomal proteins, of which 22 were confirmed as novel components. An antibody screen covering 4000 genes revealed an additional 113 candidates. We illustrate the power of our methods by identifying a novel set of five proteins preferentially associated with mother or daughter centrioles, comprising genes implicated in cell polarity. Pulsed labelling demonstrates a remarkable variation in the stability of centrosomal protein complexes. These spatiotemporal proteomics data provide leads to the further functional characterization of centrosomal proteins.

Proteomics and the dynamic plasma membrane: Quo Vadis?

Quo Vadis: where are you going? Advances in MS-based proteomics have enabled research to move from obtaining the basic protein inventory of cells and organelles to the ability of monitoring their dynamics, including changes in abundance, location and various PTMs. In this respect, the cellular plasma membrane is of particular interest, by not only serving as a barrier between the "cell interior" and the external environment, but moreover by organizing and clustering essential components to enable dynamic responses to internal and external stimuli. Defining and characterizing the dynamic plasma membrane proteome is crucial for understanding fundamental biological processes, disease mechanisms and for finding drug targets. Protein identification, characterization of dynamic PTMs and protein-ligand interactions, and determination of transient changes in protein expression and composition are among the challenges in functional proteomic studies of the plasma membrane. We review the recent progress in MS-based plasma membrane proteomics by presenting key examples from eukaryotic systems, including mammals, yeast and plants. We highlight the importance of enrichment and quantification technologies required for detailed functional and comparative analysis of the dynamic plasma membrane proteome.