Quantifying protein interaction dynamics by SWATH mass spectrometry: application to the 14-3-3 system

Do hypoxia/normoxia culturing conditions change the neuroregulatory profile of Wharton Jelly mesenchymal stem cell secretome?

Introduction

The use of human umbilical cord Wharton Jelly-derived mesenchymal stem cells (hWJ-MSCs) has been considered a new potential source for future safe applications in regenerative medicine. Indeed, the application of hWJ-MSCs into different animal models of disease, including those from the central nervous system, has shown remarkable therapeutic benefits mostly associated with their secretome. Conventionally, hWJ-MSCs are cultured and characterized under normoxic conditions (21 % oxygen tension), although the oxygen levels within tissues are typically much lower (hypoxic) than these standard culture conditions. Therefore, oxygen tension represents an important environmental factor that may affect the performance of mesenchymal stem cells in vivo. However, the impact of hypoxic conditions on distinct mesenchymal stem cell characteristics, such as the secretome, still remains unclear.

Methods

In the present study, we have examined the effects of normoxic (21 % O₂) and hypoxic (5 % O₂) conditions on the hWJ-MSC secretome. Subsequently, we address the impact of the distinct secretome in the neuronal cell survival and differentiation of human neural progenitor cells.

Results

The present data indicate that the hWJ-MSC secretome collected from normoxic and hypoxic conditions displayed similar effects in supporting neuronal differentiation of human neural progenitor cells in vitro. However, proteomic analysis revealed that the use of hypoxic preconditioning led to the upregulation of several proteins within the hWJ-MSC secretome.

Conclusions

Our results suggest that the optimization of parameters such as hypoxia may lead to the development of strategies that enhance the therapeutic effects of the secretome for future regenerative medicine studies and applications.

Identification of a Set of Conserved Eukaryotic Internal Retention Time Standards for Data-independent Acquisition Mass Spectrometry

Accurate knowledge of retention time (RT) in liquid chromatography-based mass spectrometry data facilitates peptide identification, quantification, and multiplexing in targeted and discovery-based workflows. Retention time prediction is particularly important for peptide analysis in emerging data-independent acquisition (DIA) experiments such as SWATH-MS. The indexed RT approach, iRT, uses synthetic spiked-in peptide standards (SiRT) to set RT to a unit-less scale, allowing for normalization of peptide RT between different samples and chromatographic set-ups. The obligatory use of SiRTs can be costly and complicates comparisons and data integration if standards are not included in every sample. Reliance on SiRTs also prevents the inclusion of archived mass spectrometry data for generation of the peptide assay libraries central to targeted DIA-MS data analysis. We have identified a set of peptide sequences that are conserved across most eukaryotic species, termed Common internal Retention Time standards (CiRT). In a series of tests to support the appropriateness of the CiRT-based method, we show: (1) the CiRT peptides normalized RT in human, yeast, and mouse cell lysate derived peptide assay libraries and enabled merging of archived libraries for expanded DIA-MS quantitative applications; (2) CiRTs predicted RT in SWATH-MS data within a 2-min margin of error for the majority of peptides; and (3) normalization of RT using the CiRT peptides enabled the accurate SWATH-MS-based quantification of 340 synthetic isotopically labeled peptides that were spiked into either human or yeast cell lysate. To automate and facilitate the use of these CiRT peptide lists or other custom user-defined internal RT reference peptides in DIA workflows, an algorithm was designed to automatically select a high-quality subset of datapoints for robust linear alignment of RT for use. Implementations of this algorithm are available for the OpenSWATH and Skyline platforms. Thus, CiRT peptides can be used alone or as a complement to SiRTs for RT normalization across peptide spectral libraries and in quantitative DIA-MS studies.

Transformative Impact of Proteomics on Cardiovascular Health and Disease: A Scientific Statement From the American Heart Association

The year 2014 marked the 20th anniversary of the coining of the term proteomics. The purpose of this scientific statement is to summarize advances over this period that have catalyzed our capacity to address the experimental, translational, and clinical implications of proteomics as applied to cardiovascular health and disease and to evaluate the current status of the field. Key successes that have energized the field are delineated; opportunities for proteomics to drive basic science research, facilitate clinical translation, and establish diagnostic and therapeutic healthcare algorithms are discussed; and challenges that remain to be solved before proteomic technologies can be readily translated from scientific discoveries to meaningful advances in cardiovascular care are addressed. Proteomics is the result of disruptive technologies, namely, mass spectrometry and database searching, which drove protein analysis from 1 protein at a time to protein mixture analyses that enable large-scale analysis of proteins and facilitate paradigm shifts in biological concepts that address important clinical questions. Over the past 20 years, the field of proteomics has matured, yet it is still developing rapidly. The scope of this statement will extend beyond the reaches of a typical review article and offer guidance on the use of next-generation proteomics for future scientific discovery in the basic research laboratory and clinical settings.

Quantitative affinity purification mass spectrometry: a versatile technology to study protein-protein interactions

While the genomic revolution has dramatically accelerated the discovery of disease-associated genes, the functional characterization of the corresponding proteins lags behind. Most proteins fulfill their tasks in complexes with other proteins, and analysis of protein–protein interactions (PPIs) can therefore provide insights into protein function. Several methods can be used to generate large-scale protein interaction networks. However, most of these approaches are not quantitative and therefore cannot reveal how perturbations affect the network. Here, we illustrate how a clever combination of quantitative mass spectrometry with different biochemical methods provides a rich toolkit to study different aspects of PPIs including topology, subunit stoichiometry, and dynamic behavior.

Middle-Down and Chemical Proteomic Approaches to Reveal Histone H4 Modification Dynamics in Cell Cycle: Label-Free Semi-Quantification of Histone Tail Peptide Modifications Including Phosphorylation and Highly Sensitive Capture of Histone PTM Binding Proteins Using Photo-Reactive Crosslinkers

Mass spectrometric proteomics is an effective approach for identifying and quantifying histone post-translational modifications (PTMs) and their binding proteins, especially in the cases of methylation and acetylation. However, another vital PTM, phosphorylation, tends to be poorly quantified because it is easily lost and inefficiently ionized. In addition, PTM binding proteins for phosphorylation are sometimes resistant to identification because of their variable binding affinities. Here, we present our efforts to improve the sensitivity of detection of histone H4 tail peptide phosphorylated at serine 1 (H4S1ph) and our successful identification of an H4S1ph binder candidate by means of a chemical proteomics approach. Our nanoLC-MS/MS system permitted semi-quantitative label-free analysis of histone H4 PTM dynamics of cell cycle-synchronized HeLa S3 cells, including phosphorylation, methylation, and acetylation. We show that H4S1ph abundance on nascent histone H4 unmethylated at lysine 20 (H4K20me0) peaks from late S-phase to M-phase. We also attempted to characterize effects of phosphorylation at H4S1 on protein-protein interactions. Specially synthesized photoaffinity bait peptides specifically captured 14-3-3 proteins as novel H4S1ph binding partners, whose interaction was otherwise undetectable by conventional peptide pull-down experiments. This is the first report that analyzes dynamics of PTM pattern on the whole histone H4 tail during cell cycle and enables the identification of PTM binders with low affinities using high-resolution mass spectrometry and photo-affinity bait peptides.

An open-source computational and data resource to analyze digital maps of immunopeptidomes

We present a novel mass spectrometry-based high-throughput workflow and an open-source computational and data resource to reproducibly identify and quantify HLA-associated peptides. Collectively, the resources support the generation of HLA allele-specific peptide assay libraries consisting of consensus fragment ion spectra, and the analysis of quantitative digital maps of HLA peptidomes generated from a range of biological sources by SWATH mass spectrometry (MS). This study represents the first community-based effort to develop a robust platform for the reproducible and quantitative measurement of the entire repertoire of peptides presented by HLA molecules, an essential step towards the design of efficient immunotherapies.

DOI:http://dx.doi.org/10.7554/eLife.07661.001

The cells of the immune system protect us by recognizing telltale molecules produced by damaged and diseased cells, or by infection-causing microorganisms (which are also called pathogens). To help with this process, the cells in our bodies display small fragments of proteins (called peptides) on their surface that are then checked by the immune cells. Collectively, these peptides are referred to as the ‘immunopeptidome’, and deciphering the complexity of the human immunopeptidome is important for both basic research and medical science. Such an achievement would help to guide the development of next-generation vaccines and therapies against autoimmune disorders, infectious diseases and cancers.

In the past, immune peptides were mostly identified using a technique that is commonly called ‘shotgun’ mass spectrometry. However, this approach doesn't always provide reproducible results. In 2012, researchers reported the development of a new approach—which they called ‘SWATH’ mass spectrometry—that could yield more reproducible data.

Now, Caron et al.—including many of the researchers involved in the 2012 study—have developed a large collection of standardized tests that use SWATH mass spectrometry to analyze the human immunopeptidome. The workflow and the computational and data resources developed as part of this international effort are the first steps toward highly reproducible and measurable analyses of the immunopeptidome across many samples. Moreover, the large repository of assays generated by the project has been made public and will serve a large community of researchers, which should enable better collaborations.

In the future, SWATH mass spectrometry could be used as a robust technology for the reproducible detection and measurement of pathogen-specific or cancer-specific immune peptides. This could greatly help in the design of personalized immune-based therapies.

DOI:http://dx.doi.org/10.7554/eLife.07661.002

Phosphorylation-dependent Regulation of Connecdenn/DENND1 Guanine Nucleotide Exchange Factors

Connecdenn 1/2 are DENN (differentially expressed in normal and neoplastic cells) domain-bearing proteins that function as GEFs (guanine nucleotide exchange factors) for the small GTPase Rab35. Disruption of connecdenn/Rab35 function leads to defects in the recycling of multiple cargo proteins from endosomes with altered cell function, yet the regulation of connecdenn GEF activity is unexplored. We now demonstrate that connecdenn 1/2 are autoinhibited such that the purified, full-length proteins have significantly less Rab35 binding and GEF activity than the isolated DENN domain. Both proteins are phosphorylated with prominent phosphorylation sites between residues 500 and 600 of connecdenn 1. A large scale proteomics screen revealed that connecdenn 1 is phosphorylated at residues Ser-536 and Ser-538 in an Akt-dependent manner in response to insulin stimulation of adipocytes. Interestingly, we find that an Akt inhibitor reduces connecdenn 1 interaction with Rab35 after insulin treatment of adipocytes. Remarkably, a peptide flanking Ser-536/Ser-538 binds the DENN domain of connecdenn 1, whereas a phosphomimetic peptide does not. Moreover, connecdenn 1 interacts with 14-3-3 proteins, and this interaction is also disrupted by Akt inhibition and by mutation of Ser-536/Ser-538. We propose that Akt phosphorylation of connecdenn 1 downstream of insulin activation regulates connecdenn 1 function through an intramolecular interaction.

Modulation of nociceptive ion channels and receptors via protein-protein interactions: implications for pain relief

In the last 2 decades biomedical research has provided great insights into the molecular signatures underlying painful conditions. However, chronic pain still imposes substantial challenges to researchers, clinicians and patients alike. Under pathological conditions, pain therapeutics often lack efficacy and exhibit only minimal safety profiles, which can be largely attributed to the targeting of molecules with key physiological functions throughout the body. In light of these difficulties, the identification of molecules and associated protein complexes specifically involved in chronic pain states is of paramount importance for designing selective interventions. Ion channels and receptors represent primary targets, as they critically shape nociceptive signaling from the periphery to the brain. Moreover, their function requires tight control, which is usually implemented by protein-protein interactions (PPIs). Indeed, manipulation of such PPIs entails the modulation of ion channel activity with widespread implications for influencing nociceptive signaling in a more specific way. In this review, we highlight recent advances in modulating ion channels and receptors via their PPI networks in the pursuit of relieving chronic pain. Moreover, we critically discuss the potential of targeting PPIs for developing novel pain therapies exhibiting higher efficacy and improved safety profiles.

SWATH analysis of the synaptic proteome in Alzheimer's disease

Brain tissue from Alzheimer's disease patients exhibits synaptic degeneration in selected regions. Synaptic dysfunction occurs early in the disease and is a primary pathological target for treatment. The molecular mechanisms underlying this degeneration remain unknown. Quantifying the synaptic proteome in autopsy brain and comparing tissue from Alzheimer's disease cases and subjects with normal aging are critical to understanding the molecular mechanisms associated with Alzheimer pathology. We isolated synaptosomes from hippocampus and motor cortex so as to reduce sample complexity relative to whole-tissue homogenates. Synaptosomal extracts were subjected to strong cation exchange (SCX) fractionation to further partition sample complexity; each fraction received SWATH-based information-dependent acquisition to generate a comprehensive peptide-ion library. The expression of synaptic proteins from AD hippocampus and motor cortex was then compared between groups. A total of 2077 unique proteins were identified at a critical local false discovery rate <5%. Thirty of these, including 17 novel proteins, exhibited significant expression differences between cases and controls; these proteins are involved in cellular functions including structural maintenance, signal transduction, autophagy, oxidative stress, and proteasome activity, or they have synaptic-vesicle related or energy-related functions. Differentially expressed proteins were subjected to pathway analysis to identify protein-protein interactions. This revealed that the most perturbed molecular and cellular functions were cellular assembly and organization. Core analysis revealed RhoA signaling to be the top canonical pathway. Network analysis showed that differentially expressed proteins were related to cellular assembly and organization, and cellular function and maintenance. This is the first study to combine SCX fractionation with SWATH analysis. SWATH is a promising new technique that can greatly enhance protein identification in any proteome, and has many other benefits; however, there are limitations yet to be resolved.

Biomarker development for presymptomatic molecular diagnosis of preeclampsia: feasible, useful or even unnecessary?

The past decade saw the advent of a number of promising biomarkers to detect pregnancies at risk for preeclampsia (PE), the foremost being those associated with an imbalance of angiogenic factors. In late pregnancy, these are useful for the detection of imminent cases of PE, while earlier they were more predictive for early- than late-onset PE. This suggests that there may be fundamental differences between the underlying pathology of these two PE forms. Therefore, it is possible that such a biological premise may limit the development of biomarkers that will permit the efficacious detection of both early- and late-onset PE via an analysis of first-trimester maternal blood samples. Consequently, a significant increase in our understanding of the underlying pathology of PE, using a variety of approaches ranging from systems biology to animal models, will be necessary in order to overcome this obstacle.

A Double-Barrel Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS) System to Quantify 96 Interactomes per Day

The field of proteomics has evolved hand-in-hand with technological advances in LC-MS/MS systems, now enabling the analysis of very deep proteomes in a reasonable time. However, most applications do not deal with full cell or tissue proteomes but rather with restricted subproteomes relevant for the research context at hand or resulting from extensive fractionation. At the same time, investigation of many conditions or perturbations puts a strain on measurement capacity. Here, we develop a high-throughput workflow capable of dealing with large numbers of low or medium complexity samples and specifically aim at the analysis of 96-well plates in a single day (15 min per sample). We combine parallel sample processing with a modified liquid chromatography platform driving two analytical columns in tandem, which are coupled to a quadrupole Orbitrap mass spectrometer (Q Exactive HF). The modified LC platform eliminates idle time between measurements, and the high sequencing speed of the Q Exactive HF reduces required measurement time. We apply the pipeline to the yeast chromatin remodeling landscape and demonstrate quantification of 96 pull-downs of chromatin complexes in about 1 day. This is achieved with only 500 μg input material, enabling yeast cultivation in a 96-well format. Our system retrieved known complex-members and the high throughput allowed probing with many bait proteins. Even alternative complex compositions were detectable in these very short gradients. Thus, sample throughput, sensitivity and LC/MS-MS duty cycle are improved severalfold compared with established workflows. The pipeline can be extended to different types of interaction studies and to other medium complexity proteomes.

From the static interactome to dynamic protein complexes: Three challenges

Protein interactions and complexes behave in a dynamic fashion, but this dynamism is not captured by interaction screening technologies, and not preserved in protein–protein interaction (PPI) networks. The analysis of static interaction data to derive dynamic protein complexes leads to several challenges, of which we identify three. First, many proteins participate in multiple complexes, leading to overlapping complexes embedded within highly-connected regions of the PPI network. This makes it difficult to accurately delimit the boundaries of such complexes. Second, many condition- and location-specific PPIs are not detected, leading to sparsely-connected complexes that cannot be picked out by clustering algorithms. Third, the majority of complexes are small complexes (made up of two or three proteins), which are extra sensitive to the effects of extraneous edges and missing co-complex edges. We show that many existing complex-discovery algorithms have trouble predicting such complexes, and show that our insight into the disparity between the static interactome and dynamic protein complexes can be used to improve the performance of complex discovery.

MS1-based label-free proteomics using a quadrupole orbitrap mass spectrometer

Presented is a data set for benchmarking MS1-based label-free quantitative proteomics using a quadrupole orbitrap mass spectrometer. Escherichia coli digest was spiked into a HeLa digest in four different concentrations, simulating protein expression differences in a background of an unchanged complex proteome. The data set provides a unique opportunity to evaluate the proteomic platform (instrumentation and software) in its ability to perform MS1-intensity-based label-free quantification. We show that the presented combination of informatics and instrumentation produces high precision and quantification accuracy. The data were also used to compare different quantitative protein inference methods such as iBAQ and Hi-N. The data can also be used as a resource for development and optimization of proteomics informatics tools, thus the raw data have been deposited to ProteomeXchange with identifier PXD001385.

Rapid mass spectrometric conversion of tissue biopsy samples into permanent quantitative digital proteome maps

Clinical specimens are each inherently unique, limited and non-renewable. As such, small samples such as tissue biopsies are often completely consumed after a limited number of analyses. Here we present a method that enables fast and reproducible conversion of a small amount of tissue (approximating the quantity obtained by a biopsy) into a single, permanent digital file representing the mass spectrometry-measurable proteome of the sample. The method combines pressure cycling technology (PCT) and SWATH mass spectrometry (MS), and the resulting proteome maps can be analyzed, re-analyzed, compared and mined in silico to detect and quantify specific proteins across multiple samples. We used this method to process and convert 18 biopsy samples from 9 renal cell carcinoma patients into SWATH-MS fragment ion maps. From these proteome maps we detected and quantified more than 2,000 proteins with a high degree of reproducibility across all samples. The identified proteins clearly separated tumorous kidney tissues from healthy tissue, and differentiated distinct histomorphological kidney cancer subtypes.

DIA-Umpire: comprehensive computational framework for data-independent acquisition proteomics

As a result of recent improvements in mass spectrometry (MS), there is increased interest in data-independent acquisition (DIA) strategies in which all peptides are systematically fragmented using wide mass-isolation windows ('multiplex fragmentation'). DIA-Umpire (http://diaumpire.sourceforge.net/), a comprehensive computational workflow and open-source software for DIA data, detects precursor and fragment chromatographic features and assembles them into pseudo-tandem MS spectra. These spectra can be identified with conventional database-searching and protein-inference tools, allowing sensitive, untargeted analysis of DIA data without the need for a spectral library. Quantification is done with both precursor- and fragment-ion intensities. Furthermore, DIA-Umpire enables targeted extraction of quantitative information based on peptides initially identified in only a subset of the samples, resulting in more consistent quantification across multiple samples. We demonstrated the performance of the method with control samples of varying complexity and publicly available glycoproteomics and affinity purification-MS data.

Discovery and targeted proteomics on cutaneous biopsies infected by borrelia to investigate lyme disease

Lyme disease is the most important vector-borne disease in the Northern hemisphere and represents a major public health challenge with insufficient means of reliable diagnosis. Skin is rarely investigated in proteomics but constitutes in the case of Lyme disease the key interface where the pathogens can enter, persist, and multiply. Therefore, we investigated proteomics on skin samples to detect Borrelia proteins directly in cutaneous biopsies in a robust and specific way. We first set up a discovery gel prefractionation-LC-MS/MS approach on a murine model infected by Borrelia burgdorferi sensu stricto that allowed the identification of 25 Borrelia proteins among more than 1300 mouse proteins. Then we developed a targeted gel prefractionation-LC-selected reaction monitoring (SRM) assay to detect 9/33 Borrelia proteins/peptides in mouse skin tissue samples using heavy labeled synthetic peptides. We successfully transferred this assay from the mouse model to human skin biopsies (naturally infected by Borrelia), and we were able to detect two Borrelia proteins: OspC and flagellin. Considering the extreme variability of OspC, we developed an extended SRM assay to target a large set of variants. This assay afforded the detection of nine peptides belonging to either OspC or flagellin in human skin biopsies. We further shortened the sample preparation and showed that Borrelia is detectable in mouse and human skin biopsies by directly using a liquid digestion followed by LC-SRM analysis without any prefractionation. This study thus shows that a targeted SRM approach is a promising tool for the early direct diagnosis of Lyme disease with high sensitivity (<10 fmol of OspC/mg of human skin biopsy).

Using data-independent, high-resolution mass spectrometry in protein biomarker research: perspectives and clinical applications

In medicine, there is an urgent need for protein biomarkers in a range of applications that includes diagnostics, disease stratification, and therapeutic decisions. One of the main technologies to address this need is MS, used for protein biomarker discovery and, increasingly, also for protein biomarker validation. Currently, data-dependent analysis (also referred to as shotgun proteomics) and targeted MS, exemplified by SRM, are the most frequently used mass spectrometric methods. Recently developed data-independent acquisition techniques combine the strength of shotgun and targeted proteomics, while avoiding some of the limitations of the respective methods. They provide high-throughput, accurate quantification, and reproducible measurements within a single experimental setup. Here, we describe and review data-independent acquisition strategies and their recent use in clinically oriented studies. In addition, we also provide a detailed guide for the implementation of SWATH-MS (where SWATH is sequential window acquisition of all theoretical mass spectra)-one of the data-independent strategies that have gained wide application of late.

Building high-quality assay libraries for targeted analysis of SWATH MS data

Targeted proteomics by selected/multiple reaction monitoring (S/MRM) or, on a larger scale, by SWATH (sequential window acquisition of all theoretical spectra) MS (mass spectrometry) typically relies on spectral reference libraries for peptide identification. Quality and coverage of these libraries are therefore of crucial importance for the performance of the methods. Here we present a detailed protocol that has been successfully used to build high-quality, extensive reference libraries supporting targeted proteomics by SWATH MS. We describe each step of the process, including data acquisition by discovery proteomics, assertion of peptide-spectrum matches (PSMs), generation of consensus spectra and compilation of MS coordinates that uniquely define each targeted peptide. Crucial steps such as false discovery rate (FDR) control, retention time normalization and handling of post-translationally modified peptides are detailed. Finally, we show how to use the library to extract SWATH data with the open-source software Skyline. The protocol takes 2-3 d to complete, depending on the extent of the library and the computational resources available.

Multiplexed quantification for data-independent acquisition

Data-independent acquisition (DIA) strategies provide a sensitive and reproducible alternative to data-dependent acquisition (DDA) methods for large-scale quantitative proteomic analyses. Unfortunately, DIA methods suffer from incompatibility with common multiplexed quantification methods, specifically stable isotope labeling approaches such as isobaric tags and stable isotope labeling of amino acids in cell culture (SILAC). Here we expand the use of neutron-encoded (NeuCode) SILAC to DIA applications (NeuCoDIA), producing a strategy that enables multiplexing within DIA scans without further convoluting the already complex MS(2) spectra. We demonstrate duplex NeuCoDIA analysis of both mixed-ratio (1:1 and 10:1) yeast and mouse embryo myogenesis proteomes. Analysis of the mixed-ratio yeast samples revealed the strong accuracy and precision of our NeuCoDIA method, both of which were comparable to our established MS(1)-based quantification approach. NeuCoDIA also uncovered the dynamic protein changes that occur during myogenic differentiation, demonstrating the feasibility of this methodology for biological applications. We consequently establish DIA quantification of NeuCode SILAC as a useful and practical alternative to DDA-based approaches.

Emerging proteomic technologies for elucidating context-dependent cellular signaling events: A big challenge of tiny proportions

Aberrant cell signaling events either drive or compensate for nearly all pathologies. A thorough description and quantification of maladaptive signaling flux in disease is a critical step in drug development, and complex proteomic approaches can provide valuable mechanistic insights. Traditional proteomics-based signaling analyses rely heavily on in vitro cellular monoculture. The characterization of these simplified systems generates a rich understanding of the basic components and complex interactions of many signaling networks, but they cannot capture the full complexity of the microenvironments in which pathologies are ultimately made manifest. Unfortunately, techniques that can directly interrogate signaling in situ often yield mass-limited starting materials that are incompatible with traditional proteomics workflows. This review provides an overview of established and emerging techniques that are applicable to context-dependent proteomics. Analytical approaches are illustrated through recent proteomics-based studies in which selective sample acquisition strategies preserve context-dependent information, and where the challenge of minimal starting material is met by optimized sensitivity and coverage. This review is organized into three major technological themes: (i) LC methods in line with MS; (ii) antibody-based approaches; (iii) MS imaging with a discussion of data integration and systems modeling. Finally, we conclude with future perspectives and implications of context-dependent proteomics.

Quantitative variability of 342 plasma proteins in a human twin population

The degree and the origins of quantitative variability of most human plasma proteins are largely unknown. Because the twin study design provides a natural opportunity to estimate the relative contribution of heritability and environment to different traits in human population, we applied here the highly accurate and reproducible SWATH mass spectrometry technique to quantify 1,904 peptides defining 342 unique plasma proteins in 232 plasma samples collected longitudinally from pairs of monozygotic and dizygotic twins at intervals of 2–7 years, and proportioned the observed total quantitative variability to its root causes, genes, and environmental and longitudinal factors. The data indicate that different proteins show vastly different patterns of abundance variability among humans and that genetic control and longitudinal variation affect protein levels and biological processes to different degrees. The data further strongly suggest that the plasma concentrations of clinical biomarkers need to be calibrated against genetic and temporal factors. Moreover, we identified 13 cis-SNPs significantly influencing the level of specific plasma proteins. These results therefore have immediate implications for the effective design of blood-based biomarker studies.

In-depth comparative proteomic analysis of yeast proteome using iTRAQ and SWATH based MS

SWATH is capable of quantifying proteins of lower abundance as compared to iTRAQ.

Mass spectrometry-based proteomic quest for diabetes biomarkers

Diabetes mellitus (DM) is a metabolic disorder characterized by chronic hyperglycemia, which affects hundreds of millions of individuals worldwide. Early diagnosis and complication prevention of DM are helpful for disease treatment. However, currently available DM diagnostic markers fail to achieve the goals. Identification of new diabetic biomarkers assisted by mass spectrometry (MS)-based proteomics may offer solution for the clinical challenges. Here, we review the current status of biomarker discovery in DM, and describe the pressure cycling technology (PCT)-Sequential Window Acquisition of all Theoretical fragment-ion (SWATH) workflow for sample-processing, biomarker discovery and validation, which may accelerate the current quest for DM biomarkers. This article is part of a Special Issue entitled: Medical Proteomics.

Systems cell biology

Systems cell biology melds high-throughput experimentation with quantitative analysis and modeling to understand many critical processes that contribute to cellular organization and dynamics. Recently, there have been several advances in technology and in the application of modeling approaches that enable the exploration of the dynamic properties of cells. Merging technology and computation offers an opportunity to objectively address unsolved cellular mechanisms, and has revealed emergent properties and helped to gain a more comprehensive and fundamental understanding of cell biology.

Applying 'Sequential Windowed Acquisition of All Theoretical Fragment Ion Mass Spectra' (SWATH) for systematic toxicological analysis with liquid chromatography-high-resolution tandem mass spectrometry

Liquid chromatography-tandem mass spectrometry (LC-MS/MS) has become an indispensable analytical technique in clinical and forensic toxicology for detection and identification of potentially toxic or harmful compounds. Particularly, non-target LC-MS/MS assays enable extensive and universal screening requested in systematic toxicological analysis. An integral part of the identification process is the generation of information-rich product ion spectra which can be searched against libraries of reference mass spectra. Usually, 'data-dependent acquisition' (DDA) strategies are applied for automated data acquisition. In this study, the 'data-independent acquisition' (DIA) method 'Sequential Windowed Acquisition of All Theoretical Fragment Ion Mass Spectra' (SWATH) was combined with LC-MS/MS on a quadrupole-quadrupole-time-of-flight (QqTOF) instrument for acquiring informative high-resolution tandem mass spectra. SWATH performs data-independent fragmentation of all precursor ions entering the mass spectrometer in 21m/z isolation windows. The whole m/z range of interest is covered by continuous stepping of the isolation window. This allows numerous repeat analyses of each window during the elution of a single chromatographic peak and results in a complete fragment ion map of the sample. Compounds and samples typically encountered in forensic casework were used to assess performance characteristics of LC-MS/MS with SWATH. Our experiments clearly revealed that SWATH is a sensitive and specific identification technique. SWATH is capable of identifying more compounds at lower concentration levels than DDA does. The dynamic range of SWATH was estimated to be three orders of magnitude. Furthermore, the >600,000 SWATH spectra matched led to only 408 incorrect calls (false positive rate = 0.06 %). Deconvolution of generated ion maps was found to be essential for unravelling the full identification power of LC-MS/MS with SWATH. With the available software, however, only semi-automated deconvolution was enabled, which rendered data interpretation a laborious and time-consuming process.

DIANA--algorithmic improvements for analysis of data-independent acquisition MS data

MOTIVATION

Data independent acquisition mass spectrometry has emerged as a reproducible and sensitive alternative in quantitative proteomics, where parsing the highly complex tandem mass spectra requires dedicated algorithms. Recently, targeted data extraction was proposed as a novel analysis strategy for this type of data, but it is important to further develop these concepts to provide quality-controlled, interference-adjusted and sensitive peptide quantification.

RESULTS

We here present the algorithm DIANA and the classifier PyProphet, which are based on new probabilistic sub-scores to classify the chromatographic peaks in targeted data-independent acquisition data analysis. The algorithm is capable of providing accurate quantitative values and increased recall at a controlled false discovery rate, in a complex gold standard dataset. Importantly, we further demonstrate increased confidence gained by the use of two complementary data-independent acquisition targeted analysis algorithms, as well as increased numbers of quantified peptide precursors in complex biological samples.

AVAILABILITY AND IMPLEMENTATION

DIANA is implemented in scala and python and available as open source (Apache 2.0 license) or pre-compiled binaries from http://quantitativeproteomics.org/diana. PyProphet can be installed from PyPi (https://pypi.python.org/pypi/pyprophet).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A new in vivo cross-linking mass spectrometry platform to define protein-protein interactions in living cells

Protein-protein interactions (PPIs) are fundamental to the structure and function of protein complexes. Resolving the physical contacts between proteins as they occur in cells is critical to uncovering the molecular details underlying various cellular activities. To advance the study of PPIs in living cells, we have developed a new in vivo cross-linking mass spectrometry platform that couples a novel membrane-permeable, enrichable, and MS-cleavable cross-linker with multistage tandem mass spectrometry. This strategy permits the effective capture, enrichment, and identification of in vivo cross-linked products from mammalian cells and thus enables the determination of protein interaction interfaces. The utility of the developed method has been demonstrated by profiling PPIs in mammalian cells at the proteome scale and the targeted protein complex level. Our work represents a general approach for studying in vivo PPIs and provides a solid foundation for future studies toward the complete mapping of PPI networks in living systems.

A repository of assays to quantify 10,000 human proteins by SWATH-MS

Mass spectrometry is the method of choice for deep and reliable exploration of the (human) proteome. Targeted mass spectrometry reliably detects and quantifies pre-determined sets of proteins in a complex biological matrix and is used in studies that rely on the quantitatively accurate and reproducible measurement of proteins across multiple samples. It requires the one-time, a priori generation of a specific measurement assay for each targeted protein. SWATH-MS is a mass spectrometric method that combines data-independent acquisition (DIA) and targeted data analysis and vastly extends the throughput of proteins that can be targeted in a sample compared to selected reaction monitoring (SRM). Here we present a compendium of highly specific assays covering more than 10,000 human proteins and enabling their targeted analysis in SWATH-MS datasets acquired from research or clinical specimens. This resource supports the confident detection and quantification of 50.9% of all human proteins annotated by UniProtKB/Swiss-Prot and is therefore expected to find wide application in basic and clinical research. Data are available via ProteomeXchange (PXD000953-954) and SWATHAtlas (SAL00016-35).

Kinase Substrate Sensor (KISS), a mammalian in situ protein interaction sensor

Probably every cellular process is governed by protein-protein interaction (PPIs), which are often highly dynamic in nature being modulated by in- or external stimuli. Here we present KISS, for KInase Substrate Sensor, a mammalian two-hybrid approach designed to map intracellular PPIs and some of the dynamic features they exhibit. Benchmarking experiments indicate that in terms of sensitivity and specificity KISS is on par with other binary protein interaction technologies while being complementary with regard to the subset of PPIs it is able to detect. We used KISS to evaluate interactions between different types of proteins, including transmembrane proteins, expressed at their native subcellular location. In situ analysis of endoplasmic reticulum stress-induced clustering of the endoplasmic reticulum stress sensor ERN1 and ligand-dependent β-arrestin recruitment to GPCRs illustrated the method's potential to study functional PPI modulation in complex cellular processes. Exploring its use as a tool for in cell evaluation of pharmacological interference with PPIs, we showed that reported effects of known GPCR antagonists and PPI inhibitors are properly recapitulated. In a three-hybrid setup, KISS was able to map interactions between small molecules and proteins. Taken together, we established KISS as a sensitive approach for in situ analysis of protein interactions and their modulation in a changing cellular context or in response to pharmacological challenges.

The development and application of a quantitative peptide microarray based approach to protein interaction domain specificity space

Protein interaction domain (PID) linear peptide motif interactions direct diverse cellular processes in a specific and coordinated fashion. PID specificity, or the interaction selectivity derived from affinity preferences between possible PID-peptide pairs is the basis of this ability. Here, we develop an integrated experimental and computational cellulose peptide conjugate microarray (CPCMA) based approach for the high throughput analysis of PID specificity that provides unprecedented quantitative resolution and reproducibility. As a test system, we quantify the specificity preferences of four Src Homology 2 domains and 124 physiological phosphopeptides to produce a novel quantitative interactome. The quantitative data set covers a broad affinity range, is highly precise, and agrees well with orthogonal biophysical validation, in vivo interactions, and peptide library trained algorithm predictions. In contrast to preceding approaches, the CPCMAs proved capable of confidently assigning interactions into affinity categories, resolving the subtle affinity contributions of residue correlations, and yielded predictive peptide motif affinity matrices. Unique CPCMA enabled modes of systems level analysis reveal a physiological interactome with expected node degree value decreasing as a function of affinity, resulting in minimal high affinity binding overlap between domains; uncover that Src Homology 2 domains bind ligands with a similar average affinity yet strikingly different levels of promiscuity and binding dynamic range; and parse with unprecedented quantitative resolution contextual factors directing specificity. The CPCMA platform promises broad application within the fields of PID specificity, synthetic biology, specificity focused drug design, and network biology.

Glycoproteomic analysis of prostate cancer tissues by SWATH mass spectrometry discovers N-acylethanolamine acid amidase and protein tyrosine kinase 7 as signatures for tumor aggressiveness

The identification of biomarkers indicating the level of aggressiveness of prostate cancer (PCa) will address the urgent clinical need to minimize the general overtreatment of patients with non-aggressive PCa, who account for the majority of PCa cases. Here, we isolated formerly N-linked glycopeptides from normal prostate (n = 10) and from non-aggressive (n = 24), aggressive (n = 16), and metastatic (n = 25) PCa tumor tissues and analyzed the samples using SWATH mass spectrometry, an emerging data-independent acquisition method that generates a single file containing fragment ion spectra of all ionized species of a sample. The resulting datasets were searched using a targeted data analysis strategy in which an a priori spectral reference library representing known N-glycosites of the human proteome was used to identify groups of signals in the SWATH mass spectrometry data. On average we identified 1430 N-glycosites from each sample. Out of those, 220 glycoproteins showed significant quantitative changes associated with diverse biological processes involved in PCa aggressiveness and metastasis and indicated functional relationships. Two glycoproteins, N-acylethanolamine acid amidase and protein tyrosine kinase 7, that were significantly associated with aggressive PCa in the initial sample cohort were further validated in an independent set of patient tissues using tissue microarray analysis. The results suggest that N-acylethanolamine acid amidase and protein tyrosine kinase 7 may be used as potential tissue biomarkers to avoid overtreatment of non-aggressive PCa.

Mass spectrometry of natural products: current, emerging and future technologies

Although mass spectrometry is a century old technology, we are entering into an exciting time for the analysis of molecular information directly from complex biological systems. In this Highlight, we feature emerging mass spectrometric methods and tools used by the natural product community and give a perspective of future directions where the mass spectrometry field is migrating towards over the next decade.

Identification of 2R-ohnologue gene families displaying the same mutation-load skew in multiple cancers

The complexity of signalling pathways was boosted at the origin of the vertebrates, when two rounds of whole genome duplication (2R-WGD) occurred. Those genes and proteins that have survived from the 2R-WGD-termed 2R-ohnologues-belong to families of two to four members, and are enriched in signalling components relevant to cancer. Here, we find that while only approximately 30% of human transcript-coding genes are 2R-ohnologues, they carry 42-60% of the gene mutations in 30 different cancer types. Across a subset of cancer datasets, including melanoma, breast, lung adenocarcinoma, liver and medulloblastoma, we identified 673 2R-ohnologue families in which one gene carries mutations at multiple positions, while sister genes in the same family are relatively mutation free. Strikingly, in 315 of the 322 2R-ohnologue families displaying such a skew in multiple cancers, the same gene carries the heaviest mutation load in each cancer, and usually the second-ranked gene is also the same in each cancer. Our findings inspire the hypothesis that in certain cancers, heterogeneous combinations of genetic changes impair parts of the 2R-WGD signalling networks and force information flow through a limited set of oncogenic pathways in which specific non-mutated 2R-ohnologues serve as effectors. The non-mutated 2R-ohnologues are therefore potential therapeutic targets. These include proteins linked to growth factor signalling, neurotransmission and ion channels.

On-beads digestion in conjunction with data-dependent mass spectrometry: a shortcut to quantitative and dynamic interaction proteomics

With the advent of the "-omics" era, biological research has shifted from functionally analyzing single proteins to understanding how entire protein networks connect and adapt to environmental cues. Frequently, pathological processes are initiated by a malfunctioning protein network rather than a single protein. It is therefore crucial to investigate the regulation of proteins in the context of a pathway first and signaling network second. In this study, we demonstrate that a quantitative interaction proteomic approach, combining immunoprecipitation, in-solution digestion and label-free quantification mass spectrometry, provides data of high accuracy and depth. This protocol is applicable, both to tagged, exogenous and untagged, endogenous proteins. Furthermore, it is fast, reliable and, due to a label-free quantitation approach, allows the comparison of multiple conditions. We further show that we are able to generate data in a medium throughput fashion and that we can quantify dynamic interaction changes in signaling pathways in response to mitogenic stimuli, making our approach a suitable method to generate data for system biology approaches.

Twenty years of protein interaction studies for biological function deciphering

Intensive methodological developments and technology innovation have been devoted to protein-protein interaction studies over 20years. Genetic indirect assays and sophisticated large scale biochemical analyses have jointly contributed to the elucidation of protein-protein interactions, still with a lot of drawbacks despite heavy investment in human resources and technologies. With the most recent developments in mass spectrometry and computational tools for studying protein content of complex samples, the initial goal of deciphering molecular bases of biological functions is now within reach. Here, we described the various steps of this process and gave examples of key milestones in this scientific story line. This article is part of a Special Issue entitled: 20years of Proteomics in memory of Viatliano Pallini. Guest Editors: Luca Bini, Juan J. Calvete, Natacha Turck, Denis Hochstrasser and Jean-Charles Sanchez.

Proteomic analysis of phosphorylation in cancer

Constitutive activity of kinases is known to be crucial for a tumor to maintain its malignant phenotype, a phenomenon which is often referred to as oncogene addiction. The in-depth analysis of aberrant signaling pathways by the analysis of protein phosphorylation has become feasible through recent advances in proteomics technology. In this article we will review developments in the field of phosphoproteomics and its application in cancer research. The most widely used technologies for the generic enrichment of phosphopeptides are discussed as well as targeted approaches for the analysis of a specific subset of phosphopeptides. Validation experiments of phosphorylation sites using targeted mass spectrometry are also explained. Finally, we will highlight applications of phosphoproteomic technology in cancer research using cell lines and tissue.

SWATH™- and iTRAQ-based quantitative proteomic analyses reveal an overexpression and biological relevance of CD109 in advanced NSCLC

To identify cancer-related proteins, we used isobaric tags in a relative and absolute quantitation (iTRAQ) proteomic approach and SWATH™ quantification approach to analyze the secretome of an isogenic pair of highly metastatic and low metastatic non-small-cell lung cancer (NSCLC) cell lines. In addition, we compared two groups of pooled serum samples (12 early-stage and 12 late-stage patients) to mine data for candidates screened by iTRAQ-labeled proteomic analysis. A total of 110 proteins and 71 proteins were observed to be significantly differentially expressed in the cell line secretome and NSCLC sera, respectively. Among these proteins, CD109 was found to be highly expressed in both the highly metastatic cell line secretome and the group of late-stage patients. A sandwich ELISA assay also demonstrated an elevation of serum CD109 levels in individual NSCLC patients (n=30) compared with healthy subjects (n=19). Furthermore, CD109 displayed higher expression in lung cancer tissues compared with their matched noncancerous lung tissues (n=72). In addition, the knockdown of CD109 influenced several NSCLC cell bio-functions, for instance, depressing cell growth, affecting cell cycle phases. These phenomena suggest that CD109 plays a critical role in NSCLC progression.

BIOLOGICAL SIGNIFICANCE

We simultaneously applied two quantitative proteomic approaches-iTRAQ-labeling and SWATH™-to analyze the secretome of metastatic cell lines, in order to explore the cancer-associated proteins in conditioned media. In this study, our results indicate that CD109 plays a critical role in non-small-cell lung cancer (NSCLC) progression, and is overexpressed in advanced NSCLC.

Contemporary network proteomics and its requirements

The integration of networks with genomics (network genomics) is a familiar field. Conventional network analysis takes advantage of the larger coverage and relative stability of gene expression measurements. Network proteomics on the other hand has to develop further on two critical factors: (1) expanded data coverage and consistency, and (2) suitable reference network libraries, and data mining from them. Concerning (1) we discuss several contemporary themes that can improve data quality, which in turn will boost the outcome of downstream network analysis. For (2), we focus on network analysis developments, specifically, the need for context-specific networks and essential considerations for localized network analysis.

Mapping differential interactomes by affinity purification coupled with data-independent mass spectrometry acquisition

Characterizing changes in protein-protein interactions associated with sequence variants (e.g. disease-associated mutations or splice forms) or following exposure to drugs, growth factors or hormones is critical to understanding how protein complexes are built, localized and regulated. Affinity purification (AP) coupled with mass spectrometry permits the analysis of protein interactions under near-physiological conditions, yet monitoring interaction changes requires the development of a robust and sensitive quantitative approach, especially for large-scale studies where cost and time are major considerations. To this end, we have coupled AP to data-independent mass spectrometric acquisition (SWATH), and implemented an automated data extraction and statistical analysis pipeline to score modulated interactions. Here, we use AP-SWATH to characterize changes in protein-protein interactions imparted by the HSP90 inhibitor NVP-AUY922 or melanoma-associated mutations in the human kinase CDK4. We show that AP-SWATH is a robust label-free approach to characterize such changes, and propose a scalable pipeline for systems biology studies.