Evaluation of Data-Dependent and -Independent Mass Spectrometric Workflows for Sensitive Quantification of Proteins and Phosphorylation Sites

Mass spectrometry-based proteomic applications in dental implants research

Dental implants have been established as successful treatment options for missing teeth with steadily increasing demands. Today, the primary areas of research in dental implantology revolve around osseointegration, soft and hard tissue grafting as well as peri-implantitis diagnostics, prevention, and treatment. This review provides a comprehensive overview of the current literature on the application of MS-based proteomics in dental implant research, highlights how explorative proteomics provided insights into the biology of peri-implant soft and hard tissues and how proteomics facilitated the stratification between healthy and diseased implants, enabling the identification of potential new diagnostic markers. Additionally, this review illuminates technical aspects, and provides recommendations for future study designs based on the current evidence.

Proteomics for Microalgae Extracts by High-Resolution Mass Spectrometry

Two protocols of protein extraction from Arthrospira platensis (spirulina) microalgae to study their proteome by mass spectrometry (MS) are here presented. The first is based on an aqueous buffer solution of Tris-HCl and the second on cold acetone. The identification of proteins was carried out by a bottom-up approach, which involves enzymatic digestion of extracted proteins followed by either matrix-assisted laser desorption ionization with time-of-flight (MALDI-TOF) MS or liquid chromatography (LC) coupled with electrospray ionization (ESI) and Fourier-transform tandem MS. While MALDI-TOF MS allowed for a fast peptide mass fingerprinting (PMF) check yet identifying less than 20 proteins in the extracted samples, the data-dependent acquisitions (DDA) mode of reversed-phase (RP) LC-ESI tandem FTMS/MS separations allowed us to recognize more than one hundred proteins by searching into dedicated spectral libraries. The application of MALDI-TOF MS analysis was found, however, of great support for preliminary investigations of cyanobacteria samples before proceeding with the RPLC-ESI-MS/MS DDA investigation, which definitively allows for a qualitative proteome analysis also of minor spirulina proteins in processed foodstuffs. Although the protein content in spirulina can be influenced by cultivation and environmental conditions, e.g., light intensity, climate, and water/air quality, here the qualitative chemical profiles of the examined samples were characterized by similar composition in high-quality proteins as phycocyanins and phycoerythrins.

Characterizing the top-down sequencing of protein ions prior to mobility separation in a timsTOF

Mass spectrometry (MS)-based proteomics workflows of intact protein ions have increasingly been utilized to study biological systems. These workflows, however, frequently result in convoluted and difficult to analyze mass spectra. Ion mobility spectrometry (IMS) is a promising tool to overcome these limitations by separating ions by their mass- and size-to-charge ratios. In this work, we further characterize a newly developed method to collisionally dissociate intact protein ions in a trapped ion mobility spectrometry (TIMS) device. Dissociation occurs prior to ion mobility separation and thus, all product ions are distributed throughout the mobility dimension, enabling facile assignment of near isobaric product ions. We demonstrate that collisional activation within a TIMS device is capable of dissociating protein ions up to 66 kDa. We also demonstrate that the ion population size within the TIMS device significantly influences the efficiency of fragmentation. Lastly, we compare CIDtims to the other modes of collisional activation available on the Bruker timsTOF and demonstrate that the mobility resolution in CIDtims enables the annotation of overlapping fragment ions and improves sequence coverage.

Assessing the Dark Field of Metaproteome

The human gut microbiome is a complex system composed of hundreds of species, and metaproteomics can be used to explore their expressed functions. However, many lower abundance species are not detected by current metaproteomic techniques and represent the dark field of metaproteomics. We do not know the minimal abundance of a bacterium in a microbiome(depth) that can be detected by shotgun metaproteomics. In this study, we spiked 15N-labeled E. coli peptides at different percentages into peptides mixture derived from the human gut microbiome to evaluate the depth that can be achieved by shotgun metaproteomics. We observed that the number of identified peptides and peptide intensity from 15N-labeled E. coli were linearly correlated with the spike-in levels even when 15N-labeled E. coli was down to 0.5% of the biomass. Below that level, it was not detected. Interestingly, the match-between-run strategy significantly increased the number of quantified peptides even when 15N-labeled E. coli peptides were at low abundance. This is indicative that in metaproteomics of complex gut microbiomes many peptides from low abundant species are likely observable in MS1 but are not selected for MS2 by standard shotgun strategies.

Improving Phosphoproteomics Profiling Using Data-Independent Mass Spectrometry

Mass spectrometry-based profiling of the phosphoproteome is a powerful method of identifying phosphorylation events at a systems level. Most phosphoproteomics studies have used data-dependent acquisition (DDA) mass spectrometry as their method of choice. In this Perspective, we review some recent studies benchmarking DDA and DIA methods for phosphoproteomics and discuss data analysis options for DIA phosphoproteomics. In order to evaluate the impact of data-dependent and data-independent acquisition (DIA) on identification and quantification, we analyze a previously published phosphopeptide-enriched data set consisting of 10 replicates acquired by DDA and DIA each. We find that though more unique identifications are made in DDA data, phosphopeptides are identified more consistently across replicates in DIA. We further discuss the challenges of identifying chromatographically coeluting phosphopeptide isomers and investigate the impact on reproducibility of identifying high-confidence site-localized phosphopeptides in replicates.

Data-Independent Acquisition Enables Robust Quantification of 400 Proteins in Non-Depleted Canine Plasma

Mass spectrometry-based plasma proteomics offers a major advance for biomarker discovery in the veterinary field, which has traditionally been limited to quantification of a small number of proteins using biochemical assays. The development of foundational data and tools related to sequential window acquisition of all theoretical mass spectra (SWATH)-mass spectrometry has allowed for quantitative profiling of a significant number of plasma proteins in humans and several animal species. Enabling SWATH in dogs enhances human biomedical research as a model species, and significantly improves diagnostic and disease monitoring capability. In this study, a comprehensive peptide spectral library specific to canine plasma proteome was developed and evaluated using SWATH for protein quantification in non-depleted dog plasma. Specifically, plasma samples were subjected to various orthogonal fractionation and digestion techniques, and peptide fragmentation data corresponding to over 420 proteins was collected. Subsequently, a SWATH-based assay was introduced that leveraged the developed resource and that enabled reproducible quantification of 400 proteins in non-depleted plasma samples corresponding to various disease conditions. The ability to profile the abundance of such a significant number of plasma proteins using a single method in dogs has the potential to accelerate biomarker discovery studies in this species.

Review of Liquid Chromatography-Mass Spectrometry-Based Proteomic Analyses of Body Fluids to Diagnose Infectious Diseases

Rapid and precise diagnostic methods are required to control emerging infectious diseases effectively. Human body fluids are attractive clinical samples for discovering diagnostic targets because they reflect the clinical statuses of patients and most of them can be obtained with minimally invasive sampling processes. Body fluids are good reservoirs for infectious parasites, bacteria, and viruses. Therefore, recent clinical proteomics methods have focused on body fluids when aiming to discover human- or pathogen-originated diagnostic markers. Cutting-edge liquid chromatography-mass spectrometry (LC-MS)-based proteomics has been applied in this regard; it is considered one of the most sensitive and specific proteomics approaches. Here, the clinical characteristics of each body fluid, recent tandem mass spectroscopy (MS/MS) data-acquisition methods, and applications of body fluids for proteomics regarding infectious diseases (including the coronavirus disease of 2019 [COVID-19]), are summarized and discussed.

Evaluation of the Sensitivity and Reproducibility of Targeted Proteomic Analysis Using Data-Independent Acquisition for Serum and Cerebrospinal Fluid Proteins

There is a need for targeted analysis of biological fluids for diagnosis, prognosis, or monitoring the progression of diseases. Cerebrospinal fluid (CSF) and serum have been widely used for the development of protein analysis for neurodegenerative diseases and other diseases, respectively. Recently, data-independent acquisition (DIA) mass spectrometry (MS) has been developed to increase the throughput over data-dependent acquisition (DDA) on screening of a large number of samples and discovery of candidate targets. When it comes to target validation, the analytical performance of targeted analysis is critical. However, the inter- and intralaboratory analytical performances of the DIA-MS for targeted proteomic analysis of CSF and serum samples have not yet been investigated. In this study, we showed that the DIA-MS approach allowed us to identify and quantify 1732 CSF and 424 serum proteins, with 90% of proteins identified and quantified in at least 50% of DIA-MS runs. To evaluate the sensitivity, linearity, and dynamic range of the DIA approach, we included the stable isotope-labeled (SI) peptides into CSF and serum samples with serial dilutions. The lower limit of quantification (LLOQ) of peptides was 0.1-0.5 fmol, and the dynamic range was over 3.53 orders of magnitude, with excellent linearity (r2 < 0.978) in CSF and serum samples. Finally, the reproducibility of the DIA-MS approach was evaluated using entire proteins identified in CSF and serum samples. The intralaboratory three replicate results showed reliable reproducibility with 12.5 and 17.3% of the median coefficient of variation (CV) in both CSF and serum matrices, whereas the median CVs of interlaboratory three replicates were 23.8 and 32.0% in CSF and serum samples, respectively. The comparison of the quantitative result between replicates showed close similarity at intra- and interlaboratories with a median Pearson correlation value of >0.98 in CSF and serum, respectively. In conclusion, we demonstrate the capability of the DIA approach as a targeted proteomic analysis for candidate proteins from CSF and serum samples.

MS2Planner: improved fragmentation spectra coverage in untargeted mass spectrometry by iterative optimized data acquisition

Motivation

Untargeted mass spectrometry experiments enable the profiling of metabolites in complex biological samples. The collected fragmentation spectra are the metabolite’s fingerprints that are used for molecule identification and discovery. Two main mass spectrometry strategies exist for the collection of fragmentation spectra: data-dependent acquisition (DDA) and data-independent acquisition (DIA). In the DIA strategy, all the metabolites ions in predefined mass-to-charge ratio ranges are co-isolated and co-fragmented, resulting in multiplexed fragmentation spectra that are challenging to annotate. In contrast, in the DDA strategy, fragmentation spectra are dynamically and specifically collected for the most abundant ions observed, causing redundancy and sub-optimal fragmentation spectra collection. Yet, DDA results in less multiplexed fragmentation spectra that can be readily annotated.

Results

We introduce the MS2Planner workflow, an Iterative Optimized Data Acquisition strategy that optimizes the number of high-quality fragmentation spectra over multiple experimental acquisitions using topological sorting. Our results showed that MS2Planner increases the annotation rate by 38.6% and is 62.5% more sensitive and 9.4% more specific compared to DDA.

Availability and implementation

MS2Planner code is available at https://github.com/mohimanilab/MS2Planner. The generation of the inclusion list from MS2Planner was performed with python scripts available at https://github.com/lfnothias/IODA_MS.

Supplementary information

Supplementary data are available at Bioinformatics online.

Fragmentation and Mobility Separation of Peptide and Protein Ions in a Trapped-Ion Mobility Device

Ion mobility separations (IMS) have increasingly been coupled with mass spectrometry to increase peak capacity and deconvolute complex mass spectra in proteomics workflows. IMS separations can be integrated prior to or following the collisional activation step. Post-activation IMS separations have demonstrated many advantages, yet few instrument platforms are capable of this feat. Here, we present the fragmentation of peptide ions within a commercially available trapped-ion mobility spectrometry device. Fragmentation is initiated prior to mobility analysis enabling the separation of generated product ions. The added separation step deconvolutes product ion spectra and permits improved annotation of product ions. Furthermore, we demonstrate the isolation and fragmentation of mobility separated product ions with the downstream quadrupole and collisional cell. When applied to melittin and ubiquitin, this ion mobility assisted pseudo-MS³ fragmentation approach generates sequence coverage ∼50% greater than that of typical MS² analyses. We envision this ion-mobility-assisted fragmentation technique as the foundation of a powerful new pseudo-MS³ workflow for application toward middle- or top-down proteomics.

Proteomics, Personalized Medicine and Cancer

As of 2020 the human genome and proteome are both at >90% completion based on high stringency analyses. This has been largely achieved by major technological advances over the last 20 years and has enlarged our understanding of human health and disease, including cancer, and is supporting the current trend towards personalized/precision medicine. This is due to improved screening, novel therapeutic approaches and an increased understanding of underlying cancer biology. However, cancer is a complex, heterogeneous disease modulated by genetic, molecular, cellular, tissue, population, environmental and socioeconomic factors, which evolve with time. In spite of recent advances in treatment that have resulted in improved patient outcomes, prognosis is still poor for many patients with certain cancers (e.g., mesothelioma, pancreatic and brain cancer) with a high death rate associated with late diagnosis. In this review we overview key hallmarks of cancer (e.g., autophagy, the role of redox signaling), current unmet clinical needs, the requirement for sensitive and specific biomarkers for early detection, surveillance, prognosis and drug monitoring, the role of the microbiome and the goals of personalized/precision medicine, discussing how emerging omics technologies can further inform on these areas. Exemplars from recent onco-proteogenomic-related publications will be given. Finally, we will address future perspectives, not only from the standpoint of perceived advances in treatment, but also from the hurdles that have to be overcome.

MSpectraAI: a powerful platform for deciphering proteome profiling of multi-tumor mass spectrometry data by using deep neural networks

BACKGROUND

Mass spectrometry (MS) has become a promising analytical technique to acquire proteomics information for the characterization of biological samples. Nevertheless, most studies focus on the final proteins identified through a suite of algorithms by using partial MS spectra to compare with the sequence database, while the pattern recognition and classification of raw mass-spectrometric data remain unresolved.

RESULTS

We developed an open-source and comprehensive platform, named MSpectraAI, for analyzing large-scale MS data through deep neural networks (DNNs); this system involves spectral-feature swath extraction, classification, and visualization. Moreover, this platform allows users to create their own DNN model by using Keras. To evaluate this tool, we collected the publicly available proteomics datasets of six tumor types (a total of 7,997,805 mass spectra) from the ProteomeXchange consortium and classified the samples based on the spectra profiling. The results suggest that MSpectraAI can distinguish different types of samples based on the fingerprint spectrum and achieve better prediction accuracy in MS1 level (average 0.967).

CONCLUSION

This study deciphers proteome profiling of raw mass spectrometry data and broadens the promising application of the classification and prediction of proteomics data from multi-tumor samples using deep learning methods. MSpectraAI also shows a better performance compared to the other classical machine learning approaches.

Proteomic Biomarkers for the Detection of Endometrial Cancer

Endometrial cancer is the leading gynaecological malignancy in the western world and its incidence is rising in tandem with the global epidemic of obesity. Early diagnosis is key to improving survival, which at 5 years is less than 20% in advanced disease and over 90% in early-stage disease. As yet, there are no validated biological markers for its early detection. Advances in high-throughput technologies and machine learning techniques now offer unique and promising perspectives for biomarker discovery, especially through the integration of genomic, transcriptomic, proteomic, metabolomic and imaging data. Because the proteome closely mirrors the dynamic state of cells, tissues and organisms, proteomics has great potential to deliver clinically relevant biomarkers for cancer diagnosis. In this review, we present the current progress in endometrial cancer diagnostic biomarker discovery using proteomics. We describe the various mass spectrometry-based approaches and highlight the challenges inherent in biomarker discovery studies. We suggest novel strategies for endometrial cancer detection exploiting biologically important protein biomarkers and set the scene for future directions in endometrial cancer biomarker research.

Precision Profiling of the Cardiovascular Post-Translationally Modified Proteome: Where There Is a Will, There Is a Way

There is an exponential increase in biological complexity as initial gene transcripts are spliced, translated into amino acid sequence, and post-translationally modified. Each protein can exist as multiple chemical or sequence-specific proteoforms, and each has the potential to be a critical mediator of a physiological or pathophysiological signaling cascade. Here, we provide an overview of how different proteoforms come about in biological systems and how they are most commonly measured using mass spectrometry-based proteomics and bioinformatics. Our goal is to present this information at a level accessible to every scientist interested in mass spectrometry and its application to proteome profiling. We will specifically discuss recent data linking various protein post-translational modifications to cardiovascular disease and conclude with a discussion for enablement and democratization of proteomics across the cardiovascular and scientific community. The aim is to inform and inspire the readership to explore a larger breadth of proteoform, particularity post-translational modifications, related to their particular areas of expertise in cardiovascular physiology.

Rapid Identification of New Delhi Metallo-β-Lactamase (NDM) Using Tryptic Peptides and LC-MS/MS

There is significant interest in the development of mass spectrometry (MS) methods for antimicrobial resistance protein detection, given the ability of these methods to confirm protein expression. In this work, we studied the performance of a liquid chromatography, tandem MS multiple-reaction monitoring (LC-MS/MS MRM) method for the direct detection of the New Delhi metallo-β-lactamase (NDM) carbapenemase in clinical isolates. Using a genoproteomic approach, we selected three unique peptides (SLGNLGDADTEHYAASAR, AFGAAFPK, and ASMIVMSHSAPDSR) specific to NDM that were efficiently ionized and spectrally well-defined. These three peptides were used to build an assay with turnaround time of 90 min. In a blind set, the assay detected 21/24 bla _NDM-containing isolates and 76/76 isolates with negative results, corresponding to a sensitivity value of 87.5% (95% confidence interval [CI], 67.6% to 97.3%) and a specificity value of 100% (95% CI, 95.3% to 100%). One of the missed identifications was determined by protein fractionation to be due to low (∼0.1 fm/μg) NDM protein expression (below the assay limit of detection). Parallel disk diffusion susceptibility testing demonstrated this isolate to be meropenem susceptible, consistent with low NDM expression. Total proteomic analysis of the other two missed identifications did not detect NDM peptides but detected other proteins expressed from the bla _NDM-containing plasmids, confirming that the plasmids were not lost. The measurement of relative NDM concentrations over the entire isolate test set demonstrated variability spanning 4 orders of magnitude, further confirming the remarkable range that may be seen in levels of NDM expression. This report highlights the sensitivity of LC-MS/MS to variations in NDM protein expression, with implications for how this technology may be used.

Single Cell Proteomics by Data-Independent Acquisition To Study Embryonic Asymmetry in Xenopus laevis

Techniques that allow single cell analysis are gaining widespread attention, and most of these studies utilize genomics-based approaches. While nanofluidic technologies have enabled mass spectrometric analysis of single cells, these measurements have been limited to metabolomics and lipidomic studies. Single cell proteomics has the potential to improve our understanding of intercellular heterogeneity. However, this approach has faced challenges including limited sample availability, as well as a requirement of highly sensitive methods for sample collection, cleanup, and detection. We present a technique to overcome these limitations by combining a micropipette (pulled glass capillary) based sample collection strategy with offline sample preparation and nanoLC-MS/MS to analyze proteins through a bottom-up proteomic strategy. This study explores two types of proteomics data acquisition strategies namely data-dependent (DDA) and data-independent acquisition (DIA). Results from the study indicate DIA to be more sensitive enabling analysis of >1600 proteins from ∼130 μm Xenopus laevis embryonic cells containing <6 nL of cytoplasm. The method was found to be robust in obtaining reproducible protein quantifications from single cells spanning the 1-128-cell stages of development. Furthermore, we used micropipette sampling to study intercellular heterogeneity within cells in a single embryo and investigated embryonic asymmetry along both animal-vegetal and dorsal-ventral axes during early stages of development. Investigation of the animal-vegetal axis led to discovery of various asymmetrically distributed proteins along the animal-vegetal axis. We have further compared the hits found from our proteomic data sets with other studies and validated a few hits using an orthogonal imaging technique. This study forms the first report of vegetal enrichment of the germ plasm associated protein DDX4/VASA in Xenopus embyos. Overall, the method and data presented here holds promise to enable important leads in developmental biology.

Protein profiling and pseudo-parallel reaction monitoring to monitor a fusion-associated conformational change in hemagglutinin

Influenza infection requires viral escape from early endosomes into the cytosol, which is enabled by an acid-induced irreversible conformational transformation in the viral protein hemagglutinin. Despite the direct relationship between this conformational change and infectivity, label-free methods for characterizing this and other protein conformational changes in biological mixtures are limited. While the chemical reactivity of the protein backbone and side-chain residues is a proxy for protein conformation, coupling this reactivity to quantitative mass spectrometry is a challenge in complex environments. Herein, we evaluate whether electrophilic amidination coupled with pseudo-parallel reaction monitoring is an effective label-free approach to detect the fusion-associated conformational transformation in recombinant hemagglutinin (rHA). We identified rHA peptides that are differentially amidinated between the pre- and post-fusion states, and validated that this difference relies upon the fusion-associated conformational switch. We further demonstrate that we can distinguish the fusion profile in a matrix of digested cellular lysate. This fusion assay can be used to evaluate fusion competence for modified HA. Graphical abstract.

Recent advances in phosphoproteomics and application to neurological diseases

Phosphorylation has an incredible impact on the biological behavior of proteins, altering everything from intrinsic activity to cellular localization and complex formation. It is no surprise then that this post-translational modification has been the subject of intense study and that, with the advent of faster, more accurate instrumentation, the number of large-scale mass spectrometry-based phosphoproteomic studies has swelled over the past decade. Recent developments in sample preparation, phosphorylation enrichment, quantification, and data analysis strategies permit both targeted and ultra-deep phosphoproteome profiling, but challenges remain in pinpointing biologically relevant phosphorylation events. We describe here technological advances that have facilitated phosphoproteomic analysis of cells, tissues, and biofluids and note applications to neuropathologies in which the phosphorylation machinery may be dysregulated, much as it is in cancer.

Parallel reaction monitoring on a Q Exactive mass spectrometer increases reproducibility of phosphopeptide detection in bacterial phosphoproteomics measurements

Increasing number of studies report the relevance of protein Ser/Thr/Tyr phosphorylation in bacterial physiology, yet the analysis of this type of modification in bacteria still presents a considerable challenge. Unlike in eukaryotes, where tens of thousands of phosphorylation events likely occupy more than two thirds of the proteome, the abundance of protein phosphorylation is much lower in bacteria. Even the state-of-the-art phosphopeptide enrichment protocols fail to remove the high background of abundant unmodified peptides, leading to low signal intensity and undersampling of phosphopeptide precursor ions in consecutive data-dependent MS runs. Consequently, large-scale bacterial phosphoproteomic datasets often suffer from poor reproducibility and a high number of missing values. Here we explore the application of parallel reaction monitoring (PRM) on a Q Exactive mass spectrometer in bacterial phosphoproteome analysis, focusing especially on run-to-run sampling reproducibility. In multiple measurements of identical phosphopeptide-enriched samples, we show that PRM outperforms data-dependent acquisition (DDA) in terms of detection frequency, reaching almost complete sampling efficiency, compared to 20% in DDA. We observe a similar trend over multiple heterogeneous phosphopeptide-enriched samples and conclude that PRM shows a great promise in bacterial phosphoproteomics analyses where reproducible detection and quantification of a relatively small set of phosphopeptides is desired. SIGNIFICANCE: Bacterial phosphorylated peptides occur in low abundance compared to their unmodified counterparts, and are therefore rarely reproducibly detected in shotgun (DDA) proteomics measurements. Here we show that parallel reaction monitoring complements DDA analyses and makes detection of known, targeted phosphopeptides more reproducible. This will be of significance in replicated MS measurements that have a goal to reproducibly detect and quantify phosphopeptides of interest.

Application of targeted mass spectrometry in bottom-up proteomics for systems biology research

The enormous diversity of proteoforms produces tremendous complexity within cellular proteomes, facilitates intricate networks of molecular interactions, and constitutes a formidable analytical challenge for biomedical researchers. Currently, quantitative whole-proteome profiling often relies on non-targeted liquid chromatography-mass spectrometry (LC-MS), which samples proteoforms broadly, but can suffer from lower accuracy, sensitivity, and reproducibility compared with targeted LC-MS. Recent advances in bottom-up proteomics using targeted LC-MS have enabled previously unachievable identification and quantification of target proteins and posttranslational modifications within complex samples. Consequently, targeted LC-MS is rapidly advancing biomedical research, especially systems biology research in diverse areas that include proteogenomics, interactomics, kinomics, and biological pathway modeling. With the recent development of targeted LC-MS assays for nearly the entire human proteome, targeted LC-MS is positioned to enable quantitative proteomic profiling of unprecedented quality and accessibility to support fundamental and clinical research. Here we review recent applications of bottom-up proteomics using targeted LC-MS for systems biology research. SIGNIFICANCE: Advances in targeted proteomics are rapidly advancing systems biology research. Recent applications include systems-level investigations focused on posttranslational modifications (such as phosphoproteomics), protein conformation, protein-protein interaction, kinomics, proteogenomics, and metabolic and signaling pathways. Notably, absolute quantification of metabolic and signaling pathway proteins has enabled accurate pathway modeling and engineering. Integration of targeted proteomics with other technologies, such as RNA-seq, has facilitated diverse research such as the identification of hundreds of "missing" human proteins (genes and transcripts that appear to encode proteins but direct experimental evidence was lacking).

Targeted Annotation of S-Sulfonylated Peptides by Selective Infrared Multiphoton Dissociation Mass Spectrometry

Protein S-sulfinylation (R-SO₂^-) and S-sulfonylation (R-SO₃^-) are irreversible oxidative post-translational modifications of cysteine residues. Greater than 5% of cysteines are reported to occupy these higher oxidation states, which effectively inactivate the corresponding thiols and alter the electronic and physical properties of modified proteins. Such higher oxidation states are reached after excessive exposure to cellular oxidants, and accumulate across different disease states. Despite widespread and functionally relevant cysteine oxidation across the proteome, there are currently no robust methods to profile higher order cysteine oxidation. Traditional data-dependent liquid chromatography/tandem mass spectrometry (LC/MS/MS) methods generally miss low-occupancy modifications in complex analyses. Here, we present a data-independent acquisition (DIA) LC/MS-based approach, leveraging the high IR absorbance of sulfoxides at 10.6 μm, for selective dissociation and discovery of S-sulfonated peptides. Across peptide standards and protein digests, we demonstrate selective infrared multiphoton dissociation (IRMPD) of S-sulfonated peptides in the background of unmodified peptides. This selective DIA IRMPD LC/MS-based approach allows identification and annotation of S-sulfonated peptides across complex mixtures while providing sufficient sequence information to localize the modification site.

Molecular basis for blue light-dependent phosphorylation of Arabidopsis cryptochrome 2

Plant cryptochromes undergo blue light-dependent phosphorylation to regulate their activity and abundance, but the protein kinases that phosphorylate plant cryptochromes have remained unclear. Here we show that photoexcited Arabidopsis cryptochrome 2 (CRY2) is phosphorylated in vivo on as many as 24 different residues, including 7 major phosphoserines. We demonstrate that four closely related Photoregulatory Protein Kinases (previously referred to as MUT9-like kinases) interact with and phosphorylate photoexcited CRY2. Analyses of the ppk123 and ppk124 triple mutants and amiR^4k artificial microRNA-expressing lines demonstrate that PPKs catalyse blue light-dependent CRY2 phosphorylation to both activate and destabilize the photoreceptor. Phenotypic analyses of these mutant lines indicate that PPKs may have additional substrates, including those involved in the phytochrome signal transduction pathway. These results reveal a mechanism underlying the co-action of cryptochromes and phytochromes to coordinate plant growth and development in response to different wavelengths of solar radiation in nature.

Plant cryptochromes are regulated by blue-light dependent phosphorylation. Here the authors map the in vivo phosphorylation sites of Arabidopsis cryptochrome 2 and identify four closely related kinases that act to both activate and destabilize the receptor in response to blue light.

High-throughput identification of C/D box snoRNA targets with CLIP and RiboMeth-seq

High-throughput sequencing has greatly facilitated the discovery of long and short non-coding RNAs (ncRNAs), which frequently guide ribonucleoprotein complexes to RNA targets, to modulate their metabolism and expression. However, for many ncRNAs, the targets remain to be discovered. In this study, we developed computational methods to map C/D box snoRNA target sites using data from core small nucleolar ribonucleoprotein crosslinking and immunoprecipitation and from transcriptome-wide mapping of 2΄-O-ribose methylation sites. We thereby assigned the snoRNA guide to a known methylation site in the 18S rRNA, we uncovered a novel partially methylated site in the 28S ribosomal RNA, and we captured a site in the 28S rRNA in interaction with multiple snoRNAs. Although we also captured mRNAs in interaction with snoRNAs, we did not detect 2΄-O-methylation of these targets. Our study provides an integrated approach to the comprehensive characterization of 2΄-O-methylation targets of snoRNAs in species beyond those in which these interactions have been traditionally studied and contributes to the rapidly developing field of 'epitranscriptomics'.

Ribosome profiling reveals an adaptation strategy of reduced bacterium to acute stress

Bacteria of class Mollicutes (mycoplasmas) feature significant genome reduction which makes them good model organisms for systems biology studies. Previously we demonstrated, that drastic transcriptional response of mycoplasmas to stress results in a very limited response on the level of protein. In this study we used heat stress model of M. gallisepticum and ribosome profiling to elucidate the process of genetic information transfer under stress. We found that under heat stress ribosomes demonstrate selectivity towards mRNA binding. We identified that heat stress response may be divided into two groups on the basis of absolute transcript abundance and fold-change in the translatome. One represents a noise-like response and another is likely an adaptive one. The latter include ClpB chaperone, cell division cluster, homologs of immunoblocking proteins and short ORFs with unknown function. We found that previously identified read-through of terminators contributes to the upregulation of transcripts in the translatome as well. In addition we identified that ribosomes of M. gallisepticum undergo reorganization under the heat stress. The most notable event is decrease of the amount of associated HU protein. In conclusion, only changes of few adaptive transcripts significantly impact translatome, while widespread noise-like transcription plays insignificant role in translation during stress.

Quantitative analysis of human centrosome architecture by targeted proteomics and fluorescence imaging

Centrioles are essential for the formation of centrosomes and cilia. While numerical and/or structural centrosomes aberrations are implicated in cancer, mutations in centriolar and centrosomal proteins are genetically linked to ciliopathies, microcephaly, and dwarfism. The evolutionarily conserved mechanisms underlying centrosome biogenesis are centered on a set of key proteins, including Plk4, Sas-6, and STIL, whose exact levels are critical to ensure accurate reproduction of centrioles during cell cycle progression. However, neither the intracellular levels of centrosomal proteins nor their stoichiometry within centrosomes is presently known. Here, we have used two complementary approaches, targeted proteomics and EGFP-tagging of centrosomal proteins at endogenous loci, to measure protein abundance in cultured human cells and purified centrosomes. Our results provide a first assessment of the absolute and relative amounts of major components of the human centrosome. Specifically, they predict that human centriolar cartwheels comprise up to 16 stacked hubs and 1 molecule of STIL for every dimer of Sas-6. This type of quantitative information will help guide future studies of the molecular basis of centrosome assembly and function.

Modulation of Phosphopeptide Fragmentation via Dual Spray Ion/Ion Reactions Using a Sulfonate-Incorporating Reagent

The labile nature of phosphoryl groups has presented a long-standing challenge for the characterization of protein phosphorylation via conventional mass spectrometry-based bottom-up proteomics methods. Collision-induced dissociation (CID) causes preferential cleavage of the phospho-ester bond of peptides, particularly under conditions of low proton mobility, and results in the suppression of sequence-informative fragmentation that often prohibits phosphosite determination. In the present study, the fragmentation patterns of phosphopeptides are improved through ion/ion-mediated peptide derivatization with 4-formyl-1,3-benezenedisulfonic acid (FBDSA) anions using a dual spray reactor. This approach exploits the strong electrostatic interactions between the sulfonate moieties of FBDSA and basic sites to facilitate gas-phase bioconjugation and to reduce charge sequestration and increase the yield of phosphate-retaining sequence ions upon CID. Moreover, comparative CID fragmentation analysis between unmodified phosphopeptides and those modified online with FBDSA or in solution via carbamylation and 4-sulfophenyl isothiocyanate (SPITC) provided evidence for sulfonate interference with charge-directed mechanisms that result in preferential phosphate elimination. Our results indicate the prominence of charge-directed neighboring group participation reactions involved in phosphate neutral loss, and the implementation of ion/ion reactions in a dual spray reactor setup provides a means to disrupt the interactions by competing hydrogen-bonding interactions between sulfonate groups and the side chains of basic residues.

Secreted Matrix Metalloproteinase-9 of Proliferating Smooth Muscle Cells as a Trigger for Drug Release from Stent Surface Polymers in Coronary Arteries

Cardiovascular diseases are the leading causes of death in industrialized countries. Atherosclerotic coronary arteries are commonly treated with percutaneous transluminal coronary intervention followed by stent deployment. This treatment has significantly improved the clinical outcome. However, triggered vascular smooth muscle cell (SMC) proliferation leads to in-stent restenosis in bare metal stents. In addition, stent thrombosis is a severe side effect of drug eluting stents due to inhibition of endothelialization. The aim of this study was to develop and test a stent surface polymer, where cytotoxic drugs are covalently conjugated to the surface and released by proteases selectively secreted by proliferating smooth muscle cells. Resting and proliferating human coronary artery smooth muscle cells (HCASMC) and endothelial cells (HCAEC) were screened to identify an enzyme exclusively released by proliferating HCASMC. Expression analyses and enzyme activity assays verified selective and exclusive activity of the matrix metalloproteinase-9 (MMP-9) in proliferating HCASMC. The principle of drug release exclusively triggered by proliferating HCASMC was tested using the biodegradable stent surface polymer poly-l-lactic acid (PLLA) and the MMP-9 cleavable peptide linkers named SRL and AVR. The specific peptide cleavage by MMP-9 was verified by attachment of the model compound fluorescein. Fluorescein release was observed in the presence of MMP-9 secreting HCASMC but not of proliferating HCAEC. Our findings suggest that cytotoxic drug conjugated polymers can be designed to selectively release the attached compound triggered by MMP-9 secreting smooth muscle cells. This novel concept may be beneficial for stent endothelialization thereby reducing the risk of restenosis and thrombosis.

Multiple stage MS in analysis of plasma, serum, urine and in vitro samples relevant to clinical and forensic toxicology

This paper reviews MS approaches applied to metabolism studies, structure elucidation and qualitative or quantitative screening of drugs (of abuse) and/or their metabolites. Applications in clinical and forensic toxicology were included using blood plasma or serum, urine, in vitro samples, liquids, solids or plant material. Techniques covered are liquid chromatography coupled to low-resolution and high-resolution multiple stage mass analyzers. Only PubMed listed studies published in English between January 2008 and January 2015 were considered. Approaches are discussed focusing on sample preparation and mass spectral settings. Comments on advantages and limitations of these techniques complete the review.

Assessment of current mass spectrometric workflows for the quantification of low abundant proteins and phosphorylation sites

The data described here provide a systematic performance evaluation of popular data-dependent (DDA) and independent (DIA) mass spectrometric (MS) workflows currently used in quantitative proteomics. We assessed the limits of identification, quantification and detection for each method by analyzing a dilution series of 20 unmodified and 10 phosphorylated synthetic heavy labeled reference peptides, respectively, covering six orders of magnitude in peptide concentration with and without a complex human cell digest background. We found that all methods performed very similarly in the absence of background proteins, however, when analyzing whole cell lysates, targeted methods were at least 5–10 times more sensitive than directed or DDA methods. In particular, higher stage fragmentation (MS3) of the neutral loss peak using a linear ion trap increased dynamic quantification range of some phosphopeptides up to 100-fold. We illustrate the power of this targeted MS3 approach for phosphopeptide monitoring by successfully quantifying 9 phosphorylation sites of the kinetochore and spindle assembly checkpoint component Mad1 over different cell cycle states from non-enriched pull-down samples. The data are associated to the research article ‘Evaluation of data-dependent and data-independent mass spectrometric workflows for sensitive quantification of proteins and phosphorylation sites׳ (Bauer et al., 2014) [1]. The mass spectrometry and the analysis dataset have been deposited to the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) via the PRIDE partner repository with the dataset identifier PXD000964.

Signal Transduction Reaction Monitoring Deciphers Site-Specific PI3K-mTOR/MAPK Pathway Dynamics in Oncogene-Induced Senescence

We report a straightforward strategy to comprehensively monitor signal transduction pathway dynamics in mammalian systems. Combining targeted quantitative proteomics with highly selective phosphopeptide enrichment, we monitor, with great sensitivity, phosphorylation dynamics of the PI3K-mTOR and MAPK signaling networks. Our approach consists of a single enrichment step followed by a single targeted proteomics experiment, circumventing the need for labeling and immune purification while enabling analysis of selected phosphorylation nodes throughout signaling pathways. The need for such a comprehensive pathway analysis is illustrated by highlighting previously uncharacterized phosphorylation changes in oncogene-induced senescence, associated with diverse biological phenotypes and pharmacological intervention of the PI3K-mTOR pathway.

Characterization of a human pluripotent stem cell-derived model of neuronal development using multiplexed targeted proteomics

PURPOSE

Human pluripotent stem cell (hPSC)-derived cellular models have great potential to enable drug discovery and improve translation of preclinical insights to the clinic. We have developed a hPSC-derived neural precursor cell model for studying early events in human brain development. We present protein-level characterization of this model, using a multiplexed SRM approach, to establish reproducibility and physiological relevance; essential prerequisites for utilization of the neuronal development model in phenotypic screening-based drug discovery.

EXPERIMENTAL DESIGN

Profiles of 246 proteins across three key stages of in vitro neuron differentiation were analyzed by SRM. Three independently hPSC-derived isogenic neural stem cell (NSC) lines were analyzed across five to nine independent neuronal differentiations.

RESULTS

One hundred seventy-five proteins were reliably quantified revealing a time-dependent pattern of protein regulation that reflected protein dynamics during in vivo brain development and that was conserved across replicate differentiations and multiple cell lines.

CONCLUSIONS AND CLINICAL RELEVANCE

SRM-based protein profiling enabled establishment of the reproducibility and physiological relevance of the hPSC-derived neuronal model. Combined with the successful quantification of proteins relevant to neurodevelopmental diseases, this validates the platform for use as a model to enable neuroscience drug discovery.

Quantitative isoform-profiling of highly diversified recognition molecules

Complex biological systems rely on cell surface cues that govern cellular self-recognition and selective interactions with appropriate partners. Molecular diversification of cell surface recognition molecules through DNA recombination and complex alternative splicing has emerged as an important principle for encoding such interactions. However, the lack of tools to specifically detect and quantify receptor protein isoforms is a major impediment to functional studies. We here developed a workflow for targeted mass spectrometry by selected reaction monitoring that permits quantitative assessment of highly diversified protein families. We apply this workflow to dissecting the molecular diversity of the neuronal neurexin receptors and uncover an alternative splicing-dependent recognition code for synaptic ligands.

DOI:http://dx.doi.org/10.7554/eLife.07794.001

To create a protein, a gene is first copied to form an RNA molecule that contains regions known as introns and exons. Splicing removes the introns and joins the exons together to form a molecule of ‘messenger RNA’, which is translated into a protein.

Over the course of evolution, many groups—or families—of proteins have expanded and diversified their roles. One way in which this can occur is through a process known as alternative splicing, in which different exons can be included or excluded to generate the final messenger RNA. In this way, a single gene can produce a number of different proteins. These closely related proteins are known as isoforms.

The brain contains billions of neurons that communicate with one another across connections known as synapses. A family of proteins called neurexins helps neurons to form these synapses. Humans have three neurexin genes, which undergo extensive alternative splicing to produce thousands of protein isoforms. However, it is not known whether all of these isoforms are produced in neurons, as existing experimental techniques were not sensitive enough to easily distinguish one isoform from another.

A technique known as ‘selected reaction monitoring’ (or SRM for short) has recently emerged as a promising way to identify proteins. This allows proteins containing specific sequences to be separated out for analysis, in contrast to existing techniques that test randomly selected protein samples, which will result in most isoforms being missed. Schreiner, Simicevic et al. have now developed SRM further and show that this technique can detect the identity and amount of the neurexin isoforms present at synapses, including those that are only produced in very small quantities. Using SRM, Schreiner, Simicevic et al. demonstrate that neurexin isoforms differ in how they interact with synaptic receptors. Thus, alternative splicing of neurexins underlies a ‘recognition code’ at neuronal synapses.

In the future, this newly developed SRM method could be used to investigate isoforms in other protein families and tissues, and so may prove valuable for understanding how a wide range of cellular recognition processes work.

DOI:http://dx.doi.org/10.7554/eLife.07794.002

RZZ and Mad1 dynamics in Drosophila mitosis

The presence or absence of Mad1 at kinetochores is a major determinant of spindle assembly checkpoint (SAC) activity, the surveillance mechanism that delays anaphase onset if one or more kinetochores remain unattached to spindle fibers. Among the factors regulating the levels of Mad1 at kinetochores is the Rod, Zw10, and Zwilch (RZZ) complex, which is required for Mad1 recruitment through a mechanism that remains unknown. The relative dynamics and interactions of Mad1 and RZZ at kinetochores have not been extensively investigated, although Mad1 has been reported to be stably recruited to unattached kinetochores. In this study, we directly compare Mad1-green fluorescent protein (GFP) turnover dynamics on unattached Drosophila kinetochores with that of RZZ, tagged either with GFP-Rod or GFP-Zw10. We find that nearly 40 % of kinetochore-bound Mad1 has a significant dynamic component, turning over with a half-life of 12 s. RZZ in contrast is essentially stable on unattached kinetochores. In addition, we report that a fraction of RZZ and Mad1 can co-immunoprecipitate, indicating that the genetically determined recruitment hierarchy (in which Mad1 depends on RZZ) may reflect a physical association of the two complexes.