Showing papers in "Journal of Proteome Research in 2018"

Performance Evaluation of the Q Exactive HF-X for Shotgun Proteomics

Abstract: Progress in proteomics is mainly driven by advances in mass spectrometric (MS) technologies. Here we benchmarked the performance of the latest MS instrument in the benchtop Orbitrap series, the Q Exactive HF-X, against its predecessor for proteomics applications. A new peak-picking algorithm, a brighter ion source, and optimized ion transfers enable productive MS/MS acquisition above 40 Hz at 7500 resolution. The hardware and software improvements collectively resulted in improved peptide and protein identifications across all comparable conditions, with an increase of up to 50 percent at short LC–MS gradients, yielding identification rates of more than 1000 unique peptides per minute. Alternatively, the Q Exactive HF-X is capable of achieving the same proteome coverage as its predecessor in approximately half the gradient time or at 10-fold lower sample loads. The Q Exactive HF-X also enables rapid phosphoproteomics with routine analysis of more than 5000 phosphopeptides with short single-shot 15 min LC–...

Streamlined Tandem Mass Tag (SL-TMT) Protocol: An Efficient Strategy for Quantitative (Phospho)proteome Profiling Using Tandem Mass Tag-Synchronous Precursor Selection-MS3

Abstract: Mass spectrometry (MS) coupled toisobaric labeling has developed rapidly into a powerful strategy for high-throughput protein quantification. Sample multiplexing and exceptional sensitivity allow for the quantification of tens of thousands of peptides and, by inference, thousands of proteins from multiple samples in a single MS experiment. Accurate quantification demands a consistent and robust sample-preparation strategy. Here, we present a detailed workflow for SPS-MS3-based quantitative abundance profiling of tandem mass tag (TMT)-labeled proteins and phosphopeptides that we have named the streamlined (SL)-TMT protocol. We describe a universally applicable strategy that requires minimal individual sample processing and permits the seamless addition of a phosphopeptide enrichment step ("mini-phos") with little deviation from the deep proteome analysis. To showcase our workflow, we profile the proteome of wild-type Saccharomyces cerevisiae yeast grown with either glucose or pyruvate as the carbon source. Here, we have established a streamlined TMT protocol that enables deep proteome and medium-scale phosphoproteome analysis.

Enhanced Global Post-translational Modification Discovery with MetaMorpheus.

Abstract: Correct identification of protein post-translational modifications (PTMs) is crucial to understanding many aspects of protein function in biological processes. G-PTM-D is a recently developed technique for global identification and localization of PTMs. Spectral file calibration prior to applying G-PTM-D, and algorithmic enhancements in the peptide database search significantly increase the accuracy, speed, and scope of PTM identification. We enhance G-PTM-D by using multinotch searches and demonstrate its effectiveness in identification of numerous types of PTMs including high-mass modifications such as glycosylations. The changes described in this work lead to a 20% increase in the number of identified modifications and an order of magnitude decrease in search time. The complete workflow is implemented in MetaMorpheus, a software tool that integrates the database search procedure, identification of coisolated peptides, spectral calibration, and the enhanced G-PTM-D workflow. Multinotch searches are also...

S-Trap, an Ultrafast Sample-Preparation Approach for Shotgun Proteomics.

Abstract: The success of shotgun proteomic analysis depends largely on how samples are prepared. Current approaches (such as those that are gel-, solution-, or filter-based), although being extensively employed in the field, are time-consuming and less effective with respect to the repetitive sample processing, recovery, and overall yield. As an alternative, the suspension trapping (S-Trap) filter has been commercially available very recently in the format of a single or 96-well filter plate. In contrast to the conventional filter-aided sample preparation (FASP) approach, which utilizes a molecular weight cut-off (MWCO) membrane as the filter and requires hours of processing before digestion-ready proteins can be obtained, the S-Trap employs a three-dimensional porous material as filter media and traps particulate protein suspensions with the subsequent depletion of interfering substances and in-filter digestion. Due to the large (submicron) pore size, each centrifugation cycle of the S-Trap filter only takes 1 min, which significantly reduces the total processing time from approximately 3 h by FASP to less than 15 min, suggesting an ultrafast sample-preparation approach for shotgun proteomics. Here, we comprehensively evaluate the performance of the individual S-Trap filter and 96-well filter plate in the context of global protein identification and quantitation using whole-cell lysate and clinically relevant sputum samples.

Extending the Compatibility of the SP3 Paramagnetic Bead Processing Approach for Proteomics

Abstract: The diversity in protein and peptide biochemistry necessitates robust protocols and reagents for efficiently handling and enriching these molecules prior to analysis with mass spectrometry (MS) or other techniques. Further exploration of the paramagnetic bead-based approach, single-pot solid-phase-enhanced sample preparation (SP3), is carried out toward updating and extending previously described conditions and experimental workflows. The SP3 approach was tested in a wide range of experimental scenarios, including (1) binding solvents (acetonitrile, ethanol, isopropanol, acetone), (2) binding pH (acidic vs neutral), (3) solvent/lysate ratios (50–200%, v/v), (4) mixing and rinsing conditions (on-rack vs off-rack rinsing), (5) Enrichment of nondenatured proteins, and (6) capture of individual proteins from noncomplex mixtures. These results highlight the robust handling of proteins in a broad set of scenarios while also enabling the development of a modified SP3 workflow that offers extended compatibility. ...

Deep Learning Accurately Predicts Estrogen Receptor Status in Breast Cancer Metabolomics Data.

Abstract: Metabolomics holds the promise as a new technology to diagnose highly heterogeneous diseases. Conventionally, metabolomics data analysis for diagnosis is done using various statistical and machine learning based classification methods. However, it remains unknown if deep neural network, a class of increasingly popular machine learning methods, is suitable to classify metabolomics data. Here we use a cohort of 271 breast cancer tissues, 204 positive estrogen receptor (ER+), and 67 negative estrogen receptor (ER−) to test the accuracies of feed-forward networks, a deep learning (DL) framework, as well as six widely used machine learning models, namely random forest (RF), support vector machines (SVM), recursive partitioning and regression trees (RPART), linear discriminant analysis (LDA), prediction analysis for microarrays (PAM), and generalized boosted models (GBM). DL framework has the highest area under the curve (AUC) of 0.93 in classifying ER+/ER– patients, compared to the other six machine learning a...

Machine-Learning-Based Prediction of Cell-Penetrating Peptides and Their Uptake Efficiency with Improved Accuracy.

Abstract: Cell-penetrating peptides (CPPs) can enter cells as a variety of biologically active conjugates and have various biomedical applications. To offset the cost and effort of designing novel CPPs in laboratories, computational methods are necessitated to identify candidate CPPs before in vitro experimental studies. We developed a two-layer prediction framework called machine-learning-based prediction of cell-penetrating peptides (MLCPPs). The first-layer predicts whether a given peptide is a CPP or non-CPP, whereas the second-layer predicts the uptake efficiency of the predicted CPPs. To construct a two-layer prediction framework, we employed four different machine-learning methods and five different compositions including amino acid composition (AAC), dipeptide composition, amino acid index, composition–transition–distribution, and physicochemical properties (PCPs). In the first layer, hybrid features (combination of AAC and PCP) and extremely randomized tree outperformed state-of-the-art predictors in CPP p...

SearchGUI: A Highly Adaptable Common Interface for Proteomics Search and de Novo Engines

Abstract: Mass-spectrometry-based proteomics has become the standard approach for identifying and quantifying proteins. A vital step consists of analyzing experimentally generated mass spectra to identify the underlying peptide sequences for later mapping to the originating proteins. We here present the latest developments in SearchGUI, a common open-source interface for the most frequently used freely available proteomics search and de novo engines that has evolved into a central component in numerous bioinformatics workflows.

Comparison of an Optimized Ultracentrifugation Method versus Size-Exclusion Chromatography for Isolation of Exosomes from Human Serum

Abstract: Exosomes are nanosized vesicles that are abundant in biological fluids. In recent years, exosomes have attracted increasing attention as their cargo may provide promising biomarkers for the early diagnosis of and therapy for many diseases, such as cancer. In addition to ultracentrifugation (UC), many alternative methods including size-exclusion chromatography (SEC) have been developed for isolating exosomes. It has been reported that the SEC method provided improved performance relative to the UC method in isolating exosomes from plasma, where the former contained less residual blood protein contamination. We have compared the SEC method with an optimized UC method in isolating exosomes from human serum. This was based on dilution of the serum to reduce the viscosity and a prolonged cycle of UC, followed by another four cycles. We found that >95% of serum proteins were removed without a significant loss of exosome proteins relative to SEC. We also combined one cycle of UC with SEC and found that this method provided improved results relative to the SEC method, although the serum protein contamination was several times higher than that of our optimized UC method. The TEM showed that the size distribution of exosomes isolated from each of the three methods was similar.

Comparison of In-Solution, FASP, and S-Trap Based Digestion Methods for Bottom-Up Proteomic Studies

Abstract: Bottom-up proteomic strategies rely on efficient digestion of proteins into peptides for mass spectrometry analysis. In-solution and filter-based strategies are commonly used for proteomic analysis. In recent years, filter-aided sample preparation (FASP) has become the dominant filter-based method due to its ability to remove SDS prior to mass spectrometry analysis. However, the time-consuming nature of FASP protocols have led to the development of new filter-based strategies. Suspension traps (S-Traps) were recently reported as an alternative to FASP and in-solution strategies as they allow for high concentrations of SDS in a fraction of the time of a typical FASP protocol. In this study, we compare the yields from in-solution, FASP, and S-Trap based digestions of proteins extracted in SDS and urea-based lysis buffers. We performed label-free quantification to analyze the differences in the portions of the proteome identified using each method. Overall, our results show that each digestion method had a h...

Proteome-Wide Evaluation of Two Common Protein Quantification Methods.

Abstract: Proteomics experiments commonly aim to estimate and detect differential abundance across all expressed proteins. Within this experimental design, some of the most challenging measurements are small fold changes for lower abundance proteins. While bottom-up proteomics methods are approaching comprehensive coverage of even complex eukaryotic proteomes, failing to reliably quantify lower abundance proteins can limit the precision and reach of experiments to much less than the identified—let alone total—proteome. Here we test the ability of two common methods, a tandem mass tagging (TMT) method and a label-free quantitation method (LFQ), to achieve comprehensive quantitative coverage by benchmarking their capacity to measure 3 different levels of change (3-, 2-, and 1.5-fold) across an entire data set. Both methods achieved comparably accurate estimates for all 3-fold-changes. However, the TMT method detected changes that reached statistical significance three times more often due to higher precision and fewe...

Comparative Proteomic Analysis of Exosomes and Microvesicles in Human Saliva for Lung Cancer

Abstract: Extracellular vesicles (EVs) are cell-derived microparticles present in most body fluids, mainly including microvesicles and exosomes EV-harbored proteins have emerged as novel biomarkers for the diagnosis and prediction of different cancers We successfully isolated microvesicles and exosomes from human saliva, which were further characterized comprehensively Salivary EV protein profiling in normal subjects and lung cancer patients was systematically compared through utilizing LC–MS/MS-based label-free quantification 785 and 910 proteins were identified from salivary exosomes and microvesicles, respectively According to statistical analysis, 150 and 243 proteins were revealed as dysregulated candidates in exosomes and microvesicles for lung cancer Among them, 25 and 40 proteins originally from distal organ cells were found in the salivary exosomes and microvesicles of lung cancer patients In particular, 5 out of 25 and 9 out of 40 are lung-related proteins Six potential candidates were selected fo

Transformative Opportunities for Single-Cell Proteomics.

Abstract: Many pressing medical challenges, such as diagnosing disease, enhancing directed stem-cell differentiation, and classifying cancers, have long been hindered by limitations in our ability to quantify proteins in single cells. Mass spectrometry (MS) is poised to transcend these limitations by developing powerful methods to routinely quantify thousands of proteins and proteoforms across many thousands of single cells. We outline specific technological developments and ideas that can increase the sensitivity and throughput of single-cell MS by orders of magnitude and usher in this new age. These advances will transform medicine and ultimately contribute to understanding biological systems on an entirely new level.

Proteomic Characterization of Dermal Interstitial Fluid Extracted Using a Novel Microneedle-Assisted Technique.

Abstract: As wearable fitness devices have gained commercial acceptance, interest in real-time monitoring of an individual’s physiological status using noninvasive techniques has grown. Microneedles have been proposed as a minimally invasive technique for sampling the dermal interstitial fluid (ISF) for clinical monitoring and diagnosis, but little is known about its composition. In this study, a novel microneedle array was used to collect dermal ISF from three healthy human donors and compared with matching serum and plasma samples. Using a shotgun quantitative proteomic approach, 407 proteins were quantified with at least one unique peptide, and of those, 135 proteins were differently expressed at least 2-fold. Collectively, these proteins tended to originate from the cytoplasm, membrane bound vesicles, and extracellular vesicular exosomes. Proteomic analysis confirmed previously published work that indicates that ISF is highly similar to both plasma and serum. In this study, less than one percent of proteins wer...

Long-Term Stability of Human Plasma Metabolites during Storage at −80 °C

Abstract: Prolonged storage of biospecimen can lead to artificially altered metabolite concentrations and thus bias data analysis in metabolomics experiments. To elucidate the potential impact of long-term storage on the metabolite profile, a pooled human plasma sample was aliquoted and stored at −80 °C. During a time period of five years, 1012 of the aliquots were measured with the Biocrates AbsoluteIDQ p180 targeted-metabolomics assay at 193 time points. Modeling the concentration courses over time revealed that 55 out of 111 metabolites remained stable. The statistically significantly changed metabolites showed on average an increase or decrease of +13.7% or −14.5%, respectively. In detail, increased concentration levels were observed for amino acids (mean: + 15.4%), the sum of hexoses (+7.9%), butyrylcarnitine (+9.4%), and some phospholipids mostly with chain lengths exceeding 40 carbon atoms (mean: +18.0%). Lipids tended to exhibit decreased concentration levels with the following mean concentration changes: a...

EpiProfile 2.0: A Computational Platform for Processing Epi-Proteomics Mass Spectrometry Data.

Abstract: Epigenetics has become a fundamental scientific discipline with various implications for biology and medicine. Epigenetic marks, mostly DNA methylation and histone post-translational modifications (PTMs), play important roles in chromatin structure and function. Accurate quantification of these marks is an ongoing challenge due to the variety of modifications and their wide dynamic range of abundance. Here we present EpiProfile 2.0, an extended version of our 2015 software (v1.0), for accurate quantification of histone peptides based on liquid chromatography–tandem mass spectrometry (LC–MS/MS) analysis. EpiProfile 2.0 is now optimized for data-independent acquisition through the use of precursor and fragment extracted ion chromatography to accurately determine the chromatographic profile and to discriminate isobaric forms of peptides. The software uses an intelligent retention time prediction trained on the analyzed samples to enable accurate peak detection. EpiProfile 2.0 supports label-free and isotopic...

Flow Cytometry Has a Significant Impact on the Cellular Metabolome.

Abstract: The characterization of specialized cell subpopulations in a heterogeneous tissue is essential for understanding organ function in health and disease. A popular method of cell isolation is fluorescence-activated cell sorting (FACS) based on probes that bind surface or intracellular markers. In this study, we analyze the impact of FACS on the cell metabolome of mouse peritoneal macrophages. Compared with directly pelleted macrophages, FACS-treated cells had an altered content of metabolites related to the plasma membrane, activating a mechanosensory signaling cascade causing inflammation-like stress. The procedure also triggered alterations related to energy consumption and cell damage. The observed changes mostly derive from the physical impact on cells during their passage through the instrument. These findings provide evidence of FACS-induced biochemical changes, which should be taken into account in the design of robust metabolic assays of cells separated by flow cytometry.

Differential content of proteins, mRNAs, and miRNAs suggests that MDSC and their exosomes may mediate distinct immune suppressive functions

Abstract: Myeloid-derived suppressor cells (MDSC) are immature myeloid cells that accumulate in the circulation and the tumor microenvironment of most cancer patients. There, MDSC suppress both adaptive and innate immunity, hindering immunotherapies. The inflammatory milieu often present in cancers facilitates MDSC suppressive activity, causing aggressive tumor progression and metastasis. MDSC from tumor-bearing mice release exosomes, which carry biologically active proteins and mediate some of the immunosuppressive functions characteristic of MDSC. Studies on other cell types have shown that exosomes may also carry RNAs which can be transferred to local and distant cells, yet the messenger RNA and microRNA cargo of MDSC-derived exosomes has not been studied to date. Here, the cargo of MDSC and their exosomes was interrogated with the goal of identifying and characterizing molecules that may facilitate MDSC suppressive potency. Because inflammation is an established driving force for MDSC suppressive activity, we u...

Parsimonious Charge Deconvolution for Native Mass Spectrometry.

Abstract: Charge deconvolution infers the mass from mass over charge (m/z) measurements in electrospray ionization mass spectra. When applied over a wide input m/z or broad target mass range, charge-deconvolution algorithms can produce artifacts, such as false masses at one-half or one-third of the correct mass. Indeed, a maximum entropy term in the objective function of MaxEnt, the most commonly used charge deconvolution algorithm, favors a deconvolved spectrum with many peaks over one with fewer peaks. Here we describe a new “parsimonious” charge deconvolution algorithm that produces fewer artifacts. The algorithm is especially well-suited to high-resolution native mass spectrometry of intact glycoproteins and protein complexes. Deconvolution of native mass spectra poses special challenges due to salt and small molecule adducts, multimers, wide mass ranges, and fewer and lower charge states. We demonstrate the performance of the new deconvolution algorithm on a range of samples. On the heavily glycosylated plasma...

Fast Open Modification Spectral Library Searching through Approximate Nearest Neighbor Indexing.

Abstract: Open modification searching (OMS) is a powerful search strategy that identifies peptides carrying any type of modification by allowing a modified spectrum to match against its unmodified variant by using a very wide precursor mass window. A drawback of this strategy, however, is that it leads to a large increase in search time. Although performing an open search can be done using existing spectral library search engines by simply setting a wide precursor mass window, none of these tools have been optimized for OMS, leading to excessive runtimes and suboptimal identification results. We present the ANN-SoLo tool for fast and accurate open spectral library searching. ANN-SoLo uses approximate nearest neighbor indexing to speed up OMS by selecting only a limited number of the most relevant library spectra to compare to an unknown query spectrum. This approach is combined with a cascade search strategy to maximize the number of identified unmodified and modified spectra while strictly controlling the false discovery rate as well as a shifted dot product score to sensitively match modified spectra to their unmodified counterparts. ANN-SoLo achieves state-of-the-art performance in terms of speed and the number of identifications. On a previously published human cell line data set, ANN-SoLo confidently identifies more spectra than SpectraST or MSFragger and achieves a speedup of an order of magnitude compared with SpectraST. ANN-SoLo is implemented in Python and C++. It is freely available under the Apache 2.0 license at https://github.com/bittremieux/ANN-SoLo .

apQuant: Accurate Label-Free Quantification by Quality Filtering

Abstract: Label-free quantification of shotgun proteomics data is a frequently used strategy, offering high dynamic range, sensitivity, and the ability to compare a high number of samples without additional labeling effort. Here, we present a bioinformatics approach that significantly improves label-free quantification results. We employ Percolator to assess the quality of quantified peptides. This allows to extract accurate and reliable quantitative results based on false discovery rate. Benchmarking our approach on previously published public data shows that it considerably outperforms currently available algorithms. apQuant is available free of charge as a node for Proteome Discoverer.

BioSITe: A Method for Direct Detection and Quantitation of Site-Specific Biotinylation.

Abstract: Biotin-based labeling strategies are widely employed to study protein-protein interactions, subcellular proteomes and post-translational modifications, as well as, used in drug discovery. While the high affinity of streptavidin for biotin greatly facilitates the capture of biotinylated proteins, it still presents a challenge, as currently employed, for the recovery of biotinylated peptides. Here we describe a strategy designated Biotinylation Site Identification Technology (BioSITe) for the capture of biotinylated peptides for LC–MS/MS analyses. We demonstrate the utility of BioSITe when applied to proximity-dependent labeling methods, APEX and BioID, as well as biotin-based click chemistry strategies for identifying O-GlcNAc-modified sites. We demonstrate the use of isotopically labeled biotin for quantitative BioSITe experiments that simplify differential interactome analysis and obviate the need for metabolic labeling strategies such as SILAC. Our data also highlight the potential value of site-specifi...

Ultrafast Peptide Label-Free Quantification with FlashLFQ.

Abstract: The rapid and accurate quantification of peptides is a critical element of modern proteomics that has become increasingly challenging as proteomic data sets grow in size and complexity. We present here FlashLFQ, a computer program for high-speed label-free quantification of peptides following a search of bottom-up mass spectrometry data. FlashLFQ is approximately an order of magnitude faster than established label-free quantification methods. The increased speed makes it practical to base quantification upon all of the charge states for a given peptide rather than solely upon the charge state that was selected for MS2 fragmentation. This increases the number of quantified peptides, improves replicate-to-replicate reproducibility, and increases quantitative accuracy. We integrated FlashLFQ into the graphical user interface of the MetaMorpheus search software, allowing it to work together with the global post-translational modification discovery (G-PTM-D) engine to accurately quantify modified peptides. Fla...

Lysine Succinylation and Acetylation in Pseudomonas aeruginosa.

Abstract: Pseudomonas aeruginosa is a multi-drug-resistant human opportunistic pathogen largely involved in nosocomial infections. Unfortunately, effective antibacterial agents are lacking. Exploring its physiology at the post-translational modifications (PTMs) level may contribute to the renewal of combat tactics. Recently, lysine succinylation was discovered in bacteria and seems to be an interesting PTM. We present the first succinylome and acetylome of P. aeruginosa PA14 cultured in the presence of four different carbon sources using a 2D immunoaffinity approach coupled to nanoliquid chromatography tandem mass spectrometry. A total of 1520 succinylated (612 proteins) and 1102 acetylated (522 proteins) lysine residues were characterized. Citrate was the carbon source in which we identified the higher number of modified proteins. Interestingly, 622 lysine residues (312 proteins) were observed either acetylated or succinylated. Some of these proteins, were involved in virulence, adaptation, resistance, and so on. ...

Highly Reproducible Automated Proteomics Sample Preparation Workflow for Quantitative Mass Spectrometry.

Abstract: Sample preparation for protein quantification by mass spectrometry requires multiple processing steps including denaturation, reduction, alkylation, protease digestion, and peptide cleanup. Scaling these procedures for the analysis of numerous complex biological samples can be tedious and time-consuming, as there are many liquid transfer steps and timed reactions where technical variations can be introduced and propagated. We established an automated sample preparation workflow with a total processing time for 96 samples of 5 h, including a 2 h incubation with trypsin. Peptide cleanup is accomplished by online diversion during the LC/MS/MS analysis. In a selected reaction monitoring (SRM) assay targeting 6 plasma biomarkers and spiked β-galactosidase, mean intraday and interday cyclic voltammograms (CVs) for 5 serum and 5 plasma samples over 5 days were <20%. In a highly multiplexed SRM assay targeting more than 70 proteins, 90% of the transitions from 6 plasma samples repeated on 3 separate days had tota...

Progress on Identifying and Characterizing the Human Proteome: 2018 Metrics from the HUPO Human Proteome Project.

Abstract: The Human Proteome Project (HPP) annually reports on progress throughout the field in credibly identifying and characterizing the human protein parts list and making proteomics an integral part of multiomics studies in medicine and the life sciences. NeXtProt release 2018-01-17, the baseline for this sixth annual HPP special issue of the Journal of Proteome Research, contains 17 470 PE1 proteins, 89% of all neXtProt predicted PE1-4 proteins, up from 17 008 in release 2017-01-23 and 13 975 in release 2012-02-24. Conversely, the number of neXtProt PE2,3,4 missing proteins has been reduced from 2949 to 2579 to 2186 over the past two years. Of the PE1 proteins, 16 092 are based on mass spectrometry results, and 1378 on other kinds of protein studies, notably protein-protein interaction findings. PeptideAtlas has 15 798 canonical proteins, up 625 over the past year, including 269 from SUMOylation studies. The largest reason for missing proteins is low abundance. Meanwhile, the Human Protein Atlas has released its Cell Atlas, Pathology Atlas, and updated Tissue Atlas, and is applying recommendations from the International Working Group on Antibody Validation. Finally, there is progress using the quantitative multiplex organ-specific popular proteins targeted proteomics approach in various disease categories.

Differential Quantitative Determination of Site-Specific Intact N-Glycopeptides in Serum Haptoglobin between Hepatocellular Carcinoma and Cirrhosis Using LC-EThcD-MS/MS.

Abstract: Intact N-glycopeptide analysis remains challenging due to the complexity of glycopeptide structures, low abundance of glycopeptides in protein digests, and difficulties in data interpretation/quantitation. Herein, we developed a workflow that involved advanced methodologies, the EThcD-MS/MS fragmentation method and data interpretation software, for differential analysis of the microheterogeneity of site-specific intact N-glycopeptides of serum haptoglobin between early hepatocellular carcinoma (HCC) and liver cirrhosis. Haptoglobin was immunopurified from 20 μL of serum in patients with early HCC, liver cirrhosis, and healthy controls, respectively, followed by trypsin/GluC digestion, glycopeptide enrichment, and LC-EThcD-MS/MS analysis. Identification and differential quantitation of site-specific N-glycopeptides were performed using a combination of Byonic and Byologic software. In total, 93, 87, and 68 site-specific N-glycopeptides were identified in early HCC, liver cirrhosis, and healthy controls, respectively, with high confidence. The increased variety of N-glycopeptides in liver diseases compared to healthy controls was due to increased branching with hyper-fucosylation and sialylation. Differential quantitation analysis showed that 5 site-specific N-glycopeptides on sites N184 and N241 were significantly elevated in early HCC compared to cirrhosis ( p < 0.05) and normal controls ( p ≤ 0.001). The result demonstrates that the workflow provides a strategy for detailed profiles of N-glycopeptides of patient samples as well as for relative quantitation to determine the level changes in site-specific N-glycopeptides between disease states.

Mapping Extracellular Matrix Proteins in Formalin-Fixed, Paraffin-Embedded Tissues by MALDI Imaging Mass Spectrometry

Abstract: Collagens and elastin form the fundamental framework of all tissues and organs, and their expression and post-translational processing are tightly regulated in disease and health. Because of their unique structural composition and properties, it is a recognized challenge to access these protein structures within the complex tissue microenvironment to understand how localized changes modulate tissue health. We describe a new workflow using a combination of matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) with matrix metalloproteinase (MMP) enzymes to access and report on spatial localization of collagen and elastin sequences in formalin-fixed, paraffin-embedded (FFPE) tissues. The developed technology provides new access to collagens and elastin sequences localized to tissue features that were previously unattainable. This high-throughput technological advance should be applicable to any tissue regardless of disease type, tissue origin, or disease status and is thus relevant to all research: basic, translational, or clinical.

Age and Sex Effects on Plasma Metabolite Association Networks in Healthy Subjects.

Abstract: In the era of precision medicine, the analysis of simple information like sex and age can increase the potential to better diagnose and treat conditions that occur more frequently in one of the two sexes, present sex-specific symptoms and outcomes, or are characteristic of a specific age group. We present here a study of the association networks constructed from an array of 22 plasma metabolites measured on a cohort of 844 healthy blood donors. Through differential network analysis we show that specific association networks can be associated with sex and age: Different connectivity patterns were observed, suggesting sex-related variability in several metabolic pathways (branched-chain amino acids, ketone bodies, and propanoate metabolism). Reduction in metabolite hub connectivity was also found to be associated with age in both sex groups. Network analysis was complemented with standard univariate and multivariate statistical analysis that revealed age- and sex-specific metabolic signatures. Our results demonstrate that the characterization of metabolite-metabolite association networks is a promising and powerful tool to investigate the human phenotype at a molecular level.

Metabolomic Signature of Endometrial Cancer

Abstract: Endometrial cancer (EC) is the most common cancer of the female reproductive tract in developed countries. At the moment, no effective screening system is available. Here, we evaluate the diagnostic performance of a serum metabolomic signature. Two enrollments were carried out, one consisting of 168 subjects: 88 with EC and 80 healthy women, was used for building the classification models. The second (used to establish the performance of the classification algorithm) was consisted of 120 subjects: 30 with EC, 30 with ovarian cancer, 10 with benign endometrial disease, and 50 healthy controls. Two ensemble models were built, one with all EC versus controls (Model I) and one in which EC patients were aggregated according to their histotype (Model II). Serum metabolomic analysis was conducted via gas chromatography–mass spectrometry, while classification was done by an ensemble learning machine. Accuracy ranged from 62% to 99% for the Model I and from 67% to 100% for the Model II. Ensemble model showed an ac...